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[ "<title>Introduction</title>", "<p>Meiotic recombination promotes the faithful segregation of homologous chromosomes at meiosis I (MI) by creating physical linkages between the homologs ##REF##15573136##[1]##,##REF##12600308##[2]##. Recombination produces two types of products: crossovers (COs) and non-crossovers (NCOs). Only COs mature into exchanges between chromosome axes called chiasmata, which together with arm cohesion ensure homolog separation.</p>", "<p>Recombination during meiosis is initiated by the formation of double-strand breaks (DSBs) at recombination hotspots ##REF##9039264##[3]##. A protein complex containing the Spo11 core catalytic subunit is involved in DSB formation. Resection of DSB ends results in the formation of single-stranded DNA (ssDNA), which is then used in the search for homologous DNA sequences. The homology search is catalyzed by two RecA homologs, Rad51 and Dmc1 with their accessory factors ##REF##1581960##[4]##–##REF##9409820##[7]##. This homology search results in the invasion of ssDNA into duplex DNA, and the formation of a single-end invasion intermediates ##REF##11461702##[SEIs; 8]##. SEIs undergo second-end capture of the DSB to form a second prominent joint molecule, called the double-Holliday junction (dHJ), which is primarily resolved to form COs ##REF##8521495##[9]##. The intermediate required to form NCOs has yet to be identified. Importantly, the homology search resulting in SEI formation appears to be biochemically and temporally distinct from the second-end capture steps ##REF##11461702##[8]##,##REF##11461701##[10]##.</p>", "<p>CO formation is regulated by the action of a group of proteins called ZMM or SIC (synaptic initiation complex; hereafter called ZMM for simplicity). Members of the ZMM group include Zip1, Zip2, Zip3, Msh4, Msh5, Mer3, Spo16, and Spo22/Zip4 ##REF##10943844##[11]##–##REF##18297071##[16]##. Mer3 and Msh4–Msh5 possess helicase and structure-specific DNA-binding activities, respectively ##REF##11971962##[17]##,##REF##15304223##[18]##. Zip3, together with the Zip2–Spo16–Spo22 adaptor complex, is thought to catalyze the post-translational modification of target protein(s), e.g., sumoylation or ubiquitylation ##REF##16314568##[15]##,##REF##16847351##[19]##. Zip1 is a component of the synaptonemal complex ##REF##7916652##[20]##. The ZMM proteins ensure the formation of wild-type CO levels ##REF##15066280##[12]##,##REF##18297071##[16]##. In addition to the ZMM-dependent CO pathway, budding yeast has two additional pathways for recombination: a minor CO pathway and a NCO pathway, both dependent on the junction resolvase Mus81–Mms4 ##REF##15611158##[21]##,##REF##12750322##[22]##.</p>", "<p>One of the most notable features in meiosis is chromosome dynamics and morphogenesis. In most organisms, synapsis of homologous chromosomes is facilitated by the recombination. Synapsis culminates in the formation of SC, a tripartite structure seen in pachytene ##REF##9928494##[23]##,##REF##10690419##[24]##. In leptotene when DSBs are formed, sister chromatids form chromatin loops along a shared axis (the axial element). Leptotene is followed by zygotene, in which short patches of SC form between homologous axial elements. Elongation of SC occurs along entire chromosomes, resulting in the formation of full-length SC in pachytene. SCs are then disassembled in the diplotene. Importantly, SC formation is tightly coupled with CO formation. Formation of SEIs and dHJs occurs at the leptotene-zygotene and zygotene-pachytene transitions, respectively ##REF##11461702##[8]##,##REF##15066280##[12]##. Resolution of dHJs occurs during late pachytene.</p>", "<p>In the vegetative growth phase of <italic>S. cerevisiae,</italic> centromeres are present near the Spindle Pole Body (SPB), a fungal equivalent of the centrosome in other eukaryotes. In <italic>S. cerevisiae</italic>, the SPB is embedded in the nuclear envelope (NE), and telomeres are clustered and often associated with the NE in a dispersed distribution (Klein et al. 1992). This configuration of chromosomes in vegetative cells is referred to as the “Rabl” orientation. In meiotic prophase, cells undergo a drastic change in their chromosome configuration. Centromeres detach from the SPB, while telomeres cluster in one area of the nuclear membrane near the SPB. This chromosomal bouquet configuration is prominently seen only during zygotene. The bouquet is a conserved feature in the meiotic prophase of most eukaryotes, but its function remains unknown ##REF##9928494##[23]##. In <italic>S. cerevisae</italic>, a meiosis-specific telomere-binding protein, Ndj1 ##REF##9242487##[25]##,##REF##9157883##[26]##, is involved in tethering telomeres to the nuclear membrane and promoting bouquet formation ##REF##11018056##[27]##. <italic>ndj1</italic> mutation reduces spore viability and confers some defects in recombination ##REF##9242487##[25]##,##REF##9157883##[26]##,##REF##16648465##[28]##. In <italic>S. pombe</italic>, the Bqt1–Bqt2 complex promotes bouquet formation through interactions with a telomere-binding protein, Taz1 ##REF##16615890##[29]##. The bouquet is thought to facilitate pairing of homologous chromosomes by restricting the homology search to a smaller area.</p>", "<p>In this study, we found that a meiosis-specific protein, Csm4 ##REF##11470404##[30]##, promotes efficient transition from SEIs to dHJs as well as resolution of dHJs in the CO-specific recombination pathway. These results suggest that Csm4 regulates various steps during meiotic recombination. Recombination-related phenotypes in <italic>csm4</italic> mutants are very similar to those seen in <italic>ndj1</italic> mutants ##REF##16648465##[28]##. Csm4 forms a complex with Ndj1 <italic>in vivo</italic>. We also found that similar to <italic>ndj1</italic> mutants, <italic>csm4</italic> mutants are deficient in bouquet formation, but unlike <italic>ndj1</italic> mutants, they are proficient in tethering telomeres to the NE. These results suggest that chromosome architecture and/or dynamics, which are mediated by the tethering telomeres to the NE, control various biochemical steps during meiotic recombination. The accompanying paper by Wanat et al. (2008) shows similar and complementary results ##REF##18818741##[31]##.</p>" ]

[ "<title>Methods</title>", "<title>Strains and Plasmids</title>", "<p>All strains described here are derivatives of SK1 diploids, NKY1551 (<italic>MATα/MAT<bold>a</bold>, lys2/lys2, ura3/ura3, leu2::hisG/leu2::hisG, his4X-LEU2-URA3/his4B-LEU2, arg4-nsp/arg4-bgl</italic>) and NKY3230 (<italic>MATα/MAT<bold>a</bold>, lys2/lys2, ura3/ura3, leu2::hisG/leu2::hisG, his4X-LEU2-(N/Bam)-URA3/HIS4-LEU2-(N/Bam)</italic> and its derivatives with <italic>csm4::KamMX6</italic> were used for the 2D analysis. Rap1-GFP was a kind gift from Dr. Y. Hiraoka. The genotypes of each strain used in this study are described in ##SUPPL##0##Table S1##.</p>", "<title>Strain Construction</title>", "<p>\n<italic>csm4, ndj1, mms4,</italic> and <italic>msh4</italic> null alleles were constructed by PCR-mediated gene disruption using either the <italic>URA3</italic> gene or the <italic>KanMX6</italic>\n##REF##7747518##[64]##. <italic>NDJ1-3HA</italic> and <italic>MPS3-3HA</italic> were constructed by a PCR-based tagging methodology ##REF##10767539##[65]##.</p>", "<p>Primer details used for PCR amplification are available upon request.</p>", "<title>Anti-Serum Preparation and Antibodies</title>", "<p>Anti-Csm4 antibody was raised against recombinant protein purified from <italic>E. coli</italic>. The open reading frame of Csm4 was PCR-amplified and inserted into pET15b plasmid (Novagen) in which the N-terminus of <italic>CSM4</italic> gene was tagged with hexahistidine. Csm4 protein with the histidine tag was affinity-purified in accordance with the manufacturer's protocol and used for immunization (MBL Co. Ltd). Primer details for PCR amplification are available upon request.</p>", "<p>Anti-HA antibody (16B12; Babco), anti-tubulin, guinea pig anti-Rad51 ##REF##11005857##[45]##, and rabbit anti-Dmc1 ##REF##15620352##[5]## were used for staining. Antiserum against Zip1 was raised using a recombinant GST-fusion protein purified from <italic>E. coli</italic>\n##REF##18297071##[16]##.</p>", "<title>Cytology</title>", "<p>Immunostaining of chromosome spreads was performed as described previously ##REF##11005857##[45]##,##REF##12871899##[66]##. Whole cell immuno-staining was preformed as described previously ##REF##11018056##[27]## with a slight modification. Cells were fixed with formaldehyde. Stained samples were observed using an epi-fluorescent microscope (BX51; Olympus) with a 100x objective (NA 1.3). Images were captured by a CCD camera (Cool Snap; Roper), and processed using IP lab (Sillicon) and Photoshop (Adobe) software. For focus counting, more than 100 nuclei were counted at each time point.</p>", "<p>Rap1-GFP was observed as described previously ##REF##16027219##[52]##. Images were captured by a computer-assisted fluorescence microscope system (Delta Vision; Applied Precision) with an oil-immersion objective lens (100x, NA 1.35). Image deconvolution was performed using an image workstation (SoftWorks; Applied Precision).</p>", "<title>Analyses of Meiotic Recombination</title>", "<p>Time-course analyses of DNA events in meiosis and cell cycle progression were performed as described previously ##REF##11461702##[8]##,##REF##15066280##[12]##,##REF##8799151##[58]##.</p>", "<title>Immuno-Precipitation Assay and Western Blotting</title>", "<p>IP assay was performed as described previously ##REF##15620352##[5]##.</p>", "<title>Reproducibility</title>", "<p>Each result presented in the figures is representative of several experiments. The number of experiments performed is shown in ##SUPPL##1##Table S2##.</p>" ]

[ "<title>Results</title>", "<title>Csm4 Promotes Meiotic Recombination</title>", "<p>Previous analysis showed that <italic>csm4</italic> mutants are defective in the segregation of chromosomes during meiosis ##REF##11470404##[30]##. However, little is known about the functions of Csm4 in meiosis. We re-analyzed the meiotic phenotypes of <italic>csm4</italic> mutants in an SK1 background. Consistent with a previous study ##REF##11470404##[30]##, the <italic>csm4</italic> mutation reduces spore viability to 66%, as compared to 96% in the wild type. Interestingly, 4-, 2-, and 0-viable spore tetrads exceed 3- and 1-viable spore tetrads, suggesting non-disjunction of homologs at MI (##FIG##0##Figure 1A##) Similar results have been described by Wanat et al. in the accompanying paper ##REF##18818741##[31]##. Furthermore, <italic>csm4</italic> mutation delays its entry into MI by 5 h (##FIG##0##Figure 1B##). This delay is suppressed by introducing a mutant allele of <italic>SPO11</italic>, <italic>spo11-Y135F</italic>, which abolishes the catalytic function ##REF##9039264##[3]##,##REF##9121560##[32]##. Similar results have been described by Wanat et al. ##REF##18818741##[31]##, suggesting that the delay seen in the <italic>csm4</italic> mutant is due to a defect in meiotic recombination. The delay is also suppressed by the introduction of a mutation of the <italic>RED1</italic> gene (##FIG##0##Figure 1B##), which encodes a component of the axial element of the SC ##REF##9060462##[33]##, is necessary for DSB formation ##REF##8878674##[34]##, and acts as a barrier to inter-sister recombination ##REF##10526232##[35]##,##REF##16221890##[36]##.</p>", "<p>We then analyzed the turnover of meiotic DSBs at the <italic>HIS4-LEU2</italic> recombination hotspot ##REF##2190690##[37]## in the <italic>csm4</italic> mutant (##FIG##0##Figure 1C##). In the wild type, DSBs appear at 3 h after incubation in the sporulation medium (SPM) and then disappear at around 6 h (##FIG##0##Figure 1D##). The <italic>csm4</italic> mutant accumulates DSBs up to a slightly higher level compared to the wild type. Formation of DSBs in the mutant is slightly delayed, and disappearance of the DSBs is delayed by 4 h. At 8 h, DSBs are still detected in the mutant. These results indicate that <italic>CSM4</italic> is required for the efficient conversion of DSBs into later-stage recombination intermediates.</p>", "<p>Next, we examined the formation of crossovers (COs) in <italic>csm4</italic>. Consistent with delayed DSB repair, CO formation in <italic>csm4</italic> is delayed by approximately 4 h compared to the wild type (##FIG##0##Figure 1E##). Similar results have been described by Wanat et al. in the accompanying paper ##REF##18818741##[31]##. However, the final level of COs at the <italic>HIS4-LEU2</italic> locus is similar to the wild type (92% of the wild-type level).</p>", "<p>In addition to COs, meiotic recombination produces non-crossovers (NCOs). CO and NCO recombinants can be distinguished using restriction site polymorphisms around DSB site I in the <italic>HIS4-LEU2</italic> locus ##REF##7667321##[38]##. As seen for COs, NCOs in the <italic>csm4</italic> mutant are formed 5 h later than in the wild type (##FIG##0##Figure 1F##). In this assay, the final level of COs in the mutant is slightly higher (1.2-fold) than the wild type. Wanat et al. show a slight reduction of NCOs using the same assay ##REF##18818741##[31]##. The level of NCOs in <italic>csm4</italic> is reduced to 75% of the wild type. This suggests that Csm4 is required for timely and efficient formation of both types of recombinants. This was confirmed using a heteroduplex assay that detects CO and NCO at the same locus (##FIG##0##Figure 1G##). The final level of NCOs containing heteroduplex DNA at the <italic>Mlu</italic>I/<italic>Bam</italic>HI site in the mutant is reduced to 50% that of the wild-type level, while the level of COs containing heteroduplex DNA is unaffected by <italic>csm4</italic> mutation. Interestingly, the <italic>csm4</italic> mutant increases ectopic recombination between <italic>HIS4-LEU2</italic> and <italic>leu2::hisG</italic> on chromosome <italic>III</italic> (##FIG##0##Figure 1G##; ##REF##10511543##[39]##).</p>", "<title>Relationship of Csm4 with Msh4 and Mms4 during Meiotic Recombination</title>", "<p>Meiotic recombination has been grouped into two CO pathways and a single NCO pathway ##REF##15611158##[21]##. One major pathway for COs depends on ZMM proteins ##REF##15066280##[12]## and the other depends on the junction-specific resolvase, Mus81–Mms4 ##REF##12750322##[22]##. To examine a possible role for Csm4 in these pathways, we constructed a <italic>csm4</italic> mutant with a mutation in <italic>MSH4</italic>, which encodes a meiosis-specific MutS homolog that acts in the ZMM pathway ##REF##8001134##[40]##. <italic>csm4</italic> and <italic>msh4</italic> single mutants display reduced spore viability (66 and 29%, respectively; ##FIG##0##Figure 1A##). The <italic>csm4 msh4</italic> double mutant shows more severe defects in spore viability (18%) than either single mutant. In the CO/NCO assay, <italic>msh4</italic> affects formation of both COs and NCOs (##FIG##1##Figure 2A##). As reported previously ##REF##15066280##[12]##, at 30°C, <italic>msh4</italic> mutation decreases the final amount of COs to 50% that of the wild type, but increases the level of NCOs to 1.7-fold of the wild type. The <italic>csm4 msh4</italic> double mutant shows more severe defects in CO formation; the final level of COs in the double mutant is significantly reduced compared to <italic>csm4</italic> and <italic>msh4</italic> single mutants. However, the amount of NCOs in the <italic>csm4 msh4</italic> double mutant is only slightly reduced compared to the <italic>csm4</italic> single mutant. This suggests that Csm4 functions in meiotic recombination independently of Msh4 and that Csm4 promotes CO formation in the absence of Msh4. Furthermore, Msh4 is not necessary for residual NCO formation in the absence of Csm4.</p>", "<p>Next, we constructed the <italic>csm4 mms4</italic> double mutant. Unlike either single mutant, the <italic>csm4 mms4</italic> double mutant cannot form spores. In the CO/NCO assay (##FIG##1##Figure 2B##), the <italic>mms4</italic> single mutant exhibits a delay in formation of COs and reduces CO levels to 73% of the wild type ##REF##11779793##[41]##. Interestingly, NCOs in the <italic>mms4</italic> mutant appear at the same time as in the wild type and at levels that are 1.5-fold higher than the wild type. The <italic>csm4 mms4</italic> double mutant shows an effect on NCO formation similar to the <italic>csm4</italic> single mutant. Similar to <italic>csm4</italic>, CO formation in the double mutant is delayed, but reaches an almost wild-type level. These observations suggest that Csm4 works upstream of Mms4 in meiotic CO and NCO recombination pathways.</p>", "<p>We also examined the amount of DSBs formed in the <italic>csm4</italic> mutant in the <italic>rad50S</italic> background, which blocks processing of DSB ends ##REF##2185891##[42]##. The <italic>csm4 rad50S</italic> double mutant accumulates DSBs like the <italic>rad50S</italic> mutant (##FIG##1##Figure 2E##). Similar results have been described by Wanat et al. in the accompanying paper ##REF##18818741##[31]##. DSB levels in the double mutant were slightly higher than those seen in <italic>rad50S</italic>.</p>", "<title>Csm4 Is Required for Timely Formation of and Exit from Double-Holliday Junctions</title>", "<p>As shown above, Csm4 is necessary for timely CO formation recombination pathway, which mainly depends on ZMM proteins such as Msh4. In the ZMM-dependent CO pathway, single-end invasions (SEIs) and double-Holliday junctions (dHJs) have been identified as major recombination intermediates ##REF##11461702##[8]##,##REF##8521495##[9]##. We analyzed the effect of <italic>csm4</italic> mutation on the formation of these intermediates, which can be detected at <italic>HIS4-LEU2</italic> (##FIG##2##Figure 3A##) in 2D gel electrophoresis after cross-linking DNA samples with psoralen ##REF##11461702##[8]##,##REF##8521495##[9]##. In the wild type, SEIs begin to appear at 3 h, peak at 4.5 h, and disappear at around 6 h (##FIG##2##Figure 3B and 3D##). In contrast, the <italic>csm4</italic> mutant shows a slight delay in the onset of SEI formation, and SEIs persist at later times during meiosis (##FIG##2##Figure 3C and 3D##). At 8 h, a significant level of SEIs could be detected in the <italic>csm4</italic> strains. Although delayed, SEIs are turned over in the mutant at around 12 h. dHJs in the wild-type cells start to appear at 4.5 h, peak at 5 h, and then disappear (##FIG##2##Figure 3B and 3E##). In <italic>csm4</italic>, formation of dHJs is delayed by 3.5 h compared to the wild type (##FIG##2##Figure 3C and 3E##). The maximum level of dHJs in the mutant at 8 h is slightly higher than in the wild type. Furthermore, the resolution of dHJs is clearly delayed in the mutant. These data suggest that <italic>csm4</italic> mutation affects various steps of CO formation, likely during the SEI–dHJ transition and dHJ resolution. Similar results but with more quantitative analysis of recombination intermediates have been described in the accompanying paper by Wanat et al. ##REF##18818741##[31]##.</p>", "<title>Csm4 Is Necessary for Timely Disassembly of RecA Homolog Foci and Efficient Chromosome Synapsis</title>", "<p>We analyzed the localization of RecA homologs on meiotic chromosome spreads by immunostaining. Eukaryotic RecA homologs Rad51 and meiosis-specific Dmc1 both act in the homology search/strand exchange process that results in SEI and dHJ formation ##REF##1581961##[6]##,##REF##9323140##[43]##,##REF##7528104##[44]##. In the wild type, Rad51 as well as Dmc1 shows punctate staining, or foci ##REF##7528104##[44]##,##REF##11005857##[45]##. Rad51 foci begin to appear at 3 h, peak at 4 h, and then disappear at later times (##FIG##3##Figure 4A##). The kinetics of Rad51 focus formation is very similar to that of DSBs. In the <italic>csm4</italic> mutant, the formation of Rad51 foci is slightly delayed compared to the wild type (##FIG##3##Figure 4B and 4C##), consistent with a delay in DSB formation in the mutant. Disassembly of Rad51 foci is clearly delayed in the <italic>csm4</italic> mutant, indicating inefficient repair of DSBs. The average number of Rad51/Dmc1 foci in the <italic>csm4</italic> mutant at 4 h is 42.8 for Rad51 and 40.5 for Dmc1 (per total nucleus), which is higher than that seen in the wild type (22.6 and 24.8 for Rad51 and Dmc1, respectively). At later time points, much brighter and larger Rad51 foci, possibly representing aggregates, are observed in the mutant (##FIG##3##Figure 4B##). These aggregates appear to be specific to <italic>csm4</italic>, since other mutants, which also accumulate Rad51/Dmc1 foci at later times (e.g., <italic>tid1</italic>, <italic>mnd1</italic>, and <italic>hop2</italic>), do not accumulate these structures ##REF##11005857##[45]##–##REF##15120066##[47]##. Dmc1 in <italic>csm4</italic> shows a staining pattern similar to that seen for Rad51 (##FIG##3##Figure 4B and 4C##). These data suggest that <italic>CSM4</italic> is necessary for a step after loading of Rad51 and Dmc1, e.g., during the homology search.</p>", "<p>To examine the effect of <italic>csm4</italic> on chromosomal synapsis, i.e., formation of the synaptonemal complex (SC) during meiotic prophase, we stained chromosome spreads with an antibody against the Zip1 protein, which is a component of the central element of the SC ##REF##7916652##[20]##. In leptotene, Zip1 shows dotty-staining in the wild type (2–3 h; class I; ##FIG##3##Figure 4D##i). In zygotene (3–5 h), short lines of Zip1 (class II; ##FIG##3##Figure 4D##ii) are observed in addition to the Zip1 foci. At pachytene (5–7 h), Zip1 elongates along entire chromosomes (class III; ##FIG##3##Figure 4D##iii), indicating full chromosome synapsis. The <italic>csm4</italic> mutant shows a deficiency in SC formation. Similar to the wild type, Zip1 foci form in the mutant (##FIG##3##Figure 4E##i). Zip1 starts to elongate, but full chromosome synapsis is rarely seen in the mutant (class II'; ##FIG##3##Figure 4E##ii). As a result, the <italic>csm4</italic> mutant accumulates zygotene-like nuclei (##FIG##3##Figure 4F##). Consistent with a synapsis defect, most zygotene-like <italic>csm4</italic> nuclei contain an aggregate of Zip1 called polycomplex. Although pachytene-like nuclei are rare in the mutant, Zip1 dismantles when further incubated with SPM (##FIG##3##Figure 4F##). These results indicate that <italic>CSM4</italic> is required for efficient SC formation, particularly SC elongation. Similar results have been described by Wanat et al. using Zip1–Green fluorescent protein (GFP) fusion protein ##REF##18818741##[31]##.</p>", "<title>Csm4 Interacts with Meiosis-Specific Telomere-Binding Protein, Ndj1</title>", "<p>Expression of <italic>CSM4</italic> mRNA is specific to meiosis ##REF##11470404##[30]##. Western blotting analysis using an antibody against Csm4 reveals that this protein is present in lysates from meiotic cells, but not from mitotic cells (##FIG##4##Figure 5A##). Our initial immunostaining analysis of both whole cells and chromosome spreads failed to localize the protein either in nuclei or on chromosomes (HK, unpublished results). However, when expressed in vegetative cells as a GFP fusion protein, Csm4 localizes to nuclear membranes and the endoplasmic reticulum ##REF##12514182##[48]##.</p>", "<p>We noticed that the <italic>csm4</italic> and <italic>ndj1</italic> mutants share similar recombination defects ##REF##16648465##[28]##. In particular, similar to <italic>csm4</italic>, the <italic>ndj1</italic> mutant specifically decreases NCO formation in physical assays. When a <italic>csm4 ndj1</italic> double mutant was constructed and analyzed for CO/NCO formation, the double mutant exhibited a phenotype similar to <italic>csm4</italic> and <italic>ndj1</italic> single mutants (##FIG##1##Figure 2C##). Although <italic>CSM4</italic> and <italic>NDJ1</italic> appear to function in the same recombination pathway, there are several phenotypic differences between two single mutants. In general, <italic>csm4</italic> shows more severe defects than <italic>ndj1</italic> and <italic>csm4 ndj1</italic> double mutants show defects that are more similar to <italic>csm4</italic>. The spore viability of <italic>csm4</italic> is lower than that of <italic>ndj1</italic> (##FIG##0##Figure 1A##; 66% versus 77%), and the <italic>csm4</italic> mutant enters into MI 2 h later than the <italic>ndj1</italic> mutant (##FIG##1##Figure 2C##).</p>", "<p>Ndj1 is a meiosis-specific protein that binds to telomeres ##REF##9242487##[25]##,##REF##9157883##[26]## and is required to form the bouquet, where telomeres cluster near the SPB ##REF##11018056##[27]##. The similarity between <italic>csm4</italic> and <italic>ndj1</italic> phenotypes prompted us to examine the interaction of Csm4 with Ndj1. We used a strain in which Ndj1 protein is tagged with the HA epitope at its C-terminus. This strain exhibits wild-type spore viability. Immunoprecipitation (IP) using anti-HA antibody reveals the presence of Csm4 in precipitates of meiotic cell lysates from <italic>NDJ1-HA</italic> diploid, but not in those from the untagged strain (##FIG##4##Figure 5B##). Reciprocal IP using anti-Csm4 also detects Ndj1-HA in these precipitates (##FIG##4##Figure 5B##). These results demonstrate a physical association interaction of Csm4 with Ndj1 in meiotic cells. Since the <italic>csm4</italic> mutant expresses Ndj1 (##FIG##4##Figure 5A##), the defect conferred by <italic>csm4</italic> is not due to the inability of <italic>csm4</italic> cells to express Ndj1.</p>", "<title>\n<italic>CSM4</italic> Promotes Proper Clustering of Ndj1</title>", "<p>Next, we studied the localization of Ndj1-HA protein to the NE in <italic>csm4</italic> mutants. Whole cells were fixed with formaldehyde and then stained with anti-HA antibody followed by fluorescent-conjugated antibody. The cells were then observed under an epifluorescence microscope. We also analyzed the localization of Dmc1 in intact cells as a marker for meiotic cells. As reported previously ##REF##11018056##[27]##, in wild-type cells, Ndj1 shows several foci or patches near the nuclear periphery in the meiotic prophase (##FIG##4##Figure 5C##). The kinetics of accumulation and disappearance of Ndj1- and Dmc1-positive cells were very similar (##FIG##4##Figure 5E##). We sorted the staining patterns into three classes: rim, loose bouquet, and tight bouquet (##FIG##4##Figure 5F##). Loose and tight bouquets are only seen in the meiotic prophase (##FIG##4##Figure 5F##). Furthermore, a significant fraction of wild-type cells at 4 h shows clustering of Ndj1 foci (loose and tight bouquets) in one area of the NEs (##FIG##4##Figure 5F##). On the other hand, <italic>csm4</italic> cells do not show clustering of Ndj1, but rather exhibit dispersed staining of Ndj1 patches at the nuclear periphery (##FIG##4##Figure 5D and 5F##). In <italic>csm4</italic>, Ndj1 patches persist in the periphery longer than in the wild type (##FIG##4##Figure 5D and 5E##). Importantly, Ndj1 in the <italic>csm4</italic> mutant is still associated with the NE. These data indicate that Csm4 is required for efficient clustering of Ndj1 on the NE, but not for tethering, suggesting a role of Csm4 in Ndj1-mediated telomere clustering.</p>", "<title>\n<italic>CSM4</italic> Promotes Bouquet Formation</title>", "<p>Ndj1 promotes telomere clustering during meiotic prophase ##REF##11018056##[27]##. We examined bouquet formation by analyzing Rap1–GFP localization ##REF##9813109##[49]##,##REF##9973600##[50]##. Rap1 is concentrated at telomeres and is used as a marker for telomere localization ##REF##1315786##[51]##. As shown previously, Rap1–GFP is localized at the nuclear periphery as several foci in mitosis ##REF##9813109##[49]##,##REF##16027219##[52]##. Nuclei were visualized by deconvoluting Z-series images; one focal plane is shown in ##FIG##5##Figure 6##. When diploid cells enter the meiotic prophase, after 3–5 h incubation with SPM, a small fraction of diploid cells in meiotic prophase show a polarized distribution of Rap1–GFP at the cell periphery. In <italic>S. cerevisiae</italic>, the bouquet appears unstable and is possibly dynamic during the meiotic prophase ##REF##9973600##[50]##,##REF##16027219##[52]##. In the wild type, clustering of Rap1 foci is prominently seen at 4 h (##FIG##5##Figure 6A##); however, due to the very transient nature of the clustering, only 15–25% cells show the clustering. On the other hand, the <italic>csm4</italic> mutant shows a disperse distribution of Rap1 on NEs after 4 and 5 h incubation with SPM (##FIG##5##Figure 6B##). Very few cells show the clustering of Rap1–GFP in the mutant between 4 and 6 h (see ##FIG##4##Figure 5F##). This indicates that Csm4 is necessary for clustering of telomeres but not for tethering telomeres to the NE. Furthermore, some Rap1–GFP foci in the <italic>ndj1</italic> mutant are not localized at the nuclear periphery but are seen within the nucleus ##REF##11018056##[Figure 6C; 27]##. Similar results have been described by Wanat et al. ##REF##18818741##[31]##. We also noticed that most <italic>csm4</italic> nuclei were round, while the wild type as well as the <italic>ndj1</italic> mutant nuclei were irregularly shaped, suggesting a defect in nuclear deformation in the <italic>csm4</italic> mutant.</p>", "<title>\n<italic>CSM4</italic> Is Not Required for Relocalization of Mps3</title>", "<p>It was recently reported that a component of the SPB, Mps3, is necessary for telomere clustering during meiosis ##REF##17495028##[53]## and anchoring telomeres ##REF##16923827##[54]##. Mps3, which contains Sad1-Unc-84 (SUN) and trans-membrane domains, changes its localization from the SPB to the NE during meiosis ##REF##17495028##[53]##,##REF##16923827##[54]##. We examined the effect of <italic>csm4</italic> mutation on Mps3 relocalization. Whole cells containing <italic>MPS3</italic> tagged with HA were fixed with formaldehyde and stained with antibodies against the HA tag and Dmc1 protein. As reported previously ##REF##16923827##[54]##,##REF##12486115##[55]##, at 0 h, Mps3 is seen as a single spot at the nuclear periphery (##FIG##6##Figure 7A##), which is consistent with its localization near the SPB. During the meiotic prophase (at 4 h in SPM), in Dmc1-positive nuclei, Mps3 relocalizes throughout the NE and occasionally exhibits patchy staining (##FIG##6##Figure 7A##). This NE localization of Mps3 is still observed after the MI division. In the <italic>csm4</italic> mutant, Mps3 shows a distribution in the NE similar to the wild type, but remains longer than in the wild type (##FIG##6##Figure 7A and 7B##). This is consistent with a prolonged meiotic prophase in <italic>csm4</italic>. Therefore, the effect of the <italic>csm4</italic> mutation on telomere clustering appears to be independent of Mps3 relocalization. In addition, <italic>csm4</italic> does not affect Mps3 protein levels (##FIG##6##Figure 7C##).</p>" ]

[ "<title>Discussion</title>", "<title>Csm4 Functions with Ndj1</title>", "<p>Previously, the <italic>csm4</italic> mutant was isolated on the basis of its defect in chromosome segregation during meiosis ##REF##11470404##[30]##. In this paper, we show that Csm4 functions with the meiosis-specific telomere-binding protein Ndj1. Recombination defects conferred by a <italic>csm4</italic> mutation are very similar to those caused by a mutation in <italic>NDJ1</italic>\n##REF##16648465##[28]##. Indeed, the <italic>csm4 ndj1</italic> double mutant phenotype is similar to that seen for the single mutants. In addition, co-IP shows that Csm4 is physically associated with Ndj1 <italic>in vivo</italic>. Furthermore, Csm4 is required for efficient clustering of Ndj1 at the nuclear periphery. These results indicate that Csm4 and Ndj1 function in the same structural pathway.</p>", "<p>The <italic>csm4</italic> mutant, however, shows more severe defects in meiosis than the <italic>ndj1</italic> mutant. Spore viability is lower in <italic>csm4</italic> compared to <italic>ndj1</italic>. The <italic>csm4</italic> mutation delays its entry into MI to a greater extent than <italic>ndj1</italic>. These observations suggest that Csm4 has additional functions in meiosis or that <italic>ndj1</italic> is not null for related functions.</p>", "<title>\n<italic>CSM4</italic> Is Necessary for Various Steps of Meiotic Recombination Pathways</title>", "<p>Csm4 is necessary for normal functioning of all three recombination pathways of meiosis: ZMM-dependent and -independent (<italic>MMS4</italic>-dependent) CO and NCO formation. Although the final level of COs in the <italic>csm4</italic> single mutant is similar to that in the wild type, <italic>csm4</italic> reduces the level of NCOs compared to the wild type, indicating the involvement of Csm4 in NCO formation during meiosis. Csm4 is a meiosis-specific protein; this suggests that NCO formation is under the control of a meiotic program and thus is likely to be mechanistically distinct from NCO formation during mitosis. When <italic>csm4</italic> mutation is combined with a mutation in <italic>MSH4</italic>, the double mutant is almost completely deficient in CO formation. Therefore, Csm4 functions in CO formation independently of Msh4.</p>", "<p>CO formation in meiosis, about half of which depends on <italic>ZMM</italic> genes, is severely delayed in <italic>csm4</italic>, indicating that Csm4 is also required for efficient formation of COs in the major <italic>ZMM</italic>-dependent meiotic recombination pathway. Two intermediates, SEIs and dHJs, have been identified in the <italic>ZMM</italic>-dependent CO pathway ##REF##11461702##[8]##,##REF##8521495##9##. The most severe effect of <italic>csm4</italic> mutation is seen in SEI–dHJ transition and dHJ resolution, two distinct biochemical steps in the <italic>ZMM</italic> pathway. It is likely that SEI–dHJ transition is accompanied by the capture of SEI by the second end of the DSB ##REF##11461702##[8]##,##REF##18313389##[56]##. Therefore, Csm4 seems to promote the second-end capture during strand exchange. Generally, this capture is considered a simple annealing reaction between ssDNA of the second end and a displaced ssDNA in SEI ##REF##10357855##[57]##. However, our results strongly suggest that the second-end capture is not a simple biochemical reaction as believed previously; rather, it is a critical regulatory step in the CO pathway. Although the exact molecular nature of SEIs in the <italic>csm4</italic> mutant is not known, they are likely to contain D-loop structures that can be converted into COs or NCOs (Hunter et al. 2002). Thus, the transition of SEIs to dHJs could be regarded as an irreversible commitment step towards CO formation. The transition can be independently governed by both ZMM- and Csm4-dependent functions. Furthermore, disassembly of the two RecA homologs Rad51 and Dmc1 is delayed in the <italic>csm4</italic> mutant. This strongly suggests that disassembly of the RecA homologs occurs during the SEI–dHJ transition, and thus is somehow coupled with the second-end capture.</p>", "<title>Csm4 Affects Meiotic Recombination through Telomeres</title>", "<p>How does Csm4 control the various steps during meiotic recombination? One notion is that Csm4 functions as an enzyme directly involved in recombination. However, it is very difficult to assign a biochemical activity to Csm4 (∼23 kD) with no apparent structural domains, since it is likely to be involved in various steps in the aforementioned three recombination pathways. One possibility is that Csm4 acts as a component of the meiotic chromosomes. Red1, a chromosome axis protein, is involved in various recombination steps ##REF##9323140##[43]##,##REF##8799151##[58]##. However, our initial attempt to localize the protein on DNA by chromatin IP failed to detect the binding of Csm4 to a recombination hotspot (unpublished results). Our initial attempt to localize Csm4 was also unsuccessful because both N- and C-terminal tagged genes are non-functional and our anti-Csm4 did not work for immunostaining (HK, unpublished results). However, Csm4 is predominantly enriched in the NE when overexpressed as a GFP fusion protein in vegetative cells ##REF##12514182##[48]##, consistent with the fact that Csm4 contains a putative transmembrane domain. Furthermore, a Csm4 partner, Ndj1, is enriched at telomeres, that are tethered to the NE ##REF##9242487##[25]##,##REF##9157883##[26]##. These observations strongly suggest that Csm4 is localized in the telomeres. Indeed, similar to Ndj1, Csm4 binds to telomeres on nuclear spreads ##REF##18585352##[59]##. Thus, the Csm4–Ndj1 complex is likely to affect recombination indirectly through its function at telomeres and/or the NEs. In addition to Csm4–Ndj1, the Mps3 protein containing Sad1-UNC84 domain is also involved in the process ##REF##17495028##[53]##. During vegetative growth, Mps3 is localized to the SPB and then relocated to the NEs in the meiotic prophase ##REF##17495028##[53]##. Mps3 forms a complex with Ndj1 and Csm4 ##REF##18585352##[59]##. An allele of <italic>mps3</italic> shows pairing defects in meiosis similar to those seen in <italic>ndj1</italic> and <italic>csm4</italic> mutants ##REF##18585352##[59]##. Given that Mps3 is an inner nuclear membrane protein, it is likely to tether Ndj1-bound telomeres to the NE.</p>", "<title>How Does Telomere Tethering to the NE Regulate Meiotic Recombination?</title>", "<p>How do telomeres control recombination on the interstitial sites of chromosomes? The fact that <italic>ndj1</italic> and <italic>csm4</italic> mutants are defective in chromosomal bouquet formation [this study; ##REF##18585352##59]##,##REF##18585353##[60]## suggests that a polarized configuration of chromosomes in zygotene might play a positive role in meiotic recombination. As proposed previously ##REF##9928494##[23]##,##REF##11483995##[61]##, telomere clustering may restrict the arrangement of chromosomes in the nucleus, and in turn increases the probability that two allelic loci undergo colocalization. Although this could explain defects specific to zygotene, such as first end capture or SEI formation, those in second-end capture and dHJ resolution, occurring in the end of zygotene and pachytene, respectively, cannot be simply explained by telomere clustering during zygotene.</p>", "<p>Rather, we propose that chromosome dynamics accompanied by telomere movement facilitates meiotic recombination. Tethering telomeres to nuclear membranes followed by movement along the envelope might change the chromatin structure, which might indirectly promote various biochemical steps during recombination. Dynamic movement of chromosomes in the meiotic prophase has been recently described; it depends on actin polymerization ##REF##18585352##[59]##,##REF##18585353##[60]##,##REF##17939997##[62]##,##REF##12734403##[63]##. Furthermore, the dynamic nature of telomeres on the NE is somehow dependent on Ndj1 and Csm4 ##REF##18585352##[59]##,##REF##18585353##[60]##. It is likely that the global changes in the chromosome structure and/or movement of chromosomes, promoted by the anchoring of telomeres to the NE, control the biochemistry of recombination of meiotic chromosomes.</p>", "<title>Multi-Step Assembly of Chromosomal Bouquet in Budding Yeast</title>", "<p>Our analysis of <italic>csm4</italic> provides new insights into the mechanism of telomere clustering in budding yeast. Both <italic>csm4</italic> and <italic>ndj1</italic> mutants are deficient in telomere clustering, but the nature of deficiency in these mutants is qualitatively different. While <italic>NDJ1</italic> promotes tethering of telomeres to the NE, <italic>CSM4</italic> facilitates clustering of Ndj1-bound telomeres in one area of the envelope. Csm4 may promote bouquet formation by directly clustering the telomeres and/or by stabilizing them. Given that telomere movement on the envelope is a dynamic process ##REF##16027219##[52]##,##REF##17939997##[62]##, Csm4 might be involved in the movement of telomeres on the NE. However, the <italic>csm4</italic> mutant exhibits some local movement of telomeres on the membrane, which is clearly different from the movement in the presence of an actin-inhibitor ##REF##18585353##[60; HK and AS, unpublished results]##. Thus, the movements of telomeres are either Csm4-dependent or Csm4-independent. Our results suggest that meiotic telomere clustering consists of different steps including telomere tethering, movement, and clustering. Consistent with this, nuclei in <italic>csm4</italic> mutants are relatively round compared to the irregular shapes of meiotic nuclei seen in the wild type (##FIG##5##Figure 6##). Nuclear deformation may be induced by external physical forces on the nuclei. Therefore, Csm4 might be involved in the transduction of forces on the NEs.</p>" ]

[]

[ "<p>Conceived and designed the experiments: HK MS AS. Performed the experiments: HK MS AS. Analyzed the data: HK AS. Contributed reagents/materials/analysis tools: MS. Wrote the paper: HK MS AS.</p>", "<p>During meiotic prophase, chromosomes display rapid movement, and their telomeres attach to the nuclear envelope and cluster to form a “chromosomal bouquet.” Little is known about the roles of the chromosome movement and telomere clustering in this phase. In budding yeast, telomere clustering is promoted by a meiosis-specific, telomere-binding protein, Ndj1. Here, we show that a meiosis-specific protein, Csm4, which forms a complex with Ndj1, facilitates bouquet formation. In the absence of Csm4, Ndj1-bound telomeres tether to nuclear envelopes but do not cluster, suggesting that telomere clustering in the meiotic prophase consists of at least two distinct steps: Ndj1-dependent tethering to the nuclear envelope and Csm4-dependent clustering/movement. Similar to Ndj1, Csm4 is required for several distinct steps during meiotic recombination. Our results suggest that Csm4 promotes efficient second-end capture of a double-strand break following a homology search, as well as resolution of the double-Holliday junction during crossover formation. We propose that chromosome movement and associated telomere dynamics at the nuclear envelope promotes the completion of key biochemical steps during meiotic recombination.</p>", "<title>Author Summary</title>", "<p>Meiosis is a specialized cell division that produces haploid gametes. Homologous recombination plays a pivotal role in the segregation of homologous chromosomes during meiosis I by creating physical linkages between the chromosomes. Drastic reorganization of chromosomes in the nucleus is a prominent feature of meiotic prophase I, during which telomeres get associated with the nuclear envelope and move within the envelope, culminating in the formation of telomere clusters, often referred to as “chromosome bouquets.” The roles that telomere movement and clustering play in meiotic prophase I are largely unknown. In the budding yeast <italic>Saccharomyces cerevisiae</italic>, tethering of telomeres to the nuclear envelope is mediated by a meiosis-specific telomere-binding protein, Ndj1. We studied the functions of a meiosis-specific gene, <italic>CSM4</italic>, in telomere clustering and during meiotic recombination. <italic>CSM4</italic> is necessary for the clustering of Ndj1-associated telomeres. Interestingly, <italic>csm4</italic> mutants show pleiotropic defects during meiotic recombination. It is likely that the chromosome movement promotes various biochemical reactions during meiotic recombination.</p>" ]

[ "<title>Supporting Information</title>" ]

[ "<p>We are grateful to Dr. Michael Dresser for discussion. We thank Drs. Nancy Kleckner, Eric Alani, and Michael Dresser for sharing unpublished results and thank the reviewers of this paper for their insightful comments. We appreciate Dr. Hiroyuki Oshiumi for initial characterization of the <italic>csm4</italic> mutant. We are grateful to Drs. Neil Hunter, and Yasushi Hiraoka for providing strains and for advice on methods. We are also indebted to the members of the Shinohara lab for their help and support.</p>" ]

[ "<fig id=\"pgen-1000196-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000196.g001</object-id><label>Figure 1</label><caption><title>Csm4 promotes meiotic recombination.</title><p>(A) Spore viability (SV). The indicated strains were sporulated at 30°C and more than 100 tetrads were dissected per strain. The distribution of 0, 1, 2, 3, and 4 viable spores per tetrad are shown for each strain. (B) Meiotic cell cycle progression. Entry into meiosis I and II in the wild type, <italic>csm4</italic> with or without <italic>spo11-Y135F</italic> (upper panel) and <italic>red1</italic> (bottom panel) mutations were analyzed by DAPI staining. Graphs show the percent of cells that completed MI at the indicated times. (C) A schematic diagram of the <italic>HIS4-LEU2</italic> recombination hotspot. Restriction sites for <italic>Pst</italic>I, <italic>Xho</italic>I, <italic>Bam</italic>HI, and <italic>Mlu</italic>I are shown. Diagnostic fragments for analysis on double-strand break (DSB), crossover (CO), non-crossover/crossover (CO/NCO) and heteroduplex (HD) in CO and NCO are shown at the bottom. The size of each fragment (kilo-bases) is presented within parentheses. (D-G) DSBs (D), CO (E), CO/NCO (F), and heteroduplex in COs and NCOs (G) at the <italic>HIS4-LEU2</italic> locus in the wild type and <italic>csm4</italic> cells were analyzed by Southern blotting and quantified (graphs on right). Genomic DNA was digested as follows; DSBs, <italic>Pst</italic>I; CO, <italic>Xho</italic>I; CO/NCO, <italic>Xho</italic>I and <italic>Mlu</italic>I; heteroduplexes, <italic>Xho</italic>I, <italic>Bam</italic>HI and <italic>Mlu</italic>I. ER (in G) is a product of intra-chromosomal ectopic recombination. Wild type, open circles; <italic>csm4</italic> mutant, closed circles.</p></caption></fig>", "<fig id=\"pgen-1000196-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000196.g002</object-id><label>Figure 2</label><caption><title>Relationship of <italic>csm4</italic> with <italic>msh4</italic>, <italic>mms4</italic>, and <italic>ndj1</italic> in meiotic recombination.</title><p>(A-C) CO/NCO analysis of the wild type, <italic>csm4</italic> mutant with or without <italic>msh4</italic> (A) <italic>mms4</italic> (B), or <italic>ndj1</italic> (C) null mutant allele. Southern blots were prepared as shown in ##FIG##0##Figure 1## and the quantification of COs and NCOs as well as progression through meiosis I (MI) in each strain is shown in the graphs (right). Progression through MI was analyzed by DAPI staining. Wild type (A, B, C), open circles; <italic>csm4</italic> (A, B, C), closed circles; <italic>msh4</italic> (A), <italic>mms4</italic> (B), <italic>ndj1</italic> (C), open green triangles; <italic>csm4 msh4</italic> (A), <italic>csm4 mms4</italic> (B), <italic>csm4 ndj1</italic> (C), closed red triangles. (D) DSBs at the <italic>HIS4-LEU2</italic> locus in <italic>rad50S</italic> and <italic>csm4 rad50S</italic> cells were analyzed by Southern blotting (left) and quantified (right) as described in the <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>. Error bars (+/−SD) were obtained from three independent analyses.</p></caption></fig>", "<fig id=\"pgen-1000196-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000196.g003</object-id><label>Figure 3</label><caption><title>2D analysis of recombination intermediates in the <italic>csm4</italic> mutant.</title><p>(A) Schematic drawing of the <italic>HIS4-LEU2</italic> locus for the SEI and dHJ assay. (B, C) Southern blots of 2D gel analysis of the wild type (B) and the <italic>csm4</italic> mutant (C). Genomic DNA samples taken at various times were psoralen-crosslinked, digested with <italic>Xho</italic>I, and analyzed in 2D neutral/neutral gels. (D, E) Quantification of SEI and dHJ. The amount of SEIs (D) and interhomolog dHJs (E) were quantified at each time point and plotted. Wild type, open circles; <italic>csm4</italic>, closed circles.</p></caption></fig>", "<fig id=\"pgen-1000196-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000196.g004</object-id><label>Figure 4</label><caption><title>The <italic>csm4</italic> mutant is defective in disassembly of RecA homolog foci and SC formation.</title><p>(A–C) Rad51-Dmc1 focus formation in the <italic>csm4</italic> mutant. Nuclear spreads of the wild type (A) and <italic>csm4</italic> (B) were stained with anti-Rad51 (red; left graph) and anti-Dmc1 (green; right graph) as well as DAPI for DNA (blue). The percent of cells positive for Rad51 or Dmc1 foci (more than 5 foci per nucleus) were counted at each time point (C). At least 100 nuclei were counted at each time point. Wild type, open circles; <italic>csm4</italic> mutant, closed circles. Bars = 2 µm. (D, E) Chromosome synapsis in <italic>csm4</italic> mutants. Nuclear spreads were stained with anti-Zip1 (green) and DAPI (blue), categorized, and plotted as described previously ##REF##9060462##[33]##. SCs of wild-type cells shown in leptotene (D-i), zygotene (D-ii), and pachytene (D-iii). SCs of <italic>csm4</italic> mutants shown in leptotene (E-i) and zygotene-like stages (E-ii) contain the polycomplex (PC) as shown by an arrow. Bars = 2 µm. (F) Plots showing each class of SC (Wild type, left; <italic>csm4</italic> mutant, right) at indicated times in meiosis. Class I (open bars), Zip1 dots; Class II (dotted bars), partial Zip1 linear; Class III (closed bars), linear Zip1 staining; Class II', partial Zip1 linear with PC (blue bars). The formation of Zip1 PC is shown for each strain (red closed circles).</p></caption></fig>", "<fig id=\"pgen-1000196-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000196.g005</object-id><label>Figure 5</label><caption><title>Csm4 promotes the clustering of Ndj1 at the nuclear periphery.</title><p>(A) Expression of Csm4 protein. Lysates obtained from the wild type and <italic>csm4</italic> mutant strains bearing <italic>NDJ1-HA</italic> induced for meiosis were analyzed by Western blotting using anti-Csm4 (upper), anti-HA (middle), or anti-tubulin (lower) antibodies. (B) Co-immunoprecipitation of Csm4 and Ndj1. Cell lysates from strains containing or lacking <italic>NDJ1-HA</italic> were immunoprecipitated with anti-HA (left) and anti-Csm4 (right) antibodies, and probed with anti-Csm4 (left upper panel) and anti-HA (right upper panel), respectively. Whole cell extracts (WCE; bottom panels) were also analyzed by Western blotting. (C, D) Localization of Ndj1 protein in intact meiotic cells. Wild type (C) and <italic>csm4</italic> mutant strains (D) containing <italic>NDJ1-HA</italic> were induced for meiosis. Cell aliquots were collected at indicated times, fixed, stained with anti-HA and anti-Dmc1 antibodies, and examined using a fluorescence microscope. Ndj1-HA (green), Dmc1 (red), and DAPI (blue). Bars = 2 µm. (E) Kinetics of Ndj1 and Dmc1 foci. Nuclei positive for Ndj1 (left) and Dmc1 (right) localization in whole cells (C, D) were counted and plotted. Wild type, closed circles; <italic>csm4</italic> mutant, open circles. (F) Classes of Ndj1–telomere clustering. Cells with tight bouquet, loose bouquet, or peripheral staining of Ndj1-HA (green) are classified at each time after induction of meiosis. The percent of each class (per Ndj1-positive cells) is shown for the wild type (left) and the <italic>csm4</italic> mutant (right).</p></caption></fig>", "<fig id=\"pgen-1000196-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000196.g006</object-id><label>Figure 6</label><caption><title>Csm4 promotes bouquet formation.</title><p>Localization of Rap1–GFP in intact cells. Wild type (A), the <italic>csm4</italic> mutant (B) and <italic>ndj1</italic> mutant cells (C) with Rap1–GFP were directly examined as described in <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>. Rap1–GFP is shown in white. Deconvoluted images of one focal plane are shown. Bars = 2 µm.</p></caption></fig>", "<fig id=\"pgen-1000196-g007\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000196.g007</object-id><label>Figure 7</label><caption><title>Mps3 relocalization is independent of Csm4.</title><p>(A) Localization of Mps3-HA protein in intact meiotic cells. Wild type (left) and <italic>csm4</italic> mutant (right) bearing <italic>MPS3-HA</italic> were induced for meiosis, collected at the indicated times, fixed, stained with anti-HA and anti-Dmc1 antibodies, and then examined using a fluorescence microscope. Mps3-HA (green), Dmc1 (red), and DAPI (blue). Arrowheads show the possible location of the SPB. Bars = 2 µm. (B) Appearance and disappearance of Mps3- and Dmc1-positive cells. Nuclei positive for Mps3 (left) and Dmc1 (right) localization in whole-cell staining were counted and plotted. Wild type, open circles; <italic>csm4</italic> mutant, closed circles. (C) Expression of Csm4 protein. Cell lysates prepared from the wild type and <italic>csm4</italic> mutants bearing the <italic>MPS3-HA</italic> at indicated times in meiosis were analyzed by Western blotting using anti-HA (upper panel) or anti-tubulin (lower panel) antibodies. An asterisk indicates a non-specific band.</p></caption></fig>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"pgen.1000196.s001\"><label>Table S1</label><caption><p>Strain list.</p><p>(0.04 MB DOC)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000196.s002\"><label>Table S2</label><caption><p>The number of experiments.</p><p>(0.04 MB DOC)</p></caption></supplementary-material>" ]

[ "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>This work was supported by a MEXT grant of the Japanese Government and the Asahi Glass Science Foundation. Sponsors or funders had no role in study, design, data collection, analysis, and interpretation of the data, and in the preparation, review or approval of the manuscript.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"pgen.1000196.s001.doc\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000196.s002.doc\"><caption><p>Click here for additional data file.</p></caption></media>" ]

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[ "<title>Introduction</title>", "<p>Microarrays designed for one species have been used to explore expression divergence between species ##UREF##0##[1]##–##REF##12805547##[10]##. These studies deploy different types of microarrays on species with varying levels of divergence, and these experimental variables influence the potential for technical bias. In particular, the designs of experiments that deploy two-color versus one-color microarrays differ, and therefore can be differently subject to technical bias when these arrays are used to comparative expression between species. Microarrays with short oligonucleotide probes might be more profoundly impacted by a single base pair mismatch than ones with longer oligonucleotides. Additionally, studies of species that are substantially diverged have more sequence differences and other possible sources of variation (alternative splicing, repetitive elements, duplications, etc.) that increase the chance of technical bias. Differences in technical procedures between laboratories and genetic differences among populations or individuals can also contribute to variation in expression divergence.</p>", "<p>In theory, if the “target” species for which the array was designed and a “non-target” species are closely related, some probes on the array should be able to interrogate expression of genes in both species without bias if the sequences that are interrogated by the probes are still the same in both species ##REF##15871745##[11]##–##REF##15247326##[13]##. Some studies have attempted to identify and eliminate probes with biased response to the transcriptome of the target and non-target species. One tactic is to select probes on the basis of genomic DNA (gDNA) hybridizations of the target and a non-target species to the microarray chip ##REF##12805547##[10]##, ##UREF##4##[14]##, ##REF##16608451##[15]##. If the same amount of gDNA is used in the hybridization, probes that match conserved regions should hybridize with similar intensity to gDNA in both species. Recently, for example, the <italic>Xenopus laevis</italic> Affymetrix microarray chip was used to explore expression divergence between different species of clawed frogs and their hybrids ##REF##17024524##[16]##–##REF##18331635##[18]##. Comparisons were made between testis and ovary expression profiles of the target species, <italic>X. laevis</italic> (XL), a non-target species, <italic>X. muelleri</italic> (XM), and F1 hybrids from a cross between a XL female and a XM male (hereafter H_XLXM). In these studies, hybridizations of gDNA of XM and XL were performed on the XL microarray, and probes whose XM/XL genomic hybridization intensity ratio (gDNA ratio) was not between 0.99 and 1.01 ##REF##17712429##[17]## or between 0.99 and 1.10 were excluded from the analysis ##REF##17712429##[17]##, ##REF##18331635##[18]##. When expression profiles of testes or ovaries of XL and XM were compared to the same tissue in their hybrids, widespread dominance in expression was reported in hybrids wherein the expression profile of H_XLXM tended to be more similar to XL than to the non-target parental species XM ##REF##17712429##[17]##, ##REF##18331635##[18]##. About 28 times more genes were significantly divergently expressed in testes in a comparison between XM and H_XLXM than between XL and H_XLXM\n##REF##17712429##[17]## and about 4.5 times more genes were significantly divergently expressed in ovary in a comparison between XM and H_XLXM than between XL and H_XLXM\n##REF##18331635##[18]##.</p>", "<p>With a goal of further exploring these results, we analyzed new expression data from testis tissue of XL, <italic>X. borealis</italic> (XB), and F1 hybrids between XL x XB (XL female and XB male, hereafter H_XLXB). XB and XM are equivalently diverged from XL ##REF##15324848##[19]##–##REF##17435227##[21]## so our new data provide a phylogenetically meaningful comparison. All of these species are “pseudotetraploid” in that they are historically tetraploid but their genomes have diploidized (bivalents form at meiosis; each chromosome has only one homologous chromosome). XL and (XB+XM) diverged from a common tetraploid ancestor roughly 21–41 million years ago, and XB and XM diverged from a common ancestor roughly 14–25 million years ago ##REF##15324848##[19]##–##REF##16683033##[22]##. In the analysis of these new expression data, we included only those probes that interrogate sequences that are identical in XL and in XB based on 454 pyrosequencing of XB cDNA. For comparative purposes, we also performed genomic DNA hybridizations on XL, XB, and XM, and analyzed the new data and also data from other studies ##REF##17024524##[16]##–##REF##18331635##[18]##, ##REF##18174434##[23]##, ##REF##16651540##[24]## using microarray probes selected using the gDNA hybridization approach of ##REF##17712429##[17]##, ##REF##18331635##[18]##, ##REF##18174434##[23]##.</p>" ]

[ "<title>Materials and Methods</title>", "<title>Origin of animals</title>", "<p>XB expression data, gDNA, and XB parents of H_XLXB were from or were animals from Kenya. The XL expression data, gDNA, and XL parents of H_XLXB were from or were laboratory animals that probably are from Cape Province, South Africa, which is the source of most laboratory stocks ##UREF##12##[48]##. All of the H_XLXB individuals were from the same cross and are therefore full siblings. We did not analyze hybrid tissue from the reciprocal cross (from an XB female and XL male).</p>", "<p>The XM expression data, gDNA, and parents of H_XLXM in ##REF##17712429##[17]##, ##REF##18331635##[18]## were or were from animals collected in Swaziland, but the XM gDNA that we performed for gDNA hybridization originated from Tanzania. Within XM, mitochondrial DNA variation between these localities is very low so we do not anticipate as substantial levels of intraspecific variation in the nuclear genome of this species compared to XL ##REF##15324848##[19]##.</p>", "<title>Microarray hybridizations and comparisons</title>", "<p>We performed new expression analyses on testis and brain tissue from XL, XB, and H_XLXB. For each tissue from each species or hybrid, RNA was isolated using TRIzol® Reagent (Invitrogen Life Technologies) according to the manufacturer's protocol, purified with RNeasy Mini Kit (Qiagen), and its integrity assessed on an Agilent BioAnalyzer. Two micrograms of total RNA was used to prepare biotin labeled cRNA probes, which were subsequently hybridized to Affymetrix <italic>Xenopus laevis</italic> expression arrays following the manufacturer's protocol.</p>", "<p>We performed new gDNA hybridizations using gDNA from XL, XB, and XM and compared these to gDNA hybridizations on XL and XM that were performed by Malone et al. ##REF##17712429##[17]##. For our gDNA hybridizations, five micrograms of gDNA from each species was fragmented with Dpn I at 37°C for 3 hours. Fragmented gDNA was purified with Qiagen PCR clean-up kit and the fragment distribution was checked on Agilent Bioanalyzer (Agilent) using the DNA 1000 assay. 50–100 nanograms of fragmented gDNA were then amplified using the BioPrime Labeling System (Invitrogen) following the manufacturers instructions. After completion of the Klenow Pol I catalyzed reaction, the distribution of PCR products was examined on Agilent Bioanalyzer with the DNA 1000 kit. The entire volume of the product (∼50 μl) was used in the hybridization reaction on the Affymetrix <italic>Xenopus laevis</italic> Gene Chip. Hybridization, staining, washing and scanning were performed as described in the Expression Analysis Technical Manual. This protocol is similar to that used by Hammond et al. ##UREF##4##[14]##.</p>", "<p>After scanning, raw expression data were converted into CEL files using Microarray Analysis Suite version 5 (MAS 5, Affymetrix). For each pairwise comparison, CEL files were pre-normalized with the Robust Multichip Average (RMA) algorithm in RMAexpress ##UREF##13##[49]## using custom CDF files (probemasks) and the default parameters, which include a median polish and quantile normalization. The normalized data were used in the R statistical package following the protocol in ##REF##17712429##[17]##. An empirical Bayesian model was used to compute a moderated t-statistic using the limma package from Bioconductor ##UREF##14##[50]##. The TopTable function gave a P-value for differential expression for each gene that was adjusted using the Benjamini and Hochberg ##UREF##15##[51]## method to control for the false discovery rate. cDNA and gDNA hybridizations that we performed have been deposited in the Gene expression omnibus database ##REF##11752295##[52]##, GEO Series accession number GSE12625. We also analyzed other data from this database (GSM241082-4 ##REF##18174434##[23]##, GSM99995-7 ##REF##16651540##[24]##, GSM99980-2 ##REF##16651540##[24]##). Expression data and genomic hybridizations from XL and XM testis and ovary that were not found in GEO were kindly provided by Pawel Michalak.</p>", "<p>We used a re-sampling approach to test whether the proportion of divergently expressed genes in different analyses (each with a unique number of genes analyzed) were significantly different. Given two analyses with w and x genes of which y and z are significantly divergently expressed, respectively, using a PERL script we generated 1000 simulated datasets, each with w genes, by re-sampling a distribution of (w+x) total genes with (y+z) genes that are significantly divergently expressed. Where (y/w)&lt;(z/x), the two-sided probability of the null hypothesis of no difference is twice the proportion of these simulated datasets that had a proportion of divergently expressed genes lower than y/w (i.e. more different from z/x). Because some of the genes in these different analyses are the same and should therefore have correlated expression levels, the inclusion of these genes in this comparison reduces the power to reject the null hypothesis, making this test conservative.</p>" ]

[ "<title>Results</title>", "<title>Affymetrix <italic>Xenopus laevis</italic> microarray, probemasks, and tissue comparisons</title>", "<p>This study examines expression data collected from a prefabricated <italic>Xenopus laevis</italic> microarray – the Affymetrix GeneChip® <italic>Xenopus laevis</italic> Genome Array. This microarray interrogates over 14400 transcripts. A transcript is interrogated with a set of 16 probes, which is called a “probeset”. Each probe within a probeset is an oligonucleotide 25 base pairs in length that hybridizes to a unique portion of an XL transcript. For each species or hybrid in this study, three biological replicates (different individuals) were performed per tissue. Hereafter we refer to the replicated expression data from a single tissue from one species or one type of hybrid (either H_XLXB or H_XLXM) as a “treatment”.</p>", "<p>Probemasks are lists of genes that are defined <italic>a priori</italic> to be excluded from analysis (before microarray normalization is performed). In this study, we analyzed data using two types of probemasks. The first type of probemask excluded all probes except those that interrogated sequences that we confirmed were identical in XL and XB, as in ##UREF##3##[9]##, ##REF##16677401##[25]##. We used BLAST ##REF##9254694##[26]## to identify probes on the Affymetrix GeneChip® <italic>Xenopus laevis</italic> Genome Array that perfectly match sequences in XB that we obtained using 454 pyrosequencing of normalized XB testis cDNA. Normalization of XB testis cDNA (which is a procedure different from and unrelated to normalization of microarray data) was performed prior to 454 pyrosequencing in order to increase representation of genes with low expression; procedures for cDNA normalization and 454 pyrosequencing are described elsewhere ##REF##18261230##[27]##. The resulting probemask included 5268 probes in a total of 2143 probesets, for an average of 2.458 probes per probeset. Hereafter we refer to this probemask as the “XB+XL perfect match probemask”. According to a permutation test in which the same number of probes is assigned to probesets randomly one thousand times, this average number of probes per probeset is significantly higher than random expectations (P&lt;0.001; the mean number of probes per probeset of the permutations was 1.169 and the 95% confidence interval was 1.158–1.180). This is consistent with the notion that some genes are conserved across multiple regions that are interrogated by unique probes on the microarray, resulting in significantly more probes per probeset than random expectations. Despite this biologically relevant pattern, we note that the overall low number of probes per probeset is likely to be associated with more variation in expression intensities than is typical of Affymetrix probesets with 16 probes. Furthermore, because the perfect match probes identified in XB are based on 454 pyrosequencing of normalized testis cDNA, this analysis might be biased in favor of genes that are expressed in testis of this non-target species. Additionally, because we retain only those probes that are identical in XL and XB, this analysis probably is also biased towards slowly evolving genes – or at least genes that have slowly evolving regions that are interrogated by probes on the microarray.</p>", "<p>The second type of probemask was generated based on the non-target to target hybridization ratio of genomic DNA (the gDNA ratio) of XL, XB, and XM as in ##REF##17712429##[17]##, ##REF##18331635##[18]##. These probemasks include only those probes with a non-target/target gDNA ratio between 0.99 and 1.1, and hereafter we refer to them as the “XB/XL gDNA probemask” and the “XM/XL gDNA probemask”, respectively. The XB/XL gDNA probemask included a total of 1792 probes in 1672 probesets, for an average of 1.072 probes per probeset. This average is similar but still significantly higher (P = 0.003) than random expectations according to a permutation test, which had an average of 1.055 (95% confidence interval 1.045–1.067). This average is significantly lower than the average of the XB+XL perfect match probemask (P&lt;0.001, permutation test). Only 2.5% of the probes (45 out of 1792 probes) that were retained by the XB/XL gDNA probemask are also retained by the XB+XL perfect match probemask. Less than 1% of the probes (45 out of 5268 probes) that were retained by the XB+XL perfect match probemask were also retained by the XB/XL gDNA probemask.</p>", "<p>The XM/XL gDNA probemask included a total of 12888 probes in 8721 probesets and an average of 1.478 probes per probeset. This average is also similar but still significantly higher than the corresponding average of the random permutations of 1.448 (P&lt;0.001; 95% confidence interval 1.437–1.460). For comparison, the probemask of ##REF##17712429##[17]## included 11485 probesets with an average of less than 2 probes per probeset.</p>", "<p>Using both types of probemask (the XB+XL perfect match probemask and the XB/XL gDNA probemask), we evaluated interspecific expression divergence in testis between H_XLXB and each parental species (XL or XB) and in brain between XL and XB. We also used both of these probemasks to evaluate intraspecific expression divergence between various XL tissues (egg, tadpole stage 11, ovary, testis, and brain). Additionally, we used the XM/XL gDNA probemask to evaluate expression divergence in testis and ovary between H_XLXM and each parental species (XL or XM) and we used this same probemask to evaluate intraspecific expression divergence between the aforementioned XL tissues. We were not able to perform interspecific analyses between XL and XM with a perfect match probemask because sequence data from XM was not obtained.</p>", "<title>Dominant expression in hybrids?</title>", "<p>When we analyzed testis expression data from XL, XB, and H_XLXB using the XB+XL perfect match probemask, expression divergence between XL and H_XLXB is slightly less than between XB and H_XLXB, but similar in terms of the number of significantly divergently expressed genes. Out of 2143 probesets included in this analysis, 182 genes are significantly upregulated in XL testis compared to H_XLXB testis whereas 210 genes are significantly upregulated in H_XLXB testis compared to XL testis. 280 genes are significantly upregulated in XB testis compared to H_XLXB testis whereas 245 genes are significantly upregulated in H_XLXB testis compared to XB testis. The number of significantly upregulated genes in each parental species compared to H_XLXB is significantly higher in the comparison with XB than the comparison with XL (182 versus 280, G = 20.95, P&lt;0.001, two-sided test). But the number of significantly upregulated genes in H_XLXB compared to each parental species is not significantly different (210 versus 245, G = 2.69, P = 0.20, two-sided test). Therefore, the difference in the number of significantly divergently expressed genes in each comparison between a parental species and hybrids is attributable to more genes being upregulated in XB compared to H_XLXB than are upregulated in XL compared to H_XLXB. Thus, the proportion of divergently expressed genes in XB testis compared to H_XLXB testis is about 1.34 times as large as the proportion of divergently expressed genes in XL testis compared to H_XLXB testis (##TAB##0##Table 1##). But, as mentioned earlier, some or all of this bias could be because we retained probes in this analysis based on sequences of genes that are expressed in XB testis.</p>", "<p>While this 1.34 fold asymmetry in divergent expression between the parental species and hybrids is significant (525 versus 392 genes, G = 19.36, df = 1, P&lt;0.001), it is in sharp contrast with the 28 fold difference reported in comparisons between testis tissue of XL, XM, and H_XLXM where 3995 genes were divergently expressed between XM and H_XLXM but only 142 genes were divergently expressed between XL and H_XLXM [##TAB##0##Table 1##; 17]. The difference in the proportion of divergently expressed genes in this study compared to ##REF##17712429##[17]## is significant. More specifically, a re-sampling test (see Methods) indicates that there is a significantly higher proportion of divergently expressed genes between XL and H_XLXB using the XB+XL perfect match probemask than were reported between XL and H_XLXM by ##REF##17712429##[17]## using a gDNA probemask (P&lt;0.001). Likewise, there is a significantly lower proportion of divergently expressed genes between XB and H_XLXB using the XB+XL perfect match probemask than were reported between XM and H_XLXM by ##REF##17712429##[17]## (P&lt;0.001).</p>", "<p>With respect to misexpression – which we define as hybrid expression that is not intermediate with respect to the expression of each parental species – using the XB+XL perfect match probemask, we find that only 13 genes are significantly upregulated in testis of H_XLXB with respect to testis of both XL and to XB and that 16 genes are significantly upregulated in testis of XL and XB with respect to testis of H_XLXB. This difference is not significant (G = 0.31, df = 1, P = 0.58).</p>", "<title>Comparison of gDNA hybridizations within and between species</title>", "<p>To further explore the basis of the discrepancy in the level of asymmetry of divergent expression recovered by our results using the XB+XL probemask and previous studies, we re-analyzed testis expression data from XL, XB, and H_XLXB using the XB/XL gDNA probemask that was based on our new gDNA hybridizations. We also re-analyzed testis and ovary expression data from XL, XM, and H_XLXM using the XM/XL gDNA probemask that was based on our new gDNA hybridizations.</p>", "<p>We compared our gDNA hybridizations to those of ##REF##17712429##[17]##, ##REF##18331635##[18]##. We ranked all of the probes on the chip by the gDNA hybridization intensity and then divided these ranks into 25 bins. Comparison to the gDNA ratio of each probe indicates that the median intensity of hybridization was lower in the non-target species (XM or XB) than the target species (XL) for most bins (##FIG##0##Fig. 1##). Probes with a gDNA ratio near one tended to have lower gDNA hybridization intensities in both the non-target and the target species than other probes on the chip, and the target species (XL) tends to have a more dynamic relationship between probe intensity and the gDNA ratio. Thus, at least on the Affymetrix GeneChip® <italic>Xenopus laevis</italic> Genome Array, probe selection on the basis of a gDNA hybridization ratio near one appears to have an unintended consequence of retaining probes with low gDNA hybridization intensities in both species. This was true in gDNA hybridizations performed by our lab and also by another lab (##FIG##0##Fig. 1##), thus it is not attributable to differences in laboratory procedure.</p>", "<p>Our XB gDNA hybridization was less intense than our XM hybridization even though we attempted to control for the amount of gDNA used in the hybridization, and even though these species are equally diverged from XL (##FIG##0##Fig. 1##). This variation probably is technical in nature and underscores the challenge of generating comparable gDNA hybridizations for different species. Below we report results derived from analyses based on our gDNA hybridizations for XL, XB, and XM; as detailed below, these results are qualitatively similar to those recovered with the gDNA probemask of ##REF##17712429##[17]##, ##REF##18331635##[18]##.</p>", "<title>Is the ratio of genomic DNA hybridization a reliable way to detect perfect match probes on the <italic>Xenopus laevis</italic> Affymetrix chip?</title>", "<p>Probes that perfectly match sequences from XL and XB have a wide range of XB/XL gDNA ratios (##FIG##1##Fig. 2A##). Under a best-case scenario, this indicates that using the gDNA ratio as a criterion for probe retention will not retain all perfect match probes. But we also found that other probes that we know mismatch both paralogs of genes in XB have a range of XB/XL gDNA ratios that overlaps extensively with the gDNA ratios of probes that perfectly match both species (##FIG##1##Fig. 2B##). This point is also illustrated by examination of four probesets on the <italic>Xenopus laevis</italic> Affymetrix microarray that were designed to interrogate XB transcripts: XlAffx.1.5.S1_at, XlAffx.1.9.S1_at, XlAffx.1.10.S1_at, and XlAffx.1.12.S1_at. Sixty out of the 64 probes in these four probesets do not perfectly match XL, and these also have a broad range of gDNA ratios (##FIG##1##Fig. 2A##). Together these observations indicate that gDNA ratios provide a poor basis for selection of perfect match probes in non-target species on the Affymetrix GeneChip® <italic>Xenopus laevis</italic> Genome Array. In addition to not retaining many probes that perfectly match both species, this approach almost certainly results in the retention of probes that do not perfectly match the non-target species.</p>", "<title>Does it matter if some probes with differential performance between treatments are included in the analysis?</title>", "<p>When testis expression data from XL, XB, and H_XLXB are analyzed using our XB/XL gDNA probemask or using our XM/XL gDNA probemask, we recover similar results to the analysis of testis expression data from XL, XM, and H_XLXM by Malone et al. ##REF##17712429##[17]##. This suggests that evolutionary differences between XB and XM, possible differences in the geographic origin of XL animals, and variation in laboratory procedures associated with microarray hybridizations together had a much smaller impact on the results than did the type of probe mask used in the analysis. More specifically, in this analysis the asymmetry in expression divergence is significant and more substantial than results from the XB+XL perfect match probemask such that expression in the hybrid appears much more similar to the target than the non-target species (##TAB##0##Table 1##). This is because using a gDNA probemask instead of a perfect match probemask results in a significantly lower proportion of genes that are divergently expressed in the comparison between XL and H_XLXB and a significantly higher proportion of genes that are divergently expressed between XB and H_XLXB (P≤0.002 for both comparisons).</p>", "<p>We explored alternative analytical approaches including invariant set (IS) normalization ##REF##11532216##[28]## and the probe logarithmic intensity error (PLIER) method for calculating signal intensity ##UREF##5##[29]##. These procedures produce results that are qualitatively similar to those recovered with RMA normalization with each probemask. The asymmetry in divergent expression in testis between each parental species and the hybrid with the XB+XL perfect match probemask is of similar magnitude in each of these analyses (1.34, 1.45 and 1.39 for RMA, IS, and PLIER, respectively). Likewise, more than twice as much asymmetry in divergent expression in testis is recovered when RMA, IS, or PLIER normalization are used with gDNA probemasks (i.e. there are more divergently expressed genes between the non-target species and the hybrid than between XL and the hybrid with these probemasks; data not shown). Thus we conclude that the method of normalization also does not account for the substantial differences in results that are obtained from perfect match versus gDNA probemasks.</p>", "<title>Rank difference</title>", "<p>The nature of the discrepancy between results obtained from these different probemasks is further illuminated by consideration of some of the technical aspects of the analysis. When microarray data are normalized it is generally assumed that the overall distribution of expression intensities within each treatment is similar ##UREF##6##[30]##–##REF##11842121##[32]##. Moreover, most normalization methods were developed for comparisons between treatments with expression divergence at only a few genes ##REF##17008090##[33]##. When data are normalized with the quantile method ##UREF##6##[30]##, for example, which was used in this study and in ##REF##17024524##[16]##–##REF##18331635##[18]##, the expression intensities of each probe are ranked and replaced by the average intensity of each quantile (each rank). This procedure yields identical distributions of overall expression intensities across treatments, even if they were very different to begin with.</p>", "<p>If the overall distribution of expression intensities was similar in each treatment before normalization, it is reasonable to expect that the magnitude and direction of expression divergence should be unbiased – that for a given magnitude of expression divergence, a similar number of genes will be upregulated in one treatment as is upregulated in the other. To test this, we calculated the difference in expression rank for each gene included in the analysis, with the lowest rank corresponding to the gene with the lowest expression as depicted in ##FIG##2##Fig. 3##. Additionally, the skew of this distribution was quantified by the Pearson skewness coefficient ( = 3*(mean-median)/standard deviation). Departure of the observed median rank difference and skew of the distribution of rank differences from the null hypotheses of a median and skew of zero was assessed by comparison to a null distribution generated from 1000 randomized ranks using scripts written in PERL.</p>", "<p>When interspecific data from the target species and a non-target species were analyzed using a gDNA probemask, the median rank difference was negative and this median departed significantly and substantially from zero (##TAB##1##Table 2##). The skew of the distribution of rank differences was significantly and substantially positive in these interspecific comparisons (##TAB##1##Table 2##). While these metrics are not independent because the median is used in the calculation of skew, they provide qualitative information about the rank difference distributions in these analyses. Because we calculated the rank difference by subtracting the non-target rank from the target rank, a negative median indicates that the non-target sequences tend to be upregulated to a greater degree than do the target sequences. A positive skew of this distribution (##TAB##1##Table 2##) indicates a tail on the right, suggesting that some probesets have a much higher rank (higher expression) in XL but not the reverse.</p>", "<p>In contrast, when intraspecific comparisons were analyzed with gDNA probemasks, the median and skew never departed as substantially from the null expectation as the interspecific comparisons between a target and non-target species, although occasionally the intraspecific departure was significant (##TAB##1##Table 2##). When the XB+XL perfect match probemask was used in the analysis, the median and skew were not significantly different from the null expectation (##TAB##2##Table 3##). While occasional departure from the null in some intraspecific comparisons between different XL tissues probably has a biological basis and could also stem from variation between laboratories in microarray protocol, these comparisons suggest that the substantially negative median and positive skew of the rank difference in interspecies comparisons analyzed with gDNA probemasks has a technical rather than a biological basis.</p>", "<title>Spearman rank correlation</title>", "<p>When gDNA probemasks are used, we suspected that differential performance of some probesets in the non-target species could cause a spurious signal of upregulation <italic>and</italic> downregulation compared to another species (##FIG##2##Fig. 3##). One class of significantly differently expressed genes – those that appear to be upregulated in the target species (XL) – could result when probes hybridize poorly to transcripts of the non-target species. The other class of significantly differently expressed genes – those that appear to be upregulated in the non-target species (XB or XM) – could result when the ranks of some genes in the non-target species are elevated as a result of the other genes that are interrogated by biased probes having a lower rank (##FIG##2##Fig. 3##). A key difference between these two classes of divergently expressed genes is that a larger proportion of the genes that appear upregulated in XL are interrogated by probes with differential performance (bias) between species. In analyses with a gDNA probemask, therefore, we predicted that the expression rank of genes that appear to be significantly upregulated in the non-target species would be highly correlated with the expression rank of these genes in the target species. We expected this correlation to be much higher than the correlation between the ranks of genes upregulated in the target species and the rank of these same genes in the non-target species.</p>", "<p>To test this, we calculated the Spearman's rank correlation (SRC) of the rank in each treatment of (i) genes upregulated in the non-target species and (ii) genes upregulated in the target species. Under our hypothesis that many of the genes that are upregulated in the non-target species are false positives, we expected that the SRC would be much higher in (i) than in (ii). To quantify this expectation, we calculated the absolute value of the difference in the SRC in (i) and (ii) for the interspecies comparisons, and we refer to this difference as δSRC. For comparative purposes, δSRC was calculated for interspecific comparisons between XL and a non-target species, comparisons between each species and a hybrid, and intraspecific comparisons between different tissues of XL, and this was performed for analyses with each type of probemask.</p>", "<p>The data support our expectation. When the XB/XL gDNA probemask or the XM/XL gDNA probemask are used in interspecific comparisons, the δSRC of the rank of genes upregulated in the non-target species is substantially higher than that of genes upregulated in the target species or in hybrids (##TAB##1##Table 2##). When comparisons were made between tissue types in XL or within a tissue type of XL and a hybrid using these gDNA probemasks, extreme differences between δSRC of each of these classes of genes were not observed (##TAB##1##Table 2##). A high δSRC was not observed in any of the analyses with the XB+XL perfect match probemask (##TAB##2##Table 3##). Furthermore, we found other signs of technical bias in results generated with gDNA probemasks, but not the XB+XL perfect match probemask, by comparing the mean rank of significantly upregulated genes (##SUPPL##0##Supporting Information S1##, ##SUPPL##1##Table S1##, ##SUPPL##2##Table S2##).</p>", "<p>Taken together, these observations are consistent with the notion that the use of probemasks based on gDNA ratios on the Affymetrix GeneChip® <italic>Xenopus laevis</italic> Genome Array produces spurious results when comparisons are made directly between species or between a non-target species and a hybrid, irrespective of tissue type. When gDNA probemasks are used, many of the genes that are putatively upregulated in the non-target species are actually false positives whose high ranks are an artifact of the low ranks of poorly performing probesets. Of course, this group of genes may include some genes that are not false positives, but it is not clear which ones these are. We suspect then, albeit with caveats discussed below, that our analysis with the XB+XL perfect match probemask is a closer approximation of biological variation than that recovered by ##REF##17712429##[17]##, ##REF##18331635##[18]##.</p>" ]

[ "<title>Discussion</title>", "<title>Probe selection by genomic hybridization</title>", "<p>A challenge to the implementation of single-species microarrays in comparative transcriptomics is the identification of unbiased probes. Due to differences from the target species, such as sequence divergence, non-target transcripts will exhibit a range of probe hybridization efficiencies that cause technical variation in hybridization intensities. In comparative analyses, normalization may overcompensate for genes with lower than average divergence and undercompensate for genes with higher than average divergence ##REF##15867429##[34]##. Exacerbating this problem, our analysis of confirmed perfect match probes in a target and a non-target species illustrates that the gDNA ratio is an unreliable metric with which to identify unbiased probes on the Affymetrix GeneChip® <italic>Xenopus laevis</italic> Genome Array. This approach selects probes with low gDNA intensity (##FIG##0##Fig. 1##), misses probes that do perfectly match both species (##FIG##1##Fig. 2A##), and includes probes that do not perfectly match both species (##FIG##1##Fig. 2B##). The implications of this are large and affect fundamental conclusions of the analysis, such as which and how many genes are significantly or not significantly differently expressed. Notably, our analyses suggest that including biased probes in a microarray analysis leads not only to spurious results from these biased probes, but affects conclusions drawn from probes that are interrogated by probes that perform equally well in both species. We anticipate, therefore, that comparisons between species using probes that are selected by gDNA ratios, including the comparison between XB or XM and XL that are presented here, are characterized by a high level of false positives as well as false negatives. Many of the genes from this type of analysis that are putatively upregulated in the target species are actually interrogated by probes that do not perform equivalently in the non-target species. Many of the genes that are putatively upregulated in the non-target species are actually genes whose ranks have been elevated as an artifact of other probes that do not perform equivalently in the non-target species. It is therefore not only necessary to retain as many perfect match probes as possible, but also to exclude biased probes from microarray analyses.</p>", "<title>Gene duplication</title>", "<p>Another concern with the application of this microarray to non-target clawed frog species relates to whole genome duplication. Because XL, XB and XM are tetraploid, asymmetry in cross-hybridization between paralogous transcripts could influence results. For example, a probe might hybridize to only one paralog in one species but to both paralogs of genes in another species, either as a result of sequence divergence or because both are expressed in one but not the other. This problem is aggravated by species-specific pseudogenization. Estimates of the percent of duplicated genes in XL that are still expressed (not pseudogenes) range from 77% ##REF##8277859##[35]## to a probably more accurate estimate of less than 50% ##UREF##7##[36]##, ##UREF##8##[37]##. Divergence of the ancestor of XL from the ancestor of (XM+XB) occurred about halfway between the time of whole genome duplication and the present ##REF##15324848##[19]##, ##REF##16683033##[22]##, ##REF##18261230##[27]##. For this reason, the frequency of expressed orthologous transcripts in XL and non-target species such as XB and XM is far below 100% as a result of “divergent resolution” – the retention of different (non-orthologous) paralogs of genes in each species ##UREF##9##[38]##.</p>", "<p>That Affymetrix microarrays do not effectively discriminate between different but closely related duplicated genes has been suggested for allopolyploid wheat ##REF##17364174##[39]##. However, we performed a power analysis that indicated that probes on the XL microarray performed consistently in distinguishing expression of each paralog after the application of probemasks with different specificities for a target paralog [i.e. varying numbers of mismatches to the non-target paralog; 27]. But within <italic>Xenopus</italic>, orthologs are more similar to each other than are paralogs derived from genome duplication because genome duplication occurred before speciation. Orthologous but not identical sequences (from different species) thus have greater potential to be able to hybridize strongly but not equivalently to probes directed against one XL paralog, than do co-expressed paralogs within XL. These concerns are relevant to all of the analyses presented here, including the ones that use the XB+XL perfect match probemask.</p>", "<title>Conclusions</title>", "<p>Previous work has explored factors in addition to sequence divergence that influence probe hybridization efficiency in different species, such as variation in labeling, overlap of oligonucleotide probes, alternative splicing, sequence homology to non-target transcripts, insertion/deletion differences, and intraspecific polymorphism ##REF##17008090##[33]##, ##REF##15867429##[34]##, ##REF##17364174##[39]##–##REF##17480226##[43]##. Some or all of these variables might be at play here – sequence divergence, for example, has already been shown to influence microarray hybridization efficiency in clawed frogs ##REF##16397297##[44]##. While sequence mismatches might not substantially affect the ability of microarrays to detect misexpression ##UREF##10##[45]##, ##REF##17384014##[46]##, it seems probable that sequence mismatches could cause bias if it varies among treatments such as when expression of two species are compared using an array designed for only one of them. Therefore, an experimental design that has consistent bias across treatments, in which one compares ‘apples to apples’ ##UREF##11##[47]##, has the potential to provide useful information from non-target species. Examples of more appropriate experimental designs include (a) using a microarray designed for another species with a non-target species but only comparing intraspecific expression levels within the non-target species, (b) constructing custom arrays for each species (or hybrid) of interest, and (c) building a custom array with probes directed against each species ##REF##17384014##[46]##. Another important measure for comparative analyses using single-species arrays is the validation of results using microarray-independent approaches, such as real-time quantitative PCR. The biases suggested by our analyses have implications for studies that deploy Affymetrix microarrays for interspecific comparisons, particularly ##REF##17024524##[16]##–##REF##18331635##[18]##, and could also be a concern for expression studies of species or genes with population structure, high mutation rate, or large effective population size.</p>" ]

[ "<title>Conclusions</title>", "<p>Previous work has explored factors in addition to sequence divergence that influence probe hybridization efficiency in different species, such as variation in labeling, overlap of oligonucleotide probes, alternative splicing, sequence homology to non-target transcripts, insertion/deletion differences, and intraspecific polymorphism ##REF##17008090##[33]##, ##REF##15867429##[34]##, ##REF##17364174##[39]##–##REF##17480226##[43]##. Some or all of these variables might be at play here – sequence divergence, for example, has already been shown to influence microarray hybridization efficiency in clawed frogs ##REF##16397297##[44]##. While sequence mismatches might not substantially affect the ability of microarrays to detect misexpression ##UREF##10##[45]##, ##REF##17384014##[46]##, it seems probable that sequence mismatches could cause bias if it varies among treatments such as when expression of two species are compared using an array designed for only one of them. Therefore, an experimental design that has consistent bias across treatments, in which one compares ‘apples to apples’ ##UREF##11##[47]##, has the potential to provide useful information from non-target species. Examples of more appropriate experimental designs include (a) using a microarray designed for another species with a non-target species but only comparing intraspecific expression levels within the non-target species, (b) constructing custom arrays for each species (or hybrid) of interest, and (c) building a custom array with probes directed against each species ##REF##17384014##[46]##. Another important measure for comparative analyses using single-species arrays is the validation of results using microarray-independent approaches, such as real-time quantitative PCR. The biases suggested by our analyses have implications for studies that deploy Affymetrix microarrays for interspecific comparisons, particularly ##REF##17024524##[16]##–##REF##18331635##[18]##, and could also be a concern for expression studies of species or genes with population structure, high mutation rate, or large effective population size.</p>" ]

[ "<p>Conceived and designed the experiments: FJJC BJE. Performed the experiments: FJJC DI BJE. Analyzed the data: FJJC BJE. Contributed reagents/materials/analysis tools: BJE. Wrote the paper: FJJC BJE.</p>", "<title>Background</title>", "<p>Prefabricated expression microarrays are currently available for only a few species but methods have been proposed to extend their application to comparisons between divergent genomes.</p>", "<title>Methodology/Principal Findings</title>", "<p>Here we demonstrate that the hybridization intensity of genomic DNA is a poor basis on which to select unbiased probes on Affymetrix expression arrays for studies of comparative transcriptomics, and that doing so produces spurious results. We used the Affymetrix <italic>Xenopus laevis</italic> microarray to evaluate expression divergence between <italic>X. laevis</italic>, <italic>X. borealis</italic>, and their F1 hybrids. When data are analyzed with probes that interrogate only sequences with confirmed identity in both species, we recover results that differ substantially analyses that use genomic DNA hybridizations to select probes.</p>", "<title>Conclusions/Significance</title>", "<p>Our findings have implications for the experimental design of comparative expression studies that use single-species microarrays, and for our understanding of divergent expression in hybrid clawed frogs. These findings also highlight important limitations of single-species microarrays for studies of comparative transcriptomics of polyploid species.</p>" ]

[ "<title>Supporting Information</title>" ]

[ "<p>We thank Pawel Michalak for providing expression data from XL, XM and H_XLXM and genomic hybridizations from XL and XM. We also thank Jonathan Dushoff, Wilfried Haerty, John Hammond, Neil Graham, Pawel Michalak, Richard Morton, and the reviewers for advice on analyses, discussions, and comments on this manuscript, and Mohammad Iqbal Setiadi and David Anderson for assistance with rearing animals. This research was supported by the Canadian Foundation for Innovation, the National Science and Engineering Research Council, and McMaster University.</p>" ]

[ "<fig id=\"pone-0003279-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003279.g001</object-id><label>Figure 1</label><caption><title>Genomic hybridization intensities (gDNA intensity) of XL, XB, and XM vary with respect to the non-target to target ratio of these intensities (gDNA ratio).</title><p>This graph depicts the median gDNA intensities of all probes on the chip ranked by their gDNA ratio into 25 bins; each bin contains 10,000 probes except the 25^th bin, which contains 7852 probes. The area in gray corresponds with the range of gDNA ratios of probes that are retained using the method of Malone et al. (2007). XL gDNA ratios are represented by filled symbols and non-target gDNA ratios are represented by unfilled symbols. Shown are relationships between the median gDNA intensity of each bin and the median gDNA ratio of each bin for (A) our XM and XL gDNA hybridizations, (B) the XM and XL gDNA hybridizations of Malone et al. (2007), and (C) our XB and XL gDNA hybridizations.</p></caption></fig>", "<fig id=\"pone-0003279-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003279.g002</object-id><label>Figure 2</label><caption><title>The gDNA ratio of probes that perfectly match (PM) XL and XB overlaps extensively with the gDNA ratio of probes that mismatch (MM) one species.</title><p>(A) XB gDNA intensity versus gDNA ratio of PM probes in XL, XL and XB, and XB. PM probes in XL are in black, PM probes in XL and XB are in red, and PM probes in XB but not XL are in green. (B) XB gDNA intensity versus gDNA ratio of MM probes in XB. For comparative purposes, PM probes in XL are again in black. Probes that mismatch both paralogs of genes in XB with one, two, three, or four base pair differences are indicated in red, blue, green, and yellow respectively.</p></caption></fig>", "<fig id=\"pone-0003279-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003279.g003</object-id><label>Figure 3</label><caption><title>An example of how poor performance of a few probes in the non-target species can affect the rank of many genes, even ones that perform equally in both species.</title><p>Ten genes (a, b, c, d, e, f, g, h, i, and j) are ranked according to their expression intensity. In the non-target species, probes directed against genes e, h, and j perform poorly and have a low rank in the non-target species due to sequence divergence, even though there actually is no expression divergence. This elevates the rank of many other genes, causing an overall negative median rank difference (RD) and a positively skew in the RD distribution. In this example, significantly upregulated genes in the target species tend to have a higher average rank in this species (9) than the significantly upregulated genes in the non-target species do in that species (6.5). Significantly upregulated genes in the target species have a lower average rank in the non-target species (3) than the significantly upregulated genes in the non-target species do in the target species (3.5).</p></caption></fig>" ]

[ "<table-wrap id=\"pone-0003279-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003279.t001</object-id><label>Table 1</label><caption><title>Proportions of divergently expressed genes differ significantly depending on what probemask is used in the analysis.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"3\" align=\"left\" rowspan=\"1\">XL versus H</td><td colspan=\"3\" align=\"left\" rowspan=\"1\">NT versus H</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Analysis</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">SUXL</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">SUH</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Proportion divergently expressed</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">SUNT</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">SUH</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Proportion divergently expressed</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ratio of divergently expressed genes</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Number of genes analyzed</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XB + XL perfect match probemask</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">182</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">210</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.18</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">280</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">245</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.34</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2143</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XB/XL gDNA probemask</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">97</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">468</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">299</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.46</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.36</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1672</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XM/XL gDNA probemask</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">417</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">572</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1430</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1248</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.31</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.71</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8721</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Malone et al. (2007)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">92</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2236</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1759</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.35</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">28.13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11485</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Malone et al. (2008)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">777</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">839</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4349</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2930</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.63</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.50</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11485</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pone-0003279-t002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003279.t002</object-id><label>Table 2</label><caption><title>Analyses with gDNA probemasks produce different rank difference distributions in interspecific and intraspecific comparisons.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Comparisons with XB/XL gDNA probemask<xref ref-type=\"table-fn\" rid=\"nt103\">a</xref>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Interspecific comparisons</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Median</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">skew</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">δSRC</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XB_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−54*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.409*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2837^Χ\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B-XB_B\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−58*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.476*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1811^Χ\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XB_B\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−51*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.376*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1595^Χ\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B-XB_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−54.5*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.391*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2231^Χ\n</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Hybrid to parental comparisons</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-H(XLXB)_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.016</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0245</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">H(XLXB)_T-XB_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−51</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.470</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2986^Χ\n</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Intraspecific comparisons</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T_XL_E\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.140*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0794</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.004</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0304</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XL_O\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.161*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0154</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_O-XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0446</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_O_XL_E\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.166*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0578</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_E_XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.178*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1325</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T_XL_B\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.114*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0689</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B_XL_E\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.096</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1578</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B_XL_o\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.079</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0274</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B_XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.018</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1608</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">\n<bold>Comparisons with XM/XL gDNA probemask<xref ref-type=\"table-fn\" rid=\"nt104\">b</xref></bold>\n</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Interspecific comparisons</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XM_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−269*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.417*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2153^Χ\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_O-XM_O\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−390*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.568*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2501^Χ\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XM_O\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−250*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.374*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1335^Χ\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_O-XM_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−338*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.429*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1906^Χ\n</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Hybrid to parental comparisons</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-H(XLXM)_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.032</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0129</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">H(XLXM)_T-XM_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−151*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.257*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1810^Χ\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_O-H(XLXM)_O\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−32</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.092*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0077</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">H(XLXM)_O-XM_O\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−187*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.309*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1642^Χ\n</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Intraspecific comparisons</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T_XL_E\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.024</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0091</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_O-XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.031</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0642</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T_XL₀\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">109*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.198*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0535</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T_XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.081*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0043</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_O-XL_E\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−67*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.175*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0477</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_E-XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">77*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.192*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1313</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T_XL_B\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.031</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0639</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B_XL_E\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.009</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0598</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B_XL_O\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">69*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.095*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0278</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B_XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.074*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0749</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pone-0003279-t003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003279.t003</object-id><label>Table 3</label><caption><title>Analysis with the XB+XL perfect match probemasks produces results with similar rank difference statistics in interspecific and intraspecific comparisons.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Comparisons with XB+XL perfect match probemask<xref ref-type=\"table-fn\" rid=\"nt106\">a</xref>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Interspecific comparisons</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">median</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">skew</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">δSRC</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XB_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.009</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0029</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B-XB_B\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.014</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0094</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XB_B\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.035</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0076</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B-XB_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.007</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0045</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hybrid to parental comparisons</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-H(XLXB)_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.050</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0167</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XB_T-H(XLXB)_T\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.031</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0197</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Intraspecific comparisons</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T_XL_E\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−22</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.130*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0946</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.079</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0558</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T-XL_O\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.072</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0513</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_O-XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.018</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0174</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_O_XL_E\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.113*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0343</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_E_XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.030</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0408</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_T_XL_B\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.027</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0008</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B_XL_E\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−29*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.154*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0619</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B_XL_o\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.052</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0109</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">XL_B_XL_T11\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.110*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0710</td></tr></tbody></table></alternatives></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"pone.0003279.s001\"><label>Supporting Information S1</label><caption><p>Inspection of mean rank of significantly upregulated genes provides additional support for bias in gDNA probemasks.</p><p>(0.05 MB DOC)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003279.s002\"><label>Table S1</label><caption><p>Mean ranks of significantly upregulated genes when analyzed with gDNA probemasks.</p><p>(0.03 MB XLS)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003279.s003\"><label>Table S2</label><caption><p>Mean ranks of significantly upregulated genes from analysis using the XB+XL perfect match probemask.</p><p>(0.02 MB XLS)</p></caption></supplementary-material>" ]

[ "<table-wrap-foot><fn id=\"nt101\"><label/><p>Results are shown from pairwise comparisons between XL and H (XL versus H) and between a non-target species and a hybrid (NT versus H). All analyses compare testis tissue except the ones from ##REF##18331635##[18]##, which compare ovary tissue. For each comparison, the number of significantly upregulated genes in XL (SUXL), significantly upregulated genes in the hybrid (SUH), and significantly upregulated genes in the non-target species (SUNT) is listed. The proportion of divergently expressed genes is equal to the total from the (NT versus H) comparison divided by the total from the (XL versus H) comparison.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt102\"><label/><p>Median and skew of the rank difference distribution and δSRC (see text) are reported. Suffixes after species acronyms (XL, XB) refer to the tissue type analyzed: O (ovary), T (testes), T11 (tadpole stage 11), B (brain), and E (egg). Asterisks indicate significant departure from the null. For δSRC, interspecific comparisons and comparisons between a non-target species and a hybrid are higher than other comparisons, and are indicated (^Χ). In all of these cases, the correlation (i) is higher than the correlation (ii).</p></fn><fn id=\"nt103\"><label>a</label><p>1672 probesets, CI median: 0±24, CI skew: 0±0.107</p></fn><fn id=\"nt104\"><label>b</label><p>8721 probesets, CI median: 0±54, CI skew: 0±0.046</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt105\"><label/><p>Acronyms and statistics follow ##TAB##1##Table 2##.</p></fn><fn id=\"nt106\"><label>a</label><p>2143 probesets, 95% confidence interval (CI) of the median = 0±25, and CI of the skew = 0.000±0.087</p></fn></table-wrap-foot>", "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>This research was supported by the Canadian Foundation for Innovation, the National Science and Engineering Research Council, and McMaster University.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"pone.0003279.s001.doc\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003279.s002.xls\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003279.s003.xls\"><caption><p>Click here for additional data file.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>Metabolism is a complex process that takes place for producing energy and forms the driving force for cellular activity. It involves a large number of chemical reactions/conversions carried out by living organisms as they feed, grow and reproduce. A cascade of such reactions/conversions form a highly branched network. A metabolic network consists of many reactions and transport processes associated with the production and depletion of cellular metabolites. Metabolic pathways are defined as coordinated series of biochemical reactions in which the product of one reaction is the reactant of the subsequent one in the chain. Examples of metabolic pathways include Glycolysis, the Krebs cycle and the Pentose phosphate pathways.</p>", "<p>There exist various categories of data models for analyzing metabolic pathways. The huge amount of genomic data, available at present, has led to the construction of genome-scale models of metabolism [##REF##15967022##1##]. The biological information from genomes can be extracted by constructing computational models and subsequently making predictions from them [##REF##17123434##2##,##UREF##0##3##]. Flux balance analysis is a constraint-based approach [##UREF##1##4##, ####REF##11062431##5##, ##REF##16772264##6####16772264##6##] that spans the closed solution space within which many steady state solutions would lie. Optimization techniques are used to find out a single state, within this space of allowed states, which reflects the actual flux distribution of the cell under a defined set of nutrient conditions [##REF##11246024##7##,##REF##10356245##8##]. The utilities of such modeling include predicting systems behavior, identifying crucial steps in systems regulation. In [##REF##7579901##9##], Cascante et. al. have shown how this kind of modeling can be used for characterizing fermentation pathway of <italic>S. cerevisiae</italic>. Moreover, modeling and analysis of metabolic networks may be useful to perform rational drug design [##REF##11875424##10##].</p>", "<p>Reactions in a metabolic pathway are mostly enzymatic. That is, for a reaction A → B catalyzed by an enzyme E, the rate of production of B depends not only on the concentration of the substrate A but also on the concentration of E that is available for catalyzing the reaction. Assuming that sufficient amount of the substrate A being present, if the concentration of E is low (high) then the rate of production of B will also be low (high). In the extreme pathway analysis (one of the methods under flux balance approach) [##REF##10716907##11##], the authors have considered the reaction flux but not the enzyme concentration. This motivates us to develop a new method that considers both the substrate and enzyme concentration, thereby it becomes somewhat closer to real life situations than what extreme pathway analysis offers. We intend to undertake this endeavor in the present article.</p>", "<p>Here we develop a method for identification of a metabolic pathway, in terms of the level of enzyme concentration, which yields the maximum rate of production of a metabolite in the pathway starting from a given substrate. The method determines an optimal set of enzymes that is required to get an optimal metabolic pathway through which the rate of production of a metabolite is maximum. In other words, the method is able to determine a set of enzymes that needs to be expressed at a certain level for increasing the production of the target metabolite. The method, first of all, generates the possible flux vectors in the pathway. For this purpose, assuming steady state condition, we consider the basis vectors that span the null space of the given stoichiometric matrix. Then we take convex combination of these basis vectors to generate the flux vectors that satisfy certain inequality constraints. A set of weighting coefficients, corresponding to enzymes catalyzing biochemical reactions in the said pathway, is incorporated, and then a set of constraints incorporating these weighting coefficients is formulated. An objective function, in terms of these weighting coefficients, is formed, and then minimized under regularization method. The weighting coefficients corresponding to a minimum value of the objective function represent an optimal pathway. Fig. ##FIG##0##1## depicts the flowchart of the method that is easy to implement, yet workable. For simplicity, we have made some assumptions as mentioned in the methodology section.</p>", "<p>The effectiveness of the present method is demonstrated on two synthetic systems designed in [##UREF##2##12##,##UREF##3##13##], on two pentose phosphate, two glycolytic pathways [##UREF##4##14##], one large carotenoid biosynthesis pathway [##UREF##4##14##] and a network of core carbon metabolism [##REF##11708855##15##] of various organisms belonging to different phylogeny. The method is compared with the existing extreme pathway analysis [##REF##10716907##11##]. The major differences of the present method from the existing extreme pathway analysis have been pointed out. Finally, we provide biological relevance of the results. A possible validation from biological point of view along with the salient features of the method is also included. It has been demonstrated with a few examples that the method can be appropriately applied to the problems of metabolic engineering.</p>" ]

[ "<title>Method for identification of metabolic pathways</title>", "<p>In this section we describe the proposed method. First of all, we make some assumptions based on which we describe the method subsequently.</p>", "<title>Assumptions</title>", "<p>Here we assume that a large amount of substrate is present. Thus any sudden influx of the substrate from other pathways does not effect any change of the rate of production of the corresponding product. This is due to the limited availability of the enzymes in a pathway. In other words, the ratio of enzyme concentration to substrate concentration is very low. For simplicity, we have not considered any feedback inhibition on the enzyme activity. In other words, we are considering only the fraction of enzyme molecules that have not been inactivated due to feedback inhibitions.</p>", "<title>System definition</title>", "<p>A metabolic reaction network is a collection of enzymatic reactions and transport processes. A system boundary can be drawn around all these types of reactions that constitute internal fluxes operating inside the network. The system is closed to the passage of certain metabolites while others are allowed to enter and/or exit the system based on external sources and/or sinks that are operating on the network. The existence of an external source/sink on a metabolite necessitates the introduction of an exchange flux, which allows a metabolite to enter or exit the system boundary. These fluxes represent the inputs and outputs of the system.</p>", "<p>Consider a metabolic network with the substrate (starting metabolite) A and the final metabolite B (Fig. ##FIG##6##7##). Let, the metabolite B be reached through <italic>s </italic>different paths. That is, there are <italic>s </italic>biochemical reactions/conversions <italic>R</italic>₁, <italic>R</italic>₂,...<italic>R</italic>_{<italic>s </italic>}in the network involving the metabolite B. Let there be <italic>n </italic>reactions in the network, <italic>i.e</italic>., <italic>n </italic>fluxes. Some of them can be internal fluxes and the rest are exchange fluxes. If there are <italic>p </italic>internal and <italic>q </italic>exchange fluxes then <italic>n </italic>= <italic>p </italic>+ <italic>q</italic>. The internal and exchange fluxes are represented by <italic>v </italic>and <italic>b </italic>respectively. That is,</p>", "<p></p>", "<p>Now the rate of growth of the metabolite B on the substrate A, is obtained by taking algebraic sum of the weighted fluxes of reactions <italic>R</italic>₁, <italic>R</italic>₂,...<italic>R</italic>_{<italic>s</italic>}, and is given by</p>", "<p></p>", "<p>Thus the term <italic>z </italic>needs to be maximized for yielding maximum rate of growth of B. Here <italic>v</italic>_{<italic>k </italic>}is the flux of the reaction <italic>R</italic>_{<italic>k </italic>}involving only the metabolite B [##UREF##14##40##]. The term <italic>c</italic>_{<italic>k </italic>}in [0,1] denotes the weighting factor corresponding to this reaction <italic>R</italic>_{<italic>k</italic>}. <italic>c</italic>_{<italic>k </italic>}indicates the level of concentration of the enzyme catalyzing the reaction <italic>R</italic>_{<italic>k</italic>}. <italic>c</italic>_{<italic>k </italic>}= 1 indicates that the required amount of the enzyme catalyzing the reaction <italic>R</italic>_{<italic>k </italic>}is present. On the other hand, <italic>c</italic>_{<italic>k </italic>}→ 0 indicates that sufficient amount of enzyme is not present to carry out the reaction. Higher the value of <italic>c</italic>_{<italic>k</italic>}, higher is the concentration of the enzyme and vice-versa. The term <italic>v</italic>_{<italic>k </italic>}is considered to be positive if the reaction <italic>R</italic>_{<italic>k </italic>}yields the metabolite B, otherwise it is negative. A reversible reaction is considered as two separate reactions corresponding to forward and backward reactions. It is to be mentioned here that the role of <italic>c</italic>_{<italic>i </italic>}in Eq. (1) in extreme pathway analysis [##REF##10716907##11##] is different from that in the present method. In the earlier case, <bold>c </bold>is a <italic>unit vector</italic>, along a particular flux, whereas in the present method, <bold>c </bold>indicates the level of concentration of the various enzymes catalyzing the reactions in the network.</p>", "<title>Generation of flux vectors</title>", "<p>For solving the above-mentioned maximization problem, we require the values of the flux vectors <bold>v </bold>= [<italic>v</italic>₁, <italic>v</italic>₂,...,<italic>v</italic>_{<italic>n</italic>}]^{<italic>T </italic>}that cannot be obtained easily as the full dynamics is not known or becomes intrackable in most of the scenarios. In order to overcome this situation, we now propose an algorithm for generating flux vectors that satisfy approximately the quasi-steady state condition [##REF##10716907##11##]. That is, we generate those <bold>v </bold>which satisfies</p>", "<p></p>", "<p>and the inequalities in (4) and (5). Here <bold>S </bold>is the <italic>m </italic>× <italic>n </italic>stoichiometric matrix [##REF##16188931##41##] with <italic>m </italic>as the number of metabolites and <italic>n </italic>as the number of reactions. From a reaction database, <bold>S </bold>can be computed. Then the flux vectors <bold>v </bold>form the null space of <bold>S</bold>. In extreme pathway analysis, the approximately equality sign in Eq. (2) is replaced by the strict equality sign as the system is in a steady state scenario. The proposed method generates the flux vectors <bold>v </bold>as linear combinations of the basis vectors spanning the null space of <bold>S</bold>, and those satisfying the inequalities (4) and (5). We cannot always guarantee (due to finite memory, and the problem of overflow or underflow for representing a numerical value) the strict equality in Eq. (2) for the flux vectors that are generated. Thus we have considered approximate equality sign instead of strict equality. Since in practical situations, <italic>n </italic>&gt; <italic>m</italic>, Eq. (2) is under determined. So we proceed as follows:</p>", "<p>Step I:</p>", "<p>Generate basis vectors <bold>v</bold>_{<italic>b </italic>}that form the null space of the stoichiometric matrix <bold>S</bold>. Let the number of such basis vectors be <italic>l</italic>. (This is done by using standard routines and toolboxes of MATLAB.)</p>", "<p>Step II:</p>", "<p>(i) Generate <italic>l </italic>number of positive random numbers <italic>a</italic>_{<italic>p</italic>}, <italic>p </italic>= 1, 2,...,<italic>l</italic>.</p>", "<p>(ii) Generate a vector</p>", "<p></p>", "<p>until certain inequality constraint on v is satisfied for all its components. All the internal fluxes are non-negative yielding [##REF##10716907##11##]</p>", "<p></p>", "<p>The constraints on the exchange fluxes depends on their directions [##REF##10716907##11##]. These constraints can be expressed as</p>", "<p></p>", "<p>where <italic>α</italic>_{<italic>j </italic>}and <italic>β</italic>_{<italic>j </italic>}are either zero, or negative and positive infinity, respectively, based on the direction of the exchange flux. The activity of these exchange fluxes is considered to be positive if the metabolite is exiting or being produced by the system, and negative if the metabolite is entering or being consumed by the system. For all metabolites in which a source or sink may be present, the exchange flux can operate in a bidirectional manner and is unconstrained. Under the existence of a source (input), <italic>α</italic>_{<italic>j </italic>}is set to negative infinity and <italic>β</italic>_{<italic>j </italic>}to zero. On the other hand, if only a sink (output) exists on the metabolite, <italic>α</italic>_{<italic>j </italic>}is set to zero and <italic>β</italic>_{<italic>j </italic>}to positive infinity. If both a source and a sink are present for the metabolite then the exchange flux is bidirectional with <italic>α</italic>_{<italic>j </italic>}set to negative infinity and <italic>β</italic>_{<italic>j </italic>}to positive infinity leaving the exchange flux unconstrained. For further details on these issues, one may refer to [##REF##10716907##11##].</p>", "<p>Thus we generate a large number of flux vectors, satisfying the inequality constraints, which form the data set. The flux vectors along with their corresponding weighting factors are used to determine <italic>z</italic>. The optimization algorithm searches through this generated data set, and estimates the values of the weighting coefficients <italic>c</italic>_{<italic>k </italic>}(Eq. (7)) on convergence.</p>", "<title>Formulation of a new constraint</title>", "<p>Eq. (2) describes the quasi-steady state condition, which assumes that the concentration of the enzymes catalyzing various reactions in the network is present in the system at the required level. In other words, the genes that produce these enzymes need to be expressed at the required level. For a variety of reasons, in real systems, the genes that produce these enzymes may not be expressed at the required level. This imposes restrictions on the system, and for this purpose, we define a new constraint as</p>", "<p></p>", "<p>Here <bold>C </bold>is an <italic>n </italic>× <italic>n </italic>diagonal matrix whose diagonal elements are the components of the vector <bold>c</bold>. That is, if <bold>C </bold>= [<italic>γ</italic>_{<italic>ij</italic>}]_{<italic>n </italic>× <italic>n</italic>}, then <italic>γ</italic>_{<italic>ij </italic>}= <italic>δ</italic>_{<italic>ij</italic>}<italic>c</italic>_{<italic>i</italic>}, where <italic>δ</italic>_{<italic>ij </italic>}is the Kronecker delta. Note that <italic>c</italic>_{<italic>i </italic>}is the weighting factor corresponding to the enzyme catalyzing ith reaction in the network, irrespective of whether the reaction involves the metabolite B or not.</p>", "<p>Thus the problem of determining a metabolic pathway yielding maximum rate of production of a metabolite B starting from a substrate A, boils down to a maximization problem, where <italic>z </italic>is maximized with respect to <bold>c</bold>, subject to satisfying the constraint given in Eq. (6).</p>", "<title>Estimation of weighting coefficients <italic>c</italic>_{<italic>i</italic>}</title>", "<p>Combining Eq. (1) and Eq. (6), we can reformulate the objective function as</p>", "<p></p>", "<p>that needs to be minimized with respect to the weighting factors <italic>c</italic>_{<italic>i </italic>}for all <italic>i</italic>. The term <bold>Λ </bold>= [<italic>λ</italic>₁, <italic>λ</italic>₂,...,<italic>λ</italic>_{<italic>m</italic>}]^{<italic>T </italic>}is the regularizing parameter. For the sake of simplicity, we have considered here <italic>λ</italic>₁= ... = <italic>λ</italic>_{<italic>m </italic>}= <italic>λ </italic>(say). Minimization of <italic>y </italic>can be carried out in various ways. Here we have adopted the gradient descent technique [##UREF##15##42##]. Initially, a set of random values in [0, 1] corresponding to <italic>c</italic>_{<italic>i</italic>}'s are generated. The <italic>c</italic>_{<italic>i</italic>}'s are then modified iteratively using the gradient descent technique, where the amount of modification for <italic>c</italic>_{<italic>i </italic>}in each iteration is defined as</p>", "<p></p>", "<p>The term <italic>η </italic>is a small positive quantity indicating the rate of modification. For computing the values of Δ<italic>c</italic>_{<italic>i</italic>}'s, we use the following expression</p>", "<p></p>", "<p>Thus the modified value of <italic>c</italic>_{<italic>i </italic>}is given by</p>", "<p></p>", "<p><italic>c</italic>_{<italic>i</italic>}(<italic>t </italic>+ 1) is the value of <italic>c</italic>_{<italic>i </italic>}at iteration (<italic>t </italic>+ 1), which is computed based on the <italic>c</italic>_{<italic>i</italic>}-value at the iteration <italic>t</italic>. Regularization parameter <italic>λ </italic>is chosen empirically. Here we are varying the value of <italic>λ </italic>from 0.1 to 1.0 in steps of 0.1. Using the above mentioned method, for each value of <italic>λ</italic>, we finally get <italic>c</italic>_{<italic>i</italic>}-values for which <italic>y </italic>attains a minimum value. For each value of <italic>λ</italic>, <italic>y </italic>is minimized. We choose the specific <italic>λ</italic>-value for which the <italic>y</italic>-value is the minimum over all the minima obtained for different values of <italic>λ</italic>. The c-values corresponding to this minimum <italic>y </italic>are finally considered.</p>", "<p>The vector <bold>c </bold>corresponds to the flux vector <bold>v</bold>. That is, its <italic>i</italic>th component <italic>c</italic>_{<italic>i </italic>}(<italic>c</italic>_{<italic>i </italic>}<italic>ε </italic>[0,1]) is associated with the flux <italic>v</italic>_{<italic>i </italic>}of the <italic>i</italic>th reaction of a metabolic network. On minimization of <italic>y</italic>, some of the <italic>c</italic>_{<italic>i </italic>}values will attain non-zero values in [0,1] and the others are very close to zero. We consider a metabolic path to be an optimal one, if <italic>y </italic>is minimum and the <italic>c</italic>_{<italic>i</italic>}-values of all the enzymes catalyzing the reactions in that path are greater than zero. Otherwise, a low c-value (close to zero) corresponding to an enzyme catalyzing an intermediate reaction may result in an insufficient product. This will reduce the rate of the next reactions and hence the amount of the target metabolite. In other words, these non-zero <italic>c</italic>_{<italic>i</italic>}-values indicate an optimal pathway through which the rate of yield of metabolite B, being grown on the substrate A, becomes maximum. The major differences of the method from the existing extreme pathway analysis [##REF##10716907##11##] are as follows.</p>", "<p>• Unlike the extreme pathway analysis, the present method considers the presence of enzymes.</p>", "<p>• Extreme pathway analysis finds the flux vectors upon optimization, whereas the present method generates a set of some possible flux vectors and finds an optimal pathway in terms of weighting coefficients reflecting enzyme concentration.</p>", "<p>• Extreme pathway analysis considers individual reactions in the pathway in a sequential manner, whereas the present method considers all the reactions in parallel.</p>", "<p>The value of <italic>c </italic>corresponding to an enzyme E may be estimated <italic>in vitro </italic>in the following way. Let us assume the following enzymatic reactions</p>", "<p></p>", "<p>where S and P stand for substrate and product respectively. The terms <italic>k</italic>₁, <italic>k</italic>₂and <italic>k</italic>₃are rate constants. Let us also assume that <italic>x </italic>mole of S can produce <italic>y </italic>mole of P. Under this situation, assume that [<italic>E</italic>_{<italic>min</italic>}] is the minimum concentration of the enzyme E that is required to obtain the maximal rate (<italic>V</italic>_{<italic>max</italic>}) of product formation. Thus an estimate of <italic>c </italic>may be taken as</p>", "<p></p>", "<p>where [E] is the concentration level of the enzyme E which is required to get an optimal path. Note that the values of <italic>V</italic>_{<italic>max</italic>}, and the rate constants can be estimated <italic>in vitro </italic>[##UREF##16##43##]. Thus <italic>E</italic>_{<italic>min </italic>}can also be determined through [##UREF##16##43##]</p>", "<p></p>", "<p>and thereby [E] using c-value obtained by our method. On the other hand, if [E] can be determined <italic>in vitro</italic>, then the theoretical value of <italic>c </italic>(obtained by the proposed method) can be verified with the experimental value.</p>" ]

[ "<title>Results</title>", "<p>The proposed method is described in the methodology section. Here we provide a comparative analysis of the present method with extreme pathway analysis using two synthetic [##UREF##2##12##,##UREF##3##13##] and four different real life pathways. Real life pathways include pentose phosphate and glycolytic pathways of <italic>E. coli </italic>K-12 MG1655, <italic>T. pallidum </italic>and <italic>P. falciparum</italic>, a large network of carotenoid biosynthesis [##UREF##5##16##, ####UREF##6##17##, ##UREF##7##18##, ##REF##2656356##19##, ##REF##8033911##20####8033911##20##] and a network of core carbon metabolism [##REF##11708855##15##]. All these real life pathways are obtained from the KEGG database [##UREF##4##14##]. In order to restrict the size of the article, we have provided a brief account on these real-life pathways in the Additional File ##SUPPL##0##1##. Some of the results are included here while the others are provided in the Additional File ##SUPPL##0##1## for restricting the size of the article.</p>", "<title>Analysis of the results</title>", "<title>On the synthetic system in Fig. ##FIG##1##2##</title>", "<p>For the system in Fig. ##FIG##1##2##, we want to maximize the rate of yield of the metabolite P, starting from the substrate A. The system is composed of 10 reactions <italic>R</italic>₁, <italic>R</italic>₂,...,<italic>R</italic>₁₀involving 6 metabolites A, B, C, D, E and P. The reactions <italic>R</italic>₂and <italic>R</italic>₈are reversible. We associate the weighting factors <italic>c</italic>₁, <italic>c</italic>₂,...,<italic>c</italic>₁₀corresponding to the enzymes catalyzing these reactions respectively. There are 6 internal fluxes <italic>R</italic>₅, <italic>R</italic>₆,...,<italic>R</italic>₁₀and 4 exchange fluxes <italic>R</italic>₁, <italic>R</italic>₂,...,<italic>R</italic>₄as depicted in Fig. ##FIG##1##2##. The constraints as mentioned in the methodology section, and following [##UREF##3##13##] are as follows:</p>", "<p></p>", "<p>The terms <italic>α</italic>₂and <italic>α</italic>₈are -∞ because <italic>R</italic>₂and <italic>R</italic>₈are reversible. Moreover, <italic>β</italic>₂= 0 because exclusive growth on substrate A is considered. Finally, we assume that the maximal uptake rate of A is 1 (<italic>β</italic>₁= 1). Following the method described in the methodology section, we have generated a set of flux vectors.</p>", "<p>In order to maximize the rate of yield of P for growth on substrate A, the objective function <italic>y </italic>(Eq. (7)) is minimized, where <italic>z </italic>is given by <italic>z </italic>= <italic>c</italic>₉<italic>v</italic>₉+ <italic>c</italic>₁₀<italic>v</italic>₁₀- <italic>c</italic>₃<italic>v</italic>₃. We vary the value of <italic>λ </italic>from 0.1 to 1.0. Initially, we should always give stress on the maximization of the rate of yield rather than on the constraint. That is, initially <italic>λ </italic>should be kept small. As we go from <italic>λ </italic>= 0.1 to <italic>λ </italic>= 1.0, it implies that we are increasing the stress on the constraint, and finally both the rate of yield (<italic>z</italic>) and the constraint are treated equally. For each value of <italic>λ</italic>, we minimize <italic>y</italic>, and consider that set of <italic>c</italic>_{<italic>i</italic>}-values corresponding to the <italic>λ</italic>-value as the final solution, for which <italic>y </italic>becomes minimum. Here we have obtained an optimal pathway as <italic>R</italic>₁→ <italic>R</italic>₅→ <italic>R</italic>₉→ <italic>R</italic>₃, which is in accordance with earlier investigations [##UREF##3##13##]. The optimal pathway is obtained for <italic>λ </italic>= 1.0 in 85 iterations.</p>", "<p>Table ##TAB##0##1## shows a few pathways along with <italic>c</italic>-values and average amount (<italic>z</italic>) of the target P. Since, we have generated a set of flux vectors, we have considered average of these vectors to compute the average amount of the target product P. For example, the pathway <italic>R</italic>₁→ <italic>R</italic>₅→ <italic>R</italic>₉→ <italic>R</italic>₃yields the highest average <italic>z</italic>, and hence it is the optimal pathway. It is to be mentioned here that the paths involving the reactions <italic>R</italic>₆and <italic>R</italic>₇need to be activated to yield C and D respectively, as both C and D are required to produce P through these paths. The other synthetic pathway is included in Fig. 8 in Additional File ##SUPPL##0##1## in order to restrict the size of the article.</p>", "<p>We have varied the upper bound of the flux values to show the variation of enzyme concentrations (<italic>c</italic>-value) and the amount (<italic>z</italic>) of the target metabolite. The results are provided in Table ##TAB##1##2## for some high and low upper bounds. It is clear from Table ##TAB##1##2## that <italic>z</italic>-value, as expected, decreases with the decrease in upper bound. In all the cases, we have found the same optimal path although absolute <italic>c</italic>-values differ. This shows the consistency of the proposed method in determining optimal metabolic paths.</p>", "<title>On the Glycolytic Pathway in <italic>T. pallidum </italic>(Fig. ##FIG##2##3##)</title>", "<p>The glycolytic pathway in <italic>T. pallidum </italic>consists of 13 metabolites and 25 fluxes (Fig. ##FIG##2##3##). The starting metabolite is <italic>α </italic>-D-Glucose-1P and the target product is phosphoenolpyruvate. Thus we maximize the rate of yield <italic>z </italic>= <italic>c</italic>₂₅<italic>v</italic>₂₅- <italic>c</italic>₂₄<italic>v</italic>₂₄of phosphoenolpyruvate, starting from the substrate <italic>α</italic>-D-Glucose-1P. Here an optimal pathway has been obtained as <italic>α </italic>- <italic>D </italic>- <italic>Gluucose </italic>- 1<italic>P </italic>→ <italic>α </italic>- <italic>D </italic>- <italic>Gluucose </italic>- 6<italic>P </italic>→ <italic>β </italic>- <italic>D </italic>- <italic>Fructose </italic>- 6<italic>P </italic>→ <italic>β </italic>- <italic>D </italic>- <italic>Fructose </italic>- 1, 6<italic>P</italic>2 → <italic>Glyceraldehyde </italic>- 3<italic>P </italic>→ <italic>Glycerate </italic>- 1, 3<italic>P</italic>2 → <italic>Glycerate </italic>- 3<italic>P </italic>→ <italic>Glycerate </italic>- 2<italic>P </italic>→ <italic>Phosphoenolpyruvate </italic>in 100 iterations as shown by bold (black) arrows. The optimal pathway is obtained for <italic>λ </italic>= 0.9.</p>", "<title>On the Carotenoid biosynthesis pathway (Fig. 9 in Additional File ##SUPPL##0##1##)</title>", "<p>Considering the reference pathway from KEGG database, the starting metabolite for the carotenoid biosynthesis pathway is phytoene and the target metabolite is abscisic alcohol [##UREF##8##21##,##REF##7919981##22##]. There are 83 metabolites and 100 fluxes (Fig. 9 in Additional File ##SUPPL##0##1##). There are 2 reversible and 98 irreversible reactions. Applying the present methodology, optimal pathway for the carotenoid biosynthesis has been found to be: <italic>Phytoene </italic>→ <italic>Phytofluene </italic>→ <italic>ζ </italic>- <italic>Carotene </italic>→ <italic>Neurosporene </italic>→ <italic>Lycopene </italic>→ <italic>γ </italic>- <italic>Carotene </italic>→ <italic>β </italic>- <italic>Carotene </italic>→ <italic>β </italic>- <italic>Cryptoxanthin </italic>→ <italic>Zeaxanthin </italic>→ <italic>Antheraxanthin </italic>→ <italic>V iolaxanthin </italic>→ <italic>Neoxanthin </italic>→ 9' - <italic>cis </italic>- <italic>Neoxanthin </italic>→ <italic>Xanthoxin </italic>→ <italic>Abscisic aldehyde </italic>→ <italic>Abscisic alcohol</italic>. The optimal pathway is obtained for <italic>λ </italic>= 0.7 in 90 iterations, which is shown in Fig. ##FIG##3##4## by bold (black) arrows.</p>", "<title>On the core carbon metabolic network (Fig. ##FIG##4##5##)</title>", "<p>We have applied the method to analyze a simple example that represents the skeleton network of core carbon metabolism [##REF##11708855##15##,##REF##12642111##23##]. The network includes 23 reactions, seven of which are regulated by four regulatory proteins. The internal metabolites are A, B, C, D, E, F, O₂, NADH, ATP and the external metabolites are Carbon 1, Carbon 2, <italic>D</italic>_{<italic>ext</italic>}, <italic>E</italic>_{<italic>ext</italic>}, <italic>F</italic>_{<italic>ext</italic>}, <italic>H</italic>_{<italic>ext </italic>}and Oxygen. This network is a highly simplified representation of core metabolic processes including a glycolytic pathway with carbon 1 (C1) and carbon 2 (C2) as primary substrates, as well as a pentose phosphate pathway and a TCA cycle, through which amino acid H enters into the system. Fermentation pathways, amino acid biosynthesis, cell growth along with corresponding regulation (e.g. catabolite repression, aerobic/anaerobic regulation, and carbon storage regulation) are also included. The growth reaction is indicated by white arrows. For further details of the pathway, one may refer to [##REF##11708855##15##,##REF##12642111##23##].</p>", "<p>Our methodology aims at obtaining the optimal metabolic flux distribution within the solution space. In this study, <italic>R</italic>_{<italic>z</italic>}, the biomass flux, is defined as <italic>C </italic>+ <italic>F </italic>+ <italic>H </italic>+ 10<italic>AT P </italic>→ 1 <italic>Biomass</italic>, and needs to be maximized. Applying the proposed methodology, the optimal pathway has been found to be: <italic>A </italic>→ <italic>B </italic>→ <italic>C </italic>→ <italic>G </italic>→ <italic>C </italic>→ <italic>Biomass </italic>for <italic>λ </italic>= 0.8 in 95 iterations.</p>", "<title>Comparison with the extreme pathway analysis [##REF##10716907##11##,##REF##12466293##24##]</title>", "<p>Extreme pathways are a set of generating vectors that describe the conical steady-state solution space for flux distributions through an entire metabolic network. These cone-generating vectors correspond to biochemical pathways. The optimal metabolic pathways are calculated using linear optimization and are then interpreted using the extreme pathways. For details of the method, one may refer to [##REF##10716907##11##,##REF##12466293##24##]. Here we demonstrate a comparative analysis of the present method with the extreme pathway analysis [##REF##10716907##11##,##REF##12466293##24##]. The comparative analysis has been done on all the above mentioned pathways. Optimal pathways obtained by extreme pathway analysis for the two synthetic systems (Fig. ##FIG##1##2##, and Fig. 8 in Additional File ##SUPPL##0##1##) are the same as that obtained by the present method. Similarly, for pentose phosphate and glycolytic pathways in <italic>E. coli </italic>K-12MG1655 (Figs. 10 and 11 in Additional File ##SUPPL##0##1##), optimal pathways are the same as obtained by both the methods.</p>", "<p>For <italic>P. falciparum</italic>, an optimal pentose phosphate pathway <italic>α </italic>- <italic>D </italic>- <italic>Glucose </italic>- 6<italic>P </italic>→ <italic>β </italic>- <italic>D </italic>- <italic>Fructose </italic>- 6<italic>P </italic>→ <italic>D </italic>- <italic>Xylulose </italic>- 5<italic>P </italic>→ <italic>D </italic>- <italic>Glyceraldehyde </italic>- 3<italic>P </italic>as obtained by the extreme pathway analysis was found to be different from that obtained by the present method as shown in Fig. 12 in Additional File ##SUPPL##0##1##. The glycolytic pathway in the organism <italic>T. pallidum </italic>as obtained by the present algorithm has been found to be different from the previously identified optimal pathway by the extreme pathway analysis. For the latter case, it was found to be <italic>α </italic>- <italic>D </italic>- <italic>Glucose </italic>- 1<italic>P </italic>→ <italic>α </italic>- <italic>D </italic>- <italic>Glucose </italic>- 6<italic>P </italic>→ <italic>β </italic>- <italic>D </italic>- <italic>Glucose </italic>- 6<italic>P </italic>→ <italic>β </italic>- <italic>D </italic>- <italic>Fructose </italic>- 6<italic>P </italic>→ <italic>β </italic>- <italic>D </italic>- <italic>Fructose </italic>- 1, 6<italic>P</italic>2 → <italic>Glyceraldehyde </italic>- 3<italic>P </italic>→ <italic>Glycerate </italic>- 1, 3<italic>P</italic>2 → <italic>Glycerate </italic>- 3<italic>P </italic>→ <italic>Glycerate </italic>- 2<italic>P </italic>→ <italic>Phosphoenolpyruvate </italic>as shown by bold (white) arrows in Fig. ##FIG##2##3##. Using the extreme pathway analysis we have obtained a different carotenoid biosynthesis pathway: <italic>Phytoene </italic>→ 9, 9' - <italic>Di </italic>- <italic>cis </italic>- <italic>ζ </italic>- <italic>Carotene </italic>→ 7, 9, 7', 9' - <italic>Tetra </italic>- <italic>cis </italic>- <italic>lycopene </italic>→ Lycopene → <italic>γ </italic>- <italic>Carotene </italic>→ <italic>β </italic>- <italic>Carotene </italic>→ <italic>β </italic>- <italic>Cryptoxanthin </italic>→ <italic>Zeaxanthin </italic>→ <italic>Antheraxanthin </italic>→ <italic>Violaxanthin </italic>→ 9 - <italic>cis </italic>- <italic>Violaxanthin </italic>→ <italic>Xanthoxin </italic>→ <italic>Abscisic aldehyde </italic>→ <italic>Abscisic alcohol </italic>as shown by bold (white) arrows in Fig. ##FIG##3##4##.</p>", "<p>For the carotenoid biosynthesis pathway, the path obtained by the proposed method is different from the extreme pathway analysis at the starting branch and at the end of the branch of the optimal path.</p>", "<p>Starting from Phytoene there are two paths to arrive at the intermediate metabolite Lycopene. Of the two paths, the proposed method found three metabolites, Phytofluene, <italic>ζ</italic>-Carotene and Neurosporene while the extreme pathway analysis identified two metabolites, 9,9'-Di-cis-<italic>ζ</italic>-Carotene and 7,9,7',9'-Tetra-cis-lycopene, to reach Lycopene, as can be seen from Fig. ##FIG##3##4##. The paths identified by the above two methods are different till it reaches the metabolite Lycopene. From Lycopene both the methods follow the same path till they arrive at the other intermediate metabolite Violaxanthin. From Violaxanthin extreme pathway method found one intermediate metabolite, 9-cis-Violaxanthin to arrive at the other intermediate metabolite Xanthoxin. The proposed method found two intermediate metabolites Neoxanthin and 9'-cis-Neoxanthin to reach Xanthoxin. So we can conclude that from Violaxanthin the paths obtained by the above two methods are differnt till it arrives at the metabolite Xanthoxin. From Xanthoxin both the methods follow the same path to reach the target metabolite Abscisic alcohol.</p>", "<p>Each extreme pathway of core carbon metabolism was scaled to its maximum possible flux based on the maximum value of the uptake reactions (<italic>v</italic>_{<italic>max</italic>}) given in [##REF##11708855##15##]. Here we have assumed that there is no restriction on the environmental conditions and all possible inputs are available. The environment contains carbon1 (C1), F, H, <italic>O</italic>₂and the transport flux <italic>T</italic>_{<italic>c</italic>2}is repressed in the presence of C1. We have not taken into account the regulatory constraints associated with regulation of gene expression, i.e., by repressing or activating certain genes and other environmental conditions.. The regulatory and environmental constraints may further constrain the allowable functions of the network. The pathway obtained by the proposed method is similar to pathway 32 as obtained by the extreme pathway analysis in [##REF##12642111##23##]. The article also derives two sets of optimal pathways in terms of the highest biomass yield with no byproduct secretion. The optimal pathway with the highest yield obtained by our method is similar to pathway 32 of group 1 [##REF##12642111##23##]. A comparison of the flux values obtained from our methodology with the extreme pathway analysis and their percentage deviations are demonstrated in Table ##TAB##2##3##. From the table it can be inferred that the flux values obtained by both methods are more or less similar in nature although some external flux values deviate considerably. These considerable deviations may be due to the following reasons.</p>", "<p>The values of <italic>v</italic>_{<italic>max </italic>}in Table ##TAB##2##3## corresponding to extreme pathway analysis were obtained by imposing certain environmental and regulatory constraints mentioned in [##REF##12642111##23##], while the proposed method, for simplicity, does not consider such constraints. Moreover, for computing an average flux value by our method, we have taken average of a distribution of such flux values while the method of extreme pathway analysis determines the flux value. Finally, it may be mentioned here that we have developed the methodology accommodating certain characteristics of a system (i.e. within a specific metabolic system).</p>", "<p>We did not consider the characteristics outside the system, from which the external fluxes enter into it. However, the method developed here has produced consistent results that have been validated by randomizing the starting point in generating flux vectors.</p>", "<p>We have compared the flux values obtained by the proposed method with that derived by extreme pathway analysis. The results are shown for the system in Fig. ##FIG##1##2## and for the core carbon metabolic network in Fig. ##FIG##4##5##. Since the proposed method, unlike extreme pathway analysis, generates a number of flux values corresponding to a single reaction, we have taken average of these values for the reaction and used this average for comparison. Percentage deviations between average flux values (<italic>v</italic>_{<italic>av</italic>}) and flux values (<italic>v</italic>_{<italic>epa</italic>}) derived by extreme pathway analysis were calculated in Table ##TAB##3##4##. It is clear from the table that the flux values corresponding to both these methods are very close; although, as in the case of Table ##TAB##2##3##, some considerable deviations were noted mostly for external fluxes. The reasons for such deviations for the carbon metabolic pathway (Fig. ##FIG##4##5##) have been explained in the paragraph above. Note that here no constraint was considered by both the methods.</p>", "<title>Biological relevance and validation</title>", "<p>This section provides how the results obtained by the present method as well as extreme pathway analysis are relevant to some biological facts already observed by other researchers. Salient features of the present method along with true/false positive/negative scenarios are also depicted.</p>", "<title>Relevance</title>", "<p>Here we demonstrate how the results obtained by the present method are biologically more relevant than those obtained by the extreme pathway analysis.</p>", "<p>In the carotenoid biosynthesis pathway, there are two possible paths from the initial metabolite phytoene, producing 9,9'-Di-cis-<italic>ζ</italic>-Carotene in one branch and phytofluene in another branch. Of the two possible paths, the branch that produces phytofluene as the intermediate metabolite is observed in [##UREF##9##25##,##REF##17584945##26##], which is the same as obtained by our proposed method (Fig. ##FIG##3##4##). It is to be mentioned here that the other path has been identified by the extreme pathway analysis.</p>", "<p>As we proceed along the path, we observe that there are 4 possible paths emerging from the intermediate metabolite Neurosporene (Fig. 9 in Additional File ##SUPPL##0##1##). The path that produces <italic>α</italic>-Zeacarotene and Hydroxy-neurosporene are not biochemically feasible as they do not lead to the target metabolite Abscisic alcohol. Of the remaining two paths, the path producing Lycopene is obtained by the present method (Fig. ##FIG##3##4##). The path that leads from phytoene to lycopene through the intermediate paths as mentioned above can be found in fungi [##REF##4269373##27##,##REF##11842148##28##]. Lycopene is also found to be an intermediate in the biosynthesis of other carotenoids, in some bacteria, fungi and green plants [##UREF##10##29##]. Thus both the present and extreme pathway analysis have correctly identified Lycopene as an intermediate over the other alternative <italic>β</italic>-Zeacarotene (Fig. ##FIG##3##4##).</p>", "<p>There are 4 possible paths emerging from the intermediate metabolite Lycopene (Fig. 9 in Additional File ##SUPPL##0##1##). The paths producing <italic>δ</italic>-Carotene, 3,4-Dehydrolycopene and Rhodopin as the intermediate metabolites are not possible as they do not lead to the final product Abscisic alcohol. We can reach the target metabolite Abscisic alcohol only through the path that produces <italic>γ</italic>-carotene. There are 7 possible paths from the intermediate metabolite <italic>γ</italic>-carotene (Fig. 9 in Additional File ##SUPPL##0##1##). The paths yielding Chlorobactene, 1'-Hydroxy- <italic>γ</italic>-carotene, Myxol, Deoxymyxol, (2'S)-Deoxymyxol 2'-<italic>α</italic>-L- fucoside and 1'2'-Dihydro-<italic>γ</italic>-carotene do not terminate to the target metabolite Abscisic alcohol, and hence they are not biochemically feasible. The target metabolite can be obtained through the path producing <italic>β</italic>-carotene. The biosynthesis pathway for <italic>β</italic>-carotene has been determined for fungi such as Phycomyces blakesleeanus and Neurospora crassa [##REF##15345447##30##]. <italic>β</italic>-carotene is also synthesized by a number of bacteria, fungi, and most green plants [##REF##15345447##30##]. The path from <italic>β</italic>-carotene producing Echinenone terminates at the end products Adonixanthin and Astaxanthin. The other path terminates at the end product Isorenieratene. The above mentioned two paths are not possible as none of them yields Abscisic alcohol that is the desired target metabolite of carotenoid biosynthesis (Fig. 9 in Additional File ##SUPPL##0##1##). The path producing <italic>β</italic>-Cryptoxanthin is the only feasible path as it finally leads to the target metabolite.</p>", "<p>From <italic>β</italic>-Cryptoxanthin there are two paths producing Thermo-biszeaxanthin and Zeaxanthin as the two products. The path producing Zeaxanthin is followed as it terminates to the desired end metabolite. The path from <italic>β</italic>-carotene to Zeaxanthin can be found in Flavobacterium Species [##REF##4462583##31##]. The conversion between Zeaxanthin and Antheraxanthin is a reversible one, and as the forward reaction rate is greater than the reverse rate, the pathway from Zeaxanthin to Antheraxanthin is favored. As Capsanthin obtained from Antheraxanthin is not the desired end product (Fig. 9 in Additional File ##SUPPL##0##1##), this path is not followed. Violaxanthin obtained from Antheraxanthin via the reversible reaction ultimately leads to the target metabolite Abscisic alcohol. Capsorubin is obtained from Violaxanthin but this path is not followed as this does not yield the desired target metabolite Abscisic alcohol. Neoxanthin is produced from Violaxanthin. Arabidopsis is the best-characterized plant system of carotenoid biosynthesis. The path from <italic>β</italic>-carotene to Neoxanthin for xanthophyll biosynthesis has been observed in Arabidopsis. It may be emphasized that this path was identified by the present method but not by the extreme pathway analysis (Fig. ##FIG##3##4##).</p>", "<p>Similarly, there exists a single path from Neoxanthin to 9'-cis-Neoxanthin and from 9'-cis-Neoxanthin to Xanthoxin. Two paths are emerging from Xanthoxin producing Abscisic aldehyde in one branch and Xanthoxic acid in the other. The path leading to Xanthoxic acid is not followed as this does not lead to the final metabolite of carotenoid biosynthetis. The other one producing Abscisic aldehyde is followed as it terminates to the target metabolite Abscisic alcohol by the subsequent reaction. Two intermediate metabolites, 9'-cis-neoxanthin and 9-cis-violaxanthin, have been identified in light-grown and etiolated leaves, and in roots of a variety of species [##UREF##11##32##]. Biochemical evidence has suggested the occurrence of this pathway in various eukaryotes and in archaea [##REF##11042147##33##].</p>", "<p>The existence of the sugar phosphates Glyceraldehyde-3P, Ribulose-5P, Xylulose-5P, Fructose-6P and Glucose-6P in the pentose-phosphate pathway (PPP) are found in [##REF##12881455##34##, ####REF##14532041##35##, ##UREF##12##36####12##36##] (Figs. 10 and 12 in Additional File ##SUPPL##0##1##). The major pathway for glucose-6P metabolism in <italic>E. coli </italic>in [##REF##14763984##37##] is the same as obtained from our proposed methodology (Fig. 11 in Additional File ##SUPPL##0##1##).</p>", "<title>A possible biological validation</title>", "<p>Here we highlight some of the salient features of our method and try to argue that the results obtained thereof might not be a mathematical artefact. The present method maximizes the rate of production of biomass which in a way should be consistent with the law of mass action and subsumes the free energy minimization principle. Then we use the stoichiometric matrix based on these two fundamental and model independent principles. In our results, we are able to identify the true positive and the true negative scenarios correctly which could be a pointer to the fact that our method has not introduced any artefact in its formulation. As for the intermediate scenario our method for real systems so far has not produced any false positive or false negative results.</p>", "<p>Considering the pentose phosphate pathway in the organism <italic>E. coli </italic>K-12 MG1655 (Fig. 10 in Additional File ##SUPPL##0##1##) we have found the path starting from the metabolite <italic>α</italic>-D-Glucose-6P to reach the target metabolite D-Glyceraldehyde-3P and D-Fructose-6P. We have observed the relative values of the components of the flux vector <bold>v </bold>that are involved in the aforesaid resulting pathway. The value of <italic>v</italic>₆is greater than <italic>v</italic>₅, and the value of <italic>v</italic>₂₁is greater than <italic>v</italic>₂₂. This leads us to obtain the target metabolite.</p>", "<p>Starting from any intermediate metabolite, e.g., <italic>β</italic>-D-Glucose-6P, D-Glucono-1,5 lactone-6P and 6-Phospho-D-Gluconate, we were able to reach the target metabolite D-Glyceraldehyde-3P and D-Fructose-6P. While considering a particular intermediate metabolite, we noted the relative values of the components of the flux vector <bold>v</bold>. We observed that the relative values of the components of the flux vector <bold>v </bold>while obtaining the path from any intermediate metabolite to the target are of the same order of magnitude as that obtained by considering the original path from <italic>α</italic>-D-Glucose-6P to reach the target metabolite.</p>", "<p>Moreover, we considered some other metabolite as starting substrate that are not on optimal path, and found that they did not lead to the target metabolite. For example, starting from D-Ribose-5P, we were able to reach the metabolite <italic>PRPP </italic>for most of the values of <italic>λ </italic>lying between 0.1 and 1.0, in steps of 0.1. For low values of <italic>λ</italic>, we could reach the metabolite D-Erythrose-4P and D-Fructose-6P. Thus we can conclude that any intermediate metabolite on optimal path produces the target metabolite, and it is independent of the starting metabolite. As desired, the metabolite not on optimal path do not lead to the target metabolite D-Glyceraldehyde-3P and D-Fructose-6P even via the reverse path. Similar observations were found for the other pathways.</p>", "<p>We took two intermediate metabolites <italic>β</italic>-D-Glucose-6P and D-Glucono-1,5 lactone-6P that are indeed on the final path as identified by the present method, and we found that in most of the cases it led to the target. This shows that the method that we have developed is independent of choice of the initial substrate. Then we chose another substrate which also belongs to the path identified by us but this time the substrate could lead to various branches, of which one would eventually lead us to the target. We found that for certain range of values of the parameter <italic>λ</italic>, this would always lead us to the target, picking up the correct branch that is most of the time followed by the organism. Now for certain other ranges of the values of the parameter <italic>λ</italic>, the other branches in the pathway were picked up. In all the cases, the relative strength of the vector <bold>v </bold>reflects the correct strength that would drive the path from the starting substrate to the target. We have observed this feature for all the real life paths.</p>", "<p>The kind of constraint that we have imposed on to the systems must have captured the essential biochemistry of the systems. That is why the method becomes independent of the choice of the various substrates within the conscensus pathway and makes our methodology quite a general one without centering around any specific model of the system.</p>" ]

[ "<title>Discussion</title>", "<p>The method developed in this article may be useful for manipulating a metabolic pathway to achieve some desired goals constituting some tasks of metabolic engineering. Here we describe a few examples where this method may be useful.</p>", "<p>• Let us consider the synthetic pathway in Fig. ##FIG##1##2## and redraw it in Fig. ##FIG##5##6## incorporating the enzymes <italic>e</italic>₁, <italic>e</italic>₂,...<italic>e</italic>₁₀mediating the reactions <italic>R</italic>₁, <italic>R</italic>₂,...<italic>R</italic>₁₀, respectively. For this system, we have already determined <italic>R</italic>₁→ <italic>R</italic>₅→ <italic>R</italic>₉→ <italic>R</italic>₃as an optimal pathway through which the amount of the product P becomes maximum. We have also found that the concentration of the enzymes <italic>e</italic>₁, <italic>e</italic>₂,...<italic>e</italic>₁₀that is required to get this optimal pathway is 0.88 for <italic>e</italic>₁, 0.80 for <italic>e</italic>₅, 0.80 for <italic>e</italic>₉and 0.86 for <italic>e</italic>₃. For some reasons, let us say, the concentration of the enzyme <italic>e</italic>₅becomes low (~0). Under this situation, the amount of the target product will also be less. On application of the present method on this system, we would be able to identify an optimal pathway and thereby the reason behind the situation. Then we can make necessary arrangement to activate the corresponding gene and thereby leading to the formation of the enzyme in higher concentration.</p>", "<p>• Consider the example of reducing the amount of acetate in glycolytic pathway as done by Yang et al [##UREF##13##38##]. Here the authors have proposed a method of adding a new path of forming Acetoin for reducing the amount of acetate. However, this problem may boil down to determining an optimal metabolic pathway through which the amount of acetate is minimum. Then we can apply our method to this problem for determining this optimal pathway and finally inhibit the other paths but only express this optimal path. This will lead to the formation of acetate to a minimum amount. The amount of acetate will be minimum if this optimal path is only expressed and the others are inhibited.</p>", "<p>• For the third example, let us assume there are <italic>n </italic>paths <italic>P</italic>₁, <italic>P</italic>₂,...<italic>P</italic>_{<italic>n </italic>}starting from a given substrate to yield a given target metabolite. Also assume that out of these <italic>n </italic>pathways, there are multiple optimal pathways <italic>P</italic>₁, <italic>P</italic>₂,...<italic>P</italic>_{<italic>m </italic>}(<italic>m </italic>&lt;<italic>n</italic>) through which the amount of the target metabolite becomes maximum. Now if we want to avoid a particular pathway, we may inhibit (by some means) the genes producing some of the enzymes catalyzing the reactions in that pathway.</p>" ]

[ "<title>Conclusions</title>", "<p>Here we have developed a simple method for identifying an optimal metabolic pathway through which a metabolite attains a maximum rate of growth on a given substrate. The method involves formulation of the rate of yield of a metabolite incorporating weighting coefficients indicating the concentration levels of enzymes catalyzing biochemical reactions in the pathway. A new constraint incorporating these weighting coefficients has been defined. Using the method, a set of flux vectors has been generated, which has then been used to determine a set of above-mentioned weighting coefficients giving rise to a maximum rate of yield of a metabolite, starting from a given substrate. The entire method is based on well known flux balancing approach.</p>", "<p>It is to be mentioned here that the extreme pathway analysis [##REF##10716907##11##] does not consider the effect of enzymes catalyzing the reactions in a metabolic pathway. On the other hand, the method developed in this article involves enzyme concentration in its formulation; thereby it is closer to the real life situations than the extreme pathway analysis. The other difference between the said two methods is that extreme pathway analysis finds the flux vectors through optimization, whereas the present method generates a subset of possible flux vectors and finds an optimal pathway in terms of weighting coefficients reflecting enzyme concentration. Moreover, the extreme pathway analysis considers individual reactions in the pathway in a sequential manner, whereas the present method considers all the reactions in parallel.</p>", "<p>It has been observed that the method though simple enough, is able to identify the optimal pathways which conform to the results of some earlier studies. The method can suitably be used using reaction databases without going into complex mathematical calculations, and without using various kinetic parameters that are hard to be estimated. Comparative analysis of the results obtained by the present method with that of the extreme pathway analysis shows that the present method has been able to identify optimal pathways correctly for almost all the pentose phosphate and glycolytic pathways considered here. The present method has identified a carotenoid biosynthesis pathway that is closer to some earlier investigations than that obtained by the extreme pathway analysis. All the optimal real life pathways have been biologically validated. Finally, possible direct impact of the method on certain problems of metabolic engineering has been pointed out.</p>", "<p>Here we have assumed that a large amount of substrate is present. This assumption implies that any influx of the substrate from the other pathways does not have any effect on the rate of production of the corresponding product, due to limited supply of enzymes. Moreover, for simplicity, we have not considered any feedback inhibition on the enzyme activity. In other words, we are considering only the fraction of enzyme molecules that have not been inactivated due to feedback inhibitions. Incorporation of feedback inhibitions on enzyme activity forms a scope for further investigation.</p>", "<p>In biochemical networks, crosstalk often occurs, which deals with multiple inputs and overlapping outputs [##REF##14990392##39##]. Here we are dealing with metabolic networks. If crosstalk occurs in the networks under consideration then there may be more than one disjoint sources (metabolites) from which the target or any other intermediate metabolites on the pathway under consideration are found. In this case, we have to consider all these input metabolites of the other networks while constructing the stoichiometric matrix and generating the flux vectors. However, this forms a scope for further investigation.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>In the present article, we propose a method for determining optimal metabolic pathways in terms of the level of concentration of the enzymes catalyzing various reactions in the entire metabolic network. The method, first of all, generates data on reaction fluxes in a pathway based on steady state condition. A set of constraints is formulated incorporating weighting coefficients corresponding to concentration of enzymes catalyzing reactions in the pathway. Finally, the rate of yield of the target metabolite, starting with a given substrate, is maximized in order to identify an optimal pathway through these weighting coefficients.</p>", "<title>Results</title>", "<p>The effectiveness of the present method is demonstrated on two synthetic systems existing in the literature, two pentose phosphate, two glycolytic pathways, core carbon metabolism and a large network of carotenoid biosynthesis pathway of various organisms belonging to different phylogeny. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. Biological relevance and validation of the results are provided. Finally, the impact of the method on metabolic engineering is explained with a few examples.</p>", "<title>Conclusions</title>", "<p>The method may be viewed as determining an optimal set of enzymes that is required to get an optimal metabolic pathway. Although it is a simple one, it has been able to identify a carotenoid biosynthesis pathway and the optimal pathway of core carbon metabolic network that is closer to some earlier investigations than that obtained by the extreme pathway analysis. Moreover, the present method has identified correctly optimal pathways for pentose phosphate and glycolytic pathways. It has been mentioned using some examples how the method can suitably be used in the context of metabolic engineering.</p>" ]

[ "<title>Authors' contributions</title>", "<p>RKD has conceived the study, formulated the methodology, made partial analysis of the results and has prepared the manuscript. MD has implemented the algorithms, made partial analysis of the results and has modified some parts of the manuscript. SM has made partial analysis of the results and given some fruitful suggestions while preparing the manuscript.</p>", "<title>Supplementary Material</title>" ]

[ "<title>Acknowledgements</title>", "<p>Dr. S. Mukhopadhyay gratefully acknowledges the financial assistance received in the form of a grant, BT/BI/04/001/93 and BT/BI/10/019/99 from the Department of Biotechnology, Government of India.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Outline of the proposed method</bold>. Outline of the proposed method.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Synthetic reaction system 1</bold>. A chemical reaction network consisting of 6 metabolites and 10 reactions.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Glycolytic Pathway in <italic>T. pallidum</italic></bold>. Glycolytic pathway in <italic>T. pallidum </italic>consisting of 13 metabolites and 25 fluxes (reversible reactions are shown by double arrows). The starting metabolite is <italic>α</italic>-D-Glucose-1P and the target product is phosphoenolpyruvate respectively. The bold (black) arrows represent the optimal pathway obtained by the present method and the bold (white) arrows represent the optimal pathway obtained by the extreme pathway analysis.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Optimal Carotenoid biosynthesis Pathway</bold>. The bold (black) arrows represent the optimal pathway obtained by the present method and the bold (white) arrows represent that found by the extreme pathway analysis.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Core carbon metabolic network</bold>. A simplified core carbon metabolic network [##REF##11708855##15##]. The network consists of 12 metabolites and 23 reactions. The stoichiometry of the metabolic reactions are described as follows [##REF##11708855##15##]: <italic>R</italic>₁: <italic>A </italic>+ <italic>ATP </italic>→ <italic>B</italic>; <italic>R</italic>_{2<italic>a </italic>}: <italic>B </italic>→ 2<italic>ATP </italic>+ 2<italic>NADH </italic>+ <italic>C</italic>; <italic>R</italic>_{2<italic>b </italic>}: <italic>C </italic>+ 2<italic>ATP </italic>+ 2<italic>NADH → B</italic>; <italic>R</italic>₃: <italic>B </italic>→ <italic>F </italic>; <italic>R</italic>₄:<italic>C </italic>→ <italic>G</italic>; <italic>R</italic>_{5<italic>a </italic>}: <italic>G </italic>→ 0.8<italic>C </italic>+ 2<italic>NADH</italic>; <italic>R</italic>_{5<italic>b </italic>}: <italic>G </italic>→ 0.8<italic>C </italic>+ 2<italic>NADH</italic>; <italic>R</italic>₆: <italic>C </italic>→ 2<italic>ATP </italic>+ 3<italic>D</italic>; <italic>R</italic>₇: <italic>C </italic>+ 4<italic>NADH </italic>→ 3<italic>E</italic>; <italic>R</italic>_{8<italic>a </italic>}: <italic>G </italic>+ <italic>ATP </italic>+ 2<italic>NADH </italic>→ <italic>H</italic>; <italic>R</italic>_{8<italic>b </italic>}: <italic>H </italic>→ <italic>G </italic>+ <italic>ATP </italic>+ 2<italic>NADH</italic>; <italic>R</italic>_{<italic>res </italic>}:<italic>NADH </italic>+ <italic>O</italic>₂→ <italic>ATP</italic>; <italic>C </italic>→ <italic>Biomass</italic>; <italic>F </italic>→ <italic>Biomass</italic>; <italic>H </italic>→ <italic>Biomass</italic>; 10<italic>ATP </italic>→ <italic>Biomass</italic>. The stoichiometry of the transport processes are described as follows: <italic>T</italic>_{<italic>c</italic>1}: <italic>Carbon</italic>1 → <italic>A</italic>; <italic>T</italic>_{<italic>c</italic>2}: <italic>Carbon</italic>2 → <italic>A</italic>; <italic>T</italic>_{<italic>f </italic>}: <italic>Fext </italic>→ <italic>F</italic>; <italic>T</italic>_{<italic>d </italic>}: <italic>D </italic>→ <italic>Dext</italic>; <italic>T</italic>_{<italic>e</italic>}: <italic>E </italic>→ <italic>Eext</italic>; <italic>T</italic>_{<italic>h </italic>}: <italic>Hext </italic>→ <italic>H</italic>; <italic>T</italic>_{<italic>o</italic>2}: <italic>Oxygen </italic>→ <italic>O</italic>2. The biomass flux <italic>R</italic>_{<italic>z </italic>}is as follows: <italic>C </italic>+ <italic>F </italic>+ <italic>H </italic>+ 10<italic>ATP </italic>→ 1 <italic>Biomass </italic>is indicated by white arrows.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>The corresponding enzymes of the synthetic reaction system in Fig. 2</bold>. The synthetic reaction system in Fig. 2 and their corresponding enzymes.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>A hypothetical biochemical reaction network</bold>. A hypothetical biochemical reaction network.</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Some possible pathways for the system in Fig. 2 (or Fig. 6)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Serial Number</td><td align=\"left\">Some possible paths</td><td align=\"left\">Optimal c-values</td><td align=\"left\">Average quantity (z) of P</td></tr></thead><tbody><tr><td align=\"center\">1</td><td align=\"left\"><italic>R</italic>₁→ <italic>R</italic>₅→ <italic>R</italic>₉→ <italic>R</italic>₃</td><td align=\"left\"><italic>c</italic>₁= 0.88, <italic>c</italic>₅= 0.80, <italic>c</italic>₉= 0.80, <italic>c</italic>₃= 0.86</td><td align=\"left\">51.53</td></tr><tr><td align=\"center\">2</td><td align=\"left\"><italic>R</italic>₁→ <italic>R</italic>₆→ <italic>R</italic>₈→ <italic>R</italic>₉→ <italic>R</italic>₃</td><td align=\"left\"><italic>c</italic>₁= 0.88, <italic>c</italic>₆= 0.56, <italic>c</italic>₈= 0.57, <italic>c</italic>₉= 0.80, <italic>c</italic>₃= 0.86</td><td align=\"left\">12.22</td></tr><tr><td align=\"center\">3</td><td align=\"left\"><italic>R</italic>₁→ <italic>R</italic>₅→ <italic>R</italic>₈→ <italic>R</italic>₁₀→ <italic>R</italic>₃</td><td align=\"left\"><italic>c</italic>₁= 0.88, <italic>c</italic>₅= 0.80, <italic>c</italic>₈= 0.57, <italic>c</italic>₁₀= 0.04, <italic>c</italic>₃= 0.86</td><td align=\"left\">24.63</td></tr><tr><td align=\"center\">4</td><td align=\"left\"><italic>R</italic>₁→ <italic>R</italic>₆→ <italic>R</italic>₁₀→ <italic>R</italic>₃</td><td align=\"left\"><italic>c</italic>₁= 0.88, <italic>c</italic>₆= 0.56, <italic>c</italic>₁₀= 0.04, <italic>c</italic>₃= 0.86</td><td align=\"left\">19.88</td></tr><tr><td align=\"center\">5</td><td align=\"left\"><italic>R</italic>₁→ <italic>R</italic>₇→ <italic>R</italic>₁₀→ <italic>R</italic>₃</td><td align=\"left\"><italic>c</italic>₁= 0.88, <italic>c</italic>₇= 0.18, <italic>c</italic>₁₀= 0.04, <italic>c</italic>₃= 0.86</td><td align=\"left\">29.41</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Variation of c-values and average z with the upper bound on reaction fluxes for the optimal path <italic>R</italic>₁→ <italic>R</italic>₅→ <italic>R</italic>₉→ <italic>R</italic>₃of the system in Fig. 2</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Serial Number</td><td align=\"left\">Upper bound on flux value</td><td align=\"left\">Optimal c-values</td><td align=\"left\">Average quantity (z) of P</td></tr></thead><tbody><tr><td align=\"center\">1</td><td align=\"left\">5000</td><td align=\"left\"><italic>c</italic>₁= 0.93, <italic>c</italic>₅= 0.83, <italic>c</italic>₉= 0.91, <italic>c</italic>₃= 0.85</td><td align=\"left\">6670.68</td></tr><tr><td align=\"center\">2</td><td align=\"left\">4000</td><td align=\"left\"><italic>c</italic>₁= 0.91, <italic>c5 </italic>= 0.92, <italic>c</italic>₉= 0.82, <italic>c</italic>₃= 0.98</td><td align=\"left\">5458.83</td></tr><tr><td align=\"center\">3</td><td align=\"left\">3000</td><td align=\"left\"><italic>c</italic>₁= 0.91, <italic>c</italic>₅= 0.85, <italic>c</italic>₉= 0.83, <italic>c</italic>₃= 0.91</td><td align=\"left\">4308.66</td></tr><tr><td align=\"center\">4</td><td align=\"left\">2000</td><td align=\"left\"><italic>c</italic>₁= 0.88, <italic>c</italic>₅= 0.84, <italic>c</italic>₉= 0.81, <italic>c</italic>₃= 0.86</td><td align=\"left\">3451.73</td></tr><tr><td align=\"center\">5</td><td align=\"left\">1000</td><td align=\"left\"><italic>c</italic>₁= 0.87, <italic>c</italic>₅= 0.82, <italic>c</italic>₉= 0.86, <italic>c</italic>₃= 0.83</td><td align=\"left\">2347.61</td></tr><tr><td align=\"center\">6</td><td align=\"left\">50</td><td align=\"left\"><italic>c</italic>₁= 0.84, <italic>c</italic>₅= 0.89, <italic>c</italic>₉= 0.87, <italic>c</italic>₃= 0.82</td><td align=\"left\">55.69</td></tr><tr><td align=\"center\">7</td><td align=\"left\">40</td><td align=\"left\"><italic>c</italic>₁= 0.94, <italic>c</italic>₅= 0.87, <italic>c</italic>₉= 0.89, <italic>c</italic>₃= 0.81</td><td align=\"left\">47.29</td></tr><tr><td align=\"center\">8</td><td align=\"left\">30</td><td align=\"left\"><italic>c</italic>₁= 0.89, <italic>c</italic>₅= 0.86, <italic>c</italic>₉= 0.85, <italic>c</italic>₃= 0.85</td><td align=\"left\">42.57</td></tr><tr><td align=\"center\">9</td><td align=\"left\">20</td><td align=\"left\"><italic>c</italic>₁= 0.86, <italic>c</italic>₅= 0.84, <italic>c</italic>₉= 0.81, <italic>c</italic>₃= 0.82</td><td align=\"left\">38.66</td></tr><tr><td align=\"center\">10</td><td align=\"left\">10</td><td align=\"left\"><italic>c</italic>₁= 0.98, <italic>c</italic>₅= 0.93, <italic>c</italic>₉= 0.91, <italic>c</italic>₃= 0.87</td><td align=\"left\">34.96</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Comparison of the flux values obtained by the proposed method and the extreme pathway analysis for the core carbon metabolic network in Fig. 5</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Reaction</td><td align=\"left\"><italic>v</italic>_{<italic>max</italic>}</td><td align=\"left\"><italic>v</italic>_{<italic>av </italic>}</td><td align=\"left\">Percentage deviation </td></tr><tr><td/><td align=\"left\">(Extreme path- way analysis)</td><td align=\"left\">(Proposed method)</td><td align=\"left\">|<italic>v</italic>_{<italic>av </italic>}- <italic>v</italic>_{<italic>max</italic>}|/<italic>v</italic>_{<italic>av </italic>}× 100%</td></tr></thead><tbody><tr><td align=\"center\">Tc1</td><td align=\"left\">10.5</td><td align=\"left\">11.13</td><td align=\"left\">5.66</td></tr><tr><td align=\"center\">Tc2</td><td align=\"left\">10.5</td><td align=\"left\">11.43</td><td align=\"left\">8.13</td></tr><tr><td align=\"center\">Td</td><td align=\"left\">12</td><td align=\"left\">13.91</td><td align=\"left\">13.73</td></tr><tr><td align=\"center\">Te</td><td align=\"left\">12</td><td align=\"left\">10.47</td><td align=\"left\">14.61</td></tr><tr><td align=\"center\">Tf</td><td align=\"left\">5</td><td align=\"left\">7.65</td><td align=\"left\">34.64</td></tr><tr><td align=\"center\">Th</td><td align=\"left\">5</td><td align=\"left\">6.84</td><td align=\"left\">26.90</td></tr><tr><td align=\"center\">To2</td><td align=\"left\">15</td><td align=\"left\">12.63</td><td align=\"left\">18.76</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Comparison of flux values obtained by the proposed method and the extreme pathway analysis for the system in Fig. 2</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Reaction</td><td align=\"left\">Average flux value (proposed method) </td><td align=\"left\">Flux value (Extreme pathway analysis)</td><td align=\"left\">Percentage deviation</td></tr><tr><td/><td align=\"left\">(<italic>v</italic>_{<italic>av</italic>})</td><td align=\"left\">(<italic>v</italic>_{<italic>epa</italic>})</td><td align=\"left\">|<italic>v</italic>_{<italic>av </italic>}- <italic>v</italic>_{<italic>epa</italic>}|/<italic>v</italic>_{<italic>av </italic>}× 100%</td></tr></thead><tbody><tr><td align=\"center\">R1</td><td align=\"left\">48.73</td><td align=\"left\">46.21</td><td align=\"left\">5.17</td></tr><tr><td align=\"center\">R2</td><td align=\"left\">3.596</td><td align=\"left\">3.129</td><td align=\"left\">12.98</td></tr><tr><td align=\"center\">R3</td><td align=\"left\">36.286</td><td align=\"left\">32.543</td><td align=\"left\">10.31</td></tr><tr><td align=\"center\">R4</td><td align=\"left\">7.687</td><td align=\"left\">6.292</td><td align=\"left\">18.15</td></tr><tr><td align=\"center\">R5</td><td align=\"left\">49.227</td><td align=\"left\">46.341</td><td align=\"left\">5.86</td></tr><tr><td align=\"center\">R6</td><td align=\"left\">17.86</td><td align=\"left\">16.001</td><td align=\"left\">10.41</td></tr><tr><td align=\"center\">R7</td><td align=\"left\">12.35</td><td align=\"left\">12.31</td><td align=\"left\">0.32</td></tr><tr><td align=\"center\">R8</td><td align=\"left\">14.50</td><td align=\"left\">13.656</td><td align=\"left\">5.82</td></tr><tr><td align=\"center\">R9</td><td align=\"left\">68.318</td><td align=\"left\">65.734</td><td align=\"left\">3.78</td></tr><tr><td align=\"center\">R10</td><td align=\"left\">15.263</td><td align=\"left\">14.814</td><td align=\"left\">2.94</td></tr></tbody></table></table-wrap>" ]

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"<disp-formula id=\"bmcM1\"><label>(1)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\" name=\"1752-0509-2-65-i2\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>z</mml:mi><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>s</mml:mi></mml:munderover><mml:mrow><mml:msub><mml:mi>c</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:msub><mml:mi>v</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM2\"><label>(2)</label><bold>S.v </bold>≈ <bold>0</bold></disp-formula>", "<disp-formula id=\"bmcM3\"><label>(3)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M3\" name=\"1752-0509-2-65-i3\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mstyle mathvariant=\"bold\" 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"<disp-formula id=\"bmcM7\"><label>(7)</label><italic>y </italic>= 1/<italic>z </italic>+ <bold>Λ</bold>^{<italic>T</italic>}.(<bold>S</bold>.(<bold>C</bold>.<bold>v</bold>))</disp-formula>", "<disp-formula id=\"bmcM8\"><label>(8)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M4\" name=\"1752-0509-2-65-i4\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>Δ</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mi>η</mml:mi><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM9\"><label>(9)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M5\" name=\"1752-0509-2-65-i5\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>Δ</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mi>η</mml:mi><mml:mrow><mml:msup><mml:mi>z</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mi>z</mml:mi></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mi>η</mml:mi><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msup><mml:mi>Λ</mml:mi><mml:mi>T</mml:mi></mml:msup><mml:mo>.</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>S</mml:mi></mml:mstyle><mml:mo>.</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>C</mml:mi></mml:mstyle><mml:mo>.</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>v</mml:mi></mml:mstyle><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><italic>c</italic>_{<italic>i</italic>}(<italic>t </italic>+ 1) = <italic>c</italic>_{<italic>i</italic>}(<italic>t</italic>) + Δ<italic>c</italic>_{<italic>i</italic>}, ∀<italic>i</italic>, <italic>t </italic>= 0, 1, 2,...</disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M6\" name=\"1752-0509-2-65-i6\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>E</mml:mi><mml:mo>+</mml:mo><mml:mi>S</mml:mi><mml:msubsup><mml:mo>⇌</mml:mo><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msubsup><mml:mi>E</mml:mi><mml:mi>S</mml:mi><mml:msup><mml:mo>→</mml:mo><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:msup><mml:mi>E</mml:mi><mml:mo>+</mml:mo><mml:mi>P</mml:mi></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><italic>c </italic>= [<italic>E</italic>]/[<italic>E</italic>_{<italic>min</italic>}]</disp-formula>", "<disp-formula><italic>V</italic>_{<italic>max </italic>}= <italic>k</italic>₃[<italic>E</italic>_{<italic>min</italic>}]</disp-formula>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p><bold>Analysis of the results</bold>. Analysis of the results for the synthetic system, pentose phosphate pathway, glycolytic pathway and the carotenoid biosynthesis pathway for different organisms.</p></caption></supplementary-material>" ]

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[ "<title>Introduction</title>", "<p>Until relatively recently it was commonly believed that macrophages were in a post-mitotic state ##REF##4271722##[1]##, ##REF##14295560##[2]##, ##REF##14295559##[3]##. In this scenario, dead and damaged macrophages in tissue are replenished by an influx of blood monocytes that then differentiate into tissue macrophages ##REF##5666958##[4]##, ##REF##6968334##[5]##. However, there is now considerable evidence that tissue-derived macrophages retain a capacity to proliferate under suitable conditions. Macrophages can extensively proliferate <italic>in vitro</italic> when stimulated with macrophage growth factor, which is present in most tissues at concentrations sufficient to sustain cell division ##REF##4514628##[6]##, ##REF##4857127##[7]##, ##REF##650153##[8]##. In addition, a significant proportion of peritoneal macrophages and macrophages in local tissue appear to undergo mitosis <italic>in vivo,</italic> especially during inflammatory conditions ##REF##14467923##[9]##, ##REF##4956876##[10]##, ##UREF##0##[11]##, ##REF##3338096##[12]##, ##REF##2788207##[13]##, ##REF##3999178##[14]##, ##REF##7088183##[15]##, ##UREF##1##[16]##, ##UREF##2##[17]##, ##REF##1092793##[18]##. Recently, our group has shown that opsonic phagocytosis can stimulate cell cycle progression ##REF##15905568##[19]##, through an ERK signaling mechanism (data presented at Experimental Biology 2008 meeting at San Diego, CA, Abstract #LB545). These findings suggest that local proliferation of macrophages in infected tissue could provide an alternative mechanism for the replenishment of tissue macrophages upon damage by infection.</p>", "<p>Given that macrophages can undergo cell division locally in tissue under various conditions and that certain microbes can survive inside phagocytic cells, the question arises as to the fate of intracellular pathogens when the original phagocytic cell divides into two daughter cells. To our knowledge, this important question has not been systematically studied. In fact, consideration of possible outcomes reveals that macrophage cell division is potentially a double-edged sword from the viewpoint of host defense mechanisms. On one hand, cell division would double the number of phagocytic cells, an event that could conceivably benefit the host by increasing the number of effector cells at the site of infection. On the other hand, division of cells infected with a live microbe that survives intracellularly could also lead to a doubling of infected cells, an event that could facilitate the dissemination of infection and potentially harm the host. Certainly, the benefit or debit to the host of infected macrophage replication would also depend on the distribution of microbes among infected cells and on the type of pathogenic microbes. In situations where the entire microbial load would segregate to one cell, there would be a new uninfected cell generated for host defense or for new microbial invasion and proliferation, depending on the circumstances. Hence, the realization that macrophages can replicate combined with the ability of certain microbes to replicate intracellularly poses several new problems for innate immunity and microbial pathogenesis.</p>", "<p>\n<italic>Cryptococcus neoformans</italic> is a major cause of life-threatening infections such as pulmonary cryptococcosis and meningoencephalitis in patients with impaired immunity. Macrophages play a vital role in cryptococcal pathogenesis ##UREF##3##[20]##, ##REF##8665468##[21]##. <italic>C. neoformans</italic> is a facultative intracellular pathogen in macrophages. Alveolar macrophages containing intracellular <italic>C. neoformans</italic> are the important component in dormant form of <italic>C. neoformans</italic> infection which as the potential for later reactivation if the host becomes immunocompromised ##REF##11390242##[22]##, ##REF##11880650##[23]##, ##REF##10639453##[24]##. The role of macrophages in defense against <italic>C. neoformans</italic> appears to depend on the host species such that pulmonary macrophage depletion is associated with increased susceptibility and resistance, in rats and mice, respectively ##REF##16116215##[25]##. <italic>C. neoformans</italic> has a unique and remarkable intracellular pathogenic strategy that includes replication inside macrophages, accumulation of cytoplasm polysaccharide-containing vesicles and induction of phagosome leakiness ##REF##11390242##[22]##, ##REF##11880650##[23]##. Recently, a novel phenomenon known as phagosomal extrusion has been described, whereby <italic>C. neoformans</italic> can escape from the macrophage host, with survival of both yeast and host cells ##REF##17084702##[26]##, ##REF##17084701##[27]##.</p>", "<p>In the present study, we evaluated the distribution of intracellular yeasts into daughter cells following mitosis of macrophage-like cells. The data suggested that the outcome of particle distribution is a function of the intracellular cargo number, with inert particles and certain <italic>C. neoformans</italic> strains having a stochastic outcome whereas for others, the distribution was unequal and not determined by chance alone. The outcome of ingested particle distribution depends on the single phagosome formation, thus implying that phagosomal fusion events can have a dominant effect on intracellular particle distribution following phagocytic cell division.</p>" ]

[ "<title>Materials and Methods</title>", "<title>Yeast strains and culture</title>", "<p>\n<italic>C. neoformans</italic> var. <italic>neoformans</italic> strain 24067 and Cap67 (acapsular strain) was obtained from the American Type Culture Collection (Rockville, MD). <italic>C. neoformans</italic> var. <italic>grubii</italic> strain H99 and its phospholipase (PLB1) mutant were obtained from John Perfect (Durham, NC). PLB1 was documented as a virulence factor of <italic>C. neoformans</italic>\n##REF##11123698##[36]##. <italic>C. gattii</italic> (Serotype B) strain NIH 198 was provided by Thomas Mitchell (Durham, NC). All yeasts were cultured in Sabouraud dextrose broth (Difco) at 30°C with agitation (150–180 rpm). When necessary, yeasts were killed by heating to 56°C in water bath for 1 h and yeast death was confirmed by demonstrating no colony growth in Sabouraud agar. To coat polysaccharide to acapsular Cap67 cells, <italic>C. neoformans</italic> strain H99 cultures were grown in Sabouraud dextrose broth for more than 3 days and supernatant was collected and filtered by 0.45 mm filter. Acapsular Cap67 yeast cells were incubated with filtered supernatant for 2 h. The presence of surface polysaccharide was established by indirect immunofluorescence ##REF##6365785##[37]##, ##REF##3536747##[38]##.</p>", "<title>Murine macrophages and culture</title>", "<p>The murine macrophage-like J774.16 cells, originally derived from a reticulum sarcoma ##REF##1089721##[39]##, were used for all the experiments except for those specifically indicated. J774.16 cells were selected because <italic>C. neoformans</italic> infection in this host cell replicates all the phenomena observed with primary cells ##REF##11390242##[22]##, ##REF##17084702##[26]##, ##REF##16879260##[40]##. J774.16 cells were grown at 37°C with 10% CO₂ in feeding media consisting of Dulbecco's minimal essential medium (DMEM) (Life Technologies), 10% NCTC-109 medium (Gibco), 10% heat-inactivated (56°C for 30 min) FCS (Gemini Bio-products, Woodland, CA), and 1% non-essential amino acids (Mediatech Cellgro, Washington, DC). Cells were then plated on polylysine coverslip-bottom MatTek plates (MatTek Cultureware, Ashland, MA) at a density of 5×10⁴ per plate in feeding media, stimulated with 50 U/ml recombinant murine IFNγ (Genzyme, Cambridge, MA) and incubated overnight at 37°C and 10% CO₂.</p>", "<p>For primary murine macrophage harvesting, 6–8 week old BALB/c female mice (Jackson Laboratories, Bar Harbor, ME) were sacrificed by asphyxiation with CO₂. Macrophages were harvested by lavaging the peritoneal cavity with sterile PBS (10 ml washes per mice). Cells were pooled and spun down at 1200 rpm, counted, resuspended in feeding media aforementioned and plated in MatTek plates. Macrophages were allowed to adhere for at least 1 h prior to phagocytosis and subsequent microscopic time-lapse imaging.</p>", "<title>Macrophage-like phagocytosis assay and time-lapse microscopy</title>", "<p>For live cell imaging, 5×10⁴ macrophage-like cells were plated on polylysine coated coverslip bottom MatTek plates and allowed to adhere overnight as described above. Yeast cells or polystyrene beads (3.2 µm, Spherotech, Lake Forest, IL) were washed with PBS for 3 times prior to phagocytosis assays. The macrophage culture media was then removed and replaced with fresh media containing yeast cells at effector to target ratio of 10∶1 or 5∶1, or polystyrene beads at effector to target ratio of 10∶1 along with 10–50 µg/ml of purified anti-capsular mAb 18B7 when <italic>C. neoformans</italic> strain 24067 and <italic>C. gattii</italic> were used or 20% guinea pig serum when H99 strain was used. In comparing <italic>C. neoformans</italic> strains we faced the problem that there is great variability in the efficiency of antibody- and complement-mediated phagocytosis. For example, H99 is easily opsonized with complement while antibody is significantly less effective. Since the mode of opsonization does not appear to have major effect on the outcome of the fungal-macrophage interaction with respect to intracellular replication or exocytosis ##REF##15905568##[19]##, ##REF##17084702##[26]##, ##REF##12884862##[41]## and we were interested in cargo distribution after replication, we opted for using the most efficient opsonin for each strain. In all phagocytosis experiments, macrophage-like cells were activated with 0.3 µg/ml lipopolysaccharide (LPS) (Sigma, St. Louis, MO) and 50 units/ml of murine IFNγ. Macrophage-like cells and <italic>C. neoformans</italic> or beads were incubated together for 60 min to allow for completion of phagocytosis, washed once with fresh media, replenished with 2 ml feeding media and followed by time-lapse imaging every 4 minutes. Images were collected at 10× or 20× using the Axiovert 200 M inverted microscope and photographed with an AxiocamMR camera controlled by the Axio Vision 4.4 software (Carl Zeiss Micro Imaging, NY). This microscope was housed in a Plexiglas box and the temperature was stabilized at 37°C with a forced air heater system. The plate lid was kept in place to prevent evaporation, and 5% CO₂ was delivered to a chamber locally at the culture dish. Images were compiled into movies which were then used to analyze the intracellular particle distribution and phagosome formation during macrophage-like cell division. Movie animations and montages were created using ImageJ software (<ext-link ext-link-type=\"uri\" xlink:href=\"http://rsb.info.nih.gov/ij/\">http://rsb.info.nih.gov/ij/</ext-link>).</p>", "<title>Statistical analysis</title>", "<p>One-way ANOVA, Newman-Keuls multiple comparison test, one sample <italic>t</italic> test, χ² analysis and linear regression were used as indicated. Statistical analysis was done by Prism 4.0 (GraphPad Software, San Diego, CA) and <italic>p</italic>&lt;0.05 was considered to be significant.</p>" ]

[ "<title>Results</title>", "<title>Modeling of post-mitotic intracellular particle distribution</title>", "<p>Following phagocytosis of yeasts, a significant portion of J774.16 macrophage-like cells undergo cell division. This effect probably reflects the phenomenon of cell cycle progression following phagocytosis that we have described earlier ##REF##15905568##[19]##. To analyze the outcome of intracellular yeast distribution after phagocytic cell division we considered that the default outcome was stochastic, with the distribution of ingested particles or microbes after cell division reflecting chance alone. There are two possible outcomes to the division of an infected cell (##FIG##0##Figure 1A, B and C##). Outcome I occurs when all the intracellular particles in the mother cell are distributed into one of the daughter cells, a process that produces one infected and one with a clean daughter cell (##FIG##0##Figure 1A##). Outcome II occurs when the yeast cells in the mother cell are distributed to both daughter cells after cell division. Furthermore, Outcome II can be subdivided into two conditions: the intracellular yeast cells are equally distributed into both daughter cells (Outcome IIa, ##FIG##0##Figure 1B##) and unequally distributed into both daughter cells (Outcome IIb, ##FIG##0##Figure 1C##). Conceivably, these two conditions are relevant to microbial pathogenesis because Outcome IIa leaves both daughter cells with half the number of ingested yeasts and thus could allow cellular microbicidal mechanisms to overcome the microbial infection while in Outcome IIb, one daughter cell is left with larger microbial load compared to the other.</p>", "<p>To analyze the distribution of intracellular particles during macrophage-like cell division quantitatively, the skewness of numerical difference of intracellular particles between two daughter cells was measured by the distribution index (DI) which was defined as following. The number of intracellular particles in mother cell is <italic>n</italic>. During cell division, there are various outcomes for intracellular particle distribution between the two daughter cells. If the number of intracellular particles which are distributed into Daughter cell 1 is <italic>x</italic>, the number of intracellular particles which are distributed into Daughter cell 2 is <italic>n-x</italic>. The DI was defined as\n</p>", "<p>Based on the stochastic outcome we developed an algorithm to model the intracellular particle distribution after cell division. The number of intracellular particles in any given mother cell is <italic>n</italic>. During cell division, the intracellular particles are assumed to be distributed stochastically into two daughter cells. If the number of intracellular particles which are distributed into Daughter cell 1 is <italic>x</italic>, the number of intracellular particles which are distributed into Daughter cell 2 is <italic>n-x</italic>. The probability of this given event to occur is <italic>y</italic>, which can be calculated by the equationNote that is the number of possible events to distribute <italic>x</italic> intracellular particles into one daughter cell if total <italic>n</italic> intracellular particles are distributed and 2<italic>ⁿ</italic> is the total number of possible events to distribute <italic>n</italic> intracellular particles into two daughter cells.</p>", "<p>The DI for this given event (<italic>z</italic>) is the probability multiplied by the numerical difference of the intracellular particles between two daughter cellsThe theoretical DI (DI_T) for all possible distribution events of <italic>n</italic> intracellular particles is\n</p>", "<p>Stochastic probabilities of Outcome I, IIa and IIb events described above, which also vary as the function of particle number, were calculated based on Equation (2). For probability of Outcome I (P_I), either Daughter cell 1 has all the particles (<italic>x = n</italic>) or Daughter cell 2 has all the particles (<italic>x</italic> = 0)\n</p>", "<p>The probabilities of Outcome IIa (P_IIa) or Outcome IIb (P_IIb) depend on whether the number of particles in mother cells is even or odd. It is impossible to have Outcome IIa or Outcome IIb events if the number of particles in the mother cells is odd or even, respectively. Therefore, P_IIa equals 0 and P_IIb equals 1 when <italic>n</italic> is odd whereas P_IIa equals 1 and P_IIb equals 0 when <italic>n</italic> is even. Other than in these exclusionary conditions,\n\n</p>", "<p>To handle the computational complexity that results with an increasing number of ingested particles (<italic>n</italic>), we wrote two programs using sun java jdk1.6 to calculate DI_T and P_I, P_IIa and P_IIb based on Equation (4)–(7) (##SUPPL##4##Data Set S1##, ##SUPPL##5##S2## and ##SUPPL##6##S3## were the java programs for calculation of DI_T, calculation of P_I, P_IIa and P_IIb and factorial calculation respectively). Java standard library functions were used for coding. Since floating-point numbers may lose precision, the programs can only be used when <italic>n</italic>≤1000. Given macrophage-like cells in our system rarely ingested particles more than 50, the calculated numbers were sufficient for our comparison and normalization purposes.</p>", "<p>Using the above formalisms we calculated the DI_T and P for situations up to <italic>n</italic> = 100 and plotted it as a function of ingested particle number (##FIG##0##Figure 1D, E## and ##SUPPL##7##Data Set S4##, ##SUPPL##8##S5##, which were calculated results of DI_T and P_I, P_IIa and P_IIb respectively). When there is only one particle per cell, the distribution will always be an Outcome I event, which has a DI_T of 1. The DI_T drops as a function of particle number as the particles per cell increase (##FIG##0##Figure 1D##). P_I drops rapidly as a function of particle number when the particle per cell increase, such that an Outcome I event becomes extremely unlikely if the particle number is more than 10. P_IIa decreases and approach to 0, as the particle number increases given the particle number is not odd. Conversely, P_IIb increases and approach 1 as particle number increases given the particle number is not even (##FIG##0##Figure 1E##). Hence, if the distribution of ingested particles is determined by chance alone the likelihood that a clean daughter cell emerges from cell division is dramatically reduced as a function of the number of ingested particles. This analysis provided a reference point for the comparison and interpretation of the biological data obtained with phagocytic cells.</p>", "<title>Post-mitotic distribution of intracellular yeasts</title>", "<p>We measured the distribution of intracellular yeasts into the daughter cells using microscopic time-lapse imaging after phagocytosis of three different <italic>C. neoformans</italic> strains, 24067, H99 and Cap67, which is an acapsular mutant. For a comparison, a different <italic>Cryptococcus</italic> spp., <italic>Cryptococcus gattii</italic> was also used in our experiments. In addition, we investigated the distribution of inert polystyrene beads ingested by macrophage-like cells as the control. To explore whether microbial factors affected the measured outcomes, we also evaluated heat-killed H99 strain (HK H99), one H99 strain deficient in phospholipase B (H99 PLB1), heat-killed Cap67 (HK Cap67), Cap67 coated with polysaccharide derived from H99 (Cap67+PS) and heat-killed <italic>C. gattii</italic> (HK <italic>C. gattii</italic>). As predicted, both Outcomes I and II events were observed during cell division of macrophage-like cells containing yeasts or beads (##FIG##1##Figure 2A## and ##SUPPL##0##Movie S1##, ##FIG##1##Figure 2B## and ##SUPPL##1##Movie S2##, respectively). We noted that formation of a single giant phagosome often preceded Outcome I type cell division. Before cell division, phagosomes with intracellular yeasts often fused together, forming a single giant phagosome and effectively reducing intracellular particle number (<italic>n</italic>) to 1. The integrity of the giant phagosome was maintained during cell division and consequently the phagosome was sorted into one of the daughter cells after cell division. Interestingly, the likelihood of single giant phagosome formation was particularly high when phagocytic cells contained <italic>C. neoformans</italic> strains H99 PLB1 mutant, Cap67 and <italic>C. gattii</italic> yeast cells (##FIG##1##Figure 2A## and ##SUPPL##0##Movie S1##). In contrast, the single giant phagosome formation was rarely seen in Outcome II type cell division when phagocytic cells contained polystyrene beads or <italic>C. neoformans</italic> strain 24067 (##FIG##1##Figure 2B## and ##SUPPL##1##Movie S2##).</p>", "<p>In addition, similar distribution patterns were observed in yeast-infected tissue-derived macrophages during cell division in culture (data not shown). However, the frequencies of cell division events for tissue-derived macrophages were much lower than observed with macrophage-like cell lines and consequently we focused our analysis on data derived from J774.16 macrophage-like cells. These macrophage-like cells faithfully reproduce the interaction of yeasts with macrophages but have the advantage of reproducing every 12 h ##REF##15905568##[19]## and thus provide an extremely useful system for studying the outcome of phagocytic cell division in the setting of intracellular ingested cargo.</p>", "<title>Comparison of experimental and theoretical post-mitotic particle distribution</title>", "<p>To quantify the distribution of intracellular particles into daughter cells after division of macrophage-like cells the outcome data was presented as the DI. In Outcome I, the DI equals 1. In Outcome II, the DI is between 0 and 1 for any unequal distribution (Outcome IIb) whereas the DI for equal distribution (Outcome IIa) would equal to 0. The DI and the distribution outcomes of yeasts and polystyrene beads were shown in ##FIG##2##Figure 3A##. The measured experimental P_I, P_IIa and P_IIb, which were calculated by dividing outcome events by total events, were shown in ##TAB##0##Table 1##. For comparison, the theoretical probabilities of the outcomes for yeasts and beads were calculated according to the number of ingested particles using the plot in ##FIG##0##Figure 1E##. For polystyrene beads, the distribution of particles into daughter cells was that expected by chance alone as no significant difference was revealed between the experimental data and the theoretical calculation. For the intracellular yeasts, P_I for all strains except <italic>C. neoformans</italic> strain 24067 were significantly higher than expected by chance alone. Interestingly, large differences of probabilities between experimental data and theoretical calculation observed when the intracellular particles were H99 PLB1 mutant, Cap67, HK Cap67, <italic>C. gattii</italic> and HK <italic>C. gattii</italic>. These results suggest that microbial-mediated actions, possibly via phospholipase or polysaccharide, can skew the distribution of ingested particles into one of the daughter cells during cell division. We also noted that these yeasts also had particularly high single giant phagosome formation before macrophage-like cell division as described above.</p>", "<p>We originally intended to compare DI between macrophage-like cells dividing with intracellular beads and yeasts. However, the means of the calculated DI (horizontal lines in ##FIG##2##Figure 3B##) were not immediately comparable between these groups because the numbers of intracellular particles ingested by macrophage-like cells before cell division event differed between groups. Given that the stochastic outcome of the DI can vary as a function of the number of ingested particles, we resorted to comparing the experimental outcomes observed to the DI_T predicted by the stochastic modeling described previously (##FIG##0##Figure 1D## and ##SUPPL##7##Data Set S4##, ##SUPPL##8##S5##). Consequently, the normalized DI was calculated by dividing the DI obtained from the experiments (showed as dots in ##FIG##2##Figure 3A##) with the corresponding DI_T of the same number of intracellular particles (##FIG##2##Figure 3B##). Using this normalization approach, we eliminated the inaccuracy introduced by numerical variances of intracellular particles among groups. By this analysis, the distribution of intracellular particles was stochastic if the normalized DI equaled 1 (dash line in ##FIG##2##Figure 3B##). While normalized DI for yeasts and beads were either larger or smaller than 1, the distribution of intracellular particles into daughter macrophage-like cells was non-stochastic and skewed towards unequal distribution or equal distribution, respectively. Statistical analysis showed that the normalized DI were significantly larger than 1 for all the yeasts except <italic>C. neoformans</italic> strain 24067, Cap67+PS and beads. Interestingly, the H99 PLB1 mutant had significantly higher DI than its parental strain H99. When the acapsular mutant Cap67 was coated with polysaccharide from strain H99, this strain had a similar DI as that of H99.</p>", "<title>Single phagosome formation as a mechanism for non-stochastic intracellular particle distribution</title>", "<p>Phagosomes containing intracellular <italic>Cryptococcus</italic> yeasts can fuse, leading to formation of giant phagosomes in macrophages ##REF##17084702##[26]##, ##REF##17705844##[28]##. Hence, we suspected that single giant phagosome formation played an important role in the post-mitotic distribution of intracellular particles. To test this possibility, we counted single phagosome formation events in all experiments to determine the percentage of yeast containing cells demonstrating single phagosome formation. Plotting the percentage of cells forming single phagosome versus the normalized DI revealed a strong linear correlation (##FIG##3##Figure 4##). Interestingly, the best-fit line intersected the X-axis near a normalized DI of 1, implying a stochastic distribution in situations where there was no single phagosome formation.</p>", "<title>Biological consequences following Outcome I and II of intracellular particle distribution during macrophage-like cell mitosis</title>", "<p>To investigate the biological consequences of intracellular particle distribution, the two daughter cells were continuously observed by time-lapse microscopy after cell division. Four types of consequences were observed and quantified following Outcome I and II events: cell survival, cell death by lysis, phagosomal extrusion and daughter cell fusion (##TAB##1##Table 2## and ##TAB##2##3##). The overwhelming majority of daughter cells emerged from mitosis alive as indicated by movement irrespective of whether they had intracellular cargo. Lysis of the cells caused by intracellular yeasts was not commonly observed during our experimental time frame. We occasionally observed the fusion of two daughter cells with intracellular yeasts after the cell division was completed (##FIG##4##Figure 5A## and ##SUPPL##2##Movie S3##). This phenomenon was not observed with uninfected macrophage-like cells or macrophage-like cells containing intracellular beads. The fusion phenomenon was relatively rare, but its occurrence is noteworthy because it implies an altered cell membrane state for cells with ingested yeasts. In addition, phagosomal extrusion of intracellular yeasts was commonly observed before as well as after mitosis and intracellular yeast distribution (##FIG##4##Figure 5B## and ##SUPPL##3##Movie S4##). This phenomenon indicates that phagosomal extrusion does not damage the host cell sufficiently to prevent cell division. The extrusion was more commonly seen with intracellular <italic>C. gattii</italic>, less commonly seen with <italic>C. neoformans</italic> strains. No extrusion was observed with macrophage-like cells harboring intracellular beads. These results were consistent with the previous report ##REF##17084702##[26]##. Mitosis resulting in Outcome I was associated with more phagosomal extrusion events. Interestingly, compared to the phagosomal extrusion rates reported previously under no mitosis conditions ##REF##17084702##[26]##, the daughter cell with full cargo load post Outcome I distribution had significantly higher extrusion rates with intracellular <italic>C. neoformans</italic> strains Cap67, HK Cap67+PS, H99 and HK C. <italic>gattii</italic>, suggesting that post-mitotic host cells were more likely to demonstrate microbe exit events. The extrusion rates were similar if intracellular yeasts were <italic>C. neoformans</italic> strain 24067 and <italic>C. gattii</italic>. In the contrast, extrusion rates post Outcome II distribution were lower than those reported under no mitosis conditions. This implied that Outcome I events may predispose intracellular yeasts for extrusion whereas Outcome II events reduce the likelihood of phagosomal extrusion.</p>" ]

[ "<title>Discussion</title>", "<p>The predicted distribution outcomes for mitotic phagocytic cells carrying ingested particles were each experimentally observed in this study. In Outcome I, intracellular yeasts are quarantined within one of the daughter macrophage-like cells during mitosis, leaving the other daughter cell microbe- or particle-free. In Outcome II, intracellular yeasts were unequally or equally distributed into daughter macrophage-like cells. Intracellular yeast distribution via Outcome I may be beneficial to the host since it can cure a cell of infection by generating one clean cell. In contrast, equal or unequal distribution corresponding to Outcomes II produced two infected macrophages that could, in theory, perpetuate the infection and facilitate dissemination in conditions where the host cell does not kill the microbe. Our data showed that a significantly large percentage of macrophage-like cells harboring intracellular <italic>C. gattii</italic> and acapsular <italic>C. neoformans</italic> strain had Outcome I distribution, compared to those with intracellular beads and capsular <italic>C. neoformans</italic> strains. Interestingly, Outcome I distribution has been noted in at least one other system. Almost two decades ago, the similar phenomenon was observed in the distribution of intracellular <italic>Coxiella burnetti</italic> during macrophage cell division ##REF##3772338##[29]##, ##REF##8406840##[30]##. In the case of <italic>C. burnetti</italic>, unequal distribution resulted from the formation of a single large phagosome that necessarily produced an Outcome I event after cell division. Similarly, our study indicated that the mechanism responsible for Outcome I in the case of yeasts appears to be a phagosome fusion event that produces a single large phagosome sortable between the two daughter cells.</p>", "<p>Our analysis was limited to macrophage-like cells and we acknowledge the limitations inherent in using immortalized cell lines <italic>in vitro</italic>. Nevertheless, J774.16 macrophage-like cell line are a good system because they faithfully reproduce the essential events required for this process, namely phagocytosis, phagosome fusion, intracellular replication, and phagosome extrusion that have also been observed with tissue derived macrophages ##REF##15905568##[19]##, ##REF##11880650##[23]##, ##REF##17084702##[26]##. Furthermore, we did observe division of primary macrophages containing ingested particles in this study but the frequency of such events <italic>in vitro</italic> was too low for a statistically significant analysis. The fact that J774.16 macrophage-like cells reproduce many of the effects observed with primary macrophages and that similar observations were made with primary macrophages provides confidence for assuming that similar outcomes can be expected for tissue macrophages that are induced to replicate during infection.</p>", "<p>Outcome I type phagocytic cell division generates a new clean phagocytic cell for host defense while confining the infection to another cell. Given that Outcome I generates a clean daughter cell, one might surmise that this is the best outcome for the host. However, from the microbial perspective, Outcome I events also generate a new host cell for infection and potentially doom the infected daughter cell by burdening it with a higher intracellular microbial load. Furthermore, Outcome I events would not necessarily be best for the host if dividing the infectious cargo into two cells would reduce intracellular microbial burden and allow the host cell to kill or inhibit the microbe in the intracellular space. Hence, it is not clear whether Outcome I events are optimal for the host or the microbe and it is likely the beneficiary of this event will depend on the specific type of host-microbe interaction and the survivability of the microbe in the intracellular environment. From a teleological perspective, it is likely that the all-or-none nature of Outcome I events could be a decisive event on the outcome of host-microbe cellular interactions. If this is the case, one can imagine that control of the post-mitotic intracellular particle distribution is critical area of contention of host and microbial cells with the emergence of microbial and host defense mechanisms to influence this outcome. In our system, we observed that Outcome I events were associated with a higher likelihood of phagosomal extrusion, and event that theoretically benefits the microbe by allowing fungal escape from a phagocytic cell. Since phagosomal extrusion is associated with the appearance of pathological changes in the host cell ##REF##17705844##[28]##, we infer, that on balance, Outcome I benefits the <italic>C. neoformans</italic>. However, it is likely that the relative gains and debits of Outcome I and II events are system dependent and vary for individual microbe-host cell interactions.</p>", "<p>Our results show that Outcome I events are far more common that would be expected by chance alone, thus implying the existence of mechanisms that preferentially sort microbial cargo into one daughter cell. Given that the probability of Outcome I drops rapidly with increasing cargo number, Outcome I events require either ingestion of very few microbes or phagosomal fusion. Since limiting phagocytosis in an infected area would seem counterproductive for both host defense and intracellular pathogens that thrive inside cells, it would appear that phagosomal fusion is the most effective mechanism for influencing microbial cargo distribution following phagocytic cell division. Inspection of the data obtained with live and dead, wild type and PLB1 mutant, encapsulated and non-encapsulated yeasts and inert polystyrene beads suggest that both the microbial and phagocytic cells can influence the distribution of ingested particle cargo following cell division. For polystyrene beads, the distribution of ingested particles was stochastic. Since polystyrene beads are inert, the absence of Outcome I events could reflect a lack of reaction by the host cell to trigger cellular mechanisms that would promote phagosome fusion. In contrast, for yeasts, the outcome of intracellular particle sorting into daughter cells was stochastic for strain 24067 and non-stochastic for acapsular <italic>C. neoformans</italic> strain Cap67, PLB1 mutant and <italic>C. gattii</italic>.</p>", "<p>Giant phagosome formation was highly correlated with Outcome I events. In this system, phagosomal fusion is known to occur resulting in the formation of very large phagosomes ##REF##17084702##[26]##, ##REF##17705844##[28]##. The mechanism of phagosomal fusion in <italic>C. neoformans</italic> infected cells has not been elucidated, and dissection of the microbial and host factors responsible for phagosomal fusion is beyond the scope of this study. A likely microbial candidate to influence phagosomal fusion is the capsular polysaccharide given that polymers such as polyethylene glycol promote membrane fusion and polysaccharides such as dextran produce vesicle aggregation ##REF##8913598##[31]##. However, the effect of polysaccharide appears to be variable and dependent on the cell. When exogenous polysaccharide was added to the acapsular Cap67 cells we noted a decrease in Outcome I events possibly reflecting disaggregation of clumped cells as a consequence of increased surface charge ##REF##9125569##[32]##. Phospholipase could also affect phagosomal fusion through effects on integrity of phagosomal membranes ##REF##12787743##[33]##. The secreted phospholipase B was documented to play a role in cryptococcal dissemination ##REF##15039347##[34]##.</p>", "<p>In the course of this study we noted two phenomena that appeared related to the occurrence, timing and efficacy of phagosomal fusion events. First, phagosomal extrusion events commonly occurred before or after cell division of macrophage-like cells, and this phenomenon was most commonly observed for phagocytic cells harboring <italic>C. gattii.</italic> The observation that host cell division can follow phagosomal extrusion provides solid evidence that macrophage-like cells are not significantly damaged by the extrusion process. Interestingly, we noted that most of extruded phagosomes with <italic>C. gattii</italic> remained attached to the macrophage-like cells undergoing cell division, consistent with the occurrence of altered membranes in these cells. The observation that phagosomal extrusion sometimes preceded cell division is intriguing because it suggests that this phenomenon may reflect a need for phagosomes to fuse before being extruded. Phagosomal extrusion with replication produces two uninfected phagocytic cells. Second, we noted that macrophage-like cells harboring yeasts occasionally fused after cell division. This observation is intriguing given that giant cell formation is thought to be critical for granuloma formation and containment of <italic>C. neoformans</italic> infection in tissue ##REF##15990974##[35]##. We are not sure whether this process is analogous to that which occurs <italic>in vivo</italic> but we note with interest that cell fusion events were limited to yeast infected cells. The finding that the capsular polysaccharide is involved in phagosomal extrusion ##REF##17084702##[26]## suggests a mechanism whereby the polysaccharide alters the cell and phagosomal membranes to make them more likely to fuse, like other pro-fusion molecules as polyethylene glycol. Consequently, macrophage-like cell fusion events may reflect contact between two cells with altered membranes.</p>", "<p>In summary, we report that replication of phagocytic cells carrying a cargo of yeasts can produce both stochastic and non-stochastic outcomes with respect to intracellular yeast cell distribution into daughter cells depending on the yeast strains and species. This observation combined with the fact that mitotic events for cells carrying polystyrene beads produced stochastic outcomes implies that the distribution outcome is influenced by microbial factors. These results provide the first systematic analysis of particle distribution after phagocytic cell division and highlight the need to consider distribution outcome in evaluating the pathogenesis of infection. Microbial cargo distribution among daughter host cells may be as important a consideration as microbial intracellular location, mechanism of intracellular survival, and cellular exit strategy in influencing the outcome of infection. Consequently, we propose that the evaluation of microbial cargo distribution following phagocytic cell division is a new variable to be considered in studies of microbial pathogenesis and our findings suggest the need for similar studies with other intracellular pathogens.</p>" ]

[]

[ "<p>Conceived and designed the experiments: YL MA AC. Performed the experiments: YL MA. Analyzed the data: YL LX. Contributed reagents/materials/analysis tools: YL MA LX. Wrote the paper: YL AC. Built up the mathematic model, coded java programs for analysis and helped analyze data: LX.</p>", "<p>Given that macrophages can proliferate and that certain microbes survive inside phagocytic cells, the question arises as to the post-mitotic distribution of microbial cargo. Using macrophage-like cells we evaluated the post-mitotic distribution of intracellular <italic>Cryptococcus</italic> yeasts and polystyrene beads by comparing experimental data to a stochastic model. For beads, the post-mitotic distribution was that expected from chance alone. However, for yeast cells the post-mitotic distribution was unequal, implying preferential sorting to one daughter cell. This mechanism for unequal distribution was phagosomal fusion, which effectively reduced the intracellular particle number. Hence, post-mitotic intracellular particle distribution is stochastic, unless microbial and/or host factors promote unequal distribution into daughter cells. In our system unequal cargo distribution appeared to benefit the microbe by promoting host cell exocytosis. Post-mitotic infectious cargo distribution is a new parameter to consider in the study of intracellular pathogens since it could potentially define the outcome of phagocytic-microbial interactions.</p>" ]

[ "<title>Supporting Information</title>" ]

[ "<p>We thank Michael Cammer and the Analytical Imaging Facility of Albert Einstein College of Medicine for aiding the acquisition of images and compilation of movies.</p>" ]

[ "<fig id=\"pone-0003219-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003219.g001</object-id><label>Figure 1</label><caption><title>The possible post-mitotic outcomes of intracellular particle distribution and modeling of post-mitotic intracellular particle distribution.</title><p>(A) Schematic diagram of Outcome I: all the intracellular particles in the mother cell are distributed into one of the daughter cells with a clean daughter cell generated after cell division (all-or-none distribution). (B and C) Schematic diagram of Outcome II: intracellular yeast cells in the mother cell are distributed equally (B) or unequally (C) into both daughter cells after cell division (equal or unequal distribution). (D) Theoretical distribution index (DI_T) for various numbers of intracellular particles calculated from Equation (4), with the aid of a computer program, assuming that the intracellular particles were stochastically distributed into two daughter cells during cell division. DI_T for <italic>n</italic> = 1–100 were plotted in this Figure. (E) Probability of Outcome I, IIa and IIb (P_I, P_IIa and P_IIb) of various numbers of intracellular particles calculated from Equations (5)–(7), with the aid of a computer program, assuming that the intracellular particles were stochastically distributed into two daughter cells during cell division. P_I, P_IIa and P_IIb for <italic>n</italic> = 1–100 were plotted in this Figure.</p></caption></fig>", "<fig id=\"pone-0003219-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003219.g002</object-id><label>Figure 2</label><caption><title>Outcome I and II of post-mitotic intracellular particle distribution.</title><p>(A) Post-mitotic Outcome I of intracellular particle distribution. Images showing J774.16 cells infected with HK Cap67 undergoing cell division and sorting the intracellular yeasts into one of the two daughter cells. Frames are labeled according to the start of the imaging process, which is approximately 1 h after phagocytosis of yeasts was initiated. The thick arrow indicated single giant phagosome formation caused by phagosomal fusion. The thin arrow indicated macrophage-like cell division. Images were collected at 20×. Bar, 10 µm. (B) Post-mitotic Outcome II of intracellular particle distribution. Images showing J774.16 cells infected with <italic>C. neoformans</italic> strain 24067 undergoing cell division and sorting the intracellular in both daughter cells. Frames are labeled according to the start of the imaging process, which is approximately 1 h after phagocytosis of yeasts was initiated. The arrow indicated macrophage cell division. Images were collected at 20×. Bar, 10 µm.</p></caption></fig>", "<fig id=\"pone-0003219-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003219.g003</object-id><label>Figure 3</label><caption><title>Comparison of experimental and theoretical post-mitotic particle distribution.</title><p>(A) Post-mitotic DI of intracellular particle distribution. DI for <italic>C. neoformans</italic> strains 24067, H99, heat-killed H99 (HK H99), H99 phospolipase mutant (H99 PLB1), Cap67, heat-killed Cap67 (HK Cap67), Cap67 coated with polysaccharide (Cap67+PS), <italic>C. gattii</italic> or heat-killed <italic>C. gattii</italic> (HK <italic>C. gattii</italic>). The horizontal lines denoted the means of calculated DI, which were not immediately comparable between the groups as described in the <xref ref-type=\"sec\" rid=\"s2\">Results</xref>. The data were collected from three or more independent experiments for every condition. In every experiment, movies were recorded for approximately 24 h with 300–400 cells in the microscopic field from which the macrophage-like cells underwent cell division with intracellular particles were selected for analysis. (B) Normalized post-mitotic DI by stochastic modeling of intracellular particle distribution. To eliminate the inaccuracy introduced by numerical variances of intracellular particles, the normalized DI was calculated as described in the <xref ref-type=\"sec\" rid=\"s2\">Results</xref>. The distribution of intracellular particles was stochastic if the normalized DI equaled to one (dash line). While normalized DI were either larger or smaller than 1, the distribution of intracellular particles into daughter macrophage-like cells was non-stochastic and was skewed to unequal distribution or equal distribution, respectively. One-way ANOVA test revealed significant variances between the groups (<italic>p</italic>&lt;0.0001). Newman-Keuls multiple comparison test revealed significant difference between H99 and H99 PLB1 (^#\n<italic>p</italic>&lt;0.05), Cap67 and Cap67+PS (^##\n<italic>p</italic>&lt;0.01). One sample <italic>t</italic> test revealed that post-mitotic distribution of most yeasts had significantly larger DI than 1 which denoted stochastic distribution (dash line) (**<italic>p</italic>&lt;0.01, ***<italic>p</italic>&lt;0.001).</p></caption></fig>", "<fig id=\"pone-0003219-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003219.g004</object-id><label>Figure 4</label><caption><title>Linear correlation of percentage of macrophage-like cells with single phagosome formation versus the normalized DI.</title><p>The percentages of cells demonstrating single phagosome formation in all the experiments were calculated and plotted versus the normalized DI. Statistical analysis revealed a strong linear correlation. The best-fit line intersected the X-axis near a normalized distribution index of 1, which denotes a stochastic distribution in situations where there is no phagosomal fusion.</p></caption></fig>", "<fig id=\"pone-0003219-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003219.g005</object-id><label>Figure 5</label><caption><title>Extrusion of intracellular yeasts during mitosis of macrophage-like cells and fusion of two daughter macrophage cells with intracellular yeasts post mitosis.</title><p>(A) Fusion of two daughter macrophage cells with intracellular yeasts post mitosis. Frames are labeled according to the start of the imaging process, which is approximately 1 h after phagocytosis of <italic>C. neoformans</italic> strain H99 was initiated. The thick arrow indicated the fusion of two daughter cells after division and the thin arrow indicated macrophage-like division. Images were collected at 20×. Bar, 10 µm. (B) Extrusion of intracellular yeasts during mitosis of macrophage-like cells. After phagocytosis of <italic>C. neoformans</italic>, some macrophage-like cells undergoing cell division extruded intracellular yeasts either before or after the cell round-up, a morphological change that indicates the initiation of cell division (time zero). Frames are labeled according to the start of the imaging process, which is approximately an hour after phagocytosis of <italic>C. gattii</italic> was initiated. The thick arrow indicated the extrusion of intracellular <italic>C. neoformans</italic> and the thin arrow indicated macrophage-like cell division. Images were collected at 20×. Bar, 10 µm.</p></caption></fig>" ]

[ "<table-wrap id=\"pone-0003219-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003219.t001</object-id><label>Table 1</label><caption><title>Experimental and theoretical probabilities of distribution outcomes</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">%</td><td colspan=\"3\" align=\"left\" rowspan=\"1\">Experimental probability</td><td colspan=\"3\" align=\"left\" rowspan=\"1\">Theoretical probability</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Outcome</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">I</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">IIa</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">IIb</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">I</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">IIa</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">IIb</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Beads</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">77</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">79</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>24067</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">64</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>H99</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29<xref ref-type=\"table-fn\" rid=\"nt102\">*</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">61</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">67</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>HK H99</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">44<xref ref-type=\"table-fn\" rid=\"nt102\">*</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">52</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>H99 PLB1</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">44<xref ref-type=\"table-fn\" rid=\"nt102\">*</xref>\n<xref ref-type=\"table-fn\" rid=\"nt103\">#</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">59</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Cap67</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50<xref ref-type=\"table-fn\" rid=\"nt102\">**</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">74</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>HK Cap67</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50<xref ref-type=\"table-fn\" rid=\"nt102\">***</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">72</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Cap67+PS</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">76<xref ref-type=\"table-fn\" rid=\"nt102\">*</xref>\n<xref ref-type=\"table-fn\" rid=\"nt104\">§</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">59</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">26</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>C. gattii</italic></bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">53<xref ref-type=\"table-fn\" rid=\"nt102\">*</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">49</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>HK</bold>\n<bold><italic>C. gattii</italic></bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">96<xref ref-type=\"table-fn\" rid=\"nt102\">***</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pone-0003219-t002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003219.t002</object-id><label>Table 2</label><caption><title>Rates of the four consequences observed after Outcome I distribution</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">%</td><td colspan=\"4\" align=\"left\" rowspan=\"1\">Daughter cell 1</td><td colspan=\"4\" align=\"left\" rowspan=\"1\">Daughter cell 2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Particles</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ live</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ lysed</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">particle extruded</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ fused</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ live</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ lysed</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">particle extruded</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ fused</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Beads</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>24067</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">75</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>H99</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">43**</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>HK H99</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">59</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">41*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>H99 PLB1</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">71*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Cap67</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">44*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>HK Cap67</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">56*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Cap67+PS</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">93</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>C. gattii</italic></bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>HK </bold>\n<bold><italic>C. gattii</italic></bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">96</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pone-0003219-t003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003219.t003</object-id><label>Table 3</label><caption><title>Rates of the four consequences observed after Outcome II distribution</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">%</td><td colspan=\"4\" align=\"left\" rowspan=\"1\">Daughter cell 1</td><td colspan=\"4\" align=\"left\" rowspan=\"1\">Daughter cell 2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Particles</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ live</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ lysed</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">particle extruded</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ fused</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ live</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ lysed</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">particle extruded</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MΦ fused</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Beads</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>24067</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">95</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">82</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>H99</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">78</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>HK H99</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>H99 PLB1</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">67</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Cap67</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">89</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">67</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>HK Cap67</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">90</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Cap67+PS</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>C. gattii</italic></bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">63</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>HK </bold>\n<bold><italic>C. gattii</italic></bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr></tbody></table></alternatives></table-wrap>" ]

[ "<disp-formula><label>(1)</label></disp-formula>", "<disp-formula><label>(2)</label></disp-formula>", "<inline-formula></inline-formula>", "<disp-formula><label>(3)</label></disp-formula>", "<disp-formula><label>(4)</label></disp-formula>", "<disp-formula><label>(5)</label></disp-formula>", "<disp-formula><label>(6)</label></disp-formula>", "<disp-formula><label>(7)</label></disp-formula>" ]

[]

[]

[]

[]

[ "<supplementary-material content-type=\"local-data\" id=\"pone.0003219.s001\"><label>Movie S1</label><caption><p>Movie S1 showed one post-mitotic Outcome I event of intracellular particle distribution.</p><p>(1.74 MB AVI)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003219.s002\"><label>Movie S2</label><caption><p>Movie S2 showed one post-mitotic Outcome II event of intracellular particle distribution.</p><p>(4.55 MB AVI)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003219.s003\"><label>Movie S3</label><caption><p>Movie S3 showed one fusion event of two daughter macrophage cells with intracellular yeasts post mitosis.</p><p>(0.24 MB AVI)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003219.s004\"><label>Movie S4</label><caption><p>Movie S4 showed one extrusion event of intracellular yeasts during mitosis of macrophage-like cell.</p><p>(3.48 MB AVI)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003219.s005\"><label>Data Set S1</label><caption><p>Data Set S1 was the java program for calculation of DI_T.</p><p>(0.01 MB JAVA)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003219.s006\"><label>Data Set S2</label><caption><p>Data Set S2 was the java program for calculation of P.</p><p>(0.01 MB JAVA)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003219.s007\"><label>Data Set S3</label><caption><p>Data Set S3 was the java program for factorial calculation.</p><p>(0.01 MB JAVA)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003219.s008\"><label>Data Set S4</label><caption><p>Data Set S4 was the calculated results of DI_T.</p><p>(0.02 MB XLS)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003219.s009\"><label>Data Set S5</label><caption><p>Data Set S5 was the calculated results of P.</p><p>(0.03 MB XLS)</p></caption></supplementary-material>" ]

[ "<table-wrap-foot><fn id=\"nt101\"><p>Probabilities of distribution outcomes observed in the experiments (##FIG##2##Figure 3A##) were calculated. Theoretical probabilities of distribution outcomes were calculated from the plot in ##FIG##1##Figure 2B## according to the number of ingested particles. χ<italic>²</italic> analysis was used to compare P_I between different groups.</p></fn><fn id=\"nt102\"><label>*</label><p>indicates significant difference between experimental and theoretical probabilities (^*\n<italic>p</italic>&lt;0.05, ^**\n<italic>p</italic>&lt;0.01, ^***\n<italic>p</italic>&lt;0.001).</p></fn><fn id=\"nt103\"><label>#</label><p>indicates significant difference between H99 PLB1 mutant and it parent strain H99 (^#\n<italic>p</italic>&lt;0.05).</p></fn><fn id=\"nt104\"><label>§</label><p>indicates significant difference between Cap67 coated with H99 polysaccharide and Cap67 (^§\n<italic>p</italic>&lt;0.05).</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt105\"><p>Time-lapse microscopy recordings were performed after phagocytosis of different particles by macrophage-like cells. Four different consequences including conditions as daughter cells were live, lysed, fused or intracellular yeasts were extruded were observed post Outcome I and II distribution (A and B respectively). The percentages of events observed were shown. Daughter cell 1 and 2 generated by Outcome I were the cells without intracellular yeasts and with full load of intracellular yeasts, respectively. Daughter cell 1 and 2 generated by Outcome II were the cells with small number of yeasts and with large number of intracellular yeasts, respectively. χ<italic>²</italic> analysis revealed significant higher rates of phagosome extrusion occurred post Outcome I distribution if intracellular particles were yeasts except <italic>C. neoformans</italic> strain 24067 and beads compared to that of post Outcome II distribution (^*\n<italic>p</italic>&lt;0.5, ^**\n<italic>p</italic>&lt;0.01).</p></fn></table-wrap-foot>", "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>The present study was supported by NIH grants AI033142-11, AI033774-11 and HL059842. AC is also supported in part by the Northeastern Biodefense Center under grant AI057158-05. YL is also supported by the National Cancer Institute under Immunology and Immunooncology training program 5T32CA009173.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"pone.0003219.g001\"/>", "<graphic xlink:href=\"pone.0003219.e001.jpg\" mimetype=\"image\" position=\"float\"/>", "<graphic xlink:href=\"pone.0003219.e002.jpg\" mimetype=\"image\" position=\"float\"/>", "<inline-graphic xlink:href=\"pone.0003219.e003.jpg\" mimetype=\"image\"/>", "<graphic xlink:href=\"pone.0003219.e004.jpg\" mimetype=\"image\" position=\"float\"/>", "<graphic xlink:href=\"pone.0003219.e005.jpg\" mimetype=\"image\" position=\"float\"/>", "<graphic xlink:href=\"pone.0003219.e006.jpg\" mimetype=\"image\" position=\"float\"/>", "<graphic xlink:href=\"pone.0003219.e007.jpg\" mimetype=\"image\" position=\"float\"/>", "<graphic xlink:href=\"pone.0003219.e008.jpg\" mimetype=\"image\" position=\"float\"/>", "<graphic xlink:href=\"pone.0003219.g002\"/>", "<graphic xlink:href=\"pone.0003219.g003\"/>", "<graphic id=\"pone-0003219-t001-1\" xlink:href=\"pone.0003219.t001\"/>", "<graphic xlink:href=\"pone.0003219.g004\"/>", "<graphic xlink:href=\"pone.0003219.g005\"/>", "<graphic id=\"pone-0003219-t002-2\" xlink:href=\"pone.0003219.t002\"/>", "<graphic id=\"pone-0003219-t003-3\" xlink:href=\"pone.0003219.t003\"/>" ]

[ "<media xlink:href=\"pone.0003219.s001.avi\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003219.s002.avi\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003219.s003.avi\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003219.s004.avi\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003219.s005.txt\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003219.s006.txt\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003219.s007.txt\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003219.s008.xls\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003219.s009.xls\"><caption><p>Click here for additional data file.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p>Cell migration involves coordination of protrusion at the cell front, adhesion of the newly protruded domains to the substrate, pulling of the bulk of the cell towards new adhesion sites, and breaking of adhesion and retraction at the cell rear. These events are largely based on actin microfilament system: protrusion is believed to be driven by assembly of actin network at the leading edge, while generation of the tension force to pull the cell body and retract the tail depends, at least in part, on the contraction of actin network by motor protein myosin II ##REF##14657486##[1]##, ##REF##13678614##[2]##. Adhesion of actin network through plasma membrane to the extracellular matrix is mediated by specialized protein complexes termed focal adhesions (FAs) ##REF##11715046##[3]##–##REF##14519397##[5]##, which also serve as signal transduction sites where the cell gathers information about the mechanical and chemical properties of the environment ##REF##11715046##[3]##, ##REF##11082272##[6]##.</p>", "<p>The interaction between FAs and actin network is complex, in particular due to the fact that actin network in most migrating cells is not stationary with respect to the substrate, but moves away from the leading edge of the cell in a process known as retrograde flow ##UREF##0##[7]##, ##REF##9206973##[8]##. Retrograde flow is thought to be a consequence of the same forces that drive cell migration: the pressure of actin assembly against the membrane and contractile forces in the actin network ##REF##10934315##[9]##, ##REF##10588644##[10]##. FAs in some instances have been observed to move themselves ##REF##10550057##[11]##–##REF##10712904##[13]##, but the majority of FAs at the leading edge of the cell is stationary ##REF##12461561##[14]##. To rationalize the relationship between adhesion and retrograde flow, a hypothesis has been put forward likening adhesion to a clutch ##REF##10934315##[9]##, ##REF##10550057##[11]##, ##REF##3078414##[15]##, ##REF##10934316##[16]##. When the clutch is not engaged (no adhesion), actin machinery runs idle resulting in retrograde flow but no net advance of the cell; conversely, establishment of adhesion converts actin network flow into a productive advancement of the cell. Consistent with this hypothesis, inverse correlation between cell advance and retrograde flow rate was indeed demonstrated in some cases ##REF##10550057##[11]##, ##REF##15548591##[17]##, but not in other cases ##REF##15548591##[17]##, ##REF##1400580##[18]##. In most cases, retrograde flow coexists with adhesion, suggesting that FA components form a slipping interface with the actin network. Recent studies demonstrated various degrees of correlation between motion of filamentous actin and various adhesion components, establishing a hierarchical order of interaction within this interface ##REF##17204653##[19]##, ##REF##17158922##[20]##. However, a more general question of how FAs arise in the midst of flowing actin network and if and how their formation influences the flow remains unanswered.</p>", "<p>The relation between actin network and FAs is further complicated by the existence of two different types of actin network at the leading edge of the cell, each having its characteristic composition, dynamics and flow velocity ##REF##12105180##[21]##, ##REF##15375270##[22]##. The more peripheral network domain, the lamellipodium, is characterized by faster actin turnover and retrograde flow than the inner domain, the lamellum. Signature components of the lamellipodium include Arp2/3 and ADF/cofilin, involved in formation and turnover of branching actin network, while the lamellum contains tropomyosin and myosin II, proteins characteristic of contractile bundles of actin filaments. Mature FAs which are associated with contractile actin bundles, are also abundant in the lamellum ##REF##17204653##[19]##, ##REF##15375270##[22]##.</p>", "<p>The relative roles of the lamellipodium and the lamellum in the cell are controversial. One point of view is that lamellipodium is the “organ of protrusion” and the factory of actin filaments in the cell ##REF##11859023##[23]##. Other studies suggest that most of the actin filaments assembled in the lamellipodium disassemble locally and do not become incorporated into other actin structures in the cell, and that productive advance of the cell correlates with the advance of the lamellum rather than the lamellipodium ##REF##15375270##[22]##, ##REF##15716379##[24]##. However, the mechanism of the advance of the lamellum is not clear. While the lamellipodium displays a zone of intense actin assembly at its outer edge, consistent with the lamellipodial protrusion being powered by actin assembly, the lamellum is characterized by a distributed pattern of actin turnover ##REF##15375270##[22]##. Consequently, it is not clear if the expansion of the lamellum proceeds via local assembly of the lamellar actin network at its outer edge, or via acquisition of actin filaments from the lamellipodium. Importance of the lamellipodium actin assembly emerged from the analysis of the origin of actin filament bundles in the lamellum ##REF##16651381##[25]##. This study suggested that transverse actin arcs formed in Arp2/3-depended mechanism characteristic of the lamellipodium, while dorsal stress fibers arised in the arp2/3-independent, formin-dependent manner. Thus, one population of actin filaments in the lamellum may be inherited from the lamellipodium, while another population may arise locally. Recent analysis of the dynamics of cell spreading ##REF##15016377##[26]##, ##REF##17289574##[27]## suggested that edge extension is a cyclical process involving expansion and retraction of the lamellipodium, and that the lamellipodial actin provides a transient link between myosin II and adhesion sites. Irrespective of the eventual fate of the lamellipodial actin filaments, it is unclear how the position of the lamellipodium/lamellum boundary is defined and why this boundary moves during cell spreading and migration.</p>", "<p>In this report, we analyze the correlative dynamics of FAs and the boundary between the lamellum and the lamellipodium in spreading cells. We find that formation of FAs depends on actin flow and at the same time controls the dynamics of flow the by triggering the transition from fast to slow flow and defining the position of the lamellipodium/lamellum boundary.</p>" ]

[ "<title>Materials and Methods</title>", "<title>Cell culture</title>", "<p>Swiss 3T3 mouse fibroblasts, REF-52 rat fibroblasts and B16 mouse melanoma cells were cultured in DMEM supplemented with 10% fetal bovine serum and antibiotics. For microscopy, the cells were plated at low density in 35 mm Petri dishes with heated glass bottom (Bioptechs, Butler, PA), or into the home-made Petri dishes with coverslip glued over the hole in the bottom. Before the observation, the culture medium was replaced with fresh DMEM with 25 mM HEPES, 10% fetal serum and antibiotics. 1–1.5 ml per dish of mineral oil was then applied to the surface to avoid evaporation on the microscope stage. The temperature was maintained during observation using the Bioptechs (Butler, PA) dish and objective heaters, or an infrared lamp.</p>", "<title>Cytoskeletal markers</title>", "<p>Rhodamine-actin and rhodamine-myosin II were prepared and microinjected as described ##REF##7490299##[34]##, ##REF##9264457##[51]##. To produce YFP-paxillin construct, paxillin cDNA (kindly provided by K. Nakata, S. Miyamoto and K. Matsumoto, National Institute of Dental and Craniofacial Research, NIH, Bethesda, MD) was cloned into EYFP-C1 (Clontech Laboratories, Palo Alto, CA, USA). GFP-actin construct ##REF##9840457##[52]## was kindly provided by G. Marriott (Max-Plank-Institute for Biochemistry, Martinsried, Germany). To achieve transient expression of YFP-paxillin and GFP-actin Swiss 3T3 and REF-52 cells were transfected with either one or both of the constructs by nuclear microinjection.</p>", "<p>Stable GFP-actin expressing B16 melanoma cell line ##REF##9601095##[53]## was a generous gift of Christoph Ballestrem (Weizmann Institute of Science) and Bernhard Wehrle-Haller (University of Geneva). Stable YFP-paxillin expressing REF-52 cell line was produced using retroviral infection. To produce vector for retroviral infection, DNA encoding YFP-paxillin was cut from pEYFP-paxillin expression vector and ligated into a retroviral vector, pBabeNeo ##REF##2194165##[54]##. The vector was expressed in packaging cell line, as described ##REF##2194165##[54]##, and supernatant containing viral particles was used to infect REF-52 cells. Following infection, cells were selected with G418 and were further cultivated in the G418-containing medium (1 mg/ml).</p>", "<title>Microscopy</title>", "<p>Optical microscopy was performed using a Nikon Eclipse TE300 inverted microscope with CFI Plan 100x phase objective (NA 1.25) and 100 W halogen and 100 W HBO light sources for phase contrast and epifluorescence microscopy, respectively. Images were captured with a Roper Scientific (Tucson, AZ) MicroMAX-1300PB cooled CCD camera operated with Metamorph software (Universal Imaging, West Chester, PA). To switch automatically between phase contrast and multicolor epifluorescence microscopy modes, shutters in the transmitted and epifluorescence light paths and exitation and emission filter wheels were operated with Ludl Electronic Products Ltd. (Hawthorne, NY) MAC 2000 and MAC 5000 controllers driven with Metamorph software. XF52-1 Pinkel filter set (Omega Optical, Brattleboro, VT) was used to separate rhodamine and YFP fluorescence, and JP3 filter set (Chroma Technology Corp., Rockingham, VT) was used to separate GFP and YFP fluorescence.</p>", "<p>Enhanced phase contrast microscopy was performed as described ##REF##13679520##[28]## (Verkhovsky et al., 2003): briefly, single frame exposures of 500–800 ms were used to achieve intensity readout of about 50% saturating level of the camera. Background images of the cell-free areas of the same dish were subtracted from the cell images (a constant was added to the result to avoid negative intensity values), and the resulting images were scaled to a narrow contrast range for optimal visualization of the structure of the lamellum and the lamellipodium.</p>", "<p>Image acquisition rate was one frame per 4 to 6 s for enhanced phase contrast microscopy and 1 frame per 4 to 30 s for fluorescence microscopy. To estimate the velocity of retrograde flow, time-lapse image sequences were analyzed using kymograph function of Metamorph software. This function copies selected narrow regions from the sequential frames of the time-lapse sequence and pastes them side-by-side in a time-montage. Moving features are visualized in kymographs as diagonal streaks with a slope depending on the velocity of movement.</p>" ]

[ "<title>Results</title>", "<title>Two types of retrograde flow visualized by enhanced phase contrast microscopy at the periphery of spreading cells</title>", "<p>Enhanced phase contrast microscopy ##REF##13679520##[28]## allows detection of small contrast differences in the peripheral cytoplasm. It was previously used to analyze the organization ##REF##13679520##[28]## and dynamics ##REF##15635099##[29]## of the actin network in the lamellipodium of migrating fish keratocytes. Here, we applied this technique to visualize retrograde flow and the lamellipodium/lamellum transition in spreading fibroblasts and melanoma cells (##FIG##0##Fig. 1##). Comparison of enhanced phase contrast images and fluorescence actin images demonstrated that all the actin structures (lamellipodial network, filopodial-type small filament bundles, and stress fibers terminated at adhesion plaques) that were visualized in the fluorescence images were also clearly detectable in the enhanced phase contast images (##FIG##0##Figure 1B, C##). We followed the dynamics of these features in time-lapse movie sequences of the phase contrast images and fluorescence images. Two distinct zones of retrograde flow from the cell edge to its center were clearly distinguishable in time-lapse sequences and resolved by kymograph analysis (##FIG##0##Figure 1## and Supporting ##SUPPL##0##Movies S1## and ##SUPPL##1##S2##). The peripheral (faster) zone of flow coincided with the lamellipodial network of actin (##FIG##0##Figure 1##, arrowheads), while the more central (slower) zone encompassed the region of the cytoplasm containing stress fibers and commonly defined as the lamellum. Flow velocity varied depending on the type and state of the cell (0,5–7 µm/min for the lamellipodium and 0,05–2,5 µm/min for the lamellum, with faster flow typically observed at the early stages of spreading), but in the same cell flow velocity in the lamellipodium was always higher than that in the lamellum. Interestingly, the flow of the phase contrast features along the radial stress fibers in the lamellum and in the space between the stress fibers proceeded with exactly the same velocity, while the positions of the peripheral ends of the stress fibers did not change with time (##FIG##0##Figure 1A##). The lamellum and the lamellipodium were previously distinguished by their kinematics and dynamics using fluorescence speckle microscopy ##REF##12105180##[21]##, ##REF##15375270##[22]##.</p>", "<p>Consistent with the previous reports ##REF##10588644##[10]##, ##REF##15375270##[22]##, our phase contrast microscopy analysis revealed that two types of retrograde flow had different inhibitor sensitivities and therefore were likely driven by different mechanisms. The lamellipodial flow was instantly inhibited upon treatment with cytochalasin D (##FIG##0##Figure 1C##, and Supporting ##SUPPL##2##Movie S3##). At the same time, up to two-minute treatment with cytochalasin D did not affect the velocity of flow in the lamellum. Inhibition of cell contractility with the rho-kinase inhibitor HA1077 ##REF##9353125##[30]## (##FIG##0##Figure 1D## and Supporting ##SUPPL##3##Movie S4##) and the protein kinase inhibitor H7 ##UREF##1##[31]## (not shown), on the other hand, reduced the velocity of lamellar flow and did not affect lamellipodial flow.</p>", "<p>Kymograph analysis indicated that the flow velocity changed abruptly at the boundary between the two zones of flow (##FIG##0##Figure 1A##). This boundary was distinct based on both velocity analysis and the change of density and texture of the actin network seen in fluorescence as well as in phase contrast images (##FIG##0##Figure 1A–D##). Interestingly, the boundary was not smooth, but often consisted of several segments, convex in the inside direction, forming a festooned line. The apexes of this line coincided with the termini of the radially oriented stress fibers, which likely corresponded to the FAs (##FIG##0##Figure 1A##). Thus, it appeared that the position of the boundary between the two flow zones was related to the positions of FAs. Next, we analyzed the relationship between flow and adhesion sites in more detail.</p>", "<title>Formation of nascent adhesions locally blocks the lamellipodial flow</title>", "<p>We investigated simultaneous dynamics of FAs and retrograde flow using YFP-paxillin as a marker of adhesion sites. Time-lapse sequences recorded in double enhanced phase contrast /fluorescence mode demonstrated that the boundary between the fast and slow flow zones coincided with the line connecting the outmost FAs at the cell periphery (##FIG##1##Figure 2A## and Supporting ##SUPPL##4##Movie S5##). During cell spreading, new paxillin-positive sites (nascent FAs) always appeared outside of the area bordered by this line (194 events of new adhesion formation observed in 8 spreading cells), indicating that they originated within the lamellipodia. In the double-mode image sequences, nascent FAs were indeed initially detected within the lamellipodial flow zone. Within seconds of the detection of the nascent adhesion, phase contrast microscopy revealed irregularity of rapid lamellipodial flow manifesting as an accumulation of phase dense material at the site of adhesion and an abrupt reduction of the flow velocity in the zone immediately behind the adhesion site (##FIG##1##Figure 2B, C## and Suppurting ##SUPPL##5##Movies S6## and ##SUPPL##6##S7##). This disturbance of flow may be the cause for previously noted phenomenon of “ruffling” at the new adhesion sites ##REF##3126193##[32]##. As the new adhesion increased in size, the new boundary between the lamellipodial and the lamellar flow zones appeared, passing through the site of the newly formed adhesion (##FIG##1##Figure 2B, C##). The multiple events of the formation of new adhesions in the lamellipodial flow zone eventually resulted in the gradual anterograde movement of the boundary between the lamellum and the lamellipodium. Advance of the boundary was often associated with a transient decrease in the width of the lamellipodial zone, but the lamellipodium subsequently re-grew to its original width due to the protrusion at its outer boundary (##FIG##1##Figure 2C##, and Supporting ##SUPPL##6##Movie S7##). Thus, anterograde movement of the boundary between the flow zones in combination with the lamellipodial protrusion contributed to the overall spreading of the cell. The coupling between the formation of new adhesions and the advance of the lamellipodium/lamellum boundary was evident both in the enhanced phase contrast/YFP-paxillin double imaging mode and in the rhodamine-actin/YFP-paxillin double fluorescence mode (Supporting ##SUPPL##7##Movie S8##).</p>", "<p>The advance of the boundary between fast and slow flow did not always coincide with the maturation of small dot-like adhesions into large streak-like structures. In some cases, nascent adhesions did not mature into large adhesions, but still marked the boundary between the fast and slow flow. Advance of the boundary was associated with apparent rapid turnover of these adhesions (Supporting ##SUPPL##8##Movie S9##).</p>", "<p>Slow retrograde flow persisted behind the adhesion zone and, as noted above, was observed by phase contrast microscopy not only within the bulk of the lamellum, but also along the phase-dense cables likely representing the stress-fibers terminating in focal adhesion sites. Consistent with recent reports ##REF##16651381##[25]##, ##REF##17868136##[33]##, this was confirmed by double fluorescence imaging of the markers of stress fibers and focal adhesions. We used myosin II as one of the markers of stress fiber dynamics, because it was previously shown that myosin II is distributed in the lamellum and along the stress fibers in the form of distinct clusters of bipolar minifilaments ##REF##7490299##[34]##. Double florescence imaging with microinjected rhodamine-myosin II and YFP-paxillin demonstrated a flow of myosin-positive spots to the center of the cell away from the adhesion sites with velocities similar to the velocity of the lamellar flow component (##FIG##2##Figure 3A##, and Supporting ##SUPPL##9##Movie S10##). Focal adhesions remained stationary or moved at velocities much smaller than that of the myosin flow. This flow depleted myosin from the area immediately behind the adhesion, but new myosin spots continuously appeared there, so that the overall myosin density remained virtually constant. The flow along the fibers with respect to their associated adhesion sites was also confirmed using fluorescence imaging of the cells either injected with fluorescent actin probe or expressing GFP-actin probe. Distinct specific features along the fiber (e.g. bifurcations) and non-uniformity of actin density served as reference points (##FIG##2##Figure 3B## and Supporting ##SUPPL##10##Movie S11##).</p>", "<p>The above findings suggested that FAs defined the position of the boundary between two flow zones. To analyze the organization of the flow in the absence of FAs, we plated the cells on poly-L-lysine-coated surface in the absence of serum and extracellular matrix proteins. In these conditions, the cells attach and spread, but the specific FA complexes and stress fibers do not develop ##REF##8557752##[35]##. The cells initially spread rapidly on poly-L-lysine, but subsequently arrested in a “fried-egg” state with the bulk of the cytoplasm remaining in the center. The spreading domain appeared uniform in its organization and dynamics from the edge to the perinuclear cytoplasm. It exhibited retrograde flow with the velocity either uniform or gradually decreasing towards the cell center and intermediate in value between typical fast and slow flow velocities (##FIG##1##Figure 2D## and Supporting ##SUPPL##11##Movie S12##). Thus, two zones of flow did not develop in the absence of FAs, suggesting that the boundary between these zones not only coincided with, but indeed depended on FAs.</p>", "<title>Inhibition of the lamellipodial flow blocks the formation of nascent adhesions</title>", "<p>In the previous section, we have demonstrated that nascent adhesions formed exclusively in the fast flow zone. Next, we investigate if the experimental inhibition of fast flow affects the formation of nascent adhesions. We studied paxillin dynamics during a short-term treatment with cytochalasin D, which, as shown in the first section, rapidly abolished the fast flow. We did not observe formation of any new nascent adhesions at the cell periphery, moreover, the small peripheral adhesions present before addition of the drug disappeared after approximately two minutes of treatment (##FIG##3##Figure 4A##). At the same time, large mature adhesions remained unaffected.</p>", "<p>In contrast to the inhibition of fast flow, inhibition of slow flow with the inhibitors of cell contractility increased the number of small dot-like adhesions (##FIG##3##Fig. 4B##). This can be explained by the inhibition of maturation of these small adhesions into large ones upon block of contractility as was previously reported in several studies ##UREF##1##[31]##, ##REF##10375527##[36]##, ##REF##8682874##[37]##. Consistent with the results of the same studies, rho-kinase inhibition also led to disassembly of previously existing mature adhesions (##FIG##3##Figure 4B##). Finally, combined treatment with rho-kinase inhibitors and cytochalasin D resulted in total disappearance of both types of adhesions (##FIG##3##Figure 4C##).</p>" ]

[ "<title>Discussion</title>", "<p>In this study, we analyzed comparative dynamics of actin retrograde flow and focal adhesions at the leading edge of the cell. Many previous studies investigated actin dynamics at the leading edge and the dynamics of focal adhesions separately ##REF##10588644##[10]##–##REF##12461561##[14]##, ##REF##12105180##[21]##, ##REF##15375270##[22]##, ##REF##12677069##[38]##. A few recent studies also analyzed simultaneous dynamics of actin and the FA components ##REF##17204653##[19]##, ##REF##17158922##[20]##, but did so in a steady state situation where the formation of new FAs was not observed. In contrast to these studies, we concentrated on the changes of actin flow associated with the formation of new FAs during cell spreading and on the interaction between actin dynamics and adhesion dynamics.</p>", "<p>Two zones of retrograde flow at the cell periphery were previously observed with fluorescent speckle microscopy technique ##REF##12105180##[21]##, and distinguished using automated analysis of speckle dynamics ##REF##15375270##[22]##. Here we visualized flow using enhanced phase contrast microscopy ##REF##13679520##[28]##, ##REF##15635099##[29]##. The advantages of this technique are that no exogenous markers for actin network need to be introduced into the cell, and that the flow is analyzed in the phase contrast images that simultaneously show overall cell morphology. Enhanced phase contrast microscopy clearly visualized two types of the flow. The boundary between two flow zones was clearly distinguishable in our images both morphologically and with kymograph analysis, which demonstrated an abrupt change of the flow velocity typically by a factor of two or more at the boundary. The velocities of flow varied significantly, with the maximum values (7 µm/min for fast flow, and 2,5 µm/min for slow flow) being higher than previously reported (e.g., 0.5–2 µm/min for fast flow, and 0.1–0.3 µm/min for slow flow in ##REF##12105180##[21]##, ##REF##15375270##[22]##), suggesting that flow velocities in both zones may depend on the type and/or physiological state of the cell. In particular, high flow velocities may be associated with the early state of radial cell spreading. Interestingly, velocities of the fast retrograde flow in spreading cells measured here are of the same order of magnitude as the migration velocities of rapidly migrating cells, e.g., fish epidermal keratocytes ##REF##15548591##[17]##, ##REF##8165580##[39]##. This finding indicates that maximal actin assembly rates may be similar in different types of cells, while the difference in protrusion rate between the “slow” cells (fibroblasts) and “fast” cells (keratocytes) may be due to the difference in retrograde flow rate (high in spreading fibroblasts and low in migrating keratocytes).</p>", "<p>We have correlated the patterns of retrograde flow with the dynamics of FAs in the same cells and found that 1) positions of FAs at the cell periphery coincided with the boundary between the fast and slow flow; 2) nascent adhesions first appeared in the fast flow zone; 3) the boundary between the fast and slow flow advanced to the new adhesion sites within seconds of the formation of new adhesions; and 4) in the absence of FAs (on polylysine-coated substrate) flow was apparently homogeneous and not separated in two zones. Thus, FAs not only appeared necessary for the formation of the lamellipodium/lamellum boundary, but their location seemed to define the position of the boundary. Since formation of new FAs was a transient event and was followed by rapid advance of the lamellum to the new FA sites, static observations demonstrated co-localization of the lamellipodium-lamellum boundary with the most peripheral row of Fas. This co-localization was also noted in a previous study ##REF##17204653##[19]##, although the relation of the movement of the lamellum/lamellipodium boundary to the formation of new adhesions was not analyzed. Also consistent with our findings, sharp boundary between the two zones of flow was not observed in sea urchin coelomocytes cultured on polylysin-coated surfaces ##REF##10588644##[10]##, , although inhibitor analysis revealed two different components of flow velocity. Taken together, these findings strongly suggest that the formation of FAs is the critical event controlling the transition from the fast to the slow flow type and that FAs define the position of the boundary between the lamellipodium and the lamellum.</p>", "<p>How FAs influence the flow and what exactly are the features of FAs that trigger the formation of the boundary between the fast and slow flow is a question for future research. Our observations that the boundary advances within seconds of the formation of FAs and that small FAs that turn over at the edge of the cell and do not mature into larger long-living Fas (see Supporting ##SUPPL##8##Movie S9##) are sufficient for the formation of the boundary suggest that maturation of FAs is not necessary for the effect on actin flow. The simplest hypothetical mechanism of how nascent adhesions modulate the flow is the mechanical resistance due to the anchoring of actin filaments to nascent adhesion complexes. Similar event may have been previously observed in an artificial situation: arrest of the flow upon anchorage to a synthetic bead on the apical cell surface ##REF##9531561##[40]##. One can imagine that at first a few filaments become anchored, than more filaments arrive with the flow and become entangled and anchored, and eventually the network flow is locally arrested. However, initial mechanical arrest of the flow may represent just a first step in the formation of the lamellipodiam/lamellum boundary and may be by itself not sufficient for the demarcation of the two types of actin network. Initial mechanical events may trigger a signaling cascade resulting in a change in the overall organization and composition of the actin network, which could me mediated by a change of activity of actin-interacting proteins, e.g. controlling actin filament stability such as cofilin and tropomyosin ##REF##17331727##[41]##.</p>", "<p>Another open question is how the discrete FAs create continuous boundary for the network flow. One possibility is that the continuous barrier forms due to the cross-linking and entanglement of the filament network between discrete pin-down points of individual FAs. In this mechanism, one could expect that the segments of the boundary between adjacent FAs would bend inward due to the exterior pressure from the fast flow, apparently consistent with the observed shape of the boundary (convex inward between FAs, see ##FIG##0##Fig. 1A##). Quantitative analysis of the stress distribution pattern around FAs in the moving actin network is a challenge for biophysical modeling and may help to understand the nature of the lamellipodium/lamellum boundary.</p>", "<p>Several recent studies ##REF##15375270##[22]##, ##REF##17289574##[27]## suggested a partial overlap between the lamellum and the lamellipodium. In our experiments, the boundary between the two zones always appeared distinct, except immediately after the formation of new adhesion, when the boundary shifted its position. Further time-resolved analysis is necessary to determine whether the overlap between the two zones is limited to the phases of cell protrusion when the boundary is shifting, or exists continuously.</p>", "<p>While formation of FAs apparently creates a barrier for the fast flow, actin network inside the area bordered by FAs is not completely immobilized, but undergoes slower flow characteristic of the lamellum and probably driven by myosin-dependent contraction. The flow of the components of stress fibers away from the stationary FAs associated with their tips suggests that stress fibers grow at these tips. The growth may represent either a de novo assembly of actin filaments, or an association with the fiber tip of the pre-assembled actin filaments delivered by flow. In any case, the growth mechanism should allow for continuous connection and force transmission between the growing stress fiber tip and focal adhesion complex. Recent study ##REF##16651381##[25]## suggested that formin-family proteins that form leaky cups at the barbed ends of actin filaments ##REF##14561409##[42]##, ##REF##17373907##[43]## may be responsible for assembly of at least a part of the population of actin filaments in filament bundles in the lamellum.</p>", "<p>Our results suggest that protrusion at the leading edge of the cell could be considered as a cycle of alternating steps of protrusion of the lamellipodium and advance of the lamellum, with the formation of FAs linking these steps together (##FIG##4##Fig. 5##). This cycle may be related to the recently described periodic lamellipodial contractions in spreading epithelial cells ##REF##15016377##[26]##, ##REF##17289574##[27]##. However, unlike ##REF##17289574##[27]##, we do not find a correlation between the retraction phase of the cycle and the formation of new FAs. In our experiments, new FAs appeared during lamellipodial advance as well as during its temporal retraction.</p>", "<p>Ponti et al. ##REF##15375270##[22]## argued that the advance of the cell edge is correlated with the advance of the lamellum rather than the lamellipodium. These authors considered actin dynamics in the lamellum autonomous from the lamellipodium, with very little material transfer between the two. However, single speckle tracking techniques may tend to underestimate the transfer of polymer mass at the lamellipodium/lamellum transition where the speckle velocity changes abruptly and individual speckles may be difficult to follow. Part of the actin filaments of the lamellum may originate in the lamellipodium ##REF##16651381##[25]##. Irrespective of whether any actin filaments from the lamellipodium are transferred to the lamellum or not, our findings indicate important role of the lamellipodium in the protrusion cycle. The lamellipodium emerges as the site where nascent adhesions arise. Moreover, we found that the inhibition of the lamellipodial actin flow abolishes nascent adhesions, suggesting that flow is necessary for their formation and maintenance. What are the specific features of the actin dynamics in the lamellipodium that promote the initiation of adhesions? One of the possibilities is that intensive actin polymerization in the lamellipodium creates conditions where the adhesion formation is favored. DeMali et al. ##REF##17373907##[43]## suggested that Arp2/3 complex involved in branching actin assembly may recruit FA component vinculin and promote its association with actin. Alternatively, the flow itself may stimulate adhesions mechanically, e.g. promote clustering of the adhesion molecules or trigger hypothetical mechanical sensors activating the adhesion process. We find the last possibility especially intriguing, as it would suggest a peculiar symmetry between the initiation and maturation of the focal adhesions. It is well established that the maturation of focal adhesions is promoted by applied tension, suggesting a stretch-activated sensor mechanism ##REF##11402062##[45]##–##REF##15314229##[48]##. We propose that nascent adhesions may also depend on force, but instead of myosin-dependent contraction force involved in maturation, nascent adhesions may depend on force associated with fast network flow in the lamellipodium. Displacement of adhesion components with respect to extracellular matrix was proposed as a key element of mechanosensing ##REF##15849245##[49]##. Analogously, relative movement of adhesion proteins and lamellipodial actin network may be involved in maintenance of nascent adhesions and their maturation. The specificity of the chemical and mechanical environment in the lamellum and the lamellipodium may define specific force- and/or displacement-triggered responses. Interestingly, “young” adhesions at the lamellipodium/lamellum boundary may experience both the compression due to the external “push” of the lamellipodial flow and stretching due the “pull” of the lamellar flow, possibly explaining strong traction at the substrate ##REF##11352946##[50]##. Irrespective of the specific molecular mechanisms of force-triggered responses that remain a challenge for the future, our study elucidates the essential reciprocal relation between dynamics of actin flow and FAs.</p>" ]

[]

[ "<p>Conceived and designed the experiments: AYA ADB ABV. Performed the experiments: AYA SS ABV. Analyzed the data: AYA SS JMV. Contributed reagents/materials/analysis tools: KA JJM. Wrote the paper: ADB ABV.</p>", "<p>Dynamic actin network at the leading edge of the cell is linked to the extracellular matrix through focal adhesions (FAs), and at the same time it undergoes retrograde flow with different dynamics in two distinct zones: the lamellipodium (peripheral zone of fast flow), and the lamellum (zone of slow flow located between the lamellipodium and the cell body). Cell migration involves expansion of both the lamellipodium and the lamellum, as well as formation of new FAs, but it is largely unknown how the position of the boundary between the two flow zones is defined, and how FAs and actin flow mutually influence each other. We investigated dynamic relationship between focal adhesions and the boundary between the two flow zones in spreading cells. Nascent FAs first appeared in the lamellipodium. Within seconds after the formation of new FAs, the rate of actin flow decreased locally, and the lamellipodium/lamellum boundary advanced towards the new FAs. Blocking fast actin flow with cytochalasin D resulted in rapid dissolution of nascent FAs. In the absence of FAs (spreading on poly-L-lysine-coated surfaces) retrograde flow was uniform and the velocity transition was not observed. We conclude that formation of FAs depends on actin dynamics, and in its turn, affects the dynamics of actin flow by triggering transition from fast to slow flow. Extension of the cell edge thus proceeds through a cycle of lamellipodium protrusion, formation of new FAs, advance of the lamellum, and protrusion of the lamellipodium from the new base.</p>" ]

[ "<title>Supporting Information</title>" ]

[ "<p>We are very grateful to Benny Geiger for helpful discussion, and to Christoph Ballestrem for B16 cell line expressing GFP-actin.</p>" ]

[ "<fig id=\"pone-0003234-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003234.g001</object-id><label>Figure 1</label><caption><title>Retrograde actin flow in spreading cells visualized by enhanced phase contrast and fluorescence microscopy.</title><p>Individual images of the time-lapse sequences are shown at the left, kymographs, at the right. Arrowheads indicate zones of fast flow (lamellipodia) , dashed lines on images indicate the regions used to generate kymographs, arrows in kymographs indicate the slope of the isointensity lines reflecting the velocity of flow. (A) Swiss 3T3 fibroblast at 1.5 h of spreading displays wide lamellipodium, which is distinguishable from the lamellum by its density and texture (left image) and the velocity of retrograde flow (see Supplementary Data, ##SUPPL##0##Movie S1A##). Kymographs are generated along the phase-dense fiber (second from the left) and along the line between the fibers (second from right). The isointensity line in kymograph was traced manually (white line) and analyzed in Matlab to generate the plot of velocity versus distance from the edge of the cell (right), which shows an abrupt change of velocity from approximately 6.2 µm/min to 2.4 µm/min at the lamellipodium/lamellum boundary. (B) REF-52 fibroblast at 5 h of spreading injected with rhodamine-actin and visualized with double mode microscopy: phase contrast (left) and fluorescence (second from left). The lamellipodium is distiguishable from the lamellum by its high actin concentration and the density and texture in the phase contrast images. The width of the lamellipodium in late spreading was smaller than in recently plated cells (compare with A). Small fibers in the lamellum are apparent in both fluorescence and phase contrast images. Kymographs generated from the phase contrast (second from right) and fluorescence (right) image sequences show identical flow velocities (0.8 µm/min in the lamellipodium, and 0.06 µm/min in the lamellum). See also Supplementary Data, ##SUPPL##0##Movie S1B##. (C) Spreading B16 melanoma cell expressing GFP-actin imaged in double phase contast/fluorescence mode and analyzed with kymographs as in (B). Dashed line marked “CD” on kymograph indicates the time of addition of cytochalasin D (2 µM). Addition of cytochalasin D resulted in the immediate arrest of spreading and the decrease of the velocity of the lamellipodial flow (from 2.6 µm/min to 0.4 µm/min), which thus became equal to the velocity of the lamellar flow. (D) Enhanced phase contrast image (left) and kymograph (right) of spreading Swiss 3T3 cell. Dashed line on kymograph indicate the time of addition of 30 µM HA1077, which results in the decrease of velocity of the lamellar flow (from 1 to 0.4 µm/min) with no change in the lamellipodial flow (4.5 µm/min). Scale bars on images, 5 µm; on kymographs, vertical bars, 2 µm, horizontal bars, 2 min.</p></caption></fig>", "<fig id=\"pone-0003234-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003234.g002</object-id><label>Figure 2</label><caption><title>FAs define the boundary between fast and slow flow.</title><p>(A) Superimposition of the enhanced phase contrast and fluorescence images (left) and kymographs (right) of spreading REF-52 cell expressing YFP-paxillin. Phase contrast image is represented in gray scale, and YFP-paxillin fluorescence image, in red. FAs marked by YFP-paxillin coincide with the boundary between the lamellipodium and the lamellum (see also Supplementary Data, ##SUPPL##1##Movie S2A##). Kymographs (phase contrast on top, fluorescence in the middle, and merge at the bottom) demonstrate that the fast flow did not penetrate behind the FAs and that the zone of slow flow advanced concomitantly with the formation of new FA. Arrows on kymographs are drawn parallel to isointensity lines indicating the velocity of fast flow (4.5 µm/min), slow flow (0.5 µm/min), and the velocity of the sliding of FAs (0.15 µm/min). (B, C) Selected frames from the time-lapse sequence showing two instances of the formation of the new FAs and associated dynamics of the boundary between the two flow zones; time is indicated in minutes:seconds. Sequence (B) represents dynamics of the region boxed in (A). New FAs (paxillin-positive spots indicated with arrowheads) form within the lamellipodia at 30 s in (B), and 20 s in (C). Disturbance of the flow is simultaneously seen in the phase contrast image as a dark zone in front of the FA. Formation of FAs is followed next by the advance of the lamellum (dark boundary between the lamellipodium and the lamellum is visible in the phase contrast mode). In (B) the lamellipodium persists throughout the sequence, while in (C) formation of the new FA is followed by the ruffling and withdrawal and then re-growth of the lamellipodium. See Supplementary Data, ##SUPPL##1##Movies S2B and S2C##. (D) REF-52 cells were plated in the serum-free media onto the coverslips coated with poly-L-lysine (1 h with 10 mg/ml aqueous solution), kymograph generated along the dashed line at the left is shown at the right. Kymograph demonstrates uniform flow velocity (1.6 µm/min) throughout the spread part of the cell. See also Supplementary Data, ##SUPPL##1##Movie S2D##. Scale bars, 5 µm; in kymographs vertical bars, 2 µm, horizontal bars, 2 min.</p></caption></fig>", "<fig id=\"pone-0003234-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003234.g003</object-id><label>Figure 3</label><caption><title>Flow of the components of stress-fibers with respect to FAs.</title><p>(A) Double fluorescence image (left) and kymograph (right) of YFP-paxillin (red) and rhodamine-myosin II (cyan) in REF-52 cell. Myosin flow velocity is 1 µm/min, FAs move at 0.05 µm/min (arrows). See Supplementary Data, ##SUPPL##2##Movie S3A##. (B) Double fluorescent image (left) and kymograph (right) of YFP-paxillin (red) and GFP-actin (cyan) in REF-52 cell. Actin flow velocity in the stress fiber is 0.75 µm/min, FAs move at 0,12 µm/min (arrows). See Supplementary Data, ##SUPPL##2##Movie S3B##. Scale bars, 2 µm; in kymographs vertical bars, 2 µm, horizontal bars, 2 min.</p></caption></fig>", "<fig id=\"pone-0003234-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003234.g004</object-id><label>Figure 4</label><caption><title>Effect of the inhibitors of retrograde flow on FAs.</title><p>FAs were visualized with YFP-paxillin. FA distribution is shown just before (top panels in A–C), and after the addition of the inhibitor (bottom panels in A–C, and middle panel in C), time after the addition is indicated in minutes. (A) Cytochalasin D treatments (2 µM) abolished small nascent adhesions; (B) H7 treatment (30 µM) abolished large mature adhesions and increased the number of small adhesions; (C) cytochalasin D treatment of the cells preincubated for 1 h with H7 abolished small adhesions which were present after treatment with H7. Bar, 5 µm.</p></caption></fig>", "<fig id=\"pone-0003234-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003234.g005</object-id><label>Figure 5</label><caption><title>Diagram of the multi-step protrusion process at the leading edge of the cell.</title><p>Fast and slow flow zones are shown with different shading; nascent and mature FAs are shown as ellipses of different sizes; and stress fibers are shown as sticks. Formation of nascent FAs within the fast flow zone locally interferes with flow, and eventually results in the advance of the boundary between the fast and slow flow zones. Therefore, the width of the slow flow zone (the lamellum) increases, while the width of the fast flow zone (the lamellipodium) decreases. Subsequently, the lamellipodium re-establishes its width, resulting in the overall advance of the cell edge.</p></caption></fig>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s001\"><label>Movie S1</label><caption><p>Enhanced phase contrast image sequence of spreading Swiss 3T3 cell (corresponds to ##FIG##0##Fig. 1A##). Field of view, 46 µm×31 µm, images taken at 4 s intervals, total elapsed time, 3 min 20 s.</p><p>(0.82 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s002\"><label>Movie S2</label><caption><p>Enhanced phase contrast and fluorescence microscopy image sequence of spreading REF-52 cell injected with rhodamine-actin (corresponds to ##FIG##0##Fig. 1B##). Field of view, 14 µm×23 µm, images in each mode are taken at 8 s intervals, total elapsed time, 10 min.</p><p>(1.09 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s003\"><label>Movie S3</label><caption><p>Enhanced phase contrast and fluorescence microscopy image sequence of spreading B16 melanoma cell expressing GFP-actin treated with cytochalasin D (corresponds to ##FIG##0##Fig. 1C##). Cytochalasin D-treatment results in the immediate arrest of protrusion and the inhibition of the retrograde flow in the lamellipodium, while the overall actin organization remains virtually unchanged up to 2 min after the addition of the drug. Field of view, 29 µm×44 µm, images in each mode are taken at 10 s intervals, total elapsed time, 8 min 30 s, cytochalasin D is added at 6 min 20 s.</p><p>(0.82 MB MPG)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s004\"><label>Movie S4</label><caption><p>Enhanced phase contrast image sequence of spreading Swiss 3T3 cell treated with rho-kinase inhibitor HA-1077 (corresponds to ##FIG##0##Fig. 1d##). Addition of HA-1077 results in overall spreading of the cell, disappearance of stress fibers in the lamellum and the inhibition of retrograde flow in the lamellum, while the lamellipodium dynamics is not affected. Field of view, 51 µm×38 µm, images are taken at 6 s intervals, total elapsed time, 15 min 30 s, HA-1077 added at 5 min 40 s.</p><p>(2.58 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s005\"><label>Movie S5</label><caption><p>Merged enhanced phase contrast and fluorescence microscopy image sequence of spreading REF-52 cell excpressing YFP-paxillin (corresponds to ##FIG##1##Fig. 2a##). Field of view, 75 µm×55 µm, phase contrast images are taken at 5 s intervals, fluorescenece images, at 20 s intervals, total elapsed time, 6 min.</p><p>(1.19 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s006\"><label>Movie S6</label><caption><p>Zoomed region of merged enhanced phase contrast and fluorescence image sequence of spreading REF-52 cell excpressing YFP-paxillin shows two instances of the formation of new FAs and the associated advance of the lamellum/lamellipodium boundary (corresponds to ##FIG##1##Fig. 2b##). Field of view, 16.3 µm×12.5 µm, phase contrast images are taken at 5 s intervals, fluorescenece images, at 20 s intervals, total elapsed time, 1 min 40 s.</p><p>(0.38 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s007\"><label>Movie S7</label><caption><p>Zoomed region of merged enhanced phase contrast and fluorescence image sequence of spreading REF-52 cell excpressing YFP-paxillin shows an instance of the formation of new FAs and the associated ruffling of the lamellipodium and the advance of the lamellum/lamellipodium boundary (corresponds to ##FIG##1##Fig. 2c##). Field of view, 12.6 µm×8.7 µm, phase contrast images are taken at 5 s intervals, fluorescenece images, at 20 s intervals, total elapsed time, 5 min.</p><p>(0.96 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s008\"><label>Movie S8</label><caption><p>Double fluorescence image sequence of spreading REF-52 cell expressing YFP-paxillin (shown in red) and microinjected with rhodamine-actin (shown in cyan). Numerous new FAs form in the lamellipodium followed by the advance of the lamellipodium/lamellum boundary to the sites of FAs. Field of view, 25 µm×19 µm, total elapsed time, 8 min.</p><p>(0.74 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s009\"><label>Movie S9</label><caption><p>Fluorescence (left) and merged fluorescence and enhanced phase contrast image sequence (right) of spreading REF-52 cell expressing YFP-paxillin (shown in yellow). The moving boundary between the lamellum and the lamellipodium coincides with the row of small FAs in the process of continuous turnover. Field of view, 12.2 µm×16.7 µm, phase contrast images are taken at 6 s intervals, fluorescenece images, at 18 s intervals, total elapsed time, 10 min</p><p>(1.30 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s010\"><label>Movie S10</label><caption><p>Enhanced phase contrast image sequence of REF-52 cell spreading on poly-L-lysin-coated glass surface shows uniform retrograde flow at the cell periphery. Field of view, 41 µm×31 µm, images are taken at 5 s intervals, total elapsed time, 12 min.</p><p>(2.34 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s011\"><label>Movie S11</label><caption><p>Movie S11. Double color fluorescence image sequence of spreading REF-52 cell expressing YFP-paxillin (red) and microinjected with rhodamine-myosin II (cyan) (corresponds to ##FIG##3##Fig. 4a##). Field of view 34 µm×24 µm, images in each channel are taken at 30 s intervals, total elapsed time,12 min.</p><p>(0.38 MB MOV)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003234.s012\"><label>Movie S12</label><caption><p>Double color fluorescence image sequence of REF-52 cell expressing YFP-paxillin (red) and GFP-actin (cyan) (corresponds to ##FIG##3##Fig. 4b##). Field of view 16 µm×4 µm, images in each channel are taken at 20 s intervals, total elapsed time, 8 min.</p><p>(0.19 MB MOV)</p></caption></supplementary-material>" ]

[ "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>Supported by the Swiss Science Foundation grants 3100-61589 and 3100-112413 (A.B.V.) and Israel Science Foundation (A.D.B.). A.D.B holds the Joseph Moss Chair of Biomedical Research.</p></fn></fn-group>" ]

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[ "<title>Introduction</title>", "<p>Many horse breeders value animals with variation in coat color. Several genes are known which diminish the intensity of the coloration and are phenotypically described as “dilutions”. Two of these are a result of the <italic>Cream (CR)</italic> locus and <italic>Silver</italic> (<italic>Z</italic>) locus. The molecular basis for <italic>Cream</italic> is the result of a single base change in exon 2 of the <italic>SLC45A2 (Solute Carrier 45 family A2</italic>, <italic>aka MATP</italic> for <italic>membrane associated transport protein</italic>) on ECA21 ##UREF##0##[1]##,##REF##11736803##[3]##. This change results in the replacement of a polar acidic aspartate with a polar neutral asparagine in a putative transmembrane region of the protein coded for by this gene ##REF##11736803##[3]##,##UREF##1##[2]##. <italic>CR</italic> has an incompletely dominant mode of expression. Heterozygosity for <italic>CR</italic> dilutes only pheomelanin (red pigment) whereas homozygosity for <italic>CR</italic> results in extreme dilution of both pheomelanin and eumelanin (black pigment) ##REF##4847742##[4]##.</p>", "<p>The Silver dilution is the result of a missense mutation of <italic>PMEL17</italic> (Premelanosomal Protein) on ECA6. The base change causes replacement of a cytosolic polar neutral arginine with non-polar neutral cysteine in <italic>PMEL17</italic>\n##UREF##1##[2]##. In contrast to <italic>CR</italic>, the <italic>Z</italic> locus is fully dominant and affects only eumelanin causing little to no visible change in the amount of pheomelanin regardless of zygosity. The change in eumelanin is most apparent in the mane and tail where the black base color is diluted to white and gray ##UREF##2##[5]##.</p>", "<p>The coat color produced by the <italic>CH</italic> locus is similar to that of the <italic>CR</italic> locus in that both can cause dilution phenotypes affecting pheomelanin and eumelanin. However, the effect of <italic>CH</italic> differs from <italic>CR</italic> in that; 1) <italic>CH</italic> dilutes both pheomelanin and eumelanin in its heterozygous form and 2) heterozygotes and homozygotes for <italic>CH</italic> are phenotypically difficult to distinguish. The homozygote may differ by having less mottling or a slightly lighter hair color than the heterozygote. ##FIG##0##Figure 1## displays images of horses with the three base coat colors chestnut, bay and black and the effect of <italic>CH</italic> upon each. ##FIG##1##Figure 2## shows that champagne foals are born with blue eyes, which change color to amber, green, or light brown and pink “pumpkin skin which acquires a darker mottled complexion around the eyes, muzzle, and genitalia as the animal matures ##UREF##3##[6]##. Foals with one copy of <italic>CR</italic> also have pink skin at birth but their skin is slightly darker and becomes black/near black with age. The champagne phenotype is found among horses of several breeds, including Tennessee Walking Horses and Quarter Horses. Here we describe family studies that led to mapping the gene and subsequent investigations leading to the identification of a genetic variant that appears to be responsible for the champagne dilution phenotype.</p>" ]

[ "<title>Materials and Methods</title>", "<title>Horses</title>", "<p>Three half-sibling families, designated 1, 2 and 3, were used for mapping studies. Family 1 consisted of a Tennessee Walking Horse (TWH) stallion, known heterozygous at the <italic>Champagne locus</italic> (<italic>CH/ch</italic>), and his 17 offspring out of non-dilute mares (ch/ch). Family 2 consisted of an American Paint Horse stallion (<italic>CH/ch</italic>) and his 10 offspring out of non-dilute (ch/ch) mares. Family 3 consisted of a TWH stallion (<italic>CH/ch</italic>), 23 offspring and their 12 non-dilute dams (ch/ch) and 1 dilute (buckskin) dam (ch/ch, <italic>CR/cr</italic>).</p>", "<p>To investigate the distribution of the gene among dilute and non-dilute horses of different horse breeds, 97 non-champagne horses were chosen from stocks previously collected and archived at the MH Gluck Equine Research Center. These horses were from the following breeds: TWH (20), Thoroughbreds (TB, 35), American Paint Horses (APHA, 32), Pintos (5), American Saddlebreds (ASB, 2), one American Quarter Horse (AQHA), one pony, and one American Miniature (AMH) Horse.</p>", "<p>Hair and blood samples from horses with the champagne dilution phenotype were submitted by owners along with pedigree information and photographs showing the champagne color and characteristics of each horse. Samples were collected from the following breeds (85 total): American Miniature Horse (9), American Cream Draft cross (1), American Quarter Horse (27), American Paint Horse (13, in addition to the family), American Saddlebred (2), Appaloosa (1), ASB/Friesian cross (1), Arabian crossed with APHA or AQHA horses (3), Missouri Foxtrotter(4), Mule (2), Pony (1), Spanish Mustang 1), Spotted Saddle Horse (1), Tennessee Walking Horse (20, in addition to the families).</p>", "<title>Color Determination</title>", "<p>To be characterized as possessing the champagne phenotype, horses exhibited a diminished intensity of color (dilution) in black or brown hair pigment and met at least two of the three following criteria: 1) mottled skin around eyes, muzzle and/or genitalia, 2) amber, green, or light brown eyes, or 3) blue eyes and pink skin at birth ##UREF##3##[6]##. This was accomplished by viewing photo evidence of these traits or by personal inspection. Due to potential confusion between phenotypes of cream dilution and champagne dilution, all DNA samples from horses with the dilute phenotype were tested for the <italic>CR</italic> allele and data from those testing positive were not included in the population data.</p>", "<title>DNA Extraction</title>", "<p>DNA from blood samples was extracted using Puregene whole blood extraction kit (Gentra Systems Inc., Minneapolis, MN) according to its published protocol. Hair samples submitted by owners were processed using 5–7 hair bulbs according to the method described by Locke et al. (2002). The hair bulbs were placed in 100 µl lysis solution of 1× FastStart Taq Polymerase PCR buffer (Roche, Mannheim, Germany), 2.5 mM MgCl₂ (Roche), 0.5% Tween 20 (JT Baker, Phillipsburg, NJ) and 0.01 mg proteinase K (Sigma-Aldrich, St Louis, MO) and incubated at 60°C for 45 minutes, followed by 95°C for 45 min to deactivate the proteinase K.</p>", "<title>Microsatellite Genome Scan</title>", "<p>The genome scan was done in polymerase chain reaction (PCR) multiplexes of 3 to 6 microsatellites per reaction. The 102 microsatellite markers used are listed in ##SUPPL##3##Table S3##. Primers for these microsatellites were made available in connection with the USDA-NRSP8 project ##REF##10582279##[20]##. Two additional microsatellites were used; <italic>TKY329</italic>\n##REF##11258798##[21]## was selected based on its map location between two microsatellites used for genome scanning (<italic>UM010</italic> and <italic>VHL209</italic>) and <italic>COOK007</italic> was developed in connection with this study based on DNA sequence information from the horse genome sequence viewed in the UCSC genome browser ##UREF##4##[8]## in order to investigate linkage within the identified interval. Primers for <italic>COOK007</italic> were designed using Primer 3 software accessed online (Forward, <named-content content-type=\"gene\">5′- 6FAM-CATTCCAAACACCAACAACC - 3′</named-content>), (Reverse, <named-content content-type=\"gene\">5′ – GGACATTCCAGCAATACAGAG – 3′</named-content>) ##UREF##9##[22]##. The initial scan was conducted on a subset of samples from Family 3; including sire 3, five non-champagne offspring and five champagne offspring. When the microsatellite allele contribution from the sire was not informative, (e.g. the sire and offspring had the same genotype), dams from family 3 were typed to determine the precise contribution from the sire. When the inheritance of microsatellite markers in family 3 appeared to be correlated with the inheritance of the <italic>CH</italic> allele, then the complete families A, B and C were typed and the data analyzed for linkage by LOD score analysis ##UREF##10##[23]##.</p>", "<p>Amplification for fragment analysis was done in 10 µl PCR reactions using 1× PCR buffer with 2.0 mM MgCl₂, 200 µM of each dNTP, 1 µl genomic DNA from hair lysate, 0.1 U FastStart Taq DNA polymerase (Perkin Elmer, Waltham, MA) and the individual required molarity of each primer from the fluorescently labeled microsatellite parentage panel primer stocks at the MH Gluck Equine Research Center. Samples were run on a PTC-200 thermocycler (MJ research, Inc., Boston, MA) at a previously determined optimum annealing temperature for each multiplex. Capillary electrophoresis of product was run on an ABI 310 genetic analyzer (Applied Biosystems Inc. ABI, Foster City, CA). Results were then analyzed using the current version of STRand microsatellite analysis software (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.vgl.ucdavis.edu/informatics/STRand/\">http://www.vgl.ucdavis.edu/informatics/STRand/</ext-link>).</p>", "<title>Sequencing</title>", "<p>PCR template for sequencing was amplified in 20 µl PCR reactions using 1× PCR buffer with 2.0 mM MgCl₂, 200 µM of each dNTP, 1 µl genomic DNA from hair lysate, 0.2 U FastStart Taq DNA polymerase (Perkin Elmer) and 50 nM of each primer. Exon 2 of <italic>SLC36A1</italic> was sequenced with the following primers: Forward (<named-content content-type=\"gene\">5′-CAG AGC CTA AGC CCA GTG TC-3′</named-content>) and Reverse (<named-content content-type=\"gene\">5′-GGA GGA CTG TGT GGA AAT GG-3′</named-content>) at an annealing temperature of 57°C. Primers used to sequence the other <italic>SLC36A1</italic> exons and primers for sequencing genomic exons of SLC36A2 are provided in parts 1 and 2 respectively of ##SUPPL##4##Table S4##. Template product was quantified on a 1% agarose gel, then amplified with BigDye Terminator v1.1 cycle sequencing kit according to manufacturer's instructions (Applied Biosystems), cleaned using Centri-Sep columns (Princeton Separations Inc., Adelphia, NJ), and run on and ABI 310 genetic analyzer (Applied Biosystems). Six samples were initially sequenced: 2 suspected homozygous champagnes (based on production of all champagne dilution offspring when bred to at least 10 non-dilute dams), 2 heterozygotes, and 2 non-dilute horses. The results were analyzed and compared by alignment using ContigExpress from the Vector NTI Advance 10.3 software package (Invitrogen Corporation, Carlsbad, California).</p>", "<title>Reverse Transcription (RT-PCR)</title>", "<p>RT-PCR was performed in 25 µl reactions a Titan One Tube RT-PCR Kit (Roche) according to enclosed protocol with the primers listed in part 3 of ##SUPPL##4##Table S4##. RNA from different tissues of non-dilute horses was used to acquire partial cDNAs containing the first two exons for <italic>SLC36A1</italic>, first three exons <italic>SLC36A2</italic> and first 4 exons of <italic>SLC36A3</italic>. The cDNA acquired was sequenced and the resulting sequences were verified for their respective genes with a BLAT search using the equine assembly v2 in ENSEMBL (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ensembl.org/Equus_caballus/index.html\">http://www.ensembl.org/Equus_caballus/index.html</ext-link>) genome browser. RT-PCR was also performed utilizing RNA extracted from skin, kidney and testes of non-dilute animals currently in lab stocks. <italic>SLC36A1</italic> cDNA was produced from the skin and blood using 50 ng RNA per reaction. <italic>SLC36A2</italic> cDNA was produced from testes using 1 mRNA per RT-PCR reaction then following up with a nested PCR for shorter product. <italic>SLC36A2</italic> cDNA was produced from skin using 50 ng mRNA per RT-PCR reaction. Nested PCR was not necessary. <italic>SLC36A3</italic> cDNA was produced from testes using 1 ug mRNA per reaction. 9 µl of initial reaction was visualized on a 2% agarose gel to check for visible bands of product. When product was not initially detected an additional 20 µl PCR was performed in reactions as outlined above using 5 µl of RT product in the place of hair lysate per reaction. Detected product was then sequenced with the protocol listed above. Sequences were then used in a BLAST search using equine genome assembly 2 on ENSEMBL genome browser to verify the correct cDNA was amplified.</p>", "<title>Custom TaqMan Probe Assay</title>", "<p>A Custom TaqMan SNP Genotyping Assay (Applied Biosystems) was designed for c.188C/G SNP in filebuilder 3.1 software (Applied Biosystems) to test the population distribution of the <italic>SLC36A1</italic> alleles. A similar assay was also designed to test for the cream SNP. These assays were run on a 7500HT Fast Real Time-PCR System (Applied Biosystems). All dilute horses tested for <italic>SLC36A1</italic> variants were concurrently tested for <italic>SLC45A</italic> variants. Horses testing positive for <italic>CR</italic> alleles were not used in the dataset to avoid any confusion over the origin of their dilution phenotype.</p>" ]

[ "<title>Results</title>", "<title>Linkage Analyses</title>", "<p>\n##TAB##0##Table 1## summarizes the evidence for linkage of the <italic>CH</italic> gene to a region of ECA14. The linkage phase for each family was apparent based on the number of informative offspring in each family. Recombination rates (θ) were based on the combined recombination rate from all families. Four microsatellites showed significant linkage to the <italic>CH</italic> locus: <italic>VHL209</italic> (LOD = 6.03 for θ = 0.14), <italic>TKY329</italic> (LOD = 3.64 for θ = 0.10), <italic>UM010</italic> (LOD = 5.41 for θ = 0.04) and <italic>COOK007</italic> (LOD = 11.74 for θ = 0.00).</p>", "<p>\n##FIG##2##Figure 3## identifies the haplotypes for offspring of a single sire showing recombination between the genetic markers and the <italic>CH</italic> locus. Pedigrees of the three sire families and haplotype information are provided in ##SUPPL##0##Figure S1## and ##SUPPL##1##Table S1## respectively. The <italic>CH</italic> locus maps to an interval between <italic>UM010</italic> and <italic>TKY329</italic> with microsatellite. No recombinants were detected among 39 informative offspring between the <italic>CH</italic> and <italic>COOK007</italic> locus.</p>", "<title>Candidate Genes</title>", "<p>Candidate genes were selected on the basis of proximity to the marker <italic>COOK007</italic> and as genes previously characterized in other species as influential in the production or migration of pigment cells.</p>", "<p>\n<italic>SPARC</italic> was located closest at ∼90 kb downstream from <italic>COOK007</italic> and is coded for on the plus strand of DNA. It has been implicated in migration of retinal pigment epithelial cells in mice ##REF##12658547##[7]##.</p>", "<p>\n<italic>SLC36A</italic> family members are solute carriers and other solute carrier families have been found to play a role in coat color. <italic>SLC36A1</italic> is located ∼250 kb downstream from <italic>COOK007</italic>. It is the first and most proximal to <italic>COOK007</italic> of three genes in this family and is coded for on the minus strand of DNA.</p>", "<p>\n<italic>SLC36A2</italic> and <italic>SLC36A3</italic> are coded for on the plus strand of DNA and are approximately 350 k and 380 k downstream from <italic>COOK007</italic> respectively. <italic>A2</italic> and <italic>A3</italic> have been found to be expressed in a limited range of tissues in humans and mice ##UREF##4##[8]##.</p>", "<title>RT-PCR</title>", "<p>RT-PCR (reverse transcription-polymerase chain reaction) was used to determine if <italic>SLC36A1</italic>, <italic>SLC36A2</italic> or <italic>SLC36A3</italic> were expressed in skin. <italic>SLC36A1</italic> and <italic>SLC36A2</italic> were expressed in skin and their genomic exons were sequenced. <italic>SLC36A3</italic> was not detected in skin and therefore not investigated for detection of SNPs. Results for RT-PCR of these three genes are shown in ##FIG##3##Figure 4##.</p>", "<title>Sequencing</title>", "<p>All 9 exons of <italic>SPARC</italic> were sequenced. Three SNPs were found in exons but none showed associations with the champagne phenotype and are shown in ##SUPPL##2##Table S2##.</p>", "<p>\n<italic>SLC36A2</italic> was sequenced with discovery of 9 SNPs in exons. None of the SNPs showed associations with CH. These SNPs and all other variations detected are described in ##SUPPL##2##Table S2##.</p>", "<p>\n<italic>SLC36A1</italic> was sequenced. Only one SNP was found, a missense mutation involving a single nucleotide change from a C to a G at base 76 of exon 2 (c.188C&gt;G) (##FIG##4##Figure 5##). These <italic>SLC36A1</italic> alleles were designated <italic>c.188[C/G]</italic>, where c.188 designates the base pair location of the SNP from the first base of <italic>SLC36A1</italic> cDNA, exon 1. Sequencing traces for the partial coding sequence of <italic>SLC36A1</italic> exon 2 with part of the flanking intronic regions for one non-champagne horse and one champagne horse were deposited in GenBank with the following accession numbers respectively: EU432176 and EU432177. This single base change at <italic>c.188</italic> was predicted to cause a transition from a threonine to arginine at amino acid 63 of the protein (T63R).</p>", "<title>Protein Alignment</title>", "<p>\n##FIG##5##Figure 6## shows the alignment of the protein sequence for exons 1 and 2 of <italic>SLC36A1</italic> for seven mammalian species with sequence information from Genbank (horse, cattle, chimpanzee, human, dog, rat and mouse). Alignment was performed using AllignX function of Vector NTI Advance 10 (Invitrogen Corp, Carlsbad, California). The alignment demonstrates that this region is highly conserved among all species. At position 63, the amino acid sequence is completely conserved among these species, with the exception of horses possessing the champagne phenotype. This replacement of threonine with arginine occurs in a putative transmembrane domain of the protein ##REF##12809675##[9]##.</p>", "<title>Population Data</title>", "<p>The distribution of <italic>c.188G</italic> allele among different horse breeds and among horses with and without the champagne phenotype was investigated. ##TAB##1##Table 2## is a compilation of the population data collected via the genotyping assay. All dilute horses (85) which did not have the <italic>CR</italic> gene, tested positive for the <italic>c.188G</italic> allele with genotypes <italic>c.188C/G</italic> or <italic>c.188G/G</italic>. No horses in the non-dilute control group (97) possessed the <italic>c.188G</italic> allele. The horses used for the population study were selected for coat color and not by random selection; therefore measures of Hardy-Weinberg equilibrium are not applicable and were not calculated.</p>" ]

[ "<title>Discussion</title>", "<p>Family studies clearly showed linkage of the gene for the champagne dilution phenotype within a 6 cM region on ECA14 ##REF##16093715##[10]## (##TAB##0##Table 1##). Based on the Equine Genome Assembly V2 as viewed in ENSEMBL genome browser (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ensembl.org/Equus_caballus/index.html\">http://www.ensembl.org/Equus_caballus/index.html</ext-link>) this region spans approximately 2.86 Mbp ##REF##12045153##[11]##. Within that region, four candidate genes were investigated; one based on known effects on melanocytes (eg. <italic>SPARC</italic>) and three for their similarity to other genes previously shown to influence pigmentation (eg, <italic>SLC36A1</italic>, <italic>A2</italic>, <italic>and A3</italic>). While SNPs were found within the exons of SPARC, none were associated with <italic>CH</italic>. Of the other 3 candidate genes, only <italic>SLC36A1</italic> and <italic>SLC36A2</italic> were found to be expressed in skin cells. Therefore, the exons of those two genes were sequenced. A missense mutation in the second exon of <italic>SLC36A1</italic> showed complete association with the champagne phenotype across several breeds. While SNPs were found for <italic>SLC36A2</italic>, none showed associations at the population level for the champagne dilution phenotype.</p>", "<p>This observation is the first demonstration for a role of <italic>SLC36A1</italic> in pigmentation. Orthologous genes in other species are known to affect pigmentation. For example, the gene responsible for the cream dilution phenotypes in horses, <italic>SLC45A2 (MATP)</italic>, belongs to a similar solute carrier family. In humans, variants in <italic>SLC45A2</italic> have been associated with skin color variation ##REF##15714523##[12]## and a similar missense mutation (p.Ala111Thr) in <italic>SLC24A5</italic> (a member of potassium-dependent sodium-calcium exchanger family) is implicated in dilute skin colors caused from decreased melanin content among people of European ancestry ##REF##16357253##[13]##. The same gene, <italic>SLC24A5</italic> is responsible for the <italic>Golden (gol)</italic> dilution as mentioned in the review of mouse pigment research by Hoekstra (2006) ##REF##16823403##[14]##\n</p>", "<p>It is proposed, here, that the missense mutation in exon 2 of <italic>SLC36A1</italic> is the molecular basis for champagne dilution phenotype. While this study provides evidence that this is the mutation responsible for the champagne phenotype, the proof is of a statistical nature and a non-coding causative mutation can not be ruled out at this point. <italic>SLC36A1</italic>, previously referred to by the name <italic>PAT1</italic> (proton/amino acid transporter 1) in human and mouse ##UREF##5##[15]##, is a proton coupled small amino acid transporter located and most active in the brush border membranes of intestinal epithelial cells. This protein has also been characterized in rats under the name <italic>LYAAT1</italic> (<italic>lysosomal amino acid transporter 1</italic>). <italic>LYAAT1</italic> is localized in the membrane of lysozomes in association with LAMP1 (lysosomal associated protein 1) and in the cell membrane of post-synaptic junctions. In lysozomes it allows outward transport of protons and amino acids from the lysozome to the cytosol ##UREF##6##[16]##. During purification and separation of early-stage melanosomes <italic>LAMP1</italic> is found in high concentrations in the fraction containing stage II melanosomes ##UREF##7##[17]##,. Perhaps <italic>SLC36A1</italic> plays a role in transitions from lysozome-like precursor to melanosome. Since organellular pH affects tyrosine processing and sorting ##REF##14634018##[18]##, an amino acid substitution in this protein may affect pH of the early stage melanosome and the ability to process tyrosine properly. There must be an increase in pH, before the tyrosinase can be activated. The cytosolic pH gradient must also be maintained for proper sorting and delivery of the other proteins required for melanosome development ##UREF##8##[19]##. Thus, the pH gradient of the cell may be altered by this mutation.</p>", "<p>This variant, discovered in association with a coat dilution in the horse, is the first reported for the <italic>SLC36A1</italic> gene. The phenotype resulting from this mutation, a reduction of pigmentation in the eyes, skin and hair, illustrates previously unknown functions of the protein product of <italic>SLC36A1</italic>. Furthermore, now that a molecular test for champagne dilution is established, the genotyping assay can be used in concert with available tests for cream dilution and silver dilution to clarify the genetic basis of a horse's dilution phenotype. This will give breeders a new tool to use in developing their breeding programs whether they desire to breed for these dilutions or to select against them.</p>" ]

[]

[ "<p>Conceived and designed the experiments: DC SB RB EB. Performed the experiments: DC. Analyzed the data: DC SB RB EB. Contributed reagents/materials/analysis tools: EB. Wrote the paper: DC SB RB EB.</p>", "<p>Champagne coat color in horses is controlled by a single, autosomal-dominant gene (<italic>CH</italic>). The phenotype produced by this gene is valued by many horse breeders, but can be difficult to distinguish from the effect produced by the <italic>Cream</italic> coat color dilution gene (<italic>CR</italic>). Three sires and their families segregating for <italic>CH</italic> were tested by genome scanning with microsatellite markers. The <italic>CH</italic> gene was mapped within a 6 cM region on horse chromosome 14 (LOD = 11.74 for θ = 0.00). Four candidate genes were identified within the region, namely <italic>SPARC</italic> [<italic>Secreted protein</italic>, <italic>acidic</italic>, <italic>cysteine-rich (osteonectin)</italic>], <italic>SLC36A1</italic> (<italic>Solute Carrier 36 family A1</italic>), <italic>SLC36A2 (Solute Carrier 36 family A2)</italic>, and <italic>SLC36A3 (Solute Carrier 36 family A3)</italic>. <italic>SLC36A3</italic> was not expressed in skin tissue and therefore not considered further. The other three genes were sequenced in homozygotes for <italic>CH</italic> and homozygotes for the absence of the dilution allele (<italic>ch</italic>). <italic>SLC36A1</italic> had a nucleotide substitution in exon 2 for horses with the champagne phenotype, which resulted in a transition from a threonine amino acid to an arginine amino acid (T63R). The association of the single nucleotide polymorphism (SNP) with the champagne dilution phenotype was complete, as determined by the presence of the nucleotide variant among all 85 horses with the champagne dilution phenotype and its absence among all 97 horses without the champagne phenotype. This is the first description of a phenotype associated with the <italic>SLC36A1</italic> gene.</p>", "<title>Author Summary</title>", "<p>The purpose of this study was to uncover the molecular basis for the champagne hair color dilution phenotype in horses. Here, we report a DNA base substitution in the second exon of the horse gene <italic>SLC36A1</italic> that changes an amino acid in the transmembrane domain of the protein from threonine to arginine. The phenotypic effect of this base change is a diminution of hair and skin color intensity for both red and black pigment in horses, and the resulting dilution has become known as champagne. This is the first genetic variant reported for <italic>SLC36A1</italic> and the first evidence for its effect on eye, skin, and hair pigmentation. So far, no other phenotypic effects have been attributed to this gene. This discovery of the base substitution provides a molecular test for horse breeders to test their animals for the <italic>Champagne</italic> gene (<italic>CH</italic>).</p>" ]

[ "<title>Supporting Information</title>" ]

[ "<p>The authors wish to thank Katie Mroz-Barrett for work on the project, Bea Kinkade, Val Kleinhetz, Pam Capurso, Carolyn Sheperd and all the other horse owners who submitted samples from horses with different hair color phenotypes and to Dr. Teri Lear for critically reading the manuscript. The microsatellites used for genome scanning were provided through the auspices of the USDA-NRSP8 program and the Dorothy Russell Havemeyer Foundation. This work was conducted in connection with a project of the Morris Animal Foundation and the Agricultural Experiment Station of the University of Kentucky and is published as paper number 08-14-019.</p>" ]

[ "<fig id=\"pgen-1000195-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000195.g001</object-id><label>Figure 1</label><caption><title>Effect of <italic>Champagne</italic> gene action on base coat colors of horses (chestnut, bay, and black).</title><p>A) Chestnut – horse only produces red pigment. B) Chestnut diluted by <italic>Champagne</italic> = Gold Champagne. C) Bay – black pigment is limited to the points (e.g. mane, tail, and legs) allowing red pigment produced on the body to show. D) Bay diluted by <italic>Champagne</italic> = Amber Champagne. E) Black – red and black pigment produced, red masked by black. F) Black diluted by <italic>Champagne</italic> = Classic Champagne.</p></caption></fig>", "<fig id=\"pgen-1000195-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000195.g002</object-id><label>Figure 2</label><caption><title>Champagne Eye and Skin traits.</title><p>A, B and C) Eye and skin color of foals. D and E) Eye color and skin mottling of adult horse.</p></caption></fig>", "<fig id=\"pgen-1000195-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000195.g003</object-id><label>Figure 3</label><caption><title>Example of Recombinant Haplotypes.</title><p>Linear relationship from top to bottom between the microsatellites, phenotype, and genotype of recombinant offspring for study sire #3. Phenotype is noted in top row with offspring's ID #.</p></caption></fig>", "<fig id=\"pgen-1000195-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000195.g004</object-id><label>Figure 4</label><caption><title>RT-PCR product results for <italic>SLC36A1</italic>, <italic>A2</italic> and <italic>A3</italic>.</title><p>A) RT-PCR results for <italic>SLC36A1</italic>. B) RT-PCR results for <italic>SLC36A2</italic>. C) RT-PCR results for <italic>SLC36A3</italic>. (Faint bands observed above 400 bp on gel C were sequenced and did not show homology to <italic>SLC36A3</italic>.)</p></caption></fig>", "<fig id=\"pgen-1000195-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000195.g005</object-id><label>Figure 5</label><caption><title>Sequence Alignment and Gene Diagram.</title><p>Alignment is between homozygous champagne, non-dilute, and horse genome assembly. Reading frame is marked by alternating colors of codons. Bottom is diagram of <italic>SLC36A1</italic> with the identified SNP in exon 2. Sequence and gene layout have been verified on Ensembl genome browser equine assembly v2. Blue blocks of gene layout are exons and red boxes are the 5′ and 3′ UTRs.</p></caption></fig>", "<fig id=\"pgen-1000195-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000195.g006</object-id><label>Figure 6</label><caption><title>Seven Species Protein Sequence Alignment for <italic>SLC36A1</italic> exons 1 and 2.</title><p>The R highlighted in red is the amino acid replacement associated with the champagne phenotype.</p></caption></fig>" ]

[ "<table-wrap id=\"pgen-1000195-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000195.t001</object-id><label>Table 1</label><caption><title>Linkage Analysis between the Champagne Dilution and Microsatellite Markers; <italic>UM010</italic>, <italic>COOK007</italic>, <italic>TKY329</italic> and <italic>VHL209</italic>.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">alleles</td><td colspan=\"5\" align=\"left\" rowspan=\"1\">Sire contribution</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">Statistics</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sire Family</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(CH)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">microsatellite</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>a/b</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">N</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">a+</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">a−</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">b+</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">b−</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">LOD score</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Θ</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>UM010</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>124/108</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.41</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Σ = </bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>5.41</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>COOK007</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>332/334</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.21</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>COOK007</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>332/334</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.41</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>COOK007</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>332/324</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Σ = </bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>11.74</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>TKY329</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>117/139</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.92</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>TKY329</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>111/137</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.34</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>TKY329</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>117/139</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.64</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Σ = </bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>6.9</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>VHL209</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>95/93</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.49</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>VHL209</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>91/93</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.46</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>(+/−)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>VHL209</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>95/93</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.08</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Σ = </bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>6.03</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.14</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pgen-1000195-t002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000195.t002</object-id><label>Table 2</label><caption><title>Genotyping Results for c.188(C/G) locus.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"2\" align=\"left\" rowspan=\"1\">\n<italic>Champagne (CH/CH or CH/ch)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Non-Dilute (ch/ch, cr/cr)</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Horse Breeds</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>G/G</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>G/C</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>C/C</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Total</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">American Cream Draft Cross</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">American Miniature Horse</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">American Quarter Horse</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">28</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">American Paint Horse</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">45</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">American Saddlebred</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Appaloosa</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kentucky Mountain</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Part Arabian</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Pinto</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Pony</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Missouri Foxtrotter</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Mule</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Spanish Mustang</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Spotted Saddle Horse</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tennessee Walking Horse</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Thoroughbred</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">35</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Total</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">82</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">97</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">183</td></tr></tbody></table></alternatives></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"pgen.1000195.s001\"><label>Figure S1</label><caption><p>Pedigrees of Three Sire Families used in Genome Scan.</p><p>(0.55 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000195.s002\"><label>Table S1</label><caption><p>Haplotype Data for Three Sire Families.</p><p>(0.25 MB DOC)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000195.s003\"><label>Table S2</label><caption><p>Sequence Variants Detected in <italic>SPARC</italic>, <italic>SLC36A1</italic>, and <italic>SLC36A2</italic>.</p><p>(0.14 MB DOC)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000195.s004\"><label>Table S3</label><caption><p>Microsatellite Markers used For Genome Scan.</p><p>(0.20 MB DOC)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000195.s005\"><label>Table S4</label><caption><p>Sequencing and RT-PCR Primers.</p><p>(0.07 MB DOC)</p></caption></supplementary-material>" ]

[ "<table-wrap-foot><fn id=\"nt101\"><p>N = the number of informative meiosis.</p></fn><fn id=\"nt102\"><p>Θ = recombination frequency between that microsatelite and the <italic>champagne gene</italic> for all families combined.</p></fn><fn id=\"nt103\"><p>Σ = LOD score for which 1/10^Σ = the odds the association between the phenotype and the marker is due to chance.</p></fn></table-wrap-foot>", "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>This work was funded by Morris Animal Foundation.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"pgen.1000195.g001\"/>", "<graphic xlink:href=\"pgen.1000195.g002\"/>", "<graphic id=\"pgen-1000195-t001-1\" xlink:href=\"pgen.1000195.t001\"/>", "<graphic xlink:href=\"pgen.1000195.g003\"/>", "<graphic xlink:href=\"pgen.1000195.g004\"/>", "<graphic xlink:href=\"pgen.1000195.g005\"/>", "<graphic xlink:href=\"pgen.1000195.g006\"/>", "<graphic id=\"pgen-1000195-t002-2\" xlink:href=\"pgen.1000195.t002\"/>" ]

[ "<media xlink:href=\"pgen.1000195.s001.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000195.s002.doc\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000195.s003.doc\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000195.s004.doc\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000195.s005.doc\"><caption><p>Click here for additional data file.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p>Among the different methods cells have to monitor external or internal stresses, the surveillance mechanism associated with the p53 gene is central. Numerous molecular studies over the years have presented p53 as an essential controller of cellular and genome integrity ##REF##17457049##[1]##. p53 is a master transcription factor, functionally inactive under normal conditions due to its rapid degradation by the ubiquitin ligase MDM2. A chain of events triggered in response to cellular stress upsets this precise balance, leading to the uncoupling of MDM2-driven degradation and to the ultimate accumulation and activation of p53 ##REF##16601750##[2]##. p53 works mostly as a transcriptional activator, with few molecules in each cell ##REF##17544001##[3]##. However, p53 might also act as a repressor in some instances ##REF##16895524##[4]##. The p53 transcriptional program includes the activation of a number of cell cycle inhibitors and proapoptotic proteins, which results in apoptosis, reversible proliferative arrest or cellular senescence ##REF##9039259##[5]##, ##REF##10754563##[6]##, ##REF##12719720##[7]##.</p>", "<p>In principle, the various outcomes of p53 activation might be influenced by quantitative or qualitative mechanisms ##REF##10557106##[8]##. Some studies suggest that the level of p53 output determines whether cells will enter cell cycle arrest or apoptosis. Consistent with this view, only a subset of the genes induced by high p53 levels are induced by lower p53 levels ##REF##10783169##[9]##. Introduction of high p53 levels into tumor cell lines induces apoptosis, while the introduction of lower levels induces only cell cycle arrest ##REF##8843196##[10]##. However, other studies suggest that the outcome of p53 activation is determined by factors controlled by the tissue type or by the cell genotype.</p>", "<p>Oncogenic <italic>Ras</italic> can activate p53 to promote cellular senescence, limiting the transforming potential of excessive signalling ##REF##9765203##[11]##–##REF##9054499##[16]##. This study and others have demonstrated that conditional activation of p53 in mouse embryonic fibroblast cells (MEFs) produces reversible cell cycle arrest, whereas activation of p53 in the presence of oncogenic <italic>Ras</italic> leads to a permanent cell cycle arrest with features of cellular senescence ##REF##11971980##[17]##, ##REF##18204081##[18]##. Although oncogenic <italic>Ras</italic> may increase p53 levels, it is not clear whether this increase is sufficient to explain the induction of senescence.</p>", "<p>Two different, though not mutually exclusive, models have been proposed to explain the different biological outcomes associated with p53 activation. The quantitative model implies that p53 levels are sufficient to determine the outcome. Thus, low p53 levels induce a reversible cell cycle arrest while higher p53 levels induce senescence or apoptosis. This model is supported by studies in which p53 levels may be artificially controlled with the appropriate expression systems ##REF##10783169##[9]##, ##REF##8843196##[10]##. One potential mechanism that could explain such an effect is based on differential p53 affinity for p53 response elements, such that genes required for a reversible cell cycle arrest have protein products with greater affinities than those required for senescence or apoptosis.</p>", "<p>A qualitative model of p53 action implies that non-quantitative factors controlled by a stimulus, either the tissue origin or the cell genotype influence the outcome of p53 activation. Again, two non-mutually exclusive mechanisms might support the published data. First, certain collateral signals might directly modulate p53 activity by changing the conformation of p53 or its association with various coactivators, perhaps leading to the expression of different subsets of p53 target genes. Consistent with this possibility, ionizing radiation and UV light have been shown to induce expression of different subsets of p53-dependent target genes in the same cell type ##REF##10783169##[9]##. Interestingly, these two stimuli induce different p53 modifications ##REF##9501176##[19]##–##REF##10733583##[21]##, raising the possibility that the activating signal may modulate p53 activity in a qualitative manner by directing p53 to different promoters ##REF##11030628##[22]##. Similarly, the ability of oncogenes to promote either apoptosis or senescence is correlated with different p53 modifications.</p>", "<p>Furthermore, oncogenic Ras induces p53 phosphorylation on serine 15 and induces senescence, whereas the E1A oncoprotein does not induce serine 15 phosphorylation and promotes apoptosis. The E1A effect is dominant, since cells coexpressing E1A and Ras do not contain p53 that has been phosphorylated on serine 15, and these cells are prone to apoptosis ##REF##10950866##[23]##, ##REF##8402885##[24]##. Whether this effect leads to the expression of different p53 target genes has yet to be determined. Second, it is possible that the signal produced by p53 activation is the same in different contexts and that the outcome of p53 activation is determined by how this signal is interpreted by the cell. One may envision several mechanisms by which this might occur, but an obvious possibility involves the combined action of p53 and other transcription factors such that the action of p53 on outcome-specific targets is influenced by the presence or absence of these other factors. These other factors, in turn, would be the targets for the hypothetical collateral signal. One precedent for this involves the integration of p53 and interferon signaling on the p21 promoter, which contains both p53 and IRF-1 response elements that act to synergistically induce p21 expression during a DNA damage response ##REF##8752276##[25]##. How different signal transduction pathways integrate to produce new biological outcomes is an important biological problem that may also have an impact on the understanding of p53.</p>", "<p>How does oncogenic Ras convert p53 to a senescence inducer? Although it seems likely that a component of this response results from the ability of oncogenic Ras to produce quantitative increases in p53 activity via ARF-mediated inhibition of MDM2, there is compelling evidence for collateral signals that modify the outcome of p53 activation leading to senescence ##REF##11971980##[17]##, ##REF##15156563##[26]##. Following the discussion above, it is formally possible that oncogenic Ras directly modulates p53 activity or, instead, produces cellular changes that reinterpret the p53 signal.</p>", "<p>One potential mechanism may involve the ability of Ras to induce PPP1CA (the catalytic subunit of PP1α) expression, regulating senescence in a pRb-dependent manner ##REF##18204081##[18]##. pRb is involved in the SAHF, maintaining long-term inhibition of E2F-dependent transcription through changes in the packaging status of chromatin ##REF##12809602##[27]##.</p>", "<p>To characterize the p53 response during growth arrest and senescence, this series of experiments compares p53-dependent transcription in different situations involving proliferation, reversible arrest, replicative senescence or Ras-induced senescence.</p>" ]

[ "<title>Materials and Methods</title>", "<title>Cell Culture</title>", "<p>Primary MEFs from p53−/− mice were derived from day 13.5 embryos. Cells expressing murine p53val135 were generated by retrovirus-mediated gene transfer of p53val135 into p53−/− MEFs (p53−/− ts). Cells expressing Val12-Ras were generated by retrovirus-mediated gene transfer of pWZLBlast hVal12-Ras into wild-type MEFs at passage 3 (P3-Ras) and p53−/− ts (p53−/− ts Ras cells). Cells were cultured in Dulbecco's Modified Eagle's medium (GIBCO) supplemented with 10% fetal bovine serum (Sigma), 1% penicillin G- streptomycin sulfate (GIBCO), 0.5% fungizone-amphotericin B (GIBCO) and 5 µg/ml plasmocin (InvivoGen).</p>", "<p>P53−/− MEFs, p53−/− MDM2−/− MEFs and p19−/− MEFs were cultured in Dulbeccós Modified Eaglés medium (GIBCO). All media was supplemented with 10% fetal bovine serum (Sigma), 1% penicillin G-streptomycin sulphate (GIBCO), and 0.5% fungizone-amphotericin B (GIBCO) in a humidified CO₂ incubator at 37°C.</p>", "<title>Retroviral Vectors and Gene Transfer</title>", "<p>The following retroviral vectors were used: p53val135 mutant cDNA in pWZLHygro and pWZLBlast hVal12-Ras. Retrovirus-mediated gene transfer was performed as previously described ##REF##10871344##[44]##. Briefly, 5×10⁶ LinXE retrovirus producer cells were plated in a 10 cm dish, incubated for 24 h and then transfected by calcium-phosphate precipitation with 20 µg of retroviral plasmid (16 h at 37°C). The medium was changed and the plates were maintained at 32°C for 48 h to increase viral stability. Virus-containing supernatant was filtered through a 0.45 µm filter and supplemented with 8 µg/ml polybrene (Sigma) and an equal volume of fresh medium. Prior to infection, 8×10⁵ target fibroblasts were plated per 10 cm dish and incubated for 24 h. For infections, the culture medium was replaced by the viral supernatant, and then the culture plates were centrifuged (1 h at 1,500 rpm) and incubated at 37°C for 16 h. The medium was changed and cells were split 24 h later. Infected cell populations were selected in hygromycin (20 µg/ml) for pWZLHygro-based vectors and in blasticidin (2 µg/ml) for pWZLBlast-based vectors.</p>", "<title>Northern assays</title>", "<p>Total RNA was isolated from cells using the TRI-REAGENT method (Molecular Research Center, Cincinnati, OH) according to the manufactureŕs instructions. A reverse transcription was done for each sample (20 µg of total RNA) using MMLV reverse transcriptase (Promega), oligo dT primer and dCTP³²-labeling nucleotide.</p>", "<p>The cDNA ³²P-labeled probes were hybridized to the p53 target gene array membrane (TranSignal, Panomics, CA, USA) at 42°C overnight. After removing excess substrate by gently washing twice with 2×SSC+0.5% SDS and 0.1×SSC+0.5% SDS at 62°C, the membranes were exposed to BioMax Films (Eastman Kodak Company, NY, USA). The assay normalization was done selecting β-actin as the control housekeeping gene. Analysis was done using the GS-800 Calibrated Densitometer® and the Quantity One® program from Bio-Rad.</p>", "<p>Each experiment for each condition was performed independently at least twice, the data quantified and normalized for the value of β-actin (a gene with transcription that is independent of p53).</p>", "<p>Raw data for all conditions were normalized against an internal control, β-actin, and then compared to normal proliferating MEFs.</p>", "<title>PPP1CA Northern Blot</title>", "<p>Total RNA was extracted using RNAzolB. 10 µg of total RNA were run in formaldehyde-agarose gels and transferred to a Hybond membranes. The membrane was pre-hybridized during 4 hours at 65°C. The probe was labeled by PCR with 50 µC of redivue dCTP32 (Amersham), using specific primers for mouse PPP1CA. The purified probe was denatured and added to the hybridization solution. The hybridization was performed overnight at 65°C. After extensive whashing, the membrane was exposed to a Biomax MS film (Kodak).</p>", "<title>Data Analysis</title>", "<p>The data consisted of the expression values of 122 transcriptional targets of p53 in different cellular conditions, which led either to proliferation or to growth arrest.</p>", "<title>Clustering analysis</title>", "<p>Hierarchical clustering was performed using the function hcluster (package amap) of the free statistical software R (Ihaka and Gentleman, 1996). Before statistical analysis, gene expression levels were standardized gene by gene across all conditions using the median and interquartile range (IQR). The cellular conditions were clustered using Ward linkage and uncentered Pearson metric tests. The results were visualized and analyzed with TreeView (M. Eisen; <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.microarrays.org/software\">http://www.microarrays.org/software</ext-link>). The expression level of each gene, relative to its median expression level across all conditions, was represented by a color, with red representing expression greater than the median, green representing expression less than the median, and color intensity representing the magnitude of the deviation from the median.</p>", "<title>Feature selection</title>", "<p>The problem of extracting a robust set of predictors for the proliferating status of the different cellular conditions has been formulated as a least squares regression problem. Since the number of genes is much larger than the number of conditions, we used penalized regression methods. The standard penalty used in so-called ridge regression is given by the L2-norm of the vector containing the regression coefficients. Such penalty allows stabilizing the ordinary least squares estimate, but typically will retain all regression coefficients so that no selection of the relevant variables (genes) may be done. To perform the selection task, we used an L1-norm penalty, as is done in lasso regression. This type of penalty is known indeed to promote sparsity, i.e., to force many regression coefficients to be zero; this obviates the need for pre-selection of the data. However, a known drawback of the L1 penalty for variable selection is that in a group of highly correlated genes, it may pick up only one representative. We therefore also used combined L1- and L2-norm penalties to select sparse groups of highly correlated genes; this is done in the so-called “elastic net” proposed in Zou and Hastie, 2005, ##UREF##0##[46]##. To compute the corresponding penalized least-squares solutions, we applied the iterative thresholding algorithm developed in Daubechies et al, 2004. ##UREF##1##[47]##, which is simple to implement, robust to measurement errors and works well for high-dimensional data. Despite the small number of conditions, some standard validation tests (such as leave-one-out, label and gene permutation, bootstrap sampling) were performed.</p>", "<title>Transcriptional Assays</title>", "<p>For transient transfection of cells, we seeded 2–4×105 H1299 cells per well in six-well plates. After 24 h, transfections were performed by the calcium chloride method and JetPEI reagent (Polytransfection, Illkirch, France) according to the manufacturer's recommendations. For both transfection methods, we used 1.5–2 µg each of the reporter plasmids pGL3-13X, pGL3-Bax and pGL3-p21 in the presence or absence of pBABE puro p53 wt (0.6–0.75 µg) and pLSXN Ras val 12, or active mutants of the PI3K or Raf pathway (0.6–0.75 µg).</p>", "<p>Renilla luciferase plasmid was used as an internal control for transfection efficiency. The total amount of DNA within the experiments was kept constant by adding empty vector plasmid DNA to the transfection mixtures.</p>", "<p>Reporter gene assays were performed with the Dual-Luciferase® Reporter Assay System (Promega, USA) 48 h after transfection and the results were measured with a Victor2V luminometer. The activity of the reporter luciferase was expressed relative to the activity in renilla vector-transfected cells. Similar results were obtained in at least three different experiments. All results were compared to the control and are shown in the figures as the mean±S.D. of independent triplicate cultures.</p>", "<title>Western Blot</title>", "<p>Cells were prepared in lysis-buffer and proteins were separated on SDS-PAGE gels, transferred onto PVDF membranes (Immobilon-P, Millipore) and immunostained. The following primary antibody was used: anti-p53FL393 (Santa Cruz 6243, diluted 1∶1000), anti-PP1α (from Calbiochem) anti-Rb: G3-245 from BD Pharmingen; and horseradish peroxidase-labeled rabbit anti-mouse (Promega diluted 1∶5000) and goat anti-rabbit (Calbiochem 401315, diluted 1∶4000) secondary antibodies. Proteins were visualized using the ECL detection system (Amersham Biosciences, Buckinghamshire, UK).</p>", "<title>Immunofluorescence</title>", "<title>Immunostaining and confocal analysis for 53BP1 and γH2AX foci</title>", "<p>Cells were seeded onto glass cover slips and cultured for 8 h at 39°C. Then we placed the cells at 39°C and 32°C. After 24 h (cells at 39°C and 32°C) and 48 h, 96 h and 144 h (cells at 32°C), cover slips were fixed in 4% paraformaldehyde for 5 min at room temperature, washed twice with PBS, permeabilized in Triton X-100 0.5% in PBS for 5 min and washed twice more with PBS. Samples were incubated in blocking solution (PBS containing 3% bovine serum albumin) at 37°C for 15 min, followed by incubation for 30 min at 37°C with anti-phospho-Histone H2A.X (Ser139) antibody (Millipore 05-636) or anti-53BP1 antibody (Novus Biologicals NB100-304) diluted 1∶100. After washing with PBS, cells were incubated with species-specific Alexa 488-conjugated secondary antibody diluted 1∶100 in blocking buffer for 30 min at 37°C in the dark. The nuclei were stained with Hoechst 33258 diluted 1∶1000 for 3 min at room temperature in the dark prior to mounting with mowiol (Calbiochem). Images were collected by confocal laser microscopy (model TCS-SP2-AOBS, Leica, Germany).</p>", "<title>Immunostaining and confocal analysis for PPP1CA and pRb co-localization</title>", "<p>Cells were seeded onto glass cover slips and cultured for 8 h at 39°C. Then we placed the cells at 39°C and 32°C. After 24 h (cells at 39°C and 32°C) and 48 h (cells at 32°C), cover slips were fixed in 4% paraformaldehyde for 5 min at room temperature, washed 2 times with PBS, permeabilized in Triton X-100 0.5% in PBS for 5 min and washed again 2 times with PBS. Samples were incubated in blocking solution (PBS containing 3% bovine serum albumin) at 37°C for 15 min, followed by incubation for 30 min at 37°C with anti-human Retinoblastoma Protein (RB) monoclonal antibody (BD Pharmingen 554136) diluted 1∶100. After washing with PBS, cells were incubated with species-specific Alexa 488-conjugated secondary antibody diluted 1∶100 in blocking buffer for 30 min at 37°C in the dark. Then, cells were incubated with anti-Protein Phosphatase 1α, C-terminal antibody (Calbiochem 539517) diluted 1∶100. After washing with PBS, cells were incubated with species-specific Alexa 633-conjugated secondary antibody diluted 1∶100 in blocking buffer for 30 min at 37°C in the dark. The nuclei were stained with Hoechst 33258 diluted 1∶1000 for 3 min at room temperature in the dark prior to mounting with mowiol (Calbiochem). Images were collected by confocal laser microscopy (model TCS-SP2-AOBS, Leica, Germany).</p>", "<title>SA ß-Gal activity</title>", "<p>Senescence-associated (SA) ß-galactosidase (ß-Gal) activity was measured as previously described ##REF##7568133##[45]##, except that cells were incubated in 5-bromo-4-chloro-3-indolyl-ß-D-galactopyranoside (XGal) at pH 5.5 to increase the sensitivity of the assay in MEFs. The percentage of cells expressing SA ß-Gal was quantified by inspecting &gt;400 cells per 10-cm-diameter plate three times.</p>", "<title>Protein phosphatase assays</title>", "<p>PP1 activity was determined according to standard procedures as previously described ##REF##10462539##[57]##. PP activity was assayed using 32P-labeled phosphorylase a as a substrate which detects both PP1 and PP2A activities. To selectively measure PP1 activity we used 2 nM okadaic acid to selectively inhibit PP2A. The cell pellet was homogenized in the extraction buffer containing 20 mM Tris-HCI, pH 7.5, 5 mM EDTA, 10 mM EGTA, 15 mM -mercaptoethanol, 0.25 M sucrose, 0.3% Triton X-100, 5 µg/ml leupeptin, and 5 µg/ml aprotinin and centrifuged to give a soluble supernatant. The PP activity in the clear supernatant was determined by measuring the trichloroacetic acid-soluble counts released after incubation of the 32P-labeled phosphorylase a in the cell extract. The PP activity was linear up to assay times of 10 min and 5 µg protein of the cell extract. Routinely, incubation for PP activity was carried out for 10 min with an extract containing 5 µg of protein as determined by the Bio-Rad assay (Bio-Rad, Hercules, CA). Negative controls were obtained incubating with 100 nM Okadaic acid to inhibit PP1 and PP2A activity. One unit (U) of activity is defined as the amount that catalyzes the release of 1 nmol Pi from phosphorylase a per min at 30°C.</p>", "<title>Real time PCR (qRT-PCR) experiments</title>", "<p>Total RNA were isolated form HCT 116 p53 +/+ cells (a generous gift from B. Vogelstein) treated with 400nM and 1 microM Etoposide, 0.6 µg/ml Doxorubicin, 10nM Paclitaxel (Taxol), 100nM UCN-01, 15 µM PD98059 for 8 hours. After DNAse treatment, reverse transcription was performed with 20 µg of mRNA using MMLV reverse transcriptase (Promega) and oligo dT primer according to the manufacturer's recommendations</p>", "<p>QRT- PCR experiments were carried out using SYBR® Green PCR Master Mix (Applied Biosystems, USA). Reaction mixtures contained: 5 µl cDNA sample (1/10 dilution RT product), 1.5 µl primer mix (sense and antisense, 0.6 µM final concentration), and 12.5 µl SYBR® Green PCR Master Mix. The final volume should be 25 µl. The following primers were used to amplify regions: LATS2 forward 5′- AACAGCCTCAACGTGGACCTGTATGAA-3′ and reverse <named-content content-type=\"gene\">5′-CAGGGCATGCTCCTCCTTGGCGTCGAA- 3′</named-content>; PTEN forward <named-content content-type=\"gene\">5′-CAGAAAGACTTGAAGGCGTAT-3′</named-content> and reverse <named-content content-type=\"gene\">5′- GTAACGGCTGAGGGAACT C-3′</named-content>; RB1 forward <named-content content-type=\"gene\">5′-TCTGCATTGGTGCTAAAAGTTTCTTGGA-3′</named-content> and reverse <named-content content-type=\"gene\">5′-CCTGTTCTGACCTCGCCTGGGTGTTCGA- 3′</named-content>; MAP4 forward <named-content content-type=\"gene\">5′-TGATCCCTTTAAGATGTACCATGATGAT-3′</named-content>and reverse <named-content content-type=\"gene\">5′-AATGCTTGTGCTGGTGGCCTCTCTTCTG-3′</named-content> and β-actine forward <named-content content-type=\"gene\">5 -AGGCCAACCGCGAGAAGATGAC-3</named-content> and reverse <named-content content-type=\"gene\">5 -GAAGTCCAGGGCGACGTAGCA-3′</named-content>. The samples were amplified according to the following protocol: 10 min 95°C, 50 cycles: 15 sec 95°C, 30 sec 56°C–62°C (depending on the primer), 1 min 72°C. Then in order to get the dissociation curve, a stage was added: 15 sec 95°C, 15 sec 60°C and 15 sec 95°C.</p>", "<p>The normalized values were analyzed using SDS2.2.2 program (Applied Biosystems, USA). All samples were measured in duplicates and the right formation of the products was verified by 1% agarose gel electrophoresis (data not shown).</p>" ]

[ "<title>Results</title>", "<title>p53 levels and phenotype</title>", "<p>To assess if differences in p53-dependent transcription play a role in reversible arrest or senescence, this study took advantage of the mouse embryo fibroblast (MEF) cell system that allows easy manipulation of cellular stresses in otherwise homogeneous conditions. For instance, conditional activation of the p53 pathway in MEFs is known to trigger reversible cell cycle arrest, whereas activation of p53 in the presence of oncogenic Ras leads to permanent cell cycle arrest with features of replicative senescence (##FIG##0##Figure 1A##) ##REF##9054499##[16]##.</p>", "<p>To induce replicative senescence, wild-type and p53-null (p53 −/−) embryos were generated from crosses between heterozygous p53 knock-out mice. From wild-type embryos, MEFs were generated and grown until replicative senescence was reached (approximately at passage 5, corresponding to 10 population doublings). We extracted mRNA under these conditions, i.e., terminally arrested with senescence features (P5), and also from exponentially growing MEFs (early passage, P3). Other stress conditions leading to senescence were produced as follows. Wild-type MEFs growing at early passage were infected with retroviruses carrying oncogenic Ras (Val12-Hras). Cells were selected for retrovirus insertion and once they reached senescence (corresponding to approximately passage 3), mRNA was extracted (P3+ras). P53-null MEFs were infected with viruses carrying the 135V thermosensitive mutant of p53 that induces cellular arrest at permissive temperature (32°) ##REF##2143698##[28]##. These cells (p53ts), while maintained at restrictive temperature (39°C), were infected with viruses carrying oncogenic Ras (p53ts-ras), which induces senescence when shifted to permissive temperature ##REF##11971980##[17]##. For a summary of conditions and the resulting phenotypes see ##TAB##0##Table 1##.</p>", "<p>The abundance of p53 did not change among different passages reaching replicative senescence, or between the restrictive or permissive status in the case of the overexpression of the thermosensitive mutant of p53 (##FIG##0##Figure 1C##). Therefore, this study first measured broad p53-dependent transcription (##FIG##0##Figure 1B##). We measured the expression of 122 p53 target genes using Dot Blot arrays in the different proliferating and arrested cellular scenarios discussed above (See ##SUPPL##0##Figure S1## for a list of the 122 genes analyzed). The increased transcription rates of important p53 target genes such as Bax, GADD45, p21 and PIG8 confirm the activation of p53 in both senescence systems (##FIG##0##Figure 1C##).</p>", "<p>We observed that the arrest of MEFs at senescence (P5) and after Ras-induced senescence (P3+ras) correlated with a net increase in p53-dependent transcription (##FIG##1##Figure 2A##). Similarly, cells arrested after p53 activation (ts and ts-Ras at 32°C) also showed, as expected, a significant increase in p53-dependent transcription (##FIG##1##Figure 2A##). Therefore, we had a genetically homogeneous system with different levels of p53 activity measured with respect to 122 p53 transcriptional targets. It was possible to ascribe a phenotype to each level of p53 activity (##TAB##0##Table 1##). There were three conditions of proliferating cells: (1) P3, (2) cells with mutant p53 at restrictive temperature (null p53), and (3) cells showing basal levels of p53 activity. There was also one condition of replicative senescence with a moderate increase of p53 activity (P5). Oncogenic Ras activation seems to induce higher levels of p53 activation with similar senescent phenotypes (P3+ras). However, elevated levels of p53 do not always induce senescence as p53ts cells at permissive temperature are reversibly arrested, but p53 activity is higher than in the two previous conditions displaying senescence. As before, oncogenic Ras expression switches the cell from arrest to senescence, also increasing the relative p53-dependent transcription (##TAB##0##Table 1## and ##FIG##1##Figure 2A##).</p>", "<p>Therefore, arrest vs. senescence is not determined by the relative levels of p53 activity alone.</p>", "<title>Specificity of the senescence response</title>", "<p>To study the expression pattern of p53-responsive genes during arrest or senescence in order to compare both processes and to ascertain what gene or genes may play a crucial role in the proliferating or arrested cell phenotypes, we performed a hierarchical clustering of the different cellular conditions on the basis of pattern similarity (see <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>). In ##FIG##1##Figures 2B and 2C## we observed that the conditions are separated into two groups corresponding to the arrested (right side) and proliferating (left side) phenotypes. The cell lines are grouped together on the cluster dendogram by the activation or inactivation of p53 and not by the presence or absence of the Ras oncogene. This is clear in wild-type MEFs growing at passage 3 (P3), which have low levels of p53 activation compared to arrested wild-type MEFs in passage 5 (P5), which have p53 highly activated. However, it is interesting that the most extreme condition, p53 activation in the presence of oncogenic Ras, triggers an enhanced transcriptional response (##FIG##1##Figures 2A and 2C##, lane p53ts-Ras [32°]). See below.</p>", "<p>Although all the physiological conditions that lead to growth-arrest onset are clustered together and all the transcripts considered are p53-dependent, it is clear that there are some genes whose enhanced activation (relative to their median expression level over all cellular conditions) is specific to each particular condition (##FIG##1##Figure 2C## and ##TAB##1##Table 2##). These genes might serve as specific marker genes. However, no concurrent senescence signature could be observed, indicating that the senescence program is not determined by the specificity of the p53 response.</p>", "<p>Next, applying a penalized least-squares regression technique with an L1-type penalty to the expression data (see <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>) it was possible to identify four p53 target genes among the 122 genes studied as the most relevant markers for predicting the proliferating or arrested phenotype of each cellular condition. These four relevant genes are: MAP4, PTEN, Lats2 and Rb1 (##FIG##1##Figure 2D##). Furthermore, combining L1- and L2-norm penalties allowed small subgroups of additional genes that are highly correlated with the main predictors to be extracted. This study identified five more genes closely related to MAP4 behavior: p63, caspase1, DKK1, Bcl2 and Gtse1; as well as LRDD, related to Rb1. This robust set of p53 target genes molecularly defines a minimal footprint to identify a p53-dependent arrest.</p>", "<p>In order to confirm the p53-dependent arrest footprint defined by these markers, we measured the p53-dependent transactivation of 4 among the selected genes by qRT-PCR in HCT 116 p53+/+ cells treated with different DNA-damaging agents. p53 protein is present at low levels in resting cells but after exposure to those agents as well as to other stressing stimuli, it is stabilized and activated by a series of post-translational modifications. These modifications leave p53 free from mdm2, an E3 ubiquitin ligase that ubiquitinates it and facilitates its degradation by the proteasome ##REF##9039259##[5]##. p53 stabilization and activation is followed by cell-cycle arrest. To ascertain whether the transcription of this set of genes also depends on other chemotherapeutic drugs that act through p53-independent mechanisms, we also treated the cells with compounds that do not directly cause DNA breaks. Only the treatment with the topoisomerase inhibitors Etoposide and Doxorubicin induced an activation of the transcription of PTEN, Lats2, Rb1 and MAP4 (##FIG##1##Figure 2E##). However, we did not detect increase of these genes by Taxol, flavopiridol or UCN-01.</p>", "<title>Downregulation of p53 response without senescence</title>", "<p>P53 transcription seems to define only arrest, and not senescence, suggesting the existence of a p53-independent signal necessary to convert the reversible arrest into senescence. To explore this, we analyzed whether sustained p53 activation might induce senescence without a second signal. In the same p53-induced transcriptional setting, we analyzed the activation of p53 during long periods and its correlation with the appearance of senescence. After an initial activation, general p53-induced transcription seems to decay at 24 hrs; this downregulation is maintained for long periods even in the presence of Ras activation (##FIG##2##Figure 3A##). However, senescence features are only maintained in the p53ts-Ras cells incubated at 32°C (##FIG##2##Figure 3B##). We found that in cells carrying activated p53 only, senescence is not induced despite a long period of growth arrest (up to 6 days). These data support the finding of Ferbeyre et al. ##REF##11971980##[17]##, that growth arrest and senescence are two independent phenotypes; the permanence of growth arrest does not induce senescence unless another signal is involved.</p>", "<p>Finally, to confirm that the cells in long-term arrest have not suffered molecular changes that might indicate a switch to senescence, we analyzed 53BP1 and γH2AX phosphorylation at the senescence-associated DNA foci. As before, p53ts and p53ts-Ras cells were cultured at 39°C, then were moved to 32°C and maintained for up to 6 days at restrictive temperature. Cells were taken at different time points and analyzed for the presence of DNA-damage foci labeled by 53BP1 and γH2AX phosphorylation as markers for cellular senescence (##FIG##3##Figure 4##). One or two 53BP1 and γH2AX foci appear with cell proliferation, and the same number of foci was maintained in p53ts arrested cells even after 6 days of growth arrest. However, p53ts-Ras cells showed a strong increase in the number of foci per nuclei after 48 hrs of arrest (##FIG##3##Figure 4##); this was maintained despite p53-transcriptional downregulation.</p>", "<p>These data, which are consistent with previous observations ##REF##11971980##[17]##, ##REF##15156563##[26]##, indicate that initial p53 activation is required to induce growth arrest. However, a second Ras-dependent signal seems to be required to stabilize the arrest as irreversible senescence.</p>", "<title>PPP1CA contributes to growth arrest stabilization in senescence</title>", "<p>The application of a retroviral-based genetic screen yielded an antisense RNA fragment against PPP1CA, the catalytic subunit of PP1α. Loss of PPP1CA function bypasses Ras/p53-induced growth arrest and senescence ##REF##18204081##[18]##. It was found that oncogenic Ras promotes an increase in the intracellular level of ceramides, which may increase PPP1CA activity, contributing to senescence. PP1α has been identified as the protein phosphatase responsible for the dephosphorylation of pRb ##REF##9020179##[29]##; this has been related to the growth arrest response ##REF##8380637##[30]##–##REF##9835651##[32]##. When cells are actively growing, the hyperphosphorylated form of the Rb protein (ppRb) predominates. On the contrary, when cells are delayed in their growth, the hypophosphorylated form of the Rb protein (pRb) is the most abundant. Thus, enforced pRb dephosphorylation might contribute to the arrest to senescence transition ##REF##12809602##[27]##, ##REF##16957149##[33]##.</p>", "<p>PPP1CA protein levels increase upon Ras activation (##FIG##4##Figure 5A##) ##REF##10811615##[34]##, ##REF##18204081##[18]##, but not mRNA (##FIG##4##Figure 5B##). PP1 phosphatase activity also increases upon oncogenic ras expression (##FIG##4##Figure 5C##), paralleling protein levels of PPP1CA. Expression of a specific shRNA against PPP1CA impairs pRb dephosphorylation, thus bypassing p53-induced arrest. When p53ts-Ras cells were shifted at 32°C, pRb became hypophosphorylated, in accordance with the growth-arrest induced by thermo-sensitive p53 at this permissive temperature. In contrast, p53ts-Ras cells stably transduced with shRNA against PPP1CA showed an increase in the hyperphosphorylated form of Rb protein when kept for 24 h at 32°C (##FIG##4##Figure 5D##). These data show that downregulation of PPP1CA maintains pRb in the hyperphosphorylated state, even in the presence of active p53, therefore allowing cell growth (##FIG##4##Figures 5D and 5E##). While p53ts-Ras cells at 32°C show mostly the senescent phenotype, only 22% of cells carrying the PPP1CA shRNA showed senescence features, confirming the relevance of PP1α activity to the senescence phenotype (##FIG##4##Figure 5F##). This was further confirmed by immunofluorescence studies (##FIG##5##Figure 6##). In proliferating cells, PPP1CA and pRb levels are low, increasing slightly upon growth arrest. However, these proteins showed diffuse distribution (##FIG##5##Figure 6##). Under conditions inducing senescence (p53ts-Ras at 32°C), cells increase pRb and PPP1CA levels, which showed nuclear co-localization, strengthening evidence for their functional relationship to senescence.</p>", "<title>Oncogenic stress enhances p53-dependent transcription</title>", "<p>We also observed that oncogenic Ras enhances p53-dependent transcription (##FIG##1##Figures 2A and 2C##). To study this effect in detail, we selected three different p53-responsive promoters, Bax, p21waf1 and the synthetic p53 response element (x13). We engineered a construct fusing the different promoters 5′ of the luciferase reporter gene and compared the effect of p53 alone to the effect of the combination of p53 and Ras-val12 (##FIG##6##Figure 7A##). Oncogenic Ras enhances p53-dependent transcription in all cases, but does not alter transcription when transfected alone (##FIG##6##Figure 7A##). These effects are dependent on p53 and Ras doses (##SUPPL##1##Figure S2##).</p>", "<p>To further study this effect, we selected the Bax promoter and investigated dependence of the phenomenon on Ras. To that end, we tested the N17 mutant of Hras-val12. This mutant lacks the ability to bind to Ras effectors and therefore acts as a dominant negative mutant. The N17 mutant does not alter the p53 response (##FIG##6##Figure 7A##), indicating that the Ras effect is dependent upon activation of Ras effectors. To directly discriminate between the two main effector pathways involved in this effect, the same experiment was performed with active PI3K or Raf pathway mutants. We co-transfected p53 and an active mutant of AKT (AKT-CA) (PI3K pathway), or an active mutant of Raf (BXB-Raf-CAAX). We were able to reproduce the ras-enhancing effect (##FIG##6##Figure 7B##), indicating that a strong activation of either pathway may provoke the enhancement of p53 transcription.</p>", "<p>Ras, acting through the Raf pathway, may activate p53 through p19ARF, either dependent upon or independently of MDM2, while PI3K may inhibit p53 through MDM2 phosphorylation ##REF##12719720##[7]##, ##REF##12204530##[35]##, ##REF##10995391##[36]##. To determine whether MDM2 or p19 was involved in the effect, the experiment was performed in p19-null or MDM2-null cells (##FIG##6##Figure 7C##). We observed that the p53-enhancing effect observed in the Ras oncogenic signal was dependent upon p19ARF but not on MDM2. A similar observation was made with the activated Raf oncogene. However, activated AKT showed p53-enhancing effects independent of p19ARF and MDM2 (##FIG##6##Figure 7C##). These data match those previously reported ##REF##9765203##[11]##, ##REF##9744268##[37]##–##REF##17182091##[39]##; Ras and Raf oncogenes require the p19ARF protein to activate p53.</p>" ]

[ "<title>Discussion</title>", "<p>The data presented in this study elucidate the regulation of p53-responsive genes during proliferation and senescence. We have clearly demonstrated that Ras effects on p53-dependent transcriptional activation result in quantitative rather than qualitative changes. Therefore, the senescence response depends on factors other than p53 activation. p53 activation seems to be necessary but not sufficient to induce senescence, as other signals may be needed for the full onset of senescence. We have shown that Ras-induced activation of PPP1CA, the catalytic subunit of PP1α, is necessary to induce Ras-dependent senescence ##REF##18204081##[18]##. It is therefore possible to split the senescence response into two physiological processes. The first of these involves induction of growth arrest and is dependent on p53 activation or other physiological signals activating a proliferative brake similar to that of p53, such as p73 or p63. The second process occurs later, acting on pRb to stabilize its active unphosphorylated form, independent of p53. Unphosphorylated pRb will bind and inactivate E2F factors blocking cell cycle progression and altering local chromatin ##REF##12809602##[27]##. PPP1CA activation will take part in this second process, contributing to irreversible proliferative arrest by enforcing pRb dephosphorylation.</p>", "<p>Since senescence is a safeguard mechanism that may prevent pretumoral cells from further expansion, many studies have recently emphasized the relevance of this possible new therapeutic tool against cancer (reviewed in ##REF##16081723##[41]##–##REF##16869762##[43]##). Our work has identified a set of p53 target genes that affect growth arrest in response to p53 activation. Although our work only identify these 4 genes as the minimal footprint to differentiate growing from p53-arrested cells, these 4 genes have been broadly studied and its relevance in growth arrest and senescence has been established.</p>", "<p>Tumor suppressor Lats2 has been shown to be necessary for culture-induced replicative senescence in MEFs, since Lats2−/− MEFs bypass this process ##UREF##2##[48]##. Furthermore, cells lacking Lats2 showed increased prevalence of micronuclei, chromosomal defects and aneuploidy ##UREF##2##[48]##, ##REF##17015431##[49]##. Lats2 and p53 establish a positive feedback loop that prevents tetraploidization of cells treated with the microtubule poison nocodazole ##REF##17015431##[49]##. Most important, miRNA-372 and miRNA373 microRNAs directly target Lats2 expression and have been shown to cooperate with oncogenic Val12-Ras in a way that resembles p53 inactivation, acting as oncogenes in testicular germ cell tumors ##REF##16564011##[50]##. Finally, Lats2 has been shown down-regulated through promoter hypermethylation ##REF##15746036##[51]##, ##REF##16208412##[52]##, in association with poor prognosis human breast cancers and acute lymphoblastic leukemia. Lats2 might have a role against cancer development, probably through the induction of senescence, and this could explain the link between its down-regulation and tumoral progression. The tumor-suppressor gene RB1 can suppress S phase entry and cause a transient G₁ arrest following DNA damage ##REF##17979151##[53]##–##REF##16936742##[55]## and the mutations in Rb1 pathway-related genes are associated with poor prognosis in many tumor types. The PTEN/PI3K pathway is also regarded as an effector of cellular senescence ##REF##10832053##[56]## through p27kip1 cell cycle inhibitor activation.</p>", "<p>The key findings obtained in this study may contribute to the current understanding of the molecular basis of senescence and should be of great interest in future senescence studies.</p>" ]

[]

[ "<p>Conceived and designed the experiments: AC. Performed the experiments: LR MT IF EC. Analyzed the data: JFL MK AC. Wrote the paper: AC.</p>", "<title>Background</title>", "<p>P53 activation can trigger various outcomes, among them reversible growth arrest or cellular senescence. It is a live debate whether these outcomes are influenced by quantitative or qualitative mechanisms. Furthermore, the relative contribution of p53 to Ras-induced senescence is also matter of controversy.</p>", "<title>Methodology/Principal Findings</title>", "<p>This study compared situations in which different signals drove senescence with increasing levels of p53 activation. The study revealed that the levels of p53 activation do not determine the outcome of the response. This is further confirmed by the clustering of transcriptional patterns into two broad groups: p53-activated or p53-inactivated, i.e., growth and cellular arrest/senescence. Furthermore, while p53-dependent transcription decreases after 24 hrs in the presence of active p53, senescence continues. Maintaining cells in the arrested state for long periods does not switch reversible arrest to cellular senescence. Together, these data suggest that a Ras-dependent, p53-independent, second signal is necessary to induce senescence. This study tested whether PPP1CA (the catalytic subunit of PP1α), recently identified as contributing to Ras-induced senescence, might be this second signal. PPP1CA is induced by Ras; its inactivation inhibits Ras-induced senescence, presumably by inhibiting pRb dephosphorylation. Finally, PPP1CA seems to strongly co-localize with pRb only during senescence.</p>", "<title>Conclusions</title>", "<p>The levels of p53 activation do not determine the outcome of the response. Rather, p53 activity seems to act as a necessary but not sufficient condition for senescence to arise. Maintaining cells in the arrested state for long periods does not switch reversible arrest to cellular senescence. PPP1CA is induced by Ras; its inactivation inhibits Ras-induced senescence, presumably by inhibiting pRb dephosphorylation. Finally, PPP1CA seems to strongly co-localize with pRb only during senescence, suggesting that PP1α activation during senescence may be the second signal contributing to the irreversibility of the senescent phenotype.</p>" ]

[ "<title>Supporting Information</title>" ]

[]

[ "<fig id=\"pone-0003230-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003230.g001</object-id><label>Figure 1</label><caption><title>Experimental system.</title><p>A) Molecular markers used to identify senescence. B) Scheme of the procedure (see M &amp; M) and 2 representative images of the dot blot obtained after hybridization. C) Comparison of several conditions of well-known activated targets of p53. Western blot showed no variation among p53 levels under comparable conditions.</p></caption></fig>", "<fig id=\"pone-0003230-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003230.g002</object-id><label>Figure 2</label><caption><title>Analysis of the p53-dependent transcriptional signature.</title><p>A) Comparison of the levels of 122 transcriptional targets of p53 at different conditions of p53 activity. See text. Data normalized against β-actin were compared to the proliferative condition P3 to evaluate its statistical significance. Statistical analysis was performed by paired t-test. (_*) = p&lt;0.05; (_**) = p&lt;0.005; (_***) = p&lt;0.001. B) and C) Analysis of the expression values of 122 transcriptional targets of p53 under different cellular conditions, which led either to proliferation or to growth arrest. Clustering analysis. Hierarchical clustering was performed using the function hcluster (package amap) of the free statistical software R. See M &amp; M. The expression level of each gene, relative to its median expression level across all conditions, was represented by a color, with red representing expression greater than the median, green representing expression less than the median, and the color intensity representing the magnitude of the deviation from the median. D) Feature selection. Since the number of genes is much greater than the number of conditions, we used penalized regression methods. See text and M &amp; M for more details. E) Validation of the feature selection by quantitative PCR. See text and M&amp;M. We determined Lats2, DKK1, pRb and PTEN mRNA levels after 24 hts treatment of HCT116 cells with the indicated treatment, by quantitative PCR. Cyclophilin (ref. 4326316E), an endogenous control, was used to normalize variations in cDNA quantities from different samples. Each reaction was performed in triplicate with cDNA from normal and tumor tissue from each patient studied. C shows untreated samples. E: Etoposide, D: Doxorubicin, T:Taxol, U: UCN-01, F:Flavopiridol. Data shows average of three determinations.</p></caption></fig>", "<fig id=\"pone-0003230-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003230.g003</object-id><label>Figure 3</label><caption><title>P53 activity is downregulated maintaining senescence.</title><p>Cells were plated in 10-cm-diameter plates. Cells were grown at 39°C (i.e., never incubated at 32°C) or arrested for the indicated times at 32°C. Cells were harvested and RNA collected for A, or stained for SA β-GAL for B. A) Comparison of the levels of 122 transcriptional targets of p53 at different times after p53 activation. Data normalized against β-actin was compared to the proliferative conditions at 39°C to evaluate statistical significance. Statistical analysis was performed by paired t-test. (_*) = p&lt;0.05; (_**) = p&lt;0.005; (_***) = p&lt;0.001. B) More than 400 cells were visually analyzed for SA β-GAL staining as described in ##FIG##0##Figure 1A##. Data represent the percentage of cells showing SA β-GAL staining.</p></caption></fig>", "<fig id=\"pone-0003230-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003230.g004</object-id><label>Figure 4</label><caption><title>Enforced growth arrest does not induce senescence.</title><p>P53ts or p53ts-Ras cells were grown at 39°C or incubated at 32°C for different times as indicated. Cells were fixed and stained with DAPI to identify the nuclei, or with antibodies against 53BP1 or phosphorylated gH2AH. A) Representative picture. B) Foci of &gt;60 nuclei of each condition were counted and data represented as the average of the number of foci per nuclei. Bars = StDev.</p></caption></fig>", "<fig id=\"pone-0003230-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003230.g005</object-id><label>Figure 5</label><caption><title>A)</title><p>Oncogenic Ras increased PPP1CA protein levels. P53ts (control) or p53ts-Ras (+ras) cells were grown at 39°C or incubated at 32°C for 24 hrs. Then PPP1CA protein levels were analyzed by western Blot. α-tubulin was used as a loading control. The data are representative of three independent experiments. B) PPP1CA mRNA levels were not dependent on the expression of oncogenic ras. mRNA levels were analyzed by Northern blot. A labeled probe able to specifically recognize PPP1CA isoform was used as described in M&amp;M. C) Oncogenic ras increased PP1 activity. Exponentially growing cells were keep growing or switch for 24 hrs at 32°C as indicated. Then were starved and PP1 phosphatase activity was measured as described in M&amp;M. C shows the remaining activity after 100 nM okadaic acid treatment to inhibit PP1 and PP2A activity. D, E, and F) P53ts-Ras cells carrying the shRNA against PPP1CA (shRNA) or vector alone (control) were grown at restricted temperature (39°C), or permissive temperature (32°C) as indicated, for 24 hrs. Cells were harvested for protein extraction (for D), fixed and stained with crystal violet (for E) or for SA β-GAL (for F). D) Downregulation of PPP1CA inhibits p53-induced pRb hypophosphorylation. Cells were processed for western blot, showing hyperphosphorylated (ppRb) and hypophosphorylated (pRb) forms of the protein. α-tubulin was used as a loading control. The data are representative of three independent experiments. Bottom panel shows quantification of pRb bands. E) and F) Downregulation of PPP1CA bypasses p53/ras-induced senescence. Cells (10⁴) were seeded and grown at 39° or 32°C for 1 week, then fixed and stained for colony formation with crystal violet (E) or SA β-GAL (F). In F, numbers show the percentage of cells with SA β-GAL staining.</p></caption></fig>", "<fig id=\"pone-0003230-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003230.g006</object-id><label>Figure 6</label><caption><title>PPP1CA and pRB co-localize during p53/ras-induced senescence.</title><p>P53ts or p53ts-Ras were grown at 39°C or incubated at 32°C for 24 hrs. Cells were fixed and labeled with DAPI to identify the nuclei, as well as antibodies against PPP1CA (red) or pRb (green).</p></caption></fig>", "<fig id=\"pone-0003230-g007\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003230.g007</object-id><label>Figure 7</label><caption><title>Oncogenic Ras enhances p53-transcriptional activation. A, B and C)</title><p>p53(−/−), p53(−/−); MDM2(−/−) or p19(−/−) MEFs were transfected as indicated with plasmids carrying luciferase and the indicated genes. Luciferase activity was measured as indicated in the M &amp; M. A) Assays were performed in p53(−/−) MEFs using different promoters responding to p53 (p21waf1, Bax and synthetic p53x13 carrying only the p53 binding site repeated 13 times). B) Only the Bax promoter was used in p53(−/−) MEFs. C) Only the Bax promoter was used in p53(−/−), double p53(−/−); MDM2(−/−) or p19(−/−) MEFs. Data show the averages of three independent experiments.</p></caption></fig>" ]

[ "<table-wrap id=\"pone-0003230-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003230.t001</object-id><label>Table 1</label><caption><title>Summary of cell lines and conditions used in this study.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cell line</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genotype</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">phenotype</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>p53−/−</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">p53−/− MEFs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Growth at 32° and 39°</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>p53−/−;ts</bold> (p53ts)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">p53−/− MEFs with p53val135</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Growth at 39° reversible arrest at 32°</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>p53−/−;ts-ras</bold> (p53ts-ras)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">p53−/− MEFs with p53val135</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Growth at 39°; senescence at 32°</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>p3</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">naïve MEFs, 6 population doublings</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Growth at 37°</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>p5</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">naïve MEFs, 10 population doublings</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">senescence at 37°</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>P3+Ras</bold> (Ras)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">naïve MEFs+Ras-val12, 6 population doublings</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">senescence at 37°</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pone-0003230-t002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003230.t002</object-id><label>Table 2</label><caption><title>Condition-specific genes.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">P5 Replicative</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ras Oncogenic Stress</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">p53ts(32°) Growth arrest</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IGF-R</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">p63</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GML</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">MAP4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CycB1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Bak</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ZAP70</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Krt2-8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Bax</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Wig1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Krt1-15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">PTGF</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PIG8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Pmaip1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Igfbp6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IL6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">DKK1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Bcl6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">P73</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">PUMA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">PPM1D</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Lats2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Mgmt</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tyr</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Bax</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Pold1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MDR1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Jun</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Lats2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Thbs1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">LRDD/PIDD</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kai1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Pthlh</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Btg2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Waf1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">MAP4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Igfbp3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tst</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">MST1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">RB1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">RB1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">IGFR</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hic1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">p14-ARF</td></tr></tbody></table></alternatives></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"pone.0003230.s001\"><label>Figure S1</label><caption><p>List of 122 p53 target genes used int his study</p><p>(0.11 MB EMF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003230.s002\"><label>Figure S2</label><caption><p>Oncogenic ras increases p53-induced transcription in a dose-dependent maner</p><p>(0.07 MB TIF)</p></caption></supplementary-material>" ]

[ "<table-wrap-foot><fn id=\"nt101\"><label/><p>This table represents the genes that are the most representative (relative to their median level–based on all conditions) in each particular arrested condition. A threshold equal to 2.30 was chosen for the ratio. Genes are arranged from the lowest to highest ratio.</p></fn></table-wrap-foot>", "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>This work has been supported by grants from the Ministerio de Educacion y Ciencia (SAF2005-00944), Fundación Mutua Madrileña and the EU (from VI framework, Project COMBIO, Project NETSENSOR). IF is supported by a fellowship from the Ministerio de Educacion y Ciencia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"pone.0003230.s001.emf\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003230.s002.tif\"><caption><p>Click here for additional data file.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p>Since 1990, the Federal Government, through Title XXVI of the Public Health Service (PHS) Act as currently amended by the Ryan White HIV/AIDS Treatment Modernization Act of 2006##UREF##0##[1]## (Ryan White Program, or RWP), has provided funding to states, cities, and nonprofit organizations to improve the quality and availability of medical care and supportive services for low-income, uninsured, and underinsured individuals and families affected by HIV/AIDS. Administered through the Health Resources and Services Administration (HRSA), RWP funds are provided directly to healthcare facilities (through Part C grants to Community Health Centers, University-affiliated medical centers, hospitals, or other community-based health care settings) or may support care in facilities indirectly, through grants provided to state health departments and local health departments in eligible metropolitan areas (EMAs) or transitional grant areas (TGAs).</p>", "<p>The legislation which provides these funds for HIV care and services also requires that service providers establish quality management programs to assess the extent to which HIV health services provided to patients under the grant are consistent with the most recent PHS guidelines for the treatment of HIV/AIDS and related opportunistic infections ##UREF##0##[1]##. To monitor quality of care, HRSA provides technical assistance to grant recipients for quality improvement ##UREF##1##[2]##, and grantees can use a proportion of their awards to implement a clinical quality management program ##UREF##0##[1]##.</p>", "<p>Recently, the Institute of Medicine recommended that quality of care should be measured at the broader population level, that population-based methods should be used for such evaluations, and that information on quality of care with respect to both prophylaxis and treatment should be measured ##UREF##2##[3]##. Representative data on patients in care will soon be available in the United States ##REF##17579722##[4]##; we used data from a pilot probability sample of patients in care for HIV infection in 1998 to provide historical information about the quality of care provided in facilities supported by the RWP to that in non-RWP-supported facilities, to provide baseline data for comparison with future analyses of quality of care from population-based systems, and to illustrate the use of population-based clinical outcomes surveillance data for describing quality of care.</p>" ]

[ "<title>Methods</title>", "<p>The Survey of HIV Disease and Care (SHDC) project was a pilot project to develop methods for the use of population-based sampling of persons receiving care for HIV infection as a method of HIV clinical outcomes surveillance. The methods have been previously reported ##REF##16134562##[5]##. The three participating health jurisdictions (“study sites”) were chosen by a competitive application process to CDC. Project staff at each study site first defined a geographic area for inclusion in the study; the geographic areas were the entire state of Michigan, health regions 1,2,3,4, and 9 in Louisiana (southern Louisiana, including New Orleans and Baton Rouge), and King County (including Seattle) in Washington State. The chosen geographic areas within each state reflected a number of considerations, including jurisdiction for public health surveillance, available resources, and distribution of AIDS prevalence within the area. Health department staff in each study site then constructed a sampling frame of HIV care facilities within the defined geographic area, using data on health care providers and facilities who had reported diagnosing or caring for persons with HIV infection to the health department as part of HIV/AIDS surveillance, and other data sources. Facilities that provided no clinical care, such as HIV counseling and testing facilities, were excluded. Facilities could represent a single provider, a group of providers sharing a common medical records system, or some other clinic with a single medical records system. Facilities were classified based on size of HIV patient load (small, medium, or large), urban vs. rural location, and on whether or not the provider or facility received RWP support–either directly from HRSA under Part C (formerly Title III), or indirectly through a state or local health department funded under Part A or B (formerly Title I or II). Receipt of RWP support was thus identified at the facility level; no determination was made at the patient level as to whether RWP resources supported specific aspects of that patient's care (such as provision of antiretroviral drugs). HIV care facilities were sampled, using probability proportional to size of the patient population, within size, urban/rural, and RWP-support strata. For this analysis, we excluded five facilities in Louisiana that provided only inpatient care, because the RWP is designed to pay for outpatient care.</p>", "<p>From each eligible participating HIV care facility sampled, the health department requested information about the number and demographic characteristics of patients who had been seen in the facility at least once for care for HIV infection during 1998. The numbers of patients were obtained at the facility level, such that if multiple HIV clinicians were practicing in a facility that share a common medical records system, only one tally of patients would be obtained for the whole facility. Based on this information, patients were stratified within facilities on race and sex, and sampled using systematic sampling within strata from an ordered list. The sampling interval was varied in different race/sex strata to ensure adequate representation of women and racial/ethnic minorities. Of note, we did not collect information on other qualitative aspects of the facilities, such as training or experience with providing HIV care.</p>", "<p>For each sampled patient, medical records were abstracted for the period January 1–December 31 1998. Abstractors in all study sites received the same standardized training, and used standardized definitions for clinical outcomes and laboratory measures. Data were collected on laboratory values (including CD4+ T-lymphocyte count and HIV RNA concentration [viral load]); prescription of highly active antiretroviral therapies (HAART) and prophylactic medications for the prevention of <italic>Pneumocystis jirovecii</italic> pneumonia (PCP) and <italic>Mycobacterium avium</italic> complex (MAC); provision of recommended screening tests (tuberculin skin test, Pap smear) and influenza vaccination; and information about inpatient hospital utilization. Using treatment guidelines current in 1998 ##UREF##3##[6]##, ##UREF##4##[7]##, standard definitions were constructed for which patients were eligible for recommended care (e.g., PCP prophylaxis for patients with a CD4 count &lt;200 cells/μL). Details of the definitions for recommended care are included in ##SUPPL##0##Appendix S1##. Quality assurance procedures (e.g., independent re-abstraction of a small sample of records and/or computerized checks that data were valid [within an expected range]) were implemented in all study areas.</p>", "<p>Sampling weights were constructed for each patient by multiplying the sampling weight of the facility by the sampling weight of the patient within the facility. Further details of sampling weights and calculation of variance have been previously reported ##REF##16134562##[5]##. These weights were used to estimate the number of patients in care within the geographic areas, as well as the number of patients in care at facilities supported by HRSA and at other facilities. For each geographic area, the proportion of eligible patients receiving care according to treatment guidelines, with 95% confidence intervals, was estimated. Statistically significant differences between proportions were determined using ?² tests. For some outcomes such as number of laboratory tests performed within a time period, the median number of tests per unit time, with 95% confidence intervals, was estimated; median-split ?² tests were used to test for significant differences between medians. All analyses were performed in SUDAAN to account for the complex sampling design.</p>", "<p>The SHDC project was considered to be non-research by the Centers for Disease Control and Prevention Institutional Review Board (IRB), and as such did not require IRB review. Of the three participating state and local health departments, the protocol was reviewed and received Institutional Review Board (IRB) approval in two, and in one, it was determined to be exempt from IRB review.</p>" ]

[ "<title>Results</title>", "<p>Overall, 95% (41/43) of eligible sampled health care facilities agreed to participate in the survey (range by site: 86%–100%). Information was abstracted from the medical records of 831 patients (range by site: 169–374); of these, 250 patients (30%) received their care in facilities supported by the RWP (range by site: 43–131: 20%–45%). Using weighted sums of patients in care, we estimated that our study made statistical inference to 18,720 patients in care for HIV infection: 8,490 (45%, CI = 29%–62%) in care at RWP-supported facilities, and 10,230 (55%, CI = 37%–71%) in care at facilities not supported by the RWP.</p>", "<p>Limited information was collected about the 41 participating facilities. The proportions of included facilities that were RWP-supported facilities in Michigan, King County, and Southern Louisiana were 14%, 25%, and 20%, respectively. Median numbers of patients in care in the RWP-supported facilities in each of the three areas (1130, 187, 1783) were higher than the median number of patients in care in the non-RWP facilities (24, 84, 46). There was a trend for larger patients loads in RWP-supported facilities in Michigan and southern Louisiana (p = 0.06 and p = 0.07 respectively by median test), but not in King County (p = 0.52). By categorical size of patient load, in Michigan 38% of facilities had patient loads &lt;20, 48% had patient loads 20–199, and 14% had patient loads ≥200. Corresponding proportions for Louisiana were 50%, 20%, and 30%, and for King County were 0%, 67%, and 33%.</p>", "<p>There were some statistically significant differences in the demographic and clinical characteristics of persons receiving care in RWP-supported and non-RWP supported facilities, and these differences were not consistent in the three study areas (##TAB##0##Table 1##). Women in King County were more likely to receive care in RWP-supported facilities than in non-RWP-supported facilities, whereas the opposite was true for men. Persons aged 45 years or older in King County and Louisiana were less likely to receive care in RWP-supported facilities than in facilities not supported by the RWP, while the opposite was true for persons aged 25–44. There were racial/ethnic differences in the proportions of patients receiving care in RWP-supported and non-RWP-supported facilities in King County: 66% of patients receiving care in RWP-supported facilities were white, non-Hispanic, but 82% of patients receiving care in non-RWP-supported facilities were white, non-Hispanic. There were also differences in the distribution of risk for HIV acquisition (southern Louisiana and King County) and clinical stage of disease (southern Louisiana) between patients receiving care in RWP-supported facilities and those receiving care in non-RWP-supported facilities. In Michigan, there were no significant differences in the demographic characteristics of those receiving care in RWP-supported and non-RWP-supported facilities.</p>", "<p>For most clinical care outcomes evaluated, there were no statistically significant differences in the quality of care provided to patients in RWP-supported and non-RWP-supported facilities (##TAB##1##Table 2##). Where statistically significant differences were observed, in each case, the proportion of patients receiving care according to treatment guidelines was higher for patients receiving HIV care in RWP-supported facilities. Patients receiving HIV care in RWP-supported facilities were more likely to receive indicated PCP or MAC prophylaxis during 1998 in southern Louisiana; were more likely to receive a tuberculin skin test during 1998 in King County; and were more likely to receive a Pap smear in 1998 in all three study areas. There were no significant differences in the median number of viral load tests, CD4 counts, or outpatient visits between patients receiving HIV care at RWP-supported versus non-RWP-supported facilities in any of the 3 study areas (##TAB##2##Table 3##). In Louisiana, patients receiving their HIV care in RWP-supported facilities were less likely to have had a hospital visit during the year than patients receiving care in non-RWP supported facilities.</p>" ]

[ "<title>Discussion</title>", "<p>We used data from a population-based sample of patients receiving HIV care in these three geographic areas to describe the quality of HIV care delivered in 1998 in RWP-supported and non-RWP-supported facilities. We observed that patients receiving HIV care at facilities supported thorough RWP funds, administered directly or indirectly through HRSA, received care which was in compliance with then-current treatment guidelines at least as often as patients who received care from non-RWP supported facilities. We believe the recommended standards of care we evaluated represent important and objective measures of quality of care, and are in alignment with HRSA's currently-proposed clinical performance measures ##UREF##5##[8]##. We therefore conclude that care in RWP-supported facilities was at least of equivalent quality to care supported by other payers – and in some cases, of higher quality.</p>", "<p>The primary strength of our study is that the patients included were selected using probability sampling methods, and are therefore representative of all patients in care for HIV infection in the three participating geographic areas. However, our study also had some weaknesses. In one site, two eligible sampled facilities refused participation, which, to the extent that the refusing facilities provided a different quality of care from participating facilities, could introduce some bias to our findings. In this case, none of the refusing facilities in the sample of facilities was RWP-supported. The King County site had no small facilities in their sample. As well, our data are somewhat dated, although we believe that the data are appropriate for documenting baseline measures of quality of care by RWP status, and demonstrating how the Institute of Medicine's recommendation to use population-based data to evaluate quality of care can be operationalized using data from a population-based, clinical outcomes surveillance project.</p>", "<p>Also, data were only collected about care reflected in the medical records of the facility where the patient was sampled. Therefore, for patients who received HIV care in multiple facilities, the extent to which indicated care was received may have been underestimated. If patients receiving care in non-RWP-supported facilities were more likely than those receiving care in RWP-supported facilities to receive certain services, such as tuberculin skin tests or Pap smears, outside of the facility where they were sampled, then our observed differences in the proportions of patients receiving recommended screening tests may be due to misclassification. Certain of our data, such as the low estimate of receipt of viral load tests in Michigan, suggest that the extent of incomplete data due to this limitation may be pronounced for some variables in some project sites. This concern may be especially relevant to the provision of certain services, that are more likely to be provided by specialists (e.g., Pap tests) or may be more accessible and less expensive outside of HIV care facilities (e.g., influenza vaccine). Our analysis of data from women with HIV infection from a different study indicated that women who received their gynecological care at the same clinic as their HIV care were more likely to receive Pap tests as recommended ##UREF##6##[9]##.</p>", "<p>The designation of RWP-support is somewhat artificial, in that RWP support was only identified at the facility level. In reality, some patients may receive support for HIV care which is received in a facility not directly supported by the RWP. For example, patients receiving care from private facilities may receive funding for purchasing medicines through the AIDS Drug Assistance Program (ADAP). Thus, our data may underestimate the extent of RWP support for care, but unless receiving ADAP impacted the care outcomes we analyzed, this should not represent a source of misclassification bias with respect to our primary conclusions.</p>", "<p>In some cases, our precision was low and our statistical power to detect actual differences may have been limited, even when point estimates appeared very different. This occurred for two reasons. For some clinical care outcomes, our survey had large design effects ##REF##16134562##[5]##; for example, the design effect in the Michigan sample for influenza vaccination was 29.6. In other cases, for example for MAC prophylaxis in Michigan, there were relatively small numbers of patients for whom the clinical service was indicated; this also led to broad confidence intervals and nonsignificant ?² tests.</p>", "<p>HRSA has long-standing quality of care standards, provides technical assistance to grantees for evaluating quality of care, and has supported independent evaluations of quality of care in RWP-supported facilities ##UREF##1##[2]##. Our analysis complements those previous efforts in several ways. For example, data from the HIV Costs and Services Utilization Study (HCSUS), a nationally representative sample of patients in care for HIV infection ##REF##10591268##[10]##, was evaluated to describe how patients receiving care at RWP-supported facilities were different from those receiving care in other facilities and evaluated differences in the types of services provided at clinics ##REF##15385240##[11]##. They found that patients in RWP-supported facilities in 1996–1997 were more likely to be younger, less educated, poorer, female, non-white, and uninsured. We found similar results with respect to sex, age, and race in some or all of our sites for care received in 1998. The HCSUS analysis reported that RWP-supported clinics provided more types of support services than other clinics##REF##15385240##[11]##, but did not report individual level care outcomes, as we do in our analysis. Other evaluations have also addressed programmatic issues of service provision at the facility level, but not at the client level ##REF##16548717##[12]##.</p>", "<p>HRSA has recently taken steps towards development of a client-level reporting system to capture information on care at the client level, including supporting pilot activities ##UREF##7##[13]##. However, comparable data from a representative sample of non-RWP-supported facilities will remain an important point of comparison when client-level data are reported directly to HRSA in the future.</p>", "<p>Other reports have evaluated quality of care within RWP-supported facilities, but did not have a comparative group of non-RWP facilities in the same analysis. For example, Wilson et al conducted an in-depth analysis of quality of care within 68 RWP-supported facilities, and reported on similar care outcomes as do we, but further stratified their analyses by provider type. A separate analysis on the same data suggested that, because clinical care outcome measures were not highly correlated within facilities, multiple care outcomes should be evaluated ##REF##17967827##[14]##. We evaluated five of the outcome measures that were identified in that previous work (HAART prescription, PCP prophylaxis, tuberculosis screening, cervical cancer screening, and influenza vaccination) as well as MAC prophylaxis and measures of frequency of CD4 count and HIV viral load tests.</p>", "<p>We report data from a public health surveillance project, not from a health services research study. It is important to recognize that the view of care offered by a clinical outcomes surveillance system is necessarily and appropriately different from the view that may be offered by health services research studies. A research study to describe the drivers of compliance with care guidelines may consider factors such as patient load, number of infectious disease specialists, sex of provider, and provider training ##REF##16287794##[15]##–##REF##16765286##[17]##. Clinical outcomes surveillance data, on the other hand, seek to describe differences when they occur–a high level view of quality and opportunities for improvement in care, regardless of their underlying drivers. To the extent that differences are suggested by surveillance data, more in-depth evaluations and research studies may be needed to suggest steps for quality improvement. The Institute of Medicine report explicitly recognized that, despite the fact that measures of quality derived from population-based systems reflect “the cumulative effects of many influences”, population-based measures are “essential in monitoring HIV care … and identifying areas for improvement” ##UREF##2##[3]##. These data were designed to be interpreted for this purpose within the local sites; therefore we did not combine data across sites in this analysis.</p>", "<p>Although we set out to determine whether differences in quality of care existed for patients in RWP-supported facilities, it is equally important to recognize that there were important opportunities for improving quality of care across the practice settings that we included in our analysis. For example, the proportions of patients receiving indicated TB screening, influenza vaccination, PCP prophylaxis, and Pap screening were all relatively low. Separate analyses of patient-level factors associated with lack of receipt of TB screening ##UREF##8##[18]##, influenza vaccination ##REF##17597447##[19]## and PCP prophylaxis ##REF##17304464##[20]## have been recently published from other US clinical outcomes surveillance data. As well, it has been recently suggested that gaps in recommended HIV care in the United States may sometimes result from prioritization of limited resources by health care providers ##REF##17967827##[14]##. Again, more specific research is needed to determine whether observed gaps in compliance with treatment guidelines should be addressed with more training, better systems to track delivery of needed care and identify needs at the client level, more resources for care provision, or a combination of these interventions. Additionally, new models for care delivery should be evaluated for their ability to improve delivery of indicated clinical preventive services ##REF##18482970##[21]##.</p>", "<p>The experiences of this pilot project have been used to inform the development of a nationwide surveillance system of HIV clinical outcomes and health care, called the Medical Monitoring Project (MMP) ##REF##17579722##[4]##. MMP is a national probability sample of patients in care for HIV infection, constructed by multi-stage probability sampling including a probability proportional to size (PPS) sample of states, a PPS sample of facilities within selected states, and an equal probability sample of patients within selected facilities ##UREF##9##[22]##. The sampling strategy in MMP is based on the methods used in SHDC, but aims to improve these by using equal probability sampling methods (EPSEM). Based on the findings from this analysis that the numbers of persons in care at RWP-supported facilities are considerable, we have also decided that stratification of the sampling frame of facilities on the basis of receipt of RWP funding is not necessary to obtain a sufficient number of patients receiving care in RWP-supported facilities in our sample. Therefore, stratification of the facility sampling frame by RWP support status will not be included in future sampling designs.</p>", "<p>As part of future chart abstractions, in addition to documenting whether selected facilities were RWP-supported, abstractors will attempt to document RWP-supported care or services, even when they are not received in a RWP-supported facility (e.g., ADAP funding), in order to better document the impact of the provision of RWP funds to support HIV care and services (information on ADAP support of an individual patient's care would only be informative for receipt of medications, such as PCP or MAC prophylaxis, or HAART). In addition, MMP will ascertain all HIV-related care received by abstracting medical records at all facilities at which HIV-related care was received for each enrolled patient. Data from the first nationally-representative sample from MMP will be publicly available in 2009.</p>", "<p>Data from clinical outcomes surveillance projects are primarily used for resource planning, allocation, and prioritization in state and local health departments ##REF##10897179##[23]##. This analysis demonstrates that data already collected as part of ongoing surveillance efforts may also be useful for evaluation of quality of care on a population level. As HRSA moves to develop client-level data systems to better document the provision of care and services supported by RWP funds ##UREF##7##[13]##, CDC will continue to ensure that population-based clinical outcomes surveillance data collected by state and local health departments are measured in ways that are consistent with publicly available standards for clinical performance outcomes developed by HRSA. By so doing, CDC will collect comparable data from non-RWP supported facilities that can provide a context in which to interpret the quality of care evaluations conducted by HRSA and its grantees.</p>" ]

[]

[ "<p>Conceived and designed the experiments: PSS MD EM SB SB ADM. Performed the experiments: MD EM SB SB ADM. Analyzed the data: PSS. Wrote the paper: PSS MD EM SB SB ADM.</p>", "<title>Background</title>", "<p>The Ryan White HIV/AIDS Care Act (now the Treatment Modernization Act; Ryan White Program, or RWP) is a source of federal public funding for HIV care in the United States. The Health Services and Resources Administration requires that facilities or providers who receive RWP funds ensure that HIV health services are accessible and delivered according to established HIV-related treatment guidelines. We used data from population-based samples of persons in care for HIV infection in three states to compare the quality of HIV care in facilities supported by the RWP, with facilities not supported by the RWP.</p>", "<title>Methodology/Principal Findings</title>", "<p>Within each area (King County in Washington State; southern Louisiana; and Michigan), a probability sample of patients receiving care for HIV infection in 1998 was drawn. Based on medical records abstraction, information was collected on prescription of antiretroviral therapy according to treatment recommendations, prescription of prophylactic therapy, and provision of recommended vaccinations and screening tests. We calculated population-level estimates of the extent to which HIV care was provided according to then-current treatment guidelines in RWP-supported and non-RWP-supported facilities. For all treatment outcomes analyzed, the compliance with care guidelines was at least as good for patients who received care at RWP-supported (vs non-RWP supported) facilities. For some outcomes in some states, delivery of recommended care was significantly more common for patients receiving care in RWP-supported facilities: for example, in Louisiana, patients receiving care in RWP-supported facilities were more likely to receive indicated prophylaxis for <italic>Pneumocystis jirovecii</italic> pneumonia and <italic>Mycobacterium avium</italic> complex, and in all three states, women receiving care in RWP-supported facilities were more likely to have received an annual Pap smear.</p>", "<title>Conclusions/Significance</title>", "<p>The quality of HIV care provided in 1998 to patients in RWP-supported facilities was of equivalent or better quality than in non-RWP supported facilities; however, there were significant opportunities for improvement in all facility types. Data from population-based clinical outcomes surveillance data can be used as part of a broader strategy to evaluate the quality of publicly-supported HIV care.</p>" ]

[ "<title>Supporting Information</title>" ]

[ "<p>Acknowledgements are due to the following scientists who comprise the SHDC Pilot Study Group whose input contributed to this study: Hien L. Guyer, and Linda L. Wotring, of HIV/AIDS Surveillance, Michigan Department of Community Health, Detroit and Lansing, MI; and Debra L. Hanson, John T. Brooks, David Swerdlow, and Mitchell I. Wolfe, of the Division of HIV/AIDS Prevention-Surveillance and Epidemiology, National Center for HIV, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta GA. We also thank Faye Malitz for her invaluable assistance with determining RWP support status of sampled facilities.</p>" ]

[]

[ "<table-wrap id=\"pone-0003250-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003250.t001</object-id><label>Table 1</label><caption><title>Estimated characteristics of persons in care for HIV infection by Ryan White Program support status, King County, Washington, southern Louisiana and Michigan, 1998.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"2\" align=\"left\" rowspan=\"1\">King County (n = 288)</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">Southern Louisiana (n = 169)</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">Michigan (n = 374)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">RWP supported (n = 131)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">non RWP supported (n = 157)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">RWP supported (n = 43)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">non RWP supported (n = 126)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">RWP supported (n = 76)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">non RWP supported (n = 298)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Characteristic</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated % (SEM)</td></tr></thead><tbody><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">\n<bold>Sex</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">82.1 (2.3)<xref ref-type=\"table-fn\" rid=\"nt102\">*</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">92.1 (2.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">68.1 (3.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">75.3 (4.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">67.1 (8.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">72.7 (3.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17.9 (2.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.9 (2.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31.9 (3.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">24.7 (4.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">32.9 (8.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">27.3 (3.3)</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">\n<bold>Age</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">13–24</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.9 (0.9)<xref ref-type=\"table-fn\" rid=\"nt103\">†</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.7 (1.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.5 (5.1)<xref ref-type=\"table-fn\" rid=\"nt102\">*</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9.8 (3.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.1 (3.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.1 (1.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">25–44</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">87.8 (3.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">63.9 (3.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">85.2 (3.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">59.0 (6.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">64.0 (0.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">71.0 (3.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">45+</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10.3 (3.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33.4 (4.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.3 (1.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31.2 (5.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">30.9 (2.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25.9 (2.9)</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">\n<bold>Race</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">White, non-Hispanic</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">66.4 (4.5)<xref ref-type=\"table-fn\" rid=\"nt104\">‡</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">82.4 (2.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">30.9 (14.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">43.6 (4.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">28.5 (8.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">35.5 (6.6)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Black, non-Hispanic</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">18.1 (3.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10.5 (1.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">65.3 (13.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">48.9 (5.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">63.1 (10.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">59.9 (6.1)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">All other/unknown</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15.5 (3.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.1 (1.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.8 (1.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.5 (3.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.4 (2.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.6 (1.5)</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">\n<bold>Risk category</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">MSM</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">42.0 (5.6) <xref ref-type=\"table-fn\" rid=\"nt103\">†</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">58.3 (4.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">32.1 (15.3)<xref ref-type=\"table-fn\" rid=\"nt103\">†</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33.3 (5.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29.1 (9.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">24.5 (5.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">MSM/IDU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13.8 (4.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.0 (2.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.2 (0.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.1 (2.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.0 (5.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.9 (1.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IDU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9.8 (2.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.4 (3.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">43.9 (16.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.4 (2.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14.6 (4.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15.4 (2.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">HRH</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13.7 (2.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.1 (2.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10.6 (1.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13.0 (3.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9.5 (6.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.8 (1.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Other/unknown</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20.7 (4.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25.2 (2.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.2 (0.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40.2 (5.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40.8 (7.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">49.4 (5.2)</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">\n<bold>Clinical/immunological</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CD4 0-99 or AIDS-defining opportunistic illness</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">35.7 (5.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">41.1 (8.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19.7 (1.6)<xref ref-type=\"table-fn\" rid=\"nt103\">†</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">41.5 (4.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">45.5 (5.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">41.0 (2.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CD4 100-349</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29.3 (5.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22.8 (5.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">43.9 (2.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25.3 (4.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">18.9 (5.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10.6 (2.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CD4≥350 or unknown</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">35.0 (5.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">36.1 (4.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">36.4 (4.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33.2 (4.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">35.6 (9.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">48.4 (3.1)</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pone-0003250-t002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003250.t002</object-id><label>Table 2</label><caption><title>Estimated proportions of persons in care for HIV infection treated according to guidelines, by Ryan White Program funding status King County Washington, southern Louisiana and Michigan, 1998.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"6\" align=\"left\" rowspan=\"1\">Patients receiving treatment</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"2\" align=\"left\" rowspan=\"1\">King County n = 288</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">Southern Louisiana n = 169</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">Michigan n = 374</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">RWP-supported n = 131</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">not RWP-supported n = 157</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">RWP-supported n = 43</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">not RWP-supported n = 126</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">RWP-supported n = 76</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">not RWP-supported n = 298</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Care provided</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Treated/eligible<xref ref-type=\"table-fn\" rid=\"nt106\">*</xref> Estimated % (95% CI)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Treated/eligible Estimated % (95% CI)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Treated/eligible Estimated % (95% CI)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Treated/eligible Estimated % (95% CI)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Treated/eligible Estimated % (95% CI)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Treated/eligible Estimated % (95% CI)</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">HAART<xref ref-type=\"table-fn\" rid=\"nt107\">†</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">76/111 66% (55, 77)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">90/131 69% (58, 80)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22/34 61% (56, 66)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">70/98 71% (57, 82)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">36/55 74% (47, 100)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">125/194 65% (55, 75)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PCP prophylaxis<xref ref-type=\"table-fn\" rid=\"nt108\">‡</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">56/66 85% (74, 96)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">66/83 76% (52, 100)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13/15 86% (66, 95)<xref ref-type=\"table-fn\" rid=\"nt109\">§</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">44/67 60% (47, 73)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">28/32 87% (85, 89)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">116/142 84% (76, 93)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">MAC prophylaxis<xref ref-type=\"table-fn\" rid=\"nt110\">∥</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14/21 71% (47, 95)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14/29 60% (40, 81)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4/5 87% (41, 98)<xref ref-type=\"table-fn\" rid=\"nt111\">‡‡</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12/28 43% (22, 66)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9/13 81% (73, 89)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">27/64 45% (14, 76)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TB test<xref ref-type=\"table-fn\" rid=\"nt112\">**</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">98/130 79% (70, 88)<xref ref-type=\"table-fn\" rid=\"nt113\">¶</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">66/157 44% (28, 61)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">39/43 88% (24, 99)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">51/126 42% (32, 52)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">26/76 39% (0, 87)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">103/297 30% (14, 47)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Influenza vaccine</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">36/131 31% (21, 41)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">46/157 25% (15, 35)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13/43 28% (26, 29)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50/126 36% (24, 50)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">21/76 38% (0, 86)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29/298 7% (2, 12)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Pap smear<xref ref-type=\"table-fn\" rid=\"nt114\">††</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">37/62 61% (49, 72)<xref ref-type=\"table-fn\" rid=\"nt109\">§</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">21/47 40% (24, 55)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12/15 81% (42, 96)<xref ref-type=\"table-fn\" rid=\"nt111\">‡‡</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12/34 41% (24, 60)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13/22 62% (32, 92)<xref ref-type=\"table-fn\" rid=\"nt111\">‡‡</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20/84 19% (5, 32)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Any Viral load assay</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">119/131 91% (85, 98)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">150/157 96% (93, 100)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">39/43 88% (24, 99)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">96/126 71% (60, 80)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">46/76 73% (37, 100)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">155/298 51% (28, 74)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Any CD4 measurement</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">120/131 92% (86, 98)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">151/157 97% (93, 100)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">39/43 88% (24, 99)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">102/126 80% (72, 87)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">48/76 77% (46, 100)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">190/298 61% (49, 72)</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pone-0003250-t003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003250.t003</object-id><label>Table 3</label><caption><title>Estimated health care utilization of persons in care for HIV infection, by Ryan White Program funding status, King County Washington, southern Louisiana and Michigan, 1998.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"6\" align=\"left\" rowspan=\"1\">Patients receiving treatment</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"2\" align=\"left\" rowspan=\"1\">King County n = 288</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">Southern Louisiana n = 169</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">Michigan n = 374</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">RWP-supported n = 131</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">not RWP-supported n = 157</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">RWP-supported n = 43</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">not RWP-supported n = 126</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">RWP-supported n = 76</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">not RWP-supported n = 298</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Utilization measure</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated Median # (95% CI) or % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated Median # (95% CI) or % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated Median # (95% CI) or % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated Median # (95% CI) or % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated Median # (95% CI) or % (SEM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated Median # (95% CI) or % (SEM)</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Viral loads<xref ref-type=\"table-fn\" rid=\"nt116\">*</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 (0.8, 1.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.3 (1.0, 1.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1 (0.5, 1.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1 (0.6, 1.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.5 (0.0, 1.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1 (0.0, 1.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CD4 counts<xref ref-type=\"table-fn\" rid=\"nt116\">*</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 (0.8, 1.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.2 (1.0, 1.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1 (0.5, 1.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1 (0.9, 1.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.6 (0.0, 1.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.6 (0.0, 1.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Outpatient visits<xref ref-type=\"table-fn\" rid=\"nt117\">†</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.1 (4.1, 5.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.4 (5.7, 8.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.3 (3.3, 6.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.3 (3.6, 7.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.4 (2.9, 12.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.2 (0.0, 4.5)</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Hospital visits<xref ref-type=\"table-fn\" rid=\"nt117\">†</xref>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">None</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">85% (3.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">85% (9.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">91% (4.7)<xref ref-type=\"table-fn\" rid=\"nt118\">‡</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">65% (6.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17% (11.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10% (4.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">One</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9% (2.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10% (6.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7% (3.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">24% (5.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">53% (11.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">61% (7.1)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Two or more</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6% (2.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5% (3.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2% (1.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11% (4.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">30% (10.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29% (5.0)</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Hospital days<xref ref-type=\"table-fn\" rid=\"nt119\">§</xref>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">1–2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12% (7.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">45% (13.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NC<xref ref-type=\"table-fn\" rid=\"nt120\">∥</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NC<xref ref-type=\"table-fn\" rid=\"nt120\">∥</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14% (5.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">16% (3.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">3–7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">46% (13.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">27% (16.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NC<xref ref-type=\"table-fn\" rid=\"nt120\">∥</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NC<xref ref-type=\"table-fn\" rid=\"nt120\">∥</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40% (10.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33% (4.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">8 or more</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">42% (12.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">28% (6.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NC<xref ref-type=\"table-fn\" rid=\"nt120\">∥</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NC<xref ref-type=\"table-fn\" rid=\"nt120\">∥</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">46% (13.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">51% (3.0)</td></tr></tbody></table></alternatives></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"pone.0003250.s001\"><label>Appendix S1</label><caption><p>(0.03 MB DOC)</p></caption></supplementary-material>" ]

[ "<table-wrap-foot><fn id=\"nt101\"><p>RWP = Ryan White Program; SEM = standard error of the mean; MSM = men who have sex with men; IDU = injecting drug user; HRH = high risk heterosexual; CD4 = CD4+ T-lymphocyte count, in cells/μL. Indicated p-values are for the <underline>overall</underline> test for difference and do not necessarily identify the specific levels of the variable that differ between RWP and non-RWP funded providers.</p></fn><fn id=\"nt102\"><label>*</label><p>p≤0.01 for ?² test comparing patients at RWP and non-RWP funded providers within study site.</p></fn><fn id=\"nt103\"><label>†</label><p>p≤0.001 for ?² test comparing patients at RWP and non-RWP funded providers within study site.</p></fn><fn id=\"nt104\"><label>‡</label><p>p≤0.05 for ?² test comparing patients at RWP and non-RWP funded providers within study site.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt105\"><p>RWP: Ryan White Program; HAART: Highly active antiretroviral therapy; PCP: <italic>Pneumocystis jirovecii</italic> pneumonia; MAC: <italic>Mycobacterium avium</italic> complex; TB: tuberculosis; CD4: CD4+ T-lymphocyte count in cells/μL; CI: confidence interval.</p></fn><fn id=\"nt106\"><label>*</label><p>Treated/eligible = number of persons receiving care/number of persons eligible for care</p></fn><fn id=\"nt107\"><label>†</label><p>Eligibility for HAART was defined as CD4&lt;500, PCR&gt;20000, bDNA&gt;10000 or AIDS-defining opportunistic illness diagnosis (any time before or during 1998)</p></fn><fn id=\"nt108\"><label>‡</label><p>Eligibility for PCP prophylaxis was defined as CD4&lt;200 or PCP diagnosis (any time before or during 1998)</p></fn><fn id=\"nt109\"><label>§</label><p>p≤.001 for ?² comparing percentages of patients at RWP and non-RWP funded providers within study site</p></fn><fn id=\"nt110\"><label>∥</label><p>Eligibility for MAC prophylaxis was defined as CD4&lt;50 cells/μL or <italic>M. avium</italic> complex diagnosis (any tine before or during 1998)</p></fn><fn id=\"nt111\"><label>‡‡</label><p>p≤.05 for ?² comparing percentages of patients at RWP and non-RWP funded providers within study site</p></fn><fn id=\"nt112\"><label>**</label><p>Eligibility for TB test was defined as follows: no tuberculosis diagnosis or sputum culture positive to <italic>M. tuberculosis</italic> prior to 1998.</p></fn><fn id=\"nt113\"><label>¶</label><p>p≤.01for ?² comparing percentages of patients at RWP and non-RWP funded providers within study site</p></fn><fn id=\"nt114\"><label>††</label><p>The only exclusions from this analysis were men, and women with a diagnosis of invasive cervical cancer and age 13–17 with non-sexual transmission mode.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt115\"><p>RWP: Ryan White Program; SEM: standard error of the mean; CD4: CD4+ T-lymphocyte.</p></fn><fn id=\"nt116\"><label>*</label><p>Number of tests per 6 months</p></fn><fn id=\"nt117\"><label>†</label><p>Number of visits in study year</p></fn><fn id=\"nt118\"><label>‡</label><p>p≤.01 for ?² comparing percentages of patients at RWP and non-RWP funded providers within study site. The p-value is for the <underline>overall</underline> test for difference and does not necessarily identify the specific levels of the variable that differ between RWP and non-RWP funded providers.</p></fn><fn id=\"nt119\"><label>§</label><p>Number of days <underline>for those who were hospitalized</underline> during the study year. The number of patients in each calculation is: King County, RWP = 23, non-RWP = 16; MI, RWP = 37, non-RWP = 181.</p></fn><fn id=\"nt120\"><label>∥</label><p>NC: Not calculated. For Louisiana, RWP and non-RWP funded providers are not compared on the number of hospital days due to the very small number of patients (n = 3) at RWP supported providers who had been hospitalized during the study year.</p></fn></table-wrap-foot>", "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>This work and the effort of its authors was supported by a cooperative agreement from the Centers for Disease Control and Prevention (CDC). CDC authors conducted the analyses reported in this manuscript.</p></fn></fn-group>" ]

[ "<graphic id=\"pone-0003250-t001-1\" xlink:href=\"pone.0003250.t001\"/>", "<graphic id=\"pone-0003250-t002-2\" xlink:href=\"pone.0003250.t002\"/>", "<graphic id=\"pone-0003250-t003-3\" xlink:href=\"pone.0003250.t003\"/>" ]

[ "<media xlink:href=\"pone.0003250.s001.doc\"><caption><p>Click here for additional data file.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p>Understanding the extent to which sex-specific processes shape human genetic diversity has long been a matter of great interest for human population geneticists ##REF##9889112##[1]##,##REF##17067791##[2]##. To date, as detailed in ##TAB##0##Table 1##, the focus has mainly been on the analysis of uniparentally inherited markers: mitochondrial DNA (mtDNA) and the non-recombining portion of the Y chromosome (NRY). A large number of studies have found that the level of differentiation was greater for the Y chromosome than for mtDNA, both at a global ##REF##9806547##[3]## and a local scale ##REF##8751879##[4]##–##REF##17208185##[11]##, for a review see ##REF##16479583##[12]##. This result has mainly been explained by patrilocality, a widespread tendency for men to stay in their birthplace while women move to their husband's house ##UREF##0##[13]## (see ##TAB##0##Table 1## for more detailed interpretations). This hypothesis of a higher migration rate of women has been especially strengthened by the comparison of patrilocal and matrilocal populations at a local scale ##REF##11528385##[14]##–##REF##16916941##[17]##. These studies have shown that in patrilocal populations, genetic differentiation is stronger among men than among women, while the reverse is observed in matrilocal populations. It is also noteworthy that the absolute difference between male and female genetic structure is more pronounced in patrilocal than in matrilocal populations ##REF##15894624##[16]##. Interestingly, while social practices seem to consistently influence the sex-specific demography at a local scale, the robustness of a sex-specific genetic structure at a global scale is still a challenging issue (see ##TAB##0##Table 1##). A recent analysis of mtDNA and NRY variation at a global scale, which used the same panel of populations for both categories of markers (an omission that was criticized in Seielstad et al.'s ##REF##9806547##[3]## study ##REF##9806532##[18]##) showed no difference between the male and female genetic structure ##REF##15378061##[19]##. Consistent with this result, an analysis of the autosomal and X-linked microsatellite markers in the HGDP-CEPH Human Genome Diversity Cell Line Panel showed no major differences between the demographic history of men and women ##REF##15601537##[20]##. The apparent paradox between local and global trends can be resolved though, since the geographical clustering of populations with potentially different lifestyles may minimize the differences in sex-specific demography at a global scale ##REF##16617372##[21]##,##REF##11420360##[22]##. It may also be that the global structure reflects more ancient, pre-agricultural, social patterns, as patrilocality may only have increased in human societies only with the recent transition to agriculture ##REF##16479583##[12]##.</p>", "<p>The higher differentiation level found on the NRY as compared to mtDNA at a local scale could also be the consequence of a higher effective number of women, for example through the practice of polygyny, a tendency for men (but not for women) to have multiple mates ##REF##8751879##[4]##, ##REF##12532283##[7]##, ##REF##15190128##[15]##, ##REF##12962309##[23]##–##UREF##1##[25]##, and/or through the paternal transmission of reproductive success ##REF##17208185##[11]##. However, the influence of such processes on genetic structure has often been considered as negligible, since realistic rates of polygyny cannot create large differences in male and female genetic structure ##REF##9806547##[3]##,##REF##10364534##[5]##,##REF##11528385##[14]##. Hence, until now, the effect of local social processes on male and female effective numbers has not been investigated directly, possibly because current methods fail to unravel the relative contribution of effective number and migration rate on the differentiation level ##REF##11023812##[26]##. The consequence is that the vast majority of studies fail to show whether the observed differentiation arises from sex-specific differences in migration rate, effective numbers, or both (see ##TAB##0##Table 1##). New methods need therefore to be developed in order to appreciate the relative influence of sex-biased dispersal and differences in effective numbers on genetic structure.</p>", "<p>Another limitation to the use of uniparentally inherited markers stems from the fact that each of them is, in effect, a single genetic locus. For that reason, we cannot test for the robustness of the sex-specific genetic structure on these markers. We cannot either rule out the possibility that mtDNA and NRY, which contain multiple linked genes, may be shaped by selection ##REF##17912352##[27]##,##REF##16645093##[28]##. This raises the question of whether results based on uniparentally inherited markers simply reflect stochastic variation, or real differences in sex-specific demography. To answer this question, we propose a novel approach based on the joint analysis of autosomal and X-linked markers. This multi-locus analysis has the potential of providing more robust information, as these markers give an independent picture of sex-specific demography. This approach also aims to disentangle the effects of sex-biased dispersal and effective numbers on genetic structure.</p>", "<p>In order to recognize the impact of social organization on these differences, we investigate sex-specific genetic structure in human populations of Central Asia (##FIG##0##Figure 1##), where various ethnic groups, characterized by different languages, lifestyles and social organizations, co-exist. Although all groups share a patrilocal organization, Tajiks (sedentary agriculturalists) are bilineal, i.e. they are organized into nuclear or extended families where blood links and rights of inheritance through both male and female ancestors are of equal importance, and they preferentially establish endogamous marriages with cousins. By contrast, Kazaks, Karakalpaks, Kyrgyz and Turkmen (traditionally nomadic herders) are patrilineal, i.e. they are organized into paternal descent groups (tribes, clans, lineages), and they practice exogamous marriages, in which a man chooses a bride from a different clan.</p>" ]

[ "<title>Methods</title>", "<title>DNA Samples</title>", "<p>We sampled 10 populations of bilineal agriculturalists and 11 populations of patrilineal herders from West Uzbekistan to East Kyrgyzstan, representing 780 healthy adult men from 5 ethnic groups (Tajiks, Kyrgyz, Karakalpaks, Kazaks, and Turkmen) (see ##TAB##1##Table 2##). The geographic distribution of the samples and information about lifestyle is provided in ##FIG##0##Figure 1##. Also living in Central Asia, Uzbeks are traditionally patrilineal herders too, but they have recently lost their traditional social organization ##REF##17208185##[11]##, and we therefore chose not to include any sample from this ethnic group for the purpose of this study. We collected ethnologic data prior to sampling, including the recent genealogy of the participants. Using this information, we retained only those individuals that were unrelated for at least two generations back in time. All individuals gave their informed consent for participation in this study. Total genomic DNA was isolated from blood samples by a standard phenol-chloroform extraction ##UREF##7##[50]##.</p>", "<title>Uniparentally Inherited Markers</title>", "<p>The mtDNA first hypervariable segment of the mtDNA control region (HVS-I) was amplified using primers L15987 (<named-content content-type=\"gene\">5′TCAAATGGGCCTGTCCTTGTA</named-content>) and H580 (<named-content content-type=\"gene\">5′TTGAGGAGGTAAGCTACATA</named-content>) in 18 populations out of 21 (674 individuals, see ##TAB##1##Table 2##). The amplification products were subsequently purified with the EXOSAP standard procedure. The sequence reaction was performed using primers L15925 (<named-content content-type=\"gene\">5′TAATACACCAGTCTTGTAAAC</named-content>) and HH23 (<named-content content-type=\"gene\">5′AATAGGGTGATAGACCTGTG</named-content>). Sequences from positions 16 024–16 391 were obtained. Eleven Y-linked microsatellite markers (see ##TAB##2##Table 3##) were genotyped in the same individuals, following the protocol described by Parkin et al. ##REF##16289902##[51]##.</p>", "<title>Multi-Locus Markers</title>", "<p>27 autosomal and 9 X-linked microsatellite markers (see ##TAB##3##Table 4##) were genotyped in the same individuals. We used the informativeness for assignment index <italic>I</italic>\n_n\n##REF##14631557##[52]## to select subsets of microsatellite markers on the X chromosome and the autosomes from the set of markers used in Rosenberg et al.'s worldwide study ##REF##12493913##[43]##. This statistic measures the amount of information that multiallelic markers provide about individual ancestry ##REF##14631557##[52]##. This index was calculated among a subset of 14 populations, chosen from the Rosenberg et al.'s dataset ##REF##12493913##[43]## to be genetically the closest to the Central Asian populations (Balochi, Brahui, Burusho, Hazara, Pathan, Shindi, Uygur, Han, Mongola, Yakut, Adygei, Russian, Druze and Palestinian). The rationale was to infer the information provided by individual loci about ancestry from this subset of populations, and to extrapolate the results to the populations studied here. For the X chromosome data, we pooled the ‘Screening Set10’ and ‘Screening Set52’ from the HGDP-CEPH Human Genome Diversity Cell Line Panel ##REF##11954565##[53]## analyzed by Rosenberg et al. ##REF##12493913##[43]## which represented a total of 36 microsatellites. We chose 9 markers among the 11 with the highest <italic>I</italic>\n_n. For autosomal data, we used the ‘Screening Set10’, which represented a total of 377 microsatellites, and chose 27 markers among the 30 with the highest <italic>I</italic>\n_n. All markers were chosen at a minimum of 2 cM apart from each others ##REF##10961910##[54]##. PCR amplifications were performed in a 20 µl final volume composed of 1× Eppendorf buffer, 125 µM each dNTP, 0.5U Eppendorf Taq polymerase, 125 nM of each primer, and 10 ng DNA. The reactions were performed in a Eppendorf Mastercycler with an initial denaturation step at 94°C for 5 min; followed by 36 cycles at 94°C for 30 s, 55°C for 30 s, 72°C for 20 s, and 72°C for 10 min as final extension. Forward primers were fluorescently labeled and reactions were further analyzed by capillary electrophoresis (ABI 310, Applied Biosystems). We used the software package Genemarker (SoftGenetics LLC) to obtain allele sizes from the analysis of PCR products (allele calling).</p>", "<title>Statistical Analyses</title>", "<p>We calculated the total allelic richness (<italic>AR</italic>) (over all populations), the unbiased estimate of expected heterozygosity <italic>H</italic>\n_e\n##REF##17248844##[55]##, the total number of polymorphic sites and <italic>F</italic>\n_ST for mtDNA using Arlequin version 3.1. ##UREF##8##[56]##. Genetic differentiation among populations for the autosomes, the X and the Y chromosome was measured both per locus and overall loci using Weir and Cockerham's <italic>F</italic>\n_ST estimator ##UREF##9##[57]##, as calculated in G<sc>enepop</sc> 4.0. ##UREF##10##[58]##. The 95% confidence intervals were obtained by bootstrapping over loci ##UREF##10##[58]##, using the approximate bootstrap confidence intervals (ABC) method described by DiCiccio and Efron ##UREF##11##[59]##. Isolation by distance (i.e. the correlation between the genetic and the geographic distances) was analyzed by computing the regression of pairwise <italic>F</italic>\n_ST/(1−<italic>F</italic>\n_ST) estimates between pairs of populations to the natural logarithm of their geographical distances, and rank correlations were tested using the Mantel permutation procedure ##REF##6018555##[60]##, as implemented in G<sc>enepop</sc> 4.0. ##UREF##10##[58]##. All other statistical tests were performed using the software package R v. 2.2.1 ##UREF##12##[61]##.</p>", "<title>Sex-Biased Dispersal in the Island Model</title>", "<p>Let us consider an infinite island model of population structure ##REF##17246615##[62]##, with two classes of individuals (males and females), which describes a infinite set of populations with constant and equal sizes that are connected by gene flow. Then the expected values of <italic>F</italic>\n_ST for uniparentally inherited markers depend on the effective number <italic>N</italic>\n_m (resp. <italic>N</italic>\n_f) of adult males (resp. females) per population and the migration rate <italic>m</italic>\n_m (resp. <italic>m</italic>\n_f) of males (resp. females) per generation, as: (see, e.g., ##REF##17976181##[63]##). We can therefore calculate the female-to-male ratio of the effective number of migrants per generation as: .</p>", "<p>In this model, we can also compute for the autosomes and the X chromosome the reproductive values for each class (sex), which are interpreted here as the probability that an ancestral gene lineage was in a given class in a distant past ##UREF##13##[64]##. From these, we can obtain the well-known expressions of effective size <italic>N</italic>\n_e for autosomal and X-linked genes: , respectively ##UREF##5##[45]##. Note that <italic>N</italic>\n_e is expressed here as a number of gene copies (i.e., twice the effective number of diploid individuals for autosomes). Likewise, the effective migration rate, i.e. the average dispersal rate of an ancestral gene lineage, is given by for autosomal genes, and for X-linked genes, respectively. Substituting these expressions into the well-known equation: <italic>F</italic>\n_ST≈1/(1+2<italic>N</italic>\n_e\n<italic>m</italic>\n_e) ##UREF##13##[64]##, we get:for autosomal genes, andfor X-linked genes.</p>", "<title>Evaluation of the Approach through Stochastic Simulations</title>", "<p>We performed coalescent simulations, using an algorithm in which coalescence and migration events are considered generation-by-generation until the common ancestor of the whole sample has been reached (see ##REF##12654930##[65]##). We simulated a finite island model with 50 demes, each made of <italic>N</italic> = <italic>N</italic>\n_f+<italic>N</italic>\n_m = 500 diploid individuals, with a migration parameter <italic>m</italic> = <italic>m</italic>\n_f+<italic>m</italic>\n_m = 0.2. Using these total values for <italic>N</italic> and <italic>m</italic>, we then varied the sex-specific parameters to cover the (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) parameter space evenly. Note that the parameter <italic>m</italic> is the total migration rate, which corresponds to twice the effective migration rate for autosomal markers. Hence, for each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, the total number of individuals is 500 (although the number of females may vary from 1 to 499) and the effective migration rate for autosomal markers is . We chose these total values for <italic>N</italic> and <italic>m</italic> such that, for a ratio <italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m = 21.6 (as observed for the herder populations), the distribution of <italic>F</italic>\n_ST estimates on uniparentally-inherited markers in the simulations were close to the observations: for mtDNA, the 95% highest posterior density interval (see ##UREF##14##[66]##, pp. 38–39) for the distribution of <italic>F</italic>\n_ST estimates in the simulations was [0.007; 0.033] with a mode at 0.014 (estimated value from the real dataset: among the herders) while for the NRY, the 95% highest posterior density interval was [0.088; 0.374] with a mode at 0.187 (estimated value from the real dataset: ).</p>", "<p>Each simulated sample consisted in 330 sampled males from 11 populations (30 males per population), genotyped at 27 autosomal, 9 X-linked markers as well as 10 Y-linked markers and a single mtDNA locus. Each locus was assumed to follow a Generalized Stepwise Model (GSM) ##REF##12207711##[67]## with a possible range of 40 contiguous allelic states, except the mtDNA, which was assumed to follow an infinite allele model of mutation. The average mutation rate was 5.10⁻³, and the mean parameter of the geometric distribution of the mutation step lengths for microsatellites was set to 0.2 ##REF##12207711##[67]##,##REF##8600387##[68]##.</p>" ]

[ "<title>Results/Discussion</title>", "<title>Uniparentally-Inherited Markers</title>", "<p>We sampled 780 healthy adult men from 10 populations of bilineal agriculturalists and 11 populations of patrilineal herders from West Uzbekistan to East Kyrgyzstan, representing 5 ethnic groups (Tajiks, Kyrgyz, Karakalpaks, Kazaks, and Turkmen) (see ##FIG##0##Figure 1## and ##TAB##1##Table 2##). We genotyped all bilineal populations, and 8 out of 11 patrilineal populations at the HVS-I locus of mtDNA, and at 11 microsatellite markers on the NRY (for more details on the markers used, see ##TAB##2##Table 3##). The overall genetic differentiation was higher for NRY, as compared to mtDNA, both among the 10 bilineal agriculturalist populations , and among the subset of 8 patrilineal herder populations . Assuming an island model of population structure, this implies that female migration rate (<italic>m</italic>\n_f), and/or the effective number of females (<italic>N</italic>\n_f), is higher than of the corresponding parameters for males (<italic>m</italic>\n_m and <italic>N</italic>\n_m). These results also suggest that the differences in sex-specific genetic structure are much more pronounced in the patrilineal herders than in the bilineal agriculturalists. From the above <italic>F</italic>\n_ST estimates, we obtained the female-to-male ratio of the effective number of migrants per generation (see the <xref ref-type=\"sec\" rid=\"s3\">Methods</xref> section for details): <italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m≈2.1 for bilineal populations and <italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m≈21.6 for patrilineal populations. The ratio in patrilineal populations is thus one order of magnitude higher than in bilineal populations. However, since each of these markers is a single genetic locus, we cannot test for the robustness of the sex-specific genetic structure on these markers. We therefore examined the amount of information contained in multi-locus data on autosomal and X-linked markers, both of which average over male and female histories.</p>", "<title>A New Multi-Locus Approach</title>", "<p>In the infinite island model of population structure with two classes of individuals (males and females), we obtained the following expressions of <italic>F</italic>\n_ST (see the <xref ref-type=\"sec\" rid=\"s3\">Methods</xref> section for details):for autosomal genes, andfor X-linked genes. A special case of interest occurs when , i.e. when the differentiation of X-linked genes exactly equals that of autosomal genes. Combining eqs (1) and (2), we find that this occurs for , with <italic>N</italic> = <italic>N</italic>\n_f+<italic>N</italic>\n_m and <italic>m</italic> = <italic>m</italic>\n_f+<italic>m</italic>\n_m. Furthermore, as shown in ##FIG##1##Figure 2##, if we observe a lower genetic differentiation of autosomal markers, as compared to X-linked markers (blue zone in ##FIG##1##Figure 2##), this suggests that . This may happen, e.g., for <italic>N</italic>\n_f = <italic>N</italic>\n_m and <italic>m</italic>\n_f = <italic>m</italic>\n_m, i.e. for equal effective numbers of males and females and unbiased dispersal. But if autosomal markers are more differentiated than X-linked markers (, see the red upper-right triangle in ##FIG##1##Figure 2##), this implies that . In this case, since <italic>m</italic>\n_f/<italic>m</italic> and <italic>N</italic>\n_f/<italic>N</italic> are ratios varying between 0 and 1, the effective number of females must be higher than that of males (<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m), and the female migration rate must be higher than half the male migration rate (<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m/2). Hence, a prediction from this model is that when , the effective number of females is higher than that of males, whatever the pattern of sex-specific dispersal. This suggests that it is indeed possible to test for differences in effective numbers between males and females from the joint analysis of autosomal and X-linked data. We note however that when , we cannot conclude on the relative male and female effective numbers and migration rates.</p>", "<p>We tested the above prediction in the 10 bilineal agriculturalist populations and 11 patrilineal herder populations sampled in Central Asia by comparing the genetic structure estimated from 27 unlinked polymorphic autosomal microsatellite markers (<italic>AR</italic> = 16.2, <italic>H</italic>\n_e = 0.803 on average) to that from 9 unlinked polymorphic X-linked microsatellite markers (<italic>AR</italic> = 12.6, <italic>H</italic>\n_e = 0.752 on average) (for more details on the markers used, see ##TAB##3##Table 4##). Overall heterozygosity was not significantly different between X-linked and autosomal markers, neither in the pooled sample (two-tailed Wilcoxon sum rank test; <italic>p</italic> = 0.09), nor in the bilineal agriculturalists (<italic>p</italic> = 0.13) or the patrilineal herders (<italic>p</italic> = 0.12). The overall population structure was significantly higher for autosomal as compared to X-linked markers among patrilineal herders: (one-tailed Wilcoxon sum rank test; ; <italic>p</italic> = 0.02). Among bilineal agriculturalists, the result was not significant: (<italic>p</italic> = 0.36). From these results, and following our model predictions, we conclude that in patrilineal herders (where ), the effective number of females is higher than that of males. This conclusion does not hold for the bilineal agriculturalists.</p>", "<p>From our model, it is possible to get more precise indications on the sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values that are compatible with our data. Rearranging eqs (1–2), we get:i.e.:\n</p>", "<p>For any given set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, we can therefore calculate from eq. (4) the expected value of for each estimate in the dataset. We can then test the null hypothesis by comparing the distribution of observed and expected values. If the hypothesis can be rejected at the <italic>α</italic> = 0.05 level, then the corresponding set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values can also be rejected. Following Ramachandran et al. ##REF##15601537##[20]##, we varied the values of the ratios <italic>N</italic>\n_f/<italic>N</italic> and <italic>m</italic>\n_f/<italic>m</italic> (respectively, the female fraction of effective number, and the female fraction of the total migration rate) from 0 to 1, with an interval of 0.01 between consecutive values. For each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, we applied the transformation in eq. (4) to each of the 27 locus-specific values observed. Thus, for each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, we obtained 27 expected values of , given our data. These expected values of were then compared to the 9 observed locus-specific in our dataset, and we calculated the <italic>p</italic>-value for a two-sided Wilcoxon sum rank test between the list of 27 expected values and the 9 observed in the dataset. The results are depicted in ##FIG##2##Figure 3##. Significant <italic>p</italic>-values (<italic>p</italic>≤0.05) correspond to a significant difference between the observed and expected values, thus to sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values that are rejected, given our data (see the blue region in ##FIG##2##Figure 3##). Conversely, non-significant <italic>p</italic>-values (<italic>p</italic>&gt;0.05) correspond to sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values that cannot be rejected (see the red region in ##FIG##2##Figure 3##).</p>", "<p>For the patrilineal herder populations (##FIG##2##Figures 3A–3B##), most sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values are rejected, except those corresponding to larger effective numbers for females (from ##FIG##2##Figures 3A–3B##: <italic>N</italic>\n_f/<italic>N</italic>&gt;0.55, i.e. <italic>N</italic>\n_f&gt;1.27<italic>N</italic>\n_m) and <italic>m</italic>\n_f&gt;0.67<italic>m</italic>\n_m. Because the multi-locus estimate of is significantly higher than the estimate of , we expected to find such patterns of non-significant values (see ##FIG##1##Figure 2##). For the bilineal agriculturalist populations, we could not reject the hypothesis that the effective numbers and migration rates are equal across males and females or even lower in females (see ##FIG##2##Figures 3C–3D##). This is also reflected by the fact that the estimates of were not significantly higher than the estimates of in those populations.</p>", "<p>Finally, we have shown that the effective number of women is higher than that of men among patrilineal herders, but not necessarily among bilineal agriculturalists. Furthermore, a close inspection of the results depicted in ##FIG##2##Figures 3A and 3B## reveals that, among herders, we reject all the sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values for which <italic>m</italic>\n_f&lt;<italic>m</italic>\n_m at the <italic>α</italic> = 0.10 level. This is not true for agriculturalists. This suggests that the migration rates are also likely to be higher for women than for men in patrilineal populations, as compared to bilineal populations (compare ##FIG##2##Figures 3B and 3D##). Although both groups are patrilocal, such a difference in sex-specific migration patterns might be expected, since patrilineal herders are exogamous (among clans) and bilineal agriculturalists are preferentially endogamous. For example, it was observed that in patrilocal and matrilocal Indian populations, where migrations are strictly confined within endogamous groups, sex-specific patterns were not influenced by post-marital residence ##REF##16617372##[21]##.</p>", "<title>What Could Explain a Larger Effective Number of Females?</title>", "<p>While an influence of post-marital residence on the migration rate of women and men has already been widely proposed ##REF##11528385##[14]##–##REF##16916941##[17]## (see also ##TAB##0##Table 1##), the factors that may locally affect the effective number of women, relatively to that of men, are not well recognized. As seen in ##TAB##0##Table 1##, although a number of studies have compared matrilocal and patrilocal populations, few have compared contrasting groups of populations with respect to other factors as, e.g., the tendency for polygyny ##REF##15190128##[15]##. Furthermore, a number of these studies lack ethnological information a priori, concerning social organization, marriage rules, etc., which makes interpretation somewhat difficult (see ##TAB##0##Table 1##). Here, we compared two groups of patrilocal populations with contrasting social organizations, and at least five non-mutually exclusive interpretations for a larger effective number of females can be invoked:</p>", "<p>\n<italic>Social organization</italic>, i.e. the way children are affiliated to their parents, can deeply affect sex-specific genetic variation. In Central Asia, herder populations are organized in patrilineal descent groups (tribes, clans, lineages). This implies that children are systematically affiliated with the descent groups of the father. Chaix et al. ##REF##17208185##[11]## showed that the average number of individuals carrying the same Y chromosome haplotype was much higher in patrilineal herder populations than in bilineal agriculturalist populations (where children are affiliated both to the mother and the father). These “identity cores” would be the direct consequence of the internal dynamics of their patrilineal organization. Indeed, the descent groups are not formed randomly and related men tend to cluster together, e.g. through the recurrent lineal fission of one population into new groups. This particular dynamics increases relatedness among men, and may therefore reduce the effective number of men, as compared to women.</p>", "<p>Indirectly, the social organization can also deflate the effective number of men through <italic>the transmission of reproductive success</italic>\n##REF##15797619##[29]## if this success is culturally transmitted exclusively from fathers to sons. Because herders are patrilineal (so that inheritance is organized along paternal descent groups), social behaviors are more likely to be inherited through the paternal line of descent only. It has recently been argued that the rapid spread of Genghis Khan's patrilineal descendants throughout Central Asia was explained by this social selection phenomenon ##REF##12592608##[30]##. The correlation of fertility through the patriline has also been described in patrilineal tribes in South America ##REF##5473413##[31]##. By contrast, in bilineal societies such as the agriculturalists of Central Asia, social behaviors that influence reproductive success are more likely to be transmitted by both sexes. Furthermore, differences of cultural transmission of fitness between hunter-gatherers and agriculturalists have already been reported ##REF##16933997##[32]##. Interestingly, a slightly higher matrilineal intergenerational correlation in offspring number has been observed in the Icelandic population, which suggests that in some populations, reproductive behaviors can be maternally-inherited ##REF##12721957##[33]##.</p>", "<p>\n<italic>Polygyny</italic>, in which the husband may have multiple wives, has often been invoked as a factor that could reduce the effective number of men ##REF##8751879##[4]##, ##REF##12532283##[7]##, ##REF##15190128##[15]##, ##REF##12962309##[23]##–##UREF##1##[25]##. While we could not find any evidence of polygyny in present-day Central Asian populations, this custom was traditionally practiced in the nomadic herder Kazak populations, although limited to the top 10 percent of men from the highest social rank ##REF##10364534##[5]##,##UREF##2##[34]##. Hence, even though we lack ethnological data to determine to what extent herders are or were practicing polygyny in a recent past, the practice of polygyny among herders in Central Asia might have influenced (at least partially) the observed differences in men and women effective numbers.</p>", "<p>\n<italic>Recurrent bottlenecks in men</italic> due to a higher pre-reproductive mortality could also severely reduce the effective numbers of men. From the study of several groups in West Papua and Papua New Guinea ##REF##12532283##[7]##,##UREF##3##[35]##, it appears that warfare may indeed lead to the quasi-extinction of adult men in some communities, while the mass killing of adult women is far more rarely reported. However, this differential mortality could also be balanced by potentially high death rates of women during childbirth. In any case, a differential mortality is equally likely to arise in herder and agriculturalist populations. It may therefore not be relevant in explaining why we detect higher effective numbers of women (as compared to men) in patrilineal herders and not in bilineal agriculturalists.</p>", "<p>Since our approach implicitly assumes equal male and female generation time, the observed higher effective number of women, relatively to that of men, could result from a <italic>shorter generation time for women</italic>, due to the tendency of women to reproduce earlier in life than men and the ability of men to reproduce at a later age than women. This has indeed been described in a number of populations with different lifestyles, from complete genealogical records or mean-age-at-first-marriage databases ##REF##12721957##[33]##,##REF##10677323##[36]##,##REF##15795887##[37]##. It has even been proposed to be a nearly universal trait in humans, although its magnitude varies across regions and cultures ##REF##15795887##[37]##. Tang et al. ##REF##12019257##[38]## suggested that accounting for longer generation time in males could minimize the difference between maternal and paternal demography. However, the differences in sex-specific generation times that have been reported (e.g., 28 years for the matrilines and 31 years for the patrilines in Iceland ##REF##12721957##[33]##, 29 years for the matrilines and 35 years for the patrilines in Quebec ##REF##10677323##[36]##) are unlikely to explain the observed differences in male and female effective numbers ##REF##15317874##[24]##.</p>", "<title>Limits of the Approach</title>", "<p>There might also be non-biological explanations of our results, however, as they are based on the simplifying assumptions of Wright's infinite island model of population structure ##REF##10098262##[39]##. This model assumes (<italic>i</italic>) that there is no selection and that mutation is negligible, (<italic>ii</italic>) that each population has the same size, and sends and receives a constant fraction of its individuals to or from a common migrant pool each generation (so that geographical structure is absent), and (<italic>iii</italic>) that equilibrium is reached between migration, mutation and drift. On the first point, we did not find any evidence of selection, for any marker, based on Beaumont and Nichols' method ##UREF##4##[40]## for detecting selected markers from the analysis of the null distribution generated by a coalescent-based simulation model (data not shown). As for the second point, we tested for the significance of the correlation between the pairwise <italic>F</italic>\n_ST/(1−<italic>F</italic>\n_ST) estimates and the natural logarithm of their geographical distances ##REF##9093870##[41]##. We found no evidence for isolation by distance, either for X-linked markers (<italic>p</italic> = 0.47 for agriculturalists, <italic>p</italic> = 0.24 for herders), or for autosomal markers (<italic>p</italic> = 0.92 for agriculturalists, <italic>p</italic> = 0.45 for herders). As for the third point, the X-to-autosomes (X/A) effective size ratio can significantly deviate from the expected three-quarters (assuming equal effective numbers of men and women) following a bottleneck or an expansion ##REF##17971168##[42]##. This is because X-linked genes have a smaller effective size, and hence reach equilibrium more rapidly. After a reduction of population size, the X/A diversity ratio is lower than expected, while after an expansion, the diversity of X-linked genes recovers faster than on the autosomes, and the X/A diversity ratio is then closer to unity. In the latter case, would be reduced and could then tend towards . However, neither reduction nor expansion should lead to , as we found in herder populations of Central Asia. Therefore, we do not expect the limits of Wright's island model to undermine our approach.</p>", "<title>Evaluation by Means of Stochastic Simulations</title>", "<p>We aimed to investigate to what extent the approach proposed here is able to detect differences in male and female effective numbers. To do this, we performed coalescent simulations in a finite island model, for a wide range of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values. The simulation parameters were set to match those of our dataset: 11 sampled demes, 30 males genotyped at 27 autosomal and 9 X-linked markers per deme (for further details concerning the simulations, see the <xref ref-type=\"sec\" rid=\"s3\">Methods</xref> section). We used 1421 sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, covering the whole parameter space (represented as white dots in ##FIG##3##Figure 4B##). For each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) parameter values, we simulated 100 independent datasets. For each dataset, we calculated the estimates of at all loci, and we calculated the <italic>p</italic>-value for a one-sided Wilcoxon sum rank test for the list of estimates . Hence, for each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) parameter values, we could calculate the proportion of significant tests at the <italic>α</italic> = 0.05 level, among the 100 independent datasets. ##FIG##3##Figure 4## shows the distribution of the percentage of significant tests in the (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) parameter space. Theory predicts that in the upper-right triangle where , we should have . One can see from ##FIG##3##Figure 4## that, given the simulation parameters used, the method is conservative: the proportion of significant tests at the <italic>α</italic> = 0.05 level is null outside of the upper-right triangle. However, we find a fairly large proportion of significant tests for large <italic>N</italic>\n_f/<italic>N</italic> and <italic>m</italic>\n_f/<italic>m</italic> ratios which indicates (<italic>i</italic>) that the method presented here has the potential to detect differences in male and female effective numbers, but (<italic>ii</italic>) that only strong differences might be detected, for similarly sized datasets as the one considered here.</p>", "<title>Robustness to the Sampling Scheme</title>", "<p>We also aimed to investigate whether the results obtained here were robust to our sampling scheme, and that our results were not biased by the inclusion of particular populations. To do this, we re-analyzed both the bilineal agriculturalists and the patrilineal herders datasets, removing one population at a time in each group. For each of these jackknifed datasets, we calculated the <italic>p</italic>-value of a one-sided Wilcoxon sum rank test , as done on the full datasets. The results are given in ##TAB##4##Table 5##. We found no significant test for any of the bilineal agriculturalist groupings (<italic>p</italic>&gt;0.109), which supports the idea that, in those populations, both the migration rate and the number of reproductive individuals can be equal for both sexes. In patrilineal herders, the tests were significant at the <italic>α</italic> = 0.05 level for 8 out of 11 population groupings. For the 3 other groupings, the <italic>p</italic>-values were 0.068, 0.078 and 0.073 (see ##TAB##4##Table 5##). Overall, the ratio of multi-locus estimates ranged from 1.7 to 3.5 in patrilineal herders (and from 0.9 to 1.2 in bilineal agriculturalists). Although in some particular groupings of patrilineal herder populations, the difference in the distributions of may not be strong enough to be significant, we can clearly distinguish the pattern of differentiation for autosomal and X-linked markers in patrilineal and bilineal groups. Results from coalescent simulations (see above) suggest that this lack of statistical power might be expected for ratios close to unity. Indeed, we found that the tests were more likely to be significant for fairly large <italic>N</italic>\n_f/<italic>N</italic> and <italic>m</italic>\n_f/<italic>m</italic> ratios (the upper-right red region in ##FIG##3##Figure 4##) which would correspond to ratios much greater than one.</p>", "<title>Comparison with Uniparentally-Inherited Markers</title>", "<p>Importantly, our results on X-linked and autosomal markers are consistent with those obtained from NRY and mtDNA (see ##FIG##2##Figures 3B–3D##): in these figures, the dashed line gives all the sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values that are compatible with the observed estimates. These are the sets of values that satisfy for the bilineal populations, and for the patrilineal populations, since we inferred <italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m≈2.1 and <italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m≈21.6, respectively, for the two groups. For the bilineal agriculturalists (##FIG##2##Figure 3D##), the set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values inferred from the estimates fall within the range that was not rejected, given our data on X-linked and autosomal markers. For the patrilineal herders (##FIG##2##Figure 3B##), the overlap is only partial: from the NRY and mtDNA data only, low <italic>N</italic>\n_f/<italic>N</italic> ratios associated with high <italic>m</italic>\n_f/<italic>m</italic> ratios are as likely as high <italic>N</italic>\n_f/<italic>N</italic> ratios associated with low <italic>m</italic>\n_f/<italic>m</italic> ratios. Yet, it is clear from this figure that a large set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values inferred from the single-locus estimates can be rejected, given the observed differentiation on X-linked and autosomal markers. All genetic systems (mtDNA, NRY, X-linked and autosomal markers) converge toward the notion that patrilineal herders, in contrast to bilineal agriculturalists, have a strong sex-specific genetic structure. Yet, the information brought by X-linked and autosomal markers is substantial, since we show that this is likely due to both higher migration rates and larger effective numbers for women than for men.</p>", "<title>Comparison with Other Studies</title>", "<p>Our results, based on the X chromosome and the autosomes, also confirm previous analyses based on the mtDNA and the NRY, showing that men are genetically more structured than women in other patrilocal populations ##REF##9806547##[3]##–##REF##15996169##[10]##, ##REF##11528385##[14]##–##REF##16916941##[17]## (see also ##TAB##0##Table 1##). A handful of studies have also shown a reduced effective number of men compared to that of women, based on coalescent methods ##REF##12962309##[23]##,##REF##15317874##[24]##, but none have considered the influence of social organization on this dissimilarity (see ##TAB##0##Table 1##).</p>", "<p>In some respects, our results contrast with those of Wilder and Hammer ##UREF##1##[25]##, who studied sex-specific population genetic structure among the Baining of New Britain, using mtDNA, NRY, and X-linked markers. Interestingly, they found that <italic>N</italic>\n_f&gt;<italic>N</italic>\n_m, but <italic>m</italic>\n_f&lt;<italic>m</italic>\n_m, and claimed that a similar result, although left unexplored by the authors, was to be found in a recent study by Hamilton et al. ##REF##15894624##[16]##. This raises the interesting point that sex-specific proportions of migrants (<italic>m</italic>) are likely to be shaped by factors that may only partially overlap with those that affect the sex-specific effective numbers (<italic>N</italic>). Further studies of human populations with contrasted social organizations, as well as further theoretical developments, are needed to appreciate this point.</p>", "<p>In order to ask to what extent our results generalize to other human populations, we investigated sex-specific patterns in the 51 worldwide populations represented in the HGDP-CEPH Human Genome Diversity Cell Line Panel dataset ##REF##12493913##[43]##, for which the data on the differentiation of 784 autosomal microsatellites and 36 X-linked microsatellites are available (data not shown). By doing this, we found a larger differentiation for X-linked than for autosomal markers . Therefore, we confirmed Ramachandran et al.'s ##REF##15601537##[20]## result that no major differences in demographic parameters between males and females are required to explain the X-chromosomal and autosomal results in this worldwide sample. Ramachandran et al.'s approach ##REF##15601537##[20]## is based upon a pure divergence model from a single ancestral population, which is very different from the migration-drift equilibrium model considered here. In real populations, however, genetic differentiation almost certainly arises both through divergence and limited dispersal, which places these two models at two ends of a continuum. Yet, importantly, if we apply Ramachandran et al.'s ##REF##15601537##[20]## model to the Central Asian data, our conclusions are left unchanged. In their model, the differentiation among populations is , where <italic>t</italic> is the time since divergence from an ancestral population and <italic>N</italic>\n_e the effective size of the populations (see, e.g., ##REF##17246175##[44]##). Hence, we get for autosomal and X-linked markers, respectively. Therefore, our observation that implies that , which requires that <italic>N</italic>\n_f&gt;7<italic>N</italic>\n_m since (see, e.g., ##UREF##5##[45]##). In this case, the female fraction of effective number is larger than that of males, which is consistent with our findings in a model with migration.</p>", "<p>The HGDP-CEPH dataset does not provide any detailed ethnic information for the sampled groups, and we can therefore not distinguish populations with different lifestyles. However, at a more local scale in Pakistan, we were able to analyze a subset of 5 populations (Brahui, Balochi, Makrani, Sindhi and Pathan), which are presumed to be patrilineal ##UREF##6##[46]##. For this subset, we found a higher differentiation for autosomal than for X-linked markers , although non-significantly (<italic>p</italic> = 0.12). This result seems to suggest that other patrilineal populations may behave like the Central Asian sample presented here. Therefore, because the geographical clustering of populations with potentially different lifestyles may minimize the differences in sex-specific demography at a global scale ##REF##16617372##[21]##,##REF##11420360##[22]##, and/or because the global structure may reflect ancient (pre-agricultural) marital residence patterns with less pronounced patrilocality ##REF##16479583##[12]##, we emphasize the point that large-scale studies may not be relevant to detect sex-specific patterns, which supports a claim made by many authors.</p>", "<title>Conclusion</title>", "<p>In conclusion, we have shown here that the joint analysis of autosomal and X-linked polymorphic markers provides an efficient tool to infer sex-specific demography and history in human populations, as suggested recently ##REF##16479583##[12]##,##REF##17208169##[47]##. This new multilocus approach is, to our knowledge, the first attempt to combine the information contained in mtDNA, NRY, X-linked and autosomal markers (see ##TAB##0##Table 1##), which allowed us to test for the robustness of a sex-specific genetic structure at a local scale. Unraveling the respective influence of migration and drift upon neutral genetic structure is a long-standing quest in population genetics ##REF##17402974##[48]##,##REF##11472529##[49]##. Here, our analysis allowed us to show that differences in sex-specific migration rates may not be the only cause of contrasted male and female differentiation in humans and that, contrary to the conclusion of a number of studies (see ##TAB##0##Table 1##), differences in effective numbers may also play an important role. Indeed, we have demonstrated that sex-specific differences in population structure in patrilineal herders may be the consequence of both higher female effective numbers and female effective dispersal. Our results also illustrate the importance of analyzing human populations at a local scale, rather than global or even continental scale ##REF##17067791##[2]##,##REF##15378061##[19]##,##REF##16617372##[21]##. The originality of our approach lies in the comparison of identified ethnic groups that differ in well-known social structures and lifestyles. In that respect, our study is among the very few which compare patrilineal vs. bilineal or matrilineal groups (see ##TAB##0##Table 1##), and we believe that it contributes to the growing body of evidence showing that social organization and lifestyle have a strong impact on the distribution of genetic variation in human populations. Moreover, our approach could also be applied on a wide range of animal species with contrasted social organizations. Therefore, we expect our results to stimulate research on the comparison of X-linked and autosomal data to disentangle sex-specific demography.</p>" ]

[ "<title>Results/Discussion</title>", "<title>Uniparentally-Inherited Markers</title>", "<p>We sampled 780 healthy adult men from 10 populations of bilineal agriculturalists and 11 populations of patrilineal herders from West Uzbekistan to East Kyrgyzstan, representing 5 ethnic groups (Tajiks, Kyrgyz, Karakalpaks, Kazaks, and Turkmen) (see ##FIG##0##Figure 1## and ##TAB##1##Table 2##). We genotyped all bilineal populations, and 8 out of 11 patrilineal populations at the HVS-I locus of mtDNA, and at 11 microsatellite markers on the NRY (for more details on the markers used, see ##TAB##2##Table 3##). The overall genetic differentiation was higher for NRY, as compared to mtDNA, both among the 10 bilineal agriculturalist populations , and among the subset of 8 patrilineal herder populations . Assuming an island model of population structure, this implies that female migration rate (<italic>m</italic>\n_f), and/or the effective number of females (<italic>N</italic>\n_f), is higher than of the corresponding parameters for males (<italic>m</italic>\n_m and <italic>N</italic>\n_m). These results also suggest that the differences in sex-specific genetic structure are much more pronounced in the patrilineal herders than in the bilineal agriculturalists. From the above <italic>F</italic>\n_ST estimates, we obtained the female-to-male ratio of the effective number of migrants per generation (see the <xref ref-type=\"sec\" rid=\"s3\">Methods</xref> section for details): <italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m≈2.1 for bilineal populations and <italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m≈21.6 for patrilineal populations. The ratio in patrilineal populations is thus one order of magnitude higher than in bilineal populations. However, since each of these markers is a single genetic locus, we cannot test for the robustness of the sex-specific genetic structure on these markers. We therefore examined the amount of information contained in multi-locus data on autosomal and X-linked markers, both of which average over male and female histories.</p>", "<title>A New Multi-Locus Approach</title>", "<p>In the infinite island model of population structure with two classes of individuals (males and females), we obtained the following expressions of <italic>F</italic>\n_ST (see the <xref ref-type=\"sec\" rid=\"s3\">Methods</xref> section for details):for autosomal genes, andfor X-linked genes. A special case of interest occurs when , i.e. when the differentiation of X-linked genes exactly equals that of autosomal genes. Combining eqs (1) and (2), we find that this occurs for , with <italic>N</italic> = <italic>N</italic>\n_f+<italic>N</italic>\n_m and <italic>m</italic> = <italic>m</italic>\n_f+<italic>m</italic>\n_m. Furthermore, as shown in ##FIG##1##Figure 2##, if we observe a lower genetic differentiation of autosomal markers, as compared to X-linked markers (blue zone in ##FIG##1##Figure 2##), this suggests that . This may happen, e.g., for <italic>N</italic>\n_f = <italic>N</italic>\n_m and <italic>m</italic>\n_f = <italic>m</italic>\n_m, i.e. for equal effective numbers of males and females and unbiased dispersal. But if autosomal markers are more differentiated than X-linked markers (, see the red upper-right triangle in ##FIG##1##Figure 2##), this implies that . In this case, since <italic>m</italic>\n_f/<italic>m</italic> and <italic>N</italic>\n_f/<italic>N</italic> are ratios varying between 0 and 1, the effective number of females must be higher than that of males (<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m), and the female migration rate must be higher than half the male migration rate (<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m/2). Hence, a prediction from this model is that when , the effective number of females is higher than that of males, whatever the pattern of sex-specific dispersal. This suggests that it is indeed possible to test for differences in effective numbers between males and females from the joint analysis of autosomal and X-linked data. We note however that when , we cannot conclude on the relative male and female effective numbers and migration rates.</p>", "<p>We tested the above prediction in the 10 bilineal agriculturalist populations and 11 patrilineal herder populations sampled in Central Asia by comparing the genetic structure estimated from 27 unlinked polymorphic autosomal microsatellite markers (<italic>AR</italic> = 16.2, <italic>H</italic>\n_e = 0.803 on average) to that from 9 unlinked polymorphic X-linked microsatellite markers (<italic>AR</italic> = 12.6, <italic>H</italic>\n_e = 0.752 on average) (for more details on the markers used, see ##TAB##3##Table 4##). Overall heterozygosity was not significantly different between X-linked and autosomal markers, neither in the pooled sample (two-tailed Wilcoxon sum rank test; <italic>p</italic> = 0.09), nor in the bilineal agriculturalists (<italic>p</italic> = 0.13) or the patrilineal herders (<italic>p</italic> = 0.12). The overall population structure was significantly higher for autosomal as compared to X-linked markers among patrilineal herders: (one-tailed Wilcoxon sum rank test; ; <italic>p</italic> = 0.02). Among bilineal agriculturalists, the result was not significant: (<italic>p</italic> = 0.36). From these results, and following our model predictions, we conclude that in patrilineal herders (where ), the effective number of females is higher than that of males. This conclusion does not hold for the bilineal agriculturalists.</p>", "<p>From our model, it is possible to get more precise indications on the sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values that are compatible with our data. Rearranging eqs (1–2), we get:i.e.:\n</p>", "<p>For any given set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, we can therefore calculate from eq. (4) the expected value of for each estimate in the dataset. We can then test the null hypothesis by comparing the distribution of observed and expected values. If the hypothesis can be rejected at the <italic>α</italic> = 0.05 level, then the corresponding set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values can also be rejected. Following Ramachandran et al. ##REF##15601537##[20]##, we varied the values of the ratios <italic>N</italic>\n_f/<italic>N</italic> and <italic>m</italic>\n_f/<italic>m</italic> (respectively, the female fraction of effective number, and the female fraction of the total migration rate) from 0 to 1, with an interval of 0.01 between consecutive values. For each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, we applied the transformation in eq. (4) to each of the 27 locus-specific values observed. Thus, for each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, we obtained 27 expected values of , given our data. These expected values of were then compared to the 9 observed locus-specific in our dataset, and we calculated the <italic>p</italic>-value for a two-sided Wilcoxon sum rank test between the list of 27 expected values and the 9 observed in the dataset. The results are depicted in ##FIG##2##Figure 3##. Significant <italic>p</italic>-values (<italic>p</italic>≤0.05) correspond to a significant difference between the observed and expected values, thus to sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values that are rejected, given our data (see the blue region in ##FIG##2##Figure 3##). Conversely, non-significant <italic>p</italic>-values (<italic>p</italic>&gt;0.05) correspond to sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values that cannot be rejected (see the red region in ##FIG##2##Figure 3##).</p>", "<p>For the patrilineal herder populations (##FIG##2##Figures 3A–3B##), most sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values are rejected, except those corresponding to larger effective numbers for females (from ##FIG##2##Figures 3A–3B##: <italic>N</italic>\n_f/<italic>N</italic>&gt;0.55, i.e. <italic>N</italic>\n_f&gt;1.27<italic>N</italic>\n_m) and <italic>m</italic>\n_f&gt;0.67<italic>m</italic>\n_m. Because the multi-locus estimate of is significantly higher than the estimate of , we expected to find such patterns of non-significant values (see ##FIG##1##Figure 2##). For the bilineal agriculturalist populations, we could not reject the hypothesis that the effective numbers and migration rates are equal across males and females or even lower in females (see ##FIG##2##Figures 3C–3D##). This is also reflected by the fact that the estimates of were not significantly higher than the estimates of in those populations.</p>", "<p>Finally, we have shown that the effective number of women is higher than that of men among patrilineal herders, but not necessarily among bilineal agriculturalists. Furthermore, a close inspection of the results depicted in ##FIG##2##Figures 3A and 3B## reveals that, among herders, we reject all the sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values for which <italic>m</italic>\n_f&lt;<italic>m</italic>\n_m at the <italic>α</italic> = 0.10 level. This is not true for agriculturalists. This suggests that the migration rates are also likely to be higher for women than for men in patrilineal populations, as compared to bilineal populations (compare ##FIG##2##Figures 3B and 3D##). Although both groups are patrilocal, such a difference in sex-specific migration patterns might be expected, since patrilineal herders are exogamous (among clans) and bilineal agriculturalists are preferentially endogamous. For example, it was observed that in patrilocal and matrilocal Indian populations, where migrations are strictly confined within endogamous groups, sex-specific patterns were not influenced by post-marital residence ##REF##16617372##[21]##.</p>", "<title>What Could Explain a Larger Effective Number of Females?</title>", "<p>While an influence of post-marital residence on the migration rate of women and men has already been widely proposed ##REF##11528385##[14]##–##REF##16916941##[17]## (see also ##TAB##0##Table 1##), the factors that may locally affect the effective number of women, relatively to that of men, are not well recognized. As seen in ##TAB##0##Table 1##, although a number of studies have compared matrilocal and patrilocal populations, few have compared contrasting groups of populations with respect to other factors as, e.g., the tendency for polygyny ##REF##15190128##[15]##. Furthermore, a number of these studies lack ethnological information a priori, concerning social organization, marriage rules, etc., which makes interpretation somewhat difficult (see ##TAB##0##Table 1##). Here, we compared two groups of patrilocal populations with contrasting social organizations, and at least five non-mutually exclusive interpretations for a larger effective number of females can be invoked:</p>", "<p>\n<italic>Social organization</italic>, i.e. the way children are affiliated to their parents, can deeply affect sex-specific genetic variation. In Central Asia, herder populations are organized in patrilineal descent groups (tribes, clans, lineages). This implies that children are systematically affiliated with the descent groups of the father. Chaix et al. ##REF##17208185##[11]## showed that the average number of individuals carrying the same Y chromosome haplotype was much higher in patrilineal herder populations than in bilineal agriculturalist populations (where children are affiliated both to the mother and the father). These “identity cores” would be the direct consequence of the internal dynamics of their patrilineal organization. Indeed, the descent groups are not formed randomly and related men tend to cluster together, e.g. through the recurrent lineal fission of one population into new groups. This particular dynamics increases relatedness among men, and may therefore reduce the effective number of men, as compared to women.</p>", "<p>Indirectly, the social organization can also deflate the effective number of men through <italic>the transmission of reproductive success</italic>\n##REF##15797619##[29]## if this success is culturally transmitted exclusively from fathers to sons. Because herders are patrilineal (so that inheritance is organized along paternal descent groups), social behaviors are more likely to be inherited through the paternal line of descent only. It has recently been argued that the rapid spread of Genghis Khan's patrilineal descendants throughout Central Asia was explained by this social selection phenomenon ##REF##12592608##[30]##. The correlation of fertility through the patriline has also been described in patrilineal tribes in South America ##REF##5473413##[31]##. By contrast, in bilineal societies such as the agriculturalists of Central Asia, social behaviors that influence reproductive success are more likely to be transmitted by both sexes. Furthermore, differences of cultural transmission of fitness between hunter-gatherers and agriculturalists have already been reported ##REF##16933997##[32]##. Interestingly, a slightly higher matrilineal intergenerational correlation in offspring number has been observed in the Icelandic population, which suggests that in some populations, reproductive behaviors can be maternally-inherited ##REF##12721957##[33]##.</p>", "<p>\n<italic>Polygyny</italic>, in which the husband may have multiple wives, has often been invoked as a factor that could reduce the effective number of men ##REF##8751879##[4]##, ##REF##12532283##[7]##, ##REF##15190128##[15]##, ##REF##12962309##[23]##–##UREF##1##[25]##. While we could not find any evidence of polygyny in present-day Central Asian populations, this custom was traditionally practiced in the nomadic herder Kazak populations, although limited to the top 10 percent of men from the highest social rank ##REF##10364534##[5]##,##UREF##2##[34]##. Hence, even though we lack ethnological data to determine to what extent herders are or were practicing polygyny in a recent past, the practice of polygyny among herders in Central Asia might have influenced (at least partially) the observed differences in men and women effective numbers.</p>", "<p>\n<italic>Recurrent bottlenecks in men</italic> due to a higher pre-reproductive mortality could also severely reduce the effective numbers of men. From the study of several groups in West Papua and Papua New Guinea ##REF##12532283##[7]##,##UREF##3##[35]##, it appears that warfare may indeed lead to the quasi-extinction of adult men in some communities, while the mass killing of adult women is far more rarely reported. However, this differential mortality could also be balanced by potentially high death rates of women during childbirth. In any case, a differential mortality is equally likely to arise in herder and agriculturalist populations. It may therefore not be relevant in explaining why we detect higher effective numbers of women (as compared to men) in patrilineal herders and not in bilineal agriculturalists.</p>", "<p>Since our approach implicitly assumes equal male and female generation time, the observed higher effective number of women, relatively to that of men, could result from a <italic>shorter generation time for women</italic>, due to the tendency of women to reproduce earlier in life than men and the ability of men to reproduce at a later age than women. This has indeed been described in a number of populations with different lifestyles, from complete genealogical records or mean-age-at-first-marriage databases ##REF##12721957##[33]##,##REF##10677323##[36]##,##REF##15795887##[37]##. It has even been proposed to be a nearly universal trait in humans, although its magnitude varies across regions and cultures ##REF##15795887##[37]##. Tang et al. ##REF##12019257##[38]## suggested that accounting for longer generation time in males could minimize the difference between maternal and paternal demography. However, the differences in sex-specific generation times that have been reported (e.g., 28 years for the matrilines and 31 years for the patrilines in Iceland ##REF##12721957##[33]##, 29 years for the matrilines and 35 years for the patrilines in Quebec ##REF##10677323##[36]##) are unlikely to explain the observed differences in male and female effective numbers ##REF##15317874##[24]##.</p>", "<title>Limits of the Approach</title>", "<p>There might also be non-biological explanations of our results, however, as they are based on the simplifying assumptions of Wright's infinite island model of population structure ##REF##10098262##[39]##. This model assumes (<italic>i</italic>) that there is no selection and that mutation is negligible, (<italic>ii</italic>) that each population has the same size, and sends and receives a constant fraction of its individuals to or from a common migrant pool each generation (so that geographical structure is absent), and (<italic>iii</italic>) that equilibrium is reached between migration, mutation and drift. On the first point, we did not find any evidence of selection, for any marker, based on Beaumont and Nichols' method ##UREF##4##[40]## for detecting selected markers from the analysis of the null distribution generated by a coalescent-based simulation model (data not shown). As for the second point, we tested for the significance of the correlation between the pairwise <italic>F</italic>\n_ST/(1−<italic>F</italic>\n_ST) estimates and the natural logarithm of their geographical distances ##REF##9093870##[41]##. We found no evidence for isolation by distance, either for X-linked markers (<italic>p</italic> = 0.47 for agriculturalists, <italic>p</italic> = 0.24 for herders), or for autosomal markers (<italic>p</italic> = 0.92 for agriculturalists, <italic>p</italic> = 0.45 for herders). As for the third point, the X-to-autosomes (X/A) effective size ratio can significantly deviate from the expected three-quarters (assuming equal effective numbers of men and women) following a bottleneck or an expansion ##REF##17971168##[42]##. This is because X-linked genes have a smaller effective size, and hence reach equilibrium more rapidly. After a reduction of population size, the X/A diversity ratio is lower than expected, while after an expansion, the diversity of X-linked genes recovers faster than on the autosomes, and the X/A diversity ratio is then closer to unity. In the latter case, would be reduced and could then tend towards . However, neither reduction nor expansion should lead to , as we found in herder populations of Central Asia. Therefore, we do not expect the limits of Wright's island model to undermine our approach.</p>", "<title>Evaluation by Means of Stochastic Simulations</title>", "<p>We aimed to investigate to what extent the approach proposed here is able to detect differences in male and female effective numbers. To do this, we performed coalescent simulations in a finite island model, for a wide range of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values. The simulation parameters were set to match those of our dataset: 11 sampled demes, 30 males genotyped at 27 autosomal and 9 X-linked markers per deme (for further details concerning the simulations, see the <xref ref-type=\"sec\" rid=\"s3\">Methods</xref> section). We used 1421 sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, covering the whole parameter space (represented as white dots in ##FIG##3##Figure 4B##). For each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) parameter values, we simulated 100 independent datasets. For each dataset, we calculated the estimates of at all loci, and we calculated the <italic>p</italic>-value for a one-sided Wilcoxon sum rank test for the list of estimates . Hence, for each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) parameter values, we could calculate the proportion of significant tests at the <italic>α</italic> = 0.05 level, among the 100 independent datasets. ##FIG##3##Figure 4## shows the distribution of the percentage of significant tests in the (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) parameter space. Theory predicts that in the upper-right triangle where , we should have . One can see from ##FIG##3##Figure 4## that, given the simulation parameters used, the method is conservative: the proportion of significant tests at the <italic>α</italic> = 0.05 level is null outside of the upper-right triangle. However, we find a fairly large proportion of significant tests for large <italic>N</italic>\n_f/<italic>N</italic> and <italic>m</italic>\n_f/<italic>m</italic> ratios which indicates (<italic>i</italic>) that the method presented here has the potential to detect differences in male and female effective numbers, but (<italic>ii</italic>) that only strong differences might be detected, for similarly sized datasets as the one considered here.</p>", "<title>Robustness to the Sampling Scheme</title>", "<p>We also aimed to investigate whether the results obtained here were robust to our sampling scheme, and that our results were not biased by the inclusion of particular populations. To do this, we re-analyzed both the bilineal agriculturalists and the patrilineal herders datasets, removing one population at a time in each group. For each of these jackknifed datasets, we calculated the <italic>p</italic>-value of a one-sided Wilcoxon sum rank test , as done on the full datasets. The results are given in ##TAB##4##Table 5##. We found no significant test for any of the bilineal agriculturalist groupings (<italic>p</italic>&gt;0.109), which supports the idea that, in those populations, both the migration rate and the number of reproductive individuals can be equal for both sexes. In patrilineal herders, the tests were significant at the <italic>α</italic> = 0.05 level for 8 out of 11 population groupings. For the 3 other groupings, the <italic>p</italic>-values were 0.068, 0.078 and 0.073 (see ##TAB##4##Table 5##). Overall, the ratio of multi-locus estimates ranged from 1.7 to 3.5 in patrilineal herders (and from 0.9 to 1.2 in bilineal agriculturalists). Although in some particular groupings of patrilineal herder populations, the difference in the distributions of may not be strong enough to be significant, we can clearly distinguish the pattern of differentiation for autosomal and X-linked markers in patrilineal and bilineal groups. Results from coalescent simulations (see above) suggest that this lack of statistical power might be expected for ratios close to unity. Indeed, we found that the tests were more likely to be significant for fairly large <italic>N</italic>\n_f/<italic>N</italic> and <italic>m</italic>\n_f/<italic>m</italic> ratios (the upper-right red region in ##FIG##3##Figure 4##) which would correspond to ratios much greater than one.</p>", "<title>Comparison with Uniparentally-Inherited Markers</title>", "<p>Importantly, our results on X-linked and autosomal markers are consistent with those obtained from NRY and mtDNA (see ##FIG##2##Figures 3B–3D##): in these figures, the dashed line gives all the sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values that are compatible with the observed estimates. These are the sets of values that satisfy for the bilineal populations, and for the patrilineal populations, since we inferred <italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m≈2.1 and <italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m≈21.6, respectively, for the two groups. For the bilineal agriculturalists (##FIG##2##Figure 3D##), the set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values inferred from the estimates fall within the range that was not rejected, given our data on X-linked and autosomal markers. For the patrilineal herders (##FIG##2##Figure 3B##), the overlap is only partial: from the NRY and mtDNA data only, low <italic>N</italic>\n_f/<italic>N</italic> ratios associated with high <italic>m</italic>\n_f/<italic>m</italic> ratios are as likely as high <italic>N</italic>\n_f/<italic>N</italic> ratios associated with low <italic>m</italic>\n_f/<italic>m</italic> ratios. Yet, it is clear from this figure that a large set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values inferred from the single-locus estimates can be rejected, given the observed differentiation on X-linked and autosomal markers. All genetic systems (mtDNA, NRY, X-linked and autosomal markers) converge toward the notion that patrilineal herders, in contrast to bilineal agriculturalists, have a strong sex-specific genetic structure. Yet, the information brought by X-linked and autosomal markers is substantial, since we show that this is likely due to both higher migration rates and larger effective numbers for women than for men.</p>", "<title>Comparison with Other Studies</title>", "<p>Our results, based on the X chromosome and the autosomes, also confirm previous analyses based on the mtDNA and the NRY, showing that men are genetically more structured than women in other patrilocal populations ##REF##9806547##[3]##–##REF##15996169##[10]##, ##REF##11528385##[14]##–##REF##16916941##[17]## (see also ##TAB##0##Table 1##). A handful of studies have also shown a reduced effective number of men compared to that of women, based on coalescent methods ##REF##12962309##[23]##,##REF##15317874##[24]##, but none have considered the influence of social organization on this dissimilarity (see ##TAB##0##Table 1##).</p>", "<p>In some respects, our results contrast with those of Wilder and Hammer ##UREF##1##[25]##, who studied sex-specific population genetic structure among the Baining of New Britain, using mtDNA, NRY, and X-linked markers. Interestingly, they found that <italic>N</italic>\n_f&gt;<italic>N</italic>\n_m, but <italic>m</italic>\n_f&lt;<italic>m</italic>\n_m, and claimed that a similar result, although left unexplored by the authors, was to be found in a recent study by Hamilton et al. ##REF##15894624##[16]##. This raises the interesting point that sex-specific proportions of migrants (<italic>m</italic>) are likely to be shaped by factors that may only partially overlap with those that affect the sex-specific effective numbers (<italic>N</italic>). Further studies of human populations with contrasted social organizations, as well as further theoretical developments, are needed to appreciate this point.</p>", "<p>In order to ask to what extent our results generalize to other human populations, we investigated sex-specific patterns in the 51 worldwide populations represented in the HGDP-CEPH Human Genome Diversity Cell Line Panel dataset ##REF##12493913##[43]##, for which the data on the differentiation of 784 autosomal microsatellites and 36 X-linked microsatellites are available (data not shown). By doing this, we found a larger differentiation for X-linked than for autosomal markers . Therefore, we confirmed Ramachandran et al.'s ##REF##15601537##[20]## result that no major differences in demographic parameters between males and females are required to explain the X-chromosomal and autosomal results in this worldwide sample. Ramachandran et al.'s approach ##REF##15601537##[20]## is based upon a pure divergence model from a single ancestral population, which is very different from the migration-drift equilibrium model considered here. In real populations, however, genetic differentiation almost certainly arises both through divergence and limited dispersal, which places these two models at two ends of a continuum. Yet, importantly, if we apply Ramachandran et al.'s ##REF##15601537##[20]## model to the Central Asian data, our conclusions are left unchanged. In their model, the differentiation among populations is , where <italic>t</italic> is the time since divergence from an ancestral population and <italic>N</italic>\n_e the effective size of the populations (see, e.g., ##REF##17246175##[44]##). Hence, we get for autosomal and X-linked markers, respectively. Therefore, our observation that implies that , which requires that <italic>N</italic>\n_f&gt;7<italic>N</italic>\n_m since (see, e.g., ##UREF##5##[45]##). In this case, the female fraction of effective number is larger than that of males, which is consistent with our findings in a model with migration.</p>", "<p>The HGDP-CEPH dataset does not provide any detailed ethnic information for the sampled groups, and we can therefore not distinguish populations with different lifestyles. However, at a more local scale in Pakistan, we were able to analyze a subset of 5 populations (Brahui, Balochi, Makrani, Sindhi and Pathan), which are presumed to be patrilineal ##UREF##6##[46]##. For this subset, we found a higher differentiation for autosomal than for X-linked markers , although non-significantly (<italic>p</italic> = 0.12). This result seems to suggest that other patrilineal populations may behave like the Central Asian sample presented here. Therefore, because the geographical clustering of populations with potentially different lifestyles may minimize the differences in sex-specific demography at a global scale ##REF##16617372##[21]##,##REF##11420360##[22]##, and/or because the global structure may reflect ancient (pre-agricultural) marital residence patterns with less pronounced patrilocality ##REF##16479583##[12]##, we emphasize the point that large-scale studies may not be relevant to detect sex-specific patterns, which supports a claim made by many authors.</p>", "<title>Conclusion</title>", "<p>In conclusion, we have shown here that the joint analysis of autosomal and X-linked polymorphic markers provides an efficient tool to infer sex-specific demography and history in human populations, as suggested recently ##REF##16479583##[12]##,##REF##17208169##[47]##. This new multilocus approach is, to our knowledge, the first attempt to combine the information contained in mtDNA, NRY, X-linked and autosomal markers (see ##TAB##0##Table 1##), which allowed us to test for the robustness of a sex-specific genetic structure at a local scale. Unraveling the respective influence of migration and drift upon neutral genetic structure is a long-standing quest in population genetics ##REF##17402974##[48]##,##REF##11472529##[49]##. Here, our analysis allowed us to show that differences in sex-specific migration rates may not be the only cause of contrasted male and female differentiation in humans and that, contrary to the conclusion of a number of studies (see ##TAB##0##Table 1##), differences in effective numbers may also play an important role. Indeed, we have demonstrated that sex-specific differences in population structure in patrilineal herders may be the consequence of both higher female effective numbers and female effective dispersal. Our results also illustrate the importance of analyzing human populations at a local scale, rather than global or even continental scale ##REF##17067791##[2]##,##REF##15378061##[19]##,##REF##16617372##[21]##. The originality of our approach lies in the comparison of identified ethnic groups that differ in well-known social structures and lifestyles. In that respect, our study is among the very few which compare patrilineal vs. bilineal or matrilineal groups (see ##TAB##0##Table 1##), and we believe that it contributes to the growing body of evidence showing that social organization and lifestyle have a strong impact on the distribution of genetic variation in human populations. Moreover, our approach could also be applied on a wide range of animal species with contrasted social organizations. Therefore, we expect our results to stimulate research on the comparison of X-linked and autosomal data to disentangle sex-specific demography.</p>" ]

[ "<title>Conclusion</title>", "<p>In conclusion, we have shown here that the joint analysis of autosomal and X-linked polymorphic markers provides an efficient tool to infer sex-specific demography and history in human populations, as suggested recently ##REF##16479583##[12]##,##REF##17208169##[47]##. This new multilocus approach is, to our knowledge, the first attempt to combine the information contained in mtDNA, NRY, X-linked and autosomal markers (see ##TAB##0##Table 1##), which allowed us to test for the robustness of a sex-specific genetic structure at a local scale. Unraveling the respective influence of migration and drift upon neutral genetic structure is a long-standing quest in population genetics ##REF##17402974##[48]##,##REF##11472529##[49]##. Here, our analysis allowed us to show that differences in sex-specific migration rates may not be the only cause of contrasted male and female differentiation in humans and that, contrary to the conclusion of a number of studies (see ##TAB##0##Table 1##), differences in effective numbers may also play an important role. Indeed, we have demonstrated that sex-specific differences in population structure in patrilineal herders may be the consequence of both higher female effective numbers and female effective dispersal. Our results also illustrate the importance of analyzing human populations at a local scale, rather than global or even continental scale ##REF##17067791##[2]##,##REF##15378061##[19]##,##REF##16617372##[21]##. The originality of our approach lies in the comparison of identified ethnic groups that differ in well-known social structures and lifestyles. In that respect, our study is among the very few which compare patrilineal vs. bilineal or matrilineal groups (see ##TAB##0##Table 1##), and we believe that it contributes to the growing body of evidence showing that social organization and lifestyle have a strong impact on the distribution of genetic variation in human populations. Moreover, our approach could also be applied on a wide range of animal species with contrasted social organizations. Therefore, we expect our results to stimulate research on the comparison of X-linked and autosomal data to disentangle sex-specific demography.</p>" ]

[ "<p><bold>¤:</bold> Current address: Unidad de Biología Evolutiva, Departamento de Ciencias Experimentales y de la Salud, Universidad Pompeu Fabra, Barcelona, Spain</p>", "<p>Conceived and designed the experiments: EH RV. Performed the experiments: LS BMC LQM PB MG. Analyzed the data: LS RV. Contributed reagents/materials/analysis tools: BMC TH AA FN MJ EH. Wrote the paper: LS RV. Collected the samples: LS, BMC, EH.</p>", "<p>In the last two decades, mitochondrial DNA (mtDNA) and the non-recombining portion of the Y chromosome (NRY) have been extensively used in order to measure the maternally and paternally inherited genetic structure of human populations, and to infer sex-specific demography and history. Most studies converge towards the notion that among populations, women are genetically less structured than men. This has been mainly explained by a higher migration rate of women, due to patrilocality, a tendency for men to stay in their birthplace while women move to their husband's house. Yet, since population differentiation depends upon the product of the effective number of individuals within each deme and the migration rate among demes, differences in male and female effective numbers and sex-biased dispersal have confounding effects on the comparison of genetic structure as measured by uniparentally inherited markers. In this study, we develop a new multi-locus approach to analyze jointly autosomal and X-linked markers in order to aid the understanding of sex-specific contributions to population differentiation. We show that in patrilineal herder groups of Central Asia, in contrast to bilineal agriculturalists, the effective number of women is higher than that of men. We interpret this result, which could not be obtained by the analysis of mtDNA and NRY alone, as the consequence of the social organization of patrilineal populations, in which genetically related men (but not women) tend to cluster together. This study suggests that differences in sex-specific migration rates may not be the only cause of contrasting male and female differentiation in humans, and that differences in effective numbers do matter.</p>", "<title>Author Summary</title>", "<p>Human evolutionary history has been investigated mainly through the prism of genetic variation of the Y chromosome and mitochondrial DNA. These two uniparentally inherited markers reflect the demographic history of males and females, respectively. Their contrasting patterns of genetic differentiation reveal that women are more mobile than men among populations, which might be due to specific marriage rules. However, these two markers provide only a limited understanding of the underlying demographic processes. To obtain an independent picture of sex-specific demography, we developed a new multi-locus approach based on the analysis of markers from the autosomal and X-chromosomal compartments. We applied our method to 21 human populations sampled in Central Asia, with contrasting social organizations and lifestyles. We found that, in patrilineal populations, not only the migration rate but also the number of reproductive individuals is likely to be higher for women. This result does not hold for bilineal populations, for which both the migration rate and the number of reproductive individuals can be equal for both sexes. The social organization of patrilineal populations is the likely cause of this pattern. This study suggests that differences in sex-specific migration rates may not be the only cause of contrasting male and female differentiation in humans, and that differences in effective numbers do matter.</p>" ]

[]

[ "<p>We thank all the people who volunteered to participate in this study, or who helped us in the field. We are grateful to Sylvain Théry for valuable help in handling geographic data, to Hélène Fréville and Nicolas Perrin for helpful comments on previous versions of this manuscript, as well as to three anonymous reviewers for insightful and constructive comments. We acknowledge the “Service de Systématique Moléculaire” (SSM) at the Museum National d'Histoire Naturelle (MNHN) and the Biological Resource Center of the Foundation Jean Dausset-CEPH for genotyping facilities. Part of this work was carried out by using the resources of the Computational Biology Service Unit from the Museum National d'Histoire Naturelle (MNHN) which was partially funded by Saint Gobain.</p>" ]

[ "<fig id=\"pgen-1000200-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000200.g001</object-id><label>Figure 1</label><caption><title>Geographic map of the sampled area, with the 21 populations studied.</title><p>Bilineal agriculturalist populations are in blue (Tajiks); Patrilineal herders with a semi-nomadic lifestyle are in red (Kazaks, Karakalpaks, Kyrgyz and Turkmen).</p></caption></fig>", "<fig id=\"pgen-1000200-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000200.g002</object-id><label>Figure 2</label><caption><title>Diagram representing the relative values of expected genetic differentiation for autosomal markers and for X-linked markers .</title><p>In the red upper right triangle, the <italic>F</italic>\n_ST estimates for autosomal markers are higher than for X-linked markers. In this case, <italic>N</italic>\n_f/<italic>N</italic> is necessarily larger than 0.5. In the blue region of the figure, the <italic>F</italic>\n_ST estimates for autosomal markers are lower than for X-linked markers. The white plain line, at which , represents the set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values where the autosomal and X-linked <italic>F</italic>\n_ST estimates are equal. In this case , if <italic>N</italic>\n_f = <italic>N</italic>\n_m, then the lower effective size of X-linked markers (which would be three-quarters that of autosomal markers) can only be balanced by a complete female-bias in dispersal (<italic>m</italic>\n_f/<italic>m</italic> = 1). Conversely, if <italic>m</italic>\n_f = <italic>m</italic>\n_m, the large female fraction of effective numbers compensates exactly the low effective size of X-linked markers only for <italic>N</italic>\n_f = 7<italic>N</italic>\n_m. Last, if <italic>m</italic>\n_f = <italic>m</italic>\n_m/2, then the autosomal and X-linked <italic>F</italic>\n_ST estimates can only be equal as the number of males tends towards zero.</p></caption></fig>", "<fig id=\"pgen-1000200-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000200.g003</object-id><label>Figure 3</label><caption><title>\n<italic>p</italic>-values of Wilcoxon tests plotted in the (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) parameter space.</title><p>For each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, we applied the transformation in eq. (4), and tested whether our data on autosomal and X-linked markers were consistent, given the hypothesis defined by the set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values. (A) Surface plot of the <italic>p</italic>-values, as a function of the female fraction of effective number and the female fraction of migration rate, for the herders (11 populations). The arrow indicates the line that separates the region where <italic>p</italic>≤0.05 from that where <italic>p</italic>&gt;0.05. Non-significant <italic>p</italic>-values (<italic>p</italic>&gt;0.05) correspond to the values of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) that could not be rejected, given our data. (B) Contour plots, for the same data. The dashed line indicates the range of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values inferred from the ratio of NRY and mtDNA population structure, as obtained from the relationship: . The dotted lines correspond to the cases where <italic>N</italic>\n_f = <italic>N</italic>\n_m (vertical line) and <italic>m</italic>\n_f = <italic>m</italic>\n_m (horizontal line). (C) and (D) as (A) and (B), respectively, for the agriculturalists (10 populations).</p></caption></fig>", "<fig id=\"pgen-1000200-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000200.g004</object-id><label>Figure 4</label><caption><title>Percentage of significant tests in the (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) parameter space, for simulated data.</title><p>We chose a range of 49 (<italic>N</italic>\n_f\n<italic>m</italic>\n_f/<italic>N</italic>\n_m\n<italic>m</italic>\n_m) ratios, varying from 0.0004 to 2401, and for each of these ratios we chose 29 sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values. By doing this, we obtained 1421 sets of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values, represented as white dots in the right-hand side panel B, covering the whole parameter space. For each set, we simulated 100 independent datasets using a coalescent-based algorithm, and taking the same number of individuals and the same number of loci for each genetic system as in the observed data. For each dataset, we calculated the <italic>p</italic>-value for a one-sided Wilcoxon sum rank test , and for each set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values we calculated the percentage of significant <italic>p</italic>-values (at the <italic>α</italic> = 0.05 level). A. Surface plot of the proportion of significant <italic>p</italic>-values (at the <italic>α</italic> = 0.05 level), as a function of the female fraction of effective number and the female fraction of migration rate. B. Contour plot, for the same data. The dotted line, at which , represents the set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values where the autosomal and X-linked <italic>F</italic>\n_ST's are equal. The theory predicts that we should only find in the upper-right triangle defined by the dotted line. Hence, the proportion of significant <italic>p</italic>-values for any set of (<italic>N</italic>\n_f/<italic>N</italic>, <italic>m</italic>\n_f/<italic>m</italic>) values in this upper right triangle gives an indication of the power of the method.</p></caption></fig>" ]

[ "<table-wrap id=\"pgen-1000200-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000200.t001</object-id><label>Table 1</label><caption><title>Human sex-specific demography inferred from genetic data.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Region</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Markers</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Method</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Social organization<xref ref-type=\"table-fn\" rid=\"nt102\">a</xref>\n</td><td colspan=\"3\" align=\"left\" rowspan=\"1\">Differences in demographic parameters between males and females<xref ref-type=\"table-fn\" rid=\"nt103\">b</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">References</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sex-biased migration</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Skewed effective population size</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GLOBAL</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY SNPs<xref ref-type=\"table-fn\" rid=\"nt104\">c</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (AMOVA<xref ref-type=\"table-fn\" rid=\"nt105\">d</xref>)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA<xref ref-type=\"table-fn\" rid=\"nt106\">e</xref>\n</td><td colspan=\"3\" align=\"left\" rowspan=\"1\">None</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##15378061##[19]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GLOBAL</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Autosomal STRs<xref ref-type=\"table-fn\" rid=\"nt107\">f</xref>, X-linked STRs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (AMOVA)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td colspan=\"3\" align=\"left\" rowspan=\"1\">None</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##15601537##[20]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GLOBAL</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY SNPs<xref ref-type=\"table-fn\" rid=\"nt104\">c</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Coalescent-based (TMRCA<xref ref-type=\"table-fn\" rid=\"nt108\">g</xref> estimates)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m (polygyny)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##15317874##[24]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GLOBAL<xref ref-type=\"table-fn\" rid=\"nt109\">h</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY STRs+SNPs, Autosomal STRs+SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (<italic>F</italic>\n_ST)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Considered as negligible<xref ref-type=\"table-fn\" rid=\"nt110\">i</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##9806547##[3]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GLOBAL<xref ref-type=\"table-fn\" rid=\"nt109\">h</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NRY SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Coalescent-based (mismatch distributions)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Not considered<xref ref-type=\"table-fn\" rid=\"nt111\">j</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m (polygyny)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##12962309##[23]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">India</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (<italic>R</italic>\n_ST, haplotype sharing)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Endogamy, patrilocality</td><td colspan=\"3\" align=\"left\" rowspan=\"1\">None</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##16617372##[21]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">NRY STRs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Endogamy, matrilocality</td><td colspan=\"3\" align=\"left\" rowspan=\"1\">None</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sinai peninsula</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic diversity</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Endogamy and rare patrilocal exogamy, polygyny</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m (polygyny)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##8751879##[4]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">West New Guinea</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY STRs+SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure and diversity (<italic>F</italic>\n_ST, <italic>R</italic>\n_ST, haplotype diversity)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Exogamy, patrilocality, patrilineality, polygyny</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m (polygyny, warfare)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##12532283##[7]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sub-Saharan Africa</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY STRs+SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (AMOVA)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">FPP<xref ref-type=\"table-fn\" rid=\"nt112\">k</xref>: patrilocality, high polygyny</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m (polygyny)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##15190128##[15]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">HGP<xref ref-type=\"table-fn\" rid=\"nt113\">l</xref>: moderate patrilocality, low polygyny</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&lt;<italic>m</italic>\n_m (multilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&lt;<italic>N</italic>\n_m\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Thailand</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY STRs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Coalescent-based (Approximate Bayesian Computation)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Patrilocality</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##15894624##[16]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Matrilocality</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&lt;<italic>m</italic>\n_m (matrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&lt;<italic>N</italic>\n_m (matrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Eastern North America</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY STRs+SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (AMOVA), coalescent-based (MIGRATE<xref ref-type=\"table-fn\" rid=\"nt114\">m</xref>)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Patrilocality, patrilineality</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##16916941##[17]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Matrilocality, matriliny</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&lt;<italic>m</italic>\n_m (matrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&lt;<italic>N</italic>\n_m (matrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Central Asia (pastoral populations)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY STRs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure and diversity (AMOVA, <italic>R</italic>\n_ST)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Exogamy, patrilineality</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilineality, exogamy)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and/or</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m (patrilineality,VRS<xref ref-type=\"table-fn\" rid=\"nt115\">n</xref>)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##17208185##[11]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">New Britain</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY SNPs, X-linked loci</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Coalescent-based (θ<xref ref-type=\"table-fn\" rid=\"nt116\">o</xref> and TMRCA estimates)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">No strong endogamy, ambilocality, polygyny</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&lt;<italic>m</italic>\n_m\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">and</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n_f&gt;<italic>N</italic>\n_m (polygyny)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##UREF##1##[25]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Central Asia</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY STRs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (AMOVA)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Exogamy, patrilocality, polygyny</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Considered as negligible</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##10364534##[5]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Thailand</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY STRs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure and diversity (haplotype diversity, <italic>R</italic>\n_ST)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Patrilocality</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Considered as negligible</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##11528385##[14]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Matrilocality</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&lt;<italic>m</italic>\n_m (matrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Considered as negligible</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sub-Saharan Africa<xref ref-type=\"table-fn\" rid=\"nt109\">h</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure and diversity (haplotype diversity, AMOVA)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA<xref ref-type=\"table-fn\" rid=\"nt104\">c</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&lt;<italic>m</italic>\n_m\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Not considered</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##11420360##[22]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Continental Asia<xref ref-type=\"table-fn\" rid=\"nt109\">h</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (<italic>F</italic>\n_ST)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA<xref ref-type=\"table-fn\" rid=\"nt104\">c</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Not considered</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##12012367##[6]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Russia</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (<italic>F</italic>\n_ST)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Patrilocality, patrilineality</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Not considered</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##15974299##[8]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Caucasus</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (AMOVA)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Not considered</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##15180701##[9]##\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Turkey</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA, NRY STRs+SNPs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic structure (AMOVA)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>m</italic>\n_f&gt;<italic>m</italic>\n_m (patrilocality)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Not considered</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n##REF##15996169##[10]##\n</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pgen-1000200-t002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000200.t002</object-id><label>Table 2</label><caption><title>Sample description.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sampled populations (area)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Acronym</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Location</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Long.</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Lat.</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n_X\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n_A\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n_Y\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n_mt\n</td></tr></thead><tbody><tr><td colspan=\"9\" align=\"left\" rowspan=\"1\">\n<bold>Bilineal agriculturalists</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Samarkand)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Uzbekistan/Tajikistan border</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>39.54</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>66.89</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">32</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Samarkand)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Uzbekistan/Tajikistan border</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>39.5</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>67.27</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Ferghana)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJR</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajikistan/Kyrgyzstan border</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>40.36</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>71.28</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Ferghana)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJK</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajikistan/Kyrgyzstan border</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>40.25</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>71.87</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Gharm)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJE</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Northern Tajikistan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>39.12</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>70.67</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Gharm)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJN</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Western Tajikistan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>38.09</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>68.81</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">35</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Gharm)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJT</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Northern Tajikistan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>39.11</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>70.86</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">32</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Penjinkent)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TDS</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Uzbekistan/Tajikistan border</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>39.28</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>67.81</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Penjinkent)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TDU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Uzbekistan/Tajikistan border</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>39.44</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>68.26</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajiks (Yagnobs from Douchambe)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJY</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Western Tajikistan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>38.57</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>68.78</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40</td></tr><tr><td colspan=\"9\" align=\"left\" rowspan=\"1\">\n<bold>Patrilineal herders with a semi-nomadic lifestyle</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Karakalpaks (Qongrat from Karakalpakia)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">KKK</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Western Uzbekistan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>43.77</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>59.02</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">55</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Karakalpaks (On Tört Uruw from Karakalpakia)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">OTU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Western Uzbekistan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>42.94</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>59.78</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">53</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kazaks (Karakalpakia)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">KAZ</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Western Uzbekistan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>43.04</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>58.84</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kazaks (Bukara)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">LKZ</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Southern Uzbekistan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>40.08</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>63.56</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kyrgyz (Andijan)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tajikistan/Kyrgyzstan border</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>40.77</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>72.31</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">48</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kyrgyz (Narin)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Middle Kyrgyzstan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>41.6</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>75.8</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kyrgyz (Narin)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRM</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Middle Kyrgyzstan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>41.45</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>76.22</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">26</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kyrgyz (Narin)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRL</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Middle Kyrgyzstan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>41.36</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>75.5</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kyrgyz (Narin)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRB</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Middle Kyrgyzstan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>41.25</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>76</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Kyrgyz (Issyk Kul)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRT</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Eastern Kyrgyzstan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>42.16</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>77.57</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Turkmen (Karakalpakia)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TUR</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Western Uzbekistan</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>41.55</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>60.63</bold>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">51</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pgen-1000200-t003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000200.t003</object-id><label>Table 3</label><caption><title>Level of diversity and differentiation for NRY markers and mtDNA.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">NRY markers</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"2\" align=\"left\" rowspan=\"1\">\n<italic>F</italic>\n_ST\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Locus name</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Allelic richness (AR)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>H</italic>\n_e\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Herders</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Agriculturalists</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS426</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.500</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3326</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0068</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS393</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.492</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1095</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0517</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS390</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.739</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1229</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1253</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS385 a/b</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.858</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1414</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0278</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS388</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.531</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3003</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0736</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.743</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1081</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1310</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS392</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.516</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1345</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0701</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS391</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.495</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2533</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0686</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS389I</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.541</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1537</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1395</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS439</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.725</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1638</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0291</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DYS389II</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.763</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1556</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0395</td></tr></tbody></table><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">mtDNA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"2\" align=\"left\" rowspan=\"1\">\n<italic>F</italic>\n_ST\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Locus name</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Polymorphic sites</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>H</italic>\n_e\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Herders</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Agriculturalists</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">HVS-I</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">121</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0156</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0098</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0343</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pgen-1000200-t004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000200.t004</object-id><label>Table 4</label><caption><title>Level of diversity and differentiation for X-linked and autosomal markers.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"2\" align=\"left\" rowspan=\"1\">\n<italic>F</italic>\n_ST\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Locus name</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Allelic richness (AR)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>H</italic>\n_e\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Herders</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Agriculturalists</td></tr></thead><tbody><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">\n<bold>X-linked markers</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CTAT014</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.746</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0018</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0225</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA124E07</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.847</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0024</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0136</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA31D10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.697</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0069</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0007</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ATA28C05</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.722</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0086</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0179</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFM150xf10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.832</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.0021</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0152</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA100G03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.734</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.0019</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0084</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AGAT121P</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.593</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.0016</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0048</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ATCT003</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.797</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0095</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0261</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA31F01</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.804</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0069</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0053</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">\n<bold>Autosomal markers</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFM249XC5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.848</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0080</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0081</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ATA10H11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.680</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0128</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0193</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFM254VE1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.837</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0105</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0086</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFMA218YB5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.852</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0030</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0151</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGAA7G08</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.896</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0096</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0138</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA11H10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.776</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0017</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0056</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA12A07</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.857</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0001</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0163</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA193A07</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.825</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0064</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0087</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFMB002ZF1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.820</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0028</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0169</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFMB303ZG9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.858</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0090</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0148</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ATA34G06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.675</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0088</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0132</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA72G09</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.884</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">−0.0023</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0131</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA22F11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.897</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0152</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0144</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGAA6D03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.831</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0048</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0176</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA88H02</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.892</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0063</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0056</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">SE30</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.762</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0084</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0103</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA43C11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.870</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0028</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0093</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFM203YG9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.753</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0105</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0084</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFM157XG3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.753</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0147</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0196</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UT2095</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.738</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0032</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0112</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA28D01</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.896</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0156</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0139</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGAA4B09</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.707</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0034</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0208</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ATA3A07</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.746</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0078</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0070</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFM193XH4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.716</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0164</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0129</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATA11B12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.896</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0104</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0265</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFM165XC11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.785</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0058</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0185</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AFM248VC5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.620</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0246</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0145</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pgen-1000200-t005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000200.t005</object-id><label>Table 5</label><caption><title>Autosomal and X-linked differentiation on jackknifed samples.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sample removed</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>-value</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n\n</td></tr></thead><tbody><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">\n<bold>Patrilineal groups</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">KAZ</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0084</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0050</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.068</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">KKK</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0085</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0050</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.078</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0078</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0027</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.022</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRB</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0080</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0030</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0078</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0035</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.037</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRL</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0086</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0038</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.018</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRM</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0069</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0023</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.018</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">KRT</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0081</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0044</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.047</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">LKZ</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0088</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0025</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">OTU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0089</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0038</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.022</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TUR</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0054</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0025</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.073</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.2</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">\n<bold>Bilineal groups</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TDS</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0125</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0109</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.443</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TDU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0132</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0153</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.705</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0144</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0123</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.109</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJE</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0140</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0133</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.148</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJK</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0134</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0131</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.457</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJN</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0148</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0144</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.387</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJR</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0140</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0141</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.401</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJT</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0139</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0121</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.225</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0139</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0127</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.283</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TJY</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0139</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0116</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.259</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.2</td></tr></tbody></table></alternatives></table-wrap>" ]

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[ "<table-wrap-foot><fn id=\"nt101\"><label/><p>This table summarizes the observed patterns of sex-specific differences in demographic parameters reported in a number of recent studies. The first column lists the location of the sampled populations, or indicates whether the study is conducted at a global scale. The second column gives the markers used, and the third column indicates the statistical methods employed. The fourth column provides indications on social organization, available a priori for the populations under study. In the fifth and sixth columns, the authors' interpretations of sex-specific differences in demographic parameters are given, with respect to skewed gene flow and/or effective numbers.</p></fn><fn id=\"nt102\"><label>a</label><p>Indications on social organization, marriage rules, etc., as provided by the authors.</p></fn><fn id=\"nt103\"><label>b</label><p>The differences in demographic parameters between males and females, as inferred by the authors, are given in terms of sex-biased gene flow, and skewed effective numbers; the authors' interpretation to the observed pattern is given in parentheses, when available.</p></fn><fn id=\"nt104\"><label>c</label><p>Single nucleotide polymorphisms.</p></fn><fn id=\"nt105\"><label>d</label><p>Analysis of molecular variance ##REF##1644282##[69]##.</p></fn><fn id=\"nt106\"><label>e</label><p>Not available (no detailed information given by the authors concerning social organization, marriage rules, etc.).</p></fn><fn id=\"nt107\"><label>f</label><p>Short tandem repeats.</p></fn><fn id=\"nt108\"><label>g</label><p>Time to the most recent common ancestor.</p></fn><fn id=\"nt109\"><label>h</label><p>mtDNA and NRY were not sampled in the same individuals or populations.</p></fn><fn id=\"nt110\"><label>i</label><p>The authors discussed a possible difference in demographic parameters between males and females, but considered it as negligible.</p></fn><fn id=\"nt111\"><label>j</label><p>The authors did not consider this pattern.</p></fn><fn id=\"nt112\"><label>k</label><p>Food-producer populations.</p></fn><fn id=\"nt113\"><label>l</label><p>Hunter-gatherer populations.</p></fn><fn id=\"nt114\"><label>m</label><p>Monte Carlo Markov chain method to estimate population sizes and migration rates ##REF##11287657##[70]##.</p></fn><fn id=\"nt115\"><label>n</label><p>Variance in Reproductive Success.</p></fn><fn id=\"nt116\"><label>o</label><p>population-mutation parameter.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt117\"><label/><p>Long., longitude; Lat., latitude. <italic>n</italic>\n_X, <italic>n</italic>\n_A, <italic>n</italic>\n_Y and <italic>n</italic>\n_mt: sample size for X-linked, autosomal, Y-linked and mitochondrial markers, respectively.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt118\"><label/><p>We calculated the total allelic richness (<italic>AR</italic>) (over all populations) and the expected heterozygosity <italic>H</italic>\n_e\n##REF##17248844##[55]## using Arlequin version 3.1 ##UREF##8##[56]##. Genetic differentiation among populations was measured both per locus and overall loci, using Weir and Cockerham's <italic>F</italic>\n_ST estimator ##UREF##9##[57]##, as calculated in G<sc>enepop</sc> 4.0 ##UREF##10##[58]##. We calculated the total number of polymorphic sites, the unbiased estimate of expected heterozygosity <italic>H</italic>\n_e\n##REF##17248844##[55]##, and <italic>F</italic>\n_ST using Arlequin version 3.1 ##UREF##8##[56]##.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt119\"><label/><p>We calculated the allelic richness (<italic>AR</italic>) and unbiased estimates of expected heterozygosity <italic>H</italic>\n_e\n##REF##17248844##[55]##, obtained both by locus and on average with Arlequin version 3.1 ##UREF##8##[56]##. Genetic differentiation among populations was measured both per locus and overall loci, using Weir and Cockerham's <italic>F</italic>\n_ST estimator ##UREF##9##[57]## as calculated in G<sc>enepop</sc> 4.0 ##UREF##10##[58]##.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt120\"><label/><p>For each group, we removed one sample in turn and calculated the differentiation on autosomal and X-linked markers. The <italic>p</italic>-value gives the result of a one-sided Wilcoxon sum rank test , as performed on the full dataset.</p></fn></table-wrap-foot>", "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>This work was supported by the Centre National de la Recherche Scientifique (CNRS) ATIP programme (to EH), by the CNRS interdisciplinary programme “Origines de l'Homme du Langage et des Langues” (OHLL) and by the European Science Foundation (ESF) EUROCORES programme “The Origin of Man, Language and Languages” (OMLL). We also thank the “Fondation pour la Recherche Médicale” (FRM) for financial support. LS is financed by the French Ministry of Higher Education and Research. MAJ is supported by a Wellcome Trust Senior Fellowship in Basic Biomedical Science (grant number 057559).</p></fn></fn-group>" ]

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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>" ]

[ "<title>Review</title>", "<p><italic>We all as humans stay at the parasites' place which in turn reside in us! Nous demeurons chez des microbes qui nous habitent </italic>!</p>", "<p>Are we totally wrong in thinking that we are, as humans, like all other living organisms on Earth, (just) hosts and resources for parasites! On the contrary, would we be just staying at a parasites' home, since that our point of view on parasites and pathogens is totally flawed by an anthropogenic, too medically-oriented perspective? At a time where we are demonstrating that the human genome is not a single-formed entity but it is made for a substantial part of past genetic interactions between environmentally-resident pathogens and our species, the book co-edited by Richard S. Ostfeld, Felicia Keesing, and Valerie T. Eviner comes at just the right moment in offering some refreshing ideas and opening new research avenues on the exact role played by parasites in ecosystems, and <italic>vice versa</italic>. The book is composed of four parts – Part I, <italic>Effects of Ecosystems on Disease</italic>; Part II, <italic>Effects of Disease on Ecosystems</italic>; Part III, <italic>Management and Applications</italic>; Part IV, <italic>Concluding Comments: Frontiers in the Ecology of Infectious Diseases</italic>. It puts together 22 different chapters, most having being written by the more talented and influential research individuals in the fields of ecology of infectious diseases and disease management. People more interested in vectors and parasites will find some important reconsiderations on vector-parasite interactions since, in reality, many parasites may be capable of adapting on multiple vector species <italic>en route </italic>for transmission and many vectors may support multiple pathogens as well. With this book, following a series of some previous contributions, we are definitely entering into a new research dimension in which parasites are not just considered as killers that might be fought, not put at the centre of all Earth ecosystems organization, but put at their right place: they constitute an important component of biological diversity and organization. Some new ideas and theories exposed in the book, like the development of adequate models to deal with complex systems, the extension of the Red Queen hypothesis at a community-scale level or of the resilience theory to socioecological systems, and the application of ecological theories to disease management and policies, will be of particular interest to the reader. I did appreciate reading this important masterpiece of ecology of infectious diseases' science, which in its Part III opens a necessary and important discussion, that still needs to be further pursued, with management- and policy-makers (I am not totally convinced that it is to only research ecologists to make the first step in approaching these categories of professionals, but it is a two-way process as ecology informs us!). Time is gone to reconsider our viewpoint on parasites and to help every citizen understanding the basic linkages between ecosystems, diseases and health. Again, micro-organisms and others viruses and prions are not just like enemies in ecosystems, but they constitute for sure the bulk of living diversity on the planet, thus contributing to its organization and evolution.</p>", "<title>Competing interests</title>", "<p>The author declares that they have no competing interests.</p>" ]

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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>" ]

[ "<p>BioMed Central have removed this article from the public domain as the article was published in error.\n</p>" ]

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[ "<title>Introduction</title>", "<p>In most societies mental illness carries a substantial stigma [##REF##10945080##1##,##REF##15863750##2##]. The mentally ill are often blamed for bringing on their own illnesses, while others may see them as victims of unfortunate fate, religious and moral transgression, or even witchcraft. Such stigma may lead to denial on the part of the family that one of their members is psychiatrically ill. Some families may hide or overprotect a member with mental illness, thus keeping the person from receiving potentially effective care.</p>", "<p>Stigma remains a powerful negative attribute in all social relations. It is considered as an amalgamation of three related problems: a lack of knowledge (ignorance), negative attitudes (prejudice), and exclusion or avoidance behaviours (discrimination) [##UREF##0##3##,##UREF##1##4##].</p>", "<p>The mentally ill are labelled as different from other people and are viewed negatively by others. Stigmatisation can lower a person's self esteem, contribute to disrupted family relationships, and affect employability [##REF##7973472##5##]. It is a barrier to the provision of mental health services by health planners [##UREF##2##6##].</p>", "<p>Many studies have demonstrated that persons labelled as mentally ill are perceived with more negative attributes and rejection regardless of their behaviour [##UREF##3##7##, ####UREF##4##8##, ##REF##7822113##9####7822113##9##]. Research has shown that people who are labelled as mentally ill associate themselves with society's negative conceptions of mental illness, and that society's negative reactions contribute to the incidence of mental disorder. [##UREF##5##10##]. However, other studies have demonstrated that negative societal reactions are the result, rather than the cause, of mental illness [##UREF##6##11##].</p>", "<p>Individuals who perpetuate stigma are likely to socially distance themselves from persons with mental illness. Social distance may manifest itself in such discriminatory practices as, for example, not renting property to or hiring people who have psychiatric disabilities [##REF##7973472##5##,##UREF##7##12##].</p>", "<p>Stigmatising views about mental illness are not limited to uninformed members of the general public; even well-trained professionals from most mental health disciplines subscribe to stereotypes about mental illness [##UREF##8##13##,##REF##8585582##14##]. Medical students have been shown to have stigmatising attitudes toward mental illness which they hold onto in their professional lives [##UREF##9##15##]. Therefore, research on attitudes toward mental illness, specifically of those in mental health related fields, is necessary to ensure quality care to persons with mental illness. This is important because interventions directed at these target groups may be more cost effective than interventions directed at the general public [##REF##16946814##16##].</p>", "<p>This study aimed to evaluate the effect of a psychiatric label attached to an apparently normal person on the attitude of final year medical students in a Nigerian university.</p>" ]

[ "<title>Methods</title>", "<p>This was a cross sectional questionnaire based study conducted among the final year medical students of Olabisi Onabanjo University, Ogun State. Participation was on a voluntary basis. A questionnaire containing demographic information, a single-paragraph case description illustrating a normal person, a social distance scale and questions on expected burden was used to elicit response from the students.</p>", "<p>The students were randomly assigned into two groups using their matriculation numbers. Group A received a case description with a psychiatric label attached while group B received the same case description but without a psychiatric label.</p>", "<p>The case description is as follows 'Mr AB is a young man who can express his feelings and thoughts among those close to him, although he sometimes gets anxious while talking in a group consisting of strangers. He gets along all right with his family most of the time. Generally he also gets along with other people. Compared to those of his age, his life can be considered as organised. He is generally an optimistic and happy person. In summary, he establishes a good balance between his social life and study'. The students were assigned to one of the two conditions of the case description. One condition involved adding the sentence 'This young man has been diagnosed as having mental illness by the doctor who examined him' to the end of the case description. In the second condition no psychiatric label was attached to the case description.</p>", "<p>Each case description was followed by 16 questions to be rated on a 4-point scale ranging from definitely agree to definitely disagree. The questions from 1 to 13 were designed to measure social distance between oneself and the person depicted in the case description while questions 14, 15 and 16 assess the possible burden expected from a mentally ill person one may associate with. The case description and the questionnaire were modified versions of those used in previous studies that concerned psychiatric label and attitude to mental illness [##UREF##10##17##].</p>", "<p>The data derived from the responses of the students were analysed using SPSS v.10 (SPSS Inc., Chicago, IL, USA). Results are presented in frequencies and percentages. The Chi square test was used to determine statistical difference between proportions while the Student t test was used to determine the statistical difference between means. A p value less than 0.05 was considered as statistically significant.</p>" ]

[ "<title>Results</title>", "<p>A total of 144 students responded to the questionnaire out of a class of 167. Thus, the response rate was 86.2%. In all, 81 (56.2%) were males while 63 (43.8%) were females. A total of 68 (47.2%) of the students responded to the questionnaire with the attached psychiatric label (male 55.9%, female 44.1%) while 76 (52.8%) responded to the questionnaire without the attached psychiatric label (male 56.6%, female 43.4%) (p = 0.933). The mean (SD) age of the students that responded to the questionnaire with the attached psychiatric label was 27.07 (3.33) years compared with 26.96 (2.18) years for those that responded to the questionnaire without the psychiatric label (p = 0.187).</p>", "<p>Table ##TAB##0##1## shows the responses of the students to the questions about the man depicted in the case description. The students that responded to the questionnaire with the attached psychiatric label were significantly more unwilling to rent out their houses to the man depicted in the case description compared to those that responded to the questionnaire without the attached label (p = 0.003). Similarly, they were unwilling to have him as their next-door neighbour (p = 0.004) or have him as their barber or hairdresser (p = 0.000) compared with the group that responded to the questionnaire without the attached label. They were also not willing to share an office with him (p = 0.000) or allow their sister to get married to him (p = 0.000).</p>", "<p>Significantly, the students that responded to the questionnaire with the attached label felt that the man in the case description will exhaust them both physically (p = 0.005) and emotionally (p = 0.021) in their relationship with him, compared with those that responded to the questionnaire without the psychiatric label.</p>" ]

[ "<title>Discussion</title>", "<p>This study set out to investigate the effect of a psychiatric label attached to an apparently normal person in a case description on the attitude of final year medical students toward psychiatrically ill patients. The students have had previous clinical exposure to psychiatry in the course of their medical training. The finding in this study indicated that a label of mental illness on the person depicted in the case description elicited negative attitude that resulted in the students wanting to maintain a significant distance from the person that was labelled mentally ill. The results provided strong support for the influence of labelling on certain attitudes. These attitudes were more obvious in circumstances that could bring a closer relationship between the respondents and the person depicted in the case description. They were not willing to have him as their barber/hairdresser, they would discourage their sister from planning to get married to him, and they were uncomfortable with the thought of sharing an office with him. These findings are consistent with previous studies on the influence of psychiatric label on attitude towards mental illness [##UREF##4##8##,##REF##7822113##9##,##UREF##10##17##]. Furthermore, the students felt that friendship with the labelled person would be a burden on them physically and emotionally. This could further worsen the social distance between them and the labelled person. These stigmatising attitudes have been shown to increase psychological distress in people labelled to be mentally ill [##REF##9212538##18##]. Moreover such attitudes may inhibit help seeking among individuals with a mental disorder [##REF##7939964##19##,##REF##15491256##20##] and provide barriers to their successful reintegration into the society [##REF##11354589##21##].</p>", "<p>The findings in this study provide support for an earlier report by Adewuya and Makanjuola [##REF##16234984##22##] on the attitudes of students generally toward the mentally ill in a Nigerian university. This however, challenges studies where less stigmatisation of mental illness was reported for non-Western cultures especially of Asian and African countries [##REF##1778081##23##,##UREF##11##24##]. Although a dearth of research on this issue was given for the observation in these cultures, Fabrega, however, noted that lack of differentiation between psychiatric and non-psychiatric disorders in non-Western cultures could be an important factor for less stigmatisation [##REF##1778081##23##,##UREF##11##24##]. It is however important to note that this study was conducted in a group of students who are medically inclined, and hence they should be able to differentiate between psychiatric and non-psychiatric disorders.</p>", "<p>Although a considerable number of studies have consistently reported improvement in attitude of medical students toward psychiatry after clinical exposure [##REF##16379188##25##,##REF##16223898##26##], follow-up studies on such students have queried the sustenance of the observed improvement in attitude toward psychiatry over time even before they eventually graduate from medical school [##REF##11319003##27##]. Thus, the finding of a stigmatising attitude of the final year medical students in this study may be a reflection of this decay. The challenge then will be to find a way of sustaining the initial improvement reported in the literature. The focus will be to provide a more cost effective approach of educating the medical students on stigma reduction in mental health. This is particularly important because stigma involves different but related facets [##UREF##0##3##,##UREF##1##4##]. This is however, hindered by deficiencies in the mental health curriculum in medical schools where little or no attention is given to stigma as an issue. Moreover, the common medical textbooks in psychiatry fail to devote elaborate attention to issues on stigmatisation of mental illness.</p>", "<p>In conclusion, medical students are not exonerated from the list of people that express stigmatising attitude toward the mentally ill. There is therefore the need to equip the students with more knowledge on stigma reduction in mental health.</p>" ]

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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The aim of this study is to evaluate the effect of a psychiatric label attached to an apparently normal person on the attitude of final year medical students at a Nigerian university.</p>", "<title>Methods</title>", "<p>A questionnaire with sections on demographic information, a single-paragraph case description illustrating a normal person, a social distance scale and questions on expected burden was used to elicit responses from 144 final year medical students who have had previous exposure to psychiatric posting. The students consisted of two randomly assigned groups; group A received a case description with a psychiatric label attached while group B received the same case description but without a psychiatric label.</p>", "<title>Results</title>", "<p>A total of 68 (47.2%) of the students responded to the questionnaire with the attached psychiatric label, while 76 (52.8%) responded to the questionnaire without the attached label. There was no statistical difference in age (p = 0.187) and sex (p = 0.933) between the two groups of students. The students who responded to the questionnaire with the attached psychiatric label would not rent out their houses (p = 0.003), were unwilling to have as their next-door neighbour (p = 0.004), or allow their sister to get married (p = 0.000) to the man depicted in the case description compared with those that responded to the questionnaire without label. This group also felt that the man would exhaust them both physically (p = 0.005) and emotionally (p = 0.021) in any relationship with him.</p>", "<title>Conclusion</title>", "<p>These results strengthen the view that stigma attached to mental illness is not limited to the general public; medical students are also part of the stigmatising world. There is, therefore, a need to incorporate issues concerning stigma and its reduction as a core component of the mental health curriculum of medical schools.</p>" ]

[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>OOO conceived the study, and participated in its design, acquisition, analysis and interpretation of data, and in the drafting of the manuscript. OO participated in its coordination, statistical analysis and helped to draft the manuscript. MOO participated in the design of the study, its coordination and the draft of the manuscript. All authors read and approved the final manuscript.</p>" ]

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[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>responses of the students to the person depicted in the case description</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\"><bold>Label attached</bold><break/><bold> frequency, % (n = 68)</bold></td><td align=\"left\"><bold>No label attached:</bold><break/><bold> frequency, % (n = 76)</bold></td><td align=\"left\"><bold>p Value</bold></td></tr></thead><tbody><tr><td align=\"left\">Uncomfortable sitting close to him on public transport</td><td align=\"left\">36 52.9</td><td align=\"left\">31 40.8</td><td align=\"left\">0.144</td></tr><tr><td align=\"left\">Disturbed by shopping from a market which he runs</td><td align=\"left\">13 19.1</td><td align=\"left\">16 21.1</td><td align=\"left\">0.774</td></tr><tr><td align=\"left\">Willing to let your house to him</td><td align=\"left\">39 57.4</td><td align=\"left\">61 80.3</td><td align=\"left\">0.003</td></tr><tr><td align=\"left\">Ill at ease by his working as a gateman at your house</td><td align=\"left\">31 45.6</td><td align=\"left\">25 32.9</td><td align=\"left\">0.112</td></tr><tr><td align=\"left\">Disturbed participating in a social gathering to which he has been invited</td><td align=\"left\">13 19.1</td><td align=\"left\">20 26.3</td><td align=\"left\">0.308</td></tr><tr><td align=\"left\">Willing to play cards with him at a social gathering</td><td align=\"left\">54 79.4</td><td align=\"left\">50 65.8</td><td align=\"left\">0.087</td></tr><tr><td align=\"left\">Willing to chat with him on political matters at a social gathering</td><td align=\"left\">41 60.3</td><td align=\"left\">49 64.5</td><td align=\"left\">0.614</td></tr><tr><td align=\"left\">Willing to tell him about your own private problems</td><td align=\"left\">19 27.9</td><td align=\"left\">27 35.5</td><td align=\"left\">0.305</td></tr><tr><td align=\"left\">Disturbed by his becoming your next-door neighbour</td><td align=\"left\">20 29.4</td><td align=\"left\">08 10.5</td><td align=\"left\">0.004</td></tr><tr><td align=\"left\">Will have my hair cut/styled by him if he was a barber/hairdresser</td><td align=\"left\">26 38.2</td><td align=\"left\">57 75.0</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">Disturbed by working in the same place as him</td><td align=\"left\">03 04.1</td><td align=\"left\">05 06.6</td><td align=\"left\">0.616</td></tr><tr><td align=\"left\">Will be worried sharing the same room with him if you work at the same place</td><td align=\"left\">34 50.0</td><td align=\"left\">12 15.8</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">Disturbed by your sister wanting to marry him</td><td align=\"left\">49 72.1</td><td align=\"left\">28 36.8</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">Will be an emotional burden on you in your friendship with him</td><td align=\"left\">17 25.0</td><td align=\"left\">09 11.8</td><td align=\"left\">0.021</td></tr><tr><td align=\"left\">Will exhaust your physical energy in your friendship with him</td><td align=\"left\">20 29.4</td><td align=\"left\">09 11.8</td><td align=\"left\">0.005</td></tr><tr><td align=\"left\">Your friendship with him will have a negative influence on your mental health</td><td align=\"left\">08 11.7</td><td align=\"left\">10 13.2</td><td align=\"left\">0.884</td></tr></tbody></table></table-wrap>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>Aggressive angiomyxoma is a rare mesenchymal tumor occurring predominantly in the pelvi-perineal region of females. We report such a case presented as abdominal as well as gluteal lump, a very unusual presentation. Patient underwent laparotomy and tumor was successfully excised.</p>" ]

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[ "<title>Discussion</title>", "<p>Aggressive angiomyxoma is an uncommon mesenchymal neoplasm occurring predominantly in the pelvi-perineal region of adults, first described in 1983 by Steeper and Rosai [##UREF##0##1##]. About 90% of patients are women, usually of reproductive age [##REF##16317491##2##]. A few cases have been described in males, usually in scrotum. It presents as a painless, poorly circumscribed gelatinous vulvar mass and clinically simulates a bartholin gland cyst or an inguinal hernia. On gross examination the tumors are lobulated, soft to rubbery, solid masses. The cut surface reveals a glistening, soft homogeneous appearance. Recurrent tumors show more prominent areas of hemorrhage and fibrosis. Histologically angiomyxoma is a mesenchymal tumor, composed of fibroblasts within a strong myxoid background. Vascular proliferation is also prominent, and virtually no mitoses are present [##REF##15735978##3##]. The vast majority of cases demonstrate positivity for desmin in the myxoid bundles and/or stromal cells, while actins and CD34 may be variably positive [##REF##15735978##3##]. The estrogen and progesterone receptor positivity suggests that aggressive angiomyxoma might be hormone dependent as rapid growth has been observed during pregnancy. The tumor grows slowly, and its benign nature is suggested by the histology and by the fact that it shows no tendency to metastasize. However, it is locally aggressive and tends to recur (36–72%) after resection [##UREF##1##4##]. Imaging of these tumors is important to determine extent and, thus, the optimal surgical approach. Sonography shows a mass that is hypoechoic or appears frankly cystic. Angiography usually shows a generally hypervascular mass. It has a characteristic appearance on CT and MR imaging and these techniques reveal the extent of the tumor as well. On CT, the tumor has a well-defined margin and attenuation less than that of muscle. On T2-weighted MR imaging, the tumor has high signal intensity [##UREF##1##4##]. Treatment is usually surgery in form of wide local excision. Preoperative angiographic embolization, preoperative external beam irradiation and intraoperative electron beam radiotherapy are useful to decrease the chances of local recurrence [##REF##9559638##5##]. Hormonal treatment with a gonadotropin-releasing hormone agonist can be applied for small primary aggressive angiomyxomas in addition to adjuvant therapy for residual tumors [##REF##15157218##6##].</p>" ]

[ "<title>Conclusion</title>", "<p>Although a rare diagnosis, aggressive angiomyxoma can present with unusual features. Detailed radiological examination is helpful in suspecting the problem, but histology is gold standard for diagnosis. Wide excision is curative and prognosis of such tumor is good.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Agressive angiomyxoma is a rare mesenchymal neoplasm. It mainly presents in females. We here present a case of angiomyxoma presenting as huge abdominal lump along with gluteal swelling. Case note is described along with brief review of literature.</p>" ]

[ "<title>Case presentation</title>", "<p>An eighteen year old unmarried female presented with progressive distension of abdomen and swelling in left gluteal region for last ten months. It was associated with mild dull aching pain in lower abdomen and bilateral flanks. She was having normal menstrual history. Examination revealed distended abdomen and a non-tender, diffuse, firm and dull mass was felt all over abdomen. Free fluid in peritoneum could not be demonstrated. There was another 10 × 8 cm boggy swelling on upper postero-medial aspect of left thigh having cross fluctuation with abdominal swelling. On ultrasonography 30 × 18 × 16 cm complex cystic mass was found occupying whole abdomen and pelvis with internal septations and echoes displacing bowel loops posteriorly with mild right sided hydro-ureteronephrosis. CECT scan (Figure ##FIG##0##1##) showed heterogeneous soft tissue mass, adjacent to coccyx, involving left gluteal region and extending superiorly into pelvis and abdomen with anterolateral displacement of urinary bladder and lateral displacement of bowel with right hydronephrosis. Rectum was displaced anteriorly and to the right. The lesion was complex with variable soft tissue attenuation (+23.0 HU). Fat plane between the mass lesion and pelvic musculature and abdominal wall muscles was intact. Fine needle aspiration cytology was inconclusive and trucut needle biopsy showed round to spindle shaped cells in a loose to fibrous stroma with fair number of intervening vessels, suggestive of spindle cell tumor.</p>", "<p>Patient underwent laparotomy and the tumor was excised. The tumor was found to be bilobed, weighing about 15 Kg. (Figure ##FIG##1##2##). Rectum was displaced anteriorly and to right. Right ureter was displaced laterally with hydroureter. Tumor was extending into thigh through left obturator foramen. Urinary bladder was adherent to mass and displaced anteriorly. Uterus and fallopian tubes were normal, tumor was found to be originating from rectovaginal septum. Liver was normal and there was no ascites or lymphadenopathy.</p>", "<p>The histopathology of the specimen showed capillary and cavernous vascular spaces stuffed with blood and separated by edematous fibrous and myxomatous tissue. The fibrous tissue is in form of interlacing or parallel bands of collagen with edema. The myxomatous tissue comprise of stellate cells with fibrillary in mucinous setting (Figure ##FIG##2##3##). Immuno-histochemistry was positive for vimentin and desmin while negative for actin and myosin. These findings were consistent with the diagnosis of aggressive angiomyxoma.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>AKK and RK: operating surgeons, SK: collected clinical details including photographs, summarized the case history and prepared final draft, NA: conducted a literature search and prepared first draft. All authors read and approved the final manuscript.</p>", "<title>Consent</title>", "<p>A fully informed written consent was obtained from the patient for the publication of this case report and accompanying images. A copy of the written consent can be sent to Editor-in-Chief of this journal if article is accepted for publication.</p>" ]

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[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>CECT of the abdomen.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Intraoperative photograph of the tumor.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Microscopic photograph of the tumor.</p></caption></fig>" ]

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[ "<graphic xlink:href=\"1757-1626-1-131-1\"/>", "<graphic xlink:href=\"1757-1626-1-131-2\"/>", "<graphic xlink:href=\"1757-1626-1-131-3\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p>Increasing activity in the fields of cardiac and vascular surgery has served to revive interest in the developmental and adult anatomy of the aortic arches and the great vessels derived therefrom. In the course of study of specimens in the Laboratory of Gross Anatomy, it became abundantly and strictly evident that the \"standard\" type of branching from the aortic arch not only existed in the preponderant number of cases, but also, when placed with a rather ordinary variation thereof, gave a combined total that represented over 90 per cent of cases in a series of 1000 specimens [##UREF##0##1##].</p>", "<p>The summit of the arch is usually 2,5 cm approximately below the superior sternal border but may diverge from this. Sometimes the aorta curves over the right pulmonary hilum (as a right aortic arch) descending to the right of the vertebral column, accompanied by a transposition of thoracic and abdominal viscera. Less often, after arching over the right hilum, it passes behind the oesophagus to its usual position; this is not accompanied by visceral transposition. The aorta may divide into ascending and descending trunks, sometimes dividing near its origin and the two branches soon reuniting; the oesophagus and trachea usually pass through the interval between them [##UREF##1##2##].</p>", "<p>As far as the branches of the aortic arch are concerned, there is a plenty of variations in the origins of them. An analysis of variation in branches from 1000 aortic arches showed the following findings: In 27%, the left common carotid artery originates from the brachiocephalic trunk. In 2,5%, each of the four arteries originate independently from the arch of the aorta, while in 1,2%, right and left brachiocephalic trunks originate from the arch of the aorta. The most common pattern in 65% is formed by the separate origination of three branches springing from the vessel's convex aspect: the brachiocephalic trunk, left common carotid and left subclavian arteries [##UREF##2##3##]. In our work we present a rare type of left common carotid artery origin from the initial portion of the brachiocephalic trunk.</p>" ]

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[ "<title>Discussion</title>", "<p>Three branches, as it is known, spring from aortic arch convex aspect: the brachiocephalic trunk, left common carotid and left subclavian arteries [##UREF##3##4##]. The ascending aorta arises from the base of the left ventricle behind the left sternal margin opposite the third costal cartilage. The arch of the aorta lies behind the lower part of the sternal manubrium. It begins behind the right border of the sternum at the level of the second rib cartilage, and extends dorsally to the left to reach the spine at the left of the body of the fourth thoracic vertebra [##UREF##1##2##]. The excision of the right lobe of the thyroid gland reveals the relation of the carotid and the subclavian arteries and the intervening portion of the aortic arch to the trachea [##UREF##0##1##]. They may branch from the beginning of the arch of the upper part of the ascending aorta; the distance between these origins varies, the most frequent being approximation of the left common carotid artery to the brachiocephalic trunk [##UREF##1##2##]. Primary branches may be reduced to one, more commonly two, the left common carotid arising from the brachiocephalic trunk (7%) [##UREF##1##2##], while Anson [##UREF##2##3##] rise this incidence to 27%. However Anson [##UREF##2##3##] referred to the presence of a left common carotid artery arising from the initial portion of the brachiocephalic trunk in a frequency of 0,2%. Because of the many changes involved in transformation of the embryonic aortic arch system into the adult arterial pattern, it is understandable that variations may occur. Most anomalies result from the persistence of parts of the aortic arches that normally disappear or from disappearance of parts that normally persist. Several kinds of uncommon defect occur when arches persist instead of becoming obliterated or vice versa. A right aortic arch occurs when the left fourth arch and dorsal aorta disappear. If the left fourth arch alone (and not the dorsal aorta as well) disappears, the condition of interrupted arch arises; the first part of the arch gives off the brachiocephalic and left common carotid vessels, and beyond the gap the pulmonary trunk and a persistent patent ductus (sixth arch) are required to complete an \"arch\" with the left dorsal aorta. If the right dorsal aorta persists as well as the left, a double arch ensues with the trachea and oesophagus clasped between the two [##UREF##3##4##,##UREF##4##5##]. Of course the analysis of aortic arch variability in morphology is beyond the aim of our study.</p>", "<p>In Anson's analysis of variation in branches from 1000 aortic arches there was a 65% of the usual pattern, a 25% of the four large arteries branching separately, the remaining 5% showed a great variety of patterns, the commonest (1,2%) being symmetrical right and left brachiocephalic trunks [##UREF##2##3##].</p>", "<p>More rarely, the left common carotid and subclavian arteries arise from a left brachiocephalic or right common carotid and subclavian arteries arise separately, in which case the latter more often branches from the left end of the arch and passes behind the oesophagus [##UREF##0##1##,##UREF##1##2##,##UREF##5##6##,##UREF##6##7##]. This anomaly assumes some importance in the adult as well as in the child, as a cause of esophageal compression. The abnormal course of the \"recurrent\" laryngeal nerve, which accompanies this anomaly, is also important [##UREF##0##1##].</p>", "<p>The left vertebral artery may arise between the left common carotid and the subclavian. Very rarely, external and internal carotid arteries arise separately, the common carotid being absent on one or both sides, or both carotids and one or both vertebrals may be separate branches. In about 12% the right common carotid artery arises above the level of the sternoclavicular joint or it may be a separate branch from the aorta; again it may arise with its fellow. The left common carotid artery varies in origin more than the right.</p>", "<p>When a \"right aorta\" occurs, the arrangement of its three branches is reversed. The common carotids may have a single trunk, the subclavians separate, the right arising from the left end of the arch. Other arteries may branch from it, most commonly one or both bronchial arteries and the arteria thyroide ima [##UREF##1##2##].</p>", "<p>As it is known specific interest is shown in surgery with respect to the relation of an anomalous arch or arches to the viscera in the neck and the thorax. Additionally, a variant of origin and course of a great vessel arising from the aortic arch is of great clinical value, because the ignorance on behalf the surgeon of such a variation may cause serious surgical complications during procedures occurring in the superior mediastinum and the bare of neck.</p>" ]

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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>An abnormal origin of the left common carotid artery from the initial portion of the brachiocephalic trunk was found in the superior mediastinum in a 81-year-old Caucasian male cadaver during dissection practice. We report on the exact morphology of that variant that is appeared in an incidence of 0,2% in the literature. We discuss the relative literature and pay attention on the significance of such a variation for clinicians in its recognition and protection.</p>" ]

[ "<title>Case presentation</title>", "<p>We dissected a 31-year-old, Caucasian, male, formaline-fixed cadaver. His ethnicity was Greek. His weight was 83 kg and the height 1,78 m. He had no past medical history and was on no medication. He did not use to smoke or drink (according to his next of kin). The dissection was carried out as part of the practice for the medical students and was approved by the Ethical Committee of the University. During the anatomical preparation we came across a variation referring to the branches springing from the aortic arch. After resection of the anterior thoracic wall we removed carefully the fat tissue and the pericardium covering the ascending aorta and the great vessels arising from it. Having obtained a clear view of the great vessels we noticed the presence of a left common carotid artery arising from the left surface of the origin site of the brachiocephalic trunk (Figure ##FIG##0##1##).</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>GP did the dissection and supervised the manuscript writing. AP performed the literature review and obtained the written consent. MS and AS obtained the photos and wrote the draft of the manuscript. AT helped to the final writing of the paper. All authors read and approved the final manuscript.</p>", "<title>Consent</title>", "<p>A written consent was obtained by the cadaver's next of kin for publication of the article.</p>" ]

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[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Picture of the cadaveric preparation.</bold> The left common carotid artery (b) is shown arising from the initial portion of the brachiocephalic trunk (c) (a: aortic arch, d: ascending aorta).</p></caption></fig>" ]

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[ "<graphic xlink:href=\"1757-1626-1-83-1\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>Finland is a country situated in the north of Europe, consisting of approximately 5 million inhabitants. The climate is characterized by cold winters and relatively warm summers. Most inhabitants, 95%, speak Finnish, a language that is difficult to learn for foreigners since its vocabulary lacks common roots with most other European languages, and has a structure that differs significantly from the classical languages. English was not taught as a foreign language to all primary school pupils until the early 1970s. As a result the Finnish people was not strongly influenced by the Anglo-American culture.</p>", "<p>The previous main sources of income were forestry, agriculture, fishery and heavy industry. In the period between its liberation from Russia in 1917 until World War II, the economy was weak. Finland was deeply scarred by its participation in World War II, and the economy improved only slowly thereafter, partly due to the large post-war \"fine\" that had to be paid to the Soviet Union. Over the past decades, an advanced electronic industry has developed and much of the rural population has become urbanized. The standard of living is now high, as is the educational standard, and much emphasis is put on various public health measures [##UREF##0##1##, ####UREF##1##2##, ##UREF##2##3####2##3##].</p>", "<p>Traditionally, folk medicine has played a large role, and still does, particularly among people in the rural areas. It is therefore not surprising that Finland was late to \"discover\" chiropractic. Not until in the early 1950s did the first North American-educated chiropractor set up practice, followed by a second chiropractor 20 years later. Subsequently, the profession grew very slowly with competition from folk healers and manual therapists, who were typically trained through weekend courses.</p>", "<p>Attempts have been made to ensure a world-wide minimum standard for all chiropractic institutions, through the use of an eductional control organisation, the Council on Chiropractic Eduction (CCE). There are branches in different parts of the world, and the European branch (ECCE) inspects and certifies all European chiropractic institutions, whether private or state funded. Chiropractic educational institutions that have achieved full certifiction by the ECCE are the Anglo-European College of Chiropractic, UK, the University of Glamorgan, UK, l'Institut Franco Européen de Chiropratique, France, and the University of Southern Denmark, Denmark. There is no ECCE-approved chiropractic education in Finland, so a chiropractic degree must be obtained in foreign countries.</p>", "<p>During the late 1980s and early 1990s legislation for chiropractors was introduced in the other Nordic countries. In Finland, a new law introduced in 1994 licenced chiropractors with an academic degree from foreign chiropracic educational institutions, and grants were made available for students to study chiropractic abroad. The legal situation for the chiropractic profession has improved, but the working conditions are still unsatisfactory. Although the professional title recently became protected by law, chiropractors are unable to refer patients to other health care providers, cannot perform their own radiological examination, do not have direct access to imaging services, and may not prescribe sick leave. Unlike in the other Nordic countries, chiropractic patients in Finland are not entitled to government-subsidized reimbursement.</p>", "<p>Despite this situation, the size of the Finnish Chiropractic Union membership has, according to personal communication with its secretariat, increased five-fold during the last 15 years. To date there are, according to communication with the Finnish National Authority for Medicolegal Affairs, 70 chiropractors in Finland with either a DC degree or an academic chiropractic degree, of which 52 (Jan 2008) are members of the Finnish Chiropractic Union.</p>", "<p>It was the purpose of this study to describe the chiropractic profession in Finland, in terms of demographic and educational background. The demographics of their clinics included location, practice pattern, interactions with other health care practitioners, and some of the chiropractors' plans for the future.</p>" ]

[ "<title>Methods</title>", "<p>A survey was conducted using a structured questionnaire [Additional file ##SUPPL##0##1##: Demographic questionnaire for chiropractors in FCU]. All chiropractors who at the time of the study were members of the Finnish Chiropractic Union (N = 50) were invited to participate. The selection of participants was limited to members of the Finnish Chiropractic Union to ensure participation of graduates from CCE/ECCE accredited educational institutions.</p>", "<p>Appropriately qualified graduates who were non-members of the Finnish professional association were not approached because they, from experience, are unwilling to participate in any communal activities. The questionnaire was first tested in a pilot study on 10% of the members for face validity, and the main survey was then administered in June 2005.</p>", "<p>Approval was sought from the Helsinki University Ethics Committee, but because the survey was considered a quality assurance project approval was not needed. However, all questionnaires were coded to avoid recognition of respondents and the code key was destroyed when data collection was completed. Return of questionnaire implied consent from the participant. In order to respect the anonymity of the participants, in such a small group of practitioners, no comparison was made between responders and non-responders.</p>", "<p>Data were entered into the SPSS 11.0 spreadsheet by a person experienced in data entry. Eleven questionnaires were randomly selected and each item was manually checked versus the entered data. No errors in the data entry were identified, which was considered satisfactory.</p>", "<p>Therefore it was not considered necessary to undertake a double entry.</p>", "<p>Analysis was done using SPSS 11.0 and Minitab. Some of the variables were grouped into fewer categories, based on the frequency of responses. The results were reported as descriptive data in tables and summarized in the text.</p>" ]

[ "<title>Results</title>", "<title>Description of study sample</title>", "<p>Forty-four of the 50 distributed questionnaires were returned, a response rate of 88%. Eighty percent of the respondents were men and 77% were aged 30 to 44 years [Additional file ##SUPPL##1##2##]. Eighty percent practised in a city suburb or city center. Forty-eight percent had one practice only, followed by 40% with two practices, and 12% with more than two practices. However, about one third expected to enter a partnership with a colleague within the next two years. Forty-five percent worked together (in the same clinic) with another health care provider and another 25% expected to do so within two years. Two of the respondents expected not to be working as a chiropractor at that time.</p>", "<p>Fourteen percent employed a (non-chiropractic) assistant and a further 23% expected to do so within the next two years. About half had access to a receptionist. The majority (77%) graduated between 1990 and 2004 and 53% reported to have practised actively for a maximum of 10 years [Additional file ##SUPPL##2##3##]. Only 32% had a diploma of Doctor of Chiropractic, the remaining had either a university-based bachelor or master degree. Nine percent would consider undertaking an additional university degree. In addition, almost half of the members subscribe to a professional journal, usually the Journal of Manipulative and Physiological Therapeutics. In relation to interactions with other health care professions, these seem to be reasonably good [Table ##TAB##0##1##]. For example, the mean number of conversations/phone calls with other health care personnel in the past week was 3.3.</p>", "<title>Scope of practice</title>", "<p>The survey instrument also included questions on scope of practice, type of technique used, and adjunctive therapies used [Additional file ##SUPPL##3##4##]. The vast majority described their scope of practice to be based on a musculoskeletal approach. Almost all used the Diversified Technique, the vast majority performed Soft Tissue Therapy, and about two-third also made use of an Activator Instrument.</p>", "<p>Various adjunctive therapies were used but none of these was used by all or even by the majority. Ice was most commonly reported, by 46%. Seventy-seven percent had a viewing box for radiology readings, 40% had the possibility to, indirectly, refer patients for X-ray examination via medical practitioners, of which 9% could refer for MRI or CT scans, whereas the use of ultrasound was very rare.</p>", "<title>Patients</title>", "<p>The mean and median patient numbers, respectively, during the third week of 2005 was reported to be 59 and 47. However, there was a wide range, from 5 to 228. The mean and median number of new patients in that week was 9 and 2, respectively, indicating that the spread of data were skewed. The number of new patients in the past week preceding the study was also stated to be 9.</p>", "<p>Eighty percent of the participants spent 30–45 minutes with their patients at the first visit, 75% spent 20–30 minutes on \"new old\" patients, whereas in subsequent visits 80% of respondents spent 15–30 minutes. At one extreme, one respondent reported spending one minute only on subsequent visits. The number of patient seen wasconsidered to be \"about right\" by 55%, and 9% reported they were seeing more patients than they would like to. However, about one third (36%) would have been happy to see some more patients.</p>", "<p>Eighty percent had the (mandatory) malpractice insurance, 73% had a private pension scheme and almost as many (68%) had a private health care insurance.</p>" ]

[ "<title>Discussion</title>", "<p>The Finnish Chiropractic profession is relatively young and small, compared to other national chiropractic associations in Europe, and the demographics and practice procedures of the profession have never previously been documented [##UREF##3##4##]. Perhaps for this reason, Finnish chiropractors were eager responders to this survey with 88% returning the questionnaire.</p>", "<p>According to the present survey, the members of the Finnish Chiropractic Union consisted of mainly young men (80%), who, in the majority of cases, graduated from university based or university affiliated chiropractic institutions. Information acquired from the administrative offices of corresponding associations in Sweden, Norway and Denmark reveals a different gender distribution, with 70%, 71% and 51%, respectively of men in the three countries. The proportion of male practitioners was lower (63%) also in a recent study of German chiropractors (response rate 72%) [##REF##17480221##5##].</p>", "<p>Despite the young age of the Finnish chiropractors, their current practice pattern was similar to that of the early years of chiropractic in Finland. Typically a Finnish chiropractor is working in solo practice, sharing his time between one or two practices. Sixty percent reported working in a solo practice, whereas, according to a previous study (response rate 70%), the estimated proportion was 41% among European chiropractors in general [##REF##8046278##6##]. In the more recent German study, 45% of the respondents worked in a solo practice setting [##REF##17480221##5##].</p>", "<p>The Finnish Chiropractic Union subscribe to the Chiropractic Report for its members, a cost that is included in the membership fee. Additionally does almost 50% of the members subscribe to one more professional scientic journal. The Finnish chiropactor thus seem to be academically updated. However, regarding future development is only a small number interested in further education at a university level. This is understandable at the present time, considering the isolated position of the chiropractic profession, and the absence of chiropractic academic institutions in Finland. The time, money and effort spent on further education, would lead to no additional career possibilities.</p>", "<p>Respondents were satisfied with the number of patients and they seemed to enjoy reasonable contacts with other health care practitioners. This may indicate that their professional activities are felt to be fulfilling. Nevertheless, two of the respondents were planning to leave the profession, although they were not close to retirement age.</p>", "<p>Most reported to have a musculoskeletal approach, using mainly Diversified Manipulation Technique, Soft Tissue Techniques and Activator Instrument. These are methods previously reported frequently to be used in Europe [##REF##17480221##5##, ####REF##8046278##6##, ##REF##7790791##7####7790791##7##].</p>", "<p>The use of adjunctive therapies showed a less distinct pattern, perhaps because chiropractors determined that different patients require different approaches. It was also interesting that about one-third of the respondents had some sort of rehabilition equipment in their clinic, indicating that they also have the facility to assist patients with general or specific training following the acute treatment stage.</p>", "<p>Regarding professional activities, only some of our data are comparable with information from previous European surveys, such as time spent with patients. The time spent on the first visit appeared to be similar in Finland and the Netherlands (36 and 41 minutes, respectively) [##REF##11050613##8##]. Subsequent visits took 22 minutes in Finland and 15 minutes in the Netherlands.</p>", "<p>Most of the Finnish chiropractors had made sure that they were covered with insurances both for pension scheme and private healthcare but, a small number appeared not to have the obligatory malpractice insurance.</p>", "<p>The limitations of this study are that, despite the high response rate, not all chiropractors with a CCE/ECCE-approved education are members of the Finnish Chiropractors' Union and that not all members of the professional association participated in the survey. It is possible that non-participants in the study have a profile that differs from that of the responders. Other limitations are, of course, that the questionnaire was not exhaustive. For example, the description of practice procedures might have been designed differently by other groups of researchers, and the participants were not encouraged to extend their answers beyond the the questions stated in the questionnaire. Therefore, it is possible that some nuances of clinical practice failed to be recorded. However, the results of the pilot testing of the survey instrument did not indicate that the questionnaire failed to provide meaningful answering options.</p>" ]

[ "<title>Conclusion</title>", "<p>The educational background of the chiropractic participants in this study reflects the recent development in chiropractic education, with university affiliations and masters degrees. Although the Finnish chiropractic profession is relatively young, these chiropractors appeared to have a traditional practice profile: solo practice, a musculoskeletal approach, allowing good time for examination and treatment.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The Finnish chiropractic profession is young and not fully accepted by Finnish healthcare authorities. The demographic profile and style of practice has not been described to date. However, as the profession seems to be under rapid development, it would be of interest to stakeholders, both chiropractic and political, to obtain a baseline description of this profession with a view to the development of future goals and strategies for the profession. The purpose of this study was to describe the chiropractic profession in Finland in relation to its demographic background, the demographics of their clinics, practice patterns, interactions with other health care practitioners and some of the professions' plans for the future.</p>", "<title>Methods</title>", "<p>A structured questionnaire survey was conducted in 2005, in which all 50 members of the Finnish Chiropractic Union were invited to participate.</p>", "<title>Results</title>", "<p>In all, 44 questionnaires were returned (response rate 88%). Eighty percent of the respondents were men, and 77% were aged 30 to 44 years old, most of whom graduated after 1990 with either a university-based bachelors' or masters' degree in chiropractic. Solo practice was their main practice pattern. The vast majority described their scope of practice to be based on a musculoskeletal approach, using the Diversified Technique, performing Soft Tissue Therapy and about two-thirds also used an Activator Instrument (mechanical adjusting instrument). The mean number of patient visits reported to have been seen weekly was 59 of which nine were new patients. Most practitioners found this number of patients satisfactory. At the initial consultation, 80% of respondents spent 30–45 minutes with their patients, 75% spent 20–30 minutes with \"new old\" patients and on subsequent visits 80% of respondents spent 15–30 minutes. Interactions with other health care professions were reasonably good and most of chiropractors intended to remain within the profession.</p>", "<title>Conclusion</title>", "<p>The Finnish chiropractic profession is relatively young. Consequently, many of the practitioners have a university-degree, which reflects recent developments in undergraduate chiropractic education. Their practice profile and the manner in which they practice appear to be fairly traditional.</p>" ]

[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>SM was responsible for planning and executing the demographic survey, participated in the data collection and drafted the manuscript. CL–Y supervised the process. Both SM and CL–Y participated in the design of the study and performed the analysis. Both authors read, finalized and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]

[ "<title>Acknowledgements</title>", "<p>The authors would like to acknowledge the assistance of the chiropractors who participated in the survey and, in particular, the members of the Research Group of the Finnish Chiropractic Union, who helped collect the data.</p>" ]

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[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>A description of professional interactions between 44 Finnish chiropractors and other health care practitioners.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Variables</td><td align=\"left\">Subgroups</td><td align=\"left\">Frequency</td><td align=\"left\">Percentage</td></tr></thead><tbody><tr><td align=\"left\">Received at least one referral last week from...</td><td align=\"left\">Medical practitioner</td><td align=\"left\">28</td><td align=\"left\">64</td></tr><tr><td/><td align=\"left\">Physiotherapist</td><td align=\"left\">12</td><td align=\"left\">27</td></tr><tr><td/><td align=\"left\">Masseur</td><td align=\"left\">23</td><td align=\"left\">52</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Sent at least one report in relation to referral last week</td><td align=\"left\">Yes</td><td align=\"left\">18</td><td align=\"left\">41</td></tr><tr><td/><td align=\"left\">No</td><td align=\"left\">26</td><td align=\"left\">59</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Had at least one conversation/phone call with other health care personnel last week</td><td align=\"left\">Yes</td><td align=\"left\">23</td><td align=\"left\">52</td></tr><tr><td/><td align=\"left\">No</td><td align=\"left\">21</td><td align=\"left\">48</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Quality of co-operation with other health care providers</td><td align=\"left\">Mainly good</td><td align=\"left\">21</td><td align=\"left\">48</td></tr><tr><td/><td align=\"left\">Both good and bad</td><td align=\"left\">12</td><td align=\"left\">27</td></tr><tr><td/><td align=\"left\">Mainly lack of co-operation</td><td align=\"left\">11</td><td align=\"left\">25</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Demographic questionnaire for chiropractors in FCU. A translation of the original Finnish questionnaire.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p>Table 2. Description of 44 Finnish chiropractors and their practice patterns, I.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional file 3</title><p>Table 3. Description of 44 Finnish chiropractors and their practice patterns, II.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional file 4</title><p>Table 4. A description of scope of practice, techniques and adjunctive therapies used according to a survey of 44 Finnish chiropractors.</p></caption></supplementary-material>" ]

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[ "<title>Background</title>", "<p>Identification of the mechanisms of autoimmune inflammatory disease development in the gastrointestinal tract is an emerging topic of research. Inflammatory bowel disease (IBD) is thought to result from immune-mediated tissue injury, primed by the enteric microflora, in genetically predisposed subjects. IBD phenotypically occurs in the form of Crohn's disease (CD) or ulcerative colitis (UC). In these disorders, chronic mucosal inflammation results from inappropriate and overreactive mucosal response to intestinal bacteria followed by activation of inflammatory cells and production of inflammatory mediators [##REF##12167685##1##]. Primary sclerosing cholangitis (PSC) is a chronic inflammatory disease of the hepatic bile ducts that likely develops as a result of an inappropriate immune mediated process [##REF##17100974##2##]. Nearly 80% of northern European PSC patients also have concomitant IBD, mostly in the form of UC [##UREF##0##3##]. Primary biliary cirrhosis (PBC) is another autoimmune chronic cholestatic liver disorder that may also develop as a result of an abnormal immune response to stimulating environmental or infectious agents [##REF##16126958##4##].</p>", "<p>As in other autoimmune diseases, the specific alleles of the human leukocyte antigen (HLA) complex represent genes associated with susceptibility to PSC [##REF##11584356##5##, ####REF##12235090##6##, ##REF##17907284##7####17907284##7##], PBC [##REF##16034472##8##,##REF##15884119##9##] and IBD [##REF##17628615##10##]. It has been reported that the genetic predisposition to CD is caused by alterations in several genes, including <italic>NOD2/CARD15, OCTN1</italic>, <italic>OCTN2</italic>, <italic>DLG</italic>, <italic>ATG16L1 </italic>and <italic>IL23R </italic>[##REF##11385577##11##, ####REF##17068223##12##, ##REF##17786191##13##, ##REF##17206667##14####17206667##14##]. Several other non-HLA genes are also involved in PCS and PBC disease susceptibility [##REF##17907284##7##,##REF##17074031##15##].</p>", "<p>According to our current understanding of CD, PSC and PBC are complex diseases in which the genetic determinants contributing to disease susceptibility interact with environmental factors. However, the mechanisms underlying these interactions remain unclear. Considering high frequency at which IBD and PSC are diagnosed together [##UREF##0##3##], close spatial proximity of affected organs, and the fact that common immune mediated processes are likely involved in development of these diseases [##REF##15557349##16##], searching for common risk alleles for IBD and autoimmune cholestatic liver disorders seems to be relevant</p>", "<p>Most of the susceptibility genes are considered to not be directly involved in disease pathogenesis, but rather act as environmental response modifiers. Given their subtle individual impact on disease susceptibility, independently they may have little to no effect on disease progression and likely act in collaboration with other factors to promote disease development. Moreover, disease genetic markers vary among the populations studied, despite the phenotypic similarity of disease. Thus, analyzing the genetic background of disease is an important challenge, and several studies have been performed with the purpose of finding similarities in the molecular mechanism of development of different inflammatory disorders of the alimentary tract. The aim of this study was to investigate whether polymorphisms of CD susceptibility genes are predisposing factors to the development of PSC and PBC in a group of Polish patients.</p>" ]

[ "<title>Methods</title>", "<title>Patients</title>", "<p>The study included 60 patients with CD (28 men, range of 22 – 78 years old; median 39), 77 patients with PSC (45 men, range of 20 – 68 years old; median 33), of which 61 exhibited IBD as confirmed by endoscopy and histology (40 UC, 8 CD, and 13 indeterminate colitis), and 144 patients with PBC (8 men, range of 40 – 72 years old; median 58). All patients were diagnosed at the Department of Gastroenterology and Hepatology, Medical Center for Postgraduate Education and Cancer Center, Warsaw, Poland. Diagnosis of CD was based on generally accepted clinical, endoscopical and histological criteria. The diagnosis of PSC was based on characteristic biochemical and radiological features (irregularity of the intrahepatic and extrahepatic bile ducts in endoscopic retrograde or magnetic resonance cholangiopancreatography). PBC was diagnosed based on generally accepted clinical, biochemical, serological (antimitochondrial antibody positivity by indirect immunofluorescence) and histological findings [##REF##8900092##17##].</p>", "<p>The control group consisted of 139 healthy volunteers (74 men, range of 21 – 66 years old, median 32) recruited from hospital staff, medical students, and healthy subjects selected for screening colonoscopy. All patients and controls were Polish Caucasians. The study was approved by the local ethics committee (Medical Center for Postgraduate Education and Cancer Center, Warsaw, Poland), and all the participants provided appropriate consent. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki.</p>", "<title>Genotyping</title>", "<p>Genomic DNA was extracted from whole blood treated with EDTA using the NucleoSpin Blood Quick Pure kit (Macherey-Nagel, Germany), following the manufacturer's protocol.</p>", "<p>The selected polymorphisms of genes with previously confirmed evidence for association with CD were as follows: <italic>NOD2/CARD15</italic>: rs2066842 (Pro268Ser), rs2066844 (Arg702Trp), rs2066845 (Gly908Arg), rs5743293 (1007fs); in the <italic>SLC22A4/OCTN1</italic>/<italic>SLC22A5/OCTN2 </italic>genes: rs1050152 (Leu503Phe)/rs2631367 (-207G&gt;C); in the <italic>DLG5 </italic>gene: rs1248696 (Arg30Gln); in the <italic>ATG16L1 </italic>gene: rs2241880 (Thr300Ala); and in the <italic>IL23R </italic>gene: rs11209026 (Arg381Gln), rs1884444 (His3Gln), rs10889677 (exon-3'UTR).</p>", "<p>Genotyping was carried out using TaqMan SNP Genotyping Assays (Applied Biosystems; Foster City, CA). The PCR reactions were performed in 96-well plates on the ABI PRISM 7000 Sequence Detection System (SDS) (Applied Biosystems; Foster City, CA). Each well contained 20 ng genomic DNA, 1.25 μL TaqMan SNP Genotyping Assay (probes and primers mix), 12.5 μL TaqMan Universal PCR Master Mix, No AmpErase UNG (Applied Biosystems; Foster City, CA), and 9.25 μL water. Two non-template-control wells were included on each plate. After DNA amplification (95°C for 10 min, followed by 40 cycles of 92°C for 15 sec and 60°C for 1 min), fluorescence was acquired and analyzed for allelic discrimination using the ABI Prism 7000 SDS Software.</p>", "<title>Statistical analyses</title>", "<p>The frequency distribution of alleles, genotypes, and haplotypes was compared using standard or Yates corrected χ²test and Fisher's exact test when appropriate. Statistical significance threshold level was Bonferroni corrected for multiple hypothesis testing, according to the number of genes tested. The p-value significance threshold level was defined as 0.008. For the Fisher's exact tests, the STATISTICA software was used. ORs with 95% confidence intervals (95% CI) were calculated using a calculator available at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.hutchon.net/ConfidOR.htm\"/>. LD analysis and calculation of the Hardy-Weinberg equilibrium were performed using the Haploview v3.2 software, available at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.broad.mit.edu/mpg/haploview/\"/>. Statistical power analyses were done using G*power's (ver. 3.0.10) [##REF##17695343##18##] post-hoc procedure for the Fisher's exact test.</p>" ]

[ "<title>Results and Discussion</title>", "<p>Allelic distributions for all polymorphisms studied in the patient and control groups are shown in Table ##TAB##0##1##, while genotype counts for each of the studied groups are presented in Additional file ##SUPPL##0##1## [see Additional file ##SUPPL##0##1##]. In the control group, the genotype distributions for all polymorphisms were in Hardy-Weinberg equilibrium (p &gt; 0.05).</p>", "<p>The <italic>Caspase Recruitment Activation Domain 15 </italic>(<italic>NOD2/CARD15</italic>) gene is located in the proximal region of chromosome 16 (16q12) and encodes the nucleotide-binding oligomerization domain protein 2 (NOD2), which is a cytosolic receptor for a bacterial peptidoglycan response pathway [##REF##17379562##19##]. Mutations in NOD2 result in immune system dysfunctions [##REF##11385577##11##,##REF##11385576##20##] and are considered causative genetic factors in the development of CD. <italic>NOD2/CARD15 </italic>mutations are also associated with susceptibility to other granulomatous inflammatory disorders, such as early-onset sarcoidosis and Blau syndrome [##REF##11528384##21##,##REF##15459013##22##].</p>", "<p>Three of the four <italic>NOD2/CARD15 </italic>variants tested (rs2066842, rs2066844, rs5743293) were strongly associated with CD (Table ##TAB##1##2##). In particular, a strong association was observed with Pro268Ser and 1007fs SNPs with odds ratio (OR) values over 10. Increased values of OR for homozygous risk genotypes confirm previously described codominance of these variants. The observed allele distribution was similar to those reported in other Caucasian CD patient groups [##UREF##1##23##, ####REF##12055616##24##, ##REF##12105838##25####12105838##25##].</p>", "<p><italic>The autophagy-related 16-like 1 </italic>(<italic>ATG16L1</italic>) gene encodes the protein component of the autophagosome pathway of intracellular bacteria processing. The T300A variant (rs2241880) was previously considered to be a risk factor for CD development [##REF##17200669##26##,##REF##17435756##27##]. In our CD patients, however, this variant demonstrated only a weak associaf2tion with CD, as the p-value (= 0.022) did not pass the Bonferroni corrected significance threshold level of 0.008.</p>", "<p>The organic cation transporters from the family of solute carrier protein (<italic>SLC22A4/OCTN1 </italic>and <italic>SLC22A5/OCTN2</italic>) genes are located on chromosome 5 (5q31) within the IBD5 region [##REF##16344054##28##]. The 1672C/T (Leu503Phe) missense variant of <italic>SLC22A4/OCTN1 </italic>(rs1050152) potentially alters the carnitine adsorption and the exchange of other positively charged compounds between the cell and extracellular matrix [##REF##16835882##29##]. Also included in this study is the G-to-C transversion in the <italic>SLC22A5/OCTN2 </italic>promoter (rs2631367), which is thought to affect a heat shock transcription factor binding element [##REF##18005709##30##]. The variants of both genes were previously found in linkage disequilibrium, enabling the selection of a two-allele CD risk haplotype [##REF##16344054##28##,##REF##15107849##31##]. This finding, however, has not been confirmed by other studies [##REF##16835882##29##].</p>", "<p>As shown in Table ##TAB##2##3##, there was a weak association between the protective <italic>OCTN1/OCTN2 </italic>CC haplotype and CD (OR = 0.28; CI = 0.08 – 0.94; p = 0.0298). A strong linkage disequilibrium was found between the <italic>NOD2/CARD15 </italic>Pro268Ser loci and three other studied loci of <italic>NOD2/CARD15</italic>. Analyses of reconstructed haplotypes for each patient group showed that the odds of TC haplotype of Pro268Ser and 1007fs loci is approximately ten times higher (OR = 10.26; CI = 4.52 – 23.29; p = 0.0000000000671) among patients with CD compared to healthy controls.</p>", "<p>The <italic>interleukine-23 receptor </italic>(<italic>IL23R</italic>) gene is located on chromosome region 1p31, and encodes a subunit of the IL23 receptor. IL23 and IL6 activate the STAT3 transcription factor which in turn leads to differentiation of CD4+ helper T cells into Th17 cells that function in driving autoimmune inflammation by producing pro-inflammatory cytokines, such as IL-17A, IL-17F and IL-22 [##REF##17786191##13##,##REF##16670770##32##, ####REF##17030949##33##, ##REF##17277312##34##, ##REF##18039580##35####18039580##35##]. Genetic variants of <italic>IL23R </italic>were identified as disease-associated factors in chronic inflammatory disorders, such as IBD [##REF##17068223##12##], psoriasis, T-cell-mediated inflammatory disease of the skin [##REF##17236132##36##], and ankylosing spondylitis [##REF##17952073##37##], indicating the involvement of the IL23/IL23R pathway in the etiology of various autoimmune-related disorders. Several genetic non-coding variants of this gene demonstrated independent risk association with CD, while the rare variant in the coding region of <italic>IL23R </italic>(1142G/A; Arg381Gln; rs11209026) has been regarded as a CD protective variant [##REF##17068223##12##,##REF##17786191##13##].</p>", "<p>We did not find any association of CD not only with Arg381Gln, but also two other more frequent variants of <italic>IL23R</italic>. The frequency of the G/A genotype of rs11209026 SNP was 3.3% in CD (2 of 60) compared to 5.8% (8 of 139) in controls, and only one of the patients and controls was homozygous for the AA genotype (not significant). Thus, although this rare variant was found at a similar proportion to that described from the genome-wide scan-based population studies (2.5–3.0% <italic>versus </italic>6.2–6.8%; [##REF##17786191##13##,##REF##17484863##38##]), our small group of CD patients (N = 60) was insufficient to reach statistical significance for this disproportion.</p>", "<p>The <italic>Drosophila </italic>disc large homolog 5 (<italic>DLG5</italic>) gene, mapped to chromosome 10 (10q23), is a member of the membrane-associated guanylate kinase gene family [##REF##17628615##10##,##REF##16773680##39##] and encodes a protein involved in maintaining the correct shape, polarity and growth of cells. The functional variant (113G/A, Arg30Gln, rs1248696) has been proposed to potentially lead to serious abnormalities in both the structure and function of cells. Although the first study of Stoll <italic>et al. </italic>[##REF##15107852##40##] reported an association between genetic variants in <italic>DLG5 </italic>and IBD, the more recent meta-analysis of published data on Arg30Gln question this association [##REF##17628615##10##]. Our study did not show any association between the Arg30Gln variant of <italic>DLG5 </italic>and CD.</p>", "<p>In contrast to CD patients, none of the polymorphisms showed association with PSC and PBC, two disorders of autoimmune etiology. Furthermore, no significant associations were detected in PSC patients with concurrent IBD. These negative results are consistent with the recently published studies in Scandinavian PSC patients, the largest PSC population in which genotyping has been performed [##REF##17100974##2##], and in a group of Hungarian and Polish patients with PBC [##UREF##2##41##].</p>", "<p>The present study confirms a strong association of <italic>NOD2/CARD15 </italic>gene variants, and to a lesser extent the coding <italic>ATG16L1 </italic>variant and the <italic>OCTN1/OCTN2 </italic>haplotype, with CD in a relatively small Polish Caucasian patient group. There was no evidence for significant association between variants of <italic>IL23R </italic>or <italic>DLG5 </italic>and CD. However, because small sample size significantly limits estimation of genetic variant disproportion, especially with a low prevalence of allelic frequency, whether the result represents a true negative association or a false negative finding is somewhat disputable, mainly due to insufficient power of statistical tests [##REF##17907284##7##]. In fact, calculation of the statistical testing power reached decent values only for the three main <italic>NOD2/CARD15 </italic>variants (Table ##TAB##3##4##). Therefore, considering SNP frequencies, small patient groups and measured effect size, our studies might not be able to confirm existing weak and even moderate disease associations.</p>" ]

[ "<title>Results and Discussion</title>", "<p>Allelic distributions for all polymorphisms studied in the patient and control groups are shown in Table ##TAB##0##1##, while genotype counts for each of the studied groups are presented in Additional file ##SUPPL##0##1## [see Additional file ##SUPPL##0##1##]. In the control group, the genotype distributions for all polymorphisms were in Hardy-Weinberg equilibrium (p &gt; 0.05).</p>", "<p>The <italic>Caspase Recruitment Activation Domain 15 </italic>(<italic>NOD2/CARD15</italic>) gene is located in the proximal region of chromosome 16 (16q12) and encodes the nucleotide-binding oligomerization domain protein 2 (NOD2), which is a cytosolic receptor for a bacterial peptidoglycan response pathway [##REF##17379562##19##]. Mutations in NOD2 result in immune system dysfunctions [##REF##11385577##11##,##REF##11385576##20##] and are considered causative genetic factors in the development of CD. <italic>NOD2/CARD15 </italic>mutations are also associated with susceptibility to other granulomatous inflammatory disorders, such as early-onset sarcoidosis and Blau syndrome [##REF##11528384##21##,##REF##15459013##22##].</p>", "<p>Three of the four <italic>NOD2/CARD15 </italic>variants tested (rs2066842, rs2066844, rs5743293) were strongly associated with CD (Table ##TAB##1##2##). In particular, a strong association was observed with Pro268Ser and 1007fs SNPs with odds ratio (OR) values over 10. Increased values of OR for homozygous risk genotypes confirm previously described codominance of these variants. The observed allele distribution was similar to those reported in other Caucasian CD patient groups [##UREF##1##23##, ####REF##12055616##24##, ##REF##12105838##25####12105838##25##].</p>", "<p><italic>The autophagy-related 16-like 1 </italic>(<italic>ATG16L1</italic>) gene encodes the protein component of the autophagosome pathway of intracellular bacteria processing. The T300A variant (rs2241880) was previously considered to be a risk factor for CD development [##REF##17200669##26##,##REF##17435756##27##]. In our CD patients, however, this variant demonstrated only a weak associaf2tion with CD, as the p-value (= 0.022) did not pass the Bonferroni corrected significance threshold level of 0.008.</p>", "<p>The organic cation transporters from the family of solute carrier protein (<italic>SLC22A4/OCTN1 </italic>and <italic>SLC22A5/OCTN2</italic>) genes are located on chromosome 5 (5q31) within the IBD5 region [##REF##16344054##28##]. The 1672C/T (Leu503Phe) missense variant of <italic>SLC22A4/OCTN1 </italic>(rs1050152) potentially alters the carnitine adsorption and the exchange of other positively charged compounds between the cell and extracellular matrix [##REF##16835882##29##]. Also included in this study is the G-to-C transversion in the <italic>SLC22A5/OCTN2 </italic>promoter (rs2631367), which is thought to affect a heat shock transcription factor binding element [##REF##18005709##30##]. The variants of both genes were previously found in linkage disequilibrium, enabling the selection of a two-allele CD risk haplotype [##REF##16344054##28##,##REF##15107849##31##]. This finding, however, has not been confirmed by other studies [##REF##16835882##29##].</p>", "<p>As shown in Table ##TAB##2##3##, there was a weak association between the protective <italic>OCTN1/OCTN2 </italic>CC haplotype and CD (OR = 0.28; CI = 0.08 – 0.94; p = 0.0298). A strong linkage disequilibrium was found between the <italic>NOD2/CARD15 </italic>Pro268Ser loci and three other studied loci of <italic>NOD2/CARD15</italic>. Analyses of reconstructed haplotypes for each patient group showed that the odds of TC haplotype of Pro268Ser and 1007fs loci is approximately ten times higher (OR = 10.26; CI = 4.52 – 23.29; p = 0.0000000000671) among patients with CD compared to healthy controls.</p>", "<p>The <italic>interleukine-23 receptor </italic>(<italic>IL23R</italic>) gene is located on chromosome region 1p31, and encodes a subunit of the IL23 receptor. IL23 and IL6 activate the STAT3 transcription factor which in turn leads to differentiation of CD4+ helper T cells into Th17 cells that function in driving autoimmune inflammation by producing pro-inflammatory cytokines, such as IL-17A, IL-17F and IL-22 [##REF##17786191##13##,##REF##16670770##32##, ####REF##17030949##33##, ##REF##17277312##34##, ##REF##18039580##35####18039580##35##]. Genetic variants of <italic>IL23R </italic>were identified as disease-associated factors in chronic inflammatory disorders, such as IBD [##REF##17068223##12##], psoriasis, T-cell-mediated inflammatory disease of the skin [##REF##17236132##36##], and ankylosing spondylitis [##REF##17952073##37##], indicating the involvement of the IL23/IL23R pathway in the etiology of various autoimmune-related disorders. Several genetic non-coding variants of this gene demonstrated independent risk association with CD, while the rare variant in the coding region of <italic>IL23R </italic>(1142G/A; Arg381Gln; rs11209026) has been regarded as a CD protective variant [##REF##17068223##12##,##REF##17786191##13##].</p>", "<p>We did not find any association of CD not only with Arg381Gln, but also two other more frequent variants of <italic>IL23R</italic>. The frequency of the G/A genotype of rs11209026 SNP was 3.3% in CD (2 of 60) compared to 5.8% (8 of 139) in controls, and only one of the patients and controls was homozygous for the AA genotype (not significant). Thus, although this rare variant was found at a similar proportion to that described from the genome-wide scan-based population studies (2.5–3.0% <italic>versus </italic>6.2–6.8%; [##REF##17786191##13##,##REF##17484863##38##]), our small group of CD patients (N = 60) was insufficient to reach statistical significance for this disproportion.</p>", "<p>The <italic>Drosophila </italic>disc large homolog 5 (<italic>DLG5</italic>) gene, mapped to chromosome 10 (10q23), is a member of the membrane-associated guanylate kinase gene family [##REF##17628615##10##,##REF##16773680##39##] and encodes a protein involved in maintaining the correct shape, polarity and growth of cells. The functional variant (113G/A, Arg30Gln, rs1248696) has been proposed to potentially lead to serious abnormalities in both the structure and function of cells. Although the first study of Stoll <italic>et al. </italic>[##REF##15107852##40##] reported an association between genetic variants in <italic>DLG5 </italic>and IBD, the more recent meta-analysis of published data on Arg30Gln question this association [##REF##17628615##10##]. Our study did not show any association between the Arg30Gln variant of <italic>DLG5 </italic>and CD.</p>", "<p>In contrast to CD patients, none of the polymorphisms showed association with PSC and PBC, two disorders of autoimmune etiology. Furthermore, no significant associations were detected in PSC patients with concurrent IBD. These negative results are consistent with the recently published studies in Scandinavian PSC patients, the largest PSC population in which genotyping has been performed [##REF##17100974##2##], and in a group of Hungarian and Polish patients with PBC [##UREF##2##41##].</p>", "<p>The present study confirms a strong association of <italic>NOD2/CARD15 </italic>gene variants, and to a lesser extent the coding <italic>ATG16L1 </italic>variant and the <italic>OCTN1/OCTN2 </italic>haplotype, with CD in a relatively small Polish Caucasian patient group. There was no evidence for significant association between variants of <italic>IL23R </italic>or <italic>DLG5 </italic>and CD. However, because small sample size significantly limits estimation of genetic variant disproportion, especially with a low prevalence of allelic frequency, whether the result represents a true negative association or a false negative finding is somewhat disputable, mainly due to insufficient power of statistical tests [##REF##17907284##7##]. In fact, calculation of the statistical testing power reached decent values only for the three main <italic>NOD2/CARD15 </italic>variants (Table ##TAB##3##4##). Therefore, considering SNP frequencies, small patient groups and measured effect size, our studies might not be able to confirm existing weak and even moderate disease associations.</p>" ]

[ "<title>Conclusion</title>", "<p>Although inappropriate immune mediated processes are also related with PSC and PBC, this study and previously published [##REF##17100974##2##,##UREF##2##41##] studies have not identified common allelic variants. In IBD, risk variants of genes encoding proteins that are involved in signalling pathways activated by bacterial products have been described. Consequently, IBD is thought to result from dysfunction of the mucosal immune response triggered by intestinal bacteria, and defects in the early immune response may play a role in the pathogenesis of CD [##REF##12167685##1##,##REF##16819502##42##,##REF##16920636##43##]. However, this hypothesis is based on statistical considerations rather than functional studies. It is noteworthy that only some CD patients carry mutated <italic>NOD2/CARD15 </italic>and/or other risk gene variants, and therefore, mechanisms for CD development likely are not simply related to mutations within described risk genes. Furthermore, the genetic background of CD possibly is based on more causative genetic factors than studied thus far, and each of them independently may have a relatively weak impact on disease development.</p>", "<p>Such high complexity of disease background requires mapping of genetic predisposition using genome-wide and large-scale studies in well-defined populations. Despite the fact that these kind of studies are still relatively expensive, they are likely the most cost effective giving real capacity to provide comprehensive insight into disease development mechanisms.</p>", "<p>Even though familial aggregation is observed in many complex diseases, including IBD, PSC and PBC, genetic factors in complex diseases should be considered as \"risk-factor genes\" rather than genes responsible for the development of a particular disease. Their presence is not synonymous with the development of the disease. Thus, functional studies should be initiated in order to clarify the contribution of genetic background in development of these diseases.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Numerous papers have addressed the association of mutations and polymorphisms of susceptibility genes with autoimmune inflammatory disorders. We investigated whether polymorphisms that confer susceptibility to Crohn's disease could be classified also as predisposing factors for the development of primary sclerosing cholangitis and primary biliary cirrhosis in Polish patients.</p>", "<title>Methods</title>", "<p>The study included 60 patients with CD, 77 patients with PSC, of which 61 exhibited IBD (40 UC, 8 CD, and 13 indeterminate colitis), and 144 patients with PBC. All the patients were screened against Crohn's disease associating genetic polymorphisms.</p>", "<p>The polymorphisms were chosen according to previously confirmed evidence for association with Crohn's disease, including Pro268Ser, Arg702Trp, Gly908Arg and 1007fs in <italic>NOD2/CARD15</italic>, Leu503Phe/-207G&gt;C in <italic>SLC22A4/OCTN1</italic>/<italic>SLC22A5/OCTN2</italic>, Arg30Gln in <italic>DLG5</italic>, Thr300Ala in <italic>ATG16L1</italic>, and Arg381Gln, His3Gln and exon-3'UTR in <italic>IL23R</italic>. Genotyping was carried out using TaqMan SNP genotyping assays.</p>", "<title>Results</title>", "<p>We confirmed a strong association between three <italic>NOD2/CARD15 </italic>gene variants (Pro268Ser, OR = 2.52, 95% CI = 1.34 – 4.75); (Arg702Trp, OR = 6.65, 95% CI = 1.99 – 22.17); (1007fs, OR = 9.59, 95% CI = 3.94 – 23.29), and a weak association between both the protective <italic>OCTN1/OCTN2 </italic>CC haplotype (OR = 0.28, 95% CI = 0.08 – 0.94), and a variant of <italic>ATG16L1 </italic>gene (Thr300Ala, OR = 0.468, 95% CI = 0.24 – 0.90) with Crohn's disease. In contrast, none of the polymorphisms exhibited association with susceptibility to primary sclerosing cholangitis and primary biliary cirrhosis, including a group of primary sclerosing cholangitis patients with concurrent IBD.</p>", "<title>Conclusion</title>", "<p>Although the clinical data indicate non-random co-occurrence of inflammatory bowel disease and primary sclerosing cholangitis, consistently with the previously published studies, no genetic association was found between the genetic variants predisposing to Crohn's disease and hepatobiliary autoimmune disorders. However, since estimation of genetic variant disproportion is limited by sample size, these negative results may also indicate that eventually shared genetic predispositions are too little to be captured by small patient groups.</p>" ]

[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>PG participated in preparation of the study design, carried out the molecular assays as well as performed statistical analyses and helped to draft the manuscript. AH provided diagnoses for the patients and enrolled patients eligible for the study. MM participated in preparation of the study design. JO conceived of the study, and participated in its design and coordination and drafted the manuscript. All authors read and approved the final manuscript.</p>", "<title>Availability &amp; requirements</title>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.hutchon.net/ConfidOR.htm\"/></p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.broad.mit.edu/mpg/haploview/\"/></p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2350/9/81/prepub\"/></p>", "<title>Supplementary Material</title>" ]

[ "<title>Acknowledgements</title>", "<p>We would like to thank MSc. Karolina Hanusek for cooperation in performing molecular assays.</p>", "<p>This work was supported by the CMKP grant 501-1-1-09-17/05.</p>" ]

[]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Allelic distribution in patients and healthy controls for all polymorphisms</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Polymorphism</td><td align=\"left\">Genotype</td><td align=\"left\">CD</td><td align=\"left\">PSC</td><td align=\"left\">PBC</td><td align=\"left\">Controls</td></tr></thead><tbody><tr><td align=\"left\">NOD2/CARD15 Pro268Ser</td><td align=\"left\">C/C</td><td align=\"left\">0.33</td><td align=\"left\">0.48</td><td align=\"left\">0.58</td><td align=\"left\">0.56</td></tr><tr><td/><td align=\"left\">C/T</td><td align=\"left\">0.40</td><td align=\"left\">0.45</td><td align=\"left\">0.36</td><td align=\"left\">0,41</td></tr><tr><td/><td align=\"left\">T/T</td><td align=\"left\">0.27</td><td align=\"left\">0.06</td><td align=\"left\">0.06</td><td align=\"left\">0.03</td></tr><tr><td align=\"left\">NOD2/CARD15 Arg702Trp</td><td align=\"left\">C/C</td><td align=\"left\">0.83</td><td align=\"left\">0.92</td><td align=\"left\">0.93</td><td align=\"left\">0.97</td></tr><tr><td/><td align=\"left\">C/T</td><td align=\"left\">0.17</td><td align=\"left\">0.08</td><td align=\"left\">0.07</td><td align=\"left\">0.03</td></tr><tr><td/><td align=\"left\">T/T</td><td align=\"left\">0.00</td><td align=\"left\">0.00</td><td align=\"left\">0.00</td><td align=\"left\">0.00</td></tr><tr><td align=\"left\">NOD2/CARD15 Gly908Arg</td><td align=\"left\">G/G</td><td align=\"left\">0.95</td><td align=\"left\">0.96</td><td align=\"left\">0.97</td><td align=\"left\">0.94</td></tr><tr><td/><td align=\"left\">G/C</td><td align=\"left\">0.05</td><td align=\"left\">0.04</td><td align=\"left\">0.03</td><td align=\"left\">0.06</td></tr><tr><td/><td align=\"left\">C/C</td><td align=\"left\">0.00</td><td align=\"left\">0.00</td><td align=\"left\">0.00</td><td align=\"left\">0.00</td></tr><tr><td align=\"left\">NOD2/CARD15 1007fs</td><td align=\"left\">-/-</td><td align=\"left\">0.63</td><td align=\"left\">0.91</td><td align=\"left\">0.92</td><td align=\"left\">0.94</td></tr><tr><td/><td align=\"left\">-/C</td><td align=\"left\">0.27</td><td align=\"left\">0.08</td><td align=\"left\">0.08</td><td align=\"left\">0.06</td></tr><tr><td/><td align=\"left\">C/C</td><td align=\"left\">0.10</td><td align=\"left\">0.01</td><td align=\"left\">0.00</td><td align=\"left\">0.00</td></tr><tr><td align=\"left\">OCTN1 Leu503Phe</td><td align=\"left\">C/C</td><td align=\"left\">0.27</td><td align=\"left\">0.35</td><td align=\"left\">0.37</td><td align=\"left\">0.37</td></tr><tr><td/><td align=\"left\">C/T</td><td align=\"left\">0.50</td><td align=\"left\">0.52</td><td align=\"left\">0.43</td><td align=\"left\">0.46</td></tr><tr><td/><td align=\"left\">T/T</td><td align=\"left\">0.23</td><td align=\"left\">0.13</td><td align=\"left\">0.20</td><td align=\"left\">0.17</td></tr><tr><td align=\"left\">OCTN2-207G&gt;C</td><td align=\"left\">G/G</td><td align=\"left\">0.25</td><td align=\"left\">0.28</td><td align=\"left\">0.30</td><td align=\"left\">0.27</td></tr><tr><td/><td align=\"left\">G/C</td><td align=\"left\">0.47</td><td align=\"left\">0.54</td><td align=\"left\">0.43</td><td align=\"left\">0.46</td></tr><tr><td/><td align=\"left\">C/C</td><td align=\"left\">0.27</td><td align=\"left\">0.18</td><td align=\"left\">0.27</td><td align=\"left\">0.27</td></tr><tr><td align=\"left\">DLG5 Arg30Gln</td><td align=\"left\">C/C</td><td align=\"left\">0.80</td><td align=\"left\">0.77</td><td align=\"left\">0.82</td><td align=\"left\">0.80</td></tr><tr><td/><td align=\"left\">C/T</td><td align=\"left\">0.18</td><td align=\"left\">0.21</td><td align=\"left\">0.17</td><td align=\"left\">0.19</td></tr><tr><td/><td align=\"left\">T/T</td><td align=\"left\">0.02</td><td align=\"left\">0.03</td><td align=\"left\">0.01</td><td align=\"left\">0.01</td></tr><tr><td align=\"left\">IL23R Arg381Gln</td><td align=\"left\">G/G</td><td align=\"left\">0.97</td><td align=\"left\">0.90</td><td align=\"left\">0.92</td><td align=\"left\">0.95</td></tr><tr><td/><td align=\"left\">G/A</td><td align=\"left\">0.03</td><td align=\"left\">0.09</td><td align=\"left\">0.08</td><td align=\"left\">0.05</td></tr><tr><td/><td align=\"left\">A/A</td><td align=\"left\">0.00</td><td align=\"left\">0.01</td><td align=\"left\">0.00</td><td align=\"left\">0.00</td></tr><tr><td align=\"left\">IL23R His3Gln</td><td align=\"left\">C/C</td><td align=\"left\">0.19</td><td align=\"left\">0.26</td><td align=\"left\">0.23</td><td align=\"left\">0.30</td></tr><tr><td/><td align=\"left\">C/A</td><td align=\"left\">0.59</td><td align=\"left\">0.57</td><td align=\"left\">0.54</td><td align=\"left\">0.49</td></tr><tr><td/><td align=\"left\">A/A</td><td align=\"left\">0,22</td><td align=\"left\">0.17</td><td align=\"left\">0.23</td><td align=\"left\">0.22</td></tr><tr><td align=\"left\">IL23R exon-3'UTR</td><td align=\"left\">C/C</td><td align=\"left\">0.40</td><td align=\"left\">0.53</td><td align=\"left\">0.51</td><td align=\"left\">0.49</td></tr><tr><td/><td align=\"left\">C/A</td><td align=\"left\">0.52</td><td align=\"left\">0.41</td><td align=\"left\">0.40</td><td align=\"left\">0.42</td></tr><tr><td/><td align=\"left\">A/A</td><td align=\"left\">0.08</td><td align=\"left\">0.07</td><td align=\"left\">0.08</td><td align=\"left\">0.09</td></tr><tr><td align=\"left\">ATG16L1 Thr300Ala</td><td align=\"left\">C/C</td><td align=\"left\">0.39</td><td align=\"left\">0.29</td><td align=\"left\">0.25</td><td align=\"left\">0.23</td></tr><tr><td/><td align=\"left\">C/T</td><td align=\"left\">0.42</td><td align=\"left\">0.51</td><td align=\"left\">0.52</td><td align=\"left\">0.50</td></tr><tr><td/><td align=\"left\">T/T</td><td align=\"left\">0.19</td><td align=\"left\">0.20</td><td align=\"left\">0.23</td><td align=\"left\">0.27</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Statistical analyses for significant genotype associations</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Patient group</td><td align=\"center\" colspan=\"2\">SNP</td><td align=\"center\">Genotype</td><td align=\"center\">OR</td><td align=\"center\">95% CI</td><td align=\"center\">p-value</td><td align=\"center\">Stat.<break/> Power</td></tr></thead><tbody><tr><td align=\"center\">Crohn's<break/> disease</td><td align=\"center\" colspan=\"1\">NOD2/CARD15</td><td align=\"center\">Pro268Ser</td><td align=\"center\">C/T+T/T <break/>T/T</td><td align=\"center\">2.52 <break/>12.18</td><td align=\"center\">1.34 – 4.75 <break/>3.86 – 38.37</td><td align=\"center\">0.005 <break/>0.0000003</td><td align=\"center\">0.59 <break/>0.98</td></tr><tr><td/><td align=\"center\">NOD2/CARD15</td><td align=\"center\">Arg702Trp</td><td align=\"center\">C/T+T/T <break/>T/T</td><td align=\"center\">6.65 <break/>n/a</td><td align=\"center\">1.99 – 22.17<break/> n/a</td><td align=\"center\">0.0013 <break/>n/a</td><td align=\"center\">0.71 <break/>n/a</td></tr><tr><td/><td align=\"center\">NOD2/CARD15</td><td align=\"center\">1007fs</td><td align=\"center\">-/C+C/C <break/>C/C</td><td align=\"center\">9.59<break/> &gt;16</td><td align=\"center\">3.94 – 23.29<break/> ---</td><td align=\"center\">0.00000002 <break/>0.0006</td><td align=\"center\">0.99<break/> 0.86</td></tr><tr><td/><td align=\"center\">ATG16L1</td><td align=\"center\">Thr300Ala</td><td align=\"center\">T/C+T/T <break/>T/T</td><td align=\"center\">0.468 <break/>0.632</td><td align=\"center\">0.24 – 0.90 <break/>0.30 – 1.34</td><td align=\"center\">0.022 <break/>0.231</td><td align=\"center\">0.32 <break/>0.05</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Statistical analyses using Haploview software (ver. 3.2) for significant haplotype associations.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Patient group</td><td align=\"center\">SNP</td><td align=\"center\">Haplotype</td><td align=\"center\">OR</td><td align=\"center\">95% CI</td><td align=\"center\">p-value</td></tr></thead><tbody><tr><td align=\"center\">Crohn's disease</td><td align=\"center\">NOD2/CARD15 Pro268Ser/Arg702Trp</td><td align=\"center\">TC</td><td align=\"center\">2.25</td><td align=\"center\">1.41 – 3.58</td><td align=\"center\">0.0005</td></tr><tr><td/><td align=\"center\">NOD2/CARD15 Pro268Ser/Gly908Arg</td><td align=\"center\">TG</td><td align=\"center\">3.01</td><td align=\"center\">1.89 – 4.78</td><td align=\"center\">0.000002</td></tr><tr><td/><td align=\"center\">NOD2/CARD15 Pro268Ser/1007fs</td><td align=\"center\">TC</td><td align=\"center\">10.26</td><td align=\"center\">4.52 – 23.29</td><td align=\"center\">0.0000000000671</td></tr><tr><td/><td align=\"center\">OCTN1/OCTN2</td><td align=\"center\">CC</td><td align=\"center\">0.28</td><td align=\"center\">0.08 – 0.94</td><td align=\"center\">0.0298</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Power of genotype association statistics given as 1-β error probability. The β represents the probability of falsely accepting H0 hypothesis (lack of association) when in fact H1 hypothesis (association) is true.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Polymorphism</td><td align=\"center\">Genet. model</td><td align=\"center\">CD</td><td align=\"center\">PSC</td><td align=\"center\">PBC</td></tr></thead><tbody><tr><td align=\"left\">NOD2/CARD15 Pro268Ser</td><td align=\"center\">dominant</td><td align=\"center\">0.59</td><td align=\"center\">0.04</td><td align=\"center\">&lt;0.01</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">0.98</td><td align=\"center\">0.03</td><td align=\"center\">0.04</td></tr><tr><td align=\"left\">NOD2/CARD15 Arg702Trp</td><td align=\"center\">dominant</td><td align=\"center\">0.71</td><td align=\"center\">0.11</td><td align=\"center\">0.08</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">n/a</td><td align=\"center\">&lt;0.01</td><td align=\"center\">n/a</td></tr><tr><td align=\"left\">NOD2/CARD15 Gly908Arg</td><td align=\"center\">dominant</td><td align=\"center\">&lt;0.01</td><td align=\"center\">&lt;0.01</td><td align=\"center\">0.05</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">n/a</td><td align=\"center\">n/a</td><td align=\"center\">n/a</td></tr><tr><td align=\"left\">NOD2/CARD15 1007fs</td><td align=\"center\">dominant</td><td align=\"center\">0.99</td><td align=\"center\">0.02</td><td align=\"center\">0.01</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">0.86</td><td align=\"center\">&lt;0.01</td><td align=\"center\">n/a</td></tr><tr><td align=\"left\">OCTN1 Leu503Phe</td><td align=\"center\">dominant</td><td align=\"center\">0.08</td><td align=\"center\">&lt;0.01</td><td align=\"center\">&lt;0.01</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">0.04</td><td align=\"center\">0.02</td><td align=\"center\">0.02</td></tr><tr><td align=\"left\">OCTN2-207G&gt;C</td><td align=\"center\">dominant</td><td align=\"center\">&lt;0.01</td><td align=\"center\">&lt;0.01</td><td align=\"center\">0.01</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">&lt;0.01</td><td align=\"center\">0.09</td><td align=\"center\">&lt;0.01</td></tr><tr><td align=\"left\">DLG5 Arg30Gln</td><td align=\"center\">dominant</td><td align=\"center\">&lt;0.01</td><td align=\"center\">0.02</td><td align=\"center\">0.01</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">&lt;0.01</td><td align=\"center\">0.02</td><td align=\"center\">&lt;0.01</td></tr><tr><td align=\"left\">IL23R Arg381Gln</td><td align=\"center\">dominant</td><td align=\"center\">&lt;0.01</td><td align=\"center\">0.08</td><td align=\"center\">0.03</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">&lt;0.01</td><td align=\"center\">&lt;0.01</td><td align=\"center\">n/a</td></tr><tr><td align=\"left\">IL23R His3Gln</td><td align=\"center\">dominant</td><td align=\"center\">0.09</td><td align=\"center\">0.01</td><td align=\"center\">0.06</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">&lt;0.01</td><td align=\"center\">0.02</td><td align=\"center\">&lt;0.01</td></tr><tr><td align=\"left\">IL23R exon-3'UTR</td><td align=\"center\">dominant</td><td align=\"center\">0.05</td><td align=\"center\">0.01</td><td align=\"center\">0.01</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">&lt;0.01</td><td align=\"center\">0.01</td><td align=\"center\">&lt;0.01</td></tr><tr><td align=\"left\">ATG16L1 Thr300Ala</td><td align=\"center\">dominant</td><td align=\"center\">0.32</td><td align=\"center\">0.04</td><td align=\"center\">0.01</td></tr><tr><td/><td align=\"center\">recessive</td><td align=\"center\">0.05</td><td align=\"center\">0.04</td><td align=\"center\">0.02</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>genotype counts. genotype counts for each of the studied groups</p></caption></supplementary-material>" ]

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[ "<media xlink:href=\"1471-2350-9-81-S1.xls\" mimetype=\"application\" mime-subtype=\"vnd.ms-excel\"><caption><p>Click here for file</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>The transport of nutrient-rich water from benthic to pelagic regions has been linked to increased levels of primary productivity in stratified lakes [##UREF##0##1##, ####UREF##1##2##, ##UREF##2##3####2##3##]. Ostrovsky <italic>et al</italic>.. (1996) suggest that seiche activity in the boundary layer of Lake Kinneret sustained a vertical flux between the hypolimnetic and epilimnetic waters enhancing biological productivity in the lake. MacIntyre <italic>et al</italic>. (1999) calculated the upward fluxes of ammonium across the nutricline in Mono Lake and suggested nearshore boundary fluxes could be the dominant pathway supplying ammonium to the deep chlorophyll maxima. Eckert <italic>et al</italic>. (2002) used microstructure measurements of temperature, oxygen and hydrogen sulphide in Lake Kinneret to conclude that following the onset of stratification, the flux of benthic nutrients to the water column controls primary productivity. In this study we have defined BBL transport as that which occurs in the layer bordering the sediments of a lake [##UREF##3##4##,##UREF##4##5##] alternatively referred to as the bottom boundary layer [##UREF##5##6##].</p>", "<p>The development of basin-scale internal waves arising from wind-induced energy are responsible for large scale water motions and most of the turbulence caused by these large-scale motions occurs in the BBL [##UREF##6##7##,##UREF##7##8##]. In order to differentiate between boundary and internal modes of vertical transport Yeates and Imberger (2004) parameterized the split between mixing in the internal and benthic boundary layer (BBL) using values of Lake number, L_N[##UREF##8##9##] and Burger number, B_N[##UREF##9##10##]. The L_Nis a measure of the amplitude of basin-scale internal waves in response to surface wind forcing, and B_Ndescribes waves that evolve from simple seiches [##UREF##3##4##]. Simulations performed on a number of monomictic lakes indicated that fluxes through the BBL were dominant during strong wind events occurring during period of stratification [##UREF##3##4##].</p>", "<p>A number of studies, aimed at identifying sources and sinks of nutrients in the photic zone have focused on bacterial mineralization [##UREF##10##11##], regeneration through planktonic organisms [##UREF##10##11##, ####UREF##11##12##, ##UREF##12##13####12##13##], nitrogen fixation [##UREF##13##14##], hypolimnetic flux and inflows and outflows [##UREF##14##15##]. Although the occurrence of BBL transport and its potential impact on primary productivity has been examined, the upward mixing of nutrient-rich hypolimnetic waters via the BBL and the consequent effect on lake-wide ecological processes deserves further analysis.</p>", "<p>Mono Lake is a nitrogen-limited saline lake with a relatively simple food web [##UREF##15##16##] and is subjected to wind-driven boundary-layer mixing events [##UREF##0##1##]. Yeates and Imberger (2004) simulated a BBL thickness in Mono Lake of 10–15 m during a sequence of strong wind event suggesting an active role in the development of the thermal structure of the lake. These features make it well-suited for examining the role of BBL-supplied nutrients and the influence of these nutrients on the seasonal plankton dynamics and overall productivity of the lake.</p>", "<p>The objective of the present study is to investigate the role of BBL transport in the supply of nutrients to the photic zone and its consequent impact on the lake's ecology. A coupled hydrodynamic and ecological model was used to quantify nitrogen biogeochemistry during a 4-yr period from 1991–1994 when the lake mixed to the bottom during the winter. Initially, we calibrated the model parameters and processes to ensure an acceptable representation of the field data. The simulated output was then used to calculate the sources and sinks of nitrogen to the photic zone. A comparison could then be made between the roles of recycled and external sources on the primary and secondary productivity in the lake. To enable quantification of the significance of BBL transport for ecological processes, a series of simulations were run in which this mechanism was switched off allowing a comparison between lake behavior with and without BBL transport.</p>" ]

[ "<title>Methods</title>", "<title>Model description</title>", "<p>The model used in this study is a modified version of the Computational Aquatic Ecosystem Dynamics Model (CAEDYM) [##UREF##26##28##,##UREF##27##29##] coupled to the Dynamic Reservoir Model (DYRESM) [##UREF##3##4##]. In DYRESM the lake is represented as a series of homogeneous horizontal layers of variable thickness [##UREF##3##4##]; as inflows and outflows enter or leave the lake, the affected layers expand or contract, respectively, and those above move up or down to accommodate the volume change. Mass, including that of the ecological state variables, is adjusted conservatively each time layers expand, contract, merge or are affected by inflows and outflows. The main processes modelled in DYRESM are surface heat, mass and momentum transfers, mixed layer dynamics, hypolimnetic mixing, benthic boundary layer mixing, inflows and outflows.</p>", "<p>Local meteorological data are used to determine heating due to short-wave radiation and surface heat fluxes due to evaporation, sensible heat, long-wave radiation and wind stress. The surface wind field introduces both momentum and turbulent kinetic energy to the surface layer contributing to vertical mixing. In addition to surface layer mixing, DYRESM includes algorithms that account for internal mixing (encompassing the effects of internal wave energized shear mixing) and benthic boundary layer (BBL) mixing (determined by the turbulent kinetic energy budget and parameterized by Lake number and the Burger number). The total volume of water (<italic>F</italic>_{<italic>i</italic>}^{<italic>T</italic>}) exchanged by deep water mixing and transport processes for layer <italic>i </italic>is determined by the following equation:</p>", "<p></p>", "<p>where <italic>N</italic>²is the buoyancy frequency, <italic>A </italic>is the layer area (m²), <italic>K</italic>_{<italic>M </italic>}is the molecular diffusion coefficient for heat, Δ<italic>t </italic>is the time step (seconds), <italic>L</italic>_{<italic>N </italic>}is the Lake number and <italic>δ</italic>_{<italic>i </italic>}the layer thickness of layer <italic>i </italic>(m) [##UREF##3##4##]. In this way mass transfer is enabled from hypolimnetic layers to the thermocline region internally and via the BBL. A recent modification of the DYRESM code is the separation of these mass transfers described in detail by Yeates and Imberger (2004). The Lagrangian layers have been separated into internal and BBL cells so volume exchange occurring beneath the surface mixing layer can be separated into that associated with internal mixing (between internal cells) and that associated with benthic boundary layer mixing (between BBL cells; Fig. ##FIG##1##2A&amp;B##). The volume exchange is partitioned into BBL (<italic>F</italic>_{<italic>i</italic>}^{<italic>B</italic>}) and internal (<italic>F</italic>_{<italic>i</italic>}^{<italic>I</italic>}) using the following equation:</p>", "<p></p>", "<p>where B_Nis the Burger number [##UREF##3##4##].</p>", "<p>The ecological model CAEDYM was set up in the form of an 'N-P-Z' (nutrients-phytoplankton-zooplankton) model (Fig. ##FIG##1##2C##) with resolution to the level of individual species or groups of species [##UREF##22##24##]. In the present study it is used to simulate phosphorus and nitrogen in both particulate and dissolved inorganic forms (POP and PO₄, PON, NO₃, NH₄), dissolved oxygen (DO), particulate organic carbon (POC), dissolved organic carbon (DOC), one phytoplankton group representing <italic>Picocystis </italic>sp. and one zooplankton group representing <italic>A. monica</italic>. A series of ordinary differential equations is used in CAEDYM to describe changes in concentrations of nutrients, detritus, dissolved oxygen, phytoplankton and zooplankton as a function of environmental forcing and ecological interactions for each cell represented by DYRESM (Table ##TAB##0##1##). The variables of irradiance, temperature, salinity and density are also passed to CAEDYM at each 1-hr time step and used in equations to determine rates of change of biomass and chemical constituents for each of the ecological state variables. The two models CAEDYM and DYRESM share the same layer structure including the division of hypolimnetic layers into two cells, BBL and internal. The BBL cells are considered adjacent to sediment cells so that sediment exchange of nutrients and dissolved oxygen occurs to and from these cells. The physical transfer of ecological variables between adjacent cells due to various mixing processes is accounted for in DYRESM. Further details of the structure of CAEDYM are given in Robson and Hamilton (2004) and Romero <italic>et al</italic>. (2004).</p>", "<p>The major nutrient fluxes represented in CAEDYM are uptake of dissolved inorganic nutrients by phytoplankton, release of dissolved nutrients from phytoplankton excretion, grazing, egestion and excretion of nutrients by zooplankton, nitrification and denitrification of inorganic nitrogen, sedimentation of nutrients in particulate form, mineralization of organic nutrients and release of dissolved nutrients from sediments (Table ##TAB##0##1##).</p>", "<p>Net change in carbon concentration of the phytoplankton at each model time step is calculated as the difference between the increment due to gross primary production and losses due to sedimentation, grazing by zooplankton, respiration, excretion and mortality. These terms are calculated using equations parameterized to represent the physiology of the main phytoplankton species. Losses due to grazing by zooplankton are calculated by multiplying the food assimilation rate for the zooplankton by a preference factor for phytoplankton over detrital POC.</p>", "<p>Net zooplankton growth is calculated as a balance between food assimilation and losses from respiration, excretion, egestion, predation and mortality. Food assimilation is calculated as the product of the maximum potential rate of grazing, assimilation efficiency, and temperature and food concentration functions. A constant internal nutrient ratio is assumed and excretion of nutrients calculated to maintain this ratio at each time step. Advective movement of zooplankton is carried out in DYRESM.</p>", "<p>Bacteria have not been directly simulated as they were not measured during the study period. However the nutrient pathways mediated by bacteria were included as mineralization of the particulate organic pools (POC, POP and PON). The POC, POP and PON pools available for zooplankton grazing include bacteria. Predation of zooplankton by grebes was included by an additional predation term for the months of August to November estimated from predation studies (Cooper <italic>et al</italic>. 1984).</p>", "<p>The advantage of using a depth resolved model DYRESM linked to the ecological model CAEDYM is that we could explore the effect of transport and mixing between the epilimnion, metalimnion and hypolimnion on the ecological processes in the lake. Of most relevance to this study is the exchange of nutrient-rich hypolimnetic waters to the photic zone via the BBL and its consequential effect on the primary productivity. The separation of internal and BBL cells in the layered structure of the current DYRESM allowed us to differentiate between the transport of nutrients in the internal and BBL and to determine the relative importance of each process on the mixed layer ecological dynamics. The ecological model also returns the attenuation coefficient (as a function of the concentration of both phytoplankton and particulate organic matter) to the hydrodynamic model at each one-hour time step. This variable is used to determine the extent of light and heat penetration that in turn governs the deepening of the surface mixed layer and the timing of winter turnover. In this way the feedback on a sub-daily time scale between the ecological and physical models is instrumental in the application of the model to aid in understanding of the interaction of various lake processes.</p>", "<title>Field sampling and analytical analysis</title>", "<p>Seasonal and year-to-year variations in the physical, chemical, and biotic environments were monitored fortnightly from March through October and monthly during November through January. Water temperature and conductivity were measured at nine buoyed, pelagic stations (2, 3, 4, 5, 6, 7, 8, 10 and 12) (Fig. ##FIG##0##1##). Profiles were taken with a high-precision, conductivity-temperature-depth profiler (CTD) (Seabird Electronics model Seacat 19) equipped with a submersible photosynthetically available radiation (PAR) (LiCor 191S), fluorescence (695 nm) (WETLabs WETStar miniature fluorometer), and transmissivity (660 nm) (WETlabs C-Star Transmissometer). Specific conductivity, salinity, and density were all calculated based on equations derived from measurements on Mono Lake brine [##UREF##28##30##]. Dissolved oxygen was measured at one centrally located station (Station 6) with a Yellow Springs Instruments temperature-oxygen meter (YSI, model 58) and probe (YSI, model 5739). The oxygen electrode was calibrated at least once each year against Miller titrations of Mono Lake water (Walker <italic>et al</italic>. 1970).</p>", "<p>Ammonium and chlorophyll profiles were determined by sampling 7–10 discrete depths at two pelagic stations (2 and 7; Fig. ##FIG##0##1##), while <italic>A. monica </italic>abundance was determined via vertical net tows collected at 10 (1991–1992) or 20 (1993–1994) pelagic stations (Fig. ##FIG##0##1##). Chlorophyll <italic>a </italic>was also determined in the upper water column from samples collected with a 9-m integrating tube sampler at 5 pelagic stations (2, 6, 7, 10, 11; Fig. ##FIG##0##1##).</p>", "<p>Nutrient and phytoplankton samples were immediately passed through a 120-μm net to remove all stages of <italic>A. monica </italic>and a sub-sample filtered through Gelman A/E glass fiber filters for analysis of nutrients (Jellison and Melack 1993a). Ammonium concentrations were measured with the indophenol blue method as described by Jellison <italic>et al</italic>. (1993). Nitrate and nitrite concentrations were measured but were always low (&lt; 1 μM) [##UREF##20##21##,##UREF##29##31##] and thus not considered in this study. Phosphate concentrations are orders of magnitude greater than the half saturations constants for phytoplankton so were not considered in this study [##UREF##20##21##]. Phytoplankton chlorophyll <italic>a </italic>was determined by spectrophotometric analysis as described by Jellison and Melack (1993a). Conversion of chlorophyll to carbon units were made by assuming a C:Chl <italic>a </italic>ration of 50 (see Jellison &amp; Melack 2001). Subsamples were filtered onto precombusted Gelman A/E filters for the determination of particulate organic carbon (POC) and nitrogen (PON). Duplicate carbon and nitrogen filters were acid fumed for 12 hours over concentrated HCl, and then dried at 40–50°C before determination by combustion in a Perkin-Elmer 240B elemental analyzer standardized with acetanilide. <italic>A. monica </italic>were collected using vertical net tows (120-μm mesh) to within 1-m of the bottom or well below the oxycline depending on stratification. <italic>A. monica </italic>biomass (dry weight) was estimated from stage-specific abundance, adult female length data, and weight-length relationship determined in the laboratory simulating in situ conditions of food and temperature [##UREF##30##32##]. Conversion from dry weight to carbon assumed 0.4 g C/g dry weight [##UREF##31##33##,##UREF##32##34##].</p>", "<title>Model inputs</title>", "<p>Model input files included data for initialization, meteorology, inflows and outflows. The initialization file was prepared from field data collected on 13 January 1991. On this day the temperature of the lake ranged from 2.5 to 3°C and the salinity from 88 to 88.5 g kg^-1. Inflow data included the daily volume, temperature and salinity for two inflows, one representing total surface inflows (streams and direct runoff) and the other, hydrothermal springs. The volume of the hydrothermal springs was set at 3888 m³day^-1, based on a ³He mass balance of Mono Lake [##UREF##33##35##]. The surface inflows were calculated based on a water mass balance using measured values of water depth and evaporation calculated by DYRESM. As ammonium, phytoplankton and zooplankton concentrations are negligible in the inflows, they were set to zero (Jellison and Melack 2001). Meteorological input data included hourly short- and long-wave radiation, air temperature, vapor pressure, wind speed and precipitation [##UREF##34##36##]. Air temperature, vapor pressure (converted from relative humidity), wind speed and precipitation were collected at a meteorological station located on Paoha, a central island (Fig. ##FIG##0##1##). Radiation data were collected from a meteorological station located approximately 7 km from the southwest shore of the lake (Fig. ##FIG##0##1##).</p>", "<p>The physical parameters used to simulate the hydrodynamics of Mono Lake were either physical constants or ones fixed according to the dimensions of the lake [##UREF##3##4##].</p>", "<p>The formulation of CAEDYM used here to describe the ecological variables and processes required 57 parameters determined by several methods (Table ##TAB##1##2##). Most phytoplankton parameters were derived from experimental analysis on the predominant phytoplankton species of Mono Lake [##UREF##20##21##,##UREF##35##37##]. The zooplankton parameters were determined where available from experiments conducted on <italic>A. monica </italic>or alternative <italic>Artemia </italic>species (Table ##TAB##1##2##). If parameters were not available, a series of model runs were performed to calibrate the simulation results against field data, maintaining parameter values within the bounds of literature values measured in other lakes.</p>", "<p>A variety of quantifiable measures of model fit are described in Alewell and Manderscheid (1998). We choose the average absolute error normalized to the mean (NMAE):</p>", "<p></p>", "<p>where <italic>s</italic>_{<italic>t </italic>}is the simulated value at time <italic>t</italic>, <italic>o</italic>_{<italic>t </italic>}is the observed value at time <italic>t</italic>, <italic>ō </italic>is the mean of the observed values over the simulation period and <italic>n </italic>is the number of observed values. <italic>NMAE </italic>is a measure of the absolute deviation of simulated values from observations, normalized to the mean; a value of zero indicates perfect agreement and greater than zero an average fraction of the discrepancy normalized to the mean. To compare the extent of variability within the observed data, we also calculated for each state variable, the standard deviation of observed data normalized to the mean over the simulation period (Table ##TAB##2##3##). In addition, the correlation coefficients and associated slope for the direct comparison of observed against simulated values for each state variable were calculated.</p>", "<p>The period from 1991–1992 was used for initial parameter calibration. Comparisons of field and model data were made for six major model state variables: (1) ammonium (NH₄); (2) particulate organic nitrogen (PON); (3) particulate organic carbon (POC); (4) dissolved oxygen (DO); (5) phytoplankton carbon and (6) zooplankton carbon. PON and POC refer to the sum of phytoplankton and detrital particulate nitrogen and carbon, respectively. Lake-wide averages of the surface to 9-m integrated concentrations of NH4, PON, POC, DO and phytoplankton carbon were compared. For zooplankton, lake-wide averaged biomass (g C m^-2) as determined by vertical net tows were compared to vertically-integrated model output.</p>", "<p>A manual calibration procedure was initially applied whereby individual parameters were adjusted and the model response observed. The particular features of the observed data that were used to adjust individual parameters were dependent on the parameter adjusted. For example, the minimum temperature for <italic>A. monica </italic>growth was adjusted to gain best representation of the timing of the spring zooplankton peak and the grazing rate adjusted to gain best representation of the magnitude of this peak. Individual parameters were adjusted in this way until an overall model average NMAE (calculated using the field data from the five variables listed above) of less than 0.5 was achieved.</p>", "<p>Once a reasonable fit was achieved through trial-and-error, the local parameter space optima was determined by applying a Levenberg-Marquant (L-M) method of optimization [##UREF##36##38##] using a predefined Matlab^®function (The MathWorks Inc., Natick, MA). In this function, parameters were adjusted to optimize the sum of the NMAE values for the same five variables listed above. Additional bounds were placed on simulated values of primary productivity and nitrogen sedimentation to fall within the ranges of those estimated by Jellison <italic>et al</italic>. (1993). The years 1993–1994 were used for model validation.</p>", "<p>Uncertainly in model predictions arises from different sources including those associated with process representation, parameter estimation, uncertainty in inputs and observed data [##UREF##37##39##, ####UREF##38##40##, ##UREF##39##41##, ##UREF##40##42####40##42##]. While a full analysis of model uncertainty is beyond the scope of this paper, we made an estimate of the uncertainty associated with parameter estimation by comparing output from simulations using ten different parameter sets. Initially, the five most sensitive parameters to model output were established by the sensitivity analysis described below. We then determined alternative parameter sets by fixing the lower and upper bound of each parameter and then optimizing the remaining parameters via the L-M method described above until appropriate calibration was achieved. A benchmark NMAE value of 0.5 was selected so that calibration was deemed successful if the NMAE was less than 0.5 (Table ##TAB##3##4##). The lower and upper bounds for each sensitive parameter were determined by experimental or literature ranges. Model output from this suite of parameter sets was then used as an estimate of the relative uncertainty in model output. As the upper and lower bound of the five most sensitive parameters were used, this should provide a conservative estimate of the model uncertainty associated with parameter estimation.</p>", "<p>To determine the five parameters most sensitive to model output, a sensitivity analysis was performed on each of the CAEDYM parameters listed in Table ##TAB##1##2##. Sensitivity coefficients (<italic>s</italic>_{<italic>ij</italic>}) to assess the relative sensitivity of variable i to parameter j were calculated according to:</p>", "<p></p>", "<p>where Δ<italic>c</italic>_{<italic>j </italic>}is the change in output variable <italic>i </italic>from the reference value <italic>c</italic>_{<italic>i </italic>}and Δ<italic>β</italic>_{<italic>j </italic>}is the change in parameter <italic>j </italic>from the reference value <italic>β</italic>_{<italic>j </italic>}[##UREF##41##43##]. Because this study is concerned with the role of physical transport mechanisms on lake-wide nitrogen fluxes, we focused on the response of the five major nitrogen fluxes (phytoplankton uptake, sediment flux, zooplankton regeneration, settling and upward flux into surface mixed layer) to parameter manipulation. Each parameter was adjusted by ± 10% or by ± 0.01 in the case of the temperature multipliers. A time variant array of sensitivity parameters was calculated for each flux and then the average taken and used to rank the parameters according to sensitivity.</p>", "<p>To enable us to quantify the significance of BBL transport on the ecological processes of the lake, a series of simulations were run in which this mechanism was switched off allowing a comparison between lake behavior with and without BBL transport. In DYRESM the lake-wide vertical fluxes are partitioned into internal and BBL contributions (see eq.36 in Yeates and Imberger (2004)). The BBL contribution was set to zero with all other parameters (physical and ecological) remaining the same. The simulated output of nitrogen fluxes and primary and secondary production were then analyzed and compared against base line output.</p>" ]

[ "<title>Results</title>", "<title>Nutrient concentrations, phytoplankton and zooplankton biomass</title>", "<p>The seasonal ammonium pattern of low winter concentrations and high summer values is reproduced by the model (Fig. ##FIG##2##3##). Similarly, peak concentrations of ammonium apparent in the observed data coinciding with the arrival of <italic>A. monica </italic>in the spring are matched in magnitude and timing by the model results. At the breakdown of stratification, the model simulated reduced ammonium concentrations corresponding to increased phytoplankton biomass (Fig. ##FIG##2##3##). However, the isolated high spikes in ammonium concentration observed in the field data during full circulation were generally not captured by the model (Fig. ##FIG##2##3##).</p>", "<p>The simulated values of phytoplankton biomass follow the low summer concentrations, timing and slope of the autumn recovery and spring decline observed in the field data (Fig. ##FIG##2##3##). However, the elevated values of phytoplankton biomass observed in the field at the end of the mixing periods (early 1992, 1993 and 1994) are not captured by the model (Fig. ##FIG##2##3##).</p>", "<p>The particulate organic nitrogen (PON) and particulate organic carbon (POC) data observed in the field closely followed that of the phytoplankton, with elevated values during the winter in the absence of grazing and low values during the summer months. These patterns were captured by the model, although elevated levels of PON were underestimated by the model during periods of full circulation (Fig. ##FIG##2##3##). However, elevated levels of POC were generally captured by the model which suggests that the model overestimated the detrital component of the particulate carbon pool (Fig. ##FIG##2##3##).</p>", "<p>The timing and slope of the early spring peak in <italic>A. monica </italic>biomass observed in the field was matched in the simulated results across the four year simulation period (Fig. ##FIG##2##3##).</p>", "<p>Simulated concentrations of dissolved oxygen are similar to those measured in the field during stratified periods (Fig. ##FIG##2##3##). The model, however, under predicted concentrations in late spring for both 1991 and 1992.</p>", "<title>Productivity and nitrogen fluxes</title>", "<p>Primary productivity in Mono Lake has been estimated using a numerical interpolative model incorporating photosynthetic uptake rates and measured vertical attenuation of PAR [##UREF##20##21##]. During the non-meromictic conditions of 1989 and 1990, Jellison and Melack (1993a) estimated an average daily productivity of 1.6 g C m^-2d^-1. This matches the value simulated by DYRESM-CAEDYM for the 1991–1994 monomictic period. During periods of stratification an average daily productivity of 1.7 g C m^-2d^-1was simulated and 1.3 g C m^-2d^-1during periods of full circulation.</p>", "<p>Average rates of lake-wide nitrogen deposition measured in 1986 and 1987 ranged from approximately 5.9 Mg N d^-1(ca. 2.5 mmol m^-2d^-1) during the summer to 2.7 Mg N d^-1(ca. 1.2 mmol m^-2d^-1) during the winter (Jellison <italic>et al</italic>. 1993). These rates are similar to those simulated by the model, i.e., 3.7 Mg N d^-1and 2.1 Mg N d^-1averaged during periods of stratification and full circulation, respectively. Areal average lake-wide nitrogen fluxes from the sediments were calculated by the model as 12.5 Mg N m^-2d^-1and 6.2 Mg N m^-2d^-1averaged during period of stratification and full circulation, respectively. Jellison <italic>et al</italic>. (1993) estimated the rate of ammonia release from the sediments based on sediment cores collected in 1988 as 58–162 Mg N m^-2d^-1(ca. 3.6–10.1 mmol m^-2d^-1). Although greater than those predicted by the model these estimates were derived under anoxic conditions so should be taken as an upper estimate.</p>", "<title>Measures of model performance</title>", "<p>The calculated values of normalized mean absolute error, correlation coefficient and slope are presented in Table ##TAB##2##3## for each of the main state variables over the full simulation period from 1991 to 1994 and compared to the calibration period from 1991 to 1992. Calculations of correlation coefficients are all equal to or greater than 0.8 with the exception of ammonium and dissolved oxygen.</p>", "<title>Sensitivity analysis</title>", "<p>The five parameters that displayed the greatest sensitivity to annual estimates of lake-wide nitrogen fluxes were: (1) release rate of NH₄from sediments (S_dNH4); (2) the fraction of zooplankton grazing excreted (f_ex); (3) background attenuation coefficient (K_d); (4) internal nitrogen to carbon ratio of the phytoplankton (IN_con) and (5) the fraction of zooplankton grazing egested (f_eg). The optimal parameter value (determined by the model best fit), and the upper and lower bounds used to determine alternative parameter sets are listed in Table ##TAB##3##4##.</p>", "<title>Nitrogen budget</title>", "<p>Five major nitrogen fluxes were extracted from the model to compare the various component of the nitrogen budget (Fig. ##FIG##3##4##). These fluxes were: (1) phytoplankton uptake, (2) sediment to water exchange, (3) bacterially mediated mineralization, (4) phytoplankton excretion, and (5) zooplankton excretion. The model results are expressed as mass flux per day with respect to the whole lake, with phytoplankton uptake as a negative flux (sink) and the other four terms as positive fluxes (source) (Fig. ##FIG##3##4##). The results indicate that mineralization of particulate nitrogen made the greatest contribution to phytoplankton uptake in the winter and zooplankton excretion during the summer (Fig. ##FIG##3##4##). Sediment-released nitrogen fluxes are comparatively low, although significant in making up the difference between phytoplankton uptake and excretion (Fig. ##FIG##3##4##).</p>", "<title>Boundary layer mixing</title>", "<p>In the absence of BBL transport a greater buildup of ammonium in the hypolimnion was simulated, the difference being greatest in the early part of the stratified period (Fig. ##FIG##4##5##). However, the difference in the epilimnion is not so pronounced. Similarly, the simulated results of the 9 m depth averaged concentrations of ammonium, PON, POC, dissolved oxygen and phytoplankton and <italic>A. monica </italic>biomass indicated little difference between the alternative scenarios of BBL mixing (Fig. ##FIG##5##6##).</p>", "<p>Calculations based on model output indicate that for 1991 to 1994 BBL transport was responsible for a 53% increase in upwards flux of ammonium across the thermocline during periods of stratification. For the corresponding periods, the simulated increase in primary production was calculated as 6% and secondary production as 5%. The model results, averaged over periods of autumn and winter mixing for the 4 years of simulation, indicated a reduction in upward ammonium flux of 28% when the BBL transport was active. This corresponded with a simulated decrease in primary production of 7% and negligible increase in secondary production of 1% for the same periods. The estimated net increase for 1991–1992 in ammonium flux across the thermocline due to BBL transport was 9%, primary productivity was 2% and secondary productivity was 3%.</p>", "<p>To place the differences in upward ammonium flux due to BBL transport in the context of the nitrogen cycle, the five major nitrogen fluxes were compared for both scenarios (Fig. ##FIG##6##7##). Almost no differences were found in the rates of regenerated nutrients, sediment flux and settling when BBL transport is inactive. Model results indicate that when the BBL transport was active ammonium flux across the thermocline accounts for 11% of the nitrogen sources to the photic zone during stratified periods. This compares to 5% when BBL transport is inactive.</p>" ]

[ "<title>Discussion</title>", "<p>Several aspects of the modeling require further examination. The step temperature function used to represent the process responsible for the hatching and initial growth of over-wintering <italic>A. monica </italic>cysts simulated well the timing and slope of the early spring peak in <italic>A. monica </italic>biomass. However, experiments have demonstrated that increases in salinity can influence the hatching process [##REF##28311791##22##]. It is anticipated, therefore, that an additional salinity factor would be required before the model could be used to predict <italic>A. monica </italic>dynamics under alternative salinities. Although simulated mid-summer concentrations of <italic>A. monica </italic>compare favorably with those observed in the field, the autumn decline was difficult to simulate well (Fig. ##FIG##2##3##). The model included three processes responsible for decreases in biomass during this period; limited grazing at low temperatures, end of life mortality and grebe predation. Improved understanding of the combination of triggers responsible for the autumn decline in <italic>A. monica </italic>will aid in the model representation of these processes. Alternatively a cohort model such as that proposed by [##UREF##21##23##] may be required to accurately represent the autumn decline.</p>", "<p>Differences between measures of fit comparing the calibration and validation periods are small. Although the ecological dynamics of the model during the validation period are similar to that of the calibration period, this result is an indication of model stability. However, this stability only relates to the representation of the interactions between the main processes responsible for determining the ecological patterns observed in the lake over the four years studied. Comparison to measures of fit for other lake ecosystem models is difficult as quantitative measures are rarely given. However, our overall measurement of NMAE compare favorably to Ross <italic>et al</italic>. (1994) (0.65) and Bruce <italic>et al</italic>. (2006) (0.52).</p>", "<p>Since we defined sensitivity in relation to estimates of lake-wide nitrogen fluxes, it follows that the parameters showing the most sensitivity are related to the nitrogen cycle. Since the inflow of nitrogen into the lake is negligible, it follows that for Mono Lake, sediment release is a critical source of nitrogen to the water column. Similarly both the fractions of zooplankton grazing that goes into either egestion (the bulk of which is deposited into sediments and thus lost from the photic zone) or excretion (providing nitrogen in a form for primary production) have a direct effect on the proportion of phytoplankton nitrogen that is recycled. The ratio of phytoplankton internal nitrogen to carbon controls both the uptake of inorganic nitrogen by phytoplankton and the flux of nitrogen recycled via the zooplankton grazing and excretion pathway. Background attenuation influences nitrogen fluxes indirectly by controlling the amount of light available for primary productivity.</p>", "<p>Model results indicated that during the summer stratified periods the N demand by phytoplankton in the surface to 9-m of Mono Lake is predominantly met by zooplankton excretion, phytoplankton leakage of dissolved organics, and bacterially mediated mineralization. Of these, the model predicted that the dominant source was zooplankton excretion. Since zooplankton biomass was well represented by the model including timing and magnitude of the initial peak, it follows that during these peaks the model has the closest fit to the ammonium data. Midwinter spikes in ammonium during period of reduced phytoplankton biomass were not reproduced in the model output. The model simulated almost constant phytoplankton biomass during the winter months that is inconsistent with the field data. From this we would conclude that the processes of phytoplankton ammonium uptake and release are not well represented by the model during winter conditions of high algal biomass and light-limitation. One of the limitations of this study was the assumption (for simplicity) of a constant internal phytoplankton C:N ratio. It is anticipated that modeling the internal nitrogen as a dynamic variable would improve the simulation of the phytoplankton-ammonium interactions particularly during periods of full circulation.</p>", "<p>This study employed optimization techniques to determine a series of parameter sets to best represent field data as described by the processes included in the current model formulation. Some field data were better represented than others and misrepresentation of field data by the simulation will serve to direct improvements in future model generations. Although the modeled fluxes sometimes over or underestimated the measured concentrations, the general seasonal patterns were captured by the simulations and thus used to provide insight into the processes that determine the ecosystem dynamics of Mono Lake.</p>", "<p>Bruce <italic>et al</italic>. (2006) in their study of the role of zooplankton in the nutrient cycles of Lake Kinneret, Israel, found zooplankton excretion to be the dominant source of dissolved nitrogen during winter overturn and sediment release the dominant source during summer stratification. For Mono Lake we also found that zooplankton excretion was most influential in the summer stratified period. Although the simulated rate of ammonium flux from the sediments was higher in Lake Kinneret [##UREF##22##24##], the main reason for finding sediment-released nutrients relatively less important in Mono Lake is due to two-fold higher rates of primary productivity and greater recycling due to zooplankton excretion in Mono Lake.</p>", "<p>MacIntyre and Jellison (2001) suggested that transport of nutrient-rich hypolimnetic water via the BBL layer is responsible for increased ammonium flux across the thermocline and consequential increase in productivity. By comparing the simulation results from the two scenarios we found that, although the increase in upward ammonium flux across the thermocline during the stratified periods of 1991–1994 due to BBL transport was 53% (± 4%), primary productivity for the same period increased only 6% (± 4%). Since the model suggested that 87% of the N demand by phytoplankton is met by regenerated sources, it is not unexpected that an increase in external supply has a limited impact. MacIntyre <italic>et al</italic>. (1999) highlighted the importance of the flux of BBL transported ammonium across the thermocline in sustaining primary productivity to the deep chlorophyll maxima. As a percentage of phytoplankton demand during the stratified periods the upward flux of ammonium across the thermocline was calculated as 12% with BBL on and 5% with BBL off. MacIntyre <italic>et al</italic>. (1999) reached a similar conclusion and, assuming that 5–10% of primary productivity occurs in the deep chlorophyll maximum during the summer, suggested that BBL may be the dominant mechanism supplying ammonium to the deep chlorophyll maximum.</p>", "<p>As anticipated, during stratified periods simulation results indicate that BBL transport leads to an increase in ammonium transport across the thermocline and concomitant increase in primary productivity. However, this pattern was reversed during periods of mixing. A greater build up of ammonium in the hypolimnion occurred during stratification in the case where BBL transport is absent (Fig. ##FIG##5##6##). Although in the absence of BBL transport, less flux was available in the photic zone during stratification, at overturn a greater mass of ammonium led to greater upwards flux of ammonium and concomitant increase in primary productivity. As a result, on an annual average, primary productivity was similar under both scenarios.</p>", "<p>Our model results have illustrated the importance of timing of BBL transport and its subsequent effect on primary and secondary productivity. The model used in this study did not include algorithms to represent differences in generations using a stage-structured zooplankton model. Inclusion of a stage structured model might enable us to determine whether the timing of BBL transport events and concomitant increases in primary productivity effect the timing and magnitude of successive generations of <italic>A. monica </italic>in Mono Lake.</p>", "<p>It is apparent that one of the reasons the transport of ammonium via the BBL does not have a significant impact on the productivity of Mono Lake is that sediment released nutrients are not a major component of the nutrient cycle. Model results have confirmed previous studies indicating that productivity is predominantly sustained by recycled nutrients (Jellison <italic>et al</italic>.. 1993). Furthermore, simulated estimates of BBL volume from 1991–1994 revealed that, on average, the benthic boundary layer comprised only 1% by volume and stored only 1% of the lake-wide nitrogen mass. To investigate the potential importance of BBL transport for shallower lakes where the volume of BBL may be larger in proportion to the lake volume we ran three additional simulations. The same Mono Lake input files for 1991–1994 were used, lowering the surface level of the lake to simulate initial depths of 35 m, 30 m and 22 m. Combining the results of these simulations we plotted the flux of ammonium transported via the BBL as a fraction of N demand by phytoplankton against daily average values of Lake Number (L_N) and primary productivity (Fig. ##FIG##7##8##). Simulated output indicated that the fraction of N demand met by hypolimnetic nutrients transported upwards in the BBL rarely exceeds 50% and only when primary productivity is minimal or for L_Nclose to 1. The L_Nis inversely proportional to the thermocline height and is both a measure of the energy available at the thermocline from wind induced surface stress and the volumetric importance of the hypolimnion [##UREF##8##9##].</p>", "<p>For Lake Kinneret, estimates of monthly primary productivity fall between 0.5 and 1.7 g C m^-2d^-1(Bruce <italic>et al</italic>. 2006). Mean daily values of L_Nestimated for Lake Kinneret range from 10^-2to 100 with a period of low L_Nassociated with strong wind events (Yeates and Imberger 2004). Given these ranges, it is predicted that the importance of BBL transport in Lake Kinneret may be greater than the 6% predicted for Mono Lake. In Lake Geneva, a study investigating the effect of internal waves on basin exchange indicated up to 40% of the hypolimnetic volume was exchanged following episodes of strong winds [##UREF##23##25##]. Primary productivity in Lake Geneva is relatively high [##UREF##24##26##]. For Lake Constance, estimates of L_Nduring stratification are relatively high (Yeates and Imberger 2004) and productivity is less than 1 g C m^-2d^-1[##UREF##25##27##] suggesting that for Lake Constance the transport of nutrients through the BBL may be less important to overall lake productivity but potentially significant during episodic events associated with low L_N.</p>", "<p>The results of this study have indicated that the relative importance of BBL transport as a source of nutrients sustaining productivity in the photic zone is determined by productivity and morphology. Future studies will be focused on comparing the effect of BBL transport on the ecology of other lakes. By differentiating between physical and ecological process we will be able to determine what limnological features alter the importance of BBL transport.</p>" ]

[]

[ "<p>The significance of the transport of nutrient-rich hypolimnetic water via the benthic boundary layer (BBL) to the productivity of Mono Lake was studied using a coupled hydrodynamic and ecological model validated against field data. The coupled model enabled us to differentiate between the role of biotic components and hydrodynamic forcing on the internal recycling of nutrients necessary to sustain primary productivity. A 4-year period (1991–1994) was simulated in which recycled nutrients from zooplankton excretion and bacterially-mediated mineralization exceeded sediment fluxes as the dominant source for primary productivity. Model outputs indicated that BBL transport was responsible for a 53% increase in the flux of hypolimnetic ammonium to the photic zone during stratification with an increase in primary production of 6% and secondary production of 5%. Although the estimated impact of BBL transport on the productivity of Mono Lake was not large, significant nutrient fluxes were simulated during periods when BBL transport was most active.</p>" ]

[ "<title>Study Site</title>", "<p>Mono Lake (38°N: 119°W) is a large saline lake with a salinity of 85–92 g kg^-1, a maximum depth 45 m, mean depth 17 m and surface area approximately 160 km²(Fig. ##FIG##0##1##). The lake was monomictic during the period studied (1991–1994), and vertically mixed in winter (December to February) with thermal stratification beginning in early spring and persisting through autumn [##UREF##16##17##]. At other times following large runoff years, the lake experienced multi-year periods of chemical stratification (i.e., meromixis; 1982–1988, Jellison and Melack 1993b; 1995–2003, Jellison unpublished data). The present study examines four monomictic years (1991–94) to assess the effects of BBL on nutrient cycling and productivity during stratified and holomictic periods.</p>", "<p>The planktonic community of Mono Lake has few species as is typical of hypersaline waters. The phytoplankton is dominated by a newly described picoplanktonic (2–3 μm) green alga, <italic>Picocystis salinarum </italic>Lewin (Lewin <italic>et al</italic>., 2000), and several bacillarophytes, mainly <italic>Nitzschia </italic>spp. (20–30 μm) (Lovejoy &amp; Dana, 1977; Mason, 1967). A brine shrimp, <italic>Artemia monica </italic>Verill, is the only macrozooplankter (Lenz, 1980; Lenz, 1984). While pelagic ciliates and rotifers may also be present at times (Mason, 1967; Jellison et al. 2001), they contribute a negligible amount to the total zooplankton biomass.</p>", "<p>There is a strong seasonal pattern in the nutrient and plankton dynamics of Mono Lake [##UREF##17##18##]. The seasonal patterns are driven by biotic and abiotic forces affecting productivity via bottom-up and top-down controls. Water temperatures of the surface mixed-layer ranged from 2–5°C in winter to 12–22°C in summer. Seasonal stratification and high productivity result in anoxic conditions in the hypolimnion where ammonium accumulates. The flux of this ammonium to the photic zone is limited until winter overturn mixes the whole lake providing nutrients for a pronounced spring algal bloom. Daily primary productivity rates are relatively high (Jellison and Melack 1993a).</p>", "<p>The lake's only macrozooplankter, <italic>A. monica</italic>, produces over-wintering cysts that lie dormant on the bottom during the winter and hatch during early spring (February-April) [##UREF##18##19##]. <italic>A. monica </italic>biomass usually peaks in the late spring, remains high during the summer and gradually declines during the autumn as food is scarce and temperatures decline. The spring growth of <italic>A. monica </italic>biomass is associated with a simultaneous decline in phytoplankton biomass due to grazing and rise in surface concentrations of ammonium from zooplankton excretion. Phytoplankton biomass remains low during the summer and only increases toward the end of the year when grazing pressure is reduced [##UREF##19##20##].</p>", "<p>As phosphorus concentrations are always high (&gt;400 μM; Jellison et al. 1993), nitrogen limits primary production in the photic zone (Jellison &amp; Melack 1993a, 2001). Nitrogen inputs from inflowing streams and planktonic nitrogen fixation are very low relative to internal fluxes where the main sources are from sediment release in the hypolimnion, phytoplankton and zooplankton excretion, and bacterial mineralization of particulate detrital organic nitrogen. Peak concentrations in the photic zone are observed at the breakdown of stratification as nutrient-rich hypolimnetic waters become entrained into the epilimnion. Towards the end of the mixed period and onset of stratification ammonium levels are generally low. When the zooplankton become abundant in late spring, grazing reduces phytoplankton biomass and internal phytoplankton nitrogen is converted to ammonium via the zooplankton grazing and excretion. Zooplankton excretion and reduced ammonium uptake due to low phytoplankton biomass results in an increase in epilimnetic ammonium concentrations.</p>", "<title>List of abbreviations used</title>", "<p>BBL: Benthic boundary layer; CAEDYM: Computational aquatic ecosystem dynamic model; DYRESM: Dynamic reservoir simulation model.</p>", "<title>Declaration of competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>All authors participated in the conception and design of the study. RJ collected data used in model validation while LCB conducted model simulations and drafted the initial manuscript. All authors contributed to revising and editing the manuscript, and have read and approved the final version.</p>" ]

[ "<title>Acknowledgements</title>", "<p>The first author was funded by an Australian Postgraduate (Industry) scholarship, sponsored by the Wheatbelt Development Commission. Data were collected as part of a long-term limnological monitoring program supported by grants from the Los Angeles Department of Water and Power. Facilities at the Sierra Nevada Aquatic Research Laboratory (Natural Reserve System, University of California) were utilized for laboratory analyses and field sampling. We thank Sally MacIntyre from USCB who has provided valuable advice and direction to the research. The Contract Research Group at the Centre for Water Research (CWR) and in particular Jose Romero, Peter Yeates and Matt Hipsey provided technical support for the project.</p>" ]

[ "<fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p><bold>Mono Lake</bold>. Bathymetric map of Mono Lake showing sampling stations. Depth contours are in meters.</p></caption></fig>", "<fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p><bold>Model schematic</bold>. Schematic representation of (A) the model layer structure (B) internal and boundary layer mixing in the physical model DYRESM. (BBL: benthic boundary layer; Internal: internal cells; BC: benthic boundary layer cells) and (C) the carbon and nitrogen fluxes represented in the ecological model, CAEDYM. Dotted lines indicate that these variables are not included in model.</p></caption></fig>", "<fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p><bold>Model simulations</bold>. Comparison of model simulation results (lines) and field data (crosses) for Mono Lake from 1991 to 1994 for 9-m depth integrated averages of ammonium (NH₄), total phytoplankton carbon (phytoplankton), total organic nitrogen (TPON), total organic carbon (TPOC) and dissolved oxygen (DO), vertical net tows of <italic>Artemia monica </italic>(Artemia).</p></caption></fig>", "<fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p><bold>Nitrogen fluxes</bold>. Nitrogen fluxes (Mg N day^-1) for total phytoplankton uptake (PhyUp) against sediment flux (SedFlux), mineralization of PON (Mineral), phytoplankton excretion (PhyEx), and zooplankton excretion (ZoopEx). Corresponding periods of stratification and full circulation are demarked by dashed lines.</p></caption></fig>", "<fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p><bold>BBL ammonium transport</bold>. Comparison of NH₄(g m^-3) depth profiles for the scenarios of BBL transport activated (solid line) and absent (dotted line) and field data (solid dots) for selected dates from 1991.</p></caption></fig>", "<fig id=\"F6\" position=\"float\"><label>Figure 6</label><caption><p><bold>Effect of BBL transport</bold>. Comparison of model simulation results with BBL transport activated (solid lines) and absent (dotted lines) from 1991 to 1992 for 9-m depth integrated averages of ammonium (NH₄), total phytoplankton carbon (phytoplankton), organic nitrogen (PON), organic carbon (POC) and dissolved oxygen (DO) and vertical net tows of <italic>Artemia monica </italic>(Artemia).</p></caption></fig>", "<fig id=\"F7\" position=\"float\"><label>Figure 7</label><caption><p><bold>Lakewide nitrogen fluxes</bold>. Comparison of lake-wide nitrogen fluxes for phytoplankton uptake (Phy Uptake), total regenerated sources (Tot Regen), sediment flux (Sed Flux), flux across the thermocline (Hyp Flux) and settling of particulate nitrogen (Settling) averaged annually, during the stratified periods and during the mixed periods from 1991 to 1994 from the boundary mixing on (black bars) and off (white bars) scenarios. Error bars indicate one standard deviation from mean.</p></caption></fig>", "<fig id=\"F8\" position=\"float\"><label>Figure 8</label><caption><p><bold>Ammonium flux versus lake number</bold>. Flux of ammonium transported via the BBL as a fraction of lake-wide vertical fluxes (closed circles) versus daily average values of Lake Number (L_N) and Burger Number (B_N).</p></caption></fig>" ]

[ "<table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Model process equations. Equations used to describe the processes included in the ecological model CAEDYM</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">∂Z_i/∂t = [G_iA_i<italic>f</italic>(Z)_i<italic>f</italic>₁(T)(1-f_ex-f_eg) - (R_i+M_i)<italic>f</italic>₂(T) - Pred_i]Z_i</th></tr><tr><th align=\"left\"> = (assimilation - excretion - egestion) - (respiration + mortality) - predation</th></tr><tr><th align=\"left\">∂P/∂t = [P_max,j<italic>f</italic>₁(T)min(<italic>f</italic>(I),<italic>f</italic>(P),<italic>f</italic>(N)) - (R_j)<italic>f</italic>₂(T) - Pred_j]P_j± S_j</th></tr><tr><th align=\"left\"> = photosynthetic uptake - (respiration + excretion + mortality) - predation ± settling</th></tr><tr><th align=\"left\">∂POC/∂t = Σ[G_i<italic>f</italic>(Z)_i<italic>f</italic>₁(T)_i((1-A_i) + A_if_eg) + M_i<italic>f</italic>₂(T)_i]Z_i+ Σ[R_j(1-f_res)(1-f_DOM)<italic>f</italic>₂(T)]P_j- Pred_POCPOC - R_POC<italic>f</italic>(DO)<italic>f</italic>₁(T)POC ± S_POM</th></tr><tr><th align=\"left\"> = (unassimilated zooplankton food + zooplankton egestion + zooplankton mortality) + phytoplankton mortality - zooplankton predation - POC decomposition ± settling</th></tr><tr><th align=\"left\">∂DOC/∂t = Σ[R_j(1-f_res)f_DOM<italic>f</italic>₂(T)]P_j+ R_POC<italic>f</italic>_POC(POC)<italic>f </italic>(DO)<italic>f</italic>₂(T)POC - R_DOC<italic>f </italic>(DO)<italic>f</italic>₂(T)DOC</th></tr><tr><th align=\"left\"> = phytoplankton excretion + POC decomposition - DOC mineralisation</th></tr><tr><th align=\"left\">∂POP/∂t = Σ[G_i<italic>f</italic>(Z)_i<italic>f</italic>₁(T)_i((1-A_i) + A_if_eg) + M_i<italic>f</italic>₂(T)_i]IP_ZiZ_i+ Σ[R_j(1-f_res)(1-f_DOM)<italic>f</italic>₂(T)]IP_j- Pred_POCPOP - R_POP<italic>f</italic>(DO)<italic>f</italic>₁(T)POP ± S_POM</th></tr><tr><th align=\"left\"> = (unassimilated zooplankton food + zooplankton egestion + zooplankton mortality) + phytoplankton mortality - zooplankton predation - POP decomposition ± settling</th></tr><tr><th align=\"left\">∂DOP/∂t = Σ[R_j(1-f_res)f_DOM<italic>f</italic>₂(T)]IP_j+ Σ[A_if_exG_i<italic>f</italic>(Z)_i<italic>f</italic>₁(T)_i]IP_ZiZ_i+ R_POP<italic>f </italic>(DO)<italic>f</italic>₁(T)POP - R_DOP<italic>f </italic>(DO)<italic>f</italic>₁(T)DOP</th></tr><tr><th align=\"left\"> = phytoplankton release + zooplankton excretion + POP decomposition - DOP mineralisation</th></tr><tr><th align=\"left\">∂PO4/∂t = R_DOP<italic>f </italic>(DO)<italic>f</italic>₂(T)DOP - Σ[UN_max,j<italic>f</italic>₁(T)_j<italic>f</italic>(IP)_j<italic>f</italic>(P)_j]P_j+ S_dPO4<italic>f</italic>(DO)<italic>f</italic>₂(T)LA/LV</th></tr><tr><th align=\"left\"> = DOP mineralisation - phytoplankton uptake + PO4 sediment flux</th></tr><tr><th align=\"left\">∂PON/∂t = Σ[G_i<italic>f</italic>(Z)_i<italic>f</italic>₁(T)_i((1-A_i) + A_if_eg) + M_i<italic>f</italic>₂(T)_i]IN_ZiZ_i+ Σ[R_j(1-f_res)(1-f_DOM)<italic>f</italic>₂(T)]IN_j- Pred_PONPON - R_PON<italic>f</italic>(DO)<italic>f</italic>₁(T)PON ± S_POM</th></tr><tr><th align=\"left\"> = (unassimilated zooplankton food + zooplankton egestion + zooplankton mortality) + phytoplankton mortality - zooplankton predation - PON decomposition ± settling</th></tr><tr><th align=\"left\">∂DON/∂t = Σ[R_j(1-f_res)f_DOM<italic>f</italic>₂(T)]IN_j+ Σ[A_if_exG_i<italic>f</italic>(Z)_i<italic>f</italic>₁(T)_i]IN_ZiZ_i+ R_PON<italic>f</italic>(DO)<italic>f</italic>₂(T)PON - R_DON<italic>f</italic>(DO)<italic>f</italic>₂(T)DON</th></tr><tr><th align=\"left\"> = phytoplankton release + zooplankton excretion + PON decomposition - DON mineralisation</th></tr><tr><th align=\"left\">∂NH4/∂t = R_DON<italic>f</italic>(DO)<italic>f</italic>₁(T)DON - Σ[UN_max,jP_N<italic>f</italic>₁(T)_j<italic>f</italic>(IN)_j<italic>f</italic>(N)_j]P_j- R_NO<italic>f</italic>(DO)<italic>f</italic>₂(T)NH4 + S_dNH4<italic>f</italic>(DO)<italic>f</italic>₂(T)LA/LV</th></tr><tr><th align=\"left\"> = PON mineralisation - phytoplankton uptake - nitrification + NH4 sediment flux</th></tr><tr><th align=\"left\">∂NO3/∂t = R_NO<italic>f</italic>(DO)<italic>f</italic>₂(T)NH4 - R_N2<italic>f</italic>(DO)<italic>f</italic>₂(T)NO3 - Σ[UN_max,j(1-P_N)<italic>f</italic>₁(T)_j<italic>f</italic>(IN)_j<italic>f</italic>(N)_j]P_j</th></tr><tr><th align=\"left\"> = nitrification - denitrification - phytoplankton uptake</th></tr></thead><tbody><tr><td align=\"left\">∂DO/∂t = k_O2(DO_atm - DO) + Σ[P_max,j<italic>f</italic>₁(T)_jmin(<italic>f</italic>(I),<italic>f</italic>(P),<italic>f</italic>(N)) - R_j<italic>f</italic>₂(T)_j]P_jY_O2:CΣ[R_i<italic>f</italic>₂(T)_i]Z_iY_O2:C- R_DOC<italic>f </italic>(DO)<italic>f</italic>₁(T)DOCY_O2:C- R_NO<italic>f </italic>(DO)<italic>f</italic>₂(T)NH4 - S_dO2<italic>f</italic>(DO)<italic>f</italic>₂(T)LA/LV</td></tr><tr><td align=\"left\"> = atmospheric flux + (phytoplankton oxygen production - phytoplankton respiratory consumption) - zooplankton respiratory consumption - utilisation of oxygen in mineralisation of DOM - utilisation of oxygen in nitrification - sediment oxygen demand.</td></tr><tr><td colspan=\"1\"><hr/></td></tr><tr><td align=\"left\">Temperature functions</td></tr><tr><td align=\"left\"><italic>f</italic>₁(T) = θ^T-20- θ^k(T-a)+ b</td></tr><tr><td align=\"left\">where k, a and b are constants solved numerically to satisfy the following conditions:</td></tr><tr><td align=\"left\"><italic>f</italic>₁(T) = 1; at T = Tsta</td></tr><tr><td align=\"left\">∂<italic>f</italic>₁(T)/∂T = 0; at T = Topt</td></tr><tr><td align=\"left\"><italic>f</italic>₁(T) = 0; at T = Tmax</td></tr><tr><td align=\"left\"><italic>f</italic>₂(T) = θ^T-20</td></tr><tr><td colspan=\"1\"><hr/></td></tr><tr><td align=\"left\">Limitation equations</td></tr><tr><td align=\"left\"><italic>f</italic>(Z)_i=(ΣP_j+ΣZ_k+POC)/(K_i+ΣP_j+ΣZ_k+POC)</td></tr><tr><td align=\"left\"><italic>f</italic>(I)_j= I/I_sexp(1-I/I_s)</td></tr><tr><td align=\"left\">f(IP)_j= [IP_max/(IP_max-IP_min)] [1-IP_min/IP]</td></tr><tr><td align=\"left\">f(IN)_j= [IN_max/(IN_max-IN_min)] [1-IN_min/IN]</td></tr><tr><td align=\"left\">f(DO) = DO/(K_DO+DO)</td></tr><tr><td align=\"left\">f(P) = PO4/(K_PO4+PO4)</td></tr><tr><td align=\"left\">f(N) = (NH4+NO3)/(K_N2+NH4+NO3)</td></tr><tr><td align=\"left\">P_N= (NH4 NO3)/[(NH4+K_N)(NO3+K_N)] + (NH4 K_N)/[(NH4+K_N)(NO3+K_N)]</td></tr><tr><td colspan=\"1\"><hr/></td></tr><tr><td align=\"left\">Settling</td></tr><tr><td align=\"left\">S_j= (ws/Δz)P_j</td></tr><tr><td align=\"left\">S_POM= (g(ρ_POM- ρ_w)(D_POM)²/18μ)/Δz)POM</td></tr><tr><td colspan=\"1\"><hr/></td></tr><tr><td align=\"left\">Predation</td></tr><tr><td align=\"left\">Pred_i= Σ(G_kf(Z)_kf₁(T)_kZ_kPzZOO_k,i)</td></tr><tr><td align=\"left\">Pred_j= Σ(G_if(Z)_if₁(T)_iZ_iPzPHY_i,j)</td></tr><tr><td align=\"left\"><italic>Abbreviations</italic>: Z, zooplankton; P, phytoplankton; POC, particulate organic carbon; DOC, dissolved organic carbon; POP, particulate organic phosphorus; PO₄, phosphate; PON, particulate organic nitrogen; NH₄, ammonium; NO₃, nitrate; POM, particulate organic matter (C, N or P); IP_zi, zooplankton internal phosphorus; IN_zi, zooplankton internal nitrogen; IP_j, phytoplankton internal phosphorus; IN_j, phytoplankton internal nitrogen; DO, dissolved oxygen; DOatm, concentration of oxygen in the atmosphere; LA, layer area; LV, layer volume; Δz, layer thickness; ρ_w, density of water; μ, viscosity of water; k_O2, oxygen transfer coefficient. <italic>Subscripts</italic>: i, zooplankton group; j, phytoplankton group; k, zooplankton predator group.</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Model parameters. Parameters used in CAEDYM to simulate ecological variables in Mono Lake.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"5\"><italic>General</italic></th></tr></thead><tbody><tr><td align=\"left\">Parameter</td><td align=\"left\">Description</td><td align=\"left\">Units</td><td align=\"left\">Assigned value</td><td align=\"left\">Values from field/lit</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">K_d</td><td align=\"left\">Background extinction coefficient</td><td align=\"left\">m^-1</td><td align=\"left\">0.35</td><td align=\"left\">0.29–0.34^a</td></tr><tr><td align=\"left\">Source</td><td align=\"left\">^aCalculated from unpub data on in-situ light measurements</td><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\" colspan=\"5\"><italic>Phytoplankton</italic></td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Parameter</td><td align=\"left\">Description</td><td align=\"left\">Units</td><td align=\"left\">Assigned values:</td><td align=\"left\">Values from field/literature</td></tr><tr><td align=\"left\">P_max</td><td align=\"left\">Maximum potential growth rate</td><td align=\"left\">d^-1</td><td align=\"left\">5.96</td><td align=\"left\">7.2^a</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">I_K</td><td align=\"left\">Parameter for initial slope of PI curve</td><td align=\"left\">μEm^-2s^-1</td><td align=\"left\">25</td><td align=\"left\">25^b</td></tr><tr><td align=\"left\">Kep</td><td align=\"left\">Specific attenuation coefficient</td><td align=\"left\">m²g C^-1</td><td align=\"left\">0.008</td><td align=\"left\">0.008^c</td></tr><tr><td align=\"left\">K_P</td><td align=\"left\">Half saturation constant for phosphorus uptake</td><td align=\"left\">mg L^-1</td><td align=\"left\">0.001</td><td align=\"left\">Low value as not P limited</td></tr><tr><td align=\"left\">K_N</td><td align=\"left\">Half saturation constant for nitrogen uptake</td><td align=\"left\">mg L^-1</td><td align=\"left\">0.0573</td><td align=\"left\">Calibrated</td></tr><tr><td align=\"left\">IN_con</td><td align=\"left\">Constant internal N ratio</td><td align=\"left\">mg N (mg C)^-1</td><td align=\"left\">0.0926</td><td align=\"left\">0.17^d</td></tr><tr><td align=\"left\">IP_con</td><td align=\"left\">Constant internal P ratio</td><td align=\"left\">mg P (mg C)^-1</td><td align=\"left\">0.026</td><td align=\"left\">0.048^d</td></tr><tr><td align=\"left\">θ_j</td><td align=\"left\">Temperature multiplier for growth</td><td/><td align=\"left\">1.06</td><td align=\"left\">1.07^e</td></tr><tr><td align=\"left\">T_sta</td><td align=\"left\">Standard temperature</td><td align=\"left\">°C</td><td align=\"left\">19</td><td/></tr><tr><td align=\"left\">T_opt</td><td align=\"left\">Optimum temperature</td><td align=\"left\">°C</td><td align=\"left\">22</td><td/></tr><tr><td align=\"left\">T_max</td><td align=\"left\">Maximum temperature</td><td align=\"left\">°C</td><td align=\"left\">39.5</td><td/></tr><tr><td align=\"left\">R_j</td><td align=\"left\">Metabolic loss rate coefficient</td><td align=\"left\">d^-1</td><td align=\"left\">0.302</td><td align=\"left\">Calibrated</td></tr><tr><td align=\"left\">θ_R</td><td align=\"left\">Temperature multiplier for metabolic loss</td><td/><td align=\"left\">1.05</td><td align=\"left\">Calibrated</td></tr><tr><td align=\"left\">f_res</td><td align=\"left\">Fraction of respiration relative to total metabolic loss</td><td/><td align=\"left\">0.693</td><td align=\"left\">Calibrated</td></tr><tr><td align=\"left\">f_DOM</td><td align=\"left\">Fraction of metabolic loss rate that goes to DOM</td><td/><td align=\"left\">0.291</td><td align=\"left\">Calibrated</td></tr><tr><td align=\"left\">ws</td><td align=\"left\">Settling velocity</td><td align=\"left\">m d^-1</td><td align=\"left\">0.008</td><td align=\"left\">0.04–0.013^f</td></tr><tr><td align=\"left\">Sources</td><td align=\"left\" colspan=\"4\">^aJellison and Melack 1993a, based on maximum value of carbon uptake measured from lake samples 1983–1990 assuming 50 g C g Chl <italic>a</italic>^-1<break/>^bJellison and Melack 1993a, based on minimum value of I_Kmeasured from lake samples 1983–1990.<break/>^cJellison and Melack 1993a.<break/>^dJellison and Melack 2001, estimated from seston ratios during the summer period from monomictic years 1991–1995 1984<break/>^eJellison and Melack 1993a, based on Q10 of 1.95.<break/>^fJellison <italic>et al</italic>.. 1993.</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\" colspan=\"5\"><italic>Zooplankton</italic></td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Parameter</td><td align=\"left\">Description</td><td align=\"left\">Units</td><td align=\"left\">Assigned values:</td><td align=\"left\">Values from field/literature</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">G_i</td><td align=\"left\">Grazing rate</td><td align=\"left\">g C m^-3(g C m^-3)^-1d^-1</td><td align=\"left\">1.12</td><td align=\"left\">1.26^a</td></tr><tr><td align=\"left\">A_zi</td><td align=\"left\">Grazing efficiency</td><td align=\"left\">-</td><td align=\"left\">1.0</td><td align=\"left\">Close to 1 as filter feeders</td></tr><tr><td align=\"left\">R_i</td><td align=\"left\">Respiration rate coefficient</td><td align=\"left\">d^-1</td><td align=\"left\">0.113</td><td align=\"left\">0.035–0.1^b</td></tr><tr><td align=\"left\">M_i</td><td align=\"left\">Mortality rate coefficient</td><td align=\"left\">d^-1</td><td align=\"left\">0.0107</td><td align=\"left\">0.0033^c0.0262^d</td></tr><tr><td align=\"left\">f_eg</td><td align=\"left\">Fecal pellet fraction of grazing</td><td align=\"left\">d^-1</td><td align=\"left\">0.096</td><td align=\"left\">Kfz+kez = 0.36–0.68^e</td></tr><tr><td align=\"left\">f_ex</td><td align=\"left\">Excretion fraction of grazing</td><td align=\"left\">d^-1</td><td align=\"left\">0.49</td><td/></tr><tr><td align=\"left\">DOmz</td><td align=\"left\">Minimum DO tolerance</td><td align=\"left\">mg L^-1</td><td align=\"left\">0.0</td><td align=\"left\">0–1.2^f</td></tr><tr><td align=\"left\">θ_i</td><td align=\"left\">Temperature multiplier for growth</td><td/><td align=\"left\">1.055</td><td align=\"left\">1.22^g</td></tr><tr><td align=\"left\">Tmin</td><td align=\"left\">Minimum temperature</td><td align=\"left\">Deg C</td><td align=\"left\">6</td><td align=\"left\">6.8–9.0^h</td></tr><tr><td align=\"left\">θ_Ri</td><td align=\"left\">Respiration temperature dependence</td><td/><td align=\"left\">1.10</td><td/></tr><tr><td align=\"left\">K_i</td><td align=\"left\">Half saturation constant for grazing</td><td align=\"left\">g C m^-3</td><td align=\"left\">1.12</td><td align=\"left\">2.96ⁱ</td></tr><tr><td align=\"left\">IN_zi</td><td align=\"left\">Internal ratio of nitrogen to carbon.</td><td align=\"left\">g N g C^-1</td><td align=\"left\">0.208</td><td align=\"left\">0.197/0.218^j</td></tr><tr><td align=\"left\">IP_zi</td><td align=\"left\">Internal ratio of phosphorus to carbon</td><td align=\"left\">g P g C^-1</td><td align=\"left\">0.02</td><td align=\"left\">0.0135^k</td></tr><tr><td align=\"left\">PzPHY</td><td align=\"left\">Preference of zooplankton for phytoplankton</td><td/><td align=\"left\">0.8</td><td/></tr><tr><td align=\"left\">PzPOC</td><td align=\"left\">Preference of zooplankton for POC</td><td/><td align=\"left\">0.2</td><td/></tr><tr><td align=\"left\">Sources</td><td align=\"left\" colspan=\"4\">^a[##UREF##31##33##,##UREF##42##44##] (<italic>Artemia fransiscana </italic>optimal food 11 days old)<break/>^b[##UREF##31##33##,##UREF##42##44##] (<italic>Artemia fransiscana </italic>range of food 11 days old)<break/>^cJellison <italic>et al</italic>.. 1993 (based on survival rate over 30 days)<break/>^dDana and Lenz 1986 (based on survival rate over 26 days)<break/>^eEvjemo <italic>et al</italic>.. 2000, <italic>Artemia fransiscana</italic>.<break/>^fDO concentration at depth of deep Chl <italic>a </italic>maxima (unpub data).<break/>^gJellison <italic>et al</italic>.. 1993 (best fit to temperature function used in model)<break/>^hJellison unpub data 1991–1994. (based on temperature at which total biomass &lt; 0.01 before Spring growth)<break/>ⁱEvjemo and Olsen 1999 (<italic>Artemia fransiscana </italic>11 days old, 26–28°C, Holling Type II)<break/>^jJellison unpub data (Females/Males)<break/>^k[##UREF##43##45##]</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\" colspan=\"5\"><italic>Dissolved Oxygen and Nutrients</italic></td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Parameter</td><td align=\"left\">Description</td><td align=\"left\">Units</td><td align=\"left\">Assigned values</td><td align=\"left\">Values from field/literature</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">S_dDO</td><td align=\"left\">DO sediment exchange rate</td><td align=\"left\">g m^-2d^-1</td><td align=\"left\">0.053</td><td/></tr><tr><td align=\"left\">K_{DO_sed}</td><td align=\"left\">Half saturation constant for DO sediment flux</td><td align=\"left\">mg O L^-1</td><td align=\"left\">0.537</td><td/></tr><tr><td align=\"left\">K_{DO_POM}</td><td align=\"left\">Half saturation constant for dependence of POM/DOM decomposition on DO</td><td align=\"left\">mg O L^-1</td><td align=\"left\">1.46</td><td/></tr><tr><td align=\"left\">fanB</td><td align=\"left\">Aerobic/anaerobic factor</td><td align=\"left\">-</td><td align=\"left\">0.357</td><td/></tr><tr><td align=\"left\">θ_POM</td><td align=\"left\">Temperature multiplier</td><td align=\"left\">-</td><td align=\"left\">1.03</td><td align=\"left\">1.02–1.14^a</td></tr><tr><td align=\"left\">R_POC</td><td align=\"left\">Mineralisation rate for POC to DOC</td><td align=\"left\">d^-1</td><td align=\"left\">0.12</td><td/></tr><tr><td align=\"left\">R_POP</td><td align=\"left\">Mineralisation rate for POP to DOP</td><td align=\"left\">d^-1</td><td align=\"left\">0.1</td><td align=\"left\">0.01–0.1^a</td></tr><tr><td align=\"left\">R_PON</td><td align=\"left\">Mineralisation rate for PON to DON</td><td align=\"left\">d^-1</td><td align=\"left\">0.4</td><td align=\"left\">0.01–0.03^a</td></tr><tr><td align=\"left\">D_POM</td><td align=\"left\">Diameter of POM particles</td><td align=\"left\">m</td><td align=\"left\">0.000009</td><td/></tr><tr><td align=\"left\">ρ_POM</td><td align=\"left\">Density of POM particles</td><td align=\"left\">kg m^-3</td><td align=\"left\">1109</td><td/></tr><tr><td align=\"left\">KePOC</td><td align=\"left\">Specific light attenuation coefficient for POC</td><td align=\"left\">m²g^-1</td><td align=\"left\">0.00943</td><td/></tr><tr><td align=\"left\">R_DOC</td><td align=\"left\">Mineralisation rate for DOC</td><td align=\"left\">d^-1</td><td align=\"left\">1</td><td align=\"left\">Set to 1 to eliminate DOP pool for simplicity</td></tr><tr><td align=\"left\">R_DOP</td><td align=\"left\">Mineralisation rate for DOP to PO4</td><td align=\"left\">d^-1</td><td align=\"left\">1</td><td align=\"left\">Set to 1 to eliminate DOP pool for simplicity</td></tr><tr><td align=\"left\">R_DOP</td><td align=\"left\">Mineralisation rate for DOP to PO4</td><td align=\"left\">d^-1</td><td align=\"left\">1</td><td align=\"left\">Set to 1 to eliminate DOP pool for simplicity</td></tr><tr><td align=\"left\">R_DON</td><td align=\"left\">Mineralisation rate for DON to NH4</td><td align=\"left\">d^-1</td><td align=\"left\">1</td><td align=\"left\">onset to 1 to eliminate DON pool for simplicity.</td></tr><tr><td align=\"left\">KeDOC</td><td align=\"left\">Specific light attenuation coefficient of DOC</td><td align=\"left\">m²g^-1</td><td align=\"left\">0.001</td><td/></tr><tr><td align=\"left\">R_N2</td><td align=\"left\">Denitrification rate coefficient</td><td align=\"left\">d^-1</td><td align=\"left\">0.000864</td><td align=\"left\">0.1^a</td></tr><tr><td align=\"left\">θ_N2</td><td align=\"left\">Temperature multiplier for denitrification</td><td align=\"left\">-</td><td align=\"left\">1.08</td><td align=\"left\">1.045^a</td></tr><tr><td align=\"left\">K_N2</td><td align=\"left\">Half saturation constant for denitrification dependence on oxygen</td><td align=\"left\">mg N L^-1</td><td align=\"left\">1.75</td><td/></tr><tr><td align=\"left\">R_NO</td><td align=\"left\">Nitrification rate coefficient</td><td align=\"left\">d^-1</td><td align=\"left\">0.00553</td><td align=\"left\">0.1–0.2^a</td></tr><tr><td align=\"left\">θ_NO</td><td align=\"left\">Temperature multiplier for nitrification</td><td align=\"left\">-</td><td align=\"left\">1.08</td><td align=\"left\">1.08^a</td></tr><tr><td align=\"left\">K_NO</td><td align=\"left\">Half saturation constant for nitrification dependence on oxygen</td><td align=\"left\">mg O L^-1</td><td align=\"left\">0.5</td><td/></tr><tr><td align=\"left\">θ_sed</td><td align=\"left\">Temperature multiplier for sediment nutrient fluxes</td><td align=\"left\">-</td><td align=\"left\">1.05</td><td/></tr><tr><td align=\"left\">S_dNH4</td><td align=\"left\">Release rate of NH4 from sediments</td><td align=\"left\">g m^-2d^-1</td><td align=\"left\">0.0712</td><td align=\"left\">0.054–0.18^b</td></tr><tr><td align=\"left\">K_{DO_SdNH4}</td><td align=\"left\">Controls sediment release of NH4 via oxygen – Half saturation constant for sediment NH4 release dependence on DO</td><td align=\"left\">g m^-3</td><td align=\"left\">0.565</td><td/></tr><tr><td align=\"left\">Sources</td><td align=\"left\" colspan=\"4\">^aJorgensen and Bendoricchio 2001<break/>^bJellison <italic>et al</italic>.. 1993</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Normalised mean absolute error. </p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">NMAE</th><th align=\"left\">SD/Mean</th><th align=\"left\">r²</th><th align=\"left\">Slope</th></tr></thead><tbody><tr><td align=\"left\">NH₄</td><td align=\"left\">0.58 (0.56)</td><td align=\"left\">0.88 (0.72)</td><td align=\"left\">0.37 (0.59)</td><td align=\"left\">0.29 (0.61)</td></tr><tr><td align=\"left\">Phytoplankton</td><td align=\"left\">0.45 (0.44)</td><td align=\"left\">1.15 (1.04)</td><td align=\"left\">0.79 (0.85)</td><td align=\"left\">0.61 (0.80)</td></tr><tr><td align=\"left\"><italic>Artemia monica</italic></td><td align=\"left\">0.30 (0.30)</td><td align=\"left\">0.85 (0.95)</td><td align=\"left\">0.79 (0.83)</td><td align=\"left\">0.80 (0.87)</td></tr><tr><td align=\"left\">TPON</td><td align=\"left\">0.35 (0.34)</td><td align=\"left\">0.83 (0.82)</td><td align=\"left\">0.90 (0.94)</td><td align=\"left\">0.52 (0.57)</td></tr><tr><td align=\"left\">TPOC</td><td align=\"left\">0.43 (0.42)</td><td align=\"left\">0.91 (0.88)</td><td align=\"left\">0.86 (0.92)</td><td align=\"left\">1.02 (1.15)</td></tr><tr><td align=\"left\">Dissolved oxygen</td><td align=\"left\">0.31 (0.31)</td><td align=\"left\">0.48 (0.48)</td><td align=\"left\">0.64 (0.64)</td><td align=\"left\">0.32 (0.32)</td></tr><tr><td align=\"left\">Average</td><td align=\"left\">0.40 (0.40)</td><td align=\"left\">0.85 (0.82)</td><td align=\"left\">0.72 (0.79)</td><td align=\"left\">0.59 (0.72)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"T4\" position=\"float\"><label>Table 4</label><caption><p>Sensitivity analysis. </p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Parameter</th><th align=\"left\">Optimal</th><th align=\"left\">Lower bound</th><th align=\"left\">Upper bound</th><th align=\"left\">NMAE (lower bound)</th><th align=\"left\">NMAE (upper bound)</th></tr></thead><tbody><tr><td align=\"left\">S_dNH4</td><td align=\"left\">0.06</td><td align=\"left\">0.01</td><td align=\"left\">0.10</td><td align=\"left\">0.49</td><td align=\"left\">0.42</td></tr><tr><td align=\"left\">f_ex</td><td align=\"left\">0.50</td><td align=\"left\">0.05</td><td align=\"left\">0.70</td><td align=\"left\">0.49</td><td align=\"left\">0.44</td></tr><tr><td align=\"left\">K_d</td><td align=\"left\">0.30</td><td align=\"left\">0.35</td><td align=\"left\">0.25</td><td align=\"left\">0.41</td><td align=\"left\">0.43</td></tr><tr><td align=\"left\">IN_con</td><td align=\"left\">0.09</td><td align=\"left\">0.07</td><td align=\"left\">0.22</td><td align=\"left\">0.47</td><td align=\"left\">0.49</td></tr><tr><td align=\"left\">f_eg</td><td align=\"left\">0.16</td><td align=\"left\">0.05</td><td align=\"left\">0.20</td><td align=\"left\">0.47</td><td align=\"left\">0.42</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>Results of normalised mean absolute error (NMAE) calculations applied to compare simulated to field data for simulated years 1991–1994. The values in brackets represent the same calculations made over the 1991–1992 calibration period.</p></table-wrap-foot>", "<table-wrap-foot><p>The minimum and maximum values of the five most sensitive parameters and the corresponding results of normalised mean absolute error (NMAE) calculations applied to compare simulated to field data for simulated years 1991–1994. The values in brackets represent the same calculations made over the 1991–1992 calibration period.</p></table-wrap-foot>" ]

[ "<graphic xlink:href=\"1746-1448-4-11-1\"/>", "<graphic xlink:href=\"1746-1448-4-11-2\"/>", "<graphic xlink:href=\"1746-1448-4-11-3\"/>", "<graphic xlink:href=\"1746-1448-4-11-4\"/>", "<graphic xlink:href=\"1746-1448-4-11-5\"/>", "<graphic xlink:href=\"1746-1448-4-11-6\"/>", "<graphic xlink:href=\"1746-1448-4-11-7\"/>", "<graphic xlink:href=\"1746-1448-4-11-8\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>In recent years researchers and policy-makers have shown a growing interest in the knowledge held by indigenous peoples. The interest has been partially driven by research that suggests that indigenous knowledge might be the key to inform conservation efforts and local empowerment and development [##UREF##0##1##, ####UREF##1##2##, ##UREF##2##3##, ##UREF##3##4####3##4##].</p>", "<p>Researchers and policy-makers have debated the relations between indigenous knowledge and Western science under the assumption that merging multiple epistemologies might improve the well-being of indigenous people [##UREF##4##5##,##UREF##10##11##,##UREF##14##15##, ####UREF##15##16##, ##UREF##16##17####16##17##]. Some researchers consider knowledge held by indigenous people as a form of knowledge that contrasts with the knowledge produced by Western science [##UREF##4##5##, ####UREF##5##6##, ##UREF##6##7##, ##UREF##7##8##, ##UREF##8##9##, ##UREF##9##10####9##10##], but others argue that there is a significant overlap, and that the two do not necessarily oppose each other [##UREF##10##11##, ####UREF##11##12##, ##UREF##12##13##, ##UREF##13##14####13##14##]. What we lack are studies focussing on how indigenous peoples conceptualize the relations between indigenous knowledge and knowledge produced by Western science, and how they use those to forms of knowledge in their daily life.</p>", "<p>Understanding how indigenous peoples conceptualize local and Western science matters for the wellbeing of indigenous peoples [##REF##17320319##18##, ####REF##12408732##19##, ##REF##11297838##20##, ##REF##9261977##21##, ##REF##15177839##22####15177839##22##]. Research suggests that local medicinal knowledge is culturally appropriate [##UREF##17##23##], but it can be complemented with Western medicine. Some authors have emphasized the positive view that traditional healers have towards Western medicine and their eagerness to cooperate with doctors [##REF##17320319##18##, ####REF##12408732##19##, ##REF##11297838##20##, ##REF##9261977##21##, ##REF##15177839##22####15177839##22##]. Research in Bolivia [##REF##15177839##22##], Nigeria [##REF##17320319##18##], and Iran [##REF##11297838##20##] suggests that cooperation between ethnomedicine and biomedicine is possible and might benefit local populations and their environment, whereas lack of cooperation can generate lack of understanding of biomedicine and lead to its misuse [##UREF##18##24##,##UREF##19##25##]. By analyzing how indigenous peoples conceptualize local and Western medicine, medical practitioners might be able to use biomedicine in a more culturally appropriate way.</p>", "<title>Objectives</title>", "<p>In this research we study how the Tsimane', a native Amazonian population in Bolivia, conceptualize and use local and Western forms of medicinal knowledge. Specifically, we assess (1) whether the Tsimane' separate or integrate local and Western medicine, both conceptually and practically, and (2) whether practitioners of local and Western medicine (i.e., traditional healers and medical doctors) show readiness to cooperate to combine the two medical systems in their daily practices.</p>", "<p>From the many domains of indigenous knowledge (i.e., medicine, ecology, astrology), we focus on local medicinal knowledge because a) 70–80% of the population in the developing world depends on medicinal plants for primary health care [##REF##12408732##19##,##UREF##17##23##,##UREF##20##26##], and b) empirical studies suggest that local medicinal knowledge might be associated with better health and nutritional status [##UREF##21##27##, ####REF##17389376##28##, ##UREF##22##29####22##29##].</p>", "<p>We use the term local medicinal knowledge to refer to the cumulative body of knowledge of medicinal plants and healing practices of a culture that has been handed down through generations, that is socially shared by the members of the same generation, and that has been adapted to a particular place [##UREF##9##10##,##UREF##23##30##]. Under local medicinal knowledge we include the knowledge of the raw material from which remedies are produced and the socio-medical aspects implied in their preparation and uses [##REF##15507363##31##]. We use the terms local practitioners to refer to traditional healers (people who use local medicinal knowledge) and health assistants (villagers trained to voluntarily oversee villager's health). Traditional healer is the best word we have found in English to refer to the Tsimane' term <italic>cocojsi </italic>or to the Spanish word <italic>curandero</italic>, the words used by Tsimane' to refer to native healers. We use the term Western medicine to refer to biomedicine and the term doctor to refer to people formally trained in Western medicine even if they do not have a medical degree (e.g., nurses). We use the term doctor to ease the narrative and because Tsimane' themselves use this word to refer to any partitioner of Western medicine. We use the word affections (rather than the word diseases) because when asked about the diseases suffered, the Tsimane' reply with symptoms not with the name of diseases, and we do not have the medical training to diagnose diseases.</p>", "<p>The empirical analysis focuses on four gastrointestinal affections (stomachache, diarrhea, vomiting, and intestinal parasites) for three reasons. First, the incidence of gastrointestinal affections is high among the Tsimane' [##UREF##18##24##,##UREF##19##25##]. Second, gastrointestinal affections are recognized both by local and Western medicine and have well-identified symptoms. Selection of affections that are well recognized by the two medical systems allows us to overcome the inability of the Western medical system to identify social, cultural, and behavioral factors as causes of spiritual diseases. Third, gastrointestinal affections have a clear etiology among the Tsimane', who believe that some gastrointestinal ailments (diarrhea and intestinal parasites) can result from common causes (i.e. food poisoning) whereas others (stomachache and nausea) can result from common causes or from witchcraft.</p>" ]

[ "<title>Methods</title>", "<p>For background information, we relied on TAPS data [##UREF##30##38##]. Further information was gathered during June-July 2007 in three Tsimane' villages along the Maniqui River, province of Beni, Bolivia. PhD students in environmental sciences and cultural anthropology who were taking part in a summer training camp in methods of data collection helped in data collection. The research team lived in the villages described below in houses owned by the Tsimane' Amazonian Panel Stutdy. Three experienced Tsimane' translators helped to conduct the interviews in Tsimane' and served as key study participants.</p>", "<title>Site and sample</title>", "<p>Participants for the study included people over the age of 16 in three Tsimane' villages: Yaranda, Santa María, and San Juan de Nápoles (hereafter Nápoles). We included people over the age of 16, because Tsimane' enter adulthood at that age. Young men and women of this age can marry, plant their own plot, and are supposed to be able to form a family. Two of the villages selected, Yaranda and Santa María, are far from the market town and only accessible by a motor canoe. Yaranda has 35 households and Santa Maria has 32 households. Nápoles lies about two hours by car from San Borja and has 14 households. Nápoles differs from Yaranda and Santa María because residents in Nápoles have easier access to the hospital in the outskirts of the local town of San Borja. Villagers from Nápoles also have less access to the forest due to increasing cattle ranching in the area and therefore less availability of medicinal plants.</p>", "<p>We used different sampling strategies for each method of data collection described below. We used a snowball sample for the free listings. In San Borja we started free listing by interviewing a nurse who has been collaborating with the TAPS project for several years, and suggested the names of six study participants for free listings. In Yaranda, we started by asking our key study participants to identify the village traditional healer or other people with knowledge of medical treatments (n = 12). The total sample for the free listing exercise was 18. We used a purposive sample for semi-structured interviews and pile sorts. We conducted semi-structured interviews with six women and eignt men from 16 to 75 years of age and pile sorts with 21 women and 18 men from 16 to 75 years of age. Lastly, the sample for the survey included all adults (or people over the age of 16) in the villages of Yaranda and Nápoles (n = 87; 44 women and 43 men).</p>", "<title>Methods of data collection</title>", "<p>Methods of data collection included methods 1) to gather background information (participant observation, semi-structured interviews, and TAPS 2006 survey), 2) to assess whether Tsimane' integrate local medicinal knowledge and Western medicine at the conceptual level (free listings and pile sorts), 3) to assess to what extent Tsimane' combine local medicinal knowledge with Western medicine in actual treatments (survey), and 4) to assess the willingness of Tsimane' and Western medical specialists to cooperate with each other (workshop).</p>", "<title>Participant observation</title>", "<p>We used participant observation [##UREF##35##45##] to achieve an understanding of the diseases, treatments, and relations between different bodies of knowledge. For example, upon request of the Great Tsimane' council and the villagers, we provided Western medicines to people who came to us when they were sick. A student with basic medical training was in charge of providing first aid. Interactions with sick people allowed us to observe Tsimane' requests for Western medicines. Those interactions also allowed us to gain a better understanding of Tsimane' diseases and treatment conceptualization.</p>", "<title>Semi-structured interviews</title>", "<p>We used semi-structured interviews [##UREF##36##46##] to gather information on local medicinal knowledge, the causes of diseases, the use of medicines, and the potential interest of local and Western medical professionals in cooperating with each other. Semi-structured interviews lasted less than one hour and were done with the help of a translator.</p>", "<title>TAPS 2006 survey</title>", "<p>We used a survey of the Tsimane' Amazonian Panel Study [##UREF##30##38##] to identify the main affections self-reported by people over the age of 16 (n = 679). The survey took place in 2006 during the dry season, which lasts from June to September. Previous work suggests no markedly seasonal changes in objective or self-reported ailments, and thus it is assumed that the answers obtained for the dry period reflect health during the entire year [##UREF##18##24##,##UREF##19##25##]. We found that 65% of people surveyed reported at least one ailment during the seven days before the day of the interview. The most common affection reported was the flu, followed by gastrointestinal affections. Fifteen percent of the participants reported having suffered from stomachache, diarrhea, vomiting, and intestinal parasites during the seven days before the day of the interview. So, we selected gastrointestinal affections as the focus of this study.</p>", "<title>Free listing</title>", "<p>We asked study participants to list all the medical treatments they knew to treat gastrointestinal affections [##UREF##37##47##,##UREF##38##48##]. We instructed participants to include in their lists both local and Western treatments. To get a better understanding of the full range of treatments available to Tsimane', we asked doctors and Tsimane'to list medical treatments. The final lists included local and Western treatments.</p>", "<title>Pile sorts</title>", "<p>From the list generated with the free listing technique, we selected the ten most frequent Western treatments and the ten most frequent plant treatments available within three hours walking from the village. The final list included nine plants, one mineral, and ten pharmaceutical drugs. To facilitate the identification of the items for the pile sort, we collected the plant species in the forest and bought the drugs selected from a pharmacy. We presented participants with the 20 items and asked them to sort the items in similarity groups [##UREF##38##48##,##UREF##39##49##]. After they had placed items in piles, we asked them to explain their reasons for placing items in a pile.</p>", "<title>Survey</title>", "<p>We constructed a survey using the insights of background information. The survey included socio-demographic and health questions. We asked all adults in the sample about the name of diseases (if any) suffered the week before the day of the interview and the name of the first three treatments used for any gastrointestinal affection suffered during the same period.</p>", "<title>Workshop</title>", "<p>At the end of the research, we organized a participatory workshop [##UREF##40##50##], which had three aims: (1) to explain the findings of our own research, (2) to identify the main threats to Tsimane' health, as perceived by Tsimane', and (3) to assess the willingness of Western and local medical practitioners to cooperate with each other. We invited three doctors, one nurse, four traditional healers, and four health assistants to the workshop. However, more health assistants arrived due to interest in the workshop, so the final number of participants included 34 persons. We invited these people for practical reasons. Two of the doctors and the nurse have previously worked with Tsimane'. The third doctor was very interested in the workshop. We also had previous contacts with the traditional healers and health assistants initially selected</p>", "<title>Analysis</title>", "<p>We analyzed data from free listing and pile sorting using ANTHROPAC 4.983/X for Windows [##UREF##41##51##]. From responses to free listing, we calculated: 1) the percentage of people who mentioned each item, 2) the average rank of the order of mention of each item, and 3) the saliency of each reason (the weighted average of the inverse rank of an item across multiple free lists, where each list is weighted by the number of items on the list) [##UREF##36##46##]. The saliency index evaluates, with a range from 0 to 1, the overall importance of an item across all of the lists. The analysis of free listing allowed us to identify the most common treatments reported for the gastrointestinal affections selected.</p>", "<p>We used non-metric multidimensional scaling (MDS) to analyze pile sort data. The MDS permits an observational assessment of whether people agree in the way they sort medical treatments. The closer the items are in the MDS, the more times they were classified together in individual pile sorts.</p>", "<p>We used STATA 9 to analyze survey data. We calculated frequency of gastrointentinal affections and treatment options.</p>", "<title>Limitations</title>", "<p>This study has two main limitations. First, data was collected with the help of translators. The use of translators for interviews contains the obvious challenge of language barriers and the possible loss of information. Second, since we limited our study to gastrointestinal affections, the selection of ailments could bias results. It is possible that results found in this research apply to gastrointestinal affections, but not to other diseases.</p>" ]

[ "<title>Results</title>", "<title>Tsimane' conceptualization and use of local and Western medicine</title>", "<p>Results from free lists and pile sorts suggest that Western treatments do not belong to the Tsimane' concept of medical treatments. Tsimane' participants in free listing listed 16 different treatments for gastrointestinal affections, none a Western treatment (Table ##TAB##0##1##). On average each informant listed 5.5 different treatments for gastrointestinal affections (SD = 2.4). The shortest list had two items and the longest nine. Oveto' (<italic>Uncaria guianensis</italic>) was singled out as the most important and salient item in the lists. The 12 study participants interviewed for free listing mentioned Oveto', and most mentioned Oveto' as the first item in their lists. Eleven of the 16 treatments that appeared in free listings were listed by two or more people and five treatments were listed only by one person.</p>", "<p>Results from pile sorts concur with results from free listings and further confirm Tsimane' conceptual divide between local and Western medicine. Figure ##FIG##0##1## shows the results from a non-metric multidimensional scaling with data from pile sorts from participants in three communities. Tsimane' placed the 20 medicinal items into three categories:1) medicinal plants to cure gastrointestinal affections, 2) a mineral, curpa, used to cure illnesses caused by sorcery, and 3) pharmaceutical treatments that doctors recommend to cure gastrointestinal affections.</p>", "<p>Among the nine medicinal plants used in the pile sorting exercise, we identify six distinct groups. Information from interviews after pile sorting revealed that Tsimane' classify medicinal plants according to the affection that the plant can cure. For example, the Tsimane' pile together <italic>vejqui </italic>(1)<italic>', vuayuri </italic>(3), and <italic>oveto' </italic>(4) because they use them to cure diarrhea and stomachache. <italic>Ibam'ta </italic>(2) a plant that is used to treat stomachache and leishmaniasis (not a gastrointestinal disease) appears close to the group of plants to cure stomachache. Similarly, Tsimane' use <italic>vambason </italic>(5), <italic>mana'i </italic>root (6), and <italic>ashasha </italic>(9) to treat gastrointestinal and other diseases. For example, <italic>vambason </italic>is used with kidney problems, <italic>ashasha </italic>is used for colds, and <italic>mana'i </italic>is used to cure intestinal parasites. Since Tsimane' could only put an item into only one pile, these items appear separate from one another. Tsimane' use <italic>titij' </italic>(7) and <italic>pofi </italic>seeds (8) to treat intestinal parasites; for this reason they lie next to each other in Figure ##FIG##0##1##.</p>", "<p>In Figure ##FIG##0##1## the mineral, <italic>curpa</italic>, stands apart from both plants and Western medicines. <italic>Curpa </italic>is used to heal spiritual illnesses regardless of the symptoms. Lastly, we found a single cluster that grouped all the items belonging to the list generated by Western medical practitioners. Tsimane' put all pharmaceutical treatments in a single group. During interviews after the pile sorting exercise, when we asked participants the reasons for their classification, they answered that they put pharmaceutical treatments together because they did not know their exact use.</p>", "<p>To assess whether the results from MDS were similar in the three studied villages, we ran three non-metric multidimensional scaling with data from each village separately (results not shown). We found no substantive differences across villages regarding the classification of plant remedies, but we found some differences regarding the classification of Western remedies. In the most isolated village, Yaranda, people put Western treatments in a unique category (unknown), whereas in the other two communities, Santa Maria and Nápoles, people recognized at least four uses for Western remedies: headache, toothache, flu, and cold.</p>", "<p>Results from the survey suggest that, at the practical level, Tsimane' mix local and Western treatments to cure gastrointestinal affections. Of the 87 people interviewed, 64 (73.5%) reported having been sick the week before the interview. We analyzed information from the first ailment reported and found that the most common illnesses reported were cold (20.7% of the people who reported any ailment), headache (9.2%), and diarrhea (9.2%).17.2% of the people who reported any ailment suffered from gastrointestinal affections, including diarrhea (9.2%), stomachache (6.9%), and vomiting (1.1%). None of the respondents reported suffering from intestinal parasites. The distribution of ailments resembles the distribution of ailments reported by Byron [##UREF##19##25##] for the same population.</p>", "<p>From the people surveyed (n = 87), 18.2% did not use any medicine to cure themselves. From the ones who used some treatment (Figure ##FIG##1##2##), 35.3% used plant treatments only and 17.6% used pharmaceutical treatments only. The remaining 47.1% of the people who used any treatment to cure gastrointestinal affections combined pharmaceutical and plant treatments.</p>", "<p>The results also suggest that Tsimane' first rely on local medicinal knowledge to treat gastrointestinal affections. From the people who took any treatment for gastrointestinal affections (n = 52), 76.5% chose medicinal plants first and only 23.5% chose a pharmaceutical treatment first.</p>", "<title>Assessing the willingness to cooperate</title>", "<p>In semi-structured interviews, doctors and local practitioners emphasized the need for a medical system that allowed for cooperation between local and Western medicine. Doctors and local practitioners agreed that some diseases (i.e., tuberculosis) should be treated with Western medicines, whereas other affections (e.g. mild diarrhea) could be better treated with local plant-based medicines. There was also broad agreement that a system which allowed for the collaboration of local and Western medicine would give patients the best treatment for their disease. They also expressed a desire to learn one from each other, although doctors mistrusted the medicinal value of plants until they could be scientifically validated.</p>", "<p>To probe the willingness to cooperate, we organized a workshop with doctors and local practitioners. Participants discussed the advantages of both medical systems, arguing that while the local medical system was cheap and popular, Western medicine was effective and endorsed by scientific studies.</p>", "<p>Participants in the workshop felt that health was a high priority for them and presented ideas to improve it. Some of the ideas included: (1) to train health assistants in local and Western medicine, (2) to strengthen local medicinal knowledge, (3) to strengthen the role of traditional healers and health assistants, (4) to open medical posts in communities, (5) to improve education and prevention of diseases, (6) to take care of the natural environment, (7) to build communal gardens of medicinal plants, and (8) to build physical infrastructure that might have a preventive impact on health, such as wells or latrines.</p>", "<p>Participants discussed the case of Apillapampa (Cochabamba, Bolivia) where a traditional healer works in conjunction with the system of Western health care [##REF##15177839##22##]. The reappraisal of local medicinal knowledge turned into one of the main points of discussion of the workshop since participants had the impression that, in the more isolated communities, plant remedies were the main health resource available. Participants also felt that they could obtain an economic benefit with the revalorization of medicinal plants and also contribute to the conservation of forest lands. Participants considered that organization of training workshops, valorization of elders, cooperation with doctors, procurement of institutional help, and the maintenance of customs could help achieve the revalorization of local medicinal knowledge.</p>", "<p>Participants viewed the workshop as the first spark to encourage the interaction between doctors and local practitioners and highlighted their willingness to continue cooperating. They proposed immediate steps such as the construction of school gardens with medicinal plants; the sharing of elders' medicinal knowledge in schools, and the compilation of local medical treatments within their communities. Participants also proposed to ask the municipal government for money to conduct more workshops in which the relations between local and Western medicine could be further discussed.</p>" ]

[ "<title>Discussion</title>", "<p>Three main substantive findings emerge from this work. First, Tsimane' do not include Western medicine in their conceptualization of medical treatments. Second, at the practical level, Tsimane' do mix local and Western treatments, although the frequency of use of local treatments is higher than the frequency of use of Western treatments. Last, doctors and local health practitioners expressed willingness to cooperate to improve the health of Tsimane'.</p>", "<p>The first substantive finding, that Tsimane' do not include Western remedies in their conceptualization of medical treatments, fits with findings from another case study among the Tsimane' [##UREF##42##52##]. In an ethnobotanical study in the village of Tacuaral de Matos (35 kilometers away from San Borja), Ticona found that Tsimane' do not include pharmaceutical treatments in the category of <italic>pinidyedyes </italic>(medicine), although they interchangeably use the Tsimane' word <italic>pinidye</italic>' and the Spanish words <italic>remedios </italic>(treatments) or <italic>medicinas </italic>(medicine) to acquire drugs from traders or in pharmacies. A possible explanation of this finding is that since illness is a universal and recurrent problem and health treatments within a village are usually limited, villagers might develop shared standards of treatment [##UREF##43##53##] such as particular plants to cure gastrointestinal ailments. Tsimane' still have limited access to Western treatments due to distance from pharmacies and hospitals and low cash income. These limitations together with the short history of Western treatments in the area might explain why Tsimane' do not include Western treatments in their concept of medical treatments.</p>", "<p>We also found that Tsimane' do not have distinct categories for Western treatments. The finding has important implications for the use of Western medicine, and dovetails with ethnographic information about Tsimane' misuse of Western treatments. Because Tsimane' do not have a classificatory system for Western medical treatments, they use them independently of the ailment suffered. Tsimane' typically do not rely on an individual trained in Western medicine to provide instructions on how to take Western medicines. Because of the many dangers associated with the misuse of pharmaceutical treatments, the lack of categorization of Western medicines might have pernicious effects on Tsimane' health [##UREF##18##24##].</p>", "<p>The second substantive finding is that in practice Tsimane' do combine local and Western medicines. Medical pluralism occurs in many settings and provides insights into connections between health, knowledge, treatment behavior, and the cultural significance of medicine [##REF##17405697##54##,##REF##10812565##55##]. For the Tsimane', the explanation fits with the complex systems of responses related to resources availability, cultural beliefs about illness origin, and personal interpretation of symptoms. For example, when asked for treatment preferences one informant said: \"If I have money I buy tablets, but if I don't, I use medicinal plants. If nothing works, then I am probably sick with a bush illness. I rely more on medicinal plants than on drugs, but the bush is too far and the treatment too long\" (personal interview 6/29/07).</p>", "<p>Our results also suggest that Tsimane' still use medicinal plants more often than Western remedies as the first treatment for gastrointestinal affections. The prevalence of medicinal plants over Western medicines among the Tsimane' is consonant with findings among other indigenous groups. For example, in the village of Zapotitlán (Puebla, Mexico), Hernández et al. [##UREF##45##57##] found that 74% people interviewed used medicinal plant to treat gastrointestinal affections. In a study among the Caboclos in the Amazon estuary (Brasil), Reeve [##REF##10812565##55##] also found that the principal method of self-treatment was medicinal plants. Both practical (i.e., accesibility) and cultural (i.e., taboos) reasons can account for this preference among the 'Tsimane.</p>", "<p>The third finding from this research is that doctors and local practitioners felt that cooperation between them was important for Tsimane' health because the two medical systems could complement each other. Reeve [##REF##10812565##55##] argues that amplifying the capacity of traditional healers to refer patients in need of clinical care, and clinician training for cooperation with traditional healers would assure more effective health care. Collaboration of the two medical systems would allow Tsimane' to choose the best treatment option for each ailment. Furthermore, doctors and traditional healers expressed a desire to learn more from each other. Understanding the points of articulation that develop between local healing and Western medicine within medically plural systems will contribute to the development of more integrative models of cooperation [##REF##10812565##55##].</p>" ]

[ "<title>Conclusion</title>", "<p>In this article we have analyzed Tsimane' treatment system for gastrointestinal affections. We have found that the Tsimane' conceptualize local and Western medicine as two independent domains of knowledge, although they mix pharmaceutical and plant treatments in their daily practice. We have also found that local practitioners and doctors show willingness to cooperate so people could benefit from both medical systems simultaneously.</p>", "<p>Western medicine is regarded as having qualities of power, trust, and effectiveness by Tsimane' villagers and doctors. However a sense of revalorization of local medicinal knowledge is also present. Tsimane' feel that local medicinal knowledge helps to maintain the Tsimane' lifestyle and conserve the ecosystem. Current lack of access and poor understanding of the biomedical system may contribute to poor Tsimane' health. For this reason, the exploration of the connections between local medicinal knowledge and Western medicine and the possible co-management of health among traditional healers and doctors could lead to an improvement in the health situation as well as the conservation of their own ecosystem. As Chapman [##REF##18061760##58##] argues, local medicinal knowledge should not be fully integrated into science, nor should the reverse occur. Both are complementary, not replaceable. Both have value in their own right and need to be recognized as such, giving both equal weights.</p>", "<p>At this point what remains to be answered is how this cooperation could be effective. There is a need to further analyze the policy for implementing cooperation. This is particular relevant in a context where local medical system and Western medical system are not viewed as being of equal worth, with Western practitioners frequently expressing disdain for non-Western treatment options. Some ideas have emerged from this research, however further study is required in order to achieve the equitable cooperation of both medical systems.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Interest in ethnomedicine has grown in the last decades, with much research focusing on how local medicinal knowledge can contribute to Western medicine. Researchers have emphasized the divide between practices used by local medical practitioners and Western doctors. However, researchers have also suggested that merging concepts and practices from local medicinal knowledge and Western science have the potential to improve public health and support medical independence of local people. In this article we study the relations between local and Western medicinal knowledge within a native Amazonian population, the Tsimane'.</p>", "<title>Methods</title>", "<p>We used the following methods: 1) participant observation and semi-structured interviews to gather background information, 2) free-listing and pile-sorting to assess whether Tsimane' integrate local medicinal knowledge and Western medicine at the conceptual level, 3) surveys to assess to what extent Tsimane' combine local medicinal knowledge with Western medicine in actual treatments, and 4) a participatory workshop to assess the willingness of Tsimane' and Western medical specialists to cooperate with each other.</p>", "<title>Results</title>", "<p>We found that when asked about medical treatments, Tsimane' do not include Western treatments in their lists, however on their daily practices, Tsimane' do use Western treatments in combination with ethnomedical treatments. We also found that Tsimane' healers and Western doctors express willingness to cooperate with each other and to promote synergy between local and Western medical systems.</p>", "<title>Conclusion</title>", "<p>Our findings contrast with previous research emphasizing the divide between local medical practitioners and Western doctors and suggests that cooperation between both health systems might be possible.</p>" ]

[ "<title>The Tsimane' and their medical system</title>", "<p>The Tsimane's are the third largest ethnic group in the lowlands of Bolivia, with about 8,000 people [##UREF##24##32##]. The Tsimane' live in the Ballivian and Yacuma Provinces of the Department of Beni, and their territory spreads from the foothills of the Andes to the northeast, and reaches the edges of the Moxos savanna. The Tsimane' have traditionally been a semi-nomadic group [##UREF##25##33##] but currently reside in villages along river banks and logging roads. Villages in this study had an average of 24 nuclear households (Standard deviation (SD) = 10.88).</p>", "<p>The Tsimane' are considered a foraging-horticultural society because of their heavy reliance on forest goods and agricultural crops in household consumption [##UREF##26##34##]. Nevertheless, Tsimane' are now taking up other occupations such as wage labor in logging camps, cattle ranches, and in the homesteads of colonist farmers [##UREF##18##24##,##UREF##19##25##,##UREF##27##35##]. Further reading on Tsimane' culture and society can be found in Ellis [##UREF##25##33##], Daillant [##UREF##28##36##] and Huanca [##UREF##29##37##]. Below we provide descriptions of topics related to the analysis presented in this paper, such as Tsimane' health, Tsimane' explanations of illnesses, availability of medical treatments and use of medicine among the Tsimane'.</p>", "<title>Tsimane' health</title>", "<p>Previous research [##UREF##19##25##,##REF##15386291##39##] suggests that infectious affections are an important health problem among the Tsimane', and that respiratory and gastrointestinal affections are the most commonly reported illnesses among both Tsimane' children and adults [##UREF##19##25##]. Intestinal parasites, especially hookworm, a soil-transmitted helminth, are endemic [##UREF##18##24##] and can cause stunted growth, a common condition among the Tsimane' [##REF##15386291##39##].</p>", "<p>Acculturation and access to market goods have the potential to deteriorate Tsimane' health in the short term for at least two reasons. First, previous research has shown that Tsimane' misuse Western medicine [##UREF##18##24##]. Most Tsimane' do not have the skills to select appropriate drugs without relying on advice from traders and doctors and they lack knowledge about pharmaceutical treatments. As a consequence, Tsimane' often do not use the appropriate drugs for their condition or use inappropriate dosages. Second, acculturation might generate loss of local medicinal knowledge, which seems to protect Tsimane' health and nutritional status [##UREF##21##27##, ####REF##17389376##28##, ##UREF##22##29####22##29##]. Despite lack of empirical evidence on the topic [##UREF##31##40##], Tsimane' feel that they are loosing local medicinal knowledge, and that the young are not adequately learning this knowledge.</p>", "<title>Tsimane' explanations for the causes of illnesses</title>", "<p>As other native Amazonian societies, Tsimane' consider that the world is divided between the natural tangible environment and the supernatural or spiritual realm [##UREF##29##37##,##UREF##32##41##]. This duality is embedded in the Tsimane's interpretation of the world [##UREF##33##42##] and can be found in Tsimane' explanations of the causes of illnesses.</p>", "<p>The Tsimane' distinguish between common and spiritual illnesses [##UREF##32##41##]. Common illnesses (<italic>japacjodye </italic>in Tsimane' language) result from external or internal loci of cause, such as contact with hazardous agents or internally-initiated imbalances. Spiritual illnesses result from the purposeful intervention of a human or supernatural proxy. Spiritual illnesses are called <italic>\"</italic>bush illnesses\" (<italic>däräcansi </italic>in Tsimane') and are the result of witchcraft by malevolent spirits (or people) or the guardians of the natural environment (<italic>jichi</italic>) [##UREF##32##41##].</p>", "<title>Medical treatments available to the Tsimane'</title>", "<p>In the event of an illness, Tsimane' use medicinal plants or Western medicines or consult a traditional healer. The Tsimane', who hold an extensive body of ethnobotanical knowledge, self-medicate with plant remedies. Previous research shows that from the many useful plants known to the Tsimane', medicine is the category in which Tsimane' recognize the largest number of useful plants (n = 169, 41%) [##UREF##34##43##]. Tsimane' believe in the curative power of plant remedies and the use of medicinal plants is popular, especially in isolated villages [##UREF##34##43##]. Tsimane' argue that the use of medicinal plants is decreasing due to the effort required to prepare plant remedies. Tsimane' said that gathering and preparing medicinal plants requires more effort and time than obtaining Western pharmaceuticals. Tsimane' also say that the curative power of plant remedies is slower than the curative power of pharmaceuticals. Lastly, Tsimane' say that some curative plants come with taboos and restrictions that the patient needs to follow for the plants to work well, and these cultural restrictions constitute added costs of medicinal plants.</p>", "<p>Pharmaceuticals from pharmacies or stores in local market towns and from itinerant traders who visit Tsimane' villages are also available to the Tsimane'. Tsimane' can also ocasionally obtain free Western medicine from other visitors to their villages, i.e. researchers, vaccination campaigns. Although Tsimane' use of Western treatments has increased during the last 50 years in response to increasing exposure to Bolivian national society, pharmaceuticals are still less popular than plant remedies due to the long distances to pharmacies and the relative high price of pharmaceuticals for a population with low cash income. Missionaries introduced the first hospitals and drugs in the area during the second half of the 20^thcentury [##UREF##44##56##]. Nowadays, Tsimane' have access to a hospital in the town of San Borja and to a free clinic run by Missionaires in the vicinity of San Borja [##REF##17421012##44##]. Tsimane' needing medical attention can use those facilities. Family members are expected to accompain the sick person and provide some work for the Missionaires to pay for the expenses.</p>", "<p>Last, Tsimane' can consult traditional healers or other local experts who treat common and spiritual illnesses. Traditional healers can cure common diseases and are the only ones who can cure spiritual illnesses [##UREF##32##41##]. Byron [##UREF##19##25##] states that consulting traditional healers is more economical than going to the hospital, and this might be an important incentive for the Tsimane' to visit them.</p>", "<title>Tsimane' use of medicine</title>", "<p>Tsimane' consider that the cause of an illness determines the appropriate treatment. Common illnesses, caused by the natural world, can be cured with medicinal plants or with Western medicines, whereas illnesses caused by spiritual beings can only be cured by traditional healers [##UREF##32##41##].</p>", "<p>Sickness is first treated as a common, not as a spiritual, illness, and plant or pharmaceutical remedies are administered sequentially or simultaneously according to the symptoms of the patient. Tsimane' often self-medicate and they typically stop Western or local treatments once the disease symptoms disappear without necessarily following the full dose of treatment. If the condition persists, after self-medication, the Tsimane' typically ask for the advice of knowledgeable people in the village (typically elders). The Tsimane' begin to suspect that the illness might be caused by witchcraft if the person does not improve after several treatments. In this case, they seek the help of a traditional healer. Hospitals are only visited as a last resort, and mistrust on Western doctors is common among the Tsimane' [##UREF##18##24##,##UREF##19##25##].</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>LCM collected and analyzed data and wrote the article. VRG and ST contributed to analysis and interpretations of data and critically reviewed the manuscript. All authors read and approved the final manuscript.</p>" ]

[ "<title>Acknowledgements</title>", "<p>Research was funded by a Marie Curie Grant (MIRG-CT-2006-036532) and by a grant from the Program of Cultural Anthropology of the National Science Foundation (NSF) in the USA (BCS-0552296). Thanks go to participants in the 2007 NSF summer training camp in methods in Bolivia, to villagers of the communities of Yaranda, Santa María, and San Juan de Nápoles, to the Tsimane' Amazonian Panel Study and to the assistants to the workshop. Special thanks to José Cari, Tomás Cari, Tomás Huanca, Eloida Mazcaya, Luis Rico, Manuel Roca, Evaristo Tayo, Ina Vandebroek, and Osman Vie for help in the organization and implementation of the workshop. We are also grateful to Ricardo Godoy, Gary Martin, Roger Strand, Hugo De Boer and Andrew Lyubarsky for commenting on previous versions of this text.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Non-metric multidimensional scaling with data from pile sorts of ethnomedical and biomedical treatments for gastrointestinal affections</bold>. Vejqui'(1), Vuayuri (3), Oveto'(4). Ibam'ta (2). Vambason (5). Mana'i root (6). Titij'(7), Pofi seed (8). Ashasha (9). Curpa (10). Klosidol (11), Viadil (12), Metronidazol (13), Carbon extract (14), Amoxiciline (15), Domper (16), Metoclopramida (17), Mebendazol (18), Albendazol (19), Noxon (20).</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Reported frequencies of treatments options for gastrointestinal affections during the week before the interview (n = 18)</bold>. Pharmaceutical treatment (17.65%). Pharmaceutical and plant treatments (47.06%). Plant treatment (35.29%).</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Ten most frequent items reported in free listings of gastrointestinal treatments by Tsimane' study participants (n = 12).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Scientific name^a</bold></td><td align=\"left\"><bold>Family</bold></td><td align=\"left\"><bold>Tsimane'</bold><break/><bold> name</bold></td><td align=\"left\"><bold>Voucher</bold></td><td align=\"center\"><bold>Frequency</bold></td><td align=\"center\"><bold>Salience^b</bold></td><td align=\"left\"><bold>Gastrointestinal</bold><break/><bold> affection treated</bold></td></tr></thead><tbody><tr><td align=\"left\">Uncaria guianensis <italic>(Aubl.) Gremlin</italic></td><td align=\"left\">Rubiaceae</td><td align=\"left\">Oveto'</td><td align=\"left\">TH044</td><td align=\"center\">12</td><td align=\"center\">0.29</td><td align=\"left\">Stomachache and diarrhea</td></tr><tr><td align=\"left\"><italic>Galipea longiflora</italic></td><td align=\"left\">Rutaceae</td><td align=\"left\">Ibam'ta</td><td align=\"left\">TH257</td><td align=\"center\">9</td><td align=\"center\">0.17</td><td align=\"left\">Stomachache and vomiting</td></tr><tr><td align=\"left\"><italic>Aspidosperma rigidum</italic></td><td align=\"left\">Flacourtiaceae</td><td align=\"left\">Vambason</td><td align=\"left\">TH153</td><td align=\"center\">5</td><td align=\"center\">0.13</td><td align=\"left\">Stomachache and diarrhea</td></tr><tr><td align=\"left\">Alum. Double sulfate of Aluminum and Potassium</td><td/><td align=\"left\">Curpa</td><td/><td align=\"center\">5</td><td align=\"center\">0.09</td><td align=\"left\">Stomachache and vomiting</td></tr><tr><td align=\"left\"><italic>Ficus cf. insípida </italic>Willd</td><td align=\"left\">Moraceae</td><td align=\"left\">Titij'</td><td align=\"left\">TH123</td><td align=\"center\">5</td><td align=\"center\">0.15</td><td align=\"left\">Intestinal parasites</td></tr><tr><td align=\"left\"><italic>Hymenaea courbaril </italic>L.</td><td align=\"left\">Leguminosae-Pap</td><td align=\"left\">Vejqui'</td><td align=\"left\">TH072</td><td align=\"center\">5</td><td align=\"center\">0.12</td><td align=\"left\">Stomachache and diarrhea</td></tr><tr><td align=\"left\"><italic>Carica papaya</italic></td><td align=\"left\">Caricaceae</td><td align=\"left\">Pofi</td><td/><td align=\"center\">3</td><td align=\"center\">0.10</td><td align=\"left\">Intestinal parasites</td></tr><tr><td align=\"left\">Indetermined</td><td/><td align=\"left\">Jamo'tarara</td><td/><td align=\"center\">3</td><td align=\"center\">0.08</td><td align=\"left\">Stomachache and intestinal parasites</td></tr><tr><td align=\"left\"><italic>Citrus lemon</italic></td><td align=\"left\">Rutaceae</td><td align=\"left\">Ashasha</td><td align=\"left\">TH531</td><td align=\"center\">3</td><td align=\"center\">0.10</td><td align=\"left\">Diarrhea and vomiting</td></tr><tr><td align=\"left\"><italic>Sparattanthelium burchellii </italic>Rusby</td><td align=\"left\">Hennandaceae</td><td align=\"left\">Vayori</td><td align=\"left\">AN023</td><td align=\"center\">3</td><td align=\"center\">0.04</td><td align=\"left\">Diarrhea</td></tr><tr><td align=\"left\">Responses per person</td><td align=\"left\">Mean = 5.5</td><td/><td/><td/><td/><td/></tr><tr><td/><td align=\"left\">Minimum = 2</td><td/><td/><td/><td/><td/></tr><tr><td/><td align=\"left\">Maximum = 9</td><td/><td/><td/><td/><td/></tr><tr><td/><td align=\"left\">SD = 2.4</td><td/><td/><td/><td/><td/></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>Notes: ^aFor this research, we did not collect vouchers. Information on scientific name and vouchers commes from TAPS data. Vouchers specimens are deposited in the Herbario Nacional de Bolivia, Universidad Mayor de San Andrés, La Paz.</p><p>^bSalience (S) takes into account the frequency (F) of a given item in lists and the average rank of the item in the respondents list.</p><p>S = F/NmP</p></table-wrap-foot>" ]

[ "<graphic xlink:href=\"1746-4269-4-18-1\"/>", "<graphic xlink:href=\"1746-4269-4-18-2\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Aspirin is one of the 'cornerstone' drugs in our current management of cardiovascular disorders. However, despite the prescription of aspirin recurrent vascular events still occur in 10–20% of patients. These, data together with the observations of diminished antiaggregatory response to aspirin in some subjects have provided the basis of the current debate on the existence of so-called \"aspirin resistance\". Unfortunately, many of the tests employed to define 'aspirin resistance' lack sufficient sensitivity, specificity, and reproducibility. The prevalence of 'aspirin resistance' as defined by each test varies widely, and furthermore, the value of a single point estimate measure of aspirin resistance is questionable. The rate of 'aspirin resistance' is law if patients observed to ingest aspirin, with large proportion of patients to be pseudo-'aspirin resistant', due to non-compliance. What are the implications for clinical practice? Possible non-adherence to aspirin prescription should also be carefully considered before changing to higher aspirin doses, other antiplatelet drugs (e.g. clopidogrel) or even combination antiplatelet drug therapy. Given the multifactorial nature of atherothrombotic disease, it is not surprising that only about 25% of all cardiovascular complications can usually be prevented by any single medication. We would advocate against routine testing of platelet sensitivity to aspirin (as an attempt to look for 'aspirin resistance') but rather, to highlight the importance of clinicians and public attention to the problem of treatment non-compliance.</p>" ]

[ "<title>Editorial</title>", "<p>Aspirin is one of the 'cornerstone' drugs in our current management of cardiovascular disorders. The metaanalysis from the Antithrombotic Trialists' Collaboration of 287 randomized trials of antiplatelet therapy in patients at high risk of occlusive vascular events demonstrated a 32% reduction in nonfatal myocardial infarction (MI), nonfatal stroke, and vascular death in patients treated with aspirin [##REF##11786451##1##]. However, despite the prescription of aspirin recurrent vascular events still occur in 10–20% of patients [##REF##15892858##2##]. These, data together with the observations of diminished antiaggregatory response to aspirin in some subjects have provided the basis of the current debate on the existence of so-called \"aspirin resistance\".</p>", "<p>'Aspirin resistance' has been defined as either the failure of aspirin to fully inhibit platelet aggregation in the laboratory setting or (clinically) as its inability to prevent cardiovascular events. The problem is important as it potentially implies the need for repeated laboratory tests and/or the replacement of aspirin by other antiplatelet drugs in millions of patients [##REF##15936601##3##].</p>", "<p>Unfortunately, many of the tests employed to define 'aspirin resistance' lack sufficient sensitivity, specificity, and reproducibility [##REF##18466797##4##]. The prevalence of 'aspirin resistance' as defined by each test varies widely, and furthermore, the value of a single point estimate measure of aspirin resistance is questionable [##REF##18466797##4##,##REF##17239674##5##]. Indeed, the insufficient laboratory suppression of platelet activity may result from reduced enteral absorption of aspirin (e.g. when low doses of enteric-coated aspirin are used), the concomitant administration of other cyclooxygenase-1 inhibitors (e.g. ibuprofen and naproxen), or even increased platelet turnover (e.g. as in infection, inflammation and following major surgery) [##REF##16198840##6##, ####REF##11752357##7##, ##REF##15213849##8##, ##REF##12874188##9####12874188##9##]. Polymorphism of genes involved in the thromboxane biosynthetic pathway may also be associated with a modification of the response to aspirin and its clinical efficacy [##REF##12545150##10##]. Partial loss of antiplatelet effect during long-term aspirin treatment has also been suggested [##REF##7974569##11##,##REF##15028353##12##]. Of note, monocytes/macrophages produce large amounts of thromboxane synthase making them platelet-independent source of thromboxane A2 generation, even when platelet activity is effectively suppressed [##REF##9569176##13##].</p>", "<p>When a stroke or myocardial infarction occurs in a patient on aspirin therapy, it is unknown if the patient was taking the prescribed aspirin as prescribed prior to the event. Sadly, up to 40% of patients with cardiovascular disease do not comply with aspirin [##REF##9575390##14##, ####REF##10560261##15##, ##REF##15820166##16##, ##REF##7974570##17####7974570##17##]. Thus, poor compliance may be an important reason why aspirin is ineffective in the laboratory and clinically settings. It is worth emphasizing that a lack of drug compliance is characteristic for many chronic treatments and aspirin is not exception. The relatively high rate of gastrointestinal complications with aspirin (and patients' awareness of such symptoms) often make aspirin 'first-choice-to-stop' drug from an often long list of prescribed treatments (antihypertensives, lipid lowering drugs, antianginals, etc.) in patients with cardiovascular disease. Failure to follow aspirin prescription may be greater in patients with co-morbidities (e.g. in older patients) [##REF##17000940##18##]. Post-MI patients with low educational status (e.g. not graduating from high school) are also more likely to discontinue use of all medications; the same applies for older patients, especially women [##REF##14760328##19##].</p>", "<p>A recent multicenter prospective cohort, the Prospective Registry Evaluating Myocardial Infarction: Event and Recovery (PREMIER) study, has demonstrated shocking data on non-adherence to medications in patients after acute MI [##REF##17000940##18##]. At 1 month after hospital discharge with a prescription of aspirin, beta-blockers and statins, 12% of patients discontinued use of all 3 medications, whilst 4% discontinued use of 2 medications and 18% discontinued use of 1 drug. The patients who completely stopped their drug treatments had lower 1-year survival (88.5% vs. 97.7%) compared to those who continued to take at least one medication [##REF##17000940##18##].</p>", "<p>Another published systematic review and meta-analysis on the possible relation between 'aspirin resistance' and clinical outcomes in patients with cardiovascular disease (20 studies, 2930 patients) reported that 28% of patients can be classified as 'aspirin resistant' [##REF##18202034##20##]. The latter was associated with a sharp increase in the rate of cardiovascular related events (41% of aspirin resistant patients, with odd ratio [OR] 3.85), death (5.7%, OR 5.99) and recurrent acute coronary syndromes (39.4%, OR 4.06). Clearly, the implications are grave.</p>", "<p>What may be an especially important observation is that these 'aspirin resistant' patients usually did not benefit from other antiplatelet treatments. Does it indicate the presence of multidrug platelet resistance? Alternatively, should patients' should be questioned? The authors of the meta-analysis state that compliance was assessed by the primary study investigator in only 17 of the 20 studies included in analysis, and this was by telephone or interviews, or more directly by the patient's presence in hospital [##REF##18202034##20##]. Nonetheless, few of the studies confirmed compliance by measurement of a biochemical marker of compliance. Of note odd ratios of cardiovascular events in 'aspirin resistant' patients in this meta-analysis were very close to the hazard ratio (3.81) in those who discontinued all medications (including aspirin) in PREMIER study [##REF##17000940##18##,##REF##18202034##20##].</p>", "<p>Are patient interview data on drug adherence sufficiently reliable? Tantry et al. found that among 223 patients with coronary artery disease who were reported to use aspirin regularly, only 8 patients had long-term 'aspirin resistance' as estimated by arachidonic acid-induced light aggregation and thrombelastography [##REF##16256872##21##]. However, 7 of these 8 patients admitted to being non-compliant on repeated interviews and all became 'aspirin sensitive' after in-hospital aspirin administration, and only 1 patient (~0.4%) was truly resistant to aspirin treatment.</p>", "<p>Cotter et al. measured thromboxane B2 production in 73 acute MI survivors and found that thromboxane production was not suppressed in 21 patients (29%), indicating some degree of 'aspirin resistance' [##REF##14760328##22##]. When questioned, 12 of the 21 admitted that they were not taking aspirin as recommended. The clinical impact of aspirin non-compliance over supposed 'aspirin resistance' in MI survivors was demonstrated by the fact that patients who admitted poor drug compliance had substantially higher rates of cardiovascular events (42%) and readmissions (67%) when compared those with lack of response to aspirin administration (11% for both end points) [##REF##14760328##22##]. Of note, 'aspirin resistance' in this laboratory study did not seem to affect prognosis significantly when compared to adherent responders, with 6% of these suffering recurrent cardiovascular events and 11% had re-admissions to hospital [##REF##14760328##22##].</p>", "<p>In fact aspirin non-adherence is associated with the presence of anginal symptoms. For example, Carney et al. provided patients with coronary artery disease with an aspirin packaged equipped with an electronic adherence monitor, and found that symptomatic patients took aspirin on 62.4% of the days, compared to 77.3% of days in the patients without symptoms [##REF##9575390##14##]. These data indicate a high rate of aspirin not-compliance even in those who suffer symptoms (angina) [##REF##9575390##14##]. Tarján et al. revealed that in patients with acute MI and unstable angina 'aspirin resistance' [laboratory-defined] was seen in 34% of cases, and almost third of them did not have any traces of aspirin metabolites in their urine [##REF##10560261##15##]. We can only speculate on the possible rate of aspirin non-compliance when this drug is prescribed for primary prevention.</p>", "<p>In the current issue of the <italic>Journal of Translational Medicine</italic>, Schwartz et al. show that in a population of post MI patients observed to ingest aspirin and whose platelets studied 2 hours post ingestion had decreased light aggregation response to arachidonic acid [##REF##18759978##23##]. Only a small percentage of these patients (3.4%) could be classified as resistant. A large proportion of patients (8.4%) was found to be pseudo-'aspirin resistant', due to non-compliance and were reclassified as 'normal responders' following compliant aspirin uptake. No significant difference in aspirin antiplatelet activity was found between normal subjects and post-MI patients indicating the factors other than long-term aspirin administration per se may be involved in the reduced response to aspirin [##REF##18759978##23##]. These data provide additional and persuasive evidence that undetected non-compliance may lead to an overestimate of the rate of 'aspirin resistance' [##REF##14760328##19##]. Furthermore, Schwartz et al. [##REF##18759978##23##] introduce a novel index for measuring aspirin's platelet inhibitory effect, the net aspirin response. The net aspirin response measures the amount of aspirin induced platelet inhibition by subtracting the aggregation response on aspirin from the aggregation response off aspirin state and was shown to be statistically normally distributed. Platelets from patients with a decreased net aspirin response may for unknown reasons be relatively less dependent on the arachidonic acid pathway for activation. Indeed, the paper by Schwartz et al. may be additional basis to question the reliability of 'interview proven compliance' and advocate mandatory laboratory evaluation for aspirin's platelet inhibitory effect in future studies on 'aspirin resistance' [##REF##18759978##23##].</p>", "<p>What are the implications for clinical practice? Possible non-adherence to aspirin prescription should also be carefully considered before changing to higher aspirin doses, other antiplatelet drugs (e.g. clopidogrel) or even combination antiplatelet drug therapy [##REF##16732389##24##]. Given the multifactorial nature of atherothrombotic disease, it is not surprising that only about 25% of all cardiovascular complications can usually be prevented by any single medication [##REF##16319386##25##]. We would advocate against routine testing of platelet sensitivity to aspirin (as an attempt to look for 'aspirin resistance') but rather, to highlight the importance of clinicians and public attention to the problem of treatment non-compliance.</p>", "<title>Competing interests</title>", "<p>ES declares that he has no competing interests.</p>", "<p>GYHL was clinical adviser to the guideline development group writing the NICE Guidelines on AF management. He has received funding for research, educational symposia, consultancy and lecturing from different manufacturers of drugs used for the treatment of thrombosis.</p>", "<title>Authors' contributions</title>", "<p>Both ES and GYHL carried out the manuscript preparation. All authors read and approved the final manuscript.</p>" ]

[ "<title>Acknowledgements</title>", "<p>ES is funded by a research fellowship the Heart Failure Association of the European Society of Cardiology.</p>" ]

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[ "<title>Background</title>", "<p>In light of severe physician shortages in the developing world, the World Health Organization's strategic framework for the emergency scale up of antiretroviral therapy (ART) involves training a range of health-care staff to support the delivery and monitoring of HIV/AIDS treatment. 'Task shifting' is the name given to a process of delegation whereby tasks are moved, where appropriate, to less specialized health workers [##UREF##0##1##].</p>", "<p>Task shifting has lead nurses to be heavily involved in performing HIV testing and counselling, assessing patients for ART eligibility, assessing toxicity and treatment failure, and providing patient education, psychosocial support and adherence support [##REF##16438710##2##]. Nurses may also play a lead role in record keeping and reporting. As the volume of patients under HIV/AIDS care and treatment services grows and services are decentralized, nurses may experience a shift of responsibilities, with even larger roles in initial evaluation and staging of patients, ART initiation, and patient monitoring [##REF##17328804##3##].</p>", "<p>As nurses are becoming increasingly central points of contact for clinical care of people living with HIV and AIDS (PLWHA), they must first be ensured adequate preparatory education. Scattered reports have shown, however, that most nurses in developing countries are not well prepared during their pre-service education in the knowledge, skills and attitudes needed to provide quality HIV/AIDS-related care [##REF##17531750##4##,##UREF##1##5##].</p>", "<p>Preparing nurses to confront the HIV/AIDS epidemic is a need in Haiti where the HIV prevalence rate is 2.2% [##UREF##2##6##], approximately 10 100 patients are currently receiving antiretroviral treatment [##UREF##3##7##], and there is a critical shortage of doctors, leaving nurses as the primary care providers for much of the population.</p>", "<p>Haiti has four national nursing schools, graduating approximately 120 registered nurses per year. These schools face under-resourced infrastructure (few textbooks and teaching materials and little classroom space), variable quality of teaching with few classroom instructors prepared to educate, and few clinical instructors and sites available for clinical skills practice. Graduates often must do much of their learning on-the-job during their rotations, under limited supervision. Specific to HIV/AIDS education, a recent assessment revealed that related content is very loosely woven throughout the courses, and that inclusion of HIV is arbitrarily dependent on the interest of the faculty member assigned to the course, with key areas, such as HIV/AIDS counseling, prevention of mother-to-child transmission, and ART adherence, being largely overlooked [##UREF##4##8##].</p>", "<p>Since 2004, the International Training and Education Center on HIV (I-TECH) has worked in Haiti to build capacity to respond to the AIDS epidemic. I-TECH is a collaboration between the University of Washington and University of California San Francisco and was established by the Health Resources and Services Administration (HRSA) in collaboration with the Centers for Disease Control and Prevention (CDC).</p>", "<p>In June 2006, the Haitian Ministry of Health and Population (MSPP), specifically the directorate that is in charge of health science education, the <italic>Direction de Formation et de Perfectionnement en Sciences de la Santé </italic>(DFPSS), and I-TECH started a process of integrating current HIV/AIDS knowledge, skills and attitudes into the current curriculum using a competency-based approach. This article details the steps undertaken to develop, integrate and implement the new curriculum.</p>" ]

[ "<title>Methods</title>", "<p>In June 2006, DFPSS and I-TECH convened deans of the four public nursing schools, officials from the Haiti ministries of health and education, and selected education and HIV/AIDS experts to reflect on the status of HIV/AIDS-related education at the nursing schools and how to quickly address new content into an already overloaded curriculum in a resource-strained environment.</p>", "<p>The stakeholders chose to form two committees – a coordinating committee made up of school heads and ministry officials that would ensure broad-based support and integration of the new topic into the existing curriculum and an eight-member curriculum committee made up of Haitian nurse educators, nurse trainers and one nurse HIV/AIDS expert to draft the new curriculum.</p>", "<p>Upon review of other international projects and the education literature on various models of curriculum development and integration, stakeholders opted to use a competency-based approach for the integration process.</p>", "<p>A competency is defined as the blend of skills, abilities, and knowledge needed to perform a specific task [##UREF##5##9##]. In both developed and developing countries, the traditional approach to nursing pre-service education has been for teachers to determine what content needs to be learned, teaching it, and then testing to see if the content was learned. This approach, though long established, does not guarantee that teachers use content reflecting the needs of the workplace and often relies on passive memorization from lectures as the dominant learning method for students. The literature is full of calls for curriculum reform in nursing education, advocating curricula that are responsive to changes in the health care delivery system, are research-based, are collaborative, and apply pedagogical innovation [##REF##17315564##10##].</p>", "<p>Recent reforms support the application of competency-based education – defining, teaching, and assessing competencies and then assessing student performance in relation to these, thus focusing on the outcome of the education, rather than on the process of the education (applying knowledge and skills rather than merely gaining knowledge) [##REF##12010689##11##]. Experience shows that using competencies to define what is taught in the pre-service arena can achieve the following: provide clarity of learning direction for both faculty and students, set the framework for assessment, enable the curriculum to reflect the \"real world\" skills required to meet the health needs of the population and clarify the role of nurses vis-à-vis the other health professions [##REF##12010689##11##, ####UREF##6##12##, ##UREF##7##13##, ##REF##15344373##14##, ##REF##11769948##15####11769948##15##].</p>", "<p>A competency-based education model starts by asking the question: What will the nurse do on the job? Once this is known, specifications of learning objectives for instruction are derived. If integrating a new theme into an existing curriculum, these learning objectives can then be mapped to existing courses. Then, appropriate teaching and assessment methods are derived that will ensure mastery of the objectives, and faculty are trained in and oriented to the new curriculum. Finally, evaluation is conducted to ensure that students achieve mastery of the competencies. A schematic representation of this model appears in Figure ##FIG##0##1##.</p>" ]

[ "<title>Results</title>", "<p>When the curriculum committee began to design the new curriculum, the initial hurdle was to articulate and reach consensus on the HIV/AIDS competencies relevant for nurses. In-depth discussions among the participating experts at the beginning of the project, who had a good overview of the ongoing HIV/AIDS activities in Haiti, helped to identify a draft list of general competencies. Then, the curriculum committee reviewed HIV competencies relevant for developing country settings, which were drafted by the World Health Organization [##UREF##8##16##] and the National HIV Nursing Association [##UREF##9##17##] in the United Kingdom. Over a series of seven meetings, the committee, through facilitated discussions, adapted these competencies to the Haitian environment and formed a final list of five main HIV/AIDS competencies and 35 associated sub-competencies as shown in Table ##TAB##0##1##.</p>", "<p>The committee then defined the associated learning objectives for each sub-competency. Over 350 learning objectives were defined, with each sub-competency having multiple knowledge, skill and attitudinal learning objectives. The committee then mapped each of these objectives to existing courses in the overall nursing curriculum. Hours were not taken from existing courses to specifically make room for the new objectives, nor were additional hours added to the curriculum, but rather, the new HIV/AIDS-related learning objectives were integrated into the current courses. For example, in the Counseling and Communication course, when discussing how to use job aids and demonstrate good interpersonal skills, the faculty member is prompted to use an example of ART adherence counseling.</p>", "<p>The learning objectives build upon each other at different stages in the curriculum. For example, a student nurse in year 1 will describe the relationship between HIV and nutrition, but by the years 3 and 4, she or he is able to define dietary needs of specific sub-groups of PLWHA and how to educate patients on specific meal preparation.</p>", "<p>To support faculty with up-to-date HIV/AIDS content, the committee drafted an 'HIV/AIDS Reference Manual' that features evidence-based national and international core content, protocols, and guidelines for faculty to access.</p>", "<p>The committee also drafted a 'HIV/AIDS Teaching Guide' organized by curriculum year and course, which was approved for dissemination to the nursing schools by the MSPP in November, 2007. For each course, the associated competency and sub-competencies, learning objectives, recommended chapters of the 'Reference Manual' or other materials, learning methods and evaluation methods are listed. An excerpt of a plan for a specific course is shown in Table ##TAB##1##2##.</p>", "<p>As noted, a key tenet of competency-based education is moving away from rote memorization or knowledge acquisition to the application of knowledge and skills. As such, the 'Teaching Guide' places great emphasis on a mix of interactive teaching methods to stimulate active student participation, such as case-based learning, role plays, and group discussions. The 'Teaching Guide' ensures integration between theory and practice, as many of the course plans specify practice-based experience with nurse monitors in a clinic setting.</p>", "<p>Typically, the sole form of assessment in Haiti nursing schools is a final written examination, of essay, short answer or multiple-choice type. However, such examinations tend to reward rote recall of facts and don't assess a student's ability to apply knowledge in practice. The 'Teaching Guide' emphasizes structured observation as an alternative assessment method and emphasizes periodic assessment at regular intervals throughout each course. The final exam for nurses to obtain their license to practice will also be modified to reflect the new competencies.</p>", "<p>It has been noted that faculty development is probably the single most necessary precursor to the successful implementation and maintenance of curricular reform [##REF##9094835##18##,##REF##16564471##19##]. Unless faculty members embrace the new content, expand their own knowledge base, and successfully integrate the new content into the curricula, curriculum reform simply cannot be made. To that end, a series of faculty development workshops have recently begun on the new content of the HIV/AIDS curriculum and on how to lead interactive teaching methodologies that not only enhance student knowledge but skills and attitudes. The curriculum committee will be working with faculty from each school in the coming months to design checklists that enable observation and judgments to be made about the students' mastery of the learning objectives. Curriculum committee members are also performing periodic site visits to the nursing schools to observe teaching activities, mentor faculty, and monitor and evaluate the implementation of the curriculum package.</p>", "<p>Over the next four years, as students progress from Year 1 through Year 4 of the degree program, the HIV/AIDS curriculum will be evaluated formally in all four schools. In addition, data on faculty use of and satisfaction with the curriculum will be collected through semi-structured qualitative interviews and observation, the results of which will be used to identify any weaknesses and needed changes to the Teaching Guide or Reference Manual, as related to the level of difficulty, time allocation, content updates, or other areas. A revision schedule has not been set for the teaching material, as another goal of faculty development will be to build their skills in maintaining their currency in their field and to reflect this in lesson planning.</p>" ]

[ "<title>Discussion</title>", "<p>The effect of this change has broad implications for the Haiti nursing education community. All nursing students will now need to demonstrate mastery of HIV/AIDS-related competencies during periodic assessment with direct observation of the learner performing authentic tasks. Using what they learned in the faculty development workshops and the instructions and model exercises in the 'HIV/AIDS Teaching Guide', faculty will have the added responsibility of developing exercises to address the required competencies and creating assessment tools to demonstrate that their graduates have met the competencies. The major challenges in the next step will be creating assessment tools that are reliable, valid, and practical in this developing country setting.</p>", "<p>There were several lessons learned from the process of developing the HIV competencies and integrating them into an already established broader nursing curriculum. The first lesson was the importance in identifying the right stakeholders for both the coordinating committee and the curriculum working groups. For both groups, bringing a multidisciplinary group of officials, faculty, administrators and HIV experts enriched the process, garnered buy-in, and improved the outcome by virtue of the collaborative process. The curriculum working group was made up of dedicated nurse leaders who were passionate about elevating the profile of nursing education in Haiti and graduating students competent to care for the large number of people living with HIV and AIDS.</p>", "<p>The second lesson was that this activity brought different nursing schools together to collaborate on a shared goal that was manageable and timely using a process that could be repeated for other aspects of curriculum reform. Haiti's nursing schools face numerous challenges: lack of funds, lack of available clinical mentors, poor infrastructure, lack of curriculum developers, etc. Against this backdrop, other aspects of the overall nursing curriculum program need major reform but addressing only one topic through this systematic process gave the schools a manageable victory. It is hoped that this provided stakeholders with the experience, skills and motivation to strengthen other domains of the pre-service nursing curriculum, improve the synchronization of didactic and practical training, and develop standardized competency-based examinations for nursing licensure in Haiti. Each of these goals is part of the Ministry of Health's strategic plan for 2005–10 [##UREF##10##20##].</p>", "<p>The third lesson was that defining competencies and related learning objectives, though absolutely essential to clarifying what students must learn, was conceptually difficult for the curriculum committee. Even experienced educators may find it challenging to clearly state the knowledge, skills, and attitudes underpinning a competency. Writing clear and measurable learning objectives, particularly attitudinal objectives, was challenging for the committee, and required a great deal of debate and revision.</p>", "<p>It is necessary to develop the evidence base on the impact of pre-service curriculum strengthening initiatives in developing countries like the one described here [##UREF##11##21##]. There is not one HIV care delivery model in Haiti, meaning that pre-service programs have to provide flexible education which will allow nurses to integrate into settings with varied types of HIV-related services and with varied staffing patterns. Applied research is needed in settings like Haiti on the optimal role of nurses in support of HIV scale-up, the integration of HIV care and treatment with other components of primary care services, and the relationship between pre-service nursing training, quality of care, and patient health outcomes. On-going evaluation and documentation of Haiti's pre-service training initiative for nurses will hopefully yield insights useful for other settings and professional disciplines.</p>" ]

[ "<title>Conclusion</title>", "<p>In light of the critical role that nurses play in the care of Haiti's population, investing in pre-service nursing education institutions to improve the quality of HIV/AIDS training is a critical part of increasing the overall quality of HIV/AIDS care and treatment in the country. Education in HIV/AIDS is now an integral part of the four national nursing schools in Haiti. This was achieved using a multi-disciplinary, participatory process that can be applied to future curriculum reform efforts.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Preparing health workers to confront the HIV/AIDS epidemic is an urgent challenge in Haiti, where the HIV prevalence rate is 2.2% and approximately 10 100 people are taking antiretroviral treatment. There is a critical shortage of doctors in Haiti, leaving nurses as the primary care providers for much of the population. Haiti's approximately 1000 nurses play a leading role in HIV/AIDS prevention, care and treatment. However, nurses do not receive sufficient training at the pre-service level to carry out this important work.</p>", "<title>Methods</title>", "<p>To address this issue, the Ministry of Health and Population collaborated with the International Training and Education Center on HIV over a period of 12 months to create a competency-based HIV/AIDS curriculum to be integrated into the 4-year baccalaureate programme of the four national schools of nursing.</p>", "<title>Results</title>", "<p>Using a review of the international health and education literature on HIV/AIDS competencies and various models of curriculum development, a Haiti-based curriculum committee developed expected HIV/AIDS competencies for graduating nurses and then drafted related learning objectives. The committee then mapped these learning objectives to current courses in the nursing curriculum and created an 'HIV/AIDS Teaching Guide' for faculty on how to integrate and achieve these objectives within their current courses. The curriculum committee also created an 'HIV/AIDS Reference Manual' that detailed the relevant HIV/AIDS content that should be taught for each course.</p>", "<title>Conclusion</title>", "<p>All nursing students will now need to demonstrate competency in HIV/AIDS-related knowledge, skills and attitudes during periodic assessment with direct observation of the student performing authentic tasks. Faculty will have the responsibility of developing exercises to address the required objectives and creating assessment tools to demonstrate that their graduates have met the objectives. This activity brought different administrators, nurse leaders and faculty from four geographically dispersed nursing schools to collaborate on a shared goal using a process that could be easily replicated to integrate any new topic in a resource-constrained pre-service institution. It is hoped that this experience provided stakeholders with the experience, skills and motivation to strengthen other domains of the pre-service nursing curriculum, improve the synchronization of didactic and practical training and develop standardized, competency-based examinations for nursing licensure in Haiti.</p>" ]

[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>EK provided technical assistance to the nursing committee and drafted the manuscript. NP conceived of the intervention, participated in its coordination, and helped to draft the manuscript. AD supported the technical committee. RD and MP participated in the design and implementation of the intervention. All authors read and approved the final manuscript.</p>" ]

[ "<title>Acknowledgements</title>", "<p>Ruth Derivois, of the Institut Haitien de Sante Communautaire (INHSAC), and Mona Prismy, Training Manager for I-TECH Haiti, led the Technical Committee. The curriculum committee undertook the needs assessment and curriculum development work described in this article; it's members include: Marie Roselène M. Mécéjour, DFPSS/MSPP, Marie Danielle Neff Lemaire, DFPSS/MSPP, Marie Maud César Duvilaire, DSI/MSPP, Edite Valcin Legagneur, Family Health International, Gardénia Monrose, Independent Consultant, and Claudia Thomas Riché, Centres GHESKIO/I-TECH Consultant. Dr. Nancy Rachel Labbe Coq, Dr. J.E. Adrien Demes, and Ms. Paula Brunache of I-TECH Haiti and Dr. Paul Carrenard of INHSAC provided oversight and resources in support of the technical committee.</p>", "<p>The work described in this article was supported by the US President's Emergency Plan for AIDS Relief (PEPFAR), through funding to the University of Washington from the US Health Resources and Services Administration (HRSA) Global HIV/AIDS Bureau. The funding body (PEPFAR) was not involved in the implementation of the work described, nor in the preparation of this manuscript and decision to submit it for publication. I-TECH takes full responsibility for the needs assessment study design, data collection and analysis, development of nursing competencies, and curriculum design described in this article.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Competency-based education model.</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>HIV Competencies and sub-competencies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Competencies</td><td align=\"left\">Sub competencies</td></tr></thead><tbody><tr><td align=\"left\">A. Prevent HIV infection among individuals and the community</td><td align=\"left\">A.1 Conduct community and individual education on HIV/AIDS</td></tr><tr><td/><td align=\"left\">A.2 Perform HIV pre-test counseling</td></tr><tr><td/><td align=\"left\">A.3 Conduct HIV testing</td></tr><tr><td/><td align=\"left\">A.4 Perform HIV post-test counseling</td></tr><tr><td/><td align=\"left\">A.5 Prevent and treat accidental blood exposure</td></tr><tr><td/><td align=\"left\">A.6 Prevent Mother-To-Child-Transmission of HIV</td></tr><tr><td/><td align=\"left\">A.7 Prevent and treat sexually transmitted diseases</td></tr><tr><td/><td align=\"left\">A.8 Ensure post-exposure prophylaxis in cases of sexual violence</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\">B. Promote the health of people living with HIV</td><td align=\"left\">B.1 Provide counseling on well-being and nutrition</td></tr><tr><td/><td align=\"left\">B.2 Prevent opportunistic infections</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\">C. Evaluate the health status of people living with HIV</td><td align=\"left\">C.1 Identify the clinical signs of HIV infection</td></tr><tr><td/><td align=\"left\">C.2 Conduct biologic tests</td></tr><tr><td/><td align=\"left\">C.3 Classify the patient according to stages of infection as defined by WHO and the CDC</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\">D. Ensure the care of adults and children infected with HIV/AIDS</td><td align=\"left\"><italic>D.1 Therapeutic interventions</italic></td></tr><tr><td/><td align=\"left\">D.1.1 Identify the patients eligible for ART</td></tr><tr><td/><td align=\"left\">D.1.2 Counsel for adherence to ART</td></tr><tr><td/><td align=\"left\">D.1.3 Treat opportunistic infections</td></tr><tr><td/><td align=\"left\">D.1.4 Manage the nutrition of PLWHA</td></tr><tr><td/><td align=\"left\">D.1.5 Administer ART</td></tr><tr><td/><td align=\"left\">D.1.6 Ensure the follow-up of a patents taking ART</td></tr><tr><td/><td align=\"left\">D.1.7 Manage a pregnant women infected by HIV</td></tr><tr><td/><td align=\"left\">D.1.8 Manage a child infected by HIV</td></tr><tr><td/><td align=\"left\">D.1.9 Provide palliative care</td></tr><tr><td/><td colspan=\"1\"><hr/></td></tr><tr><td/><td align=\"left\"><italic>D.2 Psychosocial and community interventions</italic></td></tr><tr><td/><td align=\"left\">D. 2.1 Provide spiritual support</td></tr><tr><td/><td align=\"left\">D. 2.2 Provide social and economic support</td></tr><tr><td/><td align=\"left\">D. 2.3 Provide psychological support</td></tr><tr><td/><td align=\"left\">D.2.4 Support clients in managing grief</td></tr><tr><td/><td align=\"left\">D. 2.5 Link patients to legal support</td></tr><tr><td/><td align=\"left\">D. 2.6 Provide support to orphans and other vulnerable children</td></tr><tr><td/><td align=\"left\">D. 2.7 Ensure the community management of people living with HIV</td></tr><tr><td/><td align=\"left\">D.2.8 Prevent and treat burn-out among n</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\">E. Ensure the management of various aspects of the HIV/AIDS control program</td><td align=\"left\">E.1. Work as a member of a multidisciplinary team</td></tr><tr><td/><td align=\"left\">E.2. Manage the drugs and other inputs necessary for the care of people living with HIV</td></tr><tr><td/><td align=\"left\">E.3. Manage the data entry of HIV patients</td></tr><tr><td/><td align=\"left\">E.4. Utilize the resources provided in the national AIDS control program</td></tr><tr><td/><td align=\"left\">E.5. Evaluate the activities of the national AIDS control program</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Sample course plan: infectious diseases, year 2</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Competency</td><td align=\"left\">Sub-competency</td><td align=\"left\">Learning objectives</td><td align=\"left\">Content source</td><td align=\"left\">Learning methods</td><td align=\"left\">Assessment method</td></tr></thead><tbody><tr><td align=\"left\">A. Prevent HIV infection among individuals and the community</td><td align=\"left\">A.5 Prevent and treat accidental blood exposure (ABE)</td><td align=\"left\">Describe the role of nurse in the prevention and treatment of ABE (K)</td><td align=\"left\">Chapter 3 in HIV Reference Manual</td><td align=\"left\">Large group discussion and lecture</td><td align=\"left\">Written exam</td></tr><tr><td colspan=\"2\"/><td colspan=\"4\"><hr/></td></tr><tr><td/><td/><td align=\"left\">Indicate the risks and degree of risk of ABE (K)</td><td align=\"left\">Chapter 3 in HIV Reference Manual</td><td align=\"left\">Case study</td><td align=\"left\">Case analysis</td></tr><tr><td colspan=\"2\"/><td colspan=\"4\"><hr/></td></tr><tr><td/><td/><td align=\"left\">Respond with legitimating statements when a victim of ABE expresses shock (A)</td><td align=\"left\">Chapter 3 in HIV Reference Manual</td><td align=\"left\">Role play</td><td align=\"left\">Observation checklist</td></tr><tr><td colspan=\"2\"/><td colspan=\"4\"><hr/></td></tr><tr><td colspan=\"\"/><td/><td align=\"left\">Demonstrate capacity to apply universal precautions and waste management (S)</td><td align=\"left\">Chapter 3 in HIV Reference Manual</td><td align=\"left\">Clinic rotation</td><td align=\"left\">Observation checklist</td></tr></tbody></table></table-wrap>" ]

[]

[]

[]

[]

[]

[]

[ "<table-wrap-foot><p>(K) = Knowledge, (S) Skill, (A) Attitude</p></table-wrap-foot>" ]

[ "<graphic xlink:href=\"1478-4491-6-17-1\"/>" ]

[]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>Many cellular processes are characterized by substantial shape changes. For example, chemotaxing cells become polarized, assuming a highly elongated form, and crawl across solid substrates in the direction of increasing concentrations of chemoattractant [##REF##12672811##1##]. During cytokinesis, a single cell undergoes significant cytoskeletal deformation, reforming into two daughter cells [##REF##15817376##2##]. These cellular processes are fundamentally mechanical, utilizing force generation at the molecular scale to generate shape changes. Properly simulating cellular shape change requires that we have a description of the underlying mechanical properties of the cell.</p>", "<p>To understand fully the mechanisms that regulate these cell shape changes requires knowledge of the signaling pathways as well as their effect on the mechanical properties of cells. For example, a complete model of chemotaxis would require a description of the gradient sensing capability of cells together with a physical model for the cellular migration [##REF##18207721##3##]. Few such models exist, even though it is now appreciated that the response of cell-signaling pathways can be regulated in response to alterations in cell size and shape [##REF##16950104##4##]. The traditional method of simulating cellular deformations is by specifying the boundary of the cell explicitly through a finite-element model (FEM) [##REF##16121529##5##, ####REF##15909655##6##, ##REF##15165868##7####15165868##7##]. One problem is that simulation of biological shape deformations – which invariably involves solving partial differential equations on moving boundaries – can be computationally expensive particularly when the cellular deformations are not small. During many processes including cytokinesis and chemotaxis, cellular shape deformations tend to be large and occur rapidly. Here, we demonstrate how the Level Set Method (LSM) can be used to couple mechanical models of the cell with biochemical models of signaling pathways to simulate large cellular deformations.</p>", "<p>We briefly contrast the LSM approach to other methods that have been used to account for cellular deformations.</p>", "<p>The immersed boundary method (IBM), introduced by Peskin [##REF##8571229##8##] was developed to simulate the interaction of flexible tissues with the surrounding incompressible fluid. It has been used to simulate cell shape changes during motility [##UREF##0##9##]. In the IBM, the Navier-Stokes equation describing the fluid flow can be solved on a fixed grid, simplifying this computationally expensive step. The membrane and cytoskeleton is discretized by assigning a series of nodes that are connected by viscoelastic elements. As the cell deforms, nodes and their corresponding links have to be inserted or deleted. This book-keeping comes at a considerable computational complexity. For this reason, the IBM may best be used in situations where the cell shape does not change considerable [##REF##15489302##10##].</p>", "<p>More recently, the cellular Potts model (CPM) has become a popular vehicle to simulate cell shape changes [##REF##10046374##11##]. In the CPM, a cell is described by a connected domain of pixels on a regular grid. The shape of the cell is evolved by updating each pixel based on a set of probabilistic rules. This method does not use an explicit viscoelastic description of the cell. Instead, cell shape is constrained by minimizing an energy function that penalizes size-deformations as well as membrane bending. Cellular Potts models have been used to simulate two-dimensional (2-D) models of cell motility in fish keratocytes [##REF##16794915##12##] and amoebae [##REF##17113108##13##]. Unlike FEM or IBM, modeling large changes in the shape of the cell is no more computationally expensive than small changes. One drawback, however, is that the mechanical description of cells in the CPM framework is not as tightly integrated with experimentally-based measurements as the method presented here.</p>", "<p>Models of cellular shape changes have all been derived based on explicit descriptions of the cell morphology that are updated based on the simulated behavior of the underlying cytoskeleton. For example, Rubinstein <italic>et al</italic>. provide a detailed 2-D computational model of the lamellipodium keratocyte motility [##UREF##1##14##]. In this model, the cellular domain is updated at each time step based on the protrusive and retractive forces (actin polymerization and acto-myosin contraction) and re-gridded. This avoids the necessity for nodes and keeping track of the mechanical state of the system. However, the model relies on an elastic (rather than viscoelastic) network which may be appropriate for the thin keratocyte, but is not likely to be applicable to thicker cells.</p>", "<p>The rest of the paper is organized as follows. We first provide some necessary background. We then develop a mechanical model within the LSM that accounts for the viscoelastic nature of the cell. We fit this model to experimental data obtained through micropipette aspiration experiments on <italic>Dictyostelium </italic>cells. We incorporate this viscoelastic model into a level set framework and illustrate how large-scale shape deformations can be accounted by the model. This is done through simulations by showing that the model accurately captures the behavior of the aspirated cell. Finally, using a simple gradient sensing model to generate internal force profiles, we simulate the changing morphology of a cell chemotaxing in response to an externally applied chemoattractant gradient. Using the framework developed here, we obtain the force profiles needed to achieve stable migrating cell morphologies observed for several strains. The methods developed here allow us to link forces acting on the cell and mechanical properties of the cytoskeleton to cell shape deformation explicitly, and will prove useful in studying cellular processes undergoing large-scale shape changes.</p>", "<title>Biological background</title>", "<p>Cells derive their mechanical properties from actin, actin-associated proteins, and motor proteins such as myosin-II [##REF##15817376##2##], which are components of the cytoskeleton. Though distributed throughout the cell, the actin cytoskeleton is concentrated along the periphery of the cell underneath the membrane, particularly in <italic>Dictyostelium</italic>, and is the molecular machinery that generates cellular shape changes during cell division and chemotaxis.</p>", "<p>Cytoskeletal networks exhibit viscoelastic behavior, having both viscous and elastic properties [##UREF##2##15##, ####REF##15166374##16##, ##REF##8011912##17####8011912##17##]. Actin filaments alone do not create significant mechanical resistance; instead, cross-linking of actin filaments by various actin binding proteins imparts mechanical rigidity to the cell. Under applied load, cross-linked actin networks behave similarly to an elastic solid and can be described using Hooke's law. However, because cross-linking proteins bind to and dissociate from actin filaments, actin-based networks may also exhibit viscous flow. Myosin-II, in filament form, also binds to actin filaments and provides mechanical resistance of the cell, as well as influencing the binding kinetics of various actin crosslinkers [##REF##15817376##2##,##REF##18372178##18##]. The interior of the cell also contains cytoskeletal polymers, as well as organelles, a nucleus, and cytoplasmic fluid. Thus, owing to their viscoelastic nature, cells exhibit a time-dependent deformation in response to mechanical force.</p>", "<title>Introduction to level set methods</title>", "<p>Cell motion has been traditionally simulated by: discretizing the cell boundary, computing the displacement of each of the points according to the local velocity, and forming a new boundary with the displaced points (Fig. ##FIG##0##1A##). This method may run into difficulties when the spatial or temporal resolution of the simulations is not sufficiently fine, or when changes in topology occur (Fig. ##FIG##0##1B##). The Level Set Method (LSM) can be used to overcome these difficulties [##UREF##3##19##]. LSM is a numerical technique for tracking interfaces and shapes which has been widely used in various fields including computer graphics [##UREF##4##20##], image processing [##REF##16326902##21##], computational fluid dynamics [##REF##18330703##22##] and material science [##UREF##5##23##].</p>", "<p>Suppose that the cell boundary at time <italic>t </italic>is described by the closed-contour Γ(<italic>t</italic>). The LSM requires a potential function (Fig. ##FIG##0##1C##.), denoted as <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>), that is related to Γ(<italic>t</italic>) according to:</p>", "<p></p>", "<p>Thus, Γ(<italic>t</italic>) is the <italic>zero-level set </italic>of <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>). It follows that, in the LSM, the cell membrane is represented implicitly through the potential function which is defined on a fixed Cartesian grid, thus eliminating the need to parameterize the boundary. This allows the LSM to handle complex boundary geometries efficiently (Fig. ##FIG##0##1D##).</p>", "<p>One candidate for the potential function is the signed distance function [##UREF##6##24##], defined by:</p>", "<p></p>", "<p>where <italic>S </italic>identifies the area occupied by the cell and <italic>d</italic>(<bold>x</bold>, Γ) is the distance of position <bold>x </bold>to the curve Γ; see Fig. ##FIG##0##1E## for an example of a cell shape embedded in a potential function derived from the signed distance function.</p>", "<p>We now manipulate Γ(<italic>t</italic>) implicitly through the function <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>) according to the equation:</p>", "<p></p>", "<p>The vector <bold>v</bold>(<bold>x</bold>, <italic>t</italic>) is the velocity of the level set moving in the outward normal direction. In our case, <bold>v</bold>(<bold>x</bold>, <italic>t</italic>) intrinsically describes the cell's membrane protrusion and retraction velocities. These velocities can be driven by externally applied forces on the cell membrane (e.g. from a micropipette aspirator), or internally generated mechanical forces (e.g. actin polymerization or myosin-II retraction), or both. To determine how these forces translate to membrane velocity, however, first requires a mechanical model of the cell.</p>", "<p>As the potential function corresponding to the cell shape is evolved, it can become quite steep or flat. To reduce the numerical errors caused by these effects, we re-initialize the potential function periodically [##UREF##6##24##]. This can be done using the re-initialization equation [##UREF##7##25##]:</p>", "<p></p>", "<p>where <italic>S</italic>(<italic>ϕ</italic>(<bold>x</bold>, 0)) is taken as +1 inside the cell, -1 outside the cell and zero on the cell membrane.</p>" ]

[ "<title>Introduction to level set methods</title>", "<p>Cell motion has been traditionally simulated by: discretizing the cell boundary, computing the displacement of each of the points according to the local velocity, and forming a new boundary with the displaced points (Fig. ##FIG##0##1A##). This method may run into difficulties when the spatial or temporal resolution of the simulations is not sufficiently fine, or when changes in topology occur (Fig. ##FIG##0##1B##). The Level Set Method (LSM) can be used to overcome these difficulties [##UREF##3##19##]. LSM is a numerical technique for tracking interfaces and shapes which has been widely used in various fields including computer graphics [##UREF##4##20##], image processing [##REF##16326902##21##], computational fluid dynamics [##REF##18330703##22##] and material science [##UREF##5##23##].</p>", "<p>Suppose that the cell boundary at time <italic>t </italic>is described by the closed-contour Γ(<italic>t</italic>). The LSM requires a potential function (Fig. ##FIG##0##1C##.), denoted as <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>), that is related to Γ(<italic>t</italic>) according to:</p>", "<p></p>", "<p>Thus, Γ(<italic>t</italic>) is the <italic>zero-level set </italic>of <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>). It follows that, in the LSM, the cell membrane is represented implicitly through the potential function which is defined on a fixed Cartesian grid, thus eliminating the need to parameterize the boundary. This allows the LSM to handle complex boundary geometries efficiently (Fig. ##FIG##0##1D##).</p>", "<p>One candidate for the potential function is the signed distance function [##UREF##6##24##], defined by:</p>", "<p></p>", "<p>where <italic>S </italic>identifies the area occupied by the cell and <italic>d</italic>(<bold>x</bold>, Γ) is the distance of position <bold>x </bold>to the curve Γ; see Fig. ##FIG##0##1E## for an example of a cell shape embedded in a potential function derived from the signed distance function.</p>", "<p>We now manipulate Γ(<italic>t</italic>) implicitly through the function <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>) according to the equation:</p>", "<p></p>", "<p>The vector <bold>v</bold>(<bold>x</bold>, <italic>t</italic>) is the velocity of the level set moving in the outward normal direction. In our case, <bold>v</bold>(<bold>x</bold>, <italic>t</italic>) intrinsically describes the cell's membrane protrusion and retraction velocities. These velocities can be driven by externally applied forces on the cell membrane (e.g. from a micropipette aspirator), or internally generated mechanical forces (e.g. actin polymerization or myosin-II retraction), or both. To determine how these forces translate to membrane velocity, however, first requires a mechanical model of the cell.</p>", "<p>As the potential function corresponding to the cell shape is evolved, it can become quite steep or flat. To reduce the numerical errors caused by these effects, we re-initialize the potential function periodically [##UREF##6##24##]. This can be done using the re-initialization equation [##UREF##7##25##]:</p>", "<p></p>", "<p>where <italic>S</italic>(<italic>ϕ</italic>(<bold>x</bold>, 0)) is taken as +1 inside the cell, -1 outside the cell and zero on the cell membrane.</p>" ]

[ "<title>Results and Discussion</title>", "<title>Viscoelastic model of cell deformation</title>", "<p>The LSM relies on a continuum description of the material properties of the cell [##REF##15817376##2##,##UREF##8##26##]. We use mechanical models to describe the viscoelastic behavior of the cell [##REF##11192248##27##]. Our mechanical model is based on a representation of cells that assumes a viscoelastic cortex surrounding a viscous core. For cells where intracellular components, such as the nucleus, take a considerable fraction of the cellular volume and play an active role in determining cell shape, the method described here will not be applicable without explicitly modeling these internal structures.</p>", "<p>We model the cortex connecting the cell membrane and the cytoplasm with a Voigt model, which consists of the parallel connection of an elastic element <italic>k</italic>_{<italic>c </italic>}(nN/<italic>μ</italic>m³) and a viscous element <italic>τ</italic>_{<italic>c </italic>}(nNs/<italic>μ</italic>m³). The latter describes the association and dissociation dynamics of the cross-linkers. We model the cytoplasm by a purely viscous element, <italic>τ</italic>_{<italic>a </italic>}(nNs/<italic>μ</italic>m³), which is placed in series with the Voigt model (Fig. ##FIG##1##2A##). The element <italic>τ</italic>_{<italic>a </italic>}includes contributions from the interior of the cell as well as adhesion, friction and cytoskeletal reorganization. Strains of the cortex and cytoplasm are described by the variables <italic>x</italic>_{<italic>m </italic>}and <italic>x</italic>_{<italic>c</italic>}, respectively. Note that, in our simulations, we use pressure rather than force to induce the cellular deformations; this accounts for the extra <italic>μ</italic>m²found in the denominators of the parameters in our model.</p>", "<p>As shown below, this combined Voigt-dashpot viscoelastic model reasonably approximates the mechanical properties of <italic>Dictyostelium </italic>where cross-linking proteins are predominantly enriched in the cortex. Extending our framework to other cell types may require different viscoelastic models to describe the cell of interest. For example, aspiration of chondrocytes suggests that these cells obey a Kelvin model (similar to the Voigt element, but includes an elastic component in series with the viscous element) [##REF##15519342##28##]. Once the appropriate viscoelastic model is developed, the implementation in the LSM framework introduced here is straightforward.</p>", "<title>Experimental determination of model parameters</title>", "<p>To determine appropriate parameters for the viscoelastic model, we used micropipette aspiration to apply step pressures (rapid increase of pressure from 0 to 0.65 nN/<italic>μ</italic>m²) to individual cells [##REF##17027494##29##,##REF##10609514##30##]. In this technique, a small negative hydrostatic pressure is created at the tip of a micropipette. By bringing the micropipette into close proximity of the cellular surface, the cell is aspirated into the micropipette.</p>", "<p>We applied step pressures to wild type interphase cells and measured cellular deformation as a function of time (Fig. ##FIG##1##2B##). Deformation is quantified by the length of cellular protrusion into the pipette tip, denoted <italic>L</italic>_{<italic>p </italic>}(Fig. ##FIG##1##2C##). We aspirated 22 cells with a radius of 4.3–6.1 <italic>μ</italic>m, a pipette radius of 3.1 <italic>μ</italic>m and a pressure of 0.65 nN/<italic>μ</italic>m². The cells deformed in two distinct phases (Fig. ##FIG##1##2D##). Within the first several seconds after application of the aspirator, the cellular deformation increased sharply, with the length of the aspirated cortex increasing to an average value of 4 <italic>μ</italic>m. The deformation during this phase can be interpreted as being dominated by the elastic characteristics of the cytoskeletal network. Thereafter, the trajectory was dominated by slow cellular flow into the micropipette, increasing, on average, about 2 <italic>μ</italic>m over the next 25 s.</p>", "<p>The pressure applied by the micropipette aspirator is not the only pressure experienced by the cell. At rest, the cell is also under pressure from cortical tension (<italic>γ</italic>_{<italic>ten</italic>}), which maintains the spherical shape of the cell. Under the cortical shell-liquid drop model [##REF##10423462##31##], we assume that the cortical tension arises as surface tension (ignoring tangential stress). Following the Young-Laplace equation for liquid interfaces, the equilibrium pressure experienced by a spherical cell of radius <italic>R</italic>_{<italic>c </italic>}is</p>", "<p></p>", "<p>The cell's protrusion into the micropipette is driven by the aspiration pressure. As the cell is aspirated, the portion of the cell inside the micropipette will be a spherical cap of radius <italic>R</italic>_cap&lt;<italic>R</italic>_{<italic>c</italic>}. Given a measured length of protrusion <italic>L</italic>_{<italic>p</italic>}, the radius of the spherical cap <italic>R</italic>_capcan be obtained (see Additional file ##SUPPL##0##1##). The cap's smaller radius gives rise to higher local curvature, creating a rounding pressure:</p>", "<p></p>", "<p>to oppose the aspiration</p>", "<p>At the critical aspiration pressure, <italic>P</italic>_crit, the cell extends a perfect hemispherical projection with radius <italic>R</italic>_{<italic>p </italic>}into the micropipette and does not protrude any further under this constant pressure. Thus, the critical pressure is:</p>", "<p></p>", "<p>The cortical tension has been measured to be 1–1.5 nN/<italic>μ</italic>m in passive, wild type <italic>Dictyostelium </italic>cells [##REF##18372178##18##,##REF##10423462##31##,##REF##17050732##32##]. Here, assuming cortical tension of <italic>γ</italic>_ten= 1 nN/<italic>μ</italic>m, pipette radius of <italic>R</italic>_{<italic>p </italic>}= 3.1 <italic>μ</italic>m and cell radius of <italic>R</italic>_{<italic>c </italic>}= 5.1 <italic>μ</italic>m, we can compute <italic>P</italic>_critto be approximately 0.25 nN/<italic>μ</italic>m². Because the applied pressure was greater than the critical pressure, the cell was continuously aspirated into the pipette. Cells were only tracked for 30 s, as longer timescales are dominated by cortical remodeling and turnover [##REF##11782490##33##,##REF##15894626##34##].</p>", "<p>Pascal's law dictates that the hydrostatic pressure, <italic>P</italic>_ext, in the micropipette is normal to the cell membrane inside the micropipette and has the same magnitude in all directions. Similarly, the cell's equilibrium pressure is normal to the cell membrane everywhere with the same magnitude. We used the total pressure, <italic>P</italic>_total= <italic>P</italic>_ext- <italic>P</italic>_round, as the input to the cell's mechanical model. This pressure is applied to the cell membrane region around <italic>x</italic>_{<italic>m </italic>}and is transferred directly to the cell's cortex, formed of cytoskeleton and its cross-linkers, just beneath the cell membrane. The corresponding mathematical model is:</p>", "<p></p>", "<p>where <italic>w</italic>₀represents the initial position of the cell cortex when no force is applied to the system. We define ℓ such that:</p>", "<p></p>", "<p>With this variable change, the transformed system can be written as:</p>", "<p></p>", "<p>Using the Voigt-dashpot model of Eqn. 5 to account for the viscoelastic response of the cell to an applied step pressure, the aspirated cellular length into the pipette, <italic>L</italic>_{<italic>p</italic>}, is given by:</p>", "<p></p>", "<p>Data from all 22 cells were combined. The following parameters in the viscoelastic model were obtained using a least squares fit (using Matlab's curve fitting toolbox): <italic>k</italic>_{<italic>c </italic>}= 0.098 ± 0.007 nN/<italic>μ</italic>m³, <italic>τ</italic>_{<italic>c </italic>}= 0.064 ± 0.018 nNs/<italic>μ</italic>m³, and <italic>τ</italic>_{<italic>a </italic>}= 6.09 ± 1.44 nNs/<italic>μ</italic>m³(the ± value refer to a 95% confidence interval). With these parameter values, the 1-D model was able to capture the deformation trends observed in the experimental data (Fig. ##FIG##1##2D##). Note that the elastic constant obtained, when applying the methods of Theret <italic>et al</italic>. [##REF##3172738##35##] and Hochmuth [##REF##10609514##30##], is equivalent to an elastic Young's modulus of 70 pN/<italic>μ</italic>m², which is similar to the value of 95 pN/<italic>μ</italic>m²measured for <italic>Dictyostelium </italic>using different techniques [##REF##18372178##18##].</p>", "<title>Implementation of micropipette aspiration simulation</title>", "<p>During micropipette aspiration, the cell's velocity is generated by externally applied pressure, as well as internally generated cellular pressures such as cortical tension. We now outline how the contribution of each pressure is computed and applied to the cell's potential function <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>).</p>", "<p>We choose to do the simulations in two dimensions. The level set method is directly applicable to three dimensions (3-D), and all of the level set equations either carry over without change into 3-D, or have natural extensions. In practice, however, the computational burden of 3-D simulations is significant and hence we restrict ourselves to two dimensions. To differentiate the forces (and hence pressures) which are 2-D, from the scalar pressures used above, we use bold characters.</p>", "<title>Evolving the cell membrane</title>", "<p>The simulation accounts for the effects of three pressures: those generated externally by the micropipette; those generated internally to maintain constant volume; and rounding pressure corresponding to the cell's cortical tension. Together, these pressures generate a velocity field that evolves the cell's membrane.</p>", "<title>Externally applied pressure</title>", "<p>To account for the force generated by the hydrostatic pressure in the micropipette, the pressure <bold>P</bold>_ext, uniform in magnitude and normal to the cell membrane, is used in the LSM simulation. This force exists only inside the inner boundaries of the pipette.</p>", "<title>Pressure due to volume conservation</title>", "<p>We assume that, during aspiration, the cellular volume (<italic>V</italic>) remains constant. To enforce this constant volume condition numerically, we implement a pressure, acting normal to the surface:</p>", "<p></p>", "<p>where <bold>n </bold>is the outward normal. The cell's volume is evaluated by assuming the cell is radially symmetric:</p>", "<p></p>", "<p>To ensure that the cell's volume does not change during the course of the aspiration requires that <italic>K</italic>_volbe large. In our simulations, we set <italic>K</italic>_vol= 0.1 nN/<italic>μ</italic>m⁵, which was sufficiently high to ensure that volume changes were minimal (Fig. ##FIG##2##3G##) while maintaining stability of the simulations.</p>", "<title>Rounding pressure due to cortical tension</title>", "<p>Resting cells experience cortical tension [##REF##10423462##31##] which generates pressure, <bold>P</bold>_eq, as shown in Eqn. 3. When a spherical cell is aspirated, the cell's cortex resists deformation.</p>", "<p>The pressure generated depends on the local surface curvature</p>", "<p></p>", "<p>and a material property of the cortex referred to as the cortical tension (<italic>γ</italic>_ten) according to:</p>", "<p></p>", "<p>Therefore the rounding pressure produce by the cell is <bold>P</bold>_round= <bold>P</bold>_ten- <bold>P</bold>_eq. This acts inward normal to the membrane.</p>", "<p>We have chosen to define <bold>P</bold>_roundas the difference between the tension and an equilibrium pressure. This is accordance to experiments on neutrophils that found that cortical tension depends on surface area [##REF##15827090##36##]. However, the latter term can also be incorporated into the volume conservation term. In particular, combining <bold>P</bold>_voland <bold>P</bold>_roundleads to:</p>", "<p></p>", "<p>where = <italic>V</italic>_actual+ 2<italic>γ</italic>_ten/(<italic>R</italic>_{<italic>c</italic>}<italic>K</italic>_vol).</p>", "<p>The coefficient 2 in the pressure equation is introduced to account for the fact that our curvature calculation is based on the 2-D representation of cell shape, as the curvature of a sphere of radius <italic>r </italic>is 2/<italic>r</italic>, but the curvature of a 2-D circle is only 1/<italic>r</italic>. In the computation of curvature, spline-based interpolation was used to smooth out discretization noise.</p>", "<title>Total pressure and cell evolution</title>", "<p>In the above formulations, the total pressure outward normal to the cell membrane is:</p>", "<p></p>", "<p>The formulation of in Eqn. 5 provides us with the pressure-velocity relationship:</p>", "<p></p>", "<p>The velocity vector, <bold>v</bold>, is defined for points on the cell membrane. This needs to be extrapolated to a velocity field to evolve the potential function <italic>ϕ</italic>. It is only the velocity variations tangential to a given interface that dictate the interface motion [##UREF##9##37##]. A velocity field that minimizes the normal component of the field variation is achieved by extrapolating the membrane velocity with the nearest neighbor method. In other words, the velocity <bold>v</bold>(<bold>x</bold>) at a point <bold>x </bold>can be set equal to the membrane velocity <bold>v</bold>() at the membrane location closest to the point <bold>x</bold>. It has been shown that a signed distance function tends to stay a signed distance function when the closest neighbor extrapolation method is used [##UREF##10##38##]. We can now use this velocity field to evolve the cell membrane according to Eqn. 2.</p>", "<p>Eqn. 11 points to a difference between the LSM model of cellular deformation and the one-dimensional (1-D), scalar model used to obtain the viscoelastic parameters (Eqn. 6). In the latter, the pressure is co-aligned with the direction of the viscoelastic components, implying that the direction of motion is also always inline with the direction of the applied pressure. In the LSM simulation, the pressure is applied normal to the cell membrane, but the viscoelastic component, <bold>l</bold>, does not have to have the same directionality, and the resultant velocity vector is not always normal to the cell membrane. While providing us with good starting point for the parameter estimation, the 1-D formulation therefore can not be expected to explain the 2-D simulation completely.</p>", "<title>Restricting cell shape inside micropipette</title>", "<p>As the cell's level set potential function moves into the micropipette, its shape is restricted to remain inside the micropipette. This is achieved by first defining a mask potential function [##UREF##11##39##], Ψ, for the micropipette (Fig. ##FIG##2##3A##). The effect of the mask is to correct for the cell's potential function by clipping it (Fig. ##FIG##2##3B##) according to:</p>", "<p></p>", "<p>This restriction guarantees that the cell boundary never moves outside of the inner walls of the virtual micropipette. After this restriction, the net change in <italic>ϕ </italic>is: - <italic>ϕ</italic>(<italic>t</italic>), which translates (see Additional file ##SUPPL##0##1##) to an effective velocity that is normal to the cell membrane:</p>", "<p></p>", "<p>Thus, wherever clipping by the micropipette mask occurs, we must use this effective velocity to evolve the potential functions in simulation.</p>", "<title>Evolution of the viscoelastic state of the cell</title>", "<p>In our simulations, the cell can be represented by a series of parallel viscoelastic systems with the same parameters (Fig. ##FIG##2##3C##). These sub-systems are not interconnected, and the applied pressure on each system, <italic>P</italic>_totalas defined in Eqn. 10, is normal to the cell membrane. We argue that applying total pressure to the parallel unconnected spring damper systems used in this model closely approximates cellular behavior when the following conditions are met:</p>", "<p>1. Membrane pressure profile is piecewise smooth. This is a reasonable assumption as, in practice, pressure profiles are piecewise smooth. Even when a point force is applied to a particular location of the cell membrane, membrane elasticity will diffuse this force and make the pressure smooth locally.</p>", "<p>2. Simulation grid density is dense enough for simulation stability, but not much denser than the discretization of the membrane pressure profile. With this assumption, the interpolation nature of level set method acts like a low pass filter, where effects of artificial abrupt jumps in the pressure profile are smoothed.</p>", "<p>Let <bold>l</bold>(<bold>x</bold>, <italic>t</italic>), <bold>x </bold>∈ Γ(<italic>t</italic>) be the viscoelastic state of the cell at time <italic>t </italic>and at a position <bold>x </bold>on the membrane. That is, |<bold>l</bold>| represents the length of the numerous parallel unconnected spring-damper systems. At a given position, <bold>x</bold>, on the membrane, there is a vector with length given by |<bold>l</bold>(<bold>x</bold>)| = |ℓ| in Eqn. 5, representing the state of a <italic>single </italic>spring-damper system. Then</p>", "<p></p>", "<p>where <italic>D </italic>is the Jacobian operator, [<italic>D</italic><bold>l</bold>]<bold>v </bold>represents the displacement of the whole cell membrane, and as defined in Eqn. 5. The equation describing the evolution of <bold>l </bold>is:</p>", "<p></p>", "<title>Testing of model: Micropipette aspiration simulation</title>", "<p>To summarize, the flow chart of the simulation steps is shown in Fig. ##FIG##3##4##. The implementation is derived from the Level Set Toolbox [##UREF##11##39##] and is coded in Matlab (Mathworks, Natick, MA). The simulations were implemented on a fixed grid of 10 <italic>μ</italic>m in size, with density of 20 points/<italic>μ</italic>m and 4 ms time steps. Simulating 15 seconds of aspiration takes approximately 8 h on a desk-top computer.</p>", "<p>We simulated the micropipette aspiration experiment under several different aspiration pressures. Using an aspiration pressure of 0.65 nN/<italic>μ</italic>m²(the pressure used to obtain our viscoelastic model parameters), our simulation reproduced the trend observed in real cells (black line in Fig. ##FIG##2##3E##). The result of this simulation did not completely overlap the least-squares fitted data, though the fit to the experimental data is nearly as good. The fitted data has a mean square error (MSE) of 0.73 <italic>μ</italic>m and a coefficient of determination (<italic>R</italic>²) of 0.79; the simulation has 0.74 <italic>μ</italic>m and 0.78 respectively. Using different parameter values: <italic>k</italic>_{<italic>c </italic>}= 0.1 nN/<italic>μ</italic>m³, <italic>τ</italic>_{<italic>c </italic>}= 0.08 nNs/<italic>μ</italic>m³, and <italic>τ</italic>_{<italic>a </italic>}= 4.6 nNs/<italic>μ</italic>m³, we were able to reproduce the fitted data slightly more accurately (Fig. ##FIG##2##3D## and red line in Fig. ##FIG##2##3E##; MSE of 0.73 <italic>μ</italic>m and an <italic>R</italic>²value of 0.79).</p>", "<p>Using 0.35 nN/<italic>μ</italic>m²of pressure, the cell was rapidly and partially aspirated into the pipette. Thereafter, it remained nearly immobile. This simulation recreates the observed behavior of <italic>Dictyostelium </italic>cells at aspiration pressures near the critical pressure.</p>", "<p>To test our model further, we simulated the relaxation of an aspirated cell and compared this to experimental results in which a cell is aspirated into the micropipette for approximately 20 s at which point the applied pressure is released. The cell responds by rapidly retracting the aspirated portion (Fig. ##FIG##2##3F##). The retraction gradually slows to a near halt, with a significant portion of the cell remaining inside the micropipette. This behavior was reproduced in our simulations. The simulated cell retraction from the micropipette is measured in the reduction of length of protrusion (Fig. ##FIG##2##3G##), matching the retraction behavior seen in live cells. As shown in Fig. ##FIG##2##3G##, the variation in cell volume during these simulations was less than 1%.</p>", "<title>Simulating Dictyostelium cell shape changes using a simplified chemotaxis model</title>", "<p>Having established that we can recreate the cellular shape during micropipette aspiration, in which externally applied pressures are driving cell shape changes, we consider a situation in which the pressures arise as a response to external stimuli. To this end we simulated the cell shape behavior of chemotactic <italic>Dictyostelium </italic>cells.</p>", "<p><italic>Dictyostelium </italic>cells have the ability to detect spatial differences in the concentration of the extracellular chemoattractant cAMP. They interpret these spatial differences and respond by localizing signaling molecules. These signaling molecules in turn bias the locations of actin polymerization driven protrusions and myosin-II motor mediated retractions, generating internal mechanical forces to deform the cell as well as propel the cell towards the chemoattractant [##REF##12672811##1##,##REF##15473840##40##].</p>", "<p>Our goal in these simulations is not to propose new chemotaxis signaling mechanisms, or even to analyze the large number of proposed mechanisms (reviewed in [##REF##18207721##3##]). Rather, it is to illustrate how cellular signaling can be coupled to the LSM framework to drive cellular deformations. Thus, we purposely implement a simple model connecting chemoattractant gradients with intracellular markers.</p>", "<title>Implementation and testing</title>", "<p>We base our model for pressure generation on a previously published signaling model that accounts for receptor mediated localization of phosphatidylinositol (3,4,5)-trisphosphate (PI(3,4,5)P₃) [##REF##15465874##41##]. Though recent experimental data suggests that cells employ multiple parallel pathways to regulate chemotaxis [##REF##17419997##42##,##REF##17535967##43##], localization of this membrane lipid has been correlated with the appearance of pseudopods [##REF##15473840##40##]. Moreover, elevated levels of PI(3,4,5)P₃correlate temporally with increased levels of actin polymerization [##REF##12062103##44##].</p>", "<p>Rather than implementing the complete reaction-diffusion equations describing the PI(3,4,5)P₃model, we simplify it by using a steady-state distribution of PI(3,4,5)P₃along the cellular membrane. It was shown that the membrane concentration of PI(3,4,5)P₃is an amplified response of the relative cAMP concentration observed on the membrane [##REF##15465874##41##,##REF##15184679##45##]:</p>", "<p></p>", "<p>Next, we compute the pressure components contributing to cell motion, which include protrusion, retraction, volume conservation, and cortical tension pressures. To compute protrusion pressure, we first assume that actin polymerization creates a pressure wherever the PI(3,4,5)P₃concentration is above its mean level:</p>", "<p></p>", "<p>Similarly, we assume myosin-II retraction occurs wherever PI(3,4,5)P₃concentration is below its mean level:</p>", "<p></p>", "<p>Both of these act normal to the cell membrane. We let the proportionality constant in Eqn. 14 be absorbed into constants <italic>K</italic>_protand <italic>K</italic>_retr. Eukaryotic cells can generate actin mediated protrusion pressures of 0.5–5 nN/<italic>μ</italic>m²[##REF##17589565##46##]. We chose <italic>K</italic>_prot= 0.5 nN/<italic>μ</italic>m²and <italic>K</italic>_retr= 1 nN/<italic>μ</italic>m².</p>", "<p>When computing the conservation of volume pressure, we assume that the cell is flat with uniform thickness. Thus, volume conservation is equivalent to conserving the 2-D area of the cell:</p>", "<p></p>", "<p>The flat cell assumption also implies that the pressure generated by cortical tension depends only on the 2-D local surface curvature and the 2-D equilibrium pressure. The rounding pressure due to cortical tension is therefore given by:</p>", "<p></p>", "<p>Values of <italic>K</italic>_area= 0.2 nN/<italic>μ</italic>m⁴and <italic>K</italic>_ten= 1 nN/<italic>μ</italic>m were used in these simulations.</p>", "<p>Summing all these components yields the total force normal to the cell membrane:</p>", "<p></p>", "<p>Finally, the membrane velocity is computed using Eqn. 11, with the same viscoelastic parameters <italic>τ</italic>_{<italic>a</italic>}, <italic>k</italic>_{<italic>c </italic>}and <italic>τ</italic>_{<italic>c</italic>}. The simulation algorithm is similar to the micropipette aspiration case, and is summarized in Fig. ##FIG##4##5##.</p>", "<p>This simulation successfully generated chemotaxis behavior (Fig. ##FIG##5##6##). In response to a chemoattractant gradient, the cell, whose shape was initialized as a circle, changed shape and migrated in the direction of the chemoattractant gradient (Fig. ##FIG##5##6A##). The pressure profile (Fig. ##FIG##5##6B##) and displacement (Fig. ##FIG##5##6C##) are shown as functions of local cAMP concentration and time, respectively. The cell achieved a velocity of 11.7 <italic>μ</italic>m/min, which is similar to published velocities of <italic>Dictyostelium </italic>cells (e.g. 11.8 <italic>μ</italic>m/min[##REF##15189986##47##]). During the simulation, the cellular area (and hence volume) remained nearly constant (Fig. ##FIG##5##6C##).</p>", "<title>Membrane pressure profile and cell shape</title>", "<p>While our simulations of <italic>Dictyostelium </italic>recreate the motion of the cell in response to the chemoattractant gradient, the resultant cell shape change is small and the steady-state morphology does not resemble that observed experimentally in chemotaxing. Wild type chemotaxing <italic>Dictyostelium </italic>cells become elongated (Fig. ##FIG##6##7A##). Other strains, including the <italic>amiB</italic>^-mutants [##REF##15259052##48##] can move stably in fan-like shapes that are reminiscent of keratocytes (Fig. ##FIG##6##7D##). Without determining the underlining molecular methods, we hypothesized that the difference in cell shape can be accounted for by the way that the force generation is distributed along the cell membrane. Our LSM simulation framework allows us to determine how these forces are distributed along the cell to generate the resulting cell shapes, both for wild type and mutants. To this end, we set out to replace our initial model, described by Eqn. 15 and 16, by one based on the observed morphologies.</p>", "<p>Given a stable cell shape Γ₀traveling at velocity <bold>u</bold>, we let Γ_{<italic>u </italic>}be the displaced cell at time Δ<italic>t</italic>, and <italic>ϕ</italic>₀and <italic>ϕ</italic>_{<italic>u </italic>}be the potential functions representing Γ₀and Γ_{<italic>u </italic>}respectively. The effective velocity field necessary for this displacement is:</p>", "<p></p>", "<p>If the cell shape is at steady state, we can assume that the internal viscoelastic network is also in steady state, that is, . Therefore, from Eqn. 5, we compute the viscoelastic steady state ℓ = <italic>P</italic>_total/<italic>k</italic>_{<italic>c</italic>}.</p>", "<p>Moreover, the membrane speed at steady state is expressed as = <italic>P</italic>_total/<italic>τ</italic>_{<italic>a</italic>}. Combined with Eqn. 18, we find <bold>P</bold>_total:</p>", "<p></p>", "<p>Taking into account the effect of cortex tension, and assuming that there is no cell volume deviations, we can compute:</p>", "<p></p>", "<p>where <bold>P</bold>_tenis the cortical tension-driven rounding pressure defined in Eqn. 17. Using this formula, and a cell velocity of 10 <italic>μ</italic>m/min, we calculated the pressure profiles required to generate cell shapes seen in wild type cells as well as in <italic>amiB</italic>^-cells.</p>", "<p>Obtaining these pressure profiles is straight-forward computationally, taking less than one minute of CPU time on a desk-top computer. It does require, however, a smooth shape. Thus, a certain amount of image processing is needed when using segmented images from experiments. Moreover, the formula in Eqn. 18 is based on a steady-state shape. Handling transient cell shape changes, such as protrusions or retractions, needs a local description of the velocity <bold>v</bold>(<bold>x</bold>).</p>", "<p>Our results indicate that to generate polarized cell morphologies observed in wild type <italic>Dictyostelium </italic>cells, the protrusive forces must be primarily concentrated along the anterior ≈ 25% portion of the cell; see Fig. ##FIG##6##7B##. This is reminiscent of the PI(3,4,5)P₃threshold observed in cells [##REF##15184679##45##,##REF##16782813##49##]. At the sides, a smaller and less localized retractive force gives the cell its elongated shape. When this pressure profile was used to simulate a chemotaxing cell (Fig. ##FIG##6##7C##), the resulting virtual cell achieved an elongated shape and chemotaxed successfully to the source of chemoattractant achieving a stable velocity of 11.1 <italic>μ</italic>m/min.</p>", "<p>Clearly, a different pressure profile is needed to generate a fan like shape as observed in <italic>amiB</italic>^-cells (Fig. ##FIG##6##7D##). Here, the maximum protrusive force is spread out considerably more at the front, while large amount of retraction force is still needed to pull the tail region along. Using this pressure profile in the chemotaxis simulation led to a migrating cell with stable shape similar to that seen experimentally (Fig. ##FIG##6##7F##). The resultant fan-shaped cell achieved the stable velocity of 9.7 <italic>μ</italic>m/min.</p>" ]

[ "<title>Results and Discussion</title>", "<title>Viscoelastic model of cell deformation</title>", "<p>The LSM relies on a continuum description of the material properties of the cell [##REF##15817376##2##,##UREF##8##26##]. We use mechanical models to describe the viscoelastic behavior of the cell [##REF##11192248##27##]. Our mechanical model is based on a representation of cells that assumes a viscoelastic cortex surrounding a viscous core. For cells where intracellular components, such as the nucleus, take a considerable fraction of the cellular volume and play an active role in determining cell shape, the method described here will not be applicable without explicitly modeling these internal structures.</p>", "<p>We model the cortex connecting the cell membrane and the cytoplasm with a Voigt model, which consists of the parallel connection of an elastic element <italic>k</italic>_{<italic>c </italic>}(nN/<italic>μ</italic>m³) and a viscous element <italic>τ</italic>_{<italic>c </italic>}(nNs/<italic>μ</italic>m³). The latter describes the association and dissociation dynamics of the cross-linkers. We model the cytoplasm by a purely viscous element, <italic>τ</italic>_{<italic>a </italic>}(nNs/<italic>μ</italic>m³), which is placed in series with the Voigt model (Fig. ##FIG##1##2A##). The element <italic>τ</italic>_{<italic>a </italic>}includes contributions from the interior of the cell as well as adhesion, friction and cytoskeletal reorganization. Strains of the cortex and cytoplasm are described by the variables <italic>x</italic>_{<italic>m </italic>}and <italic>x</italic>_{<italic>c</italic>}, respectively. Note that, in our simulations, we use pressure rather than force to induce the cellular deformations; this accounts for the extra <italic>μ</italic>m²found in the denominators of the parameters in our model.</p>", "<p>As shown below, this combined Voigt-dashpot viscoelastic model reasonably approximates the mechanical properties of <italic>Dictyostelium </italic>where cross-linking proteins are predominantly enriched in the cortex. Extending our framework to other cell types may require different viscoelastic models to describe the cell of interest. For example, aspiration of chondrocytes suggests that these cells obey a Kelvin model (similar to the Voigt element, but includes an elastic component in series with the viscous element) [##REF##15519342##28##]. Once the appropriate viscoelastic model is developed, the implementation in the LSM framework introduced here is straightforward.</p>", "<title>Experimental determination of model parameters</title>", "<p>To determine appropriate parameters for the viscoelastic model, we used micropipette aspiration to apply step pressures (rapid increase of pressure from 0 to 0.65 nN/<italic>μ</italic>m²) to individual cells [##REF##17027494##29##,##REF##10609514##30##]. In this technique, a small negative hydrostatic pressure is created at the tip of a micropipette. By bringing the micropipette into close proximity of the cellular surface, the cell is aspirated into the micropipette.</p>", "<p>We applied step pressures to wild type interphase cells and measured cellular deformation as a function of time (Fig. ##FIG##1##2B##). Deformation is quantified by the length of cellular protrusion into the pipette tip, denoted <italic>L</italic>_{<italic>p </italic>}(Fig. ##FIG##1##2C##). We aspirated 22 cells with a radius of 4.3–6.1 <italic>μ</italic>m, a pipette radius of 3.1 <italic>μ</italic>m and a pressure of 0.65 nN/<italic>μ</italic>m². The cells deformed in two distinct phases (Fig. ##FIG##1##2D##). Within the first several seconds after application of the aspirator, the cellular deformation increased sharply, with the length of the aspirated cortex increasing to an average value of 4 <italic>μ</italic>m. The deformation during this phase can be interpreted as being dominated by the elastic characteristics of the cytoskeletal network. Thereafter, the trajectory was dominated by slow cellular flow into the micropipette, increasing, on average, about 2 <italic>μ</italic>m over the next 25 s.</p>", "<p>The pressure applied by the micropipette aspirator is not the only pressure experienced by the cell. At rest, the cell is also under pressure from cortical tension (<italic>γ</italic>_{<italic>ten</italic>}), which maintains the spherical shape of the cell. Under the cortical shell-liquid drop model [##REF##10423462##31##], we assume that the cortical tension arises as surface tension (ignoring tangential stress). Following the Young-Laplace equation for liquid interfaces, the equilibrium pressure experienced by a spherical cell of radius <italic>R</italic>_{<italic>c </italic>}is</p>", "<p></p>", "<p>The cell's protrusion into the micropipette is driven by the aspiration pressure. As the cell is aspirated, the portion of the cell inside the micropipette will be a spherical cap of radius <italic>R</italic>_cap&lt;<italic>R</italic>_{<italic>c</italic>}. Given a measured length of protrusion <italic>L</italic>_{<italic>p</italic>}, the radius of the spherical cap <italic>R</italic>_capcan be obtained (see Additional file ##SUPPL##0##1##). The cap's smaller radius gives rise to higher local curvature, creating a rounding pressure:</p>", "<p></p>", "<p>to oppose the aspiration</p>", "<p>At the critical aspiration pressure, <italic>P</italic>_crit, the cell extends a perfect hemispherical projection with radius <italic>R</italic>_{<italic>p </italic>}into the micropipette and does not protrude any further under this constant pressure. Thus, the critical pressure is:</p>", "<p></p>", "<p>The cortical tension has been measured to be 1–1.5 nN/<italic>μ</italic>m in passive, wild type <italic>Dictyostelium </italic>cells [##REF##18372178##18##,##REF##10423462##31##,##REF##17050732##32##]. Here, assuming cortical tension of <italic>γ</italic>_ten= 1 nN/<italic>μ</italic>m, pipette radius of <italic>R</italic>_{<italic>p </italic>}= 3.1 <italic>μ</italic>m and cell radius of <italic>R</italic>_{<italic>c </italic>}= 5.1 <italic>μ</italic>m, we can compute <italic>P</italic>_critto be approximately 0.25 nN/<italic>μ</italic>m². Because the applied pressure was greater than the critical pressure, the cell was continuously aspirated into the pipette. Cells were only tracked for 30 s, as longer timescales are dominated by cortical remodeling and turnover [##REF##11782490##33##,##REF##15894626##34##].</p>", "<p>Pascal's law dictates that the hydrostatic pressure, <italic>P</italic>_ext, in the micropipette is normal to the cell membrane inside the micropipette and has the same magnitude in all directions. Similarly, the cell's equilibrium pressure is normal to the cell membrane everywhere with the same magnitude. We used the total pressure, <italic>P</italic>_total= <italic>P</italic>_ext- <italic>P</italic>_round, as the input to the cell's mechanical model. This pressure is applied to the cell membrane region around <italic>x</italic>_{<italic>m </italic>}and is transferred directly to the cell's cortex, formed of cytoskeleton and its cross-linkers, just beneath the cell membrane. The corresponding mathematical model is:</p>", "<p></p>", "<p>where <italic>w</italic>₀represents the initial position of the cell cortex when no force is applied to the system. We define ℓ such that:</p>", "<p></p>", "<p>With this variable change, the transformed system can be written as:</p>", "<p></p>", "<p>Using the Voigt-dashpot model of Eqn. 5 to account for the viscoelastic response of the cell to an applied step pressure, the aspirated cellular length into the pipette, <italic>L</italic>_{<italic>p</italic>}, is given by:</p>", "<p></p>", "<p>Data from all 22 cells were combined. The following parameters in the viscoelastic model were obtained using a least squares fit (using Matlab's curve fitting toolbox): <italic>k</italic>_{<italic>c </italic>}= 0.098 ± 0.007 nN/<italic>μ</italic>m³, <italic>τ</italic>_{<italic>c </italic>}= 0.064 ± 0.018 nNs/<italic>μ</italic>m³, and <italic>τ</italic>_{<italic>a </italic>}= 6.09 ± 1.44 nNs/<italic>μ</italic>m³(the ± value refer to a 95% confidence interval). With these parameter values, the 1-D model was able to capture the deformation trends observed in the experimental data (Fig. ##FIG##1##2D##). Note that the elastic constant obtained, when applying the methods of Theret <italic>et al</italic>. [##REF##3172738##35##] and Hochmuth [##REF##10609514##30##], is equivalent to an elastic Young's modulus of 70 pN/<italic>μ</italic>m², which is similar to the value of 95 pN/<italic>μ</italic>m²measured for <italic>Dictyostelium </italic>using different techniques [##REF##18372178##18##].</p>", "<title>Implementation of micropipette aspiration simulation</title>", "<p>During micropipette aspiration, the cell's velocity is generated by externally applied pressure, as well as internally generated cellular pressures such as cortical tension. We now outline how the contribution of each pressure is computed and applied to the cell's potential function <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>).</p>", "<p>We choose to do the simulations in two dimensions. The level set method is directly applicable to three dimensions (3-D), and all of the level set equations either carry over without change into 3-D, or have natural extensions. In practice, however, the computational burden of 3-D simulations is significant and hence we restrict ourselves to two dimensions. To differentiate the forces (and hence pressures) which are 2-D, from the scalar pressures used above, we use bold characters.</p>", "<title>Evolving the cell membrane</title>", "<p>The simulation accounts for the effects of three pressures: those generated externally by the micropipette; those generated internally to maintain constant volume; and rounding pressure corresponding to the cell's cortical tension. Together, these pressures generate a velocity field that evolves the cell's membrane.</p>", "<title>Externally applied pressure</title>", "<p>To account for the force generated by the hydrostatic pressure in the micropipette, the pressure <bold>P</bold>_ext, uniform in magnitude and normal to the cell membrane, is used in the LSM simulation. This force exists only inside the inner boundaries of the pipette.</p>", "<title>Pressure due to volume conservation</title>", "<p>We assume that, during aspiration, the cellular volume (<italic>V</italic>) remains constant. To enforce this constant volume condition numerically, we implement a pressure, acting normal to the surface:</p>", "<p></p>", "<p>where <bold>n </bold>is the outward normal. The cell's volume is evaluated by assuming the cell is radially symmetric:</p>", "<p></p>", "<p>To ensure that the cell's volume does not change during the course of the aspiration requires that <italic>K</italic>_volbe large. In our simulations, we set <italic>K</italic>_vol= 0.1 nN/<italic>μ</italic>m⁵, which was sufficiently high to ensure that volume changes were minimal (Fig. ##FIG##2##3G##) while maintaining stability of the simulations.</p>", "<title>Rounding pressure due to cortical tension</title>", "<p>Resting cells experience cortical tension [##REF##10423462##31##] which generates pressure, <bold>P</bold>_eq, as shown in Eqn. 3. When a spherical cell is aspirated, the cell's cortex resists deformation.</p>", "<p>The pressure generated depends on the local surface curvature</p>", "<p></p>", "<p>and a material property of the cortex referred to as the cortical tension (<italic>γ</italic>_ten) according to:</p>", "<p></p>", "<p>Therefore the rounding pressure produce by the cell is <bold>P</bold>_round= <bold>P</bold>_ten- <bold>P</bold>_eq. This acts inward normal to the membrane.</p>", "<p>We have chosen to define <bold>P</bold>_roundas the difference between the tension and an equilibrium pressure. This is accordance to experiments on neutrophils that found that cortical tension depends on surface area [##REF##15827090##36##]. However, the latter term can also be incorporated into the volume conservation term. In particular, combining <bold>P</bold>_voland <bold>P</bold>_roundleads to:</p>", "<p></p>", "<p>where = <italic>V</italic>_actual+ 2<italic>γ</italic>_ten/(<italic>R</italic>_{<italic>c</italic>}<italic>K</italic>_vol).</p>", "<p>The coefficient 2 in the pressure equation is introduced to account for the fact that our curvature calculation is based on the 2-D representation of cell shape, as the curvature of a sphere of radius <italic>r </italic>is 2/<italic>r</italic>, but the curvature of a 2-D circle is only 1/<italic>r</italic>. In the computation of curvature, spline-based interpolation was used to smooth out discretization noise.</p>", "<title>Total pressure and cell evolution</title>", "<p>In the above formulations, the total pressure outward normal to the cell membrane is:</p>", "<p></p>", "<p>The formulation of in Eqn. 5 provides us with the pressure-velocity relationship:</p>", "<p></p>", "<p>The velocity vector, <bold>v</bold>, is defined for points on the cell membrane. This needs to be extrapolated to a velocity field to evolve the potential function <italic>ϕ</italic>. It is only the velocity variations tangential to a given interface that dictate the interface motion [##UREF##9##37##]. A velocity field that minimizes the normal component of the field variation is achieved by extrapolating the membrane velocity with the nearest neighbor method. In other words, the velocity <bold>v</bold>(<bold>x</bold>) at a point <bold>x </bold>can be set equal to the membrane velocity <bold>v</bold>() at the membrane location closest to the point <bold>x</bold>. It has been shown that a signed distance function tends to stay a signed distance function when the closest neighbor extrapolation method is used [##UREF##10##38##]. We can now use this velocity field to evolve the cell membrane according to Eqn. 2.</p>", "<p>Eqn. 11 points to a difference between the LSM model of cellular deformation and the one-dimensional (1-D), scalar model used to obtain the viscoelastic parameters (Eqn. 6). In the latter, the pressure is co-aligned with the direction of the viscoelastic components, implying that the direction of motion is also always inline with the direction of the applied pressure. In the LSM simulation, the pressure is applied normal to the cell membrane, but the viscoelastic component, <bold>l</bold>, does not have to have the same directionality, and the resultant velocity vector is not always normal to the cell membrane. While providing us with good starting point for the parameter estimation, the 1-D formulation therefore can not be expected to explain the 2-D simulation completely.</p>", "<title>Restricting cell shape inside micropipette</title>", "<p>As the cell's level set potential function moves into the micropipette, its shape is restricted to remain inside the micropipette. This is achieved by first defining a mask potential function [##UREF##11##39##], Ψ, for the micropipette (Fig. ##FIG##2##3A##). The effect of the mask is to correct for the cell's potential function by clipping it (Fig. ##FIG##2##3B##) according to:</p>", "<p></p>", "<p>This restriction guarantees that the cell boundary never moves outside of the inner walls of the virtual micropipette. After this restriction, the net change in <italic>ϕ </italic>is: - <italic>ϕ</italic>(<italic>t</italic>), which translates (see Additional file ##SUPPL##0##1##) to an effective velocity that is normal to the cell membrane:</p>", "<p></p>", "<p>Thus, wherever clipping by the micropipette mask occurs, we must use this effective velocity to evolve the potential functions in simulation.</p>", "<title>Evolution of the viscoelastic state of the cell</title>", "<p>In our simulations, the cell can be represented by a series of parallel viscoelastic systems with the same parameters (Fig. ##FIG##2##3C##). These sub-systems are not interconnected, and the applied pressure on each system, <italic>P</italic>_totalas defined in Eqn. 10, is normal to the cell membrane. We argue that applying total pressure to the parallel unconnected spring damper systems used in this model closely approximates cellular behavior when the following conditions are met:</p>", "<p>1. Membrane pressure profile is piecewise smooth. This is a reasonable assumption as, in practice, pressure profiles are piecewise smooth. Even when a point force is applied to a particular location of the cell membrane, membrane elasticity will diffuse this force and make the pressure smooth locally.</p>", "<p>2. Simulation grid density is dense enough for simulation stability, but not much denser than the discretization of the membrane pressure profile. With this assumption, the interpolation nature of level set method acts like a low pass filter, where effects of artificial abrupt jumps in the pressure profile are smoothed.</p>", "<p>Let <bold>l</bold>(<bold>x</bold>, <italic>t</italic>), <bold>x </bold>∈ Γ(<italic>t</italic>) be the viscoelastic state of the cell at time <italic>t </italic>and at a position <bold>x </bold>on the membrane. That is, |<bold>l</bold>| represents the length of the numerous parallel unconnected spring-damper systems. At a given position, <bold>x</bold>, on the membrane, there is a vector with length given by |<bold>l</bold>(<bold>x</bold>)| = |ℓ| in Eqn. 5, representing the state of a <italic>single </italic>spring-damper system. Then</p>", "<p></p>", "<p>where <italic>D </italic>is the Jacobian operator, [<italic>D</italic><bold>l</bold>]<bold>v </bold>represents the displacement of the whole cell membrane, and as defined in Eqn. 5. The equation describing the evolution of <bold>l </bold>is:</p>", "<p></p>", "<title>Testing of model: Micropipette aspiration simulation</title>", "<p>To summarize, the flow chart of the simulation steps is shown in Fig. ##FIG##3##4##. The implementation is derived from the Level Set Toolbox [##UREF##11##39##] and is coded in Matlab (Mathworks, Natick, MA). The simulations were implemented on a fixed grid of 10 <italic>μ</italic>m in size, with density of 20 points/<italic>μ</italic>m and 4 ms time steps. Simulating 15 seconds of aspiration takes approximately 8 h on a desk-top computer.</p>", "<p>We simulated the micropipette aspiration experiment under several different aspiration pressures. Using an aspiration pressure of 0.65 nN/<italic>μ</italic>m²(the pressure used to obtain our viscoelastic model parameters), our simulation reproduced the trend observed in real cells (black line in Fig. ##FIG##2##3E##). The result of this simulation did not completely overlap the least-squares fitted data, though the fit to the experimental data is nearly as good. The fitted data has a mean square error (MSE) of 0.73 <italic>μ</italic>m and a coefficient of determination (<italic>R</italic>²) of 0.79; the simulation has 0.74 <italic>μ</italic>m and 0.78 respectively. Using different parameter values: <italic>k</italic>_{<italic>c </italic>}= 0.1 nN/<italic>μ</italic>m³, <italic>τ</italic>_{<italic>c </italic>}= 0.08 nNs/<italic>μ</italic>m³, and <italic>τ</italic>_{<italic>a </italic>}= 4.6 nNs/<italic>μ</italic>m³, we were able to reproduce the fitted data slightly more accurately (Fig. ##FIG##2##3D## and red line in Fig. ##FIG##2##3E##; MSE of 0.73 <italic>μ</italic>m and an <italic>R</italic>²value of 0.79).</p>", "<p>Using 0.35 nN/<italic>μ</italic>m²of pressure, the cell was rapidly and partially aspirated into the pipette. Thereafter, it remained nearly immobile. This simulation recreates the observed behavior of <italic>Dictyostelium </italic>cells at aspiration pressures near the critical pressure.</p>", "<p>To test our model further, we simulated the relaxation of an aspirated cell and compared this to experimental results in which a cell is aspirated into the micropipette for approximately 20 s at which point the applied pressure is released. The cell responds by rapidly retracting the aspirated portion (Fig. ##FIG##2##3F##). The retraction gradually slows to a near halt, with a significant portion of the cell remaining inside the micropipette. This behavior was reproduced in our simulations. The simulated cell retraction from the micropipette is measured in the reduction of length of protrusion (Fig. ##FIG##2##3G##), matching the retraction behavior seen in live cells. As shown in Fig. ##FIG##2##3G##, the variation in cell volume during these simulations was less than 1%.</p>", "<title>Simulating Dictyostelium cell shape changes using a simplified chemotaxis model</title>", "<p>Having established that we can recreate the cellular shape during micropipette aspiration, in which externally applied pressures are driving cell shape changes, we consider a situation in which the pressures arise as a response to external stimuli. To this end we simulated the cell shape behavior of chemotactic <italic>Dictyostelium </italic>cells.</p>", "<p><italic>Dictyostelium </italic>cells have the ability to detect spatial differences in the concentration of the extracellular chemoattractant cAMP. They interpret these spatial differences and respond by localizing signaling molecules. These signaling molecules in turn bias the locations of actin polymerization driven protrusions and myosin-II motor mediated retractions, generating internal mechanical forces to deform the cell as well as propel the cell towards the chemoattractant [##REF##12672811##1##,##REF##15473840##40##].</p>", "<p>Our goal in these simulations is not to propose new chemotaxis signaling mechanisms, or even to analyze the large number of proposed mechanisms (reviewed in [##REF##18207721##3##]). Rather, it is to illustrate how cellular signaling can be coupled to the LSM framework to drive cellular deformations. Thus, we purposely implement a simple model connecting chemoattractant gradients with intracellular markers.</p>", "<title>Implementation and testing</title>", "<p>We base our model for pressure generation on a previously published signaling model that accounts for receptor mediated localization of phosphatidylinositol (3,4,5)-trisphosphate (PI(3,4,5)P₃) [##REF##15465874##41##]. Though recent experimental data suggests that cells employ multiple parallel pathways to regulate chemotaxis [##REF##17419997##42##,##REF##17535967##43##], localization of this membrane lipid has been correlated with the appearance of pseudopods [##REF##15473840##40##]. Moreover, elevated levels of PI(3,4,5)P₃correlate temporally with increased levels of actin polymerization [##REF##12062103##44##].</p>", "<p>Rather than implementing the complete reaction-diffusion equations describing the PI(3,4,5)P₃model, we simplify it by using a steady-state distribution of PI(3,4,5)P₃along the cellular membrane. It was shown that the membrane concentration of PI(3,4,5)P₃is an amplified response of the relative cAMP concentration observed on the membrane [##REF##15465874##41##,##REF##15184679##45##]:</p>", "<p></p>", "<p>Next, we compute the pressure components contributing to cell motion, which include protrusion, retraction, volume conservation, and cortical tension pressures. To compute protrusion pressure, we first assume that actin polymerization creates a pressure wherever the PI(3,4,5)P₃concentration is above its mean level:</p>", "<p></p>", "<p>Similarly, we assume myosin-II retraction occurs wherever PI(3,4,5)P₃concentration is below its mean level:</p>", "<p></p>", "<p>Both of these act normal to the cell membrane. We let the proportionality constant in Eqn. 14 be absorbed into constants <italic>K</italic>_protand <italic>K</italic>_retr. Eukaryotic cells can generate actin mediated protrusion pressures of 0.5–5 nN/<italic>μ</italic>m²[##REF##17589565##46##]. We chose <italic>K</italic>_prot= 0.5 nN/<italic>μ</italic>m²and <italic>K</italic>_retr= 1 nN/<italic>μ</italic>m².</p>", "<p>When computing the conservation of volume pressure, we assume that the cell is flat with uniform thickness. Thus, volume conservation is equivalent to conserving the 2-D area of the cell:</p>", "<p></p>", "<p>The flat cell assumption also implies that the pressure generated by cortical tension depends only on the 2-D local surface curvature and the 2-D equilibrium pressure. The rounding pressure due to cortical tension is therefore given by:</p>", "<p></p>", "<p>Values of <italic>K</italic>_area= 0.2 nN/<italic>μ</italic>m⁴and <italic>K</italic>_ten= 1 nN/<italic>μ</italic>m were used in these simulations.</p>", "<p>Summing all these components yields the total force normal to the cell membrane:</p>", "<p></p>", "<p>Finally, the membrane velocity is computed using Eqn. 11, with the same viscoelastic parameters <italic>τ</italic>_{<italic>a</italic>}, <italic>k</italic>_{<italic>c </italic>}and <italic>τ</italic>_{<italic>c</italic>}. The simulation algorithm is similar to the micropipette aspiration case, and is summarized in Fig. ##FIG##4##5##.</p>", "<p>This simulation successfully generated chemotaxis behavior (Fig. ##FIG##5##6##). In response to a chemoattractant gradient, the cell, whose shape was initialized as a circle, changed shape and migrated in the direction of the chemoattractant gradient (Fig. ##FIG##5##6A##). The pressure profile (Fig. ##FIG##5##6B##) and displacement (Fig. ##FIG##5##6C##) are shown as functions of local cAMP concentration and time, respectively. The cell achieved a velocity of 11.7 <italic>μ</italic>m/min, which is similar to published velocities of <italic>Dictyostelium </italic>cells (e.g. 11.8 <italic>μ</italic>m/min[##REF##15189986##47##]). During the simulation, the cellular area (and hence volume) remained nearly constant (Fig. ##FIG##5##6C##).</p>", "<title>Membrane pressure profile and cell shape</title>", "<p>While our simulations of <italic>Dictyostelium </italic>recreate the motion of the cell in response to the chemoattractant gradient, the resultant cell shape change is small and the steady-state morphology does not resemble that observed experimentally in chemotaxing. Wild type chemotaxing <italic>Dictyostelium </italic>cells become elongated (Fig. ##FIG##6##7A##). Other strains, including the <italic>amiB</italic>^-mutants [##REF##15259052##48##] can move stably in fan-like shapes that are reminiscent of keratocytes (Fig. ##FIG##6##7D##). Without determining the underlining molecular methods, we hypothesized that the difference in cell shape can be accounted for by the way that the force generation is distributed along the cell membrane. Our LSM simulation framework allows us to determine how these forces are distributed along the cell to generate the resulting cell shapes, both for wild type and mutants. To this end, we set out to replace our initial model, described by Eqn. 15 and 16, by one based on the observed morphologies.</p>", "<p>Given a stable cell shape Γ₀traveling at velocity <bold>u</bold>, we let Γ_{<italic>u </italic>}be the displaced cell at time Δ<italic>t</italic>, and <italic>ϕ</italic>₀and <italic>ϕ</italic>_{<italic>u </italic>}be the potential functions representing Γ₀and Γ_{<italic>u </italic>}respectively. The effective velocity field necessary for this displacement is:</p>", "<p></p>", "<p>If the cell shape is at steady state, we can assume that the internal viscoelastic network is also in steady state, that is, . Therefore, from Eqn. 5, we compute the viscoelastic steady state ℓ = <italic>P</italic>_total/<italic>k</italic>_{<italic>c</italic>}.</p>", "<p>Moreover, the membrane speed at steady state is expressed as = <italic>P</italic>_total/<italic>τ</italic>_{<italic>a</italic>}. Combined with Eqn. 18, we find <bold>P</bold>_total:</p>", "<p></p>", "<p>Taking into account the effect of cortex tension, and assuming that there is no cell volume deviations, we can compute:</p>", "<p></p>", "<p>where <bold>P</bold>_tenis the cortical tension-driven rounding pressure defined in Eqn. 17. Using this formula, and a cell velocity of 10 <italic>μ</italic>m/min, we calculated the pressure profiles required to generate cell shapes seen in wild type cells as well as in <italic>amiB</italic>^-cells.</p>", "<p>Obtaining these pressure profiles is straight-forward computationally, taking less than one minute of CPU time on a desk-top computer. It does require, however, a smooth shape. Thus, a certain amount of image processing is needed when using segmented images from experiments. Moreover, the formula in Eqn. 18 is based on a steady-state shape. Handling transient cell shape changes, such as protrusions or retractions, needs a local description of the velocity <bold>v</bold>(<bold>x</bold>).</p>", "<p>Our results indicate that to generate polarized cell morphologies observed in wild type <italic>Dictyostelium </italic>cells, the protrusive forces must be primarily concentrated along the anterior ≈ 25% portion of the cell; see Fig. ##FIG##6##7B##. This is reminiscent of the PI(3,4,5)P₃threshold observed in cells [##REF##15184679##45##,##REF##16782813##49##]. At the sides, a smaller and less localized retractive force gives the cell its elongated shape. When this pressure profile was used to simulate a chemotaxing cell (Fig. ##FIG##6##7C##), the resulting virtual cell achieved an elongated shape and chemotaxed successfully to the source of chemoattractant achieving a stable velocity of 11.1 <italic>μ</italic>m/min.</p>", "<p>Clearly, a different pressure profile is needed to generate a fan like shape as observed in <italic>amiB</italic>^-cells (Fig. ##FIG##6##7D##). Here, the maximum protrusive force is spread out considerably more at the front, while large amount of retraction force is still needed to pull the tail region along. Using this pressure profile in the chemotaxis simulation led to a migrating cell with stable shape similar to that seen experimentally (Fig. ##FIG##6##7F##). The resultant fan-shaped cell achieved the stable velocity of 9.7 <italic>μ</italic>m/min.</p>" ]

[ "<title>Conclusion</title>", "<p>We have shown that the simulation framework we have developed can be used to model cell shape deformations as well as cell motility. The simulations can produce deformations seen during micropipette aspiration experiments. This requires parameter values for the viscoelastic model which can be obtained experimentally. It should be noted, however, that 2-D simulations using parameters based on a 1-D model may not reproduce the 1-D model simulation precisely.</p>", "<p>In the simulations of cell shape changes during chemotaxis, we saw that our simple model for generating the cell's protrusive and retractive forces in response to a chemoattractant gradient does not produce experimentally observed cell shapes. However, our techniques allow us to work backwards from shape to obtain the required forces. We determined that generating the elongated cell shape requires a large protrusive force at the front (the pressure profile there is positive). At the sides, there is a large retractive force (the pressure profile there is negative). While measuring this pressure profile directly would be difficult, it is possible to image fluorescently-tagged myosin-II to infer a measure of the forces acting on the cell. Under the assumption that the retractive force is being generated by myosin-II, we expect that myosin-II would be greatly enriched at the sides. Quantitatively, the spatial distribution of myosin could be used to estimate how much force is being generated along the membrane (as has been done during cytokinesis [##REF##11860600##50##]).</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Many cellular processes involve substantial shape changes. Traditional simulations of these cell shape changes require that grids and boundaries be moved as the cell's shape evolves. Here we demonstrate that accurate cell shape changes can be recreated using level set methods (LSM), in which the cellular shape is defined implicitly, thereby eschewing the need for updating boundaries.</p>", "<title>Results</title>", "<p>We obtain a viscoelastic model of <italic>Dictyostelium </italic>cells using micropipette aspiration and show how this viscoelastic model can be incorporated into LSM simulations to recreate the observed protrusion of cells into the micropipette faithfully. We also demonstrate the use of our techniques by simulating the cell shape changes elicited by the chemotactic response to an external chemoattractant gradient.</p>", "<title>Conclusion</title>", "<p>Our results provide a simple but effective means of incorporating cellular deformations into mathematical simulations of cell signaling. Such methods will be useful for simulating important cellular events such as chemotaxis and cytokinesis.</p>" ]

[ "<title>Authors' contributions</title>", "<p>LY implemented the LSM simulations and drafted the manuscript. JCE performed experiments to measure the viscoelastic properties of cells, under the guidance of DNR. BLK and SES participated in the implementation of the LSM algorithm. PAI conceived of the study, and participated in its design and coordination. LY, JCE, DNR and PAI wrote the manuscript which was read and approved by all the authors.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Supplementary Material</title>" ]

[ "<title>Acknowledgements</title>", "<p>We thank Charles Wolgemuth (U. Connecticut) who first suggested to us the use of level set methods for simulating chemotaxis. We also thank Dr Taro Q. P. Uyeda (Tsukuba University, Japan) for images of the <italic>amiB</italic>^-cells. This work was supported in part by grants from the NIH (NIGMS R01-71920) and the NSF (0621740).</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Introduction to level set methods</bold>. A. The traditional method of tracking moving boundaries involves discretization of the boundary (dotted red) into a set of points, moving each point <bold>x </bold>= (<italic>x</italic>, <italic>y</italic>) according to the local velocity (<italic>v</italic>(<bold>x</bold>, <italic>t</italic>)), leading to a new boundary at the new locations (solid red). B. Difficulties can arise, however, when the geometry of the boundary becomes irregular. In this case, the point tracking method often fails to preserve the boundary topology. Special attention is required to resolve these errors, increasing computational costs. C. In the Level Set Method (LSM), the boundary Γ(<italic>t</italic>) is embedded into a higher dimensional potential function (<italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>)) as the zero-contour. Γ(<italic>t</italic>)moves as <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>)evolves in time. D. Because the boundary is defined implicitly, the LSM framework overcomes some of the difficulties of boundary point tracking. E. This example illustrates how an arbitrary cell shape (black contour) can be embedded into a signed distance function to form the level set potential function <italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>). In this case, the potential function is given by the Euclidean distance to the cell boundary, with positive (resp. negative) sign when outside (resp. inside) the cell.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Viscoelastic model of cell</bold>. A. Representation of the viscoelastic model of the cell. <italic>x</italic>_{<italic>c </italic>}and <italic>x</italic>_{<italic>m </italic>}denote the location of the cell cytoplasm and membrane, respectively; <italic>τ</italic>_{<italic>c </italic>}and <italic>k</italic>_{<italic>c </italic>}define the mechanical model of the cell cortex; <italic>τ</italic>_{<italic>a </italic>}includes the viscous deformation of the cytoplasm as well as other components including adhesion. B. To validate our model and determine model parameters, we utilized micropipette aspiration technique. Relevant parameters include the radius of the micropipette (<italic>R</italic>_{<italic>p</italic>}), the radius of the cell (<italic>R</italic>_{<italic>c</italic>}), and the length of protrusion into the micropipette (<italic>L</italic>_{<italic>p</italic>}). C. Example of a <italic>Dictyostelium </italic>cell being aspirated into the micropipette at 0.65 nN/<italic>μ</italic>m². Time stamps are in seconds, scale bar shows 10 <italic>μ</italic>m. D. Protrusion into the pipette was measured and was accounted for by the model. Different colored circles represent data from 22 individual experiments. Solid line represents the deformation defined by Eqn. 6 with parameters <italic>k</italic>_{<italic>c </italic>}= 0.098 nN/<italic>μ</italic>m³, <italic>τ</italic>_{<italic>c </italic>}= 0.064 nNs/<italic>μ</italic>m³and <italic>τ</italic>_{<italic>a </italic>}= 6.09 nNs/<italic>μ</italic>m³.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Simulation of micropipette aspiration</bold>. A. To account for the solid surface of the micropipette, we introduce a mask potential function (Ψ) defined by the micropipette walls (black line). B. A cross section illustrates how the masking potential function is used to clip the evolving level set potential function. Based on the driving equations, the potential function evolves from <italic>ϕ</italic>(<italic>t</italic>)(solid blue) to <italic>ϕ</italic>(<italic>t </italic>+ Δ<italic>t</italic>)(dashed blue). However, this new position makes the cell cross into the pipette (defined by the mask function Ψ – green line). The level set function is then clipped to to account for the solid surface. C. Parallel spring-dashpot units are used to represent the viscoelastic state of the cell as the boundary evolves from Γ(<italic>t</italic>) to Γ(<italic>t </italic>+ Δ<italic>t</italic>). Each component consists of a viscoelastic model as defined in Fig. 2A. D. Simulation of micropipette aspiration implementing the viscoelastic model of the cell in the LSM (using the adjusted parameters; see main text). Shown is an overlay of simulation frames at <italic>t </italic>= 0 s (spherical cell, light grey), 0.5 s, 1 s, 5 s and 20 s (farthest protrusion, black). E. Measurements from aspiration simulations. Assuming an aspiration pressure of 0.65 nN/<italic>μ</italic>m², the protrusion into the cell from the simulation (black line) can account for the experimental data (grey dots; mean square error, MSE, is 0.74 <italic>μ</italic>m; coefficient of determination, <italic>R</italic>², of 0.78) nearly as well as the data fit (dotted line) from Fig. 2D. (MSE: 0.73, <italic>R</italic>²: 0.79). With slightly different parameters (see main text) the simulation (red line) overlaps the fitted data better (MSE: 0.73, <italic>R</italic>²: 0.79). Aspiration forces near the critical pressure (0.35 nN/<italic>μ</italic>m²) can deform the cell initially, but do not draw it in further (green line). F. After 20 s of micropipette aspiration, the pressure in the aspirator is dropped, leading to a relaxation in the protrusion distance; a typical example is shown here. Time stamp indicate seconds after release of aspiration pressure, scale bar corresponds to10 <italic>μ</italic>m. Note that the cell does not fully retract the aspirated portion. G. In a LSM simulation, the cell's retraction can be shown as the decrease of length of protrusion. Simulation of cell relaxation accurately demonstrates the lack of complete retraction observed (red). Also shown is the cell's volume (blue) during the simulation demonstrating that any volume changes are minimal.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Algorithm for LSM simulation of micropipette aspiration.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Algorithm for LSM simulation of cell shape changes in response to external chemotaxis gradients</bold>. This algorithm includes only general steps required to generate the pressure profile. To simulate chemotaxis also requires that the chemoattractant gradient be generated and that the protrusive (<bold>P</bold>_prot) and retractive (<bold>P</bold>_retr) pressures be computed. These would be determined by specific models of chemotactic response. In our simulations, these were generated by Eqn. 15 and Eqn. 16, respectively.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>LSM simulations of cell shape changes during chemotaxis</bold>. A. We simulated the change in cellular morphology of a <italic>Dictyostelium </italic>cell exposed to a point source of chemoattractant (1 <italic>μ</italic>M of cAMP). Shown is the resultant chemoattractant field (computed by solving the diffusion equation) as well as the location of the cell at times 0, 1.5, 10, 40, 60, 80 and 100 s. Initially, the cell is assumed to be round (red circle). B. In this model, pressure was determined by the concentration of PI(3,4,5)P₃on the membrane as described in the text. The maximum and minimum refer to the concentrations experienced by the cell around the membrane. C. The position of the cell (blue) was plotted as a function of time, showing fairly constant velocity (11.7 <italic>μ</italic>m/min). Also shown is the cell's area (red) during the simulation demonstrating that changes are also minimal.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Pressure profile drives cell shape</bold>. A. During chemotaxis, wild type <italic>Dictyostelium </italic>cells acquire a polarized, elongated morphology. B. Eqn. 19 was used to compute the pressure profile (red dots) necessary to maintain the elongated cell shape (inset) along the cell membrane, and this is plotted as a function of the local chemoattractant (cAMP) concentration. The maximum and minimum refer to the concentrations experienced by the cell around the membrane. Pressure profile used in the simulations (blue line) was obtained by fitting the computed pressure profile, details are given in the Additional file ##SUPPL##0##1##. C. Chemotaxing cell using the pressure profile of panel B. The shapes of the cell are shown at times 0, 1.5, 10, 20, 40, 60, 80 and 100 s. Other details in the simulation are as in Fig. 6. D-F. Simulations of chemotaxis in <italic>Dictyostelium amiB</italic>^-cells. These mutant cells acquire a fan-like morphology (panel D) and move along their broad axis. This form of movement was recreated using the pressure profile of panel E (colors as in panel B). F. Chemotaxing cell using the force profile of panel E. Times of the shapes are as in panel C.</p></caption></fig>" ]

[]

[ "<disp-formula>Γ(<italic>t</italic>) = {<bold>x</bold>|<italic>ϕ</italic>(<bold>x</bold>, <italic>t</italic>) = 0}.</disp-formula>", "<disp-formula id=\"bmcM1\"><label>(1)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\" name=\"1752-0509-2-68-i1\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtext>signd</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mi>Γ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mo>−</mml:mo><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mi>Γ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mi>Γ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow><mml:mtext>if </mml:mtext><mml:mi>x</mml:mi><mml:mo>∈</mml:mo><mml:mi>S</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow><mml:mtext>if </mml:mtext><mml:mi>x</mml:mi><mml:mo>∉</mml:mo><mml:mi>S</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow><mml:mtext>if </mml:mtext><mml:mi>x</mml:mi><mml:mo>∈</mml:mo><mml:mi>Γ</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM2\"><label>(2)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\" name=\"1752-0509-2-68-i2\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>v</mml:mi></mml:mstyle><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>⋅</mml:mo><mml:mo>∇</mml:mo><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mn>0.</mml:mn></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M3\" name=\"1752-0509-2-68-i3\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mi>S</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>|</mml:mo><mml:mo>∇</mml:mo><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>|</mml:mo><mml:mo>−</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM3\"><label>(3)</label><italic>P</italic>_eq= 2<italic>γ</italic>_ten/<italic>R</italic>_{<italic>c</italic>}.</disp-formula>", "<disp-formula><italic>P</italic>_round(<italic>L</italic>_{<italic>P</italic>}) = 2<italic>γ</italic>_ten/<italic>R</italic>_cap- <italic>P</italic>_eq,</disp-formula>", "<disp-formula id=\"bmcM4\"><label>(4)</label><italic>P</italic>_crit= <italic>P</italic>_round(<italic>R</italic>_{<italic>p</italic>}) = 2<italic>γ</italic>_ten(1/<italic>R</italic>_{<italic>p </italic>}- 1/<italic>R</italic>_{<italic>c</italic>}).</disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M4\" name=\"1752-0509-2-68-i4\" overflow=\"scroll\"><mml:semantics><mml:mtable columnalign=\"left\"><mml:mtr><mml:mtd><mml:msub><mml:mi>P</mml:mi><mml:mrow><mml:mtext>total</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>x</mml:mi><mml:mo>˙</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>x</mml:mi><mml:mo>˙</mml:mo></mml:mover><mml:mi>c</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>w</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:msub><mml:mi>P</mml:mi><mml:mrow><mml:mtext>total</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:msub><mml:mover accent=\"true\"><mml:mi>x</mml:mi><mml:mo>˙</mml:mo></mml:mover><mml:mi>c</mml:mi></mml:msub><mml:mo>,</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:semantics></mml:math></disp-formula>", "<disp-formula><italic>x</italic>_{<italic>c </italic>}= <italic>x</italic>_{<italic>m </italic>}- ℓ - <italic>w</italic>₀.</disp-formula>", "<disp-formula id=\"bmcM5\"><label>(5)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M5\" name=\"1752-0509-2-68-i5\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>x</mml:mi><mml:mo>˙</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mover accent=\"true\"><mml:mi>ℓ</mml:mi><mml:mo>˙</mml:mo></mml:mover></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mn>0</mml:mn></mml:mtd><mml:mtd><mml:mrow><mml:mo>−</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mn>0</mml:mn></mml:mtd><mml:mtd><mml:mrow><mml:mo>−</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>ℓ</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mrow><mml:mtext>total</mml:mtext></mml:mrow></mml:msub><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM6\"><label>(6)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M6\" name=\"1752-0509-2-68-i6\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mi>p</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>P</mml:mi><mml:mrow><mml:mtext>total</mml:mtext></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mi>t</mml:mi><mml:mo>/</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow></mml:msup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mfrac><mml:mi>t</mml:mi><mml:mrow><mml:msub><mml:mi>τ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM7\"><label>(7)</label><bold>P</bold>_vol= <italic>K</italic>_vol(<italic>V</italic>_resting- <italic>V</italic>_actual)<bold>n</bold></disp-formula>", "<disp-formula id=\"bmcM8\"><label>(8)</label><italic>V</italic>_actual= ∫_{cell length}<italic>πr</italic>(<italic>x</italic>)²<italic>dx</italic>.</disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M7\" name=\"1752-0509-2-68-i7\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δ</mml:mi><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M8\" name=\"1752-0509-2-68-i8\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>κ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>x</mml:mi></mml:mstyle><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>∇</mml:mo><mml:mo>⋅</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:mo>∇</mml:mo><mml:mi>ϕ</mml:mi></mml:mrow><mml:mrow><mml:mo>|</mml:mo><mml:mo>∇</mml:mo><mml:mi>ϕ</mml:mi><mml:mo>|</mml:mo></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM9\"><label>(9)</label><bold>P</bold>_ten(<italic>x</italic>) = 2<italic>γ</italic>_ten<italic>κ</italic>(<italic>x</italic>)<bold>n</bold>.</disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M9\" name=\"1752-0509-2-68-i9\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>vol</mml:mtext></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>round</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>K</mml:mi><mml:mrow><mml:mtext>vol</mml:mtext></mml:mrow></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mtext>resting</mml:mtext></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mtext>actual</mml:mtext></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle><mml:mo>−</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>2</mml:mn><mml:msub><mml:mi>γ</mml:mi><mml:mrow><mml:mtext>ten</mml:mtext></mml:mrow></mml:msub><mml:mi>κ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle><mml:mo>−</mml:mo><mml:mn>2</mml:mn><mml:msub><mml:mi>γ</mml:mi><mml:mrow><mml:mtext>ten</mml:mtext></mml:mrow></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>K</mml:mi><mml:mrow><mml:mtext>vol</mml:mtext></mml:mrow></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mtext>resting</mml:mtext></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>V</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mrow><mml:mtext>actual</mml:mtext></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle><mml:mo>−</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>2</mml:mn><mml:msub><mml:mi>γ</mml:mi><mml:mrow><mml:mtext>ten</mml:mtext></mml:mrow></mml:msub><mml:mi>κ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M10\" name=\"1752-0509-2-68-i10\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>V</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mrow><mml:mtext>actual</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM10\"><label>(10)</label><bold>P</bold>_total= <bold>P</bold>_ext+ <bold>P</bold>_vol- <bold>P</bold>_round.</disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M11\" name=\"1752-0509-2-68-i11\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>x</mml:mi><mml:mo>˙</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM11\"><label>(11)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M12\" name=\"1752-0509-2-68-i12\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>v</mml:mi></mml:mstyle><mml:mo>=</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mover accent=\"true\"><mml:mi>x</mml:mi><mml:mo>˙</mml:mo></mml:mover></mml:mstyle><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>m</mml:mi></mml:mstyle></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>total</mml:mtext></mml:mrow></mml:msub><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M13\" name=\"1752-0509-2-68-i13\" overflow=\"scroll\"><mml:semantics><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mover accent=\"true\"><mml:mi>x</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:mstyle></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M14\" name=\"1752-0509-2-68-i13\" overflow=\"scroll\"><mml:semantics><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mover accent=\"true\"><mml:mi>x</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:mstyle></mml:semantics></mml:math></inline-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M15\" name=\"1752-0509-2-68-i14\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δ</mml:mi><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:mi>min</mml:mi><mml:mo>⁡</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δ</mml:mi><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo><mml:mi>ψ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M16\" name=\"1752-0509-2-68-i7\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δ</mml:mi><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM12\"><label>(12)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M17\" name=\"1752-0509-2-68-i15\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mover accent=\"true\"><mml:mi>v</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:mstyle><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δ</mml:mi><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mover><mml:mo>−</mml:mo><mml:mi>ϕ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>Δ</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mfrac><mml:mrow><mml:mo>∇</mml:mo><mml:mi>ϕ</mml:mi></mml:mrow><mml:mrow><mml:mo>|</mml:mo><mml:mo>∇</mml:mo><mml:mi>ϕ</mml:mi><mml:msup><mml:mo>|</mml:mo><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M18\" name=\"1752-0509-2-68-i16\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:mfrac><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:mfrac><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle><mml:mo stretchy=\"false\">]</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>v</mml:mi></mml:mstyle><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M19\" name=\"1752-0509-2-68-i17\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mover accent=\"true\"><mml:mi>ℓ</mml:mi><mml:mo>˙</mml:mo></mml:mover><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM13\"><label>(13)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M20\" name=\"1752-0509-2-68-i18\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle><mml:mo>+</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>τ</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>total</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle><mml:mo stretchy=\"false\">]</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>v</mml:mi></mml:mstyle><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mo>∂</mml:mo><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM14\"><label>(14)</label>PI(3,4,5)P₃∝ [cAMP/mean(cAMP)]³.</disp-formula>", "<disp-formula id=\"bmcM15\"><label>(15)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M21\" name=\"1752-0509-2-68-i19\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>prot</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>K</mml:mi><mml:mrow><mml:mtext>prot</mml:mtext></mml:mrow></mml:msub><mml:mi>max</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mfrac><mml:mrow><mml:mtext>PI</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>3</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:mtext>mean</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>PI</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>max</mml:mi><mml:mo>⁡</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>PI</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:mtext>mean</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>PI</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM16\"><label>(16)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M22\" name=\"1752-0509-2-68-i20\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>retr</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:msub><mml:mi>K</mml:mi><mml:mrow><mml:mtext>retr</mml:mtext></mml:mrow></mml:msub><mml:mi>max</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mfrac><mml:mrow><mml:mtext>mean</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>PI</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:mtext>PI</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>3</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:mtext>mean</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>PI</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:mi>max</mml:mi><mml:mo>⁡</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>PI</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><bold>P</bold>_area= <italic>K</italic>_area(<italic>A</italic>₀- <italic>A</italic>)<bold>n</bold>.</disp-formula>", "<disp-formula id=\"bmcM17\"><label>(17)</label><bold>P</bold>_ten= <italic>K</italic>_ten(<italic>κ</italic>(<italic>x</italic>) - 1/<italic>R</italic>_{<italic>c</italic>})<bold>n</bold>.</disp-formula>", "<disp-formula><bold>P</bold>_total= <bold>P</bold>_prot+ <bold>P</bold>_retr+ <bold>P</bold>_area- <bold>P</bold>_ten.</disp-formula>", "<disp-formula id=\"bmcM18\"><label>(18)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M23\" name=\"1752-0509-2-68-i21\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mover accent=\"true\"><mml:mi>u</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:mstyle><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>u</mml:mi></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>/</mml:mo><mml:mi>Δ</mml:mi><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mo>|</mml:mo><mml:mo>∇</mml:mo><mml:mi>ϕ</mml:mi><mml:mo>|</mml:mo></mml:mrow></mml:mfrac><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M24\" name=\"1752-0509-2-68-i22\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>l</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M25\" name=\"1752-0509-2-68-i11\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>x</mml:mi><mml:mo>˙</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M26\" name=\"1752-0509-2-68-i23\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>total</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mfrac><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>u</mml:mi></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>/</mml:mo><mml:mi>Δ</mml:mi><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mo>|</mml:mo><mml:mo>∇</mml:mo><mml:mi>ϕ</mml:mi><mml:mo>|</mml:mo></mml:mrow></mml:mfrac><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM19\"><label>(19)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M27\" name=\"1752-0509-2-68-i24\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>prot</mml:mtext></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>retr</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mfrac><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>u</mml:mi></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>/</mml:mo><mml:mi>Δ</mml:mi><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mo>|</mml:mo><mml:mo>∇</mml:mo><mml:mi>ϕ</mml:mi><mml:mo>|</mml:mo></mml:mrow></mml:mfrac><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>n</mml:mi></mml:mstyle><mml:mo>+</mml:mo><mml:msub><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>P</mml:mi></mml:mstyle><mml:mrow><mml:mtext>ten</mml:mtext></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>This document presents detailed derivation of several of the formulae in the text.</p></caption></supplementary-material>" ]

[]

[ "<graphic xlink:href=\"1752-0509-2-68-1\"/>", "<graphic xlink:href=\"1752-0509-2-68-2\"/>", "<graphic xlink:href=\"1752-0509-2-68-3\"/>", "<graphic xlink:href=\"1752-0509-2-68-4\"/>", "<graphic xlink:href=\"1752-0509-2-68-5\"/>", "<graphic xlink:href=\"1752-0509-2-68-6\"/>", "<graphic xlink:href=\"1752-0509-2-68-7\"/>" ]

[ "<media xlink:href=\"1752-0509-2-68-S1.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>Musculoskeletal disorders have been described as the most notorious and common causes of severe long-term pain and physical disability that affect hundreds of millions of people across the world [##UREF##0##1##]. In the work place, the health care professionals are vulnerable to sustaining musculoskeletal disorders during the course of their work routine [##REF##12236647##2##, ####UREF##1##3##, ##REF##3157196##4##, ##REF##8710962##5##, ##REF##10416574##6####10416574##6##]. Salisk and Ozkan [##UREF##2##7##] defined WRMDs among physiotherapists as musculoskeletal injuries that result from a work-related event and several studies [##REF##3157196##4##, ####REF##8710962##5##, ##REF##10416574##6##, ##UREF##2##7##, ##REF##10758519##8##, ##REF##9279486##9####9279486##9##] have documented that work-related musculoskeletal disorders (WRMDs) are frequently experienced by physiotherapists. Literature has indeed suggested that physiotherapists are particularly susceptible to WRMDs because of the nature of their profession which is often repetitive, labour intensive and involving direct contact with patients [##REF##8710962##5##,##REF##10758519##8##,##UREF##3##10##]. However, Lotters et al [##REF##14712849##11##] emphasized the complexity of attributing musculoskeletal disorders to work while Palmer and Smedley [##REF##17572827##12##] submitted that work only partly contributes to the occurrence of musculoskeletal disorders.</p>", "<p>The life time prevalence of WRMDs among physiotherapists has been reported to be 68% in the United Kingdom [##UREF##4##13##], 55% [##REF##11552874##14##] and 91% [##REF##10758519##8##] in Australia, and 85% in Turkey [##UREF##2##7##]. Low back pain is the most common WRMD among physiotherapists [##REF##8710962##5##, ####REF##10416574##6##, ##UREF##2##7##, ##REF##10758519##8####10758519##8##,##REF##11552874##14##] with career and annual prevalence of low back pain among physiotherapists in the United Kingdom being reported as 68% and 58% respectively [##UREF##4##13##]. In the United States, prevalence of low back pain among physiotherapists ranged from 45% [##REF##8710962##5##] to 62% [##REF##10416574##6##]. Mierzejewski and Kumar [##REF##9279486##9##] found the prevalence of low back pain in Canada to be 49%, while Shehab et al [##UREF##5##15##] reported a 70% prevalence of low back pain in Kuwait.</p>", "<p>Some research indicates that WRMDs among physiotherapists may be age related and also associated with professional years of experience. Bork et al [##REF##8710962##5##] indicated that physiotherapists aged more than 50 years had the lowest prevalence of WRMDs, while others [##UREF##2##7##,##REF##9279486##9##,##UREF##4##13##,##UREF##6##16##] reported that most physiotherapists first developed symptoms before the age of 30 years and that majority of these initial episodes occurred within five years after graduation. The physically demanding nature of the physiotherapy profession may contribute to the occurrence of WRMDs and result in a high prevalence. Elements of physiotherapy practice which have been suggested as risk factors include: treatments which demand repetitive movements or continuous bending, lifting/transferring dependent patients, responding to unanticipated or sudden movements by patients, performing manual therapy, restricted work place, understaffing, age and sex [##REF##10758519##8##,##UREF##3##10##,##REF##11552874##14##]. Scientific literature from various parts of the world has also reported significant association between occupational risk factors involving high repetition rates, excessive forces, and awkward postures and musculoskeletal disorders [##UREF##7##17##].</p>", "<p>It has been opined that the cultural values of physical therapists may make it difficult for practitioners to avoid the risks of WRMDS during their work [##REF##11991799##18##]. Since these cultural values are generic and unique to physiotherapy, Nigerian physiotherapists are expected to be part of this picture despite the difference in contextual practice settings. However, little seems to be known about the occupational hazards of physiotherapy practice in Nigeria, despite the wealth of information on WRMDs among physiotherapists around the world. We speculated that investigating the prevalence and work factors of work-related musculoskeletal disorders among physiotherapists in an underserved health system as Nigeria may present a different picture from what obtains in the advanced countries of the world. This study therefore investigated the 12- month prevalence and work factors of WRMDs among physiotherapists in Nigeria.</p>" ]

[ "<title>Methods</title>", "<p>Approval of the University of Ibadan/University College Hospital joint Institutional Review Committee on human research was obtained before the commencement of the study. Participants in the study were physiotherapists practicing in the 26 accredited secondary and tertiary health institutions in the six geopolitical zones and federal capital territory of Nigeria [##UREF##8##19##]. The respondents were neither interns nor on administrative duties at the time of the study. Two hundred and seventeen copies of a respondent administered questionnaire and informed consent forms were posted to physiotherapists working in the accredited health institutions by surface mail. A letter of introduction explaining the purpose of the study was attached to the questionnaire. Pre- addressed stamped envelopes with the address of the corresponding authour were also included in the package sent to each hospital. After two weeks, reminders were sent to the respondents while another reminder was sent two weeks after the first one. The questionnaire was returned by only those who agreed to participate in the study.</p>", "<p>The questionnaire for this study was based on previous published surveys [##REF##10758519##8##,##REF##11552874##14##] but adapted for use among Nigerians. The questionnaire had two sections and contained 27 questions (see Additional file ##SUPPL##0##1##). Section A of the questionnaire sought information on the demographic characteristics of the respondents, their years of experience, work setting, work status and whether they have had ergonomic training or not. Its section B contained items on WRMDs, work factors and coping strategies. Respondents were asked whether they had experienced WRMDs that we defined as discomfort, injuries or pain due to their work and lasting more than three days in the last 12 months in any part of the body [##REF##11552874##14##]. Respondents who indicated experience of WRMDs symptoms in any of the body areas were asked to choose the areas of disorder that they considered as the most significant and asked further questions about their disorder. Previous studies have shown the Nordic questionnaire to be valid for assessing WRMDs [##REF##17353966##20##,##UREF##9##21##]</p>", "<p>The data for this study were analyzed using SPSS version 10 with the alpha level set at 0.05. Items that represented continuous variables such as age and years of experience as a physiotherapist were converted into categorical variables in accordance with cut-offs identified in previous related studies [##UREF##2##7##,##REF##10758519##8##,##REF##14712849##11##]. The cut-off for Body Mass Index (BMI) was however based on the WHO (2000) classification [##UREF##10##22##] for normal weight, overweight and obesity. Descriptive statistics of percentages and inferential statistics of χ²were used as appropriate. Fisher's exact tests were performed in cases where there were fewer than 5 expected counts in a cell. Alpha level was set at 0.05.</p>" ]

[ "<title>Results</title>", "<p>Two hundred and seventeen questionnaires were distributed but only 126 were returned, thus giving a percentage response of 58.1%. The 126 respondents comprising (80) 63.5% males and (46) 36.5% females had a mean age of 33.7 ± 6.8 years and body mass index (BMI) of 24.1 ± 3.5 kg/m². Seventy one (56.3%) of the respondents were working in secondary health institutions while 122 (96.8%) were working full-time. Table ##TAB##0##1## shows the detailed demographic characteristics of the respondents.</p>", "<title>Prevalence</title>", "<p>One hundred and fifteen physiotherapists (91.3%) reported experiencing WRMDs during the 12 months preceding the study. The 12- month prevalence of WRMDs in different body parts compared to the findings of previous similar studies is presented in Table ##TAB##1##2##. The low back was the most common site of disorders (69.8%) while the elbow joint (5.6%) was the least affected body part.</p>", "<p>Table ##TAB##2##3## presents the respondents' characteristics. Gender (p = 0.007) and BMI (p = 0.04) were the only factors that differed between those who did or did not report a WRMD.</p>", "<title>Onset of Disorder</title>", "<p>Fifty-eight (46.0%) of the respondents first experienced their WRMDs within the first five years of graduation, while only 2 (1.6%) had it more than 15 years after graduation (Figure ##FIG##0##1##). The onset of WRMDs was gradual in 83 (65.9%) of the respondents, sudden in 30 (23.8%) and resulted from an accident in 2 (1.6%). (Data not shown).</p>", "<title>Effects of WRMDs</title>", "<p>Seventy two (62.6%) of the physiotherapists have changed/modified their treatment for the patients as a result of WRMDs, but 99 (88.4%) did not change their area of practice and 94 (87.0%) did not leave the profession due to their WRMDs. (Data not shown).</p>", "<title>Work Factors</title>", "<p>Respondents with WRMDs were asked to consider 17 work factors that have been identified by previous studies [##REF##14712849##11##,##REF##17572827##12##] and indicate the extent to which each contributed to the occurrence of their WRMDs. A likert scale ranging from 1 (indicating not important) to 4 (indicating major importance) was used to ascertain which job risk factor was important in the occurrence of WRMDs. For each work factor the responses were dichotomized into categories of 'not important' (1 and 2) and 'important' (3 and 4). Results were obtained by expressing important responses as percentages of the total responses for each work factor. Eight of the work factors were chosen by 50% or more of the respondents as being important. The two most important work factors commonly identified by physiotherapists were treating large number of patients in a day (83.5%), and working in the same position for long (71.3%). Working with confused or agitated patients (28.9%) and reaching or working away from the body (17.4%) were cited as the most unimportant work factors (Table ##TAB##3##4##).</p>", "<title>Coping Strategies</title>", "<p>The coping strategies adopted by physiotherapists with WRMDs in Nigeria are shown in Table ##TAB##4##5##. The two most commonly adopted coping strategies were therapists modifying their positions or the positions of their patients (64.3%) and selecting techniques that will not aggravate or provoke their discomfort (47.0%). The two least adopted coping strategies were the use of electrotherapy instead of manual therapy (9.6%) and warming up and stretching before performing manual technique (5.2%).</p>" ]

[ "<title>Discussion</title>", "<p>The aim of this study was to investigate the 12- month prevalence and work factors of work related musculoskeletal disorders (WRMDs) among physiotherapists in Nigeria. The percentage response for this study was 58.1% which is consistent with responses in similar studies from Turkey [##UREF##2##7##] (59%) and Australia [##REF##11552874##14##] (53%) but lower than the 74% reported by Glover et al [##UREF##4##13##] in the United Kingdom and the 80% by Bork et al [##REF##8710962##5##] in the United States of America (USA). Although, Glover et al [##UREF##4##13##] utilized the effect of incentives and the influence of the professional association to maximize the response to their study, the relative lower response in our study when compared to others may suggest a lukewarm predisposition to research participation among physiotherapists in Nigeria.</p>", "<p>Our finding that there were more male than female physiotherapists in the survey is a reflection of the population from which our sample was drawn. This finding is contrary to the findings from previous studies that reflected more female than male physiotherapists [##REF##8710962##5##, ####REF##10416574##6##, ##UREF##2##7##, ##REF##10758519##8####10758519##8##,##UREF##4##13##,##REF##11552874##14##]. This result is understandable since unlike in Europe and America, the physiotherapy profession in Nigeria is male dominated. Indeed, 62.3% of the registered physiotherapists in Nigeria are males [##UREF##11##23##]. The gender distribution of the respondents in our study is hence largely representative of the population of physiotherapists in Nigeria.</p>", "<p>We observed a significantly higher prevalence of WRMDs among female physiotherapists with all the female physiotherapists in comparison to 86.3% of the males reporting WRMDs. Our finding is consistent with findings from previous related studies [##REF##8710962##5##,##UREF##2##7##,##UREF##4##13##]. Borke et al [##REF##8710962##5##] implicated the female gender as a potential risk factor for the occurrence of WRMDs while Glover et al [##UREF##4##13##] reported a higher prevalence of work related low back pain, neck pain, shoulder pain and wrist/hand pain among female physiotherapists. A higher but not statistically significant prevalence of WRMDs has also been reported among female Turkish physiotherapists [##UREF##2##7##]. Cromie et al [##REF##10758519##8##] however reported a higher prevalence of WRMDs among male physiotherapists and attributed their finding to a greater usage of mobilizations and manipulations by male physiotherapists than their female counterparts in their study. It has been suggested that the usually higher prevalence of WRMDs in female physiotherapists may be related to their height and body weight which put them at a disadvantage during patients' treatment and/or transfer [##REF##8710962##5##]. Also, women do have a higher prevalence than men for many upper extremity musculoskeletal disorders, even after controlling for cofounders such as age or work factors [##REF##15204301##24##]. It is interesting however that the prevalence of WRMDs in our study was higher in individuals with normal body weight (94.6%) than obese ones (71.4%).</p>", "<p>The 12- month prevalence of WRMDs among Nigerian physiotherapists was found to be 91.3%. This prevalence is higher than the 12-month prevalence of 58% reported by Glover et al [##UREF##4##13##], 40% by West and Gardner [##REF##11552874##14##], 61% by Bork et al [##REF##8710962##5##] and 62.5% by Cromie et al [##REF##10758519##8##]. The only comparable findings in the literature were the life time career prevalence of 91% and 85% reported by Cromie et al [##REF##10758519##8##] and Salik and Ozcan [##UREF##2##7##], respectively. The higher 12-month prevalence found in our study suggests that physiotherapy practice in Nigeria highly predisposes to WRMDs. This may be a reflection of the conditions under which physiotherapists practice in Nigeria. Physiotherapy practice in Nigeria, like in many other developing countries is largely bedeviled by unwholesome work settings, understaffing and lack of appropriate equipments including those as basic as standard plinths. This is beside the influence of peculiar cultural values of physiotherapists such as skills, relationships with patients and attitudes of caring and working hard that have been opined as making it difficult for physiotherapists to do their job in a way that minimizes the risk of WRMDs [##REF##11991799##18##]. It should be noted however that the extent to which work contributed to the etiology of musculoskeletal disorders in participants in our study cannot be readily ascertained and is hence largely debatable. It is plausible that some of the physiotherapists in our study probably misconstrued all musculoskeletal disorders as WRMDs regardless of whether these were caused by work or not since the consequence of musculoskeletal disorders and WRMDs in terms of work absenteeism may be similar.</p>", "<p>In this study, the low back was reported as the most common site of WRMDs among Nigerian physiotherapists, with a 12- month prevalence of 69.8%. Internationally, the prevalence of work-related low back pain ranged between 22% and 74% [##REF##10758519##8##,##UREF##4##13##,##UREF##12##25##]. Our finding is consistent with those of previous studies that have overwhelmingly implicated low back as the body part most commonly affected by WRMDs among physiotherapists [##REF##8710962##5##,##UREF##2##7##, ####REF##10758519##8##, ##REF##9279486##9####9279486##9##,##UREF##4##13##,##REF##11552874##14##]. In the United Kingdom, the 12-month prevalence of work-related low back pain among physiotherapists was found to be 22% [##UREF##4##13##], while the prevalence varied between 22% [##REF##11552874##14##] and 62.5% [##REF##10758519##8##] in Australia. Bork et al [##REF##8710962##5##] found the annual prevalence of WRMDs low back pain to be 45% in the U.S.A. Our finding may be a further reflection of the overall picture of the poor conditions of practice that may cause high prevalence of WRMDs among Nigerian physiotherapists.</p>", "<p>The majority of the physiotherapists in this study were found to have experienced their first episode of WRMDs within five years of graduation. This is similar to the findings of the majority of studies on WRMDs among physiotherapists [##REF##8710962##5##,##REF##10416574##6##,##REF##10758519##8##,##REF##9279486##9##,##UREF##4##13##,##REF##11552874##14##]. We also observed the prevalence of WRMDs to be higher among physiotherapists that were younger than 30 years of age. This finding is consistent with those of Salisk and Ozcka [##UREF##2##7##] in Turkey, Glover et al [##UREF##4##13##] in the United Kingdom, West and Gardner [##REF##11552874##14##] in Australia, Mierzejewski and Kumar [##REF##9279486##9##] in Canada and Bork et al [##REF##8710962##5##] in the United States of a higher prevalence of WRMDs among physiotherapists younger than 30 years of age. Our finding in this regard is particularly important when viewed against the background of a higher mean age at graduation of physiotherapists in Nigeria compared to their counterparts in Europe, USA and Australia. Our finding hence suggests that physiotherapists in Nigeria may enjoy a shorter WRMD-free career life than their counterparts in other parts of the world. However, findings relating to the onset of injury need to be viewed with caution as it may be very difficult to ascertain the onset of WRMDs without a substantial recall bias.</p>", "<p>The work factors commonly identified by physiotherapists in this study as contributing to the occurrence of their WRMDs in decreasing order of importance were: treating a large number of patients in one day, working in the same position for long and lifting or transferring dependent patients, and performing manual therapy techniques. Previous studies have similarly identified treating large number of patients in a day and working in the same position for long periods of time [##REF##8710962##5##,##REF##10758519##8##,##REF##11552874##14##], lifting or transferring dependent patients [##REF##10416574##6##, ####UREF##2##7##, ##REF##10758519##8####10758519##8##,##UREF##6##16##] and performing manual therapy techniques [##REF##11552874##14##] as the work factors most commonly found to cause WRMDs among physiotherapists. In our study, physiotherapists selected reaching or working away from the body and working with confused or agitated patients as the least important work factors to the occurrence of their WRMDs. It should however be noted that the work factors identified in our study were not specific to individual musculoskeletal disorder but rather cut across various musculoskeletal disorders. This is an important limitation of our study given that previous related studies have submitted that work factors are to some extent specific to individual musculoskeletal disorders. Thus, mobilization and manipulation have been identified as work factors to the occurrence of upper limb, neck, and upper back pain [##REF##10758519##8##]; while performing the same task over and over [##UREF##4##13##] and lifting and transferring dependent patients [##REF##11552874##14##] have been reported to be related to the occurrence of low back symptoms However, since physiotherapists in our study self- reported the work factors, their responses might have been a reflection of their belief rather than the actual contributions of the work factors to their disorder.</p>", "<p>The most commonly adopted coping strategies among physiotherapists in our study were therapists modifying their position or the position of their patients, therapists selecting techniques that will not aggravate or provoke their discomfort, and therapists adjusting bed or plinth height. This finding is similar to that of Glover et al [##UREF##4##13##], which reported the four most important preventive strategies commonly adopted by physiotherapists in response to sustaining musculoskeletal disorder at work as: therapists adjusting plinth or bed height, therapists modifying their position or the position of their patients, obtaining assistance when handling heavy patients, and ceasing a patient's treatment if such treatment aggravates or provokes their symptoms. Further, most of the physiotherapists in our study would also change or modify a patient treatment in the face of their WRMDs thus suggesting that physiotherapists in Nigeria who had experienced WRMDs might have sometimes selected treatment methods for reasons other than the needs of their patients – their own comfort. This attitude may not augur well for the application of the principle of altruistic care needed for effective patient treatment and optimal recovery.</p>", "<p>Despite the high prevalence of WRMDs among Nigerian physiotherapists, we found that the majority of the physiotherapists did not leave the profession and only a few changed their area of practice/specialty. Our finding is consistent with those of majority of studies that found that few physiotherapists will change their areas of practice [##UREF##2##7##,##REF##10758519##8##,##UREF##4##13##] and majority will not leave the profession [##REF##8710962##5##,##REF##10758519##8##,##REF##9279486##9##,##UREF##4##13##,##REF##11552874##14##] as a result of WRMDs. While previous studies which were conducted mostly in developed countries explained their findings in the context of 'survival bias' developed during career [##REF##8710962##5##,##REF##11552874##14##], adaptation to injury [##UREF##4##13##], flexibility of work change within profession [##REF##10758519##8##,##REF##11552874##14##] and the culture of physical therapy [##REF##11991799##18##], our finding may be suggestive of limited career-change option among Nigerian physiotherapists. This view is buttressed by our finding that 96.5% of Nigerian physiotherapists work full time and that only about a quarter had postgraduate training- an option which might have enhanced their choice of area of practice/specialty. Further, the economic vagaries and palpable financial insecurity in Nigeria may actually make physiotherapists in Nigeria to stay put within the profession despite its attendant high risk of WRMDs. The real reason or reasons why Nigerian physiotherapists do not leave the profession in spite of the high rate of WRMDs would however need to be further investigated by future studies.</p>", "<title>Limitations</title>", "<p>This study is limited by the sampling technique employed, as the non-probability sampling employed in our study may prevent generalization of our results. We could not randomize because the list of registered physiotherapists in Nigeria as contained in the Nigerian Medical Rehabilitation Therapists Bulletin [##UREF##8##19##] did not reflect the addresses and workplaces of registered members. We however tried to minimize this effect by administering our survey in all the 26 accredited tertiary and secondary health institutions in the six geopolitical zones and the federal capital territory of Nigeria, in the hope that our sample will reflect the geopolitical diversity and heterogeneity of Nigeria.</p>", "<p>Like all other cross-sectional studies involving recall, our respondents might have given vague answers to questions asked in this study as they might not have remembered the information requested of them easily. In an attempt to curtail the influence of this in our study, we restricted our survey to a 12-month prevalence which would have tasked the participants' memory lesser than the conventional lifetime and career prevalence. We however defined WRMDs as any pain or discomfort that lasted more than three days in the last 12 months in the hope that the respondents would be able to remember significant periods of their discomfort. We equally appreciate that work may only be a contributory factor in the etiology of musculoskeletal disorders among workers and that it may be difficult to distinguish between WRMDs and musculoskeletal disorders since their consequences in response to work demands may be similar. It is thus plausible that some of the respondents in our study perceived their musculoskeletal disorders as WRMDs regardless of whether they were caused by work or not.</p>", "<p>Despite these limitations, our study has provided for the first time data on the prevalence and work factors of work-related musculoskeletal disorders among physiotherapists in Nigeria. It has also underscored the need for further studies on the behavioural consequences of WRMDs and career attitudes of Nigerian Physiotherapists to them.</p>" ]

[ "<title>Conclusion</title>", "<p>This study reveals that the 12-month prevalence of WRMDs among physiotherapists in Nigeria is higher than most values reported for their counterparts around the world, but reflected similar work risk factors and coping strategies. Further studies on the consequences of WRMDs and why Nigerian physiotherapists remain in the profession despite the inherent high prevalence of WRMDs are imperative and hence suggested.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Physiotherapists are known to be prone to Work- related musculoskeletal disorders (WRMDs) but its prevalence among physiotherapists in Nigeria has not been reported. This study investigated the prevalence and work factors of WRMDs among physiotherapists in Nigeria.</p>", "<title>Methods</title>", "<p>A cross- sectional survey was administered to physiotherapists in different parts of Nigeria using a 2- part questionnaire with items adopted from questionnaires used for similar studies around the world. Two hundred and seventeen copies of the questionnaire were distributed for self administration but 126 physiotherapists returned completed surveys for a 58.1% response. The data were analyzed using SPPS version 10 at alpha level of 0.05. Descriptive statistics of frequency and percentages and inferential statistics of <italic>x</italic>²were used as appropriate for data analysis.</p>", "<title>Results</title>", "<p>Reported 12- month prevalence of WRMDs among Nigerian physiotherapists was 91.3%. Prevalence of WRMDs was significantly higher in female physiotherapists (p = 0.007) and those with lower body mass index (p = 0.045). The low back (69.8%) was the most commonly affected body part, followed by the neck (34.1%). Fifty percent of the physiotherapists first experienced their WRMDs within five years of graduation and the highest prevalence (61.7%) was found among physiotherapists younger than 30 years. Treating large number of patients in a day was cited by most (83.5%) of the respondents as the most important work factor for their WRMDs. The most commonly adopted coping strategy identified was for the therapists to modify their position and/or the patient's position (64.3%). Majority of the respondents (87.0%) did not leave the profession but 62.6% changed and/or modified their treatment because of their WRMDs.</p>", "<title>Conclusion</title>", "<p>The prevalence of WRMDs among physiotherapists in Nigeria is higher than most values reported for their counterparts around the world. The coping strategies and work factors of WRMDs among Nigerian physiotherapists are mostly similar to those of their counterparts elsewhere.</p>" ]

[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>BOA conceptualized and designed the study, was involved in data interpretation and read the final manuscript. AK was involved in data acquisition and drafting of the manuscript. AL analyzed the data and revised the manuscript for important intellectual content. All authors read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2474/9/112/prepub\"/></p>", "<title>Supplementary Material</title>" ]

[ "<title>Acknowledgements</title>", "<p>The authors acknowledge the contribution of the Nigeria Society of Physiotherapy (NSP) that mobilized its members to participate in this study and the suggestions of physiotherapists that attended the 2006 Scientific Conference of the NSP where a platform presentation of the work was made. We also appreciate the editorial assistance of Dr. A.O.Akinpelu of the Department of Physiotherapy, College of Medicine, University of Ibadan, Nigeria.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Onset of work related musculoskeletal disorders among participants.</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Socio-demographic characteristics of participants</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Characteristics</td><td/></tr></thead><tbody><tr><td align=\"left\">Age (yrs) (n = 126)</td><td/></tr><tr><td align=\"left\"> Mean (SD)</td><td align=\"left\">33.7 (6.8)</td></tr><tr><td align=\"left\"> Range</td><td align=\"left\">22–57</td></tr><tr><td align=\"left\">Height (m) (n = 126)</td><td/></tr><tr><td align=\"left\"> Mean (SD)</td><td align=\"left\">1.68 (0.09)</td></tr><tr><td align=\"left\"> Range</td><td align=\"left\">1.45–1.92</td></tr><tr><td align=\"left\">Weight (Kg) (n = 126)</td><td/></tr><tr><td align=\"left\"> Mean (SD)</td><td align=\"left\">68.1 (10.3)</td></tr><tr><td align=\"left\"> Range</td><td align=\"left\">50–100</td></tr><tr><td align=\"left\">BMI* (kg/m2) (n = 126)</td><td/></tr><tr><td align=\"left\"> Mean (SD)</td><td align=\"left\">24.1 (3.5)</td></tr><tr><td align=\"left\"> Range</td><td align=\"left\">18.84–42.42</td></tr><tr><td align=\"left\">Years of PT Experience (yrs) (n = 126)</td><td/></tr><tr><td align=\"left\"> Mean (SD)</td><td align=\"left\">8.43 (6.1)</td></tr><tr><td align=\"left\"> Range</td><td align=\"left\">1–32</td></tr><tr><td align=\"left\">Gender (n = 126)</td><td/></tr><tr><td align=\"left\"> Female</td><td align=\"left\">46 (36.5%)</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">80 (63.5%)</td></tr><tr><td align=\"left\">Work Status (n = 126)</td><td/></tr><tr><td align=\"left\"> Full Time</td><td align=\"left\">122 (96.8%)</td></tr><tr><td align=\"left\"> Part Time</td><td align=\"left\">4 (3.2%)</td></tr><tr><td align=\"left\">Work Setting (n = 126)</td><td/></tr><tr><td align=\"left\"> Tertiary</td><td align=\"left\">54 (42.9%)</td></tr><tr><td align=\"left\"> Secondary</td><td align=\"left\">71 (56.3%)</td></tr><tr><td align=\"left\"> No Response</td><td align=\"left\">1 (0.8%)</td></tr><tr><td align=\"left\">Postgraduate Training (n = 126)</td><td/></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">32 (25.4%)</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">94 (74.6%)</td></tr><tr><td align=\"left\">Ergonomic Training (n = 126)</td><td/></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">70 (55.6%)</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">47 (37.3%)</td></tr><tr><td align=\"left\"> No Response</td><td align=\"left\">9 (7.1%)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Comparison of 12-month prevalence by body parts from different studies.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Body areas (n)</td><td align=\"left\">Adegoke et al<break/>Nigeria (%)</td><td align=\"left\">Glover et al [##REF##14712849##11##]<break/>United Kingdom (%)</td><td align=\"left\">West and Gardner [##REF##17572827##12##]<break/>Australia (%)</td></tr></thead><tbody><tr><td align=\"left\">Low Back (88)</td><td align=\"left\">69.8</td><td align=\"left\">37.2</td><td align=\"left\">22.0</td></tr><tr><td align=\"left\">Neck (43)</td><td align=\"left\">31.1</td><td align=\"left\">25.7</td><td align=\"left\">20.0</td></tr><tr><td align=\"left\">Shoulders (28)</td><td align=\"left\">22.2</td><td align=\"left\">14.8</td><td align=\"left\">10.0</td></tr><tr><td align=\"left\">Wrists/Hands (26)</td><td align=\"left\">20.6</td><td align=\"left\">12.5</td><td align=\"left\">14.0</td></tr><tr><td align=\"left\">Knees (20)</td><td align=\"left\">15.9</td><td align=\"left\">7.8</td><td align=\"left\">3.0</td></tr><tr><td align=\"left\">Upper Back (18)</td><td align=\"left\">14.3</td><td align=\"left\">18.4</td><td align=\"left\">11.0</td></tr><tr><td align=\"left\">Thumbs (14)</td><td align=\"left\">11.1</td><td align=\"left\">17.8</td><td align=\"left\">-</td></tr><tr><td align=\"left\">Ankles/Feet (12)</td><td align=\"left\">9.5</td><td align=\"left\">4.1</td><td align=\"left\">2.0</td></tr><tr><td align=\"left\">Hips/Thighs (8)</td><td align=\"left\">6.3</td><td align=\"left\">4.8</td><td align=\"left\">3.0</td></tr><tr><td align=\"left\">Elbow/Forearm (7)</td><td align=\"left\">5.6</td><td align=\"left\">5.5</td><td align=\"left\">3.3</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Prevalence of WRMDs according to demographic characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Characteristics</td><td align=\"center\" colspan=\"2\">WRMD</td><td align=\"center\" colspan=\"2\">No WRMD</td><td align=\"left\">Chi Statistics</td></tr><tr><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td><td/></tr><tr><td/><td align=\"left\">n</td><td align=\"left\">(%)</td><td align=\"left\">n</td><td align=\"left\">(%)</td><td/></tr></thead><tbody><tr><td align=\"left\">Age Group (yrs)</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> &lt; 30</td><td align=\"left\">44</td><td align=\"left\">(93.6)</td><td align=\"left\">3</td><td align=\"left\">(6.4)</td><td/></tr><tr><td align=\"left\"> &gt; 30</td><td align=\"left\">71</td><td align=\"left\">(89.9)</td><td align=\"left\">8</td><td align=\"left\">(10.1)</td><td align=\"left\">0.52 (p = 0.536)</td></tr><tr><td align=\"left\">BMI Group</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> 18.5–24.9</td><td align=\"left\">88</td><td align=\"left\">(94.6)</td><td align=\"left\">5</td><td align=\"left\">(5.4)</td><td/></tr><tr><td align=\"left\"> 25.0–29.9</td><td align=\"left\">22</td><td align=\"left\">(84.6)</td><td align=\"left\">4</td><td align=\"left\">(15.4)</td><td/></tr><tr><td align=\"left\"> &gt; 30</td><td align=\"left\">5</td><td align=\"left\">(71.4)</td><td align=\"left\">2</td><td align=\"left\">(28.6)</td><td align=\"left\">6.22 (p = 0.045)*</td></tr><tr><td align=\"left\">Years of PT experience (yrs)</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> 1–5</td><td align=\"left\">43</td><td align=\"left\">(87.8)</td><td align=\"left\">6</td><td align=\"left\">(12.2)</td><td/></tr><tr><td align=\"left\"> 6–15</td><td align=\"left\">54</td><td align=\"left\">(94.7)</td><td align=\"left\">3</td><td align=\"left\">(5.3)</td><td/></tr><tr><td align=\"left\"> &gt; 16</td><td align=\"left\">18</td><td align=\"left\">(90.0)</td><td align=\"left\">2</td><td align=\"left\">(10.0)</td><td align=\"left\">1.67 (p = 0.436)</td></tr><tr><td align=\"left\">Gender</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Female</td><td align=\"left\">46</td><td align=\"left\">(100)</td><td align=\"left\">0</td><td align=\"left\">(0.0)</td><td/></tr><tr><td align=\"left\"> Male</td><td align=\"left\">69</td><td align=\"left\">(86.3)</td><td align=\"left\">11</td><td align=\"left\">(13.8)</td><td align=\"left\">6.93 (p = 0.007)*</td></tr><tr><td align=\"left\">Work Status</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Full Time</td><td align=\"left\">111</td><td align=\"left\">(91.0)</td><td align=\"left\">11</td><td align=\"left\">(9.0)</td><td/></tr><tr><td align=\"left\"> Part Time</td><td align=\"left\">4</td><td align=\"left\">(100.0)</td><td align=\"left\">0</td><td align=\"left\">(0.0)</td><td align=\"left\">0.39 (p = 1.000)</td></tr><tr><td align=\"left\">Work Setting</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Tertiary</td><td align=\"left\">47</td><td align=\"left\">(87.0)</td><td align=\"left\">7</td><td align=\"left\">(13.0)</td><td/></tr><tr><td align=\"left\"> Secondary</td><td align=\"left\">67</td><td align=\"left\">(94.4)</td><td align=\"left\">4</td><td align=\"left\">(5.6)</td><td align=\"left\">2.05 (p = 0.205)</td></tr><tr><td align=\"left\">Ergonomic Training</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">63</td><td align=\"left\">(96.9)</td><td align=\"left\">7</td><td align=\"left\">(13.0)</td><td/></tr><tr><td align=\"left\"> No</td><td align=\"left\">44</td><td align=\"left\">(93.6)</td><td align=\"left\">3</td><td align=\"left\">(6.4)</td><td align=\"left\">0.69 (p = 0.648)</td></tr><tr><td align=\"left\">Postgraduate Training</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">29</td><td align=\"left\">(90.6)</td><td align=\"left\">3</td><td align=\"left\">(9.4)</td><td/></tr><tr><td align=\"left\"> No</td><td align=\"left\">86</td><td align=\"left\">(91.5)</td><td align=\"left\">8</td><td align=\"left\">(8.5)</td><td align=\"left\">0.02 (p = 1.000)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Work factors that physiotherapists identified as contributors to WRMDs</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Risks</td><td align=\"left\">Number of Respondents</td><td align=\"left\">(%)</td></tr></thead><tbody><tr><td align=\"left\">Treating large number of patients in a day</td><td align=\"left\">96/115</td><td align=\"left\">(83.5)</td></tr><tr><td align=\"left\">Working in same position for long</td><td align=\"left\">82/115</td><td align=\"left\">(71.3)</td></tr><tr><td align=\"left\">Lifting/transferring dependent patients</td><td align=\"left\">78/115</td><td align=\"left\">(67.8)</td></tr><tr><td align=\"left\">Performing manual orthopaedic techniques</td><td align=\"left\">78/115</td><td align=\"left\">(67.8)</td></tr><tr><td align=\"left\">Working in awkward or cramped position</td><td align=\"left\">73/113*</td><td align=\"left\">(64.6)</td></tr><tr><td align=\"left\">Bending or twisting your back in awkward way</td><td align=\"left\">72/115</td><td align=\"left\">(62.6)</td></tr><tr><td align=\"left\">Not having enough rest break during the day</td><td align=\"left\">71/115</td><td align=\"left\">(61.7)</td></tr><tr><td align=\"left\">Carrying, lifting or moving heavy materials or equipments</td><td align=\"left\">64/115</td><td align=\"left\">(55.7)</td></tr><tr><td align=\"left\">Continuing to work when injured</td><td align=\"left\">60/115</td><td align=\"left\">(52.2)</td></tr><tr><td align=\"left\">Performing same task over</td><td align=\"left\">60/115</td><td align=\"left\">(52.2)</td></tr><tr><td align=\"left\">Working at or near your physical limits</td><td align=\"left\">54/115</td><td align=\"left\">(46.9)</td></tr><tr><td align=\"left\">Unanticipated sudden movement or fall by patients</td><td align=\"left\">47/115</td><td align=\"left\">(40.9)</td></tr><tr><td align=\"left\">Work scheduling (overtime, irregular shift, length of workday)</td><td align=\"left\">5/114*</td><td align=\"left\">(3.5)</td></tr><tr><td align=\"left\">Assisting patients during gait activities</td><td align=\"left\">41/115</td><td align=\"left\">(35.7)</td></tr><tr><td align=\"left\">Inadequate training in injury prevention</td><td align=\"left\">34/115</td><td align=\"left\">(29.6)</td></tr><tr><td align=\"left\">Working with confused or agitated patients</td><td align=\"left\">33/114*</td><td align=\"left\">(28.9)</td></tr><tr><td align=\"left\">Reaching or working away from the body</td><td align=\"left\">20/115</td><td align=\"left\">(17.4)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Coping strategies used by physiotherapists with WRMDs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Strategies</td><td align=\"center\" colspan=\"8\">Percentages</td></tr><tr><td/><td colspan=\"8\"><hr/></td></tr><tr><td/><td align=\"center\" colspan=\"2\">Almost always</td><td align=\"center\" colspan=\"2\">Sometimes</td><td align=\"center\" colspan=\"2\">Almost Never</td><td align=\"center\" colspan=\"2\">No Response</td></tr><tr><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td></tr><tr><td/><td align=\"center\">Number</td><td align=\"center\">(%)</td><td align=\"center\">Number</td><td align=\"center\">(%)</td><td align=\"center\">Number</td><td align=\"center\">(%)</td><td align=\"center\">Number</td><td align=\"center\">(%)</td></tr></thead><tbody><tr><td align=\"left\">I modify patient's position/my position</td><td align=\"left\">74/115</td><td align=\"left\">(64.3)</td><td align=\"left\">31/115</td><td align=\"left\">(27.0)</td><td align=\"left\">3/115</td><td align=\"left\">(2.6)</td><td align=\"left\">7/115</td><td align=\"left\">(6.1)</td></tr><tr><td align=\"left\">I select techniques that will not aggravate or provoke my discomfort</td><td align=\"left\">54/115</td><td align=\"left\">(47.0)</td><td align=\"left\">38/115</td><td align=\"left\">(33.0)</td><td align=\"left\">15/115</td><td align=\"left\">(3.0)</td><td align=\"left\">8/115</td><td align=\"left\">(7.0)</td></tr><tr><td align=\"left\">I adjust plinth/bed height before treating a patient</td><td align=\"left\">45/115</td><td align=\"left\">(39.1)</td><td align=\"left\">35/115</td><td align=\"left\">(30.4)</td><td align=\"left\">27/115</td><td align=\"left\">(23.5)</td><td align=\"left\">8/115</td><td align=\"left\">(7.0)</td></tr><tr><td align=\"left\">I pause regularly so I can stretch and change posture</td><td align=\"left\">43/115</td><td align=\"left\">(37.4)</td><td align=\"left\">44/115</td><td align=\"left\">(38.3)</td><td align=\"left\">22/115</td><td align=\"left\">(19.1)</td><td align=\"left\">6/115</td><td align=\"left\">(5.2)</td></tr><tr><td align=\"left\">I get someone else to help me handle a heavy patient</td><td align=\"left\">38/115</td><td align=\"left\">(33.1)</td><td align=\"left\">50/115</td><td align=\"left\">(43.5)</td><td align=\"left\">22/115</td><td align=\"left\">(19.1)</td><td align=\"left\">5/115</td><td align=\"left\">(4.3)</td></tr><tr><td align=\"left\">I stop treatment if it causes or aggravate my discomfort</td><td align=\"left\">28/115</td><td align=\"left\">(24.3)</td><td align=\"left\">50/115</td><td align=\"left\">(43.5)</td><td align=\"left\">29/115</td><td align=\"left\">(25.2)</td><td align=\"left\">8/115</td><td align=\"left\">(7.0)</td></tr><tr><td align=\"left\">I use different part of my body to administer a manual technique</td><td align=\"left\">19/115</td><td align=\"left\">(16.5)</td><td align=\"left\">39/115</td><td align=\"left\">(33.9)</td><td align=\"left\">49/115</td><td align=\"left\">(42.6)</td><td align=\"left\">8/115</td><td align=\"left\">(7.0)</td></tr><tr><td align=\"left\">I use electrotherapy instead of manual techniques to avoid stressing an injury</td><td align=\"left\">11/115</td><td align=\"left\">(9.6)</td><td align=\"left\">45/115</td><td align=\"left\">(39.1)</td><td align=\"left\">51/115</td><td align=\"left\">(44.3)</td><td align=\"left\">8/115</td><td align=\"left\">(7.0)</td></tr><tr><td align=\"left\">I warm up and stretch before performing a manual technique</td><td align=\"left\">6/115</td><td align=\"left\">(5.2)</td><td align=\"left\">27/115</td><td align=\"left\">(23.5)</td><td align=\"left\">75/115</td><td align=\"left\">(65.2)</td><td align=\"left\">7/115</td><td align=\"left\">(6.1)</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Occupational Health and the Practice of Physiotherapy Questionnaire.</p></caption></supplementary-material>" ]

[ "<table-wrap-foot><p>Values are means (SD), range or percentages.</p><p>*- BMI: Body Mass Index</p></table-wrap-foot>", "<table-wrap-foot><p>WRMDs: Work-Related Musculoskeletal Disorders</p><p>BMI: Body Mass Index</p><p>PT: Physical Therapy</p><p>*: indicates significance between group differences in the observed proportions for each variable.</p></table-wrap-foot>", "<table-wrap-foot><p>*- Missing value due to no response by participants</p></table-wrap-foot>" ]

[ "<graphic xlink:href=\"1471-2474-9-112-1\"/>" ]

[ "<media xlink:href=\"1471-2474-9-112-S1.doc\" mimetype=\"application\" mime-subtype=\"msword\"><caption><p>Click here for file</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>We present an update about the use of a special type of parametric designs in fMRI research that can be very useful in investigations involving natural and multi-featured stimuli such as pictures or words. This method has already been developed by Büchel and colleagues [##REF##9740757##1##] but unfortunately it has not been used as frequently as it deserves. For this reason, we present a summary of the logic behind the use of parametric designs in fMRI research, discuss shortly its mathematical background and applicability, and present an empirical example where parametric regressors carry the most relevant modulation of the fMRI signal.</p>", "<p>In several fields of neurocognition, stimuli can be assigned to experimental conditions so that they (i) are homogeneous within each cell of an experimental design and (ii) differ only with regard to a single aspect across the different levels of an experimental factor. Each statistical contrast unequivocally isolates therefore one and only one neurocognitive process. However, in the case of natural stimuli, such as pictures (e.g. kitchen utensils vs. garage tools) or written words (e.g. varying in length, number of syllables, frequency, neighbourhoods, regularities, consistencies etc.), the task of matching different groups of items for their attributes is particularly challenging, because there is often only a finite a number of stimuli to fit into each of the different cells of the experimental design that vary simultaneously in <italic>more than one </italic>feature. In these cases, different dimensions of stimuli can only be matched on average. Words, for instance, can vary in the number of letters, the frequency of occurrence, the number of lexical neighbours as well as the frequency of occurrence of orthographic or phonological sub-lexical units. Different words may have for example 1, 2 or 8 different lexical neighbours. Therefore, for each stimulus dimension (i.e. frequency of occurrence, number of lexical neighbours, etc.) there is a non-zero distance between each single item and the average for each of the different dimensions, characterizing the amount of variation within condition.</p>", "<p>Due to variation within condition, the statistics for the size of fMRI signal elicited by the different items pertaining to the very same condition may vary considerably. Consequently, the type-II error for detecting a difference in fMRI signal between two different conditions may be inflated. The main problem for the interpretation of the results of such an experiment is whether it is acceptable to consider the variation within condition as measurement error or not. If the variation within condition is small in comparison with the variation between different conditions, treating it as measurement error is not problematic. However, if the variation within a cell of the experimental design increases due to systematic variation in known dimensions of multi-featured stimuli, the validity of the whole study may be questioned.</p>", "<p>In the present paper we examine a method proposed by Büchel and colleagues [##REF##9740757##1##] for dealing with systematic variation between items. The method involves the definition of parametric regressors representing each of the several dimensions of complex stimuli. These parametric regressors absorb the systematic variation inherent in different dimensions of complex stimuli such as words, sentences or arithmetic problems, and allow for separating it from genuine measurement error. In the following, we will present the method, discuss its main applications, and present an example in which the variation between items (and their exact scaling properties) was the most relevant aspect of the experimental design.</p>", "<title>Overview of the method</title>", "<p>Activation <italic>Y</italic>_{<italic>ij </italic>}in a particular voxel can be described as in (1) for each replication <italic>i </italic>(for every <italic>i </italic>from 1 to <italic>p</italic>) of an experimental condition <italic>j </italic>(for every <italic>j </italic>from 1 to <italic>q</italic>):</p>", "<p></p>", "<p>having <italic>a </italic>as the intercept, <italic>X</italic>_{<italic>j </italic>}as a (continuous) parameter describing the present experimental design, <italic>β</italic>_{<italic>j </italic>}as the regression coefficient for the parameter <italic>X</italic>_{<italic>j </italic>}and <italic>ε</italic>_{<italic>ij </italic>}as residual error. The coefficient <italic>β</italic>_{<italic>j </italic>}describes the event- or block-specific expected BOLD-response under a given experimental condition <italic>j </italic>assuming that within an experimental condition the BOLD-response induced by event- or block-specific stimulation will be a constant. A corollary of this assumption is that variation in the BOLD response occurring within an experimental condition will be considered residual error.</p>", "<p>When stimuli in an experimental condition are sampled from a universe of natural items, some variation among items will always be present. An artificial increase of residual error <italic>ε </italic>due to variation in the BOLD-response produced by variation within condition contributes negatively to the sensitivity of the fMRI design. Importantly, when the variance among items not only represents a confounding factor but genuine scaling properties of stimulus features, it is mandatory to deal with them appropriately by modelling this variance within conditions.</p>", "<p>Parametric modelling always allows for the description of variation in the event- or block specific BOLD-response, when the source (or sources) of variation is known <italic>a priori </italic>and can be specified numerically as parametric regressor. Importantly, the variation within conditions may be due not only to one single stimulus feature, but rather be due to two or more features. In this case, for each of the dimensions of multi-featured stimuli a regressor can be defined, which absorbs the contribution of that dimension for the variation within a given experimental condition (but see the section on the limitations of this approach in the discussion, below). The specification of parametric regressors is given as follows: the parameter <italic>X</italic>_{<italic>j </italic>}described by a canonical hemodynamic function in common fMRI designs, which has exactly the same form across all replications <italic>i </italic>of a given experimental condition <italic>j</italic>, can be expressed as the average effect <italic>X</italic>_{<italic>j </italic>}of a predictor <italic>X </italic>on brain activation. Moreover, in parametric designs a second set of predictors may be complemented by a set of <italic>k </italic>dimensions (for every <italic>k </italic>from 1 to <italic>r</italic>) which are <italic>nested </italic>under each condition j and which absorb the variation within each condition. The full model presented in (2) contains a predictor representing the average effect of experimental condition <italic>j </italic>plus an additional parameter for each parametric regressor <italic>k </italic>considered. <italic>X</italic>_{<italic>ij</italic>1}, <italic>X</italic>_{<italic>ij</italic>2}, ... <italic>X</italic>_{<italic>ijk </italic>}... <italic>X</italic>_{<italic>ijr </italic>}contain the variation in each of k different stimulus dimensions. Note that parameters <italic>β</italic>_{<italic>jk </italic>}are hierarchically bound to the average parameter <italic>β</italic>_{<italic>j </italic>}and that the number of parameters <italic>β</italic>_{<italic>jk </italic>}associated with each average parameter <italic>β</italic>_{<italic>j </italic>}may differ. Therefore, (1) can be generalized by assuming a set of <italic>r </italic>&gt; 1 dimensions:</p>", "<p></p>", "<p>By entering parametric regressors in the fMRI design, the proportion of variance which can be accounted for by the variation within conditions is separated from the residual error <italic>ε</italic>_{<italic>ij</italic>}. This extension of the model presented in (1) has two consequences: (i) the statistical test on the significance of null-order parameter <italic>β</italic>_{<italic>j </italic>}will not be biased by variation within conditions, which can be explained by predictors <italic>β</italic>_{<italic>j</italic>1}to <italic>β</italic>_{<italic>jr</italic>}. (ii) Furthermore, the relevance of regression coefficients <italic>β</italic>_{<italic>j</italic>1}to <italic>β</italic>_{<italic>jr </italic>}may be assessed.</p>", "<p>The definition of parametric regressors with the single purpose of isolating variation within conditions as a confounding factor is trivial and has been employed regularly in fMRI research. The sole purpose of this application is to control for the impact of undesired sources of variance affecting statistics about the effects of interest. In this case, variation within conditions can be considered an effect of non-interest, the impact of which on the statistics can be partialled out from residual error.</p>", "<p>Nevertheless, parametric regressors also may be defined with the aim of directly investigating theoretical predictions with respect to the fMRI activation observed. In the following, we will concentrate on the advantages and limitations of such an application. In fact, parametric regressors make possible an investigation of the direction and actual scaling properties of variation of fMRI activation. Examination of the impact of quantitative regressors on the fMRI activation has been presented by Büchel and colleagues [##REF##9740757##1##]. In that study the authors defined one single parametric regressor and applied polynomial expansions (i.e. quadratic, cubic, etc.) to investigate non-linear relationships between the BOLD-response and this single experimental parameter. Here we use the method [##REF##9740757##1##] for two purposes: (i) instead of examining the impact of polynomial expansion of a single parametric predictor on fMRI activation, defining a set of predictors which, according to some theoretical expectation, may account for a significant amount of variability among trials produced by known and quantifiable properties of stimuli. Furthermore, the method is useful for (ii) assessing the significance of each single parameter for brain activation (i.e. one-sample t-tests) to the comparison between different models (i.e. statistics for two or more samples), which normally differ only with respect to one out of a set of parametric regressors. With this second type of application, we are able to statistically test hypotheses about the exact form of variation in fMRI activation.</p>", "<p>In the following example, we compare the model fit obtained for different numerical compressions of the predictors employed (i.e. logarithmic vs. linear scale). Results of these comparisons may help to determine the exact form of variation and the underlying rate of neuronal response to each of the different stimulus dimensions examined.</p>", "<title>An empirical example</title>", "<p>Numerical cognition provides a straightforward example for the usefulness of parametric regression. Numbers do naturally differ in their parametric properties, such as, for instance, their magnitude [##REF##18155348##2##, ####REF##15928716##3##, ##REF##11697933##4####11697933##4##]. Since no number shares the same magnitude with another, naturally there is variation in this dimension within every experimental condition in which different numbers are used. Number magnitude is assumed to be represented in the cortex around the intraparietal sulcus (IPS) [##REF##18155348##2##,##REF##15928716##3##]. Behavioral studies [##REF##2144576##5##] and a neural network model [##UREF##0##6##] have indicated that numerical distance is logarithmically compressed. Some recent single-cell recording studies reported that cells in pre-frontal and parietal cortex are tuned to specific magnitudes [##REF##16960005##7##, ####REF##12215649##8##, ##REF##12526780##9##, ##REF##15200715##10##, ##REF##15123797##11####15123797##11##]; their signal is best described by a logarithmically compressed scale [##REF##15200715##10##,##REF##15123797##11##]. Similar results have been obtained in fMRI studies [##REF##15504333##12##,##REF##17224409##13##]. Furthermore, studies on two-digit number processing have shown that participants may not be able to compare the magnitude of decade digits while ignoring the unit digits, even when the units are totally irrelevant for the task at hand [see [##REF##2144576##5##,##REF##11672709##14##,##UREF##1##15##] for a review, [##REF##16253524##16##,##REF##18022606##17##]].</p>", "<p>Given this theoretical background, we ask two empirical questions about the fMRI signal that can be investigated more precisely by means of parametric than by conventional categorical methods. The first question is whether the fMRI signal in the intraparietal cortex is better accounted for by the overall distance when participants are asked to choose the larger from two two-digit Arabic numbers or by the distance between decade digits. Since there are no two-digit numbers \"without\" a unit digit to serve as stimuli for a control condition, the only way to examine this problem empirically is to compare the BOLD-response evoked by overall distance with that evoked by decade distance (decade digit_{larger number}– decade digit_{smaller number}). If the statistical fit for overall distance is better than for decade distance, one may infer that the fMRI activation in the intraparietal cortex due to the contrast (overall distance &gt; decade distance) is associated with the processing of the overall magnitude of numbers.</p>", "<p>A second empirical question is whether the fMRI signal in intraparietal cortex is better accounted for by the logarithm of the distance than by the linear distance between two-digit numbers. This question has been investigated first in an fMRI study by Pinel and colleagues [##REF##11697933##4##]. These authors found that in six out of seven regions of interest the percent signal change dropped in accord with the logarithm of the distance between numbers rather than with the linear distance. Nevertheless, the authors examined the effect of logarithmic scaling on fMRI signal by splitting the range of distances into three arbitrary categories (i.e. small, medium and large distances) instead of treating distance as a continuum. This approach presents disadvantages in comparison with the modelling with parametric regressors: The method employed by Pinel and colleagues [##REF##11697933##4##] may fail to distinguish between the impact of decade distance and overall distance on fMRI signal (i.e. the first empirical question examined in the present example). This may have affected the determination of the exact spatial distribution of the neurons responding more strongly to the logarithmically compressed magnitude of numbers.</p>", "<p>In the following, we describe the results of the parametric analysis of an fMRI experiment examining the two empirical questions stated above.</p>", "<title>Procedure</title>", "<p>Fourteen male right-handed volunteers (mean age = 27, range 21–38 years) took part in the study after giving their written consent to the imaging protocol which has been approved by the local Ethics Committee of the Medical Faculty and is in compliance with the Helsinki Declaration. Participants had to select the larger number of a pair of two-digit Arabic numbers (range: 21–98) and press a key [for further details on the design of experiment and characteristics of the task as well as on behavioural data, see [##REF##16253524##16##], including supplementary material]. Overall distance, decade distance, unit distance and problem size were matched both absolutely and logarithmically between stimulus categories [##REF##16253524##16##]. The four digits chosen as units and decades of the two-digit number pair were always different. Furthermore, in the present study unit numbers were totally irrelevant for magnitude comparison since no within decade comparisons were included.</p>", "<title>MRI acquisition</title>", "<p>For each participant, a high-resolution T1-weighted anatomical scan was acquired with a Philips 1.5T Gyroscan MRI system (TR = 30 ms, matrix = 256 × 256 mm, 170 slices, voxel size = 0.86 × 0.86 × 2 mm; FOV = 220 mm, TE = 4.6 ms; flip angle = 30°). The anatomical scans were normalized using the standard T1 template of SPM2.</p>", "<title>fMRI acquisition</title>", "<p>Two functional imaging runs sensitive to blood oxygenation level-dependent (BOLD) contrast were recorded for each participant with a Philips 1.5T Gyroscan MRI system (T2*-weighted echo-planar sequence, TR = 2800 ms; TE = 50 ms; flip angle = 90°; FOV = 220 mm, 64 × 64 matrix; 30 slices, voxel size = 3.4 × 3.4 × 4 mm). In each run, 316 scans + 5 dummy scans were acquired. In a rapid event-related design, a total of 576 trials (480 experimental trials + 96 null events) were presented at a rate of 3 seconds. The fMRI time series was corrected for movement and unwarped in SPM2. Images were resampled every 4-mm and normalized to a standard EPI template using the sinc interpolation method. Moreover, functional images were co-registered with the normalized anatomical pictures. Finally, functional images were smoothed with an 8-mm Gaussian kernel.</p>", "<title>Parametric design</title>", "<p>We convolved brain activity for all experimental trials with the canonical hemodynamic response function (HRF) in <italic>a single experimental condition </italic>and defined three parametric regressors representing overall distance, decade distance, and problem size. The correlations between the different parameters and the in-line correlations (i.e. the correlations obtained after convolution with the HRF function) between the parametric regressors and the average hemodynamic response are shown in Table ##TAB##0##1## and Table ##TAB##1##2##, respectively. In order to scale the estimated regression parameters uniformly, the parametric regressors representing overall distance, decade distance, and problem size were standardized to a mean of 0 and a standard deviation of 1.</p>", "<p>In order to examine whether the fMRI signal in the intraparietal cortex can be better accounted for by the overall distance than by decade distance alone, we estimated two separate models. In one model, overall distance and problem size were entered as parametric regressors and, in a separate model, decade distance and problem size were entered as parametric regressors. A summary of the procedure for definition, estimation, and statistical assessment of the different parametric models is presented in Table ##TAB##2##3##.</p>", "<title>ROI analysis</title>", "<p>To avoid the problem of multiple comparisons typical for whole brain analysis when assessing the empirical hypotheses about the amount of signal captured by parametric predictors, small volume analysis was carried out in specific sub-regions of parietal cortex. For the analysis of the regions of interest (ROI), 6 mm-spheres in the left and right parietal cortex were extracted from the brain images using the toolbox MARSBAR. Selection of these ROIs was based on regions showing significant differences in the experimental contrasts in the whole brain analysis.</p>" ]

[ "<title>Overview of the method</title>", "<p>Activation <italic>Y</italic>_{<italic>ij </italic>}in a particular voxel can be described as in (1) for each replication <italic>i </italic>(for every <italic>i </italic>from 1 to <italic>p</italic>) of an experimental condition <italic>j </italic>(for every <italic>j </italic>from 1 to <italic>q</italic>):</p>", "<p></p>", "<p>having <italic>a </italic>as the intercept, <italic>X</italic>_{<italic>j </italic>}as a (continuous) parameter describing the present experimental design, <italic>β</italic>_{<italic>j </italic>}as the regression coefficient for the parameter <italic>X</italic>_{<italic>j </italic>}and <italic>ε</italic>_{<italic>ij </italic>}as residual error. The coefficient <italic>β</italic>_{<italic>j </italic>}describes the event- or block-specific expected BOLD-response under a given experimental condition <italic>j </italic>assuming that within an experimental condition the BOLD-response induced by event- or block-specific stimulation will be a constant. A corollary of this assumption is that variation in the BOLD response occurring within an experimental condition will be considered residual error.</p>", "<p>When stimuli in an experimental condition are sampled from a universe of natural items, some variation among items will always be present. An artificial increase of residual error <italic>ε </italic>due to variation in the BOLD-response produced by variation within condition contributes negatively to the sensitivity of the fMRI design. Importantly, when the variance among items not only represents a confounding factor but genuine scaling properties of stimulus features, it is mandatory to deal with them appropriately by modelling this variance within conditions.</p>", "<p>Parametric modelling always allows for the description of variation in the event- or block specific BOLD-response, when the source (or sources) of variation is known <italic>a priori </italic>and can be specified numerically as parametric regressor. Importantly, the variation within conditions may be due not only to one single stimulus feature, but rather be due to two or more features. In this case, for each of the dimensions of multi-featured stimuli a regressor can be defined, which absorbs the contribution of that dimension for the variation within a given experimental condition (but see the section on the limitations of this approach in the discussion, below). The specification of parametric regressors is given as follows: the parameter <italic>X</italic>_{<italic>j </italic>}described by a canonical hemodynamic function in common fMRI designs, which has exactly the same form across all replications <italic>i </italic>of a given experimental condition <italic>j</italic>, can be expressed as the average effect <italic>X</italic>_{<italic>j </italic>}of a predictor <italic>X </italic>on brain activation. Moreover, in parametric designs a second set of predictors may be complemented by a set of <italic>k </italic>dimensions (for every <italic>k </italic>from 1 to <italic>r</italic>) which are <italic>nested </italic>under each condition j and which absorb the variation within each condition. The full model presented in (2) contains a predictor representing the average effect of experimental condition <italic>j </italic>plus an additional parameter for each parametric regressor <italic>k </italic>considered. <italic>X</italic>_{<italic>ij</italic>1}, <italic>X</italic>_{<italic>ij</italic>2}, ... <italic>X</italic>_{<italic>ijk </italic>}... <italic>X</italic>_{<italic>ijr </italic>}contain the variation in each of k different stimulus dimensions. Note that parameters <italic>β</italic>_{<italic>jk </italic>}are hierarchically bound to the average parameter <italic>β</italic>_{<italic>j </italic>}and that the number of parameters <italic>β</italic>_{<italic>jk </italic>}associated with each average parameter <italic>β</italic>_{<italic>j </italic>}may differ. Therefore, (1) can be generalized by assuming a set of <italic>r </italic>&gt; 1 dimensions:</p>", "<p></p>", "<p>By entering parametric regressors in the fMRI design, the proportion of variance which can be accounted for by the variation within conditions is separated from the residual error <italic>ε</italic>_{<italic>ij</italic>}. This extension of the model presented in (1) has two consequences: (i) the statistical test on the significance of null-order parameter <italic>β</italic>_{<italic>j </italic>}will not be biased by variation within conditions, which can be explained by predictors <italic>β</italic>_{<italic>j</italic>1}to <italic>β</italic>_{<italic>jr</italic>}. (ii) Furthermore, the relevance of regression coefficients <italic>β</italic>_{<italic>j</italic>1}to <italic>β</italic>_{<italic>jr </italic>}may be assessed.</p>", "<p>The definition of parametric regressors with the single purpose of isolating variation within conditions as a confounding factor is trivial and has been employed regularly in fMRI research. The sole purpose of this application is to control for the impact of undesired sources of variance affecting statistics about the effects of interest. In this case, variation within conditions can be considered an effect of non-interest, the impact of which on the statistics can be partialled out from residual error.</p>", "<p>Nevertheless, parametric regressors also may be defined with the aim of directly investigating theoretical predictions with respect to the fMRI activation observed. In the following, we will concentrate on the advantages and limitations of such an application. In fact, parametric regressors make possible an investigation of the direction and actual scaling properties of variation of fMRI activation. Examination of the impact of quantitative regressors on the fMRI activation has been presented by Büchel and colleagues [##REF##9740757##1##]. In that study the authors defined one single parametric regressor and applied polynomial expansions (i.e. quadratic, cubic, etc.) to investigate non-linear relationships between the BOLD-response and this single experimental parameter. Here we use the method [##REF##9740757##1##] for two purposes: (i) instead of examining the impact of polynomial expansion of a single parametric predictor on fMRI activation, defining a set of predictors which, according to some theoretical expectation, may account for a significant amount of variability among trials produced by known and quantifiable properties of stimuli. Furthermore, the method is useful for (ii) assessing the significance of each single parameter for brain activation (i.e. one-sample t-tests) to the comparison between different models (i.e. statistics for two or more samples), which normally differ only with respect to one out of a set of parametric regressors. With this second type of application, we are able to statistically test hypotheses about the exact form of variation in fMRI activation.</p>", "<p>In the following example, we compare the model fit obtained for different numerical compressions of the predictors employed (i.e. logarithmic vs. linear scale). Results of these comparisons may help to determine the exact form of variation and the underlying rate of neuronal response to each of the different stimulus dimensions examined.</p>" ]

[ "<title>Results</title>", "<p>One-sample t-contrasts revealed that overall- and decade distance as well as problem size predicted activation in parietal cortex – especially in the cortical regions in the vicinity of the intraparietal sulcus – as well as in occipital, premotor, and prefrontal cortices (Figure ##FIG##0##1##). Interestingly, the paired two-sample contrast \"overall distance &gt; decade distance\" revealed strong fMRI activation in the intraparietal cortex (Table ##TAB##3##4##). ROI analyses pointed out that significantly more activation in response to overall distance than for decade distance was found in the left and right parietal cortex centered at Talairach coordinates x = -40, y = -34, z = 41 (<italic>t</italic>(13) = 5.54; <italic>p </italic>&lt; .001) and x = 45, y = -40, z = 51 (<italic>t</italic>(13) = 4.21; <italic>p </italic>= .001; Figure ##FIG##1##2##). The contrast \"decade distance &gt; overall distance\" revealed no activation in intraparietal cortex but only a slight deactivation in the left angular gyrus. ROI analysis revealed a significant deactivation for logarithmic decade distance in comparison with logarithmic overall distance in the left angular gyrus (x = -44, y = -62, z = 31; <italic>t</italic>(13) = -4.63; <italic>p </italic>&lt; .001; Figure ##FIG##1##2##).</p>", "<p>To examine whether the fMRI signal in the intraparietal cortex could be better accounted for by the logarithm of the distance than by the linear overall distance between the two two-digit numbers, we estimated two separate models: In one model, overall distance was entered as a parametric regressor; in a separate model, the logarithm of overall distance was employed as a parametric regressor. Both linear and logarithmic overall distance were significant predictors of activation in intraparietal cortex. Interestingly, the logarithmic overall distance was a better predictor of fMRI activation in a broad network of brain regions including the right and left posterior intraparietal cortex, left anterior intraparietal cortex, left extrastriate cortex, left premotor cortex, right frontal operculum, right SMA, right ventrolateral prefrontal cortex, right premotor cortex and the right orbitofrontal cortex (Table ##TAB##3##4##). In the contrast \"linear overall distance &gt; logarithmic overall distance\" no activation was observed at the threshold of <italic>p </italic>= .001, uncorrected, k = 10.</p>", "<p>To examine the differential impact of logarithmic overall distance on the MRI activation in IPS, four ROIs were extracted for the contrast \"logarithmic overall distance &gt; linear overall distance\". Posterior left (x = -32, y = -53, z = 44, <italic>t</italic>(13) = 7.15; <italic>p </italic>&lt; .001) and right intraparietal cortex (x = 37, y = -56, z = 49; <italic>t</italic>(13) = 8.10; <italic>p </italic>&lt; .001) as well as left (x = -52, y = -46, z = 38; <italic>t</italic>(13) = 5.97; <italic>p </italic>&lt; .001) and right anterior intraparietal cortex (x = 40, y = -33, z = 41; <italic>t</italic>(13) = 7.10; <italic>p </italic>&lt; .001) were activated more strongly by logarithmic than by linear overall distance.</p>" ]

[ "<title>Discussion</title>", "<p>In the present paper we have examined the applicability of the parametric methods presented in [##REF##9740757##1##] in two ways: (i) the specific impact of each one out of a set of parametric regressors on fMRI activation (Figure ##FIG##0##1##) and (ii) the comparison of different quantitative models of the scaling properties of fMRI activation (Figures ##FIG##1##2## and ##FIG##2##3##). Using parametric modelling of fMRI data, we have shown that the hemodynamic response in the intraparietal cortex, bilaterally, is sensitive to the overall magnitude of two-digit numbers (decade + unit distances), since the parametric model containing overall distance predicted fMRI activation significantly better than the model containing decade distance only. These results indicate that participants are not able to ignore the magnitude of unit digits when comparing two-digit numbers: unit magnitudes are processed – behaviorally as well as neurofunctionally – even if they are irrelevant for the comparison [##REF##11697933##4##,##REF##2144576##5##,##REF##11672709##14##, ####UREF##1##15##, ##REF##16253524##16##, ##REF##18022606##17####18022606##17##]. Furthermore, by examining the influence of decades and units on brain activation, we found that the left angular gyrus was deactivated more in response to decade distances than to overall distances. Commonly, deactivation of the angular gyrus is interpreted as the product of an enhancement of visuospatial attention [##REF##14683697##18##]. In the present case we tentatively interpret the stronger deactivation in response to decade distances as a product of the effort implied in the selection of just the decade distances for comparison. Since the correct result for the magnitude comparison could be reached in the experimental task by comparing the decade digits alone, participants may have tried to process their magnitude in more detail than unit magnitudes. For doing so, they need more visuospatial attention to select decade digits in the visual display. To our knowledge this is the first report of that effect, which should be investigated in further studies.</p>", "<p>Together, results also support the view that number magnitude is represented in the intraparietal cortex in a logarithmically compressed fashion, that irrelevant unit magnitudes determine fMRI activation, and that participants may engage visuospatial attention in order to select decade digits for processing. In general, the present data are in line with an extensive behavioural and fMRI literature [[##REF##18155348##2##,##REF##11697933##4##,##REF##2144576##5##,##REF##15504333##12##,##REF##11672709##14##], and [##REF##15928716##3##] for a review].</p>", "<p>In the following, we will discuss the relevance of the present results as an illustration of the advantages of modelling the fMRI signal with parametric regressors. Specifically, three points will be emphazised: (i) the impact of continuous predictors on fMRI signal, (ii) the control of variation within experimental conditions due to known features of complex stimuli and (iii) the isolation of specific contributions by quantifiable features of single complex stimuli, especially in the case of stimuli sampled from a pool of natural items. In the final section we will discuss some limitations of the parametric method.</p>", "<title>Continuous predictors of fMRI signal</title>", "<p>The most important feature of the parametric method is the modelling of variation in different dimensions of natural stimuli in a natural way that is not constrained by the necessity of generating (sometimes arbitrary) categories of stimuli in order to look at average differences between these categories. Therefore, no categorization of distances is necessary when comparing for instance linear and logarithmic scales, since the distances themselves are entered in the model as predictors of activation.</p>", "<p>One could argue that instead of employing parametric modelling, the experimenter could mask the decade or unit digit of different numbers in order to isolate the effects of overall magnitude and decades in the different experimental conditions. This approach is, however, problematic, since it is not clear whether the single digits at the decade and unit positions in two-digit numbers also have their own magnitude representation. For this reason, presenting digits of a two-digit number separately from each other would possibly lead to activation of separate magnitudes [##REF##18022606##17##,##UREF##2##19##] and cannot be considered as a valid test about two-digit number processing. With parametric regressors, the selective contribution of one stimulus feature can be assessed in experimental settings in which other stimulus features cannot be held constant without destroying their usual perceptual or semantic structure. Among these features, which cannot be held constant, one may differentiate between those which are of experimental interest and those which are simply confounds without any special theoretical meaning. This latter aspect of sampled stimuli will be examined in the next section.</p>", "<title>Variation of interest and of no-interest within an experimental condition</title>", "<p>As mentioned in the overview about this method, parametric regressors may represent the only source of variance of interest in an experiment. In the empirical example, parametric regressors were shown to be suited for testing non-trivial empirical hypotheses about fMRI signal. Parametric regressors were not entered in the model only in order to control for known sources of inhomogeneity, but they were the actual experimental factor.</p>", "<p>As already pointed out in [##REF##9740757##1##], the experimenter may isolate linear and non-linear contributions of the same predictor to the BOLD-response. In this sense, parametric models may be able to better approximate scaling properties of activity in very specialized groups of neurons. The empirical hypotheses tested in the present study involved predominantly the scaling of the fMRI signal (linear vs. logarithmic distances). Comparing the relative fit obtained by modelling data with linear predictors vs. logarithmically compressed regressors was made possible by employing parametric models. The aim of modelling data with linear vs. logarithmic predictors was to compare the relative fit obtained by modelling fMRI signal either with linear overall distance or with the logarithm of overall distance. As illustrated by the present results, the parametric method allows for detecting even very subtle details of the scaling properties of the BOLD response. Inspection of Table ##TAB##0##1## reveals that the parametric regressors representing overall distance and decade distance are highly correlated (<italic>r</italic>(240) = .96, <italic>p </italic>&lt; .05). This means that the advantage of overall distance in predicting fMRI activation in the intraparietal cortex bilaterally is due to fine-grained differences in the scaling properties of regressors representing overall distance and decade distance, which can only be captured in a parametric model. Voxels significantly better tuned to the logarithm of overall distance than to the overall distance itself could be observed in the intraparietal cortex. Similar arguments can be put forward for comparing the relative response of voxels in the angular gyrus to decade distance and overall distance. Our analyses suggest that overall distance rather than decade distance is generally more closely related to IPS activation while decade distance led to deactivation of the left angular gyrus.</p>", "<p>As mentioned before, in parametric analyses, predictors may represent different features of stimuli, which, even being theoretically different constructs, may selectively contribute to the activation in a single voxel. Importantly, the covariance structure between different predictors should be taken into consideration when designing a parametric fMRI study. In the optimal case the parametric predictors should be orthogonal to each other. If this condition cannot be reached in a specific case, the correlations between the different predictors should be held low (see the section on the limitations of parametric models, below). Furthermore, the number of predictors entering a parametric model should be much smaller than the number of scans in the individual time series from which the beta coefficients for each condition are estimated. In general this is not problematic, since the number of scans acquired for reach participant is very large in comparison with the number of experimental conditions of interested.</p>", "<p>In the example presented above, the activation produced by numerical distance and by problem size in a given voxel could be disentangled using the parametric model: Both overall distance and problem size activated highly overlapping regions in the intraparietal cortex bilaterally. Nevertheless, there is no doubt that overall distance and problem size remain different features of two-digit numbers. We also have demonstrated a selective increase in fMRI signal in the intraparietal cortex which was better explained by the overall distance than by decade distance alone. Moreover, we also found a small cluster of voxels in the left angular gyrus, which responded more strongly to decade distance alone rather than to overall distance (Figure ##FIG##1##2##). Only with parametric regressors it was possible to \"extract\" and assess the selective contribution of the decades for the fMRI signal to a complex stimulus, which by commonly activates different perceptual, motor and cognitive representations [##REF##15928716##3##].</p>", "<title>Isolating the contribution made by different features of a single complex stimulus</title>", "<p>Natural stimuli are complex objects which carry different perceptual as well as different conceptual features. These features may present variation across the different objects selected to form a category of objects in an empirical investigation. Ollinger and colleagues [##REF##11133323##20##] presented a method for separating processes within a trial in event-related fMRI designs. The authors have shown that perceptual, cognitive, and motor processes may be confounded in complex tasks. They presented a method for isolating the relative impact of each one of these single processes by means of a manipulation of the sequence of trials. One important precondition for employing this method is that the different processes activated within a trial can be assessed separately. Unfortunately, this assumption is not valid for all experimental designs. In the domain of number processing, examination of the relative impact of decade and unit digits on brain activation cannot be investigated in a natural way without presenting decade and unit digits in every trial, for there is no two-digit number without a decade or a unit digit. In parametric models the impact of different features of a complex stimulus can be modelled simultaneously, their specific effects can be isolated from the effects of other features, and most importantly, the specific effect of each feature on the activation observed in a given voxel can be assessed statistically. In the example presented above, the amount of signal produced by problem size was controlled for in the different analyses, since problem size was entered as a second predictor in every model examined. In the present case, the correlation between regressors representing those numerical distances and that representing problem size was not different from 0 (Table ##TAB##0##1##). Therefore, the effect of problem size on fMRI activation did not interfere substantially with the impact of overall and decade distances on fMRI activation.</p>", "<p>However, in specific applications the correlation between different stimulus dimensions may differ from 0. In such cases it is imperative to define the different stimulus dimensions in the same model, in order to isolate the specific contribution of each dimension to the fMRI activation and to obtain the correct statistics regarding each of these correlated dimensions.</p>", "<p>Parametric designs are not a substitute for the careful selection of items for an empirical investigation. The correlation matrix of the different properties of stimuli is the most adequate index for assessing the suitability of parametric modelling of fMRI data. When the correlations between different predictors are moderate or high, their interpretability as separate conceptual entities is compromised. For this reason, the selection of adequate predictors for a parametric study may turn out to be a non-trivial task, since the correlations between the different parametric properties of items, which are controlled for in a given empirical investigation, should be held small or even non-significant. In the present study, we were able to isolate the impact of overall distance and problem size on fMRI signal because the correlation between these two properties of items could be kept very low (<italic>r</italic>(240) = -.03, n.s.). Accordingly, in the study by Wood and colleagues [##REF##16253524##16##] the selective impact of decade and unit distance could be well separated in a parametric analysis since the correlation between the parametric regressors representing them was low, too (<italic>r</italic>(240) = .18, <italic>p </italic>&lt; .05).</p>", "<p>The only case in which parametric modelling with highly correlated predictors is informative is the comparison between the relative fit of <italic>two </italic>different models, one containing one of the two predictors and the other model containing the other one. A paired two-sample t-test may reveal whether one of these predictors explains more variance of fMRI signal (see the section on linear vs. logarithmic scaling of overall distance, above). In this case, the only difference between the two models should be produced by the scaling of the parametric predictor. Neurons in intraparietal cortex bilaterally respond to the magnitude of two-digit numbers in a logarithmically compressed fashion. This piece of evidence is in line with current theories of number magnitude processing [##REF##15928716##3##] and with evidence from behavioural [##REF##11672709##14##], fMRI [##REF##11697933##4##,##REF##15504333##12##] as well as single-cell recording studies [##REF##16960005##7##, ####REF##12215649##8##, ##REF##12526780##9##, ##REF##15200715##10##, ##REF##15123797##11####15123797##11##]. Moreover, a discussion on the compression of magnitude representation has been put forward by Dehaene [##UREF##0##6##], who argues that the magnitude representation may assume a more linear scaling with training in arithmetical tasks. The question whether the neural response in the intraparietal cortex also changes from a logarithmically compressed scaling to a more linear one can be directly assessed with parametric models, indicating that the parametric method represents not only an alternative method for data analysis but also a tool for testing specific empirical hypotheses with more precision.</p>", "<title>Some limitations</title>", "<title>Multi-collinearity</title>", "<p>As in every implementation of the general linear model, only the orthogonal part of the variance of a parametric regressor has its impact on fMRI signal tested for statistical significance [##REF##11305903##21##]. When the different parametric regressors are highly correlated, the orthogonal part of each parametric regressor may become very small and lose empirical relevance. For this reason, the interpretation of parametric designs in the presence of multi-collinearity is problematic. Before carrying out a parametric fMRI study, a careful selection of items should be conducted in order to avoid large correlations between the predictors of interest. In any case, the correlations between the different predictors in an fMRI design should be inspected and reported in the manuscript.</p>", "<title>\"Deactivation\" and inverted contrasts</title>", "<p>Since the parametric regressors represent the deviation of single items from the average of the experimental condition and not the average activation itself, the notion of \"deactivation\" must be viewed differently in parametric models. \"Deactivation\" in a paramentric regressor means that the direction of the association between variation within condition and fMRI signal is inverted.</p>" ]

[ "<title>Conclusion</title>", "<p>The parametric method can be very useful for investigations involving complex stimuli characterised by several different features. The more complex the tasks (e.g. complex arithmetic tasks or reading words from very specific word classes), the more adequate is the parametric modelling of stimulus features, since in many occasions authentic variation in stimulus properties cannot be matched exactly between different conditions, but only on average. In these cases genuine variance within conditions will be present and should be treated as such and not as measurement error. Modelling fMRI data using parametric regressors allows for the simultaneous quantification of variation in many stimulus dimensions and may be very useful for simultaneously isolating and statistically assessing the contribution of variation in different dimensions. However, the careful choice of items in each experimental condition cannot be substituted by adding parametric regressors to the statistical models at the high cost of interpretability of results. Moreover, the correlations between the different parametric predictors entering the statistical model should be zero or close to zero. The only exception from this rule is the comparison between linear and non-linear transformation of the same parametric predictor. Finally, when the (statistical) assumptions for their use are fulfilled, parametric models represent a very useful tool for assessing empirical hypotheses in fMRI studies more precisely.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>FMRI data observed under a given experimental condition may be decomposed into two parts: the average effect and the deviation of single replications from this average effect. The average effect is represented by the mean activation over a specific condition. The deviation from this average effect may be decomposed into two components as well: systematic variation due to known empirical factors and pure measurement error. In most fMRI designs deviations from mean activation may be treated as measurement error. Nevertheless, often deviation from the average also may contain systematic variation that can be distinguished from simple measurement error. In these cases, the average fMRI signal may provide only a coarse picture of real brain activation. The larger the variation within-condition, the coarser the average effect and the more relevant is the impact of deviations from it. Systematic deviation from the mean activation may be examined by defining a set of parametric regressors. Here, the applicability of parametric methods to refine the evaluation of fMRI studies is discussed with special emphasis on (i) examination of the impact of continuous predictors on the fMRI signal, (ii) control for variation within each experimental condition and (iii) isolation of specific contributions by different features of a single complex stimulus, especially in the case of a sampled stimulus. The usefulness and applicability of this method are discussed and an example with real data is presented.</p>" ]

[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>GW Participated in the design of the study and carried out behavioral and fMRI measures and statistical analyses, DS Carried out the region-of-interest analyses, HCN and KW participated in the conceptual formulation of the research question and the design of the study. All authors read and approved the final version of the manuscript.</p>" ]

[ "<title>Acknowledgements</title>", "<p>None.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Activation produced by (A) logarithm of overall distance, (B) overall distance and (C) problem size (<italic>p </italic>&lt; .001, uncorrected, k = 10 voxels).</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Voxels showing stronger activation for the overall distance than for decade distance are coloured blue while voxels showing stronger activation for the decade distance than for overall distance are coloured red (<italic>p </italic>&lt; .001, uncorrected, k = 10 voxels).</bold> While overall distance activated large portions of the anterior intraparietal cortex bilaterally in comparison with decade distance only, as well as in the extrastriate cortex, decade distance only deactivated voxels in the left angular gyrus relative to overall distance. ROI analyses revealed stronger activation in the intraparietal cortex bilaterally in response to overall distance compared to decade distance as well as a slight deactivation in the left angular gyrus.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>ROI analysis of the contrast logarithmic overall distance &gt; linear overall distance.</bold> Both, in the anterior and posterior intraparietal cortex, bilaterally, more activation was found for the logarithm of overall distance.</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Means and correlation matrix for the parametric regressors (n = 240 items, variances in the main diagonal)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\">dist10</td><td align=\"center\">logdist10</td><td align=\"center\">dist</td><td align=\"center\">logdist</td><td align=\"center\">size</td></tr></thead><tbody><tr><td align=\"center\">dist10</td><td align=\"center\"><bold>391</bold></td><td/><td/><td/><td/></tr><tr><td align=\"center\">logdist10</td><td align=\"center\">0.97</td><td align=\"center\"><bold>0.09</bold></td><td/><td/><td/></tr><tr><td align=\"center\">dist</td><td align=\"center\">0.98</td><td align=\"center\">0.95</td><td align=\"center\"><bold>377</bold></td><td/><td/></tr><tr><td align=\"center\">logdist</td><td align=\"center\">0.95</td><td align=\"center\">0.96</td><td align=\"center\">0.97</td><td align=\"center\"><bold>0.08</bold></td><td/></tr><tr><td align=\"center\">size</td><td align=\"center\">-0.03</td><td align=\"center\">-0.05</td><td align=\"center\">-0.03</td><td align=\"center\">-0.05</td><td align=\"center\"><bold>652</bold></td></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td align=\"center\">mean</td><td align=\"center\">36.63</td><td align=\"center\">1.48</td><td align=\"center\">36.72</td><td align=\"center\">1.49</td><td align=\"center\">118.43</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>In line correlation matrix for the parametric regressors</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"2\">Model 1<break/> \"overall distance\"</td><td align=\"center\" colspan=\"2\">Model 2<break/> \"decade distance\"</td></tr></thead><tbody><tr><td/><td align=\"center\">Overall<break/> distance</td><td align=\"center\">Problem<break/> size</td><td align=\"center\">Decade<break/> distance</td><td align=\"center\">Problem<break/> size</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Average BOLD function</td><td align=\"center\">-0.05</td><td/><td align=\"center\">-0.05</td><td/></tr><tr><td align=\"left\">Overall distance</td><td align=\"center\">-0.03</td><td align=\"center\">-0.03</td><td align=\"center\">-0.03</td><td align=\"center\">-0.02</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Summary of model definition, estimation and statistical comparison using parametric predictors</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td align=\"center\">Model 1</td><td align=\"center\">Model 2</td></tr></thead><tbody><tr><td/><td align=\"center\">Model name</td><td align=\"center\">\"overall distance\"</td><td align=\"center\">\"decade distance\"</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">First level<break/> (coefficient estimation)</td><td align=\"center\">predictors</td><td align=\"center\">\"overall distance\"<break/>, \"problem size\"</td><td align=\"center\">\"decade distance\"<break/>, \"problem size\"</td></tr><tr><td align=\"left\">Second level<break/> Paired two-sample t-tests</td><td/><td align=\"center\" colspan=\"2\">\"overall distance\" &gt; \"decade distance\"</td></tr><tr><td/><td/><td align=\"center\" colspan=\"2\">\"overall distance\" &lt; \"decade distance\"</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td/><td align=\"center\">Model name</td><td align=\"center\">\"log-overall distance\"</td><td align=\"center\">\"overall distance\"</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">First level<break/> (coefficient estimation)</td><td align=\"center\">predictors</td><td align=\"center\">\"log overall distance\",<break/> \"problem size\"</td><td align=\"center\">\"overall distance\",<break/> \"problem size\"</td></tr><tr><td align=\"left\">Second level<break/> Paired two-sample t-tests</td><td/><td align=\"center\" colspan=\"2\">\"log-overall distance\" &gt; \"overall distance\"</td></tr><tr><td/><td/><td align=\"center\" colspan=\"2\">\"log-overall distance\" &lt; \"overall distance\"</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Brain areas activated more by overall distance or decade distance, respectively</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Overall distance &gt; decade distance</td><td/><td/><td/><td/></tr></thead><tbody><tr><td align=\"left\">Region</td><td align=\"center\">Talairach<break/>coordinates x, y,<break/> z§</td><td align=\"center\"><italic>t</italic>-value<break/> df = 13</td><td align=\"center\">BA</td><td align=\"center\">Cluster<break/> size k</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Left extrastriate cortex</td><td align=\"center\">-24, -93, 8</td><td align=\"center\">10.10**</td><td align=\"center\">19</td><td align=\"center\">356</td></tr><tr><td align=\"left\">Right extrastriate cortex</td><td align=\"center\">32, -86, -2</td><td align=\"center\">10.02**</td><td align=\"center\">19</td><td align=\"center\">-</td></tr><tr><td align=\"left\">Left anterior intraparietal cortex</td><td align=\"center\">-51, -33, 38</td><td align=\"center\">8.39**</td><td align=\"center\">40</td><td align=\"center\">32</td></tr><tr><td align=\"left\">Left striate cortex</td><td align=\"center\">-16, -66, 7</td><td align=\"center\">5.87**</td><td align=\"center\">40</td><td align=\"center\">15</td></tr><tr><td align=\"left\">Right anterior intraparietal cortex</td><td align=\"center\">44, -37, 42</td><td align=\"center\">5.15**</td><td align=\"center\">40</td><td align=\"center\">20</td></tr><tr><td align=\"left\">Left superior parietal lobule</td><td align=\"center\">-4, -60, 51</td><td align=\"center\">6.07**</td><td align=\"center\">7</td><td align=\"center\">18</td></tr><tr><td align=\"left\">Right fusiform gyrus</td><td align=\"center\">36, -13, -20</td><td align=\"center\">5.17**</td><td align=\"center\">19</td><td align=\"center\">10</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Decade distance &gt; overall distance</td><td/><td/><td/><td/></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Left angular gyrus</td><td align=\"center\">-44, -60, 33</td><td align=\"center\">-5.38*</td><td align=\"center\">39</td><td align=\"center\">10</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Logarithmic overall distance &gt; overall distance</td><td/><td/><td/><td/></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Left posterior intraparietal cortex</td><td align=\"center\">-33, -52, 44</td><td align=\"center\">7.42**</td><td align=\"center\">7</td><td align=\"center\">75</td></tr><tr><td align=\"left\">Left anterior intraparietal cortex</td><td align=\"center\">-32, -30, 41</td><td align=\"center\">5.61**</td><td/><td align=\"center\">40</td></tr><tr><td align=\"left\">Left extrastriate cortex</td><td align=\"center\">-28, -84, 18</td><td align=\"center\">6.18**</td><td/><td align=\"center\">26</td></tr><tr><td align=\"left\">Left premotor cortex</td><td align=\"center\">-24, -5, 48</td><td align=\"center\">7.49**</td><td/><td align=\"center\">26</td></tr><tr><td align=\"left\">Left premotor cortex</td><td align=\"center\">-43, 1, 44</td><td align=\"center\">7.07**</td><td/><td align=\"center\">46</td></tr><tr><td align=\"left\">Right posterior intraparietal cortex</td><td align=\"center\">37, -56, 50</td><td align=\"center\">8.45**</td><td align=\"center\">7</td><td align=\"center\">234</td></tr><tr><td align=\"left\">Right anterior intraparietal cortex</td><td align=\"center\">40, -33, 41</td><td align=\"center\">7.65**</td><td align=\"center\">-<bold>a</bold></td><td align=\"center\">40</td></tr><tr><td align=\"left\">Right frontal operculum</td><td align=\"center\">34, 18, 10</td><td align=\"center\">7.21**</td><td/><td align=\"center\">21</td></tr><tr><td align=\"left\">Right SMA</td><td align=\"center\">4, 7, 51</td><td align=\"center\">6.41**</td><td/><td align=\"center\">29</td></tr><tr><td align=\"left\">Right ventrolateral prefrontal cortex</td><td align=\"center\">45, 16, -2</td><td align=\"center\">6.06**</td><td/><td align=\"center\">21</td></tr><tr><td align=\"left\">Right premotor cortex</td><td align=\"center\">44, -1, 49</td><td align=\"center\">6.05**</td><td/><td align=\"center\">34</td></tr><tr><td align=\"left\">Right orbitofrontal cortex</td><td align=\"center\">31, 39, -9</td><td align=\"center\">5.80**</td><td/><td align=\"center\">16</td></tr><tr><td align=\"left\">Overall distance &gt; logarithmic overall distance</td><td/><td/><td/><td/></tr><tr><td align=\"left\">No suprathreshold clusters</td><td/><td/><td/><td/></tr></tbody></table></table-wrap>" ]

[ "<disp-formula id=\"bmcM1\"><label>(1)</label>Y_ij= <italic>α </italic>+ <italic>β</italic>_jX_j+ <italic>ε</italic>_ij</disp-formula>", "<disp-formula id=\"bmcM2\"><label>(2)</label>Y_ij= <italic>α </italic>+ <italic>β</italic>_jX_j+ <italic>β</italic>_j1X_ij1+ ... + <italic>β</italic>_jkX_ijk+ ... +<italic>β</italic>_jrX_ijr+ <italic>ε</italic>_ij</disp-formula>" ]

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[ "<table-wrap-foot><p>dist10: decade distance, logdist10: base-10 logarithm of decade distance, dist: overall distance, logdist: base-10 logarithm of overall distance, size: problem size</p></table-wrap-foot>", "<table-wrap-foot><p>§ transformed from the MNI coordinates with the SPM tool mni2tal; ** p-value at the cluster level &lt; .05, corrected; <bold>a</bold>: local maximum in the same cluster as the right posterior intraparietal cortex</p></table-wrap-foot>" ]

[ "<graphic xlink:href=\"1744-9081-4-38-1\"/>", "<graphic xlink:href=\"1744-9081-4-38-2\"/>", "<graphic xlink:href=\"1744-9081-4-38-3\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>The PRIDE methodology</title>", "<p>In original PRIDE version, a protein structure in defined by the distributions of the distances between C_{<italic>α</italic>i}and C_{<italic>α</italic>(i+n)}atoms, where n, which ranges from 3 to 30, is the number of C_{<italic>α </italic>}atoms between them in the backbone joint. The comparison between two protein 3D structures is reduced to the comparison between distributions of inter-residue distances. This is performed by chi-square contingency table analysis, which estimates whether two distributions represent the same overall population and allows one to compute a probability of identity P, ranging from 0 and 1. Since 28 pairs of histograms are compares, 28 P values are obtained and then averaged to give the overall PRobability of IDEntity (PRIDE) between the two protein 3D structures. Such a similarity score can range, by definition, from 0 to 1, the latter value indicating the identity between the two protein structures. In the next sections, four modifications, introduced into this computational procedure, will be described.</p>" ]

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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Accurate and fast tools for comparing protein three-dimensional structures are necessary to scan and analyze large data sets.</p>", "<title>Findings</title>", "<p>The method described here is not only very fast but it is also reasonable precise, as it is shown by using the CATH database as a test set. Its rapidity depends on the fact that the protein structure is represented by vectors that monitors the distribution of the inter-residue distances within the protein core and the structure of which is optimized with the Freedman-Diaconis rule.</p>", "<title>Conclusion</title>", "<p>The similarity score is based on a <italic>χ</italic>²test, the probability density function of which can be accurately estimated.</p>" ]

[ "<title>Findings</title>", "<p>Although numerous methods for comparison protein three-dimensional (3D) structures were designed, we still lack a unique, commonly accepted procedure to measure the structural diversity between proteins [##REF##16678402##1##]. In particular, the structures of distantly related proteins should be expressed by the appropriate way allowing their comparison and the 3D structure representations used in modern algorithms are described in the reviews [##UREF##0##2##,##REF##17584118##3##]. The most accurate protein structure comparison methods produce protein structure alignments that are computationally intensive. Slower techniques may be preferable to analyze and classify sufficiently small data sets. However, the time criterion is crucial in the case of integrated survey of large databases, like the Protein Data Bank or the domain collections CATH and SCOP [##UREF##1##4##]. This problem is very similar to that encountered few years ago in the case of macromolecular sequence databases, which was solved by the development of tools like FASTA [##REF##3162770##5##], BLAST [##REF##2231712##6##] or PSI-BLAST [##REF##9254694##7##] that allow one to effectively scan enormous databases like UniProt [##UREF##2##8##], which presently contain several millions of entries. Although protein 3D structure databases are still much smaller, several representations of protein structure suitable for rapid comparison without alignment were proposed [##REF##11812155##9##, ####REF##12506205##10##, ##REF##15229882##11##, ##REF##17688700##12##, ##REF##14985506##13####14985506##13##]. One of the fast and automatic techniques for protein structural comparison is PRIDE [##REF##11812155##9##]. In this method the protein structure is represented via a series of distributions of inter-atomic distances allowing the use rapid comparison procedure without alignment.</p>", "<p>In the present communication, some improvements of the original PRIDE technology are presented. They make it more accurate than the original version without decreasing its speed. The classification ability of the method was tested on the CATH database.</p>", "<title>Amount of structural information</title>", "<p>The maximal value of n, which was equal to 30 in the old PRIDE version, is now selected as a function of the protein dimension. Obviously, the histograms, in which inter-residue distances are binned, must have a sufficiently high number of observations to be compared via any statistical tool. The number of observations in the histograms increases with the length of the protein and decreases with n. Therefore, histograms were generated for all n values larger than 3 and lower than n_max, where n_maxis the value for which there are only 20 C_{<italic>α</italic>i}-C_{<italic>α</italic>(i+n)}distances. Clearly, if n &gt; n_max, the histograms would contain less than 20 observations and they were thus ignored. Therefore, the numbers of histograms are different for proteins of different length in the modified PRIDE version. In the comparison of two domains, represented by series of C_{<italic>α</italic>i}-C_{<italic>α</italic>(i+n)}histograms, with 3 ≤ n ≤ n_max1for the first domain and 3 ≤ n ≤ n_max2for the second domain, the maximal value of n (n_max) was defined as</p>", "<p></p>", "<p>Moreover, only distances between residues belonging to helices and/or strands were taken into account in the modified PRIDE version, in order to increase the computational speed of the method. The STRIDE package, based on the detection of hydrogen bonds patterns and backbone torsions, was used for secondary structure assignment [##REF##8749853##14##].</p>", "<title>Optimization of the dimension of the histogram intervals</title>", "<p>The building of a regular histogram from continuous data demands a cautious specification of the number of bins. In the old version of PRIDE, each bin width was arbitrarily set to 0.5 Å, and adjacent bins were merged together so that at least 5% of the observations were included in each bin. Here a more rigorous approach was followed. Firstly, inter-residue distances were binned in the histograms with a fixed bin width of 0.1 Å, a value close to the average expected uncertainty of protein atomic coordinates obtained with crystallographic methods [##UREF##3##15##]. Then bin widths are changed automatically to their optimal value BS by using the Freedman-Diaconis rule [##UREF##4##16##]</p>", "<p></p>", "<p>where k is the number of observations in the sample x; iqr(x) is the interquartile range of the data of sample x, that is the range between the third and first quartiles. The iqr is expected to include about half of the data. The optimal BS values are computed for a query protein structure, and then they are used to change the histogram bins for all domains in the scanned database. New optimal BS values must be recomputed for a new query. Despite this might seem to be rather complicated and time consuming, we verified that once the histograms for the entire database are pre-computer and stored with very small bins of 0.1 Å, all of them can be re-shaped to the optimal BS very rapidly (see the paragraph \"Computational speed\" below).</p>", "<title>Distribution comparisons</title>", "<p>While in the original version of PRIDE, the C_{<italic>α</italic>i}-C_{<italic>α</italic>(i+n)}distance distributions were compared using the contingency tables [##UREF##5##17##], another statistical procedure is applied now. Contingency tables are more suitable to analyze relationships between nominal (categorical) variables and can be applied to compare continuous distributions only by carefully selecting an arbitrary bin size in such a way that each bin contains sufficient data. Here we adopted another approach that is more suitable to compare continuous distributions and that is computationally not more demanding than the contingency table analysis. By assuming that the distributions of both binned data sets of inter-residue distances are equally unknown, it is possible to use the chi-square test to disprove the null hypothesis that the two data sets can be described by the same distribution. If R_iis the number of observations in bin i for the first protein and S_iis the number of observations in the same bin i for the second protein, then the chi-square statistics is</p>", "<p></p>", "<p>where</p>", "<p></p>", "<p>and</p>", "<p></p>", "<p><italic>χ</italic>²ranges from 0 to the positive infinity. A large value of <italic>χ</italic>²indicates that the null hypothesis is rather unlikely and that the two proteins are considerably different, and <italic>χ</italic>²can thus be used as a statistical measure of proximity between two protein 3D structures. On the contrary, two identical protein 3D models are associated with a <italic>χ</italic>²value equal to 0.</p>", "<p>Furthermore, the degree of proximity between two protein structures can be also expressed by an incomplete gamma function determining the chi-square probability density function:</p>", "<p></p>", "<p>where N_bis the number of histogram bins, that corresponds to a number of degrees of freedom for histograms with an unequal number of observations. In this case the proximity measure P ranges from 0 to 1 corresponding, respectively, to the completely different and to the identical protein folds. and P_nare computed for each pair of histograms of the C_{<italic>α</italic>i}-C_{<italic>α</italic>(i+n)}distances for 3 = n = n_max. Then they are averaged to estimate the global degree of protein structural proximity. It must be observed that while <italic>χ</italic>²is a distance measure of proximity, with lower values associated with two domains that are similar, P is a measure of similarity, with higher values associated with two domains that are similar. Beside this difference, both can be used as structural similarity scores and monitor exactly the same protein structural features. However, P has the definite lowest and highest limits that are equivalent to the similarity score used in the old PRIDE version.</p>", "<title>Computational speed</title>", "<p>Given the extreme simplicity of the algorithm, it is not surprising that computations can be very fast. The most time consuming step is the computation of the histograms of the C_{<italic>α</italic>i}-C_{<italic>α</italic>(i+n)}distributions. However, they can be pre-computed and stored in about 850 seconds (Xenon 3 GHz processor) for the 34,035 protein domains of Table ##TAB##0##1##, 29,098 of which are long enough to be represented by at least 30 histograms and 4,937 of which are smaller and can be represented by 10–30 histograms. The comparison of a query with all the database entries takes on average 170 seconds (by using all the queries of Table ##TAB##0##1##), 20 of which are needed for the optimization of the bin size, according to the Freedman-Diaconis rule. The overall speed is nearly identical to the speed of the old PRIDE version. By comparison, the same amount of computations can be performed in about 4,000 seconds by using the SHEBA downloaded software [##REF##16613604##18##]. Other computer programs, like for example VAST [##REF##16613604##18##], are available only as web-servers and it is thus impossible to compare their computational speed with that of the new PRIDE version. However, it was observed the VAST server is not particularly fast [##REF##14696188##19##], though this does not demonstrate that the VAST algorithm is not.</p>", "<title>Data sets</title>", "<p>The new structure comparison method was benchmarked against the CATH v3.0.0 database [##REF##9309224##20##], which is a hierarchical classification of protein domains according to the class C (prevalence of secondary structural types), architecture A (the number, type, and reciprocal orientation of the secondary structural elements), topology T (the topological connection of the secondary structural elements) and homologous superfamily H (a common evolutionary origin supported either by significant sequence similarity or significant structural and functional similarity). Two datasets were created (Table ##TAB##0##1##), one with domains large enough to be represented by at least 30 distributions of C_{<italic>α</italic>i}-C_{<italic>α</italic>(i+n)}distances, and the other with smaller domains, for which 10 &lt; n_max&lt; 30. Domains containing more then one polypeptide chain were disregarded since, by definition, PRIDE cannot handle them.</p>", "<title>Query lists</title>", "<p>A non-redundant series of CATH entries were randomly selected from different superfamilies to be used as queries, by ensuring that all the three principal classes C of the database are equally represented (Table ##TAB##0##1##). Some were large domains (n_max&gt; 30) and other small domains (10 &lt; n_max&lt; 30). About half of them were considered to be \"easy\" queries, in the sense that they belong to a CATH fold cluster containing at least 50 domains, and the others were \"difficult\" queries that belong to small CATH fold groups having no more than 3 domains.</p>", "<title>Performance evaluation</title>", "<p>The performance of the new PRIDE version can be examined by the computation and the analysis of the ROC curves. The P value, which is a similarity score, is used to calculate ROC curve in the present study. A threshold similarity is consecutively decreased, with subsequent decrements equal to 0.01, in the entire range of possible P values, from 1 to 0. At each step, each of the queries (Table ##TAB##0##1##) was compared to all the entries of the databases (Table ##TAB##0##1##). As a consequence, 4,335,602 comparisons were performed by considering the dataset of large protein domains and 207,354 comparisons were necessary by considering the dataset of small protein domains.</p>", "<p>Each comparison can be classified in one of four categories, according to the CATH classification of two domains and their P value. It can be i) a true positive (TP), if the similarity between the query and the entry is higher that the threshold value and if the query and the entry belong to the same CATH fold; ii) false positive (FP) if the similarity between the query and the entry is higher that the threshold value despite the fact that they have different CATH classification; iii) a false negative (FN), if the entry and the query are in the same fold cluster despite their estimated similarity is lower than the threshold value; iv) a true negative (TN), if the similarity is estimated to be smaller that the threshold value and if the query and the entry are actually classified into different CATH fold groups. On the basis of these definitions it is possible to compute, for each threshold value, the sensitivity and the specificity</p>", "<p></p>", "<p></p>", "<p>and the ROC curve is obtained by potting Sensitivity against (1-Specificity) for the entire range of possible threshold values. Figure ##FIG##0##1## shows the ROC curves obtained as described above. It is necessary to remember that the line through the origin with slope 1, that is the diagonal, would correspond to the similarity detection based on a random measure. Therefore, the area under ROC curve equal to 0.5 is related to a random similarity measure, larger values indicate better than random estimations, and a value equal to 1 indicates perfect similarity. The areas under the ROC curves, shown in Figure ##FIG##0##1##, are 0.87 and 0.82 for the first and second datasets of Table ##TAB##0##1##, respectively. Not surprisingly, the area under the ROC curve is larger (0.87) for the first dataset of Table ##TAB##0##1##, which contains larger protein domains that can be described with at least 30 histograms of C_{<italic>α</italic>i}-C_{<italic>α</italic>(i+n)}distances, and smaller (0.82) for the second dataset, which contains smaller proteins that are represented by a lower number of histograms. Such values are considerably better than that obtained by using the old version of PRIDE (0.55). These values are also comparable to those obtained with two other procedures for evaluating protein structure similarity – SHEBA (0.93) and VAST (0.90) that are computationally much more demanding then the methods described in the present manuscript [##REF##16613604##18##]. The areas under the ROC curves were also computed by using separately queries that are classified into the <italic>α</italic>, <italic>β</italic>, and <italic>α</italic>/<italic>β </italic>classes within the CATH database in order to estimate the performance of PRIDE on different types of proteins. Values of 0.90, 0.90, and 0.83 were obtained by scanning the database of 29,098 domains with the query sets containing 49 <italic>α </italic>proteins, 50 <italic>β </italic>proteins, and 50 <italic>α</italic>/<italic>β </italic>proteins (dataset number 1 of Table ##TAB##0##1##), indicating that proteins containing both helices and strands are more difficult to be correctly identified, probably because of the higher structural diversity of protein domains containing different types of secondary structural elements. Additional information is available at [##UREF##6##21##] (Downloads section).</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>OC supervised and coordinated the project. SK developed the algorithm, carried out the analyses, and prepared, with OC, in the writing of the manuscript. All authors read and approved the manuscript.</p>" ]

[ "<title>Acknowledgements</title>", "<p>This work was supported by the BIN-II network of the GEN-AU Austrian project.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>ROC curves</bold>. The solid line shows a ROC curve obtained by comparing 149 CATH domains with 29 098 CATH entries of the first dataset of Table 1 that contains large protein domains; the dashed line represents a ROC curve calculated for the 42 small CATH domains and 4 937 CATH entries of the second dataset of Table 1, containing small protein domains.</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>The content of the datasets and the query lists used for PRIDE testing</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Dataset</td><td align=\"center\">Number of domains in the dataset</td><td align=\"center\">Number of histograms used for the domain structure representation</td><td align=\"center\" colspan=\"7\">Number of domains in the query list</td></tr><tr><td/><td/><td/><td colspan=\"7\"><hr/></td></tr><tr><td/><td/><td/><td align=\"center\" colspan=\"3\">E*</td><td align=\"center\" colspan=\"3\">D**</td><td align=\"center\">Total</td></tr><tr><td/><td/><td/><td colspan=\"6\"><hr/></td><td/></tr><tr><td/><td/><td/><td align=\"center\"><italic>α</italic></td><td align=\"center\"><italic>β</italic></td><td align=\"center\"><italic>α</italic>/<italic>β</italic></td><td align=\"center\"><italic>α</italic></td><td align=\"center\"><italic>β</italic></td><td align=\"center\"><italic>α</italic>/<italic>β</italic></td><td/></tr></thead><tbody><tr><td align=\"center\">1</td><td align=\"center\">29 098</td><td align=\"center\">&gt; 30</td><td align=\"center\">24</td><td align=\"center\">25</td><td align=\"center\">25</td><td align=\"center\">25</td><td align=\"center\">25</td><td align=\"center\">25</td><td align=\"center\">149</td></tr><tr><td align=\"center\">2</td><td align=\"center\">4 937</td><td align=\"center\">10 – 30</td><td align=\"center\">6</td><td align=\"center\">6</td><td align=\"center\">6</td><td align=\"center\">8</td><td align=\"center\">8</td><td align=\"center\">8</td><td align=\"center\">42</td></tr></tbody></table></table-wrap>" ]

[ "<disp-formula><italic>n</italic>_max= min(<italic>n</italic>_max1, <italic>n</italic>_max2)</disp-formula>", "<disp-formula><italic>BS </italic>= 2<italic>iqr</italic>(<italic>x</italic>)<italic>k</italic>^-1/3</disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\" name=\"1756-0500-1-44-i1\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msup><mml:mi>χ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:munder><mml:mo>∑</mml:mo><mml:mi>i</mml:mi></mml:munder><mml:mrow><mml:mfrac><mml:mrow><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msqrt><mml:mrow><mml:mfrac><mml:mi>S</mml:mi><mml:mi>R</mml:mi></mml:mfrac></mml:mrow></mml:msqrt><mml:mo>−</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msqrt><mml:mrow><mml:mfrac><mml:mi>R</mml:mi><mml:mi>S</mml:mi></mml:mfrac></mml:mrow></mml:msqrt></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mstyle></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\" name=\"1756-0500-1-44-i2\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:munder><mml:mo>∑</mml:mo><mml:mi>i</mml:mi></mml:munder><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M3\" name=\"1756-0500-1-44-i3\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>S</mml:mi><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:munder><mml:mo>∑</mml:mo><mml:mi>i</mml:mi></mml:munder><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mstyle><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M4\" name=\"1756-0500-1-44-i4\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msup><mml:mi>χ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mi>Γ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msup><mml:mi>χ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>Γ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>Γ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:mstyle displaystyle=\"true\"><mml:mrow><mml:munderover><mml:mo>∫</mml:mo><mml:mrow><mml:msup><mml:mi>χ</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:mi>∞</mml:mi></mml:munderover><mml:mrow><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:mrow></mml:mstyle><mml:msup><mml:mi>t</mml:mi><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M5\" name=\"1756-0500-1-44-i5\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>χ</mml:mi><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><italic>Sensitivity </italic>= <italic>TP</italic>/(<italic>TP </italic>+ <italic>FN</italic>)</disp-formula>", "<disp-formula><italic>Specificity </italic>= <italic>TN</italic>/(<italic>TN </italic>+ <italic>FP</italic>)</disp-formula>" ]

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[ "<table-wrap-foot><p>*E corresponds to the \"easy\" cases when the queries belong to highly populated groups of investigated datasets containing at least 50 domains at the homologous superfamily classification level of CATH;</p><p>**D corresponds to the \"difficult cases\" when queries belonged to small groups having no more than 3 domains at the homologous superfamily classification level of CATH</p></table-wrap-foot>" ]

[ "<graphic xlink:href=\"1756-0500-1-44-1\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p>Nasopharyngeal angiofibroma is considered to be a reactive, malformed, benign but aggressive neoplasm. Clinical staging and tumor embolization reduce surgical morbidity. The therapy protocol is influenced by hospital-related factors. Radiofrequency-induced thermotherapy (RFITT) is a minimally invasive surgical procedure that causes thermal ablation through coagulation and is used in the treatment of both head and neck diseases. We were unable to find reported cases of angiofibroma that were treated with RFITT, subjected to follow-up evaluation and had documented histological changes with time.</p>", "<p>We present an unusual case of a 52-year-old man with nasopharyngeal angiofibroma that first appeared as a nasal polyp. Coagulation, thrombosis, sclerosis and pericyte proliferation occurred after RFITT. We looked for a change in angiofibroma cell proliferation through biopsies obtained before and after RFITT when the patient was free of bleeding episodes. The cell origin of vessel formation after thermocoagulation therapy was investigated. Our results are of interest for surgeons applying pre-operative thermal ablation therapy.</p>" ]

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[ "<title>Discussion</title>", "<p>Nasopharyngeal angiofibroma is considered a malformation in juveniles [##REF##11020189##1##, ####REF##14567719##2##, ##REF##10640200##3####10640200##3##], but does not exclude the unusual presentation of the disease in mature patients, as confirmed by this report and occasional reports from other authors [##REF##15733604##4##]. While nasal polyps are not subjected routinely to CT or magnetic resonance imaging, these are established pre-operative diagnostic tools for nasopharyngeal angiofibroma.</p>", "<p>The case presented here is of interest from both the clinical and the pathological points of view. The nasopharyngeal and sinonasal tracts are sites of different pathologies prone to epistaxis, such as the angiofibroma, angiectatic nasal polyp [##REF##10705395##5##], and necrotizing angiocentric lesion. The stroma is different in these lesions and quite typical in angiofibroma. SMA decorates the stromal cells in certain nasal polyps. It is strongly positive in the vessel wall (pericytes) and occasionally in the stroma of angiofibromas [##REF##11020189##1##,##REF##9217133##6##], which may help in differential diagnosis.</p>", "<p>In our case, two pathologies were present synchronously, a mucosal nasal polyp and an angiofibroma, making the diagnosis more complex as noticed by other authors [##REF##9217133##6##]. The association between inflammatory nasal polyps and angiofibroma is not routinely expected, but once a biopsy is obtained, there are criteria to distinguish between nasal polyps arising through different pathogenic processes [##REF##7458752##7##]. Nasopharyngeal angiofibroma is a rare event and biopsy is not advised. The first biopsy of our patient resulted from atypical extension of the tumor into the nasal cavity. The dates for the second and third biopsy were chosen with regards to the recovery period after RFITT. Although not a new disease, nasopharyngeal angiofibroma remains a clinical and scientific challenge. Thermocoagulation should be considered as a possible pre-operative protocol when embolization is not available.</p>", "<p>The origin of angiofibroma is still under investigation. Zhang et al. [##REF##14567719##2##] presented arguments for primary stromal change at the molecular level of angiofibroma organization. However, the origin of vessel formation is uncertain [##REF##12015303##8##,##REF##9118474##9##] and pericyte behavior in angiofibroma may be of interest. We were unable to find reports on pericyte proliferation in nasopharyngeal angiofibroma treated with RFITT. We find our observations of importance for the investigation of angiogenesis, angiofibroma and post-RFITT control biopsies. Our observations are in accordance with the purpose of the therapy, that is, to impede circulation and produce coagulation, thus reducing growth. The lesion was successfully treated surgically without pre-operative embolization, suggesting that RFITT might function as a pre-operative adjuvant therapy. Two years after RFITT, our patient is without symptoms or nasopharyngeal growth.</p>", "<p>Histologically, both endothelial cell and pericyte proliferation were more accurately expressed with double immunohistochemistry compared with routine Ki67 staining. Pericyte proliferation was stronger than endothelial cell proliferation prior to therapy (PEPI 16.04%, EPI 8.34%). While the PEPI increased upon coagulation and progressed with time, the EPI did not. These results support the theory of angiofibroma as a maturing vasoformative lesion. Vessel formation is observed in inflammation, malformation, neovascularization of neoplasia and as a neoplastic event. Proliferation in vascular malformations has been studied previously [##REF##16816171##10##,##REF##11932989##11##]. Vessel formation in inflammation is diffuse except in granulomas. Malformations and neoplasias, including angiofibromas, behave as a 'body' in that they are fed and can be embolized, and angiofibromas are not considered neoplastic events. Malformations occurring with age are unusual but not unexpected. Zhang et al. [##REF##14567719##2##] showed that angiofibroma stromal cells might be neoplastic. Our investigation of angiofibroma using double immunohistochemistry showed negligible proliferation outside the vascular compartment.</p>" ]

[ "<title>Conclusion</title>", "<p>We have presented a rare case of angiofibroma in a 52-year-old man with pericyte proliferation, supporting the maturation of the vessel compartment and revealing active angiogenic machinery (cooperation between endothelial cells and pericytes). We observed the divergent behavior of endothelial cells and pericytes after RFITT adjuvant therapy prior to surgery. Further studies of RFITT related to vessel behavior are needed. We found thrombosis and coagulation resulting from RFITT to function as equivalent to embolization prior to surgical therapy for angiofibroma. An analysis of vessel cell proliferation in tissues treated with thermal ablation might have broader clinical impact across medicine.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Introduction</title>", "<p>Nasopharyngeal angiofibroma presents with symptoms of nasal obstruction and epistaxis. The treatment of choice is embolization followed by surgery.</p>", "<title>Case presentation</title>", "<p>A 52-year-old man underwent surgery for nasopharyngeal angiofibroma after adjuvant radiofrequency-induced thermotherapy. To the best of the authors' knowledge, this is the first case of angiofibroma with clinical follow-up after thermocoagulation therapy supported by quantitative, double immunohistochemistry. We found this case of angiofibroma to be of interest owing to the presentation of symptoms leading to biopsy, the pathohistological observations obtained with synchronous Ki67/cluster of differentiation 34 and Ki67/smooth muscle actin immunohistochemistry and high pericyte proliferation.</p>", "<title>Conclusion</title>", "<p>Coagulation of angiofibroma vessels followed by acquisition of a thick mantle of pericytes in a patient with a nasopharyngeal growth suggests that radiofrequency-induced thermotherapy could be a useful, palliative therapy for bleeding nasopharyngeal angiofibroma, supporting vessel maturation prior to surgical tumor removal.</p>" ]

[ "<title>Case presentation</title>", "<p>A 52-year-old white man, who experienced breathing difficulties and nasal speech for 15 months, was hospitalized for nasal polyps. A radiograph of his paranasal sinuses (21 January 2005) showed a soft tissue lesion in the mediosagittal line, suggesting a nasal polyp. A biopsy (18 February 2005) of the polyp revealed that it was immovable and provoked bleeding. The provided tissue (0.5 cm³) was diagnostic for nasopharyngeal angiofibroma after routine hematoxylin and eosin (H&amp;E) staining (Figure ##FIG##0##1##), the stromal cells were negative for both cluster of differentiation (CD) 34 antigen and smooth muscle actin (SMA) antibodies and C-kit antibody was rarely detected in single cells.</p>", "<p>Digital subtraction angiography showed the pathological vascularization of the tumor (8 March 2005; Figure ##FIG##1##2A##). A computed tomography (CT) scan of the viscerocranium with intravenous contrast revealed a 56 mm × 48 mm large, soft tissue growth that filled the nasopharynx and extended to the left nasal cavity (24 February 2005; Figure ##FIG##1##2B##). A multiple slice CT carotidography (10 May 2005) revealed that there was blood supply to the tumor from the external carotid vessels (Figure ##FIG##1##2C##).</p>", "<p>With a diagnosis of nasopharyngeal angiofibroma (Radkowski's stage Ib), the patient was subjected to RFITT using a Celon AG medical instrument (radiofrequency power, 15 to 20 W and a 5-minute application time). The therapy was performed three times over a 2-month period (1 June 2005, 9 June 2005 and 31 August 2005). The lesion did not bleed but hardened. The second surgical specimen (21 September 2005) was 5 cm³of angiofibroma tissue with multiple 2 to 3 mm centers of coagulation (Figure ##FIG##2##3##). After RFITT, the clinical symptoms were alleviated despite the incomplete reduction in tumor size. Staining for Ki67 showed low overall proliferation in the first biopsy but increased proliferation in the second (1% and 10%, respectively). A control CT scan (29 September 2005) of the epipharynx revealed a residual tumor, an enlarged left maxillary sinus with a missing medial wall, thickened mucosa without post-contrast opacification and no enlarged lymph nodes.</p>", "<p>A third biopsy 10 months after RFITT provided 0.075 cm³of residual tumor with an overall Ki67 proliferation index of 10%. Plump SMA-positive and predominantly Ki67-negative cells were detached from the vessel wall and formed sheets resembling angiomyofibroblastoma after H&amp;E staining. The second and third biopsies respected the recovery time from RFITT and were not complicated by hemorrhage.</p>", "<p>One year after RFITT, angiography found no arteries feeding the residual tumor. The patient underwent surgery at another institution without prior embolization (no hypertrophic feeding arteries were found at repeated angiography before the operation).</p>", "<p>The primary intention was to reduce the tumor and alleviate the symptoms using RFITT before the operation. Double immunostaining was planned later because of increased Ki67 staining observed in the control biopsy after RFITT. Ki67 is a proliferation marker providing nuclear staining when the cell is in the S phase preparing to enter mitosis. To determine which cell type is proliferating in a tissue, a second differentiation marker is added, that is, CD34 for endothelial cells or SMA for pericytes. The immunohistochemical analysis of all three angiofibroma biopsies was repeated with a double-staining technique for both Ki67/CD34 and Ki67/SMA to distinguish between endothelial cell and pericyte proliferation over time (Figure ##FIG##3##4A, B## and ##FIG##3##4C##). Three parameters were used to quantify proliferation. The endothelial cell proliferation index (EPI) and pericyte proliferation index (PEPI) were defined as the percentage of Ki67-positive nuclei per 1000 cells for each cellular compartment. This was different from routine, less expensive single Ki67 immunostaining where the proliferation index takes into consideration all the cells in the tissue without distinguishing between vessel cells and stromal cells. The number of vessel sections per field was obtained and the results were expressed as microvessel density (MVD), which is the number of lumina per square millimeter. The proliferating capillary index (PCI) was defined as the percentage of vessel sections of any cell type whose nuclei stained positive for Ki67. The proliferation analysis results are shown in Table ##TAB##0##1##.</p>", "<p>Double immunohistochemical staining revealed higher proliferation indices for cells of the vessel compartment compared with single Ki67 staining of each routine biopsy. The EPI slightly decreased while the PEPI increased 10 months after RFITT. The third biopsy contained a large number of detached SMA-positive cells. There were scattered Ki67-positive nuclei of cells outside the vessel wall that were defined by neither CD34 nor SMA in all three biopsies. The MVD increased 20 days after RFITT and further increased with time. The PCI also increased with time. Measurements and images were obtained using a BX-40 Olympus microscope, Sony CCD-Iris color video camera and ISSA 3.1 software (Vamstec, Zagreb).</p>", "<title>Abbreviations</title>", "<p>CD; Cluster of differentiation; CT: Computed tomography; EPI: Endothelial cell proliferation index; H&amp;E: Hematoxylin and eosin; MVD: Microvessel density; PCI: Proliferating capillary index; PEPI: Pericyte proliferation index; RFITT: Radiofrequency-induced thermotherapy; SMA: Smooth muscle actin.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>MKr is the author of this study and performed the quantitative analysis of the double-stained immunohistological slides. MKu, TB and NC are surgeons who treated and observed the patient and provided the angiofibroma biopsy specimens. AH is our radiologist responsible for the acquisition of data and analysis and interpretation of data.</p>", "<title>Consent</title>", "<p>Written informed consent was obtained from the patient for publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.</p>" ]

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[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Angiofibroma prior to radiofrequency-induced thermotherapy</bold>. Hematoxylin and eosin stain, magnification ×10.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Scans of a nasopharyngeal angiofibroma</bold>. (A) Digital subtraction angiography (maximum intensity projection technique): the terminal branch of the left maxillary artery is at the hilus of the pathological angiofibroma neovascularization. (B) Computed tomography of the viscerocranium: nasopharyngeal angiofibroma seen with intravenous contrast. (C) The same tumor seen with computed tomography carotidography (volume rendering technique).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Coagulation in angiofibroma (on the right), 3 weeks after radiofrequency-induced thermotherapy</bold>. Hematoxylin and eosin stain, magnification ×10.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Proliferation of pericytes in angiofibroma</bold>. (A) Prior to radiofrequency-induced thermotherapy. (B) Three weeks after radiofrequency-induced thermotherapy. (C) Ten months after radiofrequency-induced thermotherapy, detachment of pericytes from the vessel wall. Magnification ×20. Ki67/SMA double immunohistochemistry. Ki67-positive nuclei of cycling cells were visualized using ChemMate DAB+ Chromogen. Cytoplasm of the endothelial cells and pericytes was visualized by fast red staining.</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Variables of cell proliferation and vessel proliferation in angiofibroma with time</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Variable</td><td align=\"left\" colspan=\"3\">Endothelial cell<break/> proliferation index (%)</td><td align=\"left\" colspan=\"3\">Pericyte proliferation<break/> index (%)</td><td align=\"left\" colspan=\"3\">Proliferating<break/>capillary<break/> index (%)</td><td align=\"left\" colspan=\"3\">Microvessel<break/> density per mm²</td></tr></thead><tbody><tr><td align=\"left\">Order of biopsy</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">3</td></tr><tr><td align=\"left\">Mean*</td><td align=\"left\">8.34</td><td align=\"left\">11.18</td><td align=\"left\">9.10</td><td align=\"left\">16.04</td><td align=\"left\">19.36</td><td align=\"left\">20.59</td><td align=\"left\">38.5</td><td align=\"left\">42</td><td align=\"left\">54</td><td align=\"left\">138</td><td align=\"left\">180</td><td align=\"left\">226</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>1, 2 and 3: The first, second and third biopsies. *25 microscopic fields per variable (microscopic field 0.0415265 mm²).</p></table-wrap-foot>" ]

[ "<graphic xlink:href=\"1752-1947-2-278-1\"/>", "<graphic xlink:href=\"1752-1947-2-278-2\"/>", "<graphic xlink:href=\"1752-1947-2-278-3\"/>", "<graphic xlink:href=\"1752-1947-2-278-4\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p>Lithium batteries are used in many portable consumer electronic devices (Fig. ##FIG##0##1##). The most common type of lithium cell used in consumer applications consists of lithium and manganese (Mn). Disk battery ingestions can lead to serious complications including aerodigestive tract perforation, vessel erosion, mediastinitis, and stricture formation [##REF##6848955##1##]. Mercury batteries have been reported to cause more severe complications including acute poisoning [##REF##435843##2##], but none of the disk batteries have been reported to cause protein losing-enteropathy. We report a case in which the manganese in a lithium-manganese disk battery impacted in the esophagus presumably led to eosinophilic enterocolitis and severe protein-losing enteropathy.</p>" ]

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[ "<title>Discussion</title>", "<p>The best known manifestations of chronic Mn exposure are neurological symptoms such as hypokinesia, rigidity and tremor that resemble Parkinson's disease [##REF##10385886##3##]. Rarely, allergic responses have been described as well. Metal allergy to stainless steel wire containing Mn has been reported after coronary artery bypass grafting. A refractory pruritic erythematous wheal over the body with positive Mn patch testing and peripheral eosinophilia proved this to be a systemic allergic reaction to Mn [##REF##14598128##4##]. Mn used in the manufacture of dental prosthesis has also been reported to cause contact dermatitis, manifested by diffuse oral edema, erythema and ulcerations; this was confirmed by positive patch testing [##REF##15059103##5##,##REF##15224060##6##].</p>", "<p>Epidemiological studies have reported an acute impact of particulate Mn on the pulmonary system, including reversible decrement of pulmonary functions and increase in bronchial hyperreactivity [##REF##9847277##7##,##REF##16241039##8##]. In children, peak expiratory flow was decreased with a high concentration of Mn in the air, suggesting an obstructive allergic response rather than restrictive airway disease [##REF##17431494##9##]. Exposure to high inhaled Mn concentrations has demonstrated an increased incidence of cough, rhinitis, bronchitis, and pneumonitis [##REF##3578289##10##]. A study in rhesus monkeys documented subacute bronchiolitis and alveolar duct inflammation with lymphocytes, neutrophils, and a few eosinophils following inhalational exposure to Mn [##REF##16242036##11##]. In general, serum or blood Mn does not serve as a reliable indicator of the total body burden of Mn because of its intracellular distribution and relatively short half-life [##REF##15713346##12##].</p>", "<p>We postulate that our patient developed an allergic enterocolitis and protein losing enteropathy in response to the Mn exposure in the gastrointestinal (GI) tract. The ingested battery was composed of lithium perchlorate and manganese dioxide. The possibility that some other component of the battery could have contributed to the pathogenesis cannot be ruled out, but in the literature, Mn is the only constituent that has been attributed to the allergic responses. This is supported by the previously suggested evidence that Mn can cause rhinitis, pneumonitis, and bronchial hyperreactivity. Manganese exposure from a cardiac stenting wire and dental prosthesis has also caused allergic symptoms with peripheral eosinophilia. Our patient's most recent esophageal biopsies suggest that she either had a baseline mild asymptomatic eosinophilic esophagitis that acutely worsened with exposure to Mn or the Mn was a trigger to her eosinophilic esophagitis.</p>" ]

[ "<title>Conclusion</title>", "<p>This case shows strong circumstantial evidence that the eosinophilic enterocolitis and protein-losing enteropathy were caused by the Mn leak from a retained disk battery; she was completely asymptomatic before battery ingestion with normal albumin levels and eosinophil counts before battery removal. Additionally, there was complete resolution without any treatment aside from removal of the Mn-containing disk battery. Clinicians should be vigilant about this rare complication while managing children with ingested disk batteries as symptoms might not appear immediately after battery removal.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Introduction</title>", "<p>Disk battery ingestions can lead to serious complications including airway or digestive tract perforation, blood vessel erosions, mediastinitis, and stricture formation.</p>", "<title>Case presentation</title>", "<p>We report a 20-month-old Caucasian child who developed eosinophilic enterocolitis and subsequent protein-losing enteropathy from manganese that leaked from a lithium disk battery. The disk battery was impacted in her esophagus for 10 days resulting in battery corrosion. We postulate that this patient's symptoms were due to a manganese leak from the 'retained' disk battery; this resulted in an allergic response in her gut and protein-losing enteropathy. Her symptoms improved gradually over the next 2 weeks with conservative management.</p>", "<title>Conclusion</title>", "<p>This is the first case report to highlight the potential complication of allergic enterocolitis and protein-losing enteropathy secondary to ingested manganese. Clinicians should be vigilant about this rare complication in managing patients with disk battery ingestions.</p>" ]

[ "<title>Case presentation</title>", "<p>A 20-month-old Caucasian child presented with a 10-day history of vomiting and solid food refusal. Her chest X-ray showed a disk battery impaction in the upper esophagus. A corroded lithium-manganese battery was retrieved with a flexible laryngoscope 10 days after ingestion. The patient was transferred to our institution for further monitoring. Her physical examination and laboratory tests on admission were normal, except for an albumin of 2.7 g/dL (normal 3.8 to 5.4 g/dL) which had dropped from 4.3 g/dL on the day of battery removal. She had been on a regular diet until 10 days before admission. An esophagogram revealed no perforations.</p>", "<p>A week following removal of the battery, she continued to refuse foods. An upper endoscopy was performed that revealed non-circumferential ulceration in the upper esophagus but no biopsies were performed at that time. Her stomach and duodenum were grossly normal at that time. A nasogastric (NG) tube was placed and feeding was started. Subsequently, the protein loss worsened and her serum albumin dropped to 1.1 g/dL (normal 3.8 to 5.4 g/dL). The urinalysis was normal and fecal alpha-1 antitrypsin level was 464 mg/g (normal &lt;2 mg/g dry stool). A computed tomography (CT) scan of the abdomen, chest, and pelvis was performed because of persistent abdominal distention and feeding intolerance; this showed bilateral pleural effusions and moderate ascites. She had a normal echocardiogram and liver function tests. She was diagnosed with protein-losing enteropathy. Albumin 25% was started to maintain albumin at a level of &gt;2 g/dL. A colonoscopy and repeat endoscopy were performed due to her protein-losing enteropathy; this showed complete healing of the previous esophageal ulceration, but with findings of diffuse enteritis and colitis. Small bowel biopsies were taken from the duodenum and terminal ileum. Histology revealed mild eosinophilic esophagitis and moderate eosinophilic enterocolitis (Fig. ##FIG##1##2##). In addition, the patient developed peripheral eosinophilia of 10.5% compared to 2.9% on admission. Her symptoms improved, and her albumin and eosinophilia normalized gradually over the next 2 weeks with conservative support and no steroids.</p>", "<p>At 6 months follow-up, the patient has remained well, with normal albumin levels and no symptoms of protein-losing enteropathy. She developed an upper esophageal stricture that required recurrent dilatation and steroid injections. Her most recent endoscopic biopsies showed moderate eosinophils in the esophageal mucosa. We postulate that this patient's symptoms were due to a manganese leak from the 'retained' corroded disk battery before or during the process of removal from the esophagus; this caused an allergic response in her gut resulting in a protein-losing enteropathy.</p>", "<title>Abbreviations</title>", "<p>CT: Computed Tomography; GI: Gastrointestinal; Mn: Manganese; NG: Nasogastric.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>All authors (MAA, PSG, GT) contributed in the management of the patient, writing of the manuscript and reviewing of the literature. All authors read and approved the final manuscript.</p>", "<title>Consent</title>", "<p>Written informed consent was obtained from the parent for publication of this case report, as the child was a minor. A copy of the written consent is available for review by the Editor-in-Chief of this journal.</p>" ]

[ "<title/>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Lithium disk battery.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Colonoscopic biopsy showing eosinophilic infiltration of crypts in transverse colon.</p></caption></fig>" ]

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[ "<graphic xlink:href=\"1752-1947-2-286-1\"/>", "<graphic xlink:href=\"1752-1947-2-286-2\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p>Adenoviruses (Ad) have recently been employed for a wide range of vaccination strategies [##REF##12756416##1##]. In this regard, a number of practical advantages are recognized in using Ad-based vectors for antigen gene delivery. These advantages include the ease of manipulation of the viral genome, the ability to prepare high titer stocks of recombinant virions, and the ability of the vector to infect a wide array of target cells [##REF##9811704##2##, ####REF##12020813##3##, ##REF##11135613##4####11135613##4##] relevant to the achievement of a useful vaccine effect. These considerations highlight the emerging recognition that Ad vectors embody enormous promise for the realization of diverse vaccine interventions. Of note, Ad-based vaccinations have been practically translated for human applications and have progressed in a variety of immunization contexts such as cancer and infectious diseases [##REF##7546657##5##, ####REF##8602187##6##, ##REF##11117750##7##, ##REF##11309631##8##, ##REF##14598567##9##, ##REF##15841217##10##, ##REF##15761256##11##, ##REF##15761255##12####15761255##12##].</p>", "<p>Currently, new methods to exploit Ad for vaccine purposes have been developed. These recent approaches have utilized the natural mechanisms of Ad virion immunogenicity whereby antigen epitopes can be directly incorporated into the viral capsid as the basis by which immune presentation of the epitope is achieved [##REF##15841217##10##,##REF##7509367##13##, ####REF##16699033##14##, ##REF##16699016##15##, ##REF##17942539##16####17942539##16##]. Strategies advancing this \"capsid incorporation\" paradigm have evaluated a range of virion capsid proteins as well as a variety of antigens, model and pathogenic [##REF##15841217##10##,##REF##16699033##14##, ####REF##16699016##15##, ##REF##17942539##16##, ##REF##10233980##17####10233980##17##].</p>", "<p>The major capsid protein hexon has been the focus of the majority of these capsid incorporation strategies owing to its natural role in the generation of anti-Ad immune response and its numerical representation vis a via the virion's structural organization [##REF##16699033##14##,##REF##15731232##18##]. Using this strategy, we have developed the means to incorporate heterologous peptide epitopes specifically within the major surface-exposed domains of the Ad capsid protein hexon [##REF##15731232##18##]. Of note, our previous studies have show that we can incorporate small heterologous peptides into Ad hexon hypervariable regions (HVRs) without perturbing viral viability and major biological characteristics such as replication, thermostability, or native infectivity [##REF##15731232##18##]. Other published studies have focused on incorporations at HVR5 or single site incorporations [##REF##10233980##17##]. However, it has been recognized that the ability to place antigen within multiple sites of the hexon capsid protein holds important potential for presenting multiple epitopes/antigens or several copies of the same epitope within a single Ad vector-based vaccine.</p>", "<p>In this regard, capsid surface localization of HVR sites derived from X-ray crystallography suggests that both HVR2 and HVR5 loci are potentially useful for capsid incorporation of antigens for vaccination. As noted, there have been recent reports in which HVR5 has been exploited with respect to epitope insertion [##REF##15841217##10##,##REF##16699033##14##,##REF##16699016##15##,##REF##15731232##18##, ####REF##18391209##19##, ##REF##18267072##20##, ##REF##18560416##21####18560416##21##]. Based on our abilities to manipulate both HVR2 and HVR5 sites, we sought to explore the relative merits of these two hexon locales. To compare the flexibility and capacities of HVR2 and HVR5, respectively we genetically incorporated identical epitopes of incrementally increasing size within HVR2 or HVR5 of Ad5 hexon. Our study illustrates that hexon incorporated model antigens elicit a varied immune response in the context of antigen placement or antigen size at both the HVR2 or HVR5 locales.</p>" ]

[ "<title>Materials and methods</title>", "<title>Antibodies</title>", "<p>Mouse anti-penta-His₆tag monoclonal antibody (34660) was purchased from Qiagen (Valencia, CA). Horse radish peroxidase (HRP)-conjugated goat anti-mouse secondary antibodies were purchased from DakoCytomation (Denmark).</p>", "<title>Cell culture</title>", "<p>Human embryonic kidney cells (293) were obtained from and cultured in the medium recommended by the American Type Culture Collection (Manassas, VA). All cell lines were incubated at 37°C and 5% CO₂under humidified conditions.</p>", "<title>Recombinant adenovirus construction</title>", "<p>In order to generate recombinant adenoviruses with hexon insertions of arginine-glycine-aspartic acid (RGD)-containing sequences, fragments of the Ad5 penton base gene corresponding to the RGD motifs were derived by PCR and cloned into the <italic>BamH</italic>I site in the previously described HVR2-His₆/pH5S or HVR5-His₆/pH5S plasmids [##REF##15731232##18##]. The sequences corresponding to penton base-derived peptides, 33RGD, 43RGD, 53RGD, 63RGD, 73RGD, and 83RGD, were PCR amplified from Ad5 genomic DNA with the following pairs of primers: 33RGD sense (s) and 33RGD anti-sense (as), 43RGD sense (s) and 43RGD anti-sense (as), 53RGD sense (s) and 53RGD anti-sense (as), 63RGD sense (s) and 63RGD anti-sense (as), 73RGD sense (s) and 73RGD anti-sense (as), and 83RGD sense (s) and 83RGD anti-sense (as) (Table ##TAB##0##1##). For additional details, see reference [##REF##12163581##22##]. To create Ad5 vectors containing RGD epitopes in the HVRs of hexon, these resulting plasmids were digested with <italic>EcoR</italic>I and <italic>Pme</italic>I. These resulting fragments containing the homologous recombination regions and the hexon genes were purified, then recombined with a <italic>Swa</italic>I-digested Ad5 backbone vector that lacks the hexon gene, pAd5/ΔH5 [##REF##12438602##23##]. These recombination reactions were performed in <italic>Escherichia coli </italic>BJ5183 (Strategene, La Jolla, CA). The resultant clones were designated Ad5/HVR2-33RGD-His₆, Ad5/HVR5-33RGD-His₆, Ad5/HVR5-43RGD-His₆, and Ad5/HVR5-53RGD-His₆, all of which contain the green fluorescence protein gene and firefly luciferase gene in the E1 region [##REF##15731232##18##]. The constructs were confirmed by restriction enzyme digestions and sequence analysis. Ad5, Ad5/HVR2-His₆, and Ad5/HVR5-His₆were previously constructed as described [##REF##15731232##18##].</p>", "<title>Virus rescue and preparation</title>", "<p>To rescue viruses, the constructed plasmids were digested with <italic>Pac</italic>I, and 2 μg DNA were transfected (Lipofectamine 2000 Reagent, Invitrogen, Carlsbad, CA) into the Ad-E1-expressing 293 cells. After plaques formed, they were processed for large-scale proliferation in 293 cells. Viruses were purified by double cesium chloride ultracentrifugation and dialyzed against phosphate-buffered saline containing 10% glycerol. Viruses were stored at -80°C until use. Final aliquots of virus were analyzed for physical titer using absorbance at 260 nm. The infectious viral titer (infectious particles per ml) was determined by tissue culture infectious dose (TCID₅₀) assay. Modifications of the hexon gene was confirmed by PCR analysis with the primers 5'HVR2 (s), CTCACGTATTTGGGCAGGCGCC and 3'HVR5(as), GGCATGTAAGAAATATGAGTGTCTGGG, which anneal up and downstream of the site of the insertion within the hexon open reading frame (Table ##TAB##0##1##).</p>", "<title>Whole virus ELISA and sera ELISA</title>", "<p>The enzyme-linked immunosorbent assay (ELISA) assay was performed essentially as described previously [##REF##12228019##24##]. In brief, different amounts of viruses ranging from 4 × 10⁶to 9 × 10⁹VPs were immobilized in wells of a 96-well plate (Nunc Maxisorp, Rochester, NY) by overnight incubation in (per well) 100 μl of 100 mM carbonate buffer (pH 9.5) at 4°C. After washing with 0.05% Tween 20 in Tris-buffered saline and blocking with blocking solution (2% bovine serum albumin and 0.05% Tween 20 in TBS), the immobilized viruses were incubated with anti-penta-His₆tag monoclonal antibody (Qiagen, Valencia, CA) for 2 h at room temperature, followed by AP-conjugated goat anti-mouse antibody incubation. Colormetric reaction was performed with <italic>p</italic>-nitrophenyl phosphate (Sigma-Aldrich, St. Louis, MO) as recommended by the manufacturer, and optical density at 450–650 nm (OD_450–650) was obtained with a microplate reader (Molecular Devices).</p>", "<p>For the anti-RGD33-His₆and anti-His₆response, ELISA plates (Nunc Maxisorp, Rochester, NY) were coated with 20 μM of the RGD33-His₆peptide or the His₆peptide in 100 μl of 50 mM carbonate (pH 9.6) per well according to the method we described previously [##REF##15922957##25##]. Plates were washed and then blocked with 3% BSA/PBS. After washing, 60 μl of 1:50 diluted sera was added. After incubation for at least 2 hr at RT, the plates were extensively washed, and the isotype-specific HRP-conjugated anti-mouse antibody (Southern Biotech., Birmingham, AL) was added. ELISAs were developed with TMB substrate (Sigma-Aldrich, St. Louis, MO). OD_450–650was measured on an Emax microplate reader.</p>", "<title>Mouse immunization</title>", "<p>Female C57BL/6J (H-2^b) mice at 6–8 weeks of age were obtained from the Jackson Laboratory (Bar Harbor, ME). Groups of at least three to five mice were analyzed in each experiment or at each time point. For antibody response analysis, the following adenoviral vectors were injected into each group of mice: Ad5, Ad5/HVR2-His₆, Ad5/HVR5-His₆, Ad5/HVR2-33RGD-His₆, Ad5/HVR5-33RGD-His₆, Ad5/HVR5-43RGD-His₆, and Ad5/HVR5-53RGD-His₆at 1 × 10¹⁰viral particles (VPs) per mouse using tail intravenous injection. For CD4⁺T cell response analysis, the following adenoviral vectors were injected to each group of mice: Ad5, Ad5/HVR2-33RGD-His₆, or Ad5/HVR5-33RGD-His₆at 1 × 10¹⁰VP per mouse using tail intravenous injection. On day 40, these mice were intravenously boosted with the same dose of the same vectors or peptide. These mice were then sacrificed 9 days later. All animal protocols were approved by the Institutional Animal Care and Use Committee at the University of Alabama at Birmingham.</p>", "<title>Peptide prediction and synthesis</title>", "<p>The antigenic epitope of His₆and RGD33-His₆were predicted using the Emboss program <ext-link ext-link-type=\"uri\" xlink:href=\"http://emboss.sourceforge.net/apps/antigenic.html\"/> and by the Kyle-Doolittle hydropathic plot from the FIMM database of functional molecular immunology <ext-link ext-link-type=\"uri\" xlink:href=\"http://sdmc.lit.org.sg:8080/fimm/\"/>. Peptide sequences that were given high binding scores in both prediction programs were chosen for ELISA analysis. Peptides were synthesized by GenScript Co (Piscataway, NJ) and were &gt;98% pure as indicated by analytical high-performance liquid chromatography. Peptides were dissolved in 100% DMSO at a concentration of 10 mM and stored at -20°C until use.</p>", "<title>Intracellular flow cytometry staining</title>", "<p>Intracellular analysis of cytokines produced by CD4+ T cells was carried out using FACS analysis according to the protocol of Harrington, et al. and Mangan, et al.[##REF##16200070##26##,##REF##16648837##27##]. Briefly, prior to carrying out intracellular cytokine staining, polarized whole spleen cells or CD4+ T cells were stimulated for 5 h with phorbylmyristyl acetate (50 ng/ml; Sigma-Aldrich, St. Louis, MO) and ionomycin (750 ng/ml; Sigma-Aldrich) in the presence of either GolgiStop at the recommended concentrations (BD Pharmingen, San Diego, CA). Cells were first stained extracellularly with fluorescein isothiocyanate-conjugated anti-CD4+ (RM4-5), fixed and permeabilized with Cytofix/Cytoperm solution (BD Pharmingen), and then stained intracellularly with allophycocyanin-conjugated anti-IFN-γ (XMG1.2) and anti-IL-4 (11B11). Samples were acquired on a FACSCalibur (Becton Dickinson, Franklin Lakes, NJ) and data were analyzed with FlowJo (Ashland, OR) software.</p>", "<title>Statistical evaluation</title>", "<p>The data are presented as the mean ± the standard error. Statistical analyses were performed with the nonpaired two-tailed Student <italic>t </italic>test, assuming equal variance. Statistical significance was defined as <italic>P </italic>&lt; 0.05.</p>" ]

[ "<title>Results</title>", "<title>Incorporation of antigenic epitopes within Ad5 hexon HVR2 or HVR5</title>", "<p>In order to assess the capacity of the Ad5 hexon hypervariable regions to accommodate heterologous polypeptides, we genetically incorporated incrementally increasing fragments of the Arg-Gly-Asp (RGD)-containing loop of the Ad5 penton base. Fragments were engineered to contain the RGD motif in the middle, flanked by penton base-derived sequences of equal lengths on both sides. The length of each flanking sequence in the shortest construct was 15 amino acid (aa) residues; this was increased by 10-aa increments in succeeding constructs [##REF##12163581##22##]. DNA sequences corresponding to the fragments of the penton base protein were assembled by PCR (Figure ##FIG##0##1A##. and Table ##TAB##0##1##). These PCR products were cloned between codons for Ser192 and His193 (Fig. ##FIG##0##1B–1##) of the previously modified Ad5/HVR2-His₆genome [##REF##15731232##18##] or between codons for Ser273 and His274 of the previously modified Ad5/HVR5-His₆genome (Fig. ##FIG##0##1B–2##) [##REF##15731232##18##]. A total of six fragments encoding the penton base protein ranging in size from 33, 43, 53, 63, 73, and 83 aa were amplified and incorporated into the Ad5 hexon HVR2 or HVR5 region.</p>", "<title>Ad5 hexon HVR2 or HVR5 can accommodate large heterologous polypeptides</title>", "<p>The newly designed hexon genes were transferred into the E1-deleted Ad5 genome lacking the hexon gene. Subsequent transfection of 293 cells with the resultant recombinant genomes led to the rescue of 4 of the 12 vectors. Viable viruses were produced with incorporation of 33 aa plus a 12 aa linker at HVR2 or HVR5 (Table ##TAB##1##2A##). In addition, viable viruses were rescued with incorporations of 43 and 53 aa plus linkers at HVR5 (Table ##TAB##1##2A##). The recombinant hexon viruses rescued contained the aforementioned penton base-RGD composition (Table ##TAB##1##2B##). Rescued viruses were further amplified and their identities were confirmed by PCR, non-defective revertant viruses were not detected (data not shown). These data were further confirmed by partial sequencing of the hexon genes contained in the Ad5/HVR2 or Ad5/HVR5 genomes. Having established the identities of the newly rescued Ad viruses, we next tested whether the large incorporations in hexon had any effect on virus stability and/or infectious properties. Physical titer, as well as infectious titer was determined for each virus. The viral particle/infectious particle (VP/IP) ratio was calculated for the control viruses, (Ad5, Ad/HVR2-His₆, and Ad/HVR5-His₆) as well as all of the hexon-modified viruses. We observed that, as the incorporation size at hexon increased the VP/IP ratio also increased compared to the His₆vectors or unmodified Ad5 (Table ##TAB##2##3##). A normal VP/IP ratio of unmodified Ad ranges from ~10–30.</p>", "<title>Large epitope incorporations are accessible within Ad5 hexon HVR2 or HVR5</title>", "<p>Our previous studies determined that His₆epitopes incorporated in HVR2 or HVR5 could bind to anti-His₆tag antibody via an ELISA assay, therefore surface exposed [##REF##15731232##18##]. After establishing the ability to place large epitopes into HVR2 or HVR5, we next sought to explore whether the larger epitope incorporations were also surface exposed. Only surface expressed motifs should be accessible to antibody binding, thus, we verified that the RGD-His₆motif in HVR2 or HVR5 were accessible on the virion surface by ELISA with an anti-His₆antibody (Fig. ##FIG##1##2##). In the assay, varying amounts of purified viruses were immobilized in the wells of ELISA plates and incubated with anti-His₆antibody and appropriate secondary antibody. The results demonstrated that Ad5/HVR2-33RGD-His₆, Ad5/HVR5-33RGD-His₆, Ad5/HVR5-43RGD-His₆, Ad5/HVR5-53RGD-His₆, and positive controls (Ad5/HVR2-His₆and Ad5/HVR5-His_6,) [##REF##15731232##18##] have significant levels of binding by anti-His₆antibody, while negative control Ad5 showed essentially no binding. These results indicate that the RGD-His₆epitopes incorporated in HVR2 or HVR5 are exposed on the virion surface.</p>", "<title>Incorporation of epitopes within Ad5 hexon HVR2 or HVR5 elicits an IgG immune response</title>", "<p>We next sought to establish that these modified Ad vectors could elicit an immune response in mice. In this regard, a high IgG response is in part indicative of protection for the host organism. Equal amounts of viral particles were used to immunize C57BL/6J mice. The sera from these mice were collected at multiple time points up to 70 days post-injection for analysis with ELISA binding assays (Fig. ##FIG##2##3##). For these assays synthesized His₆peptides (His6/linker) (LGSHHHHHHLGS) were first bound to the ELISA plate, the plates were then incubated with immunized mice sera. The binding of mouse anti-His₆IgG to the synthesized peptides was detected with a HRP-conjugated secondary antibody (Fig. ##FIG##2##3A–B##). The data illustrate no binding of the His₆peptide with serum from the mice immunized with the negative control Ad5 or the uninfected mice. This is in contrast to substantial binding seen with mice immunized with Ad5/HVR5-43RGD-His₆, Ad5/HVR5-53RGD-His₆, or positive controls [##REF##15731232##18##], while Ad5/HVR2-33RGD-His₆and Ad5/HVR5-33RGD-His₆showed weaker binding. Of note, all of these vectors contain His₆epitopes in the modified HVR regions. The data demonstrated that immunization with Ads containing capsid-incorporated antigen elicits an anti-His₆IgG response in mice which shows a substantial peak at 30 days (Fig. ##FIG##2##3A##) when observed over a time course of 70 days (Fig. ##FIG##2##3B##); furthermore, the magnitude of the immune response was a function of antigen locale and flanking sequences (Fig. ##FIG##2##3A–B##).</p>", "<p>Similarly, significant results were seen in response to the synthesized 33RGD-His₆antigenic peptide (which contains a core RGD residue flanked by His₆/linker). These results were observed at time points ranging from 1–70 days (Fig. ##FIG##3##4A##). Since the probe contained both the 33RGD and His₆epitopes binding was expected for all of the sera samples except sera from Ad5 immunized mice.</p>", "<title>Incorporation of epitopes within Ad5 hexon HVR2 or HVR5 elicits a variable humoral immune response</title>", "<p>We next performed experiments to determine the quantitative aspects of the isotype-specific humoral responses that were generated in response to our vectors. For IgG1 isotype antibodies, the highest levels of anti-33RGD-His₆IgG1 were seen on day 7 after immunization with Ad5/HVR5-33RGD-His₆, Ad5/HVR5-43RGD-His₆, and Ad5/HVR5-53RGD-His₆virions. These results confirm that the HVR5 loop provides the most immunogenic environment for production of anti-33RGD-His₆IgG1 isotype antibodies. Further supporting this, the IgG1 antibody response to the 33RGD-His₆in the HVR2 loop was markedly lower when directly compared to the 33RGD-His₆in the HVR5 loop (Fig. ##FIG##3##4B##). The IgG2b (Fig. ##FIG##3##4C##) and IgG2c (Fig. ##FIG##3##4D##) isotype specific antibody response to RGD33-His₆epitope followed the same pattern as IgG1, except that peak values did not occur until day 12 after immunization, and antibody levels were sustained at high levels out to day 50. These results indicate that RGD-His₆epitopes in the HVR5 loop are more immunogenic and invoke higher sera levels of total anti-33RGD-His₆IgG antibodies than RGD-His₆epitopes in the HVR2 loop.</p>", "<title>Incorporation of epitopes within Ad5 hexon HVR2 or HVR5 elicits a varied T cell and secondary response</title>", "<p>Increased antibody titers of the IgG class require help from either Th1 CD4⁺T cells that produce IFN-γ or Th2 CD4⁺T cells that produce IL-4 [##REF##11228419##28##]. Th1 is generally associated with isotype class switching to IgG2a (in IgH^dstrain of mice) or IgG2c (in IgH^bstain), whereas Th2 help is associated with class switching to IgG1 or IgG2b in mice [##REF##2456466##29##]. To determine if there is an increase in Th1 or Th2 response to the 33RGD-His₆peptide after boost of the Ad5/HVR2-33RGD-His₆or Ad5/HVR5-33RGD-His₆vector, a single-cell suspension of spleen cells was prepared on day 9 after secondary virus infection. Cells were stained with a fluorescent labeled anti-CD4 antibody and then permeabilized in intracellular stain with fluorescent conjugated antibodies against IL-4 or IFN-γ. CD4⁺T cells from mice immunized with Ad5/HVR5-33RGD-His₆produced a significant increase in IFN-γ expressing cells and a lesser increase in CD4⁺T cells that express IL-4. In C57BL/6J mice immunized with Ad5/HVR2-33RGD-His₆or Ad5, there were very low numbers of IFN-γ⁺CD4⁺T cells (Fig. ##FIG##4##5A–B##). CD4⁺cells expressing IL-4 was equally increased in mice immunized with Ad5/HVR2-33RGD His₆or with Ad5/HVR5-33RGD-His₆(Fig. ##FIG##4##5A–B##). The increased IgG antibody response to 33RGD-His₆in the HVR5 loop of Ad is associated with a significant increased Th1 T cell response.</p>", "<p>To evaluate whether immunization with our hexon-modified viruses resulted in improved secondary antibody responses, mice were immunized with Ad5, Ad5/HVR2-33RGD-His₆, or Ad5/HVR5-33RGD-His₆. Forty days later, mice were boosted with the respective hexon-modified viruses. Sera levels of antibodies against the 33RGD-His₆peptide were determined at day 9 following the booster injection. The Ad5/HVR5-33RGD group exhibited further enhancement with respect to IgG1 antibody response to the 33RGD-His₆peptide after boosting compared to Ad5 control (Fig. ##FIG##5##6##). This trend is similar to that seen after primary immunization (Fig. ##FIG##3##4B##).</p>" ]

[ "<title>Discussion</title>", "<p>We have developed novel adenovirus vectors that have the potential to optimize adenovirus vaccine approaches. This strategy involves inserting antigenic epitopes of various sizes into HVR2 or HVR5 regions of the Ad capsid protein, hexon, to stimulate epitope-specific antibody responses following vaccination. The ability to insert multiple antigens in the Ad capsid will allow vaccination with antigenic epitopes in one vector. This method offers the ability to compare a range of identical epitopes incorporated within HVRs for antigenic optimization. Our current study is the first study of its kind to compare a range of identical epitopes incorporated within HVRs for antigenic optimization. Importantly, our data ascribe a maximal antigenic incorporation size at HVR2 and HVR5 as it relates to identical antigenic epitopes.</p>", "<p>Similar studies have been performed by other groups, Worgall and colleagues describe incorporations of a neutralizing epitope from the <italic>Pseudomonas aeruginosa </italic>outer membrane protein F (OprF) into adenovirus HVR5 [##REF##15841217##10##]. The authors showed an increase in antibody response in BALB/c mice consisting of both IgG1 and IgG2a subtypes. Additionally, when mice immunized with the virus containing the OprF epitope were subjected to pulmonary challenge with <italic>P. aeruginosa</italic>, 60 to 80% survival was achieved. This was in contrast to results seen by McConnell et.al, who published that chimeric hexons containing incorporations of B. <italic>anthracis </italic>protective antigen (PA) elicited antibodies against PA in mice but failed to yield protection against anthrax toxin (lethal factor) challenge [##REF##16699016##15##]. The authors speculate that the varying results reflect a difference in the ability of the selected epitopes to elicit a neutralizing response in the varying disease models or a difference in the antibody titers necessary to achieve protection against <italic>P. aeruginosa </italic>compared to lethal factor challenge. In addition, they speculate that the latter may be related to the fact that in the anthrax model the response is directed against a secreted bacterial toxin, while in the <italic>P. aeruginosa </italic>model the response is directed against the bacterium itself. Similar studies have been performed by Krause et. al, [##REF##16699033##14##]. Krause's study compared the immune response generated by incorporating the hemagglutinin (HA) protein of the influenza A virus incorporated into the outer Ad capsid protein hexon, penton base, fiber knob, or protein IX. The HA epitope was recognized by the anti-HA antibody in all four modified virions with slightly stronger binding to the HA presented in hexon HVR5. However, this study does not investigate whether the size of the incorporated epitopes could also affect the immune response generated.</p>", "<p>The strategy we pursued involved the genetic incorporation into hexon HVR2 and 5, respectively. We chose the RGD-containing motif to incorporate into the hexon protein because the RGD motif has been demonstrated to have a critical role in Ad entry. Thus by incorporating this molecule into the Ad hexon we speculated that it might be possible to enhance Ad viral tropism. [##REF##10233980##17##]. In addition, we have previously established that these RGD motifs can be inserted into another Ad capsid protein fiber, thus modulating vector tropism [##REF##12163581##22##]. A total of six fragments of the penton base protein ranging in size from 33 to 83 aa were incorporated into the Ad5 hexon HVR2 or HVR5. Viable viruses were produced with incorporations of 33 aa at HVR2 and up to 53 aa at HVR5 (Table ##TAB##1##2A##). To effectively invoke an epitope-specific immune response, genetically incorporated epitopes must be accessible on the Ad surface. This study illustrates that RGD-His₆motifs incorporated within HVR2 or HVR5 were accessible on the adenovirus surface based on anti-His₆ELISA (Fig. ##FIG##1##2##). There was no significant difference between <italic>in vitro </italic>antibody binding of viruses that contain His₆residues at HVR2 or HVR5, or viruses that contain the 33RGD-His₆epitope at HVR2 or HVR5. This finding confirms that the 33RGD-His₆motifs incorporated within HVR2 or HVR5 are indeed accessible on the Ad surface and should therefore be available to antibodies <italic>in vivo</italic>. We observed that increasing the size of incorporations at hexon HVRs increased the virological viral particle/infectious particle ratios (Table ##TAB##2##3##), we speculate that virus assembly and stability is affected. In addition, we have observed more aggregation with inserts incorporated at the Ad hexon HVR5 locale, we also further speculate that insertions containing RGD epitopes lend to virus aggregation. Since modifications to Ad capsid proteins can influence infectivity as well as immunogenicity of Ad vaccines and transduction efficiency, it is possible that our modifications would significantly alter the infectivity of Ad. Ad infectivity occurs through the binding of the Ad capsid proteins penton base and fiber to cellular receptors [##REF##9171344##30##, ####REF##15110528##31##, ##REF##8477447##32##, ##REF##14963160##33####14963160##33##]. More recently, hexon HVR's have been implicated in liver transduction [##REF##18391209##19##, ####REF##18267072##20##, ##REF##18560416##21####18560416##21##]. We speculate that these recent findings by kalyuzhniy and colleagues, indicate that our Ad vectors are more clinically relevant due to the likelihood of less liver transduction.</p>", "<p>Successful stimulation of immune responses by Ad vaccines schemas are thought to be dependent partly on the activation of antigen presenting cells, particularly dendritic cells [##REF##10757355##34##,##REF##11356075##35##]. Indeed, genetic modifications made to the capsid in this present study impair some virological properties such as virus particle/infectious particle ratios and gene transfer efficacies (data not shown), but our data indicates that <italic>in vivo </italic>immune response was not affected. However, we will pursue investigation regarding the uptake of our hexon-modified virus by antigen presenting cells. Of note, in this study we notice higher <italic>in vivo </italic>immune response of viruses containing 43 or 53 RGD-His₆epitopes at HVR5 compared to that of 33RGD-His_6.Sequence analysis of these three epitopes show no obvious reason for this trend (ie. hydrophobic or hydrophilic patterns), therefore; detailed structural analysis must be performed.</p>", "<p>Finally, our results indicate that mice boosted with Ad5/HVR2-33RGD-His₆or Ad5/HVR5-33RGD-His₆produced an improved secondary immune response as compared to the control Ad5 vector (Fig. ##FIG##5##6##). Successful boosting is an important factor because anti-Ad exposure after administration of Ad vectors does not generally allow repeat administration with an Ad vector of the same serotype [##REF##9557710##36##, ####REF##11249767##37##, ##REF##8989999##38##, ##REF##8825871##39##, ##REF##7884845##40####7884845##40##,##REF##15841217##10##]. Anti-Ad immunity is thought to be an obstacle for the use of Ad as a gene therapy vector; re-administration of the same vector would be beneficial in the development of Ad-based vaccines to enable boosting of antigen-specific immune response. In our study, repeat immunization resulted in boosting of the anti-33RGD-His₆antibody responses. The Ad5/HVR5-33RGD-His₆vector exhibited the highest antibody response to both 33RGD-His₆peptide and His₆peptide (data not shown) after boosting; therefore the Ad5/HVR5-33RGD-His₆vector is the best construct to generate the Ad vaccine response with respect to our model antigens.</p>", "<p>Our study in contrast, to other reports illustrates the qualitative differences with respect to incorporation of large epitopes within HVR2 or HVR5, until now most reports only investigate HVR5 as a potential incorporation locale. Our study demonstrates that HVR5 is more permissible than HVR2 with respect to incorporation of our largest model antigen. Immunizations with vectors that present smaller His₆insertions at HVR2 compared to HVR5, yield similar results with respect to antibody response and insertion locale. In contrast, immunizations with viruses containing large insertions at HVR5 yielded higher antibody and Th1 responses compared to insertions at HVR2. These results were in contrast to that seen with <italic>in vitro </italic>ELISA assays, which were equal binding of insertions at HVR2 or 5 independent of insertion size (Fig. ##FIG##1##2##). Furthermore, it is likely that large insertions at HVR2 are not permissible due to the surrounding Ad protein structure/environment. However, smaller inserts may be tolerated at HVR2.</p>", "<p>We plan to investigate factors limiting insertions at HVR2 and HVR5 by means of cryoEM analysis, this work will correlate well with the Ad crystal structure and cryoEM analysis which has been recently solved [##REF##3704642##41##, ####REF##15890367##42##, ##REF##7704534##43##, ##REF##15629723##44####15629723##44##]. In the aggregate, our study demonstrates that utilization of the HVR2 or 5 locales predicate optimal antigen size and configuration. Based on this technology, we will be able to establish the critical correlates between antigen locale/accessibility within the capsid context and vaccine efficacy. Our study evaluated model antigens at HVR2 or HVR5; further studies are necessary to evaluate therapeutic antigens at these locales in the context of binding and antibody neutralization. Transitioning our dual hexon presentation platform to present therapeutic antigens will also allow us to evaluate and use challenge models for efficacy and antigen protection assays. Capsid incorporation of antigens is a highly innovative strategy to present antigens in the context of adenovirus vaccine schemas. This strategy can also be exploited to construct multivalent vaccines, which can allow vaccination against multiple strains of a particular infectious disease or protection against multiple unrelated diseases. Of particular interest to us is the potential to expand our dual hexon antigen presentation strategy to develop Ad-based vaccinations against HIV infection and many other infections or diseases.</p>" ]

[]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Despite the many potential advantages of Ad vectors for vaccine application, the full utility of current Ad vaccines may be limited by the host anti-vector immune response. Direct incorporation of antigens into the adenovirus capsid offers a new and exciting approach for vaccination strategies; this strategy exploits the inherent antigenicity of the Ad vector. Critical to exploiting Ad in this new context is the placement of antigenic epitopes within the major Ad capsid protein, hexon. In our current study we illustrate that we have the capability to place a range of antigenic epitopes within Ad5 capsid protein hexon hypervariable regions (HVRs) 2 or 5, thus producing viable Ad virions. Our data define the maximal incorporation size at HVR2 or HVR5 as it relates to identical antigenic epitopes. In addition, this data suggests that Ad5 HVR5 is more permissive to a range of insertions. Most importantly, repeated administration of our hexon-modified viruses resulted in a secondary anti-antigen response, whereas minimal secondary effect was present after administration of Ad5 control. Our study describes antigen placement and optimization within the context of the capsid incorporation approach of Ad vaccine employment, thereby broadening this new methodology.</p>" ]

[ "<title>Abbreviations</title>", "<p>Ad: Adenovirus; Ad5: Adenovirus serotype 5; aa: Amino acid; ELISA: Enzyme-linked immunosorbent assay; HA: Hemagglutinin; His: Histidine; His₆: Six-histidine; HVRs: Hypervariable regions; IP: Infectious particles; RGD: Arg-Gly-Asp; VP: Viral particles.</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors would also like to acknowledge Dr. Maaike Everts and Erin E. Thacker as well as Yizhe Tang for their critical reading of the manuscript. This work was supported by grants from the National Institutes of Health: 5T32AI07493-11 (Dr. Casey Morrow), and 1R21AI076096-01 (Dr. David T. Curiel).</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Construction of hexon modified genomes.</bold> (A) Agarose gel electrophoresis of PCR products were obtained through PCR of genomic DNA of Ad5 using epitope specific primers (Table 1). (B) HVR2 or HVR5 shuttle vector sites which were modified with a RGD motif. The DNA encoding for the respective RGD motifs were cloned into the HVRs of our previously modified shuttle vectors within the LGSHHHHHLGS linker, as indicated by the arrows.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Model epitopes incorporated in HVRs are accessible in the context of an intact virion.</bold> In the assay, varying amounts of purified viruses were immobilized in the wells of ELISA plates and incubated with anti-His₆tag antibody. The binding was detected with an AP-conjugated secondary antibody. These results suggested that the model antigens (tagged with His₆epitopes) and the His₆epitopes (controls) incorporated into HVR2 or HVR5 were accessible to anti-His₆tag antibody at the virion level, indicating that the epitopes were exposed on the virion surface. All of the Ad vectors except Ad5 present His₆or RGD-His₆within the hexon. The His₆antigenic peptide is presented by Ad5/HVR5-His₆and Ad5/HVR5-His₆.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Capsid-incorporated antigens elicit an IgG immune response.</bold> C57BL/6J mice were immunized with 10¹⁰VP of Ad vectors. Post-immunization sera were collected after (A) 30 days post-injection or (B) 0–70 days post-injection for ELISA binding assays. 20 μM of synthesized antigenic peptide His₆peptide was bound to ELISA plates. Residual unbound peptide was washed from the plates. The plates were then incubated with immunized mice sera, the binding was detected with IgG-specific HRP-conjugated anti-mouse secondary antibody. OD absorbance represents the sera levels of antibodies. Values are expressed as the mean ± standard error of three replicates. * indicates a P value of &lt;.05., ** P &lt; .001, *** P &lt; .00001. Control viruses are Ad5, Ad/HVR2-His₆and Ad/HVR5-His₆.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Capsid-incorporated antigens elicit a varied immune response.</bold> (A-D) C57BL/6J mice were immunized with 10¹⁰VP of Ad vectors. Post-immunization sera were collected after 50 days post-injection for ELISA binding assays. 20 μM of synthetic peptide 33RGD-His₆(RGD residue flanked by His₆/Linker) was bound to the plate. The plates were then incubated with immunized mice sera, the binding was detected with isotype-specific HRP-conjugated anti-mouse secondary antibody (A, IgG; B, IgG1; C, IgG2b; D, IgG2c). OD absorbance represents the sera levels of antibodies. Values are expressed as the mean ± standard error of three replicates. Control viruses are Ad5, Ad/HVR2-His₆and Ad/HVR5-His₆.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Capsid-incorporated antigens elicit a varied T cell response.</bold> (A-B) C57BL/6J mice were immunized with 10¹⁰VP of Ad vectors. On day 40, these mice were intravenously boosted with the same dose of the same vectors. A single-cell suspension of spleen cells was prepared on day 9 after secondary virus infection. Cells were stained with a fluorescent labeled anti-CD4 antibody and then permeabilized in intracellular stain with fluorescent conjugated antibodies against IL-4 or IFN-γ. Samples were acquired on a FACSCalibur and data were analyzed with FlowJo software. Values are expressed as the mean ± standard error of three replicates.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Repeat administration of hexon-modified viruses results in boosting of the anti-33RGD-His₆immune response.</bold> C57BL/6J mice were immunized with 10¹⁰VP of Ad vectors. On day 40, these mice were intravenously boosted with the same dose of the same vectors. Post-immunization sera were collected after 9 days post-injection for ELISA binding assays. 20 μM of synthetic peptide 33RGD-His₆was bound to the plate. The plates were then incubated with immunized mice sera, the binding was detected with isotype-specific HRP-conjugated anti-mouse secondary antibody. OD absorbance represents the sera levels of antibodies. Values are expressed as the mean ± standard error of three replicates.</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Primers used in this study.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\">33RGD -(as)</td><td align=\"left\">CGGGATCC<underline>TGCTTCGGCCTCAGCGCGC</underline></td></tr><tr><td align=\"left\">33RGD -(s)</td><td align=\"left\">CGGGATCC<underline>GCCGCGGCAATGCAGCC</underline></td></tr><tr><td/><td/></tr><tr><td align=\"left\">43RGD -(as)</td><td align=\"left\">CGGGATCC<underline>GGCAGCTTCGGCCGCTG</underline></td></tr><tr><td align=\"left\">43RGD -(a)</td><td align=\"left\">CGGGATCC<underline>AACTCCAACGCGGCAGCC</underline></td></tr><tr><td/><td/></tr><tr><td align=\"left\">53RGD -(as)</td><td align=\"left\">CGGGATCC<underline>TTGCGCAGCGGGGGC</underline></td></tr><tr><td align=\"left\">53RGD -(a)</td><td align=\"left\">CGGGATCC<underline>AGCGGCGCGGAAGAGAACTC</underline></td></tr><tr><td/><td/></tr><tr><td align=\"left\">63RGD -(as)</td><td align=\"left\">CGGGATCC<underline>CTTCTCGACCTCGGGTTGCG</underline></td></tr><tr><td align=\"left\">63RGD -(a)</td><td align=\"left\">CGGGATCC<underline>AGCAACAGCAGTGGCAGCG</underline></td></tr><tr><td/><td/></tr><tr><td align=\"left\">73RGD -(as)</td><td align=\"left\">CGGGATCC<underline>CGGTTTCTTCTGAGGCTTCTCG</underline></td></tr><tr><td align=\"left\">73RGD -(a)</td><td align=\"left\">CGGGATCC<underline>GGTGGCGCAGGCGG</underline></td></tr><tr><td/><td/></tr><tr><td align=\"left\">83RGD -(as)</td><td align=\"left\">CGGGATCC<underline>CAGGGGTTTGATCACCGGTTT</underline></td></tr><tr><td align=\"left\">83RGD -(a)</td><td align=\"left\">CGGGATCC<underline>ACCGAACAGGGCGGGG</underline></td></tr><tr><td/><td/></tr><tr><td align=\"left\">3'HVR5-(as)</td><td align=\"left\">GGCATGTAAGAAATATGAGTGTCTGGG</td></tr><tr><td align=\"left\">5'HVR2-(s)</td><td align=\"left\">CTCACGTATTTGGGCAGGCGCC</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Viable viruses.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>A</bold>.</td><td/><td/></tr><tr><td align=\"left\"><bold>Insert</bold></td><td align=\"center\"><bold>HVR2</bold></td><td align=\"center\"><bold>HVR5</bold></td></tr></thead><tbody><tr><td align=\"left\">33RGD Motif + 12 aa Linker</td><td align=\"center\"><bold>+</bold></td><td align=\"center\"><bold>+</bold></td></tr><tr><td align=\"left\">43RGD Motif + 12 aa Linker</td><td align=\"center\"><bold>-</bold></td><td align=\"center\"><bold>+</bold></td></tr><tr><td align=\"left\">53RGD Motif + 12 aa Linker</td><td align=\"center\"><bold>-</bold></td><td align=\"center\"><bold>+</bold></td></tr><tr><td align=\"left\">63RGD Motif + 12 aa Linker</td><td align=\"center\"><bold>-</bold></td><td align=\"center\"><bold>-</bold></td></tr><tr><td align=\"left\">73RGD Motif + 12 aa Linker</td><td align=\"center\"><bold>-</bold></td><td align=\"center\"><bold>-</bold></td></tr><tr><td align=\"left\">83RGD Motif + 12 aa Linker</td><td align=\"center\"><bold>-</bold></td><td align=\"center\"><bold>-</bold></td></tr><tr><td align=\"left\" colspan=\"3\">(+) = viable viruses (-) = not viable viruses.</td></tr><tr><td/><td/><td/></tr><tr><td align=\"left\"><bold>B</bold>.</td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">33RGD motif-</td><td align=\"left\" colspan=\"2\"><bold>AAAMQPVEDMNDHAI<italic><underline>RGD</underline></italic>TFATRAEEKRAEAEA</bold></td></tr><tr><td align=\"left\">43RGD motif-</td><td align=\"left\" colspan=\"2\"><bold>NSNAAAAAMQPVEDMNDHAI<italic><underline>RGD</underline></italic>TFATRAEEKRAEAEAAAEAA</bold></td></tr><tr><td align=\"left\">53RGD motif-</td><td align=\"left\" colspan=\"2\"><bold>SGAEENSNAAAAAMQPVEDMNDHAI<italic><underline>RGD</underline></italic>TFATRAEEKRAEAEAAAEAAAPAAQ</bold></td></tr><tr><td align=\"left\">63RGD motif-</td><td align=\"left\" colspan=\"2\"><bold>SNSSGSGAEENSNAAAAAMQPVEDMNDHAI<italic><underline>RGD</underline></italic>TFATRAEEKRAEAEAAAEAAAPAAQPEVEK</bold></td></tr><tr><td align=\"left\">73RGD motif-</td><td align=\"left\" colspan=\"2\"><bold>GGAGGSNSSGSGAEENSNAAAAAMQPVEDMNDHAI<italic><underline>RGD</underline></italic>TFATRAEEKRAEAEAAAEAAAPAAQPEVEKPQKKP</bold></td></tr><tr><td align=\"left\">83RGD motif-</td><td align=\"left\" colspan=\"2\"><bold>TEQGGGGAGGSNSSGSGAEENSNAAAAAMQPVEDMNDHAI<italic><underline>RGD</underline></italic>TFATRAEEKRAEAEAAAEAAAPAAQPEVEKPQKKPVIKPL</bold></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>We observed that, as the incorporation size at hexon increased the VP/IP ratio also increased compared to the His6 vectors or unmodified Ad5 .</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Modified Viruses</bold></td><td align=\"left\"><bold>Viral Particle (VP)</bold></td><td align=\"left\"><bold>Infectious Particles (IP)</bold></td><td align=\"left\"><bold>VP/IP</bold></td></tr></thead><tbody><tr><td align=\"left\">Ad5</td><td align=\"left\">4.58 × 10¹²vp/ml</td><td align=\"left\">3 × 10¹¹PFU/ml</td><td align=\"left\">15.26</td></tr><tr><td align=\"left\">Ad/HVR2-His₆</td><td align=\"left\">5 × 10¹²vp/ml</td><td align=\"left\">3 × 10¹¹PFU/ml</td><td align=\"left\">14.7</td></tr><tr><td align=\"left\">Ad/HVR5-His₆</td><td align=\"left\">5 × 10¹²vp/ml</td><td align=\"left\">4 × 10¹¹PFU/ml</td><td align=\"left\">14.25</td></tr><tr><td align=\"left\">Ad/HVR2-33RGD-His₆</td><td align=\"left\">4.7 × 10¹¹vp/ml</td><td align=\"left\">2 × 10⁹PFU/ml</td><td align=\"left\">236</td></tr><tr><td align=\"left\">Ad/HVR5-33RGD-His₆</td><td align=\"left\">1.85 × 10¹²vp/ml</td><td align=\"left\">1.58 × 10⁹PFU/ml</td><td align=\"left\">1,170</td></tr><tr><td align=\"left\">Ad/HVR5-43RGD-His₆</td><td align=\"left\">2.35 × 10¹²vp/ml</td><td align=\"left\">3.98 × 10⁸PFU/ml</td><td align=\"left\">5,940</td></tr><tr><td align=\"left\">Ad/HVR5-53RGD-His₆</td><td align=\"left\">1.01 × 10¹²vp/ml</td><td align=\"left\">1.25 × 10⁹PFU/ml</td><td align=\"left\">808</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>^aantisense, as; sense, s.</p><p>^bUnderlined letters represent the sequences</p><p>encoding the RGD motifs.</p></table-wrap-foot>", "<table-wrap-foot><p>Core Arg-Gly-Asp (RGD) motif italicized and underlined</p></table-wrap-foot>" ]

[ "<graphic xlink:href=\"1743-422X-5-98-1\"/>", "<graphic xlink:href=\"1743-422X-5-98-2\"/>", "<graphic xlink:href=\"1743-422X-5-98-3\"/>", "<graphic xlink:href=\"1743-422X-5-98-4\"/>", "<graphic xlink:href=\"1743-422X-5-98-5\"/>", "<graphic xlink:href=\"1743-422X-5-98-6\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<title>Description of the condition</title>", "<p>Hepatitis B is a major global problem. More than two billion people alive today have been infected worldwide [##UREF##0##1##] and approximately 350 million people are chronically infected with hepatitis B virus (HBV) [##REF##8385046##2##,##REF##9392700##3##]. Chronic hepatitis B (CHB) is associated with serious complications, including liver failure, cirrhosis, and hepatocellular carcinoma [##REF##3371868##4##, ####REF##2834034##5##, ##REF##10861647##6####10861647##6##]. Each year more than one million patients with CHB worldwide die from these diseases [##UREF##0##1##].</p>", "<p>Mother-to-child transmission(MTCT) of HBV is one of the most important causes of chronic HBV infection [##REF##1235469##7##, ####REF##7315794##8##, ##UREF##1##9####1##9##] and remains one serious problem despite passive immunization (hepatitis B immune globulin at birth) and active immunization (hepatitis B vaccination according to the standard 3-dose schedule). MTCT may occur prenatally, during delivery, or postpartum. Currently, a series of measures have been taken to prevent both prenatal and postpartum routes of transmission with progress being achieved to some extent. However, with regard to MTCT of HBV during delivery, disagreements still exist on the issue of whether different mode of delivery (mainly caesarean section <italic>versus </italic>vaginal delivery) will affect the risk of mother-to-child HBV transmission [##REF##8559532##10##,##REF##12490098##11##].</p>", "<title>Description of the intervention</title>", "<p>Of the cases of MTCT of HBV, a large population occur during the intrapartum period. Underlying mechanisms may include transfusion of the mother's blood to the fetus during labor contractions, infection after the rupture of membranes, and direct contact of the fetus with infected secretions or blood from the maternal genital tract [##REF##1113797##12##, ####REF##712120##13##, ##REF##518104##14##, ##REF##7437368##15##, ##REF##7017358##16####7017358##16##]. As elective caesarean section (ECS) is performed before the onset of labor or the rupture of membranes, it could effectively avoid the disbenefits described above. Therefore, ECS might reduce the risk of MTCT of HBV (compared with vaginal delivery or cesarean section after onset of labor or after rupture of membranes).</p>", "<p>It is well known that, in the absence of HBV infection, ECS is related to increased risks of maternal and infant morbidity [##REF##4075629##17##, ####REF##3067173##18##, ##REF##7756199##19####7756199##19##]. In a population of HBV-infected women, the procedure would be expected to be associated with the same or greater deleterious effects on both mother and infant. For instance, surgical delivery would be expected to increase the risk of fever, endometritis, and hemorrhage and severe anemia among women. Commonly, Infants born by ECS at term are at increased risk for developing respiratory disorders compared with those born by vaginal delivery. However, although the risk of neonatal respiratory morbidity is higher, the number of affected infants is small [##REF##15174788##20##]. In addition to respiratory morbidity (respiratory distress syndrome, transient tachypnea of the newborn), an increased risk of lacerations of newborn skin is also of concern with surgical delivery.</p>", "<title>Why it is important to do this overview</title>", "<p>Given the uncertainty of findings from current studies, what is more, HBV-infected pregnant women must be provided with available information with which to make informed decisions regarding ECS and other options to prevent transmission of infection to their children, we aim to determine if there is any evidence from randomized controlled trials (RCTs) that offering ECS to mothers who are infected with HBV affects the risk of MTCT of HBV.</p>" ]

[ "<title>Methods</title>", "<title>Criteria for considering studies for this review</title>", "<title>Types of studies</title>", "<p>We included RCTs only.</p>", "<title>Types of participants</title>", "<p>HBV-infected Pregnant women with HBV DNA-positive (HBV DNA &gt; 10³copies/ml) in sera of blood and their babies.</p>", "<title>Types of intervention</title>", "<p>ECS versus vaginal delivery.</p>", "<title>Types of outcome measures</title>", "<p>Primary outcomes (HBV transmission-related)</p>", "<p>HBV-infection in neonates: HBV DNA-positive in umbilical blood or peripheral blood after birth.</p>", "<p>Secondary outcomes (morbidities related to the actual method of delivery)</p>", "<p>(1)Maternal morbidity: types of maternal morbidity evaluated includes: febrile morbidity, endometritis, hemorrhage or severe anemia, pneumonia, and urinary tract infections.</p>", "<p>(2)Infant morbidity: types of infant morbidity evaluated includes: respiratory morbidity (respiratory distress syndrome and transient tachypnea of the newborn) and skin lacerations.</p>", "<title>Search methods for identification of studies</title>", "<title>Electronic searches</title>", "<p>We searched the electronic databases as follows: Cochrane Pregnancy and Childbirth Group's Trials Register (January, 2008), the Cochrane Central Register of Controlled Trials (the Cochrane Library 2008, issue 1), PubMed (1950 to 2008), EMBASE (1974 to 2008), Chinese Biomedical Literature Database (CBM) (1975 to 2008), China National Knowledge Infrastructure (CNKI) (1979 to 2008), VIP database (1989 to 2008). We also searched additional trials by scanning the reference lists of relevant trials identified. The search strategy was iterative as follows:</p>", "<p>1 HBV</p>", "<p>2 HBV INFECT*</p>", "<p>3 HBV INFECTIONS</p>", "<p>4 HBV INFECTED</p>", "<p>5 #1 OR #2 OR #3 OR #4</p>", "<p>6 DELIVERY, OBSTETRIC</p>", "<p>7 DELIVERY AND PREGNANCY</p>", "<p>8 CAESAREAN SECTION</p>", "<p>9 \"MODE OF DELIVERY\" AND PREGNANCY</p>", "<p>10 #6 OR #7 OR #8 OR #9</p>", "<p>11 INFANT MORTALITY</p>", "<p>12 INFANT MORBIDITY</p>", "<p>13 NEONATAL MORTALITY</p>", "<p>14 NEONATAL MORBIDITY</p>", "<p>15 MATERNAL MORTALITY</p>", "<p>16 MATERNAL MORBIDITY</p>", "<p>17 POSTPARTUM MORTALITY</p>", "<p>18 POSTPARTUM MORBIDITY</p>", "<p>19 #11 OR #12 OR #13 OR #14 OR #15 OR #16 OR #17 OR#18</p>", "<p>20 #5 AND #10 AND #19</p>", "<p>21 (ANIMAL OF ANIMALS) NOT HUMAN</p>", "<p>22 #20 NOT #21</p>", "<title>Other search strategies</title>", "<p>Organizations (including the World Health Organization), individual researchers working in the field were contacted in order to obtain possible additional references, unpublished trials, or ongoing trials, confidential reports and raw data of published trials.</p>", "<title>Selection of studies</title>", "<p>The titles, abstracts and keywords of every record retrieved were scanned to determine which were possibly relevant to the review. Any record that appeared likely to meet the inclusion criteria was obtained in full text. If there was any doubt regarding eligibility from the information given in the title and abstract, the full article was retrieved for clarification. Differences in opinion between reviewers were resolved by discussion.</p>", "<title>Data extraction</title>", "<p>Two review authors (ZX, MY) independently extracted data concerning details of the study population, interventions and outcomes using a standard data extraction form, specifically designed for this review. We resolved differences in data extraction by consensus, and with reference to the original article. If necessary, we sought information from the authors of the primary studies. For dichotomous outcomes, number of events and total number in each group were extracted. For continuous outcomes, mean, standard deviation and sample size of each group were extracted.</p>", "<title>Assessment of risk of bias in included trials</title>", "<p>The risk of bias was assessed based largely on the quality criteria specified by the Cochrane Handbook for Systematic Reviews of Interventions 5.0.0 [##UREF##2##22##]. In particular, the following factors were studied:</p>", "<p>• Selection bias: a) was the randomization procedure adequate? b) was the allocation concealment adequate?</p>", "<p>• Performance bias: were the patients and people performing the intervention blind to the intervention?</p>", "<p>• Attrition bias: a) were withdrawals, dropouts and losses of follow-up completely described? b) was analysis performed by intention-to-treat?</p>", "<p>• Detection bias: were outcome assessors blind to the intervention?</p>", "<p>Based on these criteria, studies were broadly divided into the following three categories. This classification was used as the basis of a sensitivity analysis. Additionally, we intended to explore the influence of individual quality criteria in a sensitivity analysis.</p>", "<p>• A: all quality criteria met – low risk of bias.</p>", "<p>• B: one or more of the quality criteria only partly met-moderate risk of bias.</p>", "<p>• C: one or more criteria not met – high risk of bias.</p>", "<p>Each trial was assessed by two reviewers independently (ZX, MY). Disagreements were resolved, where necessary, by recourse to a third reviewer (YJ). In cases of disagreement, the rest of the group were consulted and a judgment was made based on consensus.</p>", "<title>Data Analysis</title>", "<p>Statistical analysis was carried out by using Review Manager (version 4.2). Dichotomous data were presented as relative risk (RR) and continuous outcomes as weighted mean difference (WMD), both with 95% confidence intervals (CI). The overall effect was tested by using Z score with significance being set at P &lt; 0.05. Heterogeneity was tested by using the chi-squared statistic and I square (I²) with significance being set at P &lt; 0.1. Possible sources of heterogeneity were to be assessed by sensitivity and subgroup analyses. A fixed-effect model was to be used when the studies in the subgroup were sufficiently similar (P &gt; 0.10, I²&lt; 50). A random effects model was to be used in the summary analysis when there was heterogeneity between the subgroups. Publication bias was to be tested by using the funnel plot or other corrective analytical method, depending on the number of clinical trials included in the systematic review.</p>" ]

[ "<title>Results</title>", "<title>Description of studies</title>", "<title>Studies identified</title>", "<p>A total of 942 studies were identified by the searches. No unpublished studies or other information was obtained from contact with WHO and individual researchers. By scanning titles and abstracts of the 942 studies, 927 studies, including overlapped studies, reviews, case reports and meta analyses, were excluded. After referring to full texts, 11 studies were excluded upon further scrutiny due to the following reasons: 7 studies made HBV-infected women with positive hepatitis B surface antigen (HBsAg) and/or hepatitis B e antigen (HBeAg) as their participant criteria; 3 studies had other interventions potentially impacting the outcome; 1 study did not provide data to meet the outcome criteria. Finally, we included 4 studies, involving 789 people, which were all performed in china. Among them, 1 were published in English [##REF##2902274##21##] (Lee 1988), 3 in Chinese (Ji 2002, Wang 2004, Liu 2008, available only by searching the database of CNKI). Apart from Chinese and English, we did not search citations in other languages.</p>", "<title>Designs of included studies</title>", "<p>All the included studies were of a parallel design, single centre and had a control group.</p>", "<title>Participants of included studies</title>", "<p>Numbers of participants of the individual studies ranged from 97 to 244 with a total of 789 participants included in this review. All of them were HBV-infected pregnant women with HBV DNA-positive in sera of blood. The baseline characteristics (including the age, race, gravidity, parity, pregnant week, disease duration, and severity of disease, etc) were similar in the two groups (P &gt; 0.05).</p>", "<title>Interventions of included studies</title>", "<p>ECS was made as the intervention group, and vaginal delivery as the control group in each of the four studies.</p>", "<title>Outcomes of included studies</title>", "<p>None of the four studies reported the maternal morbidity or infant morbidity associated with ECS. The common outcome reported was the positive rate of HBV DNA in neonates under different mode of delivery (ECS <italic>versus </italic>vaginal delivery).</p>", "<title>Methodological quality</title>", "<title>Randomization</title>", "<p>All the four studies mentioned \"random\", \"randomize\" or \"randomized\", but did not give a clear description of the randomization procedure.</p>", "<title>Allocation concealment</title>", "<p>No allocation concealment was used in each of the four studies</p>", "<title>Blinding</title>", "<p>No blind was used in each of the four studies</p>", "<title>Description of withdrawals, dropouts, losses of follow up and intention-to-treat analysis</title>", "<p>Neither of the included studies mentioned withdrawals, dropouts, losses of follow up or performed any intention-to-treat analysis.</p>", "<p>According to the quality criteria listed above, we considered each included study was at high risk of bias and graded as category C.</p>", "<title>Effects of interventions</title>", "<title>Assessment of the efficacy of ECS versus vaginal delivery for preventing MTCT of HBV</title>", "<p>Four studies demonstrated the efficacy of ECS compared to vaginal delivery for the prevention of MTCT of HBV. According to chi-squared statistic and I square (I²), the results of the four studies showed no statistical heterogeneity (p = 0.48.I²= 0%). So we used fixed effect model for meta-analysis. After synthesizing the results, we found out that the rate of MTCT of HBV according to mode of delivery differed significantly (ECS: 10.5%; vaginal delivery: 28.0%). The difference between the two groups (ECS <italic>versus </italic>vaginal delivery) had statistical significance (RR 0.41, 95% CI 0.28 to 0.60, P &lt; 0.000001) (Figure ##FIG##0##1##). Therefore, in comparison to vaginal delivery, ECS is more efficacious for the prevention of MTCT of HBV.</p>", "<title>Subgroup analyses</title>", "<p>Of the four included studies, one trail performed the detection of HBV DNA in neonate's umbilical blood, while other three trails in neonate's peripheral blood. So subgroup analysis was carried out under the two circumstances. The trend towards elevated rate of MTCT of HBV in the ECS group compared to vaginal delivery group with HBV DNA detected in neonate's umbilical blood (RR 0.50, 95% CI 0.26 to 0.95) was similar to studies with HBV DNA detected in neonate's peripheral blood (RR 0.37, 95% CI 0.24 to 0.59) (Figure ##FIG##1##2##).</p>", "<title>Sensitivity analyses</title>", "<p>We did not carry out any of the planned sensitivity analyses as no unpublished studies were found and all included studies were at high risk of bias (graded C).</p>", "<title>Assessment of publication bias</title>", "<p>There was an insufficient number of trials for us to assess publication bias.</p>", "<title>Adverse events</title>", "<p>No postpartum morbidity (PPM) associated with ECS was reported.</p>" ]

[ "<title>Discussion</title>", "<title>Analysis of the effect of ECS for preventing MTCT of HBV</title>", "<p>Four clinical trails were identified which evaluated the efficacy of ECS for the prevention of MTCT of HBV. They indicate ECS could significantly reduce the risk of MTCT. With regard to postpartum morbidity (PPM), none of these studies had reported the maternal morbidity or infant morbidity associated with ECS. Therefore, based on the included studies, the benefit of ECS outweighs the risk of PPM among HBV-infected women. However, it is important to point out that the risk/benefit ratio should depend on the underlying rate of MTCT. With very low rates of MTCT, the risks associated with ECS among HBV-infected women may outweigh the benefits.</p>", "<p>Currently, HBV DNA-positive is mostly considered the direct index reflecting the infectivity of HBV. So we only included HBV-infected women with HBV DNA-positive, and also made neonates with HBV DNA-positive as the outcome criteria. However, the detailed HBV DNA levels in included HBV-infected women were not described, except one study (Liu 2008), which included patients with HBV DNA &gt; 10⁵copy/ml. So we can't get adequate information for the magnitude of infectivity, which may influence the effect of ECS for the prevention of MTCT of HBV in this review. Subgroup analysis demonstrates that, HBV DNA-positive either in neonate's umbilical blood or in neonate's peripheral blood indicates the existence of HBV infection, but false positive must be excluded as a result of nonstandard collection of blood. According to principles of medical ethics, we suggest neonate's umbilical blood should be chosen for detection of HBV DNA, which can largely relieve pains of newborns. But it must be performed strictly to avoid pollution by maternal blood.</p>", "<title>Limitations of this systematic review</title>", "<p>The conclusions of this review must be considered with great caution.</p>", "<p>All the retrieved studies did not give adequate descriptions of the methodology used, which may have misled us if we had not clarified the details, for example, inclusion of non-RCTs and classifying the trials into category B rather than C. It was an exhausting but necessary process to interview every primary trial author before deciding whether to include these trials, when the methodological details were not reported. Contacting authors by telephone was more effective than writing to them because of a higher response rate and left no time for the trial authors to make up artificial details. However, even after confirmation of true randomization, we found that the methodological quality of these studies remained poor.</p>", "<p>Allocation concealment is important in preventing selection bias. Each of the studies related to our question did not use any approach to conceal the allocation process, which could lead to a high risk of selection bias.</p>", "<p>No blinding was conducted with either the participants or the investigators, which led to a high risk of performance bias. None of the studies mentioned blinding to the outcome assessors, which promotes suspicion of detection bias. Publication bias may exist as no primary articles reporting negative results were found. No information of numbers of withdrawals, dropouts, losses of follow up may have led to high attrition bias in one study.</p>", "<p>In addition, included studies of ECS among HBV-infected women have been conducted exclusively in china. In other countries, the risks and benefits associated with ECS have been largely unexplored.</p>" ]

[ "<title>Conclusion</title>", "<title>Implications for practice</title>", "<p>Although studies of ECS showed a strong evidence of a reduction in the risk of MTCT of HBV, methodological concerns including lack of information on randomization procedure, lack of allocation concealment, and lack of blinding, make the role of ECS for preventing MTCT of HBV uncertain.</p>", "<title>Implications for research</title>", "<p>More high quality controlled trials are required for assessing the effects of ECS in comparison to vaginal delivery for preventing MTCT of HBV. We suggest that well-designed RCTs with adequate power to provide a definitive answer, need be conducted. The randomization procedure should be clearly described. Allocation concealment should be emphasized and the approaches should be reported. Blinding should be conducted, though this may be difficult. Additionally, more attention should be paid to PPM associated with ECS.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Caesarean section before labor or before ruptured membranes (\"elective caesarean section\", or ECS) has been introduced as an intervention for preventing mother-to-child transmission (MTCT) of hepatitis B virus (HBV). Currently, no evidence that ECS versus vaginal delivery reduces the rate of MTCT of HBV has been generally provided. The aim of this review is to assess, from randomized control trails (RCTs), the efficacy and safety of ECS versus vaginal delivery in preventing mother-to-child HBV transmission.</p>", "<title>Results</title>", "<p>We searched Cochrane Pregnancy and Childbirth Group's Trials Register (January, 2008), the Cochrane Central Register of Controlled Trials (the Cochrane Library 2008, issue 1), PubMed (1950 to 2008), EMBASE (1974 to 2008), Chinese Biomedical Literature Database (CBM) (1975 to 2008), China National Knowledge Infrastructure (CNKI) (1979 to 2008), VIP database (1989 to 2008), as well as reference lists of relevant studies. Finally, four randomized trails involving 789 people were included. Based on meta-analysis, There was strong evidence that ECS versus vaginal delivery could effectively reduce the rate of MTCT of HBV (ECS: 10.5%; vaginal delivery: 28.0%). The difference between the two groups (ECS <italic>versus </italic>vaginal delivery) had statistical significance (RR 0.41, 95% CI 0.28 to 0.60, P &lt; 0.000001). No data regarding maternal morbidity or infant morbidity according to mode of delivery were available.</p>", "<title>Conclusion</title>", "<p>ECS appears to be effective in preventing MTCT of HBV and no postpartum morbidity (PPM) was reported. However, the conclusions of this review must be considered with great caution due to high risk of bias in each included study (graded C).</p>" ]

[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>JY conceived the study and made substantial contributions to its design, acquisition, analysis and interpretation of data. XZ and YM participants in the design, acquisition, analysis and interpretation of data. LZ participated in the design and revised the manuscript critically for important intellectual content. All authors gave final approval of the version to be submitted and any revised version.</p>" ]

[ "<title>Acknowledgements</title>", "<p>We thank Tai-xiang Wu for his advice and constructive comments on this review. We also appreciate the helpful comments and suggestions from Jing Li, Guan-jian Liu. We are grateful to You-ping Li for expert suggestions and to Shu-juan Yang for her helpful advice and assistance.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Analysis of the efficacy of ECS versus vaginal delivery for preventing MTCT of HBV.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Subgroup analysis of the efficacy of ECS versus vaginal delivery for preventing MTCT of HBV.</p></caption></fig>" ]

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[ "<graphic xlink:href=\"1743-422X-5-100-1\"/>", "<graphic xlink:href=\"1743-422X-5-100-2\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>The genome of a fecal pollution indicator phage, <italic>Bacteroides fragilis </italic>ATCC 51477-B1, was sequenced and consisted of 44,929 bases with a G+C content of 38.7%. Forty-six putative open reading frames were identified and genes were organized into functional clusters for host specificity, lysis, replication and regulation, and packaging and structural proteins.</p>" ]

[ "<title>Findings</title>", "<p>Bacteriophages infecting <italic>Escherichia coli </italic>and <italic>Bacteroides fragilis </italic>serve as fecal pollution indicators (1). <italic>Bacteroides </italic>phages are more attractive fecal indicators than <italic>E. coli </italic>because <italic>Bacteroides </italic>are more abundant in fecal matter, provide higher host specificity, and are anaerobic and thus less likely to reproduce in aquatic environments than <italic>E. coli </italic>[##REF##11010920##1##, ####REF##15933020##2##, ##REF##15574920##3##, ##REF##3662510##4####3662510##4##]. Phage ATCC 700786-B1, infecting <italic>B. fragilis </italic>RYC2056, serves as an ISO standard reference phage, but the host is also susceptible to phages in other animal feces [##UREF##0##5##,##UREF##1##6##]. Phage ATCC 51477-B1 infects the host <italic>B. fragilis </italic>HSP40, which is reported to be only susceptible to phages in human feces and surface water polluted with municipal/septic wastewater [##UREF##2##7##].</p>", "<p>A drawback to using <italic>Bacteroides </italic>phages as water quality indicators results from the requirement to cultivate them on an anaerobic host [##UREF##1##6##]. This can be circumvented with direct phage detection via PCR [##REF##10996649##8##], but assay design is difficult because only one gene for one <italic>Bacteroides </italic>bacteriophage is publically available. The lack of ability to detect <italic>Bacteroides </italic>bacterial phage is startlingly inadequate because the host genera is dominant in the human gut and contains important antibiotic resistant pathogens [##REF##15574920##3##,##UREF##3##9##]. In this study, the genome of phage ATCC 51477-B1 was sequenced to promote fecal source tracking assay development and aid construction of a <italic>B. fragilis </italic>phage bioreporter [##REF##16478488##10##].</p>", "<p>Phage ATCC 51477-B1 was propagated on <italic>B. fragilis </italic>HSP40 (ATCC 51477) at 37°C in anaerobic <italic>Bacteroides </italic>phage recovery medium (BPRM) amended with kanamycin, nalidixic acid, bile salts, and Oxyrase (Oxyrase; Mansfield, OH) [##REF##1514815##11##,##REF##11311351##12##]. Phage were purified and concentrated from lysate using polyethylene glycol [##REF##10439405##13##] and displayed a non-elongated icosohedral head and non-contractile tail consistent with the family Siphoviridae and other <italic>B</italic>. <italic>fragilis </italic>phages isolated from municipal wastewater (Figure ##FIG##0##1##) [##REF##12567237##14##]. Structural dimensions were similar to <italic>B. fragilis </italic>phage B40-8 with respect to head diameter (60 ± 4.0 μm) and tail length (162 ± 17.0 μm), but the tail diameter was greater (13.4 ± 1.1 μm versus 9.3 ± 0.4 μm) [##REF##10439405##13##].</p>", "<p>Phage DNA was extracted with Lambda minipreps (Promega, Madison, WI) and digested with <italic>Hin</italic>dIII. Several <italic>Hin</italic>dIII restriction fragments were cloned into pUC19 [##UREF##4##15##] and sequenced using primer walking. PCR reactions bridging the cloned <italic>Hin</italic>dIII restriction fragments were cloned and sequenced to confirm fragment order. Non-redundant areas of the genome that were difficult to clone were directly sequenced. In total, dideoxynucleotide sequencing provided 2× coverage except at the 5' and 3' ends. Confirmation of sequence data was sought by GS FLX pyrosequencing (MWG Biotech, Inc., High Point, NC) which produced 23,263 reads, 58,730 base calls, and assembly of 62 contigs using the GS <italic>De Novo </italic>Assembler. Thirty eight of the contigs with more than 3 reads were co-assembled with the dideoxynucleotide sequences using DNASTAR Lasergene 7.1 software suite (DNASTAR, Inc., Madison, WI). A total of 21,935 pyrosequencing reads in 27 contigs were maintained in the final assembly which aligned at 91% similarity for 100× coverage of the phage genome, including the 5' and 3' ends. The final assembled genome was 44,929 bp with a 38.7% G+C content (Figure ##FIG##1##2##).</p>", "<p>Open reading frames (ORFs) were identified using GeneMark [##REF##15980510##16##,##REF##10481031##17##] and the NCBI ORF Finder and examined for known protein functions, structures, and motifs using a conserved domain database [##REF##15608175##18##] (Additional file ##SUPPL##0##1##, Figure ##FIG##1##2##). Ten of the 46 putative ORFs contained amino acid sequences with predictable functions or motifs (Additional file ##SUPPL##0##1##). The 5' ends of three ORFs (ORF39, ORF40, and ORF43) displayed translated similarity to previously published N-terminal amino acid sequences for <italic>B. fragilis </italic>phage B40-8 head (MP1 and MP3) and tail (MP2) proteins at 100, 80, and 90% similarity, respectively [##REF##10439405##13##]. The B40-8 MP2 gene (the only <italic>B. fragilis </italic>phage gene present in GenBank), was present in ATCC 51477-B1 but contained a 119 bp insertion and was split into three misarranged sections, one of which was inverted (Figure ##FIG##1##2##). This suggested the putative ATCC 51477-B1 tail protein was chimeric with respect to B40-8.</p>", "<p>The ATCC 51477-B1 genome contained gene functional clusters for host specificity (tail fiber), lysis, replication and regulation, and packaging and structural proteins (Figure ##FIG##1##2##) [##REF##17711456##19##]. The host specificity region contained ORF7 with translated similarity to the tail fiber of <italic>Enterococcus </italic>phage phiEF24C (Additional file ##SUPPL##0##1##) and a large size (1,897 amino acids) suggesting it may be a tape measure gene [##REF##17083467##20##]. A putative M-15 type peptidase (ORF10) was the only gene clearly linked with the lysis region. The replication and regulation region included phage anti-repressors (ORF22 and ORF26), DNA modifying enzymes (ORF15 and ORF16), and replication proteins (ORF22, ORF31, and ORF32). Within the packaging and structural cluster, an ORF with similarity to the TerL protein was identified (ORF 38), as well as ORFs with N-terminal sequences similar to the three phage B40-8 structural proteins previously mentioned (ORF39, ORF40, and ORF43) [##REF##10439405##13##].</p>", "<p>Four potential promoters were identified on the positive strand of the genome based on similarity to promoters found in <italic><underline>B. fragilis </underline></italic>[##REF##15387812##21##]. Two were in the regulation and replication region, 5' of ORFs 17 and 22. The other two potential promoters were near the beginning of the genome, 5' of ORF2, and in the lysis region, 5' of ORF12. A tandem repeats finder [##REF##9862982##22##] revealed a 23 bp repeat within an 83 bp segment having two perfect and two degenerate repeats in the replication and regulation region. Another 96 bp perfect repeat sequence was identified at the beginning of the phage genome and again at the end of the replication and regulation region (Figure ##FIG##1##2##).</p>", "<p>Eight polymorphic regions occurring within ORFs and displaying sequence variability from 4% to 13% were identified with the extensive pyrosequencing data. These regions ranged in size from 200 bp to greater than 1,000 bp. ORF7, the putative tail fiber protein, displayed the most polymorphisms, including a pyrosequencing contig with a deletion relative to the final assembly. Tail fiber variability modifies the phage host range [##UREF##5##23##] and may, for this phage, reflect the antigenic variability of <italic>B. fragilis </italic>surface components [##REF##15387812##21##,##REF##15746427##24##].</p>", "<p>The sequence for <italic>B. fragilis </italic>phage ATCC 51477-B1 was deposited in GenBank with accession number <ext-link ext-link-type=\"gen\" xlink:href=\"FJ008913\">FJ008913</ext-link>.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>SAH cloned and sequenced the genome by primer walking, assisted in the pyrosequence data analysis and annotation, and drafted the manuscript. ACL analyzed pyrosequencing data, annotated the genome, and assisted in drafting the manuscript. SR organized the study and provided final editing for the manuscript. DW provided genome cloning. GSS participated in the study design and coordination. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]

[ "<title>Acknowledgements</title>", "<p>This work was supported by the Office of Naval Research under grant E01-0178-002. We would also like to thank Sarah Kortebein, Adam Crain, Polina Iakova, and Pat Jegier for technical assistance.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Transmission electron micrographs of phage ATCC 51477</bold>. (A) Magnified view of a single phage (bar = 50 nm). (B) Four additional phage all displaying tail fibers (bar = 200 nm).</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Phage ATCC 51477-B1 genome map</bold>. The direction and size of 46 putative ORFs are illustrated with arrows. ORFs labeled in green display high similarity to proteins with known function. ORFs labeled in red display high similarity to other putative open reading frames without assigned functions. ORFs labeled in blue have low similarity (&lt; 0.1) to other putative open reading frames. Sequences with high DNA similarity to the phage B40-8 structural proteins are shown in yellow. Putative <italic>B. fragilis </italic>promoters are shown as inverted orange triangles, the location of repeats are shown as purple boxes, and polymorphisms are indicated with black lines.</p></caption></fig>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Phage ATCC 51477-B1 genome organization.</p></caption></supplementary-material>" ]

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[ "<graphic xlink:href=\"1743-422X-5-97-1\"/>", "<graphic xlink:href=\"1743-422X-5-97-2\"/>" ]

[ "<media xlink:href=\"1743-422X-5-97-S1.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>There is an active and antagonistic host-pathogen interaction during HIV-1 infection. Upon infection by HIV-1, host cells react with various innate, cellular and humoral immune responses to counteract the viral invasion. Limited and transient restriction of viral infection is normally achieved. However, HIV-1 overcomes these antiviral responses through various counteracting actions. For example, APOBEC3G (apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3G), a host innate antiviral protein [##REF##18577210##1##], was found to be responsible for the inhibition of Vif-minus-HIV-1 infection [##REF##12167863##2##]; whereas Vif counteracts this host cellular response by promoting proteasome-mediated degradation of APOBEC3G [##REF##14528300##3##].</p>", "<p>APOBEC3G is a member of cellular cytidine deaminase family. At the late phase of viral life cycle, APOBEC3G is encapsided into the virus particles through interaction with viral Gag protein [##REF##15215254##4##, ####REF##15159405##5##, ##REF##15358144##6##, ##REF##15479826##7##, ##REF##15464836##8####15464836##8##]. Specifically, N-terminal domain of APOBEC3G is known to be important for targeting the protein to viral nucleoprotein complex and confers antiviral activity [##REF##17727729##9##]. Once a virus enters a new cell, virus genomic RNA will be reverse transcribed into cDNA before integrating into the host cellular chromosome DNA. As part of the host innate immune responses, APOBEC3G prevents viral cDNA synthesis by deaminating deoxycytidines (dC) in the minus-strand retroviral cDNA replication intermediate [##REF##12809610##10##, ####REF##12808466##11##, ##REF##15098018##12##, ##REF##12750511##13##, ##REF##12808465##14####12808465##14##]. As result, it creates stop codons or G-A transitions in the newly synthesized viral cDNA that is subjective to elimination by host DNA repair machinery [##REF##15098018##12##,##REF##12808465##14##]. As part of the viral counteracting effort, HIV-1 Vif counteracts this innate host cellular defense by promoting its degradation through proteasome-mediated proteolysis [##REF##14528300##3##,##REF##14528301##15##, ####REF##14527406##16##, ##REF##14564014##17##, ##REF##14614829##18####14614829##18##]. Specifically, Vif recruits Cullin5-EloB/C E3 ligase to induce polyubiquitination of APOBEC3G [##REF##15781449##19##,##REF##16303161##20##]. Specifically, Vif uses a viral SOCX-box to recruit EloB/C [##REF##15098018##12##] and a HCCH motif to recruit Cullin 5 [##REF##16076960##21##]. By eliminating APOBEC3G from the cytoplasm, Vif prevents APOBEC3G from packaging into the viral particles thus augment HIV-1 infection in \"non-permissive\" cells [##REF##12167863##2##]. Based on the Vif-APOBEC3G antagonism at the protein level, it is conceivable that creation of proteolysis-resistant APOBEC3G could potentially strengthen the host innate anti-viral response and further inhibit HIV-1 infection. The objective of this pilot study was to test this premise.</p>", "<p>Ubiquitin-associated domain 2 (UBA2) is typically 45 amino acids long that specifically bind to both mono- and polyubiquitins [##REF##12062168##22##]. Homonuclear NMR spectroscopy revealed that UBA2 domain contains a low resolution structure composed of three α-helices folded around a hydrophobic core [##REF##9846873##23##], suggesting that UBA2 domain may be involved in multiple functions. Indeed, functions of UBA2 have been linked to protein ubiquitination, UV excision repair, and cell signaling [##REF##8871400##24##]. For example, UBA2 domain is found in a family of protein including human HHR23A, budding yeast Rad23 and fission yeast Rhp23 [##REF##12062168##22##,##REF##11788722##25##]. All of the HHR23A homologues are composed of an N-terminal ubiquitin-like (UBL) domain and two ubiquitin-associated (UBA) domains, i.e., an internal UBA1 domain and a C-terminal UBA2 domain [##REF##12062168##22##]. HHR23A interacts with 26S proteasome through its N-terminal UBL domain to promote protein degradation [##REF##9490418##26##, ####REF##10471701##27##, ##REF##10488153##28####10488153##28##]. UBA domains bind to ubiquitin [##REF##11323716##29##, ####REF##11571271##30##, ##REF##11584278##31####11584278##31##] and play a role in targeting ubiquitinated substrates to the proteasome [##REF##15117949##32##, ####REF##12051757##33##, ##REF##15242647##34####15242647##34##]. As a general rule, ubiquitination of proteins and subsequent recruitment of ubiquitinated proteins to the proteasome always results in rapid degradation of those proteins [##REF##15837425##35##]. However, binding of HHR23A or Rad23 to ubiquitin and proteasome does not lead to their degradation [##REF##9490418##26##,##REF##8246991##36##]. It was believed that there must be a specific domain in the HHR23A or its homologous proteins that serve as a protective \"stabilization signal\" and prevents them from proteasome-mediated proteolysis [##REF##12354607##37##]. Indeed, UBA2 domain was recently found to function as a cis-acting and transferable \"stabilization signal\" [##REF##15837425##35##]. This \"stabilization signal\" can be destroyed simply by introducing a point mutation at residue 392 (L392A) of the UBA2 domain [##REF##15837425##35##].</p>", "<p>Since Vif promotes APOBEC3G degradation through proteasome-mediated proteolysis of ubiquitinated proteins, and because UBA2 decreases protein degradation through this pathway, we hypothesize that UBA2, if fused with APOBEC3G, should be able to act as a \"stabilization signal\" and to protect APOBEC3G from Vif-mediated degradation. Here we tested this hypothesis by comparing protein stability of normal APOBEC3G protein with the APOBEC3G-UBA2 fusion proteins in the presence of Vif. To gain additional functional insights into the molecular mechanism underlying the ability of UBA2 to prevent protein degradation, the effects of UBA2 on APOBEC3G protein degradation under the conditions of excessive polyubiquitination or the lack of proteasome activity were examined. The effect of UBA2 on APOBEC3G stability and its impact on viral infectivity was also investigated.</p>" ]

[ "<title>Methods</title>", "<title>Cell lines and Plasmids</title>", "<p>HEK293 cell was maintained in Dulbecco's minimal essential medium (DMEM) supplemented with 10% fetal bovine serum. MAGI-CCR5 cell, a HeLa-CD4 cell derivative that expresses CCR5 and that has an integrated copy of the HIV-1 long terminal repeat (LTR)-driven β-D-galactosidase reporter gene [##REF##9094670##43##], was maintained in Dulbecco's modified Eagle's medium (DMEM) supplied with 10% (vol/vol) fetal bovine serum (FBS) (Bio Whittaker), 200 μg/ml G418, 50 U/ml hygromycin (CalBiochem), and 1 μg/ml puromycin. CEM-SS cells were grown in RPMI 1640 medium. To produce APOBEC3G, APOBEC3G-UBA2, and APOBEC3G-mutant UBA2 fusion proteins, three plasmids including pcDNA3.1(-)-Apo-E/Hygromycin (E), pcDNA3.1(-)-Apo-U/Hygromycin (U), and pcDNA3.1(-)-Apo-M/Hygromycin (M) were constructed according to the strategy shown in Figure ##FIG##0##1##. To make these plasmid constructs, the <italic>APOBEC3G </italic>gene was amplified from the plasmid pcDNA3.1-<italic>HA</italic>-<italic>APOBEC3G </italic>by PCR. The 5' primer used for the construction of all three plasmids was 5'-GCGCGCGCGCCTCGAGACCATGAAGCCTCACTT-3'; The 3' primers used for the construction of the E plasmid was 5'-ATCCAAGACGGAATTCCTAGAACTCGTTTTCCTGATTCTGGAG-3' and the 3' primer used for the U and M plasmid was 5'-ATCCAAGACGGAATTCGTTTTCCTGATTCTGGAG-3'. The <italic>UBA2 </italic>gene fragment was amplified from plasmid pcDNA3.1-<italic>HHR23A </italic>by PCR. The 5' primer used was 5'-ATCCAAGACGGAATTCACGCCGCAGGAGAAAGAAGCTATAG-3'; the 3' primer for the APOPEC3G-UBA2 fusion was 5'-ATCGTACTCGAAGCTTCTAACTCAGGAGGAAGTTGGCAG-3'; and the 3' primer for the APOPEC3G-UBA2* fusion was 5'-ATCGTACTCGAAGCTTCTAACTCAGagcGAAGTTGGCAG-3'. Purified PCR products were first cloned into the mammalian expression plasmid pcDNA3.1(-)/neo, the gene fragments were then cut off and cloned into a pcDNA3.1(-)/Hygromycin plasmid. Correct insertion and nucleotide sequence of each gene fragment was verified by restriction enzyme digestions and was confirmed by nucleotide sequencing. The pcDNA3.1-<italic>HA</italic>-<italic>Ubiquitin </italic>plasmid was used to express polyubiquitin [##REF##12297498##40##,##REF##11782481##41##]. The pNL4-3 plasmid was used to packaged virus in HEK293 cells as described previously [##REF##3016298##42##].</p>", "<title>Immunoprecipitation and immunoblot analysis</title>", "<p>Transfected HEK293 cells were harvested, washed 2 times with cold PBS, and lysed in lysis buffer (50 mM Tris, pH 7.5, with 150 mM NaCl, 1% Triton X-100, and complete protease inhibitor cocktail tablets) at 4°C for 1 h, then centrifuged at 10,000 g for 30 min. Cell lysates were mixed with anti-APOBEC3G Ab (NIH reagents program) and incubated at 4°C for overnight. The mixture of antigen and antibody was incubated with protein A agarose beads (Sigma) and incubated at 4°C for 3 h. Samples were then washed three times with washing buffer (20 mM Tris, pH 7.5, with 100 mM NaCl, 0.1 mM EDTA, and 0.05% Tween-20). Beads were eluted with loading buffer. The eluted materials were then analyzed by SDS-PAGE.</p>", "<p>For Western blot analysis, HEK293 cells were harvested and rinsed with ice-cold HEPES-buffered saline (pH 7.0), then lysed in an ice-cold cell lysis buffer [20 mM Tris-HCl, pH7.6, 150 mM NaCl, 1 mM EDTA, 0.5% Nonidet P-40, 1 mM DTT, 5 μM Trichostatin A, 1 mM sodium orthovanadate, 1 mM PMSF, 1 mM NaF and complete protease inhibitors (Roche Applied Science)]. Cellular lysates were prepared and the protein concentration was determined using the Pierce protein assay kit. For immunoblotting, an aliquot of total lysate (50 μg of proteins) in 2 × SDS-PAGE sample buffer (1:1 v/v) was electrophoresed and transferred to a nitrocellulose filter. Filters were incubated with appropriate primary antibody in Tris-buffered saline (TBS, pH 7.5) and 5% skim milk or 5% BSA overnight. The primary antibodies include anti-APOBEC3G antibody at a 1:500 dilution (NIH reagents program), anti-Vif antibody at a 1:200 dilution (NIH reagents program), anti-HA antibody at a 1:1000 dilution, and anti-β-actin (Sigma) antibody at a 1:3,000 dilution. After washing, the filter was further incubated with secondary antibody in TBS-Tween-20 (TBS-T) buffer for 1 h. Protein bands were visualized by an ECL detection system. Goat anti-mouse or anti-mouse IgG-HRP conjugate (dilution of 1:3,000) were used as secondary antibodies according to the corresponding primary antibodies.</p>", "<title>Transfection and Viral Packaging</title>", "<p>Plasmid DNA was transfected into HEK293 or CEM-SS cells by using Lipofectamine 2000 transfection reagent (Invitrogen) according to the manufacturer's instructions. To create stable APOBEC3G-expressing cell lines, the plasmid DNA of pcDNA3.1(-)-Apo-E/Hygromycin, pcDNA3.1(-)-Apo-U/Hygromycin, pcDNA3.1(-)-Apo-M/Hygromycin was transfected into HEK293 cells. HEK293 cells that stably produce a high level of APOBEC3G, APOBEC3G-UBA2 or APOBEC3G-UBA2* were first established by selection of hygromycin resistant cells (300 μg/ml) for 2 weeks and verified by the Western blot analyses (Fig. ##FIG##4##5A–a##). To generate infectious viral particles, HEK293 was inoculated into 6-well plate one day before pNL4-3 plasmid transfection to ensure adequate (50–60%) cell confluency. The full-length molecular clone pNL4-3 and pNL4-3 ΔVif plasmid was then transfected into HEK293 cells to produce wild type Vif(+) or mutant Vif(-) HIV-1 viruses [##REF##3016298##42##]. Forty-eight hours <italic>p.t</italic>., HIV-1 viral particles were harvested from the supernatants of HEK293 cells by centrifugation at 1,000 rpm/min for 5 min. The isolated viral particles were split into 2 ml aliquots and stored in -80°C. To ensure equal levels of viral infection, the viral stocks were normalized by determining levels of p24 antigens in each viral stock. The level of p24 antigen was determined by using a commercial p24 antigen kit from ZeptoMetrix Co. (Buffalo, NY) following the manufacture's instructions.</p>", "<title>Viral infections</title>", "<p>To evaluate the suppressive effect of APOBEC3G, APOBEC3G-UBA2 and APOBEC3G-UBA2* on viral replication in proliferating CD4+ T-lymphocytes, CEM-SS cells (APOBEC3G negative CD4+ T-lymphocytes) that stably express a plasmid control, APOBEC3G, APOBEC3G-UBA2 and APOBEC3G-UBA2* were established. 1 × 10⁶CEM-SS cells were either mock infected or infected with 3000 TCID50 of HIV-1_NL4-3and HIV-1_NL4-3ΔVif. The viral replication was measured by p24 antigenemia.</p>", "<title>MAGI assay</title>", "<p>MAGI assay was used to determine the viral infectivity as previously described [##REF##9094670##43##]. Briefly, MAGI-CCR5 cells were cultured in 6-well plates one day before infection so that cells can reach approximately 40–50% confluency on the day of infection. The medium of each well was removed before the viral supernatant was added to infect cells in a total volume of 300 μl of complete DMEM with 20 μg/ml of DEAE-dextran. Cells in virus-containing medium were incubated at 37°C in a 5% CO₂incubator. After 2 hours incubation, 1.5 ml complete DMEM medium was added to each well. The cells were further incubated under the same condition for 48 hrs after incubation. The media were removed and 2 ml fixing solution (1% formaldehyde, 0.2% glutaraldehyde in PBS) was added to each well. Cells were washed twice with PBS and then fixed for 5 min. with 600 μl of staining solution (20 μl of 0.2 M potassium ferrocyanide, 20 μl of 0.2 M potassium ferricyanide, 2 μl of 1 M MgCl₂, 10 μl of 40 mg/ml X-gal in PBS). Cells were then incubated for 2 hrs at 37°C in a non-CO₂incubator. Staining was stopped by removing the staining solution and washed twice with PBS. Blue dots were counted as infected cells as described previously [##REF##9094670##43##] under the microscope. The viral infectivity was determined by comparing the total number of infected cells with uninfected cells in each well. Each experiment was performed in triplicate and results of these experiments were repeated at least three times. Statistic student <italic>t</italic>-test was used to determine potential significant difference among different treatment groups.</p>" ]

[ "<title>Results</title>", "<title>APOBEC3G fused with UBA2 is more resistant to Vif-mediated protein degradation than APOBEC3G</title>", "<p>To test whether UBA2 can stabilize APOBEC3G protein, UBA2 was fused at the C-terminal end of APOBEC3G (Fig. ##FIG##0##1A##). The APOBEC3G without the UBA2 fusion (Fig. ##FIG##0##1B##) or fused with a mutant L392A UBA2 that is incapable of stabilizing proteins (Fig. ##FIG##0##1C##; [##REF##15837425##35##]), was used as controls. The fusion products were cloned into a mammalian gene expression plasmid pCDNA3.1 and the resulting plasmids were designated as pcDNA3.1(-)-Apo-E/Hygromycin (E) for the untagged APOBEC3G, pcDNA3.1(-)-Apo-U/Hygromycin (U) for the APOBEC3G-UBA2 fusion, and pcDNA3.1(-)-Apo-M/Hygromycin (M) for the APOBEC3G-UBA2* mutant fusion. Protein stability of APOBEC3G was determined either by expression of these plasmids individually or by co-transfection of each individual APOBEC3G-carrying plasmid construct with a Vif-carrying plasmid (Vif-VR1012) in HEK293 cells. As shown in Fig. ##FIG##1##2A##, expression of untagged APOBEC3G produced a strong protein band at approx. 46 kD consistent with the size of APOBEC3G (Fig. ##FIG##1##2A##, lane 2). Slight increase in molecular weight was detected in the APOBEC3G-UBA2 and APOBEC3G-UBA2* fusion products (Fig. ##FIG##1##2A##, lanes 3–4).</p>", "<p>Approximately equal amount of protein was produced in each of these plasmid constructs without <italic>vif </italic>gene expression (Fig. ##FIG##1##2A–b##). When <italic>vif </italic>is expressed in the APOBEC3G-producing HEK293 cells, a significant decrease of APOBEC3G with more than 10-fold reduction was noticed in the untagged APOBEC3G cells (Fig. ##FIG##1##2A##, lane 5). In contrast, a small with about 2-fold decease of APOBEC3G-UBA was detected when APOBEC3G was fused with the wild type UBA2 (Fig. ##FIG##1##2A##, lane 6). Consistent with the finding that a single point mutation of APOBEC3G (L392A) abolishes the ability of APOBEC3G to stabilize proteins [##REF##15837425##35##], production of Vif in these cells reduced the APOBEC3G-UBA2* protein level to the level that is similar to the untagged APOBEC3G (Fig. ##FIG##1##2A## lane 7 <italic>vs</italic>. lane 5). Together, these data suggested that the wild type UBA2, when it is fused with APOBEC3G, is indeed able to stabilize APOBEC3G and renders it more resistant to Vif than the untagged APOBEC3G.</p>", "<p>One possibility for the observed resistance of APOBEC3G-UBA2 to Vif could be explained by the reduced binding of APOBEC3G-UBA2 to Vif. To test this possibility, Myc-tagged Vif was pull-down by immunoprecipitation in the APOBEC3G-producing HEK293 cells. Western blot analyses were carried out to measure the bindings of different APOBEC3G constructs to Vif. As shown in Fig. ##FIG##1##2B##, no obvious reduction of the binding of APOBEC3G-UBA2 to Vif was observed (Fig. ##FIG##1##2B##, lane 5). In fact, binding of APOBEC3G-UBA2 to Vif appeared to be stronger than the untagged APOBEC3G or APOBEC3G with the mutated UBA2. This increase binding could potentially be due to presence of the excessive APOBEC3G-UBA2, which is clearly shown by the high level of APOBEC3G remained in the supernatant (Fig. ##FIG##1##2B##, lane 2). Nevertheless, these data suggest that the observed resistance of APOBEC3G to Vif is not caused by reduction binding.</p>", "<title>Overexpression of polyubiquitin diminishes the ability of UBA2 to stabilize APOBEC3G against Vif</title>", "<p>Most cellular proteins are targeted for degradation by the proteasome. Prior to proteasome-mediated proteolysis, the proteins are covalently attached to ubiquitin. A poly-ubiquitin chain will be formed and function as a degradation signal. The poly-ubiquitinated protein can then be recognized by the 26S proteasome for degradation [##REF##9797452##38##]. If the ubiquitin chain elongation is interrupted, this protein cannot be recognized by the 26 S proteasome and thus it cannot be degraded. UBA2 binds to ubiquitin directly and inhibits elongation of polyubiquitin chains by capping conjugated ubiquitin [##REF##11571271##30##,##REF##10980700##39##]. Since Vif mediates APOBEC3G degradation by promoting protein ubiquitination of APOBEC3G [##REF##14528300##3##]<italic>via </italic>Cullin5-EloB/C E3 ligase to induce polyubiquitination of APOBEC3G [##REF##15781449##19##,##REF##16303161##20##], it is possible that UBA2 may either sequester ubiquitin from APOBEC3G or prevent polyubiquitin chain elongation. As results, the un-ubiquitinated APOBEC3G becomes resistant to proteasome-mediated proteolysis. To test this possibility, polyubiquitin was overproduced through a pcDNA3.1-HA-Ubiquitin plasmid [##REF##12297498##40##,##REF##11782481##41##] in the HEK293 cells co-producing Vif and various APOBEC3G products. As shown in Fig. ##FIG##2##3A##, APOBEC3G-UBA2 fusion protein showed relative strong intensity in comparison with the untagged APOBEC3G (Fig. ##FIG##2##3A–a##, lane 3 <italic>vs</italic>. lane 1). However, production of excessive polyubiquitin completely abolished the difference between the protein level of APOBEC3G-UBA2 and APOBEC3G (Fig. ##FIG##2##3A–a##, lane 5 <italic>vs</italic>. lane 4). Western protein blotting with anti-Vif and anti-HA for ubiquitin detection confirmed proper production of Vif and polyubiquitin in these cells. Therefore, over-production of polyubiquitin can diminish the ability of UBA2 for APOBEC3G stabilization.</p>", "<p>To further verify whether fusion of APOBEC3G to UBA2 results in less binding to polyubiquitin, APOBEC3G in the presence or absence of Vif was collected by immunoprecipitation using anti-APOBEC3G monoclonal antibody. The pull-down protein products were subject to Western blot analyses as shown in Fig. ##FIG##2##3B##. Approximately equal amount of APOBEC3G was collected in all cells with the exception of the control cells (Fig. ##FIG##2##3B–a##, lane 4), in which only endogenous APOBEC3G was pull-down. Without Vif, minimal and background level of polyubiquitin was detected in all APOBEC3G-producing cells (Fig. ##FIG##2##3B–a##, lanes 1–3). In contrast, strong polyubiquitin was detected in the <italic>vif</italic>-expressing cells with untagged APOBEC3G or APOBEC3G-UBA2* (Fig. ##FIG##2##3B–a##, lanes 5 and 7). However, much reduced level of polyubiquitination was observed in <italic>vif</italic>-expressing cells carrying the APOBEC3G-UBA2 (Fig. ##FIG##2##3B–a##, lane 6). This observation provides direct support to the notion that UBA2 may prevent polyubiquitin chain elongation on APOBEC3G.</p>", "<title>Treatment of HEK293 cells with proteasome inhibitor MG132 alleviated degradation of APOBEC3G and APOBEC3G-UBA2 fusion proteins</title>", "<p>To test whether inhibition of the 26S proteasome activity has any impact on the ability of UBA2 to stabilize APOBEC3G against Vif, APOBEC3G-producing HEK293 cells were treated the proteasome inhibitor MG132 in the presence of Vif. APOBEC3G protein levels were measured and compared between cells with or without the MG132 treatment. Similar to what we have shown in Fig. ##FIG##1##2A##, the protein intensity of APOBEC3G-UBA2 was significantly higher than that without the UBA2 tag (Fig. ##FIG##3##4A##, lane 2 <italic>vs</italic>. lane 1), suggesting the protein stabilizing capacity of UBA2. APOBEC3G fusion with a mutant UBA2* reduced its ability to stabilize APOBEC3G (Fig. ##FIG##3##4A##, lane 3). Significantly, HEK293 cells treated with the proteasome inhibitor MG132 all showed much higher protein intensities than the APOBEC3G-UBA2 producing cells without MG132 treatment (Fig. ##FIG##3##4A##, lanes 4–6 <italic>vs</italic>. lane 2). These enhanced protein levels were observed in all of the APOBEC3G protein constructs regardless whether it is fused with UBA2 or not, suggesting UBA2 stabilizes APOBEC3G through resistance to proteasome-mediated proteolysis.</p>", "<title>Viruses packaged from cells expressing APOBEC3G-UBA2 fusion protein gives stronger suppressive effect on viral infectivity than that packed from APOBEC3G</title>", "<p>To test whether APOBEC3G stabilized by UBA2 can further enhance the suppressive effect of APOBEC3G on viral infectivity, the HIV-1 viral particles were produced from HEK293 cells that expressing different constructs of APOBEC3G as described. To minimize potential differences of production of each protein construct and viral packaging, HEK293 cells that stably express APOBEC3G, APOBEC3G-UBA2, and APOBEC3G-UBA2* fusion proteins were created by proper antibiotic selection. High level expression of these proteins was further verified by Western blot analysis (Figure ##FIG##4##5A–a##). To produce APOBEC3G-carrying viral particles, the pNL4-3 plasmid was expressed in HEK293 viral producing cells that stably expressing different APOBEC3G fusion proteins. The infectious viral particles were harvested 48 hrs after transfection as previously described [##REF##3016298##42##]. Presence of different APOBEC3G constructs was detected with approx. equal amount within all three types of viral particles (Fig. ##FIG##4##5A–b##). To test whether the potential effect of the viral expressing Vif on the stability of APOBEC3G, levels of APOBEC3G in the viral particle producing HEK293 cells were further measured after viral gene expressions. Essentially the same Vif effect on APOBEC3G was seen between the viral expressing Vif and Vif expressed from a plasmid (Fig. ##FIG##4##5A–c##).</p>", "<p>To test the potential impact of different APOBEC3G constructs packaged in the viral particles on viral infectivity and replication, concentration of the viral stocks were normalized by determination of the p24 antigen levels. The viral infectivity of viruses packaged with different APOBEC3G constructs were measured with the MAGI-CCR5 assay as previously described [##REF##9094670##43##]. This assay measures viral infectivity in a single cycle of viral infection. About 50% reduction of viral infectivity was observed in viruses packed from cells producing high level of APOBEC3G than endogenous level of APOBEC3G (Fig. ##FIG##4##5B–a##, lane E <italic>vs</italic>. lane C). An additional 17% and significant reduction of viral infectivity (P &lt; 0.01) was also observed in viruses packaged from cells expressing the APOBEC3G-UBA2 fusion protein (Fig. ##FIG##4##5B–a##, lane U). In contrast, no significant difference was detected between viruses carrying untagged APOBEC3G or APOBEC3G fused with a mutant UBA2, indicating the additional reduction of viral infectivity observed in the APOBEC3G-UBA2 fusion was indeed due to the stabilizing effect of UBA2 on APOBEC3G (Fig. ##FIG##4##5B–a##, lane M <italic>vs</italic>. lane E). These differences in viral infectivity were not observed in the Vif(-) viral infections suggesting the observed differences were caused by Vif (Fig. ##FIG##4##5B–b##).</p>", "<p>To further evaluate the observed effects of APOBEC3G variants on spread viral infection, CEM-SS cells, a cell line derived from CD4-positive T-lymphocytes, were infected with the same Vif(+) and Vif(-) viral particles packaged with different APOBEC3G variants as described above. P24 antigenemia was measured from day 3 to day 21 post-viral infection. Similar suppressive effects of the APOBEC3G variant on viral infection as described above for the MAGI-CCR5 experiment were also seen in CEM-SS cells (Fig. ##FIG##4##5C##). The differences are however most pronounced in day 21 post-infection: while infection of CEM-SS with the control viral particles produced approximately 1,200 ng/ml of p24 antigen, about 400 ng/ml of p24 antigen was seen in CEM-SS cells infected with viral particles packed with either untagged APOBEC3G or the UBA2 mutant variant (Fig. ##FIG##4##5C–a##). Additional reduction of viral replication with approx. 200 ng/ml was observed in the same cells when they were infected with the viral particles packaged with the APOBEC3G-UBA2. All of the APOBEC3G variants conferred the same level of strong viral suppression against Vif(-) viral infection (Fig. ##FIG##4##5C–b##), suggesting that the observed differences as described in the Vif(+) viral infections were due to interaction between Vif and APOBEC3G.</p>" ]

[ "<title>Discussion</title>", "<p>In this report we demonstrated, proof of principle, a plausible strategy that could be used to stabilize APOBEC3G and to further reduce viral infection. Consistent with a previous study [##REF##15297452##44##], we first confirmed that virus packaged from the HEK293 cells expressing high level of APOBEC3G gives stronger suppressive effect on viral infectivity than the virus that was packaged from normal cells (Fig. ##FIG##4##5B##, lane C <italic>vs</italic>. E). Moreover, we showed that APOBEC3G protein, when it is fused with an ubiquitin-associated domain, i.e., UBA2, becomes more resistant to Vif-mediated protein degradation (Fig. ##FIG##1##2A##). Importantly, additional suppression of viral infectivity or replication was found in the APOBEC3G-UBA2-carrying virus in comparison with the APOBEC3G-carrying virus without the UBA2 fusion (Fig. ##FIG##4##5B–C##). The observed suppression of APOBEC3G-UBA2 on viral infection was diminished in Vif(-) viral infections suggesting that the observed APOBEC3G-UBA2 effect was due to its interaction with Vif. Interestingly, despite its resistance of APOBEC3G-UBA2 to Vif-induced degradation, APOBEC3G-UBA2 is packaged at essentially the same level into wild type HIV-1 virions as untagged APOBEC3G or APOBEC3G tagged with mutant UBA2 (Fig. ##FIG##4##5##). This observation seems to argue against the dogma that Vif prevents packaging of APOBEC3G by inducing its proteasomal degradation. Moreover, the wild type HIV-1 produced in the presence of APOBEC3G-UBA2 appeared to be more infectious than the Vif(-) mutant (Fig. ##FIG##4##5B a–b## [U]). This finding could potentially be even more significant than the reduction in infectivity of the wild type virus \"E\" <italic>vs</italic>. \"U\" as shown in Fig. ##FIG##4##5Ba##, as it may indicate that Vif may confer suppressive effect on APOBEC3G. in addition to the degradation effect. Indeed, a recent report by Opi et al. [##REF##17522211##45##] showed that inhibition of viral infectivity by a degradation-resistant form of APOBEC3G is still sensitive to Vif. Together these data suggest that stabilized APOBEC3G by UBA2 may have contributed to the observed viral suppression. This premise is certainly supported by our observation that the same virus that carries APOBEC3G fused with a mutant UBA2 lost its suppressive effect on viral infectivity (Fig. ##FIG##4##5B–C##).</p>", "<p>It should be mentioned that the observed suppressive effect of APOBEC3G-UBA2 on viral infection is not as pronounced as the suppressive effect observed in an APOBEC3G D128K mutant, in which the D128K mutant inhibits HIV-1 by several hundred fold [##REF##14966139##46##,##REF##14978281##47##]. One possible explanation of the discrepancy between our study and that of the cited APOBEC3G D128K study might be due to the difference in binding of Vif to these APOPEC3G variants. For example, Vif still bind to APOBEC3G-UBA2 (Fig. ##FIG##1##2B##). In contrast, Vif no longer bind to the D128K mutant [##REF##14966139##46##,##REF##14978281##47##].</p>", "<p>The molecular mechanism underlying the ability of UBA2 to stabilize APOBEC3G needs to be further delineated. There are three possibilities that could potentially explain the observed stabilizing effect of UBA2 on APOBEC3G based on the published reports and data presented here. First, similar to the finding described in the budding yeast homologue (Rad23) of HHR23A [##REF##14621999##48##], UBA2 prevents Rad23 protein degradation by binding to the UBL domain at its N-terminal end where the 26S proteasome attaches [##REF##15837425##35##,##REF##12832454##49##]. Following the same scenario, binding of UBA2 to the 26S proteasome-binding site could conceivably protect APOBEC3G from proteasome-mediated degradation. However, this possibility is unlikely because there is no UBL domain or alike which thus thus far been identified in APOBEC3G. Second, C-terminal fusion of UBA2 to APOBEC3G may stabilize APOBEC3G by hindering it from unfolding by the 19S regulatory subunit of the proteasome, a scenario that has been described previously [##REF##15837425##35##]. Prior to proteasome-mediated degradation of a protein, 19S regulatory subunit of proteasome must first unfold the polyubiquitinated protein as subsequent degradation requires an unstructured initiation site of the unfolded protein [##REF##15311270##50##]. An early <italic>in vitro </italic>study showed that tightly folded C-terminal domains can block protein unfolding and thus delay proteasomal degradation [##REF##11779508##51##]. It is possible that fusion of UBA2 with APOBEC3G created a tightly folded C-terminal end of protein that block APOBEC3G-UBA2 unfolding and proteasomal degradation. If this is the case, addition of excessive ubiquitin or inhibition of proteasome activity should not affect the level of protein observed. Therefore, this possibility should be excluded. Third, UBA2 prevents polyubiquitination of APOBEC3G the same way as described for other proteins [##REF##14621999##48##,##REF##12643283##52##]. UBA2 inhibits elongation of polyubiquitin chains by capping conjugated ubiquitin [##REF##11571271##30##,##REF##10980700##39##]. Prior to proteasome-mediated proteolysis, the protein destined to be degraded is first polyubiquitined. If the ubiquitin chain elongation is somehow restricted, this protein cannot be recognized by the 26 S proteasome and thus it cannot be degraded. To a certain extent, our results seem to support this possibility because when excessive polyubiquitin were produced, it abolishes the ability of UBA2 to stabilize APOBEC3G (Fig. ##FIG##2##3A##). Furthermore, our data showed APOBEC3G-UBA2 bound less polyubiquitin than the other APOBEC3G variants (Fig. ##FIG##2##3B##). Nevertheless, should UBA2 indeed stabilized APOBEC3G through this mechanism, the stabilization to proteasome-mediated proteolysis by UBA2 is not complete because the 26S proteasome is still able to degrade part of the APOBEC3G-UBA2 protein. This was certainly supported by the observation that inhibition of the 26S proteasome activity by MG132 resulted in further increase of the APOBEC3G-UBA2 level (Fig. ##FIG##3##4A##, lane U). In order to further explore the potential ability of UBA2 to stabilize APOBEC3G, future experiments could include testing of different UBA2 constructs isolated from various species such as budding or fission yeast. Alternatively, multiple and tandem UBA2 could potentially be used to test whether they can provide stronger stabilizing effect on APOBEC3G than a single UBA2. Additionally, it should be pointed out that introduction of therapeutic APOBEC3G-UBA2 into human cells, through whatever technique, will not eliminate preexisting endogenous (untagged) APOBEC3G. Such APOBEC3G could tie up Vif and minimize degradation-independent activities of Vif thus making APOBEC3G-UBA2 more effective. This possibility can certainly be tested by co-expression of tagged and untagged APOBEC3G.</p>" ]

[ "<title>Conclusion</title>", "<p>This is a proof of concept study that provides, for the first time, evidence showing APOBEC3G, when it is stabilized by UBA2, attenuates HIV-1 infectivity. Further refinement of this strategy is needed to develop a more efficient way to stabilize APOBEC3G. It nevertheless promises a new and testable approach in that it may contribute to future strategies against HIV infection.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Although APOBEC3G protein is a potent and innate anti-HIV-1 cellular factor, HIV-1 Vif counteracts the effect of APOBEC3G by promoting its degradation through proteasome-mediated proteolysis. Thus, any means that could prevent APOBEC3G degradation could potentially enhance its anti-viral effect. The UBA2 domain has been identified as an intrinsic stabilization signal that protects protein from proteasomal degradation. In this pilot study, we tested whether APOBEC3G, when it is fused with UBA2, can resist Vif-mediated proteasomal degradation and further inhibit HIV-1 infection.</p>", "<title>Results</title>", "<p>APOBEC3G-UBA2 fusion protein is indeed more resistant to Vif-mediated degradation than APOBEC3G. The ability of UBA2 domain to stabilize APOBEC3G was diminished when polyubiquitin was over-expressed and the APOBEC3G-UBA2 fusion protein was found to bind less polyubiquitin than APOBEC3G, suggesting that UBA2 stabilizes APOBEC3G by preventing ubiquitin chain elongation and proteasome-mediated proteolysis. Consistently, treatment of cells with a proteasome inhibitor MG132 alleviated protein degradation of APOBEC3G and APOBEC3G-UBA2 fusion proteins. Analysis of the effect of APOBEC3G-UBA2 fusion protein on viral infectivity indicated that infection of virus packaged from HEK293 cells expressing APOBEC3G-UBA2 fusion protein is significantly lower than those packaged from HEK293 cells over-producing APOBEC3G or APOBEC3G-UBA2 mutant fusion proteins.</p>", "<title>Conclusion</title>", "<p>Fusion of UBA2 to APOBEC3G can make it more difficult to be degraded by proteasome. Thus, UBA2 could potentially be used to antagonize Vif-mediated APOBEC3G degradation by preventing polyubiquitination. The stabilized APOBEC3G-UBA2 fusion protein gives stronger inhibitory effect on viral infectivity than APOBEC3G without UBA2.</p>" ]

[ "<title>List of abbreviations</title>", "<p>HIV-1-human immunodeficiency virus type 1; Vif-viral infectivity factor; APOBEC3G- apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3G; UBA2-ubiquitin-associated domain 2; UBL-ubiquitin-like domain; <italic>p.i</italic>.-post-infection; <italic>p.t</italic>.-post-transfection.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>LL and DL carried out all of the experiments. JYL provided supervision of the study and co-mentored LL's Ph.D. dissertation. RYZ supervised and directed the designed studies and co-mentored LL's Ph.D. dissertation. All authors read and approved the final manuscript.</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors would like to thank Dr. Xiao-fang Yu for providing the pcDNA3.1-HA-APOBEC3G and the Vif-VR1012 plasmids. LL was supported in part by a fellowship from the AIDS International Training Research Program (AITRP), Fogarty International Center, NIH (RYZ). Part of this study was from LL's dissertation in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Military Academy of Medical Sciences, the People's Republic of China. This study was supported in part by a research fund from the University of Maryland Medical Center and NIH NIAID-R01-AI040891 (RYZ). The anti-Vif and anti- APOBEC3G antibodies were obtained from the NIH AIDS Research and Reference Reagent program.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Schematic drawings of the APOBEC3G-carrying plasmids</bold>. E: untagged APOBEC3G-carrying plasmid (pcDNA3.1(-)-Apo-E/Hygromycin); U: same plasmid but contains an in-frame fusion of UB2A with APOBEC3G (pcDNA3.1(-)-Apo-U/Hygromycin); M: same as U but contains an in-frame fusion of a mutated UBA2* with APOBEC3G (pcDNA3.1(-)-Apo-M/Hygromycin). The asterisk * by UBA2 indicates location of a single point mutation in the UBA2 domain (L392A) that renders it incapable of stabilizing proteins [##REF##15837425##35##]. P_CMV, CMV promoter; the single letter restriction enzyme designations are: X, <italic>XhoI</italic>; E, <italic>EcoRI</italic>; H, <italic>HindIII</italic>.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>APOBEC3G fused with UBA2 domain is more resistant to Vif-mediated degradation than APOBEC3G</bold>. A-a. HEK293 cells, which is APOBEC3G-negative, was co-transfected with 1.5 μg of Vif-carrying plasmid (Vif-VR1012) DNA and 6 μg of plasmid DNA that expresses untagged APOBEC3G (E), APOBEC3G-UBA2 (U) fusion protein or APOBEC3G-UBA2* mutant fusion protein (M), respectively. Forty-five hours post-transfection (p.t.), cell lysates were subject to SDS polyacryladmide gel electrophoresis and analyzed by Western blot analysis using monoclonal anti-APOBEC3G and anti-Vif antibodies. Level of protein loading was measured by anti-β-actin antibody. A-b. The intensity of APOBEC3G protein was determined by densitometry. Value of the relative intensity of APOBEC3G was calculated in relative to the untagged APOBEC3G (E) and adjusted based on the relative intensity of β-actin in each lane to that of the control (C). B. UBA2 fusion to APOBEC3G does not affect its binding to Vif. Myc-tagged Vif was pulled-down in different APOBEC3G-producing HEK293 cells by immunoprecipitation using anti-Myc antibody. Binding of different forms of APOBEC3G to Vif was detected by using anti-APOBEC3G and anti-Vif antibodies, respectively. SUP, supernatants; IP, immunoprecipitation.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Fusion of UBA2 to APOBEC3G limits its polyubiquitination</bold>. A-a, Expression of polyubiquitin abolishes the ability of UBA2 to stabilize APOBEC3G. HEK293 cells were co-transfected as described in Fig. 2. In addition, 3 μg of a plasmid DNA (pcDNA3.1-HA-Ubiquitin) that produces polyubiquitin [##REF##12297498##40##,##REF##11782481##41##] was also co-transfected to HEK293 cells. Western blot analysis was carried out by using monoclonal anti-APOBEC3G, anti-Vif, anti-HA, and anti-β-actin antibodies respectively. A-b. The intensity of APOBEC3G protein and value of the relative intensity of APOBEC3G was determined as described in Fig. 2. Note, a protein band that migrates with similar size to APOBEC3G-UBA2 as shown in lane E sometimes react to anti- APOBEC3G antibody. This is a non-specific protein band because it only reacts to certain batches of anti-APOBEC3G antibody. To eliminate this background, the protein intensity of APOBEC3G-UBA2 and APOBEC3G-UBA2* was calculated by subtracting the level of this non-specific protein. B. Fusion of UBA2 to APOBEC3G shows reduced polyubiquitination. B-a, Vif protein was pull-down in different APOBEC3G-producing HEK293 cells by immunoprecipitation using anti-Vif antibody. Binding of high molecular weight of ubiquitin (polyubiquitin) to Vif was detected by using anti-ubiquitin antibody. B-b, the relative intensity of ubiquitin to β-actin control was determined by densitometry. Also note that there are not much protein level differences of APOBEC3G between lane 2 (U) and lane 3 (M). This is likely due to the fact that more protein is loaded in lane 3 than lane 2 as shown by the relative protein levels of β-actin.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Treatment of HEK293 cells with proteasome inhibitor MG132 alleviates degradation of APOBEC3G and APOBEC3G-UBA2 fusion proteins</bold>. HEK293 cells were co-transfected as described in Fig. 2. Transfected cells were treated with 2.5 mM of the proteasome inhibitor MG132 27 hrs <italic>p.t</italic>. Western blot analysis was carried out as described in Fig. 3. B. The intensity of APOBEC3G protein and the value of the relative intensity of APOBEC3G were calculated the same way as described in Fig. 2.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>APOBEC3G fused with UBA2 confers stronger suppressive effect on viral infectivity than APOBEC3G</bold>. A. Packaging of different APOBEC3G variants into HIV-1 viral particles from HEK293 cells that stably express high level of APOBEC3G, APOBEC3G-UBA2 or APOBEC3G-UBA2*. a, The HEK293 cells that stably producing high level of APOBEC3G, APOBEC3G-UBA2 or APOBEC3G-UBA2* were established by selection of hygromycin resistant cells (300 μg/ml) for 2 weeks and verified by the Western blot analyses; b, Different APOBEC3G variants were equally packaged into the HIV-1 viral particles and harvested from HEK293 cells by expressing pNL4-3 plasmid in the control HEK293 cells lack of APOBEC3G (C) or HEK293 cells stably expressing different APOBEC3G variants; c, effect of viral expressing Vif on protein degradation of APOBEC3G variants. B. Effect of APOBEC3G variants on viral infectivity in MAGI-CCR5 cells. The MAGI-CCR5 cells were infected with viral supernatants harvested from the HEK293 cells that stably produce either no APOBEC3G or high level of different APOBEC3G constructs. Forty-eight hours post-infection, cells were stained by β-galactosidase for HIV-infected cells as described previously [##REF##9094670##43##]. The viral infectivity of APOBEC3G-negative control HEK293 cells (C) was calibrated to 100% for comparison purpose. The viral infectivity was determined by comparing the total number of blue cells with the total number of cells counted. Data shown represent average of three independent experiments. Error Bars shown are standard errors of the means. a. results of wild type Vif(+) HIV-1_NL4-3infection; b. results of Vif(-) HIV-1_NL4-3ΔVifinfection. * <italic>p </italic>&lt; 0.01. C. Effect of APOBEC3G variants on spread viral infection in CEM-SS cells. CEM-SS cells expressing APOBEC3G(E), APOBEC3G-UBA2(U), or APOBEC3G-UBA2(M) was infected by Vif(+) or Vif(-) HIV-1_NL4-3. P24 antigen was measured post-infected day 3, 5, 7, 10 14, 21. a. results of wild type Vif(+) HIV-1_NL4-3infection; b. results of Vif(-) HIV-1_{NL4-3 Δ Vif}infection.</p></caption></fig>" ]

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[ "<title>Background</title>", "<p>Lung cancer is a major cause of cancer mortality worldwide [##REF##15761078##1##]. In the United Kingdom, it accounts for more than 33,000 cancer deaths each year (Cancer Research UK). The disease is frequently cited as a malignancy solely attributable to environmental exposure, principally tobacco smoking. It has, however, long been postulated that individuals may differ in their susceptibility and there is strong evidence from epidemiological studies for a familial risk [reviewed in [##REF##16160696##2##]]. Direct evidence for a genetic predisposition is provided by the increased risk of lung cancer associated with a number of rare Mendelian cancer syndromes, such as in carriers of germline <italic>TP53 </italic>[##REF##12802680##3##]and <italic>RB </italic>[##REF##3718823##4##,##REF##14996857##5##] mutations, as well as in patients with Bloom's [##REF##3568432##6##] and Werner's [##REF##9438005##7##] syndromes.</p>", "<p>The two major types of lung cancer, non-small cell lung cancer (NSCLC), and small cell lung cancer (SCLC) account for 75% and 25% of cases respectively. Although the histological features are different between these (reflected in differences in patterns of gene expression), there are similarities in the spectrum of underlying somatic genetic alterations suggesting commonality in pathogenesis. Moreover, the observation that the familial risks are not subtype dependent [##REF##15613665##8##, ####REF##15382071##9##, ##REF##1654203##10##, ##REF##2010787##11##, ##REF##2826845##12##, ##REF##3191498##13####3191498##13##] and that histological concordance between affected family members is poor [##REF##15382071##9##] is consistent with the hypothesis of a \"generic\" inherited susceptibility to lung cancer.</p>", "<p>The genetic basis of inherited susceptibility to lung cancer outside the context of the rare Mendelian cancer predisposition syndromes is at present undefined, but a model in which major gene loci account for the excess familial risk seems unlikely. One hypothesis about the allelic architecture of susceptibility proposes that part of the genetic risk is caused by disease loci, which include common, low penetrance alleles. This \"common-disease common-variant\" hypothesis implies that conducting association analyses based on scans of Single Nucleotide Polymorphisms (SNPs) should be a powerful strategy for identifying low-penetrance variants [##REF##8801636##14##,##REF##12610532##15##].</p>", "<p>Previous studies aimed at identifying low penetrance alleles for lung cancer susceptibility have largely been based on a candidate gene approach formulated on preconceptions as to the role of specific genes in the development of the disease. Perhaps not surprisingly most studies have to date only evaluated a restricted number of polymorphisms, primarily in genes implicated in the metabolism of tobacco-associated carcinogens and the protection of DNA from carcinogen-induced damage. However, without a clear understanding of the biology of lung cancer predisposition the definition of suitable genes for the disease is inherently problematic making an unbiased approach to loci selection highly desirable.</p>", "<p>Despite much research, few definitive low penetrance susceptibility alleles for lung cancer have been to date unequivocally been identified through candidate-based association studies. As with many other diseases, positive associations have been reported for various polymorphisms of genes such as <italic>GSTT1 </italic>[##REF##17000715##16##], <italic>GSTM1 </italic>[##REF##18270371##17##], <italic>ERCC2 </italic>[##REF##17299578##18##], <italic>CYP1A1 </italic>[##REF##10761998##19##], and <italic>TP53 </italic>[##REF##12840112##20##] from small studies, but few of the initial positive results have been replicated in subsequent studies. The inherent statistical uncertainty of case-control studies involving just a few hundred cases and controls seriously limits the power of such studies to reliably identify genetic variants conferring modest but potentially important risks.</p>", "<p>In addition to genetic variation affecting the risk of developing cancer it is increasingly being recognised that genetic variation, not necessarily in the same genes, may also affect clinical outcome. As with case-control association studies aiming to identify novel susceptibility alleles the same issues of study power pertain to the search for prognostic markers and such studies are again contingent on access to large case-series.</p>", "<p>Following the sequencing of the human genome, large-scale harvests of SNPs have been conducted and &gt; 10 million documented. Patterns of linkage disequilibrium (LD) between SNPs have been characterised allowing subsets of SNPs (tagging SNPs) to be selected that capture a large proportion of the common sequence variation in the human genome. This coupled with the advent of highly efficient analytical platforms allow whole genome-wide studies (GWAS) for disease associations to be conducted cost effectively. The relationship between patients' genotype and risk of lung cancer is now open for exploration.</p>", "<p>The identification of genes associated with cancer predisposition and determination of their contribution to disease incidence are contingent on having DNA samples from large, systematic series of cancer patients. The resulting genetic epidemiological data provides the information on which to base the identification, counselling and management of at-risk individuals. The National Cancer Research Network (NCRN) was established to provide support for clinical cancer research in England and is one of the most substantial and constructive developments in the area of cancer research to be made in recent years in the United Kingdom. In England, serving a population of 50 million people, the NCRN is made up of 34 geographically distinct Networks covering the entire country. Within each Network there are clinical research support staff and infrastructure to promote accrual of patients into trials and studies, and the collection of high quality clinico-pathological data and appropriate biological samples. Hence the NCRN presents a major scientific initiative not only in the field of clinical trials but also in the field of genetic epidemiology.</p>", "<p>To create a resource for identifying low penetrance alleles for lung cancer we established GELCAPS (GEnetic Lung CAncer Predisposition Study) in March 1999 to collect DNA and clinico-pathological data from a large series of lung cancer cases. Within 5-years of setting up the initiative by linkage with the NCRN it has been possible to create a world-class resource of biological and clinico-pathological data from over 5,000 individuals with lung cancer.</p>" ]

[ "<title>Methods/Design</title>", "<title>Eligibility criteria</title>", "<p>All patients diagnosed with lung cancer between March 1999 and July 2004 were eligible for the study. To ensure that data and samples were collected from <italic>bona fide </italic>lung cancer cases and avoid issues of bias from survivorship only incident cases with histologically or cytologically (only if not adenocarcinoma) confirmed primary disease were ascertained. Partners of recruited lung cancer patients with no personal history of cancer were recruited as controls.</p>", "<title>Procedural outline</title>", "<p>A standardised questionnaire was used to collect basic demographic characteristics-sex, date of birth, ethnic group (White, Black-Caribbean, Black-African, Black-other, Indian, Pakistani, Chinese, Other), country of birth, current area of residence – in addition to details on active and past smoking history (including type of tobacco product, amount smoked, age at first cigarette and age at any major change of smoking habits), exposure to asbestos, occupational history, and personal past medical history. All questionnaires were self-administered and no surrogate responders were used. An open question was used to obtain information on family history of cancer involving first-degree relatives. A positive history of lung cancer was only assigned when detailed information was provided identifying the family member affected by lung cancer. The referring clinician using a standard registration form supplied clinico-pathological details (type of lung cancer, stage at presentation) of patients.</p>", "<p>Coupled with patient recruitment their spouses/partners who had no known past or current history of malignancy were invited to participate for the purposes of contributing to the generation of a control series. For these individuals details of sex, date of birth, ethnic group, place of birth, current area of residence and smoking history were collected through a self-administered questionnaire. 10–20 ml EDTA-venous blood samples were collected from all participants. Consent forms, questionnaires, registration forms and blood samples were returned to the Institute of Cancer Research (ICR) by mail. Blood samples collected were stored at -80°C prior to DNA extraction and quantification.</p>", "<p>It is our intention to collect outcome data on all cases entered into GELCAPS. In the first stage of this process subsets of participating centers were asked to provide the clinical details on the outcome of the recruited lung cancer patients. Records were requested based on their date of accrual, with those accrued at the beginning of the study being requested first. A standard proforma was used to collect information on diagnosis, stage, treatment and survival. Fully informed consent was obtained from all patients alive at the time of outcome data collection. Outcome forms were returned to the ICR by mail and details were stored electronically.</p>", "<title>Statistical considerations</title>", "<p>The primary aim of establishing GELCAPS was to generate a DNA resource of lung cancer patients sufficiently large to robustly identify low penetrance alleles by association studies of genetic polymorphisms. From the outset we envisaged that at some juncture such searches would be conducted on a genome-wide basis. It is well recognised that as such studies involve typing a vast number of markers, a large number of false positive associations will inevitably be generated and only a small number of markers will be truly associated with disease susceptibility. Hence associations need to attain a high level of statistical significance to be established beyond reasonable doubt and significance levels of ~10^-7have been proposed as being appropriate [##REF##8801636##14##]. The original target of GELCAPS was to assemble a series to include ~2,000 cases. This figure had been arrived upon on the basis of upon contemporaneous views of the probable impact of common alleles on disease risk. During development of GELCAPS studies of other common diseases indicated that common disease alleles are likely to be associated with risks typically in the range of 1.1–1.5. To identify alleles conferring such risks is contingent on sample sets twice that of our original target and we therefore revised our target accordingly in order to have ~80% power to identify an association between SNP genotype and risk.</p>", "<title>Ethical considerations</title>", "<p>In generating DNA registries such as GELCAPS ethical considerations are central to study design. One of the particular strengths of studies such as GELCAPS is that once constructed the DNA database can be probed repeatedly for different existing and newly identified candidate risk factor genes. It is not feasible to contact all study entrants to seek further written consent for specific test therefore, the information sheet and study discussion was centred on the general concept of 'genetic analyses'. As these investigations were to be solely for research to find new gene(s) predisposing to cancer it was implicit that no individual results will be conveyed to persons. In publications of findings no study entrant would be identifiable. As with all studies of this nature we clearly stated that if a study entrant wished to withdraw their DNA sample and all information held on them would be destroyed. To ensure confidentiality data is held under secure conditions at the ICR Institute of Cancer Research and information held on study entrants will not be divulged to any person or agency without the prior written agreement of the study entrant.</p>", "<p>All clinical information and biological samples were obtained only after fully informed consent was obtained from participating individuals, and in accordance with the tenets of the Declaration of Helsinki. Ethical approval for the study was obtained from the London Multi-Centre Research Ethics Committee (MREC/98/2/67) and local ethical committees. Personal information was stored in accordance with the Data Protection Act (1998).</p>", "<title>Extraction of DNA, storage and quality assurance</title>", "<p>DNA was extracted from EDTA blood samples using either a standard salt extraction procedure or using the Chemagen system (Chemagen Biopolymer-Technologie AG, Arnold-Somerrfield-Ring 2, 5499 Baeswelder, Germany, Picogreen quantified (Quant-it, Invitrogen, Paisely, UK) and normalised to 100 ug/ul in TE buffer. DNAs stocks are being stored in Eppendorf tubes (Barkhausenwe 1 22339 Hamburg, Germany) at -80°C. To avoid subjecting stock DNAs being to repeated thawing and freezing we have generated a series of \"master\" 96 deep well plates of samples from which DNAs can be readily robotically abstracted for genotyping studies. Fidelity of DNA is being constantly evaluated by monitoring performance in the different genotyping platforms.</p>" ]

[ "<title>Results</title>", "<p>After securing ethical permissions at a national level through the Multi Research Ethics Committee we started recruitment to GELCAPS in March 1999. Ascertainment of cases was restricted to 28 centres and accrual was maximally 10–20 patients per month. After GELCAPS was incorporated into the NCRN (National Cancer Research Network) portfolio in March 2002 it was subsequently rolled out across England after individual centers had obtained local ethical permissions. Adoption by the NCRN was associated with a significant increase in patient and control accrual (Figure ##FIG##0##1##). Eventually 140 oncology centers (Figure ##FIG##1##2##) became active participants in GELCAPS with patient ascertainment averaging ~100 cases per month. The remit and operational procedure by which patients are accrued to NCRN adopted studies does not allow collection of compliance data within each participating center. However, we estimate based on our intimate knowledge of the clinical activities of three centers that patient accrual to GELCAPS is ~70% of those invited to participate.</p>", "<p>The original target of GELCAPS was to assemble a series of 2,000 lung cancer cases. Given the efficiency by which samples were being accrued following adoption of GELCAPS by the NCRN a new target of at least 4,000 cases was deemed to be eminently feasible within the time frame for which funding had been secured.</p>", "<p>We terminated accrual to GELCAPS in July 2004 by which time samples from 5,269 cases with primary lung cancer and 2,094 controls had been recruited. The majority of cases were male (64%) reflecting the sex preponderance of disease. Whilst the mean age of controls was comparable to cases (62.9 years, SD = 10.6) not surprisingly 69% were female (Table ##TAB##0##1##). Similarly, the prevalence of smoking was significantly higher amongst cases compared to controls. A high proportion of the cases ascertained had been diagnosed with lung cancer at a young age (Table ##TAB##0##1##); specifically 1,617 (~31%) of the cases were aged less than 60 years old at diagnosis, compared with &lt; 10% in the general population. The frequency of the various forms of lung cancer was, however, in keeping with that observed in UK general population – ~23% being affected with SCLC and ~73% with NSCLC (Table ##TAB##0##1##).</p>", "<p>To date we have acquired follow up data on 1,187 patients; specifically, information on the staging, management and clinical outcome permitting comparison patients randomly drawn from the general population. Stage at presentation for each of the different subtypes of lung cancer was similar to that observed in the general population; specifically, for patients with SCLC, somewhat less than half (43%) presented with limited disease and of the patients with NSCLC, 13% had stage I, 15% had stage II, 43% had stage III, and 29% had stage IV disease. The majority of patients with limited stage SCLC had been treated with a combination of radical radiotherapy and chemotherapy, whilst all patients received chemotherapy. The main treatment modality for SCLC patients with extensive disease was chemotherapy. Patients with early stage NSCLC (stage I and II disease) were mainly treated with surgical resection of the primary tumor whilst about one third received chemotherapy and radical radiotherapy. The mainstay treatment modality of patients with stage III and IV NSCLC was chemotherapy. Overall the median survival time (MST) for the subset of 1,187 GELCAPS patients was 18.6 months. Prognosis was significantly correlated with stage at presentation, with those presenting early have a far better survival (Figure ##FIG##2##3##). Patients with SCLC had a MST of 26.0 and 10.5 months if diagnosed with limited and extensive disease respectively. For those with NSCLC, MSTs ranged from 12.1 months for stage IV patients to 32.3 months for stage I disease.</p>" ]

[ "<title>Discussion</title>", "<p>Recent data from GWASs of breast [##REF##17529967##21##,##REF##17529973##22##], prostate [##REF##17603485##23##, ####REF##18264097##24##, ##REF##16682969##25##, ##REF##17401364##26##, ##REF##17618282##27####17618282##27##] and colorectal (CRC) cancer [##REF##18084292##28##, ####REF##17934461##29##, ##REF##17618284##30##, ##REF##17618283##31####17618283##31##] provides strong evidence for the involvement of common disease-causing alleles and suggests that a relatively large number of genes influence the aetiology in most cancers in the patient population as a whole.</p>", "<p>To exploit the advances brought about by the human genome projects, future work in cancer genetics will be dependent upon the acquisition of large well-characterised cohorts of cancer cases. Here we have demonstrated that the centralisation of cancer services in the UK offers an opportunity to establish large, well-characterised cohorts by targeting collection to the largest centres. Moreover mobilising NCRN networks provides a means of delivering consistently the data and sample collection to complete genetic epidemiology studies, relating to the detection of main effects on the required scale.</p>", "<p>Because ascertainment of cases through GELCAPS has been based on clinical centres specialising in the treatment of lung cancer a high proportion of cases have been diagnosed young. While this means cases are not fully representative of disease in the general population the distribution of age at diagnosis serves to empower GELCAPS for identifying disease-causing alleles by virtue of genetic enrichment.</p>", "<p>Given that constitutional genotypes may well influence patient prognosis it is highly desirable that survivorship is not confounding influence on sample collection. As survival rates in patients recruited to GELCAPS were not significantly different to those documented in previously published audits of lung cancer in the UK there is no evidence that \"healthy study participant\" selection will have genetically biased ascertainment. For all participants, sex, ethnicity and age at sampling have been documented. The geographical area of birth and area of residence within the UK is known for all of the individuals and this information can be used to allow analyses stratified by region of residence, reducing any effects of population stratification. The possibility of population stratification leading to false inference of disease-genotype association can readily be addressed by adjusting for known region/ethnicity or by using information on unlinked genetic markers.</p>", "<p>We acknowledge the potential problem of differential bias in genotyping samples accrued from different sources. Although the samples collected through GELCAPs have been ascertained from many clinical centres we have no evidence that this has affected sample quality as we have previously documented call rates of 99.8% in samples genotyped for 1,500 SNPs [##REF##16741161##32##, ####REF##17341484##33##, ##REF##17000706##34##, ##REF##16574953##35####16574953##35##] and Quantile-Quantile plots of test association statistics provide no evidence for differential bias</p>", "<p>The NCRN research networks are established within cancer care networks where access to partners is readily available and direct. They are not designed to collect samples from the general population so our choice of collecting samples from partners was a pragmatic one appropriate for the NCRN. Inevitably in studies such as GELCAPS a smaller number of samples from controls will be collected than from cases since in addition to lack of compliance many patients do not have a current partner. The sex of controls ascertained through initiatives such as GELCAPS will usually be of the opposite gender to cases, and controls are potentially over-matched with respect to many lifestyle risk factors. Theses limitations can be offset to a large degree by using samples collected from the healthy spouses/partners of one cancer as a source of controls for a different cancer. This is something we are currently pursuing with respect to a similar NRCN sponsored initiative the National Study of Colorectal Cancer Genetics (NSCCG)</p>", "<p>Because of the difficulty of obtaining sufficiently detailed data on environmental exposure in studies such as GELCAPS, and because there are issues to do with comparability of exposure data from controls assembled from different studies, it is acknowledged that studies of environmental risk factors including gene-environment interaction will be limited in resources such as GELCAPS. The main value of collections such as GELCAPS will be in studies of genetic risk factors and gene-gene interactions; hypotheses regarding gene-environment interaction require alternative datasets, such as the European Prospective Investigation into Cancer and nutrition (EPIC) study [##REF##12639222##36##], which are centred around population based-cohorts. Accepting such limitations our experience in developing GELCAPS serves to illustrate how large DNA databases for genetic analyses can rapidly be developed in the UK. At present we have only collected outcome data on around 20% of cases recruited to GELCAPS. By completing the collection of follow up data on all cases we shall be able to assemble a unique series for examining the influence of constitutional genotype on clinical outcome in the population setting.</p>" ]

[ "<title>Conclusion</title>", "<p>Finally, it is noteworthy that the value of GELCAPS has been demonstrated in a recent GWAS of lung cancer we have conducted in which we have been able to robustly identify a susceptibility variant for the disease mapping to 15q [##REF##18385676##37##].</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Part of the inherited risk to lung cancer is likely to include common, low risk alleles. The identification of this class of susceptibility is contingent on association-based analyses. We established GEnetic Lung CAncer Predisposition Study (GELCAPS) to collect DNA and clinico-pathological data from a large series of cases and a series of spouse/partner controls, thereby generating a key resource for the identification of low risk alleles.</p>", "<title>Methods</title>", "<p>GELCAPS was one of the first genetic epidemiological trials in the UK to be adopted by the National Cancer Research Network (NCRN) onto its portfolio with the participation of over 100 oncology departments specialising in the management of lung cancer.</p>", "<title>Results</title>", "<p>Samples from over 5,000 independent lung cancer cases and 2,000 controls have so far been assembled through GELCAPS.</p>", "<title>Conclusion</title>", "<p>GELCAPS represents one of the largest datasets of its type in the world capable of informing on the contribution of low penetrance alleles to the development of lung cancer and the influence of genetic variation on outcome. In addition our experience in developing the GELCAPS serves to illustrate how large DNA biobanks for genetic analyses can be rapidly generated within the UK using the NCRN.</p>" ]

[ "<title>Abbreviations</title>", "<p>GELCAPS: Genetic Lung Cancer Predisposition Study</p>", "<title>Compteting interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>TE and RSH were the principal investigators for the GELCAPS, devised the study. AM helped in study development and was responsible for database design, and management of study coordinators. All authors contributed to the paper.</p>", "<title>Availability &amp; requirements</title>", "<p>Cancer Research UK: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.cancerresearchuk.org\"/></p>", "<p>GELCAPS: <ext-link ext-link-type=\"uri\" xlink:href=\"http://public.ukcrn.org.uk/Search/StudyDetail.aspx?StudyID=781\"/></p>", "<p>National Cancer Research Network: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncrn.org.uk\"/></p>", "<p>NHS Cancer Plan: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.dh.gov.uk/assetRoot/04/01/45/13/04014513.pdf\"/></p>", "<p>National Study of Colorectal Cancer Genetics: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.icr.ac.uk/research/research_sections/cancer_genetics/cancer_genetics_teams/molecular_and_population_genetics/nsccg/index.shtml\"/></p>", "<p>European Prospective Investigation into Cancer and Nutrition: <ext-link ext-link-type=\"uri\" xlink:href=\"http://epic.iarc.fr/\"/></p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2407/8/244/prepub\"/></p>" ]

[ "<title>Acknowledgements</title>", "<p>We are grateful to patients for their participation. This work was undertaken with support from, HEAL, Aventis, the NCRN and Cancer Research UK. Athena Matakidou was the recipient of a clinical research fellowship from the Allan J Lerner Fund.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Accrual of cases and controls to GELCAPS.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Centres in the UK recruiting to GELCAPS after NCRN adoption.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Survival from lung cancer in patients according to stage at presentation: A) Patients with SCLC, B) Patients with NSCLC</bold>. In both SCLC and NSLC survival was significantly better (P &lt; 0.0001) in patients presenting with early stage disease compared to those presenting with late stage disease in both Log rank tests of the difference in distribution of survival curves and in Cox-proportional hazard test, adjusting for age, sex, year of presentation and treatment with platinum-based chemotherapy. Statistical analyses performed using STATA version 8.0 (College Station, Tx, USA).</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Characteristics of lung cancer patients recruited to GELCAPS</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>Cases</bold></td><td align=\"center\"><bold>Controls</bold></td></tr></thead><tbody><tr><td align=\"left\">Total</td><td align=\"center\">5,269</td><td align=\"center\">2,094</td></tr><tr><td align=\"left\">Gender (Male: Female)</td><td align=\"right\">3,382 (64.2%)</td><td align=\"center\">645 (30.8%)</td></tr><tr><td align=\"left\">Age at diagnosis (years)</td><td align=\"right\">887 (35.8%)</td><td align=\"center\">446 (69.1%)</td></tr><tr><td align=\"left\"> &lt;40</td><td align=\"right\">53 (1.0%)</td><td align=\"center\">64 (3.1%)</td></tr><tr><td align=\"left\"> 40–49</td><td align=\"right\">302 (5.7%)</td><td align=\"center\">166 (7.9%)</td></tr><tr><td align=\"left\"> 50–59</td><td align=\"right\">1,146 (21.7%)</td><td align=\"center\">514 (24.5%)</td></tr><tr><td align=\"left\"> 60–69</td><td align=\"right\">1,868 (35.4%)</td><td align=\"center\">756 (36.1%)</td></tr><tr><td align=\"left\"> 70–79</td><td align=\"right\">1,590 (30.1%)</td><td align=\"center\">510 (24.4%)</td></tr><tr><td align=\"left\"> 80+</td><td align=\"right\">310 (5.9%)</td><td align=\"center\">84 (4.0%)</td></tr><tr><td align=\"left\"> Mean (SD)</td><td align=\"right\">65.1 (10.0)</td><td align=\"center\">62.9 (10.6)</td></tr><tr><td align=\"left\">Ethnicity</td><td/><td/></tr><tr><td align=\"left\"> Arabic</td><td align=\"right\">3 (0.05%)</td><td align=\"center\">1 (0.05%)</td></tr><tr><td align=\"left\"> Asian</td><td align=\"right\">7 (0.13%)</td><td align=\"center\">6 (0.29%)</td></tr><tr><td align=\"left\"> Bangladeshi</td><td align=\"right\">1 (0.01%)</td><td align=\"center\">0</td></tr><tr><td align=\"left\"> Black-African</td><td align=\"right\">4 (0.08%)</td><td align=\"center\">1 (0.05%)</td></tr><tr><td align=\"left\"> Black-Caribbean</td><td align=\"right\">31 (0.59%)</td><td align=\"center\">8 (0.38%)</td></tr><tr><td align=\"left\"> Black-Other</td><td align=\"right\">1 (0.01%)</td><td align=\"center\">0</td></tr><tr><td align=\"left\"> Indian</td><td align=\"right\">16 (0.30%)</td><td align=\"center\">4 (0.19%)</td></tr><tr><td align=\"left\"> Jewish-Ashkenazi</td><td align=\"right\">12 (0.22%)</td><td align=\"center\">6 (0.29%)</td></tr><tr><td align=\"left\"> Pakistani</td><td align=\"right\">8 (0.15%)</td><td align=\"center\">0</td></tr><tr><td align=\"left\"> White</td><td align=\"right\">5,065 (96.1%)</td><td align=\"center\">1,947 (93.0%)</td></tr><tr><td align=\"left\"> Other/not specified</td><td align=\"right\">82 (1.6%)</td><td align=\"center\">121 (5.8%)</td></tr><tr><td align=\"left\">Reported asbestos exposure</td><td align=\"right\">807 (15.2%)</td><td align=\"center\">125 (6.0%)</td></tr><tr><td align=\"left\">Family history of lung cancer</td><td align=\"right\">750 (14.2%)</td><td align=\"center\">212 (10.1%)</td></tr><tr><td align=\"left\">Smoking habits</td><td/><td/></tr><tr><td align=\"left\"> Never-smokers</td><td align=\"right\">307 (5.8%)</td><td align=\"center\">718 (34%)</td></tr><tr><td align=\"left\"> All smokers</td><td/><td/></tr><tr><td align=\"left\"> Age first started smoking (SD)</td><td align=\"right\">16.5 (4.0)</td><td align=\"center\">17.9 (4.8%)</td></tr><tr><td align=\"left\"> Pack years in Smokers (SD)</td><td align=\"right\">47.2 (30.6)</td><td align=\"center\">30.4 (22.1%)</td></tr><tr><td/><td/><td/></tr><tr><td align=\"left\">Histology of cancer</td><td/><td/></tr><tr><td align=\"left\">Small cell (SCLC)</td><td align=\"right\">1,193 (22.6%)</td><td/></tr><tr><td align=\"left\">Non-small cell (NSCLC)</td><td align=\"right\">3,815 (72.4%)</td><td/></tr><tr><td align=\"left\"> Squamous</td><td align=\"right\">1,905 (49.9%)</td><td/></tr><tr><td align=\"left\"> Adenocarcinoma (including variants)</td><td align=\"right\">1,110 (29.1%)</td><td/></tr><tr><td align=\"left\"> Large cell</td><td align=\"right\">10 (0.3%)</td><td/></tr><tr><td align=\"left\"> Brochoalveolar</td><td align=\"right\">44 (1.2%)</td><td/></tr><tr><td align=\"left\"> Adenosquamous</td><td align=\"right\">11 (0.3%)</td><td/></tr><tr><td align=\"left\"> Neuroendocrine</td><td align=\"right\">20 (0.5%)</td><td/></tr><tr><td align=\"left\"> NSCLC unspecified</td><td align=\"right\">715 (18.7%)</td><td/></tr><tr><td align=\"left\">Sarcoma</td><td align=\"right\">5 (0.1%)</td><td/></tr><tr><td align=\"left\">Unclassified primary</td><td align=\"right\">256 (4.9%)</td><td/></tr><tr><td/><td/><td/></tr><tr><td align=\"left\">Tumour stage at presentation, by histology</td><td/><td/></tr><tr><td align=\"left\"> SCLC</td><td/><td/></tr><tr><td align=\"left\">  Limited</td><td align=\"right\">168 (50.4%)</td><td/></tr><tr><td align=\"left\">  Extensive</td><td align=\"right\">165 (59.6%)</td><td/></tr><tr><td align=\"left\"> NSCLC</td><td/><td/></tr><tr><td align=\"left\">  I</td><td align=\"right\">151 (14.1%)</td><td/></tr><tr><td align=\"left\">  II</td><td align=\"right\">140 (13.1%)</td><td/></tr><tr><td align=\"left\">  III</td><td align=\"right\">457 (42.7%)</td><td/></tr><tr><td align=\"left\">  IV</td><td align=\"right\">323 (30.2%)</td><td/></tr></tbody></table></table-wrap>" ]

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[ "<graphic xlink:href=\"1471-2407-8-244-1\"/>", "<graphic xlink:href=\"1471-2407-8-244-2\"/>", "<graphic xlink:href=\"1471-2407-8-244-3\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>Proteins are fundamental to the complex molecular and biochemical processes taking place within organisms. An understanding of their role is therefore critical in biology and bio-related areas, for purposes ranging from general knowledge to the development of targeted medicine and diagnostics. High-throughput sequencing technology has identified a tremendous number of genes with no known functional annotation. On average, as many as 70% of the genes in a genome have poorly known or unknown functions [##REF##17160037##1##]. Not surprisingly, therefore, the prediction of protein function has become an important and urgent problem in functional genomics.</p>", "<p>Protein function prediction can take many forms. The traditional and most popular methodologies use homology modeling and sequence similarity to infer biochemical function [##REF##14681378##2##,##REF##9254694##3##]. In simple cases, such as certain families of ribosomal proteins, globins, kinases or caspases, these procedures work reasonably well. Sequence similarity has been used with great success for inference of molecular function. For biological process and pathway annotation, guilt by association using functional linkage methods has been a popular choice in recent years.</p>", "<p>For example, microarrays are often used to cluster proteins into groups of genes that respond concordantly to a given environmental stimuli. When these groups are strongly enriched in proteins in a given biological process such as insulin signaling and also contain proteins without annotation we often take the leap of faith and predict the unknown proteins to be associated with this process as well. Similarly, when two proteins are found to interact in a high throughput assay we also tend to use this as evidence of functional linkage.</p>", "<p>However, enrichment and guilt by association are often highly misleading and can lead to a very high false positive rate if not used with caution. The work in [##REF##12855458##4##] and several other papers, e.g., [##REF##12826619##5##, ####REF##15567862##6##, ##REF##17396164##7####17396164##7##], attempted to frame these inference problems in a precise network-based probabilistic framework. Here we attempt to make a fundamental advance in this area, by augmenting the network-based perspective to additionally make explicit use of the structure of the GO hierarchy to compute more precise probabilities, thereby improving on the quality of predictions made by the inference algorithms.</p>", "<p>More broadly, the work in this paper is important in demonstrating that an important role can be played in this context by the knowledge captured in biological ontologies, when properly harnessed. That this should be the case is not obvious <italic>a priori</italic>. For example, while many scientists use GO in their daily research, it can be (and has been) claimed that overlap among categories, as well as the inherent ambiguity and semantic complexity of naming biological functions and processes, can frequently lead to misleading interpretations and wild goose chases. Classic statistical approaches are based on flat disjoint categories, and quantitative measures of annotation similarity such as through semantic similarity remain somewhat <italic>ad hoc</italic>.</p>", "<p>Nevertheless, despite such concerns, our work here shows that in the present context of automated protein function prediction, the leverage of hierarchies grounded in biological ontologies can yield real, quantifiable advantages over 'flat' network-based approaches.</p>", "<title>Objective</title>", "<p>Computational protein function prediction is typically treated as a classification problem. From this perspective, given a protein <italic>i </italic>and the label <italic>G </italic>of a potential function for that protein, the goal is to predict whether or not <italic>i </italic>has label <italic>G</italic>, using a classifier built from a set of training cases and additional related data. Such related data can be of many types (e.g., protein interaction data, gene expression data, protein localization data) but often can be summarized in the form of a functional linkage graph (e.g., protein-protein interaction network, gene association network). The labels <italic>G </italic>typically derive from a database of terms.</p>", "<p>Protein-protein interaction (PPI) data are common, and have been used widely in the protein function prediction problem. A functional linkage graph is used to represent the information in the PPI, where nodes represent proteins and edges indicate pairwise interactions, as in Fig. ##FIG##0##1(a)##. Numerous studies have demonstrated that proteins sharing similar functional annotations tend to interact more frequently than proteins which do not share them, both as members in relatively fixed complexes, e.g. the ribosome, or as transient interactors, such as kinases and substrates in signal transduction networks. Hence, it is natural to want to take advantage of the neighborhood information to predict protein functions. For example, to predict for the protein with a question mark in the center in Fig. ##FIG##0##1(a)##, we can try to utilize any known functional annotations of its neighbors, i.e., the proteins directly interacting with it.</p>", "<p>Databases of labels <italic>G </italic>are commonly structured in a hierarchical form (more formally, as a directed acyclic graph (DAG)). The Gene Ontology (GO) database is one such example <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.geneontology.org/\"/>. Viewed as a DAG, nodes represent labels and a link represents the <italic>is_a </italic>and <italic>part_of </italic>relations between labels. Function assignments to proteins must obey the <italic>true-path rule</italic>: if a child term (i.e., more specific term) describes the gene product, then all its parent terms (i.e., less specific terms) must also apply to that gene product. Fig. ##FIG##0##1(b)## shows a small schematic illustration. For any protein labeled with term <italic>A</italic>, it may or may not have term <italic>D</italic>, the child of <italic>A</italic>; on the other hand, if it does not have term <italic>A</italic>, it surely does not have <italic>D</italic>.</p>", "<p>This annotation rule suggests that when predicting the label of a term in the hierarchy, it is helpful to first consider whether the protein has the parent term or not. Thus, informative are not only the neighbors labeled with the term of interest but also those labeled with the parent. For instance, to predict the label of term <italic>D </italic>for the central protein in Fig. ##FIG##0##1(a)##, we want to use both the neighbors labeled with <italic>A </italic>and the neighbors labeled with <italic>D</italic>. The exploitation of GO hierarchy is not novel, and indeed is natural. It has been used in functional annotation of genes, as mentioned in the Related Work section, as well as for other purposes, such as identifying over- and under-representation of GO terms in a gene dataset, and clustering functionally related genes [##REF##14962934##8##, ####REF##15297299##9##, ##REF##15575967##10####15575967##10##].</p>", "<p>As currently practiced in most instances, prediction of protein function is done with classifiers trained separately for each possible label <italic>G</italic>, as in [##REF##12855458##4##,##REF##17396164##7##,##REF##15130933##11##,##REF##15285902##12##]. (Please also see the section of Related Work.) But, as just discussed, the overall collection of labels to be assigned generally has a <italic>hierarchical </italic>structure to it i.e., the labels are related to each other in a specific manner. This structure typically is enforced only after producing an initial set of predictions, as post-processing steps, either using transitive closure, [##REF##12855458##4##], or using more sophisticated methods, [##REF##16410319##13##,##UREF##0##14##].</p>", "<p>To further illustrate this, we show a toy GO hierarchy in Fig. ##FIG##1##2##, which contains a root and four descendant terms A, B, C and D, where term A and B are the parents for C and D, respectively. For a given protein, the label format for the terms is \"true label (predicted probability)\". For example, the protein is annotated with term A but not with term D. The probabilities of having term A and D are 0.4 and 0.5, respectively.</p>", "<p>Most existing methods, as discussed earlier, predict protein function in a term-by-term fashion, without considering the relationship among terms. Suppose the probabilities in the plot are obtained from one of such methods. If we apply a cut-off of 0.5, which is a commonly used threshold in this field, we will predict that the protein is NOT annotated with term A, since the probability of having A is 0.4, less than 0.5; and is annotated with A's child C. This violates <italic>the true-path rule</italic>, since if the protein is predicted not having term A, then it is not having any of A's descendent terms. On the other hand, the protein is predicted to be labeled with both terms B and D, with probabilities of 0.7 and 0.5, respectively, which obeys <italic>the true-path rule</italic>, with the prediction on D as a false positive. Such a violation to <italic>the true-path rule </italic>is not uncommon.</p>", "<p>The basic premise of this paper is that reference to this hierarchical relationship among labels is best incorporated in the initial stage of constructing a classifier, as a valuable source of information in and of itself. Our objective here is to demonstrate the power of this premise and to show that it may be tapped in the form of a single, coherent probabilistic classifier. In particular, we develop a probability model that integrates relational data and a hierarchy of labels, and illustrate its advantages in predicting protein function using a PPI network and the Gene Ontology (GO) hierarchy.</p>", "<title>Related Work</title>", "<p>Many methodologies have been proposed to predict protein functions. Most of the earlier methods tend to use a single source of protein information, such as PPI. Typical examples include the \"Nearest-Neighbor\" algorithm, also known as \"guilt-by-association\" principle, and the Binomial-Neighborhood (BN) method [##REF##12855458##4##].</p>", "<p>These earlier methods were followed later by a surge of interest in combining heterogeneous sources of protein information. For example, a machine learning approach integrating datasets of PPI, gene expression, hydropathy profile and amino acid sequences, in the form of different kernels, has been introduced [##REF##15130933##11##]. Various genome-wide data can also be employed in a Bayesian framework to produce posterior probability for function assignments [##REF##12826619##5##,##REF##15567862##6##]. And a Markov Random Field model combining PPI network and protein domain information was introduced in [##REF##15285902##12##]. A common characteristic of these methods is detecting protein functions individually, without considering the relationship among them. As remarked, a pitfall of this is that the predictions may conflict with the <italic>true-path rule </italic>of ontologies.</p>", "<p>Motivated in part by seminal work of [##UREF##1##15##], combining protein data and ontology structure has recently become a focus. One approach is using a Bayesian network structure to correct inconsistent function predictions, by calculating the largest posterior probability of the <italic>true-path </italic>consistent labels, given the predictions from independent classifiers for each of the proteins [##REF##16410319##13##]. Similar work has been done in [##UREF##0##14##], where multiple classifiers are built and training data are modified according to the GO hierarchy. A Bayesian model consisting of a set of nested multinomial logit models, where a prior describing correlations of parameters for nearby GO terms is trained by the hierarchy, has been proposed in [##REF##17038174##16##]. Observing the fact that a protein is actually associated with multiple GO terms, this problem can also be treated as a hierarchical multi-label classification task [##REF##16410319##13##,##UREF##2##17##]. Yielding various degrees of improvement in prediction accuracy, these methods all seek to take advantage of the hierarchical label structure. However, importantly, we note that all of those that predict at multiple depths in the GO hierarchy take a separate step to correct <italic>true-path </italic>inconsistent predictions, rather than producing them directly in a probabilistically coherent fashion.</p>", "<p>In summary, combining relational protein data, such as PPI, and hierarchical structures, as in GO, in one probabilistic model to predict <italic>true-path </italic>consistent function labels, has to the best of our knowledge not been done to date. This task is the focus of our work.</p>" ]

[ "<title>Methods</title>", "<p>Ontologies like GO are structured as directed acyclic graphs (DAG's), where a child term may have multiple parent terms. The DAG structure, with alternative paths from the root to internal and leaf terms, is one of the reasons that formal approaches to annotation predictions have been difficult. It is well known that computing the most likely assignment of values to variables in a DAG of size <italic>N </italic>given their conditional probabilities on the arcs is a classical NP-hard problem in graphical models. In fact, variants of this problem are actually formally harder by some theoretical considerations. Therefore, people routinely use tree approximations of probability distributions, which goes back to the work in [##UREF##3##18##]. In our work, clearly, a tree-based approach is the first step to something concrete, rather than <italic>ad hoc</italic>. We will show in the following sections that, as a way of balance, and in light of our results, it would appear that a tree is a good compromise between <italic>ad hoc </italic>and completely rigorous usage of the DAG.</p>", "<p>We apply a minimal spanning tree (MST) algorithm to transform a DAG into a tree-structured hierarchy, by preserving the link between the child and the parent with the heaviest weight <italic>w</italic>, where <italic>w </italic>is the empirical conditional probability of having the child term given having the parent, based on a given PPI training set. Each GO term, in such a hierarchy, may still have more than one child term, but only one parent term (if the term itself is not the root of the hierarchy).</p>", "<p>As a result of this transformation, there now exists a unique path from the root term to any non-root term. That is, let <italic>G</italic>_{<italic>d </italic>}denote a term at the <italic>d</italic>-th level below the root. For example, <italic>d </italic>= 1 if the term is a child of the root. Then in our tree-structured hierarchy there is always a unique path of the form <italic>G</italic>_{<italic>d</italic>}, <italic>G</italic>_{<italic>d</italic>-1}, ..., <italic>G</italic>₁, <italic>G</italic>₀, with <italic>G</italic>₀being the root, and <italic>G</italic>_{<italic>i</italic>-1}being the parent of <italic>G</italic>_{<italic>i</italic>}. For example, in Fig. ##FIG##0##1(b)##, the result of applying our MST algorithm would be to drop the (<italic>B</italic>, <italic>E</italic>) link.</p>", "<p>We propose to build a classifier in this setting based on the use of hierarchical conditional probabilities of the form . Here <italic>i </italic>indexes a certain protein, and <italic>G</italic>_{<italic>d </italic>}is a GO term of interest. The binary variable = 1 indicates that protein <italic>i </italic>is labeled with <italic>G</italic>_{<italic>d</italic>}; otherwise, it takes the value -1. Finally, denotes the status of all of protein <italic>i</italic>'s neighbors in the PPI network, across all GO terms, as well as the status for protein <italic>i </italic>of all of the ancestor terms of <italic>G</italic>_{<italic>d</italic>}. We will refer to as the <italic>neighborhood status </italic>of <italic>i</italic>.</p>", "<p>In the remainder of this section, we present certain model assumptions that in turn lead to a particular form for the probabilities , as well as an efficient algorithm for their computation.</p>", "<title>Assumptions</title>", "<p>We assume that labels on proteins obey a Markov property with respect to the PPI. That is, that the labeling of a protein is independent of any other proteins given that of its neighbors. Similarly, we assume that a Markov property holds on the GO tree-structured hierarchy, meaning that for a given protein the status of a GO term label is independent of that of the other terms, given that of its parent.</p>", "<p>In addition, we assume that for any given protein <italic>i</italic>, the number of its neighbors labeled with a child term, among those labeled with the parent term, follows a binomial distribution, with probability depending on whether protein <italic>i </italic>is with the child or not. More precisely, we model</p>", "<p></p>", "<p>and</p>", "<p></p>", "<p>where</p>", "<p>• <italic>G</italic>_{<italic>ch </italic>}is the child term; <italic>G</italic>_{<italic>pa </italic>}is its parent;</p>", "<p>• is the number of <italic>i</italic>'s neighbors labeled with the <italic>G</italic>_{<italic>ch</italic>}, and is the number of neighbors labeled with <italic>G</italic>_{<italic>pa</italic>};</p>", "<p>• <italic>p</italic>₁is the probability with which neighbors of <italic>i </italic>are independently labeled with <italic>G</italic>_{<italic>ch </italic>}(being already labeled with <italic>G</italic>_{<italic>pa</italic>}), given <italic>i </italic>is labeled with <italic>G</italic>_{<italic>ch</italic>};</p>", "<p>• <italic>p</italic>₀is the probability with which neighbors of <italic>i </italic>are independently labeled with <italic>G</italic>_{<italic>ch </italic>}(being already labeled with <italic>G</italic>_{<italic>pa</italic>}), given <italic>i </italic>is NOT labeled with <italic>G</italic>_{<italic>ch </italic>}but is labeled with <italic>G</italic>_{<italic>pa</italic>}.</p>", "<p>We refer to this overall set of model assumptions as the <italic>Hierarchical Binomial-Neighborhood (HBN) </italic>assumptions, in reference to their extension of the Binomial-Neighborhood (BN) assumptions of [##REF##12855458##4##]. Note that the form of the probabilities above assumes that , the number of neighbors with the child term, is independent of the neighborhood size <italic>N</italic>, given , the number of neighbors with the parent. This condition seems reasonable since, recall that, by the <italic>true-path rule</italic>, only those among <italic>i</italic>'s neighbors that are labeled with the parent term can possibly have the child term. In other words, those neighbors with the child form a subset of those neighbors with the parent.</p>", "<p>Parameters <italic>p</italic>₁and <italic>p</italic>₀are term-specific: different terms have different <italic>p</italic>₁and <italic>p</italic>₀. For a given term <italic>G</italic>_{<italic>ch</italic>}, all proteins share the same <italic>p</italic>₁and <italic>p</italic>₀. They are estimated by pseudo-likelihood approach, from the labeled training data, separately for each <italic>G</italic>_{<italic>ch </italic>}to be predicted. When calculating , , we use only the neighbors in the training set.</p>", "<p>More specifically, assume there are <italic>n </italic>proteins in the training set, with <italic>m </italic>proteins labeled with <italic>G</italic>_{<italic>ch </italic>}and <italic>n </italic>- <italic>m </italic>proteins not labeled with <italic>G</italic>_{<italic>ch</italic>}. To simplify notation, let <italic>k</italic>_{<italic>ch</italic>, <italic>i </italic>}and <italic>k</italic>_{<italic>pa</italic>, <italic>i </italic>}be protein <italic>i</italic>'s training neighbors labeled with <italic>G</italic>_{<italic>ch </italic>}and <italic>G</italic>_{<italic>pa</italic>}, respectively. For the <italic>m G</italic>_{<italic>ch</italic>}-annotated proteins, we have</p>", "<p></p>", "<p>where = 1 and <italic>i </italic>= 1, 2, ..., <italic>m</italic>. With the Markov property assumption, the likelihood function for <italic>p</italic>₁based on all <italic>G</italic>_{<italic>ch</italic>}-annotated proteins is</p>", "<p></p>", "<p>The estimator for <italic>p</italic>₁is based on all <italic>G</italic>_{<italic>ch</italic>}-annotated proteins' neighborhoods in the training set, and is the ratio of the total number of their <italic>G</italic>_{<italic>ch</italic>}-annotated neighbors and the total number of their <italic>G</italic>_{<italic>pa</italic>}-annotated neighbors, i.e.,</p>", "<p></p>", "<p>with = 1.</p>", "<p>Similarly, the estimator for <italic>p</italic>₀is based on all <italic>G</italic>_{<italic>ch</italic>}-unannoated proteins' neighborhoods in the training set, and is the ratio of the total number of their <italic>G</italic>_{<italic>ch</italic>}-annotated neighbors and the total number of their <italic>G</italic>_{<italic>pa</italic>}-annotated neighbors,</p>", "<p></p>", "<p>with = -1. Estimators and are formally pseudo-likelihood estimators.</p>", "<p>An issue of estimation is the lack of data. Few data will affect the predictability and interpretability of the terms. Thus, we focus on terms with at least 5 proteins annotated with in the GO dataset. In principle, more formal work could be done, by using smoothing techniques and Empirical Bayes approaches, which we are exploring in our current work. It appears that improvement is not uniform, and the issue clearly requires separate consideration and will likely form a substantial component of a separate paper. Its subtlety likely is due to the well-known issue of classifiers doing well for classification while still being off-target for estimation [##UREF##4##19##].</p>", "<p>Also notice that we use one-hop neighborhoods in this paper, i.e., neighbors that are directly connected to the protein of study. The extension to larger neighborhoods could be easily done, and would likely yield some improvement in predictive performance, but at the expense of some additional mathematical overhead, replacing the BN framework with one like those in [##REF##14566057##20##, ####REF##14709178##21##, ##REF##16632496##22##, ##REF##17570151##23##, ##REF##15961472##24####15961472##24##]. Our choice to use a one-hop neighborhood structure here simply reflects a desire of maintaining a certain transparency in our model development, so as to emphasize primarily the effect of adding hierarchical information.</p>", "<title>Local Hierarchical Conditional Probability</title>", "<p>By the Markov property assumed on the GO hierarchy, for any non-root term, only the parent affects its labelling. Therefore, to derive an expression for our hierarchical conditional probabilities , we first concentrate on an expression for local hierarchical conditional probabilities of the form</p>", "<p></p>", "<p>Applying Bayes' rule, we have</p>", "<p></p>", "<p>For the first term in the numerator,</p>", "<p></p>", "<p>For the second term in the numerator, we use the plug-in estimate <italic>f</italic>, where <italic>f </italic>is defined to be the empirical probability of having the child term, given its having the parent, i.e.,</p>", "<p></p>", "<p>For the denominator, we apply the law of total probability and as a result, together with the two results above, the probability in (1) can be expressed as</p>", "<p></p>", "<p>where = 1 - <italic>f</italic>.</p>", "<title>Global Hierarchical Conditional Probability</title>", "<p>Equipped with the local hierarchical conditional probability, for any non-root GO term <italic>G</italic>_{<italic>d </italic>}in the hierarchy, we now derive an expression for , the probability that protein <italic>i </italic>is annotated with <italic>G</italic>_{<italic>d </italic>}given its neighborhood status.</p>", "<p>Note that by the <italic>true-path rule </italic>we have , where <italic>G</italic>_{<italic>d</italic>-1}is the parent of <italic>G</italic>_{<italic>d</italic>}.</p>", "<p>Hence,</p>", "<p></p>", "<p>This logic easily extends recursively back through all ancestors of <italic>G</italic>_{<italic>d</italic>}, and thus the conditional probability (3) can be factorized as</p>", "<p></p>", "<p>where is the local hierarchical neighborhood information on the parent-child GO term pair, <italic>G</italic>_{<italic>m </italic>}and <italic>G</italic>_{<italic>m</italic>-1}.</p>", "<p>Importantly, note that due to the form of the factorization, the global conditional probability for <italic>G</italic>_{<italic>d </italic>}is no greater than that for its parent <italic>G</italic>_{<italic>d</italic>-1}, i.e., we have the inequality</p>", "<p></p>", "<p>As we go down along the path from the root in the hierarchy, the probability that protein <italic>i </italic>is labeled with a more specific term is always no more than the probability of any of its ancestors. If the label of a term is predicted as -1, according to some pre-chosen threshold, the labels for every descendent below will also be assigned as -1. Thus, our model is guaranteed to produce GO term label assignments that comply with the <italic>true-path rule</italic>. Most existing methods for protein function prediction use ad-hoc enforcement to correct predictions in order to maintain <italic>true-path </italic>consistency.</p>", "<title>Algorithm</title>", "<p>Classification using our Hierarchical Binomial-Neighborhood (HBN) model may be accomplished using a straightforward top-to-bottom algorithm. Specifically, for a given protein <italic>i</italic>, and a pre-determined threshold <italic>t</italic>, we proceed from the child terms of the root in the MST representation of the GO hierarchy in the following fashion.</p>", "<p><bold>initialize </bold>P<sc>ROB</sc> = 1</p>", "<p><bold>for </bold><italic>m </italic>= 1: <italic>d</italic>_{<italic>max</italic>},</p>", "<p>      <bold>while ∃ </bold>unlabeled terms <italic>G</italic>_{<italic>m </italic>}at level <italic>m</italic>,</p>", "<p>      <bold>compute </bold></p>", "<p>         </p>", "<p>      <bold>if </bold> &gt;<italic>t</italic>, set = 1</p>", "<p>      <bold>else </bold>set = -1 and propagate to all descendants of <italic>G</italic>_{<italic>m</italic>}</p>", "<p>   <bold>end</bold></p>", "<p>end</p>", "<p>Notice that setting the labels at each step is not necessary. However, doing so facilitates the computation efficiency, by avoiding the calculation of the probabilities below the threshold. By letting <italic>t </italic>= 0, we can obtain all probabilities. The fact that we can do this is a direct outcome of the fact that our predictions are guaranteed to obey <italic>the true-path rule</italic>.</p>", "<p>For a given protein, the algorithm requires at most <italic>O</italic>(<italic>N</italic>_{<italic>GO</italic>}) steps, where <italic>N</italic>_{<italic>GO </italic>}is the number of GO terms, and therefore, for <italic>N</italic>_{<italic>Protein </italic>}proteins, no more than <italic>O</italic>(<italic>N</italic>_{<italic>Protein</italic>}<italic>N</italic>_{<italic>GO</italic>}) steps are needed. Hence, the algorithm is linear in the size of both the PPI and the GO networks. In practice, it has been found to be quite fast, particularly because each protein can be expected to have a large proportion of -1 labels, and once a -1 is assigned to a term it is simply propagated to all descendant terms.</p>" ]

[ "<title>Results</title>", "<title>Data</title>", "<p>The PPI data used in this paper is from the yeast <italic>Saccharomyces cerevisiae</italic>, as updated in January 2007 at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.thebiogrid.org/\"/>. There are 5143 genes (nodes) and 31190 non-redundant physical interactions (edges), after deleting self-interacting and unannotated nodes, and genetic interactions.</p>", "<p>The Gene Ontology used is <italic>biological process</italic>, updated in June 2006, as posted at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.geneontology.org/\"/>. From the biological perspective, more specific terms are more interesting than less specific ones, and we therefore only predict for terms with 300 or less genes annotated in the database. As a result, the entire <italic>biological process </italic>ontology breaks down into 47 sub-hierarchies. In addition, to avoid extremes with little to no information, we only predict for terms with at least 5 genes. We also delete GO:0000004, biological function unknown. The total number of terms predicted is 1037. The GO term annotations used to train the model are updated in June 2006, from <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.geneontology.org/\"/>.</p>", "<p>From the initial data, a set of labels is constructed in a way that follows the <italic>true-path rule</italic>. Specifically, for any protein-term association in the data, we assign a +1 label to the term for that protein, as well as to all of the ancestors in the transitive closure of that term in the GO hierarchy. We repeat this for all protein-term associations to get the set of all positive labels. We assign -1 to all other protein-term pairs.</p>", "<p>Please visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://math.bu.edu/people/kolaczyk/software.html\"/> for the datasets used in this paper and the Matlab scripts for the HBN algorithm.</p>", "<title>Cross-Validation Study</title>", "<p>We apply our Hierarchical Binomial-Neighborhood (HBN) method, as well as the \"Nearest-Neighbor\" (NN) algorithm and the Binomial-Neighborhood (BN) method of [##REF##12855458##4##], to the data just described, using a 5-fold cross-validation design. The HBN and BN methods each produce a probability of protein-term association, while the NN algorithm similarly produces a number between 0 and 1 (i.e., the fraction of a protein's neighbors in the PPI network possessing the term in question). For each test fold, representing 20% of the proteins, all GO term annotations are taken as unknown, and predictions of protein-term associations are made with each of the three methods, based on comparison of their output to a threshold <italic>t </italic>∈ [0, 1], using the annotations in the other four folds as training data.</p>", "<title>Evaluation</title>", "<p>We use three metrics by which to evaluate the performance characteristics of each classification method. The first is the standard Receiver Operating Characteristic (ROC) curve, which evaluates a classifier's performance in a manner that aggregates across all terms. We examine ROC curves both for the overall GO hierarchy and within each of the 47 sub-hierarchies.</p>", "<p>Since the ROC curve, as a metric, is 'flat', in that it ignores any hierarchical structure among terms, we use as a second metric a hierarchical performance measure, called <italic>hF_β</italic>, proposed in [##UREF##5##25##,##UREF##6##26##] and defined as follows. For a hierarchy of GO terms and any protein <italic>i </italic>that is annotated with the hierarchy root, first take the transitive closure of all of the most specific +1 predictions and change -1's into +1's, if there is any. Note that this step is only necessary here for \"Nearest-Neighbor\" and the \"Binomial-Neighborhood\" method.</p>", "<p>Next, for each protein <italic>i</italic>, calculate the true positive (TP), false positive (FP), and false negative (FN) counts, based on the true labels of all terms in the hierarchy and the corrected predictions, denoted as <italic>TP</italic>_{<italic>i</italic>},</p>", "<p><italic>FP</italic>_{<italic>i </italic>}and <italic>FN</italic>_{<italic>i</italic>}, respectively. Define hierarchical precision (<italic>hP</italic>) and hierarchical recall (<italic>hR</italic>) as</p>", "<p></p>", "<p>The value <italic>hF_β</italic>is then defined as a weighted combination of <italic>hP </italic>and <italic>hR</italic>, in the form</p>", "<p></p>", "<p>where <italic>β </italic>∈ [0, ∞) is a tuning parameter. In this paper, we use <italic>hF</italic>₁with equal weights on precision and recall, simply denoted as <italic>hF</italic>. Note that <italic>hF</italic>, <italic>hP </italic>and <italic>hR </italic>are all scaled between 0 and 1, with higher <italic>hF </italic>indicating better performance over the hierarchy.</p>", "<p>Lastly, because accurate positive predictions are of most biological interest in this area, and because predictions of terms increasingly deeper in the GO hierarchy are of increasingly greater use, we examine the positive predictive value (PPV) of each of the methods, as a function of depth in the hierarchy. However, as the prevalence of known terms tends to decrease substantially with depth, and PPV decreases similarly with decreasing prevalence, we normalize PPV by prevalence to allow meaningful comparison across depths. Specifically, we compute a log-odds version of PPV in the form</p>", "<p></p>", "<p>where <italic>f </italic>is the prevalence of a given term. This quantity therefore indicates relative performance of a given classifier, in comparison with a method that simply predicts proteins to have a given term with <italic>a priori </italic>probability <italic>f</italic>.</p>", "<title>An Illustration</title>", "<p>To better appreciate the performance gains from HBN that we describe momentarily below, we first present an illustrative example. Consider protein YGL017W (AFT1) and its neighborhood, as depicted in Fig. ##FIG##2##3(a)##. Knowing that YGL017W is labeled with the parent term <italic>G</italic>_{<italic>pa </italic>}= GO:0045449, or <italic>regulation of transcription</italic>, we want to predict whether YGL017W is labeled with the child term <italic>G</italic>_{<italic>ch </italic>}= GO:0045941, or <italic>positive regulation of transcription</italic>. All six neighbors are in the training set, and used together with other training nodes to estimate parameters. Three out of six neighbors are labeled with <italic>G</italic>_{<italic>pa</italic>}, and two with <italic>G</italic>_{<italic>ch</italic>}. The prediction from HBN results from applying a threshold to Equation. The analogous probability for BN is given by</p>", "<p></p>", "<p>where</p>", "<p>• <italic>G </italic>is the target GO term, GO:0045941;</p>", "<p>• <italic>k </italic>is the number of training neighbors labeled with <italic>G</italic>;</p>", "<p>• <italic>N </italic>is the training neighborhood size;</p>", "<p>• is the probability with which neighbors are independently labeled with <italic>G</italic>, given protein YGL017W is labeled with <italic>G</italic>;</p>", "<p>• is the probability with which neighbors are independently labeled with <italic>G</italic>, given protein YGL017W is NOT labeled with <italic>G</italic>;</p>", "<p>• <italic>f</italic>* is the relative frequency of <italic>G </italic>in the training set, and = 1 - <italic>f</italic>*.</p>", "<p>Table ##TAB##0##1## contains the parameters for each of the three classification methods, and the output they produce. HBN provides substantially more evidence for YGL017W being labeled with GO term GO:0045941, which is in fact the case. With a threshold <italic>t </italic>= 0.5, only HBN provides a correct positive prediction. The improvement here comes from the additional information provided by including parent-term information.</p>", "<title>Cross-Validation Results</title>", "<p>A comparison of the overall performance of the three methods, by ROC curves and the <italic>hF </italic>measure, is shown in Fig. ##FIG##3##4## and Fig. ##FIG##4##5##, respectively. We are also interested in visualizing precision versus recall, shown in Fig. ##FIG##5##6##. A total of 1037 GO terms are studied on 5143 proteins. Sensitivity, specificity and <italic>hF </italic>are calculated by combining, within each of the 5 folds, the true positive (TP), false positive (FP), true negative (TN) and false negative (FN) counts, over all proteins and all terms for varying thresholds, and averaging across folds. Precision and recall are defined as , . The HBN method outperforms the other two methods by a clear margin in all figures, except at very small thresholds (<italic>t </italic>&lt; 0.1) in the <italic>hF </italic>plot. Comparison of the area under the curve (AUC) for each method, in the ROC and <italic>hF </italic>plots, through a simple paired <italic>t</italic>-test on four degrees of freedom, confirms this observation, i.e., <italic>p </italic>&lt; 10^-5for comparison of HBN with BN and with NN. The gains of HBN over BN directly reflects the benefit of effectively integrating the GO hierarchical information into the construction of our classifier.</p>", "<p>Recall that, as a result of our predicting only for GO terms annotated with less than 300 proteins in the database, the full <italic>biological process </italic>hierarchy actually breaks into 47 sub-hierarchies. Examination of performance on these sub-hierarchies provides some sense of the extent to which the HBN performance improvements are uniform across the GO hierarchy. We compute a ROC curve and <italic>hF </italic>plot for each of the sub-hierarchies (See additional file ##SUPPL##0##1##: ROC curves and <italic>hF </italic>plots for 47 sub-hierarchies in cross-validation study). Numerical comparison of the corresponding AUCs finds, at a 5% significance level that HBN improves on BN in 38 of the 47 sub-hierarchies, according to the ROC curves, 19 of the sub-hierarchies, according to the <italic>hF </italic>plots, and 18 commonly between them. Conversely, BN outperforms HBN in only 1 of the 47 sub-hierarchies, according to the ROC curves, and 9 of the sub-hierarchies, according to the <italic>hF </italic>plots. (NN was uniformly the worse performer.)</p>", "<p>These ROC plots are constructed using the original BN (and NN) predictions, without any correction for \"true-path\" consistency. However, the overwhelming improvement of HBN over BN indicated by the ROC curves is actually similar when the initial predictions of BN are post-processed by applying transitive closure. Specifically, HBN improves on BN in 28 of the sub-hierarchies, while BN outperforms HBN in only 4 sub-hierarchies. These results strongly suggest the validity of our premise as to the importance of incorporating hierarchical information in the GO database in the initial construction of a classifier. The <italic>hF </italic>plots, which incorporate transitive closure for BN (and NN) directly into their definition, and are designed to provide a more accurate summary of classification accuracy with hierarchically related class labels, support this conclusion. The gains of HBN over BN, although reduced, are still substantial, with HBN outperforming BN in just over 40% of the 47 hierarchies, and BN outperforming HBN, in less than 20%.</p>", "<p>As an illustration, consider the performance on the sub-hierarchy corresponding to Fig. ##FIG##6##7## and Fig. ##FIG##7##8##. The root term of the sub-hierarchy is GO:0050896, <italic>response to stimulus</italic>, with 72 more specific terms below it, 40 out of which are predicted for 536 proteins annotated with root. The shapes and locations of the curves in these plots are similar to those in Fig. ##FIG##3##4## and Fig. ##FIG##4##5##, with arguably a more substantial improvement from HBN in the <italic>hF </italic>plot. For instance, using a threshold of <italic>t </italic>= 0.5 for prediction, HBN produces an <italic>hF </italic>measure nearly 254% and 60% higher than NN and BN, respectively (<italic>hF</italic>_{<italic>NN </italic>}= 0.16, <italic>hF</italic>_{<italic>BN </italic>}= 0.35, and <italic>hF</italic>_{<italic>HBN </italic>}= 0.56).</p>", "<p>In contrast, Fig. ##FIG##8##9## and Fig. ##FIG##9##10## show an example of a sub-hierarchy in which the performance of HBN and BN are too close to declare one or the other better. This sub-hierarchy has root term GO:0019538, <italic>protein metabolism</italic>. Examination of the predictions seems to suggest that the comparatively poorer relative performance of HBN in this sub-hierarchy is due to its over-optimistic positive predictions, i.e., HBN produces a higher rate of false positives (FP) that lowers the hierarchical precision (<italic>hP</italic>) and hence the <italic>hF </italic>measure.</p>", "<p>Lastly, Fig. ##FIG##10##11## and Fig. ##FIG##11##12## contain plots summarizing the positive predictive value (PPV) of the three methods. In Fig. ##FIG##10##11##, we show how the averaged PPV varies against the averaged negative predictive value (NPV), over all proteins and GO terms for which all three methods produced at least one positive prediction, averaged over the five folds (PPV versus 1-NPV). We see that the HBN method has consistently higher PPV across all values of NPV. At an NPV of 0.987 (i.e., 1 - NPV = 0.013), for example, where the PPV for HBN is nearly 50% (i.e., PPV = 0.465), that for BN and NN are only roughly 30% (i.e., PPV = 0.310 and 0.326, respectively). That is, for the same rate of correct negative predictions, HBN produces nearly one in two correct positive predictions, while the other two methods produce not quite one in three. Note that the extremely high NPV values for all three methods are largely a result of the similarly high prevalence of -1 labels in the database.</p>", "<p>Shown in Fig. ##FIG##11##12## is the log-odds PPV of all three methods, for NPV = 0.987, as a function of depth in the GO hierarchy. We see that the improvement in positive predictive capabilities of HBN is fairly uniform across depths. A one-sided paired <italic>t</italic>-test at each depth confirmed the differences to be highly significant (i.e., <italic>p</italic>-values roughly 0.001 or less) at depths 3, 4, and 5, but not at depths 1, 2, 6, 7, or 8. We note, however, that the lack of significance at the latter depths is likely partly driven by sample size, since at each of these depths there were less than 30 cases of positive protein-term predictions by all three methods used in calculating LO-PPV, while at the other three depths there were well in excess of 100.</p>", "<title>In Silico Validation Results</title>", "<p>Recall that the above results are based on gene-GO term annotations in the January 2007 GO database. As an <italic>in silico </italic>proxy to <italic>in vitro </italic>validation, beyond that of the cross-validation study, we examined the performance of HBN, in comparison to NN and BN, when applied to new gene-GO term annotations found in the updated May 2007 database. Here our goal is to evaluate the robustness of our cross-validation results for predicting naturally occurring unknowns (i.e., as opposed to those left out in a random fashion through cross-validation).</p>", "<p>We applied HBN, BN, and NN in each of the 47 sub-hierarchies to genes that (i) were annotated with only the root term in the June 2006 database, and (ii) were assigned more specific functions in that sub-hierarchy in the May 2007 database. There were a total of 508 genes that had received at least one new annotation in one of the sub-hierarchies, with as few as 1 gene and as many as 74 genes per hierarchy. There were 33 sub-hierarchies having such genes. The methods were compared for their accuracy through the <italic>hF </italic>function. We present the <italic>hF </italic>plots for only those sub-hierarchies (17) with sufficiently many annotations to yield meaningful results (See additional file ##SUPPL##1##2##: <italic>hF </italic>plots for 17 sub-hierarchies in <italic>in silico </italic>study); the <italic>hF </italic>measures for the others are trivial, due to too few new annotations. Over 40% (i.e., 7 out of 17) of these <italic>hF </italic>plots find HBN to work best in correctly detecting more specific associations, over a reasonably broad range of threshold values; in the majority of the remaining plots, HBN yields results similar to the at least one of the two other methods.</p>", "<p>Overall, most of the plots are consistent with the cross-validation results. Interestingly, however, there are a number of cases where HBN clearly outperforms NN and BN by a larger margin in the <italic>in silico </italic>validation than in the cross-validation study. For example, for the sub-hierarchy with root term <italic>response to stimulus</italic>, the new <italic>hF </italic>curve for HBN exceeds that for BN by as much as 300%, dominating those for the other two methods for most of the thresholds. See Fig. ##FIG##12##13##. In addition, in some sub-hierarchies where HBN does not perform best in cross-validation, its <italic>hF </italic>curve is significantly improved in the <italic>in silico </italic>study, and in fact outperforms the other two methods. The sub-hierarchy with root term <italic>protein metabolism </italic>is of this sort. The <italic>hF </italic>curve for HBN in Fig. ##FIG##13##14## dominates the other two methods for almost 60% of the possible threshold values on the new predictions, even though HBN works no better than BN in the cross-validation study.</p>", "<p>Overall, these results suggest that the performance advantages of HBN indicated by the cross-validation study are, if anything, potentially understated.</p>" ]

[ "<title>Discussion</title>", "<p>For a well-studied organism, such as <italic>S. cerevisiae</italic>, one can make certain inferences about genes for which there is no annotated function. First, it is likely that the gene has low sequence similarity to any gene of known function, thus preventing the most straightforward computational methods of predicting gene function. Secondly, it is likely that no altered phenotype is observed upon protein overexpression, knockdown, or knockout, foiling first-pass experimental attempts to discover gene function. In these cases, the next step would involve more elaborate experimental methods, which would typically be guided by a co-expression analysis of publicly available microarray data. The experiments selected will, in general, be time-consuming, costly, different for each gene being investigated, and offer modest chances for success. Thus, the development of more sophisticated and accurate methods of computational prediction of function which could precisely guide experimental activity remains a top priority.</p>", "<p>Biological and biomedical ontologies have become a prominent, and perhaps indispensable, tool in bioinformatics and biological research. GO in particular has been used in numerous papers to detect biological process enrichment of co-expressed genes, identify biological processes associated with disease, etc. However in the vast majority of applications the hierarchical nature of GO is actually not being used directly. For example, in enrichment testing such as GSEA or GNEA we typically test for every biological process if the differentially expressed genes in some condition are associated with this process more than expected by chance.</p>", "<p>Thus while GO and other ontologies obviously organize biological knowledge in an intuitive fashion, the structure is not typically exploited for actual inference by predictive analysis tools. This is rather different from evolutionary analysis tools and genetics frameworks where probabilistic ancestor/descendant relationships in phylogenies (hierarchies) are exploited very directly with substantial practical and theoretical benefits.</p>", "<p>Our work here suggests that similar developments of probabilistic frameworks are not only feasible, but promising, for improved protein function inference with gene ontologies. In addition, it suggests the need for further research to be done to clarify the utility of different representations for such purposes. Finally, it also raises the prospect of re-engineering ontologies or other similar representations, from the perspective of seeking to provide maximal value for probabilistic inference programs.</p>" ]

[ "<title>Conclusion</title>", "<p>We have developed a probabilistic framework for automated prediction of protein function using relational information (e.g., a network of protein-protein interactions) which exploits the hierarchical structure of ontologies, and guarantees the predictions obey a 'true-path' annotation rule. We have evaluated the performance of our method and compared it with two other network-based methods by both cross-validation and an <italic>in silico </italic>study, on the genome of yeast, for terms from the biological process category in the Gene Ontology. Results showed that our proposed method, by utilizing the ontological structure, significantly improved the prediction accuracy and the capability of detecting positive annotations over the hierarchies. Furthermore, our analysis suggests that such improvement persists across the ontology depths.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.</p>", "<title>Results</title>", "<p>We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.</p>", "<title>Conclusion</title>", "<p>A cross-validation study, using data from the yeast <italic>Saccharomyces cerevisiae</italic>, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional <italic>in silico </italic>validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.</p>" ]

[ "<title>Authors' contributions</title>", "<p>XJ carried out the statistical study, implemented and performed the computation, drafted the manuscript. NN prepared the datasets and helped the computation. MS interpreted the results and took part in the analysis. SK participated in the design of the study and the analysis. EDK conceived of the study, participated in its design, supervised the analysis and finalized the manuscript. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]

[ "<title>Acknowledgements</title>", "<p>The authors thank Russ Greiner for early feedback. This work was supported in part by NHGRI grant R01 HG003367-01A1, NIH award GM078987, NSF grant ITR-048715, and ONR award N00014-06-1-0096.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Visualization small PPI network and GO DAG</bold>. This plot contains two toy examples of Protein-Protein Interaction network and the Gene Ontology structure. (a) Schematic network of local protein interactions; (b) schematic GO hierarchy, where the thicker link indicates larger weight. Among the neighbors of the central protein in (a), 4 out of 5 are labeled with term <italic>A</italic>; 2 out of 5 are labeled with term <italic>D</italic>. One neighbor is not labeled with any term. We want to predict whether or not the central protein has term <italic>D</italic>.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Illustration of obedience and disobedience to the true-path rule</bold>. The plot demonstrates a small example of GO hierarchy with four terms A, B, C and D. The true annotations and the predicted probabilities of the terms for some protein are also given, in a format of \"true annotation (probability)\". We use this to illustrate predictions that are consistent and are not consistent with the <italic>the true-path rule</italic>.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Illustration of HBN's working mechanism</bold>. The plot shows (a) protein YGL017W and its neighborhood, (b) Small GO hierarchy. Three neighbors are labeled with the parent term GO:0045449; two of them are labeled with the child term GO:0045941. We want to predict whether YGL017W is labeled with GO:0045941.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Overall method performance comparison by ROC curve</bold>. This plot demonstrates the ROC curves of the three methods based on the 5-fold cross-validation study on the whole yeast genome. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Overall method performance comparison by <italic>hF </italic>measure</bold>. This plot demonstrates the curves of <italic>hF </italic>measure of the three methods against predicting threshold, based on the 5-fold cross-validation study on the whole yeast genome. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Overall method performance comparison by precision and recall</bold>. This plot demonstrates the precision versus recall curves of the three methods based on the 5-fold cross-validation study on the whole yeast genome. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Method performance comparison by ROC curve on sub-hierarchy GO:0050896</bold>. The plot shows the ROC curves of the three methods based on the 5-fold cross-validation study on the sub-hierarchy with root GO term GO:0050896, <italic>response to stimulus</italic>. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p><bold>Method performance comparison by <italic>hF </italic>on sub-hierarchy GO:0050896</bold>. The plot shows the curves of <italic>hF </italic>measure of the three methods against predicting threshold, based on the 5-fold cross-validation study on the sub-hierarchy with root GO term GO:0050896, <italic>response to stimulus</italic>. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F9\"><label>Figure 9</label><caption><p><bold>Method performance comparison by ROC curve on sub-hierarchy GO:0019538</bold>. The plot shows the ROC curves of the three methods based on the 5-fold cross-validation study on the sub-hierarchy with root GO term GO:0019538, <italic>protein metabolism</italic>. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F10\"><label>Figure 10</label><caption><p><bold>Comparison of method performance by <italic>hF </italic>on sub-hierarchy GO:0019538</bold>. The plot shows the curves of <italic>hF </italic>measure of the three methods against predicting threshold, based on the 5-fold cross-validation study on the sub-hierarchy with root GO term GO:0019538, <italic>protein metabolism</italic>. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F11\"><label>Figure 11</label><caption><p><bold>Visualization of the averaged positive predictive value comparison</bold>. The plot contains the curves of the averaged positive predictive values (PPV) over cross-validation folds of the three methods, against 1-NPV, the averaged negative predictive value (NPV). Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F12\"><label>Figure 12</label><caption><p><bold>Visualization of the averaged log-odds positive predictive value comparison on GO hierarchy depth</bold>. The plot demonstrates the curves of the averaged log-odds PPV over cross-validation folds of the three methods for NPV = 0.987, as a function of the GO hierarchy depth. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F13\"><label>Figure 13</label><caption><p><bold><italic>hF </italic>plots for new predictions on sub-hierarchy GO:0050896</bold>. The plot shows the <italic>hF </italic>curves of the three methods based on the updated annotation for sub-hierarchy with root term GO:0050896, <italic>response to stimulus</italic>, as discussed in the <italic>in silico </italic>validation study. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>", "<fig position=\"float\" id=\"F14\"><label>Figure 14</label><caption><p><bold><italic>hF </italic>plots for new predictions on sub-hierarchy GO:0019538</bold>. The plot shows the <italic>hF </italic>curves of the three methods based on the updated annotation for sub-hierarchy with root term GO:0019538, <italic>protein metabolism</italic>, as discussed in the <italic>in silico </italic>validation study. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Parameters from Nearest-Neighbor (NN), Binomial-Neighborhood (BN) and Hierarchical Binomial-Neighborhood (HBN)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">NN</td><td align=\"center\">BN</td><td align=\"center\">HBN</td></tr></thead><tbody><tr><td align=\"center\"><italic>k </italic>= 2</td><td align=\"center\"><italic>k </italic>= 2</td><td align=\"center\"><italic>k </italic>= 2</td></tr><tr><td align=\"center\"><italic>N </italic>= 6</td><td align=\"center\"><italic>N </italic>= 6</td><td align=\"center\"><italic>N </italic>= 6</td></tr><tr><td align=\"center\">.</td><td align=\"center\"> = 0.0661</td><td align=\"center\"><italic>p</italic>₁= 0.2927</td></tr><tr><td align=\"center\">.</td><td align=\"center\"> = 0.0085</td><td align=\"center\"><italic>p</italic>₀= 0.0992</td></tr><tr><td align=\"center\">.</td><td align=\"center\"><italic>f</italic>* = 0.0106</td><td align=\"center\"><italic>f </italic>= 0.2186</td></tr><tr><td align=\"center\"><italic>P </italic>= 0.3333</td><td align=\"center\"><italic>P </italic>= 0.3381</td><td align=\"center\"><italic>P </italic>= 0.6566</td></tr></tbody></table></table-wrap>" ]

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overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M10\" name=\"1471-2105-9-350-i6\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M11\" name=\"1471-2105-9-350-i7\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M12\" name=\"1471-2105-9-350-i6\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M13\" name=\"1471-2105-9-350-i7\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula><italic>K</italic>_{<italic>ch</italic>, <italic>i </italic>}<italic>~Binomial</italic>(<italic>k</italic>_{<italic>pa</italic>, <italic>i</italic>}, <italic>p</italic>₁),</disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M14\" name=\"1471-2105-9-350-i8\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M15\" name=\"1471-2105-9-350-i9\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mn>...</mml:mn><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mn>...</mml:mn><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:msubsup><mml:mi mathvariant=\"normal\">Π</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>m</mml:mi></mml:msubsup><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:msubsup><mml:mi mathvariant=\"normal\">Π</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>m</mml:mi></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:msubsup><mml:mi>p</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msubsup><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M16\" name=\"1471-2105-9-350-i10\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>p</mml:mi><mml:mo>ˆ</mml:mo></mml:mover><mml:mn>1</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mstyle displaystyle=\"true\"><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>m</mml:mi></mml:msubsup><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mrow><mml:mrow><mml:mstyle displaystyle=\"true\"><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>m</mml:mi></mml:msubsup><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M17\" name=\"1471-2105-9-350-i8\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M18\" name=\"1471-2105-9-350-i11\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>p</mml:mi><mml:mo>ˆ</mml:mo></mml:mover><mml:mn>0</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mstyle displaystyle=\"true\"><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mo>−</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:msubsup><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mrow><mml:mrow><mml:mstyle displaystyle=\"true\"><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>m</mml:mi></mml:msubsup><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M19\" name=\"1471-2105-9-350-i8\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M20\" name=\"1471-2105-9-350-i12\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>p</mml:mi><mml:mo>ˆ</mml:mo></mml:mover><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M21\" name=\"1471-2105-9-350-i13\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>p</mml:mi><mml:mo>ˆ</mml:mo></mml:mover><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M22\" name=\"1471-2105-9-350-i1\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM1\"><label>(1)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M23\" name=\"1471-2105-9-350-i14\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi mathvariant=\"script\">X</mml:mi><mml:mrow><mml:mi>L</mml:mi><mml:mi>O</mml:mi><mml:mi>C</mml:mi><mml:mi>A</mml:mi><mml:mi>L</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M24\" name=\"1471-2105-9-350-i15\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mo>×</mml:mo><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>/</mml:mo><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M25\" name=\"1471-2105-9-350-i16\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mo>×</mml:mo><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>B</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>;</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M26\" name=\"1471-2105-9-350-i17\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>f</mml:mi><mml:mo>=</mml:mo><mml:mover accent=\"true\"><mml:mi>P</mml:mi><mml:mo>ˆ</mml:mo></mml:mover><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM2\"><label>(2)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M27\" name=\"1471-2105-9-350-i18\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>B</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:mi>f</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:mi>f</mml:mi><mml:mo>+</mml:mo><mml:mi>B</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:mover accent=\"true\"><mml:mi>f</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:mrow></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M28\" name=\"1471-2105-9-350-i19\" overflow=\"scroll\"><mml:semantics><mml:mover accent=\"true\"><mml:mi>f</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M29\" name=\"1471-2105-9-350-i1\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M30\" name=\"1471-2105-9-350-i20\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM3\"><label>(3)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M31\" name=\"1471-2105-9-350-i21\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M32\" name=\"1471-2105-9-350-i22\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:msubsup><mml:mi mathvariant=\"normal\">Π</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>d</mml:mi></mml:msubsup><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi mathvariant=\"script\">X</mml:mi><mml:mrow><mml:mi>L</mml:mi><mml:mi>O</mml:mi><mml:mi>C</mml:mi><mml:mi>A</mml:mi><mml:msub><mml:mi>L</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:msubsup><mml:mi mathvariant=\"normal\">Π</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>d</mml:mi></mml:msubsup><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M33\" name=\"1471-2105-9-350-i23\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi mathvariant=\"script\">X</mml:mi><mml:mrow><mml:mi>L</mml:mi><mml:mi>O</mml:mi><mml:mi>C</mml:mi><mml:mi>A</mml:mi><mml:msub><mml:mi>L</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M34\" name=\"1471-2105-9-350-i24\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable columnalign=\"left\"><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:msubsup><mml:mi mathvariant=\"normal\">Π</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>d</mml:mi></mml:msubsup><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi mathvariant=\"script\">X</mml:mi><mml:mrow><mml:mi>L</mml:mi><mml:mi>O</mml:mi><mml:mi>C</mml:mi><mml:mi>A</mml:mi><mml:msub><mml:mi>L</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>≤</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:msubsup><mml:mi mathvariant=\"normal\">Π</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi mathvariant=\"script\">X</mml:mi><mml:mrow><mml:mi>L</mml:mi><mml:mi>O</mml:mi><mml:mi>C</mml:mi><mml:mi>A</mml:mi><mml:msub><mml:mi>L</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign=\"left\"><mml:mtd columnalign=\"left\"><mml:mo>=</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:mi mathvariant=\"script\">X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M35\" name=\"1471-2105-9-350-i25\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mrow><mml:mtext>PROB</mml:mtext></mml:mrow><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo>←</mml:mo><mml:msub><mml:mrow><mml:mtext>PROB</mml:mtext></mml:mrow><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>×</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M36\" name=\"1471-2105-9-350-i26\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>|</mml:mo><mml:msubsup><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>;</mml:mo><mml:msub><mml:mi mathvariant=\"script\">X</mml:mi><mml:mrow><mml:mi>L</mml:mi><mml:mi>O</mml:mi><mml:mi>C</mml:mi><mml:mi>A</mml:mi><mml:msub><mml:mi>L</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M37\" name=\"1471-2105-9-350-i27\" 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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p><bold>ROC curves and <italic>hF </italic>plots for 47 sub-hierarchies in cross-validation study</bold>. This file contains the ROC curves and plots of <italic>hF </italic>score versus predicting threshold of the three methods for 47 individual sub-hierarchies in the 5-fold cross-validation study. The root term ID's and names of the root terms, the sizes of sub-hierarchies, numbers of terms and genes predicted within sub-hierarchy are also shown. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p><bold><italic>hF </italic>plots for 17 sub-hierarchies in <italic>in silico </italic>study</bold>. This file contains the plots of <italic>hF </italic>score versus threshold of the three methods for individual sub-hierarchies in the <italic>in silico </italic>validation study. The root term ID's and names of the root terms, the sizes of sub-hierarchies, numbers of terms and genes predicted within sub-hierarchy are also shown. Colors: HBN (red); BN (light blue); NN (blue).</p></caption></supplementary-material>" ]

[ "<table-wrap-foot><p>This table contains the parameters and the corresponding probabilities estimated by the three methods, as discussed in the paper, when predicting whether yeast gene YGL017W has GO term GO:0045941, <italic>positive regulation of transcription</italic>.</p></table-wrap-foot>" ]

[ "<graphic xlink:href=\"1471-2105-9-350-1\"/>", "<graphic xlink:href=\"1471-2105-9-350-2\"/>", "<graphic xlink:href=\"1471-2105-9-350-3\"/>", "<graphic xlink:href=\"1471-2105-9-350-4\"/>", "<graphic xlink:href=\"1471-2105-9-350-5\"/>", "<graphic xlink:href=\"1471-2105-9-350-6\"/>", "<graphic xlink:href=\"1471-2105-9-350-7\"/>", "<graphic xlink:href=\"1471-2105-9-350-8\"/>", "<graphic xlink:href=\"1471-2105-9-350-9\"/>", "<graphic xlink:href=\"1471-2105-9-350-10\"/>", "<graphic xlink:href=\"1471-2105-9-350-11\"/>", "<graphic xlink:href=\"1471-2105-9-350-12\"/>", "<graphic xlink:href=\"1471-2105-9-350-13\"/>", "<graphic xlink:href=\"1471-2105-9-350-14\"/>" ]

[ "<media xlink:href=\"1471-2105-9-350-S1.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2105-9-350-S2.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p>Organisms require mechanisms to survive under adverse conditions of extreme heat, osmolarity, nutrient limitation, and other stresses. Recent studies have begun to probe the cellular responses to the stress of zinc deficiency. Zinc is a critical cofactor for many proteins and plays important roles in myriad biological processes. Therefore, when zinc becomes limiting, cells must respond to maintain zinc homeostasis. In addition, cells may alter their metabolic processes to adapt to growth under conditions where certain zinc-dependent proteins are less active. We are examining the cellular responses to zinc deficiency in the yeast <italic>Saccharomyces cerevisiae</italic>.</p>", "<p>In this yeast, the Zap1 transcription factor is a central player in the response to zinc deficiency [##REF##9271382##1##]. For many of its target genes, Zap1 acts as an activator of transcription and increases gene expression when zinc levels are low. To perform this function, Zap1 binds to Zinc-Responsive Elements or \"ZREs\" in the promoters of its target genes [##REF##9786867##2##]. The consensus sequence for a ZRE is ACCTTNAAGGT. While some Zap1 target genes contain multiple functional ZREs, many others have only a single binding site [##REF##9786867##2##,##REF##10884426##3##].</p>", "<p>The Zap1 protein is 880 amino acids long. A DNA binding domain consisting of five zinc fingers is found at its carboxy terminus [##REF##12549926##4##,##REF##10747942##5##]. In addition, Zap1 contains two independent activation domains, designated AD1 and AD2, that mediate the increased transcription of target genes [##REF##10899124##6##]. Zap1 is a direct sensor of cellular zinc levels. The protein resides in the nucleus under all conditions of zinc status [##REF##10899124##6##]. When zinc levels rise, the metal binds to ligand residues in the AD1 and AD2 regions of the protein and this binding inhibits the ability of these domains to promote transcription [##REF##10899124##6##, ####REF##16045625##7##, ##REF##16829533##8##, ##REF##16483601##9##, ##REF##14517251##10####14517251##10##]. Alteration of these regulatory zinc-binding ligands by mutation decreases the ability of Zap1 to respond to zinc and the mutant protein constitutively activates transcription [##REF##16045625##7##,##REF##14517251##10##].</p>", "<p>Previous studies have identified a large number of potential Zap1 target genes in the yeast genome [##REF##10884426##3##,##REF##14660410##11##,##REF##17933919##12##]. Many of these genes act to maintain sufficient levels of cytosolic zinc available for cell growth. For example, the <italic>ZRT1</italic>, <italic>ZRT2</italic>, and <italic>FET4 </italic>genes encode zinc transporters responsible for zinc uptake across the plasma membrane [##REF##12095998##13##, ####REF##8637895##14##, ##REF##8798516##15####8798516##15##]. These genes are induced by Zap1 in zinc-limited cells. Zap1 also induces expression of the <italic>ZRT3 </italic>gene in low zinc; <italic>ZRT3 </italic>encodes a vacuolar membrane protein responsible for transporting zinc stored in the vacuole to the cytoplasm for its utilization [##REF##10856230##16##]. As a final example, Zap1 induces transcription of its own gene in a positive autoregulatory loop [##REF##9271382##1##]. Thus, Zap1 levels rise in zinc-limited cells and this may lead to increased expression of other target genes.</p>", "<p>In addition to its role in activating gene expression, Zap1 can also act as a transcriptional repressor. Previous studies have identified two different mechanisms of Zap1-mediated repression. The <italic>ZRT2 </italic>gene provided the first example. <italic>ZRT2 </italic>is unusual among Zap1 target genes in that it is induced by mild zinc limitation and repressed by more severe zinc deficiency [##REF##14976557##17##]. This paradoxical pattern of regulation is due to the presence of three ZREs in the <italic>ZRT2 </italic>promoter. Two high affinity ZREs, ZRE1 and ZRE2, are located upstream of the TATA box and these elements mediate Zap1-dependent activation of gene expression. The third ZRE, ZRE3, has a low affinity of Zap1 binding and is located downstream of the TATA box. ZRE3 is essential for repression of <italic>ZRT2 </italic>expression. Under mild conditions of zinc deficiency, Zap1 binds to ZRE1 and ZRE2 and activates gene expression. Under severe zinc deficiency, Zap1 levels rise due to autoregulation and the protein then binds to ZRE3 and interferes with <italic>ZRT2 </italic>expression possibly by blocking transcription initiation.</p>", "<p>The <italic>ADH1 </italic>and <italic>ADH3 </italic>genes provide examples of a second mechanism of Zap1-mediated repression. <italic>ADH1 </italic>and <italic>ADH3 </italic>encode zinc-dependent alcohol dehydrogenases. These genes are highly expressed in zinc-replete cells but are repressed in zinc-deficient cells [##REF##17139254##18##]. Zap1 mediates <italic>ADH1 </italic>and <italic>ADH3 </italic>repression in low zinc by means of intergenic transcripts that are activated by Zap1 and transcribed through the <italic>ADH1 </italic>and <italic>ADH3 </italic>promoters. These intergenic transcripts, designated <italic>ZRR1 </italic>and <italic>ZRR2 </italic>respectively, do not encode protein products but rather their synthesis results in the transient displacement of transcription factors normally required for <italic>ADH1 </italic>and <italic>ADH3 </italic>expression. This results in the reduced expression of two of the most abundant zinc-binding proteins in the cell. Conversely, the <italic>ADH4 </italic>gene is induced by Zap1 and this gene encodes a potential iron-dependent alcohol dehydrogenase [##REF##10884426##3##,##REF##17139254##18##,##REF##2823079##19##]. By switching from zinc-dependent to zinc-independent ADH isozymes, the cell may conserve zinc for other uses. Alternatively, Adh4 may use zinc as its cofactor [##REF##3282541##20##] but this protein is predicted to bind only one zinc per monomer while Adh1 and Adh3 each bind two. Thus, zinc conservation could occur under this scenario as well.</p>", "<p>DNA microarrays have been remarkably useful in assessing the transcriptional responses of an organism such as yeast to stress conditions [##REF##10884426##3##,##REF##14660410##11##,##REF##17933919##12##]. In a previous study, we used microarrays to identify likely Zap1 target genes in the yeast genome, a group of genes that we referred to as the Zap1 \"regulon\" [##REF##10884426##3##]. In that study, we identified a total of 46 genes in yeast that are potential targets of Zap1 activation. As described below, we have further addressed this issue using revised experimental approaches. This new analysis has led to the identification of many new potential Zap1 target genes. In addition, we have characterized the differential regulation of Zap1 target genes. We found that some genes respond to mild zinc deficiency and act as a first line of defense against this stress. These first-line defense genes participate in various mechanisms of zinc homeostasis. Other Zap1 target genes respond only to severe conditions of zinc limitation and serve as a second line of defense. Second-line defense genes act largely in the adaptation to conditions where zinc availability is insufficient to maintain optimal cell function.</p>" ]

[ "<title>Methods</title>", "<title>Growth conditions and strains</title>", "<p>Yeast cells were grown in YPD (YP medium + 2% glucose) and in synthetic defined SD medium with 2% glucose or 2% galactose and any necessary auxotrophic requirements. YPD and SD are zinc-replete media because they contain micromolar levels of zinc and lack strong zinc chelators. Yeast were made zinc limited by culturing in low zinc medium (LZM) prepared as previously described [##REF##9786854##43##]. LZM is zinc limiting because it contains 1 mM EDTA and 20 mM citrate to buffer metal availability. Therefore, only a small fraction of the total zinc in LZM medium is available for uptake by cells. Zinc was added to LZM as ZnCl₂. The wild type strain DY1457 (<italic>Matα ade6 can1 his3 leu2 trp1 ura3</italic>) was used in all experiments.</p>", "<title>Microarray analysis</title>", "<p>The data for Experiment 1 (E1) and Experiment 2 (E2) are from Lyons et al. [##REF##10884426##3##]. The new microarray analyses used cells grown under two different paired conditions and each experiment was performed in duplicate with independent cultures. In Experiment 3 (E3), wild type (DY1457) cells were transformed with the vector (pYef2) or a plasmid (pYef2-Zap1^TC) encoding a constitutive allele of Zap1 under the regulation of the galactose-inducible <italic>GAL1 </italic>promoter. The plasmid pYef2 and pYef2-Zap1^TCconstructs were previously described [##REF##16045625##7##]. These transformants were inoculated into zinc-replete SD medium + 2% galactose + 1 μM ZnCl₂and grown for 20–24 h before harvesting at an optical density measured at 600 nm (OD₆₀₀) of ~0.8. In Experiment 4 (E4), wild type (DY1457) cells were grown in a severely zinc-limiting medium (LZM + 3 μM ZnCl₂) and in a zinc-replete medium (LZM + 3 mM ZnCl₂) for 14–16 hours and harvested at an OD₆₀₀of ~0.7. For the dose-response analysis, wild type (DY1457) cells were grown in LZM + 3 mM ZnCl₂, or LZM + 300, 100, 30, 10 or 3 μM ZnCl₂. Transcript levels were assayed using microarrays in which each sample was paired with the zinc-replete (LZM + 3 mM ZnCl₂) control. For the time-course studies, wild type (DY1457) cells were grown to exponential phase in a zinc-replete medium (LZM + 1 mM ZnCl₂), washed twice in LZM with no added zinc, and then transferred to a zinc-limiting medium (LZM + 1 μM ZnCl₂) for 8 hours. Transcript levels were assayed using microarrays in which each sample was paired with the zinc-replete T_ocontrol. These experiments were also performed in duplicate with independent cultures. Total RNA was extracted from cells grown as described above with hot phenol and mRNA was isolated from total RNA using the PolyATtract mRNA Isolation System IV kit (Promega). Cy3-dUTP or Cy5-dUTP was incorporated during reverse transcription of the polyadenylated RNA [##REF##12756250##44##]. The fluorescently labeled products were recovered and hybridized to yeast whole-genome microarrays, washed, and scanned as previously described [##REF##12756250##44##].</p>", "<p>To remove intensity-dependent measurement artifacts, we normalized the log Cy3/Cy5 fold changes within each microarray slide using the locally weighted scatterplot smoothing (LOWESS) algorithm [##REF##11842121##45##]. Genes exhibiting sufficiently high fold change were screened for subsequent analysis. We chose an arbitrary cut-off value of a fold change ≥ 1.5 based on the average of two independent microarrays with the provision that both arrays showed a fold change of at least 1.4. One gene, <italic>ENO2</italic>, did not fully satisfy these criteria but was selected for further analysis because of the presence of a potential ZRE in its promoter. Subsequent S1 nuclease protection assays confirmed the zinc- and Zap1-responsive regulation of <italic>ENO2</italic>.</p>", "<title>Promoter motif and ZRE distribution analysis</title>", "<p>The ZREs in the promoters of the 46 potential Zap1 targets previously identified with the Multiple Expectation Maximization for Motif Elicitation (MEME) program (please see Availability &amp; requirements for more information) [##REF##16845028##46##] were used to generate a position-specific probability matrix with Regulatory Sequence Analysis Tools (RSAT – please see Availability &amp; requirements for more information)[##REF##12824373##47##]. Potential ZREs in the promoters (nucleotide positions -1000 to +1 where +1 is the first base of the ATG start codon) of zinc- and Zap1-responsive genes were then identified using this matrix and RSAT. To assess the distribution of potential ZREs in promoter regions, PatMatch (please see Availability &amp; requirements for more information) was used to identify ZRE-like sequences in the promoters of other yeast genes using the input sequence of ACCYKNRRKGT (Y = C or T, K = G or T, R = A or G, N = any base). 138 genes were identified that contain one or more copies of this sequence in their promoters but were not zinc- and Zap1-responsive; 156 total ZRE-like sequences were found in these promoters. The randomness of distribution of these sequences was assessed using the chi-square test. WebLogo (please see Availability &amp; requirements for more information) [##REF##2172928##48##] was used to generate the graphical representation of the ZRE consensus sequence shown in Figure ##FIG##0##1B##. To determine if the RSAT scores of ZREs in promoters that respond to mild deficiency were significantly different from those in promoters that respond only to severe deficiency, the total number of ZREs were divided into equal quartiles and then the ZREs in the two sets were divided among those quartiles. The chi-square test was then used to determine if the distribution of scores between those sets was statistically significant.</p>", "<title>RNA analysis</title>", "<p>S1 nuclease protection assays were performed with total RNA as described [##REF##1730413##49##]. The oligonucleotide probes used for these experiments are described in Additional file ##SUPPL##3##3##. For each reaction, 15 μg of total RNA was hybridized to ³²P-end-labeled DNA oligonucleotide probes before digestion with S1 nuclease and separation on a 10% polyacrylamide, 5 M urea polyacrylamide gel. Band intensities were quantitated by PhosphorImager analysis (PerkinElmer, Inc.).</p>", "<title>Electrophoretic mobility shift assays</title>", "<p>The Zap1 DNA binding domain (Zap1_DBD, residues 687–880) was expressed in <italic>E. coli </italic>as a fusion to glutathione <italic>S</italic>-transferase, purified, and then the glutathione <italic>S-</italic>transferase tag was removed with thrombin as previously described [##REF##10747942##5##]. Electrophoretic mobility shift assays were performed as previously described using purified Zap1_DBDprotein and radiolabeled ZRE oligonucleotides (Additional file ##SUPPL##4##4##). Briefly, 15 μl reactions were prepared containing 0.5 pmol of radiolabeled DNA oligonucleotide (20,000 cpm/pmol), 10 mM Tris-HCl (pH 8.0), 10 mM MgCl₂, 50 mM KCl, 1 mM DTT, 0.02 mg/ml poly(dI-dC), 0.2 mg/ml bovine serum albumin, 0.04% NP-40, 10% glycerol, and the indicated concentrations of purified Zap1_DBD. After incubation for 1 hour at room temperature, the samples were resolved on 6% polyacrylamide gels. Gels were dried onto blotting paper, and the signals were visualized by autoradiography.</p>", "<title>Microarray data</title>", "<p>All microarray data can be downloaded from the Gene Expression Omnibus database under accession number GSE11983.</p>" ]

[ "<title>Results</title>", "<title>Identification of new Zap1 target genes</title>", "<p>Our previous study suggested that Zap1 may directly activate the expression of as many as 46 different genes [##REF##10884426##3##]. That study used genome-wide transcription profiling with DNA microarrays coupled with a motif identification algorithm, MEME. Specifically, we first compared gene expression in wild type cells grown in low and high zinc and identified 458 genes expressed more highly in low zinc in duplicate microarrays. We refer to this as \"Experiment 1\" or \"E1.\" We then compared gene expression in wild type and <italic>zap1Δ </italic>mutant cells grown in low zinc and identified 214 genes that were expressed at a higher level in wild type cells in duplicate arrays. We refer to this condition as \"Experiment 2\" or \"E2.\" A set of 111 genes showed increased expression in both E1 and E2. MEME identified potential ZRE sequences located within the promoter regions of 46 of the 111 genes. These results suggested that these 46 genes are direct targets of Zap1 gene regulation.</p>", "<p>In this current study, we have further characterized the Zap1 regulon using additional microarray experiments and a more sensitive motif identification algorithm. First, we devised a third microarray experiment to identify Zap1 target genes. Since our previous analysis, we have generated alleles of Zap1 that are constitutive and poorly regulated by zinc. One such allele, designated \"Zap1^TC\", was used in this study. The Zap1^TCallele contains mutations in or near the two activation domains of Zap1 that render those domains less responsive to zinc [##REF##16045625##7##]. We predicted that Zap1 target genes would show increased expression in zinc-replete cells overexpressing the Zap1^TCallele. Microarray experiments were performed in which wild-type cells expressing the Zap1^TCallele and wild-type cells bearing the vector only were grown in high zinc conditions. We refer to this as \"Experiment 3\" or \"E3.\" Expression of 379 genes increased in Zap1^TC-expressing cells an average of ≥ 1.5-fold in duplicate arrays. When combined with our previous results, a total of 53 genes were found to have the expected alterations of expression in E1, E2, and E3 conditions (Fig. ##FIG##0##1A##).</p>", "<p>While MEME was very useful for our previous analysis, we became concerned about the ability of this algorithm to identify functional ZREs. For example, MEME was unable to identify experimentally confirmed ZREs in the promoters of the <italic>ZRT2</italic>, <italic>PIS1</italic>, and <italic>EKI1 </italic>genes [##REF##14976557##17##,##REF##16551612##21##,##REF##15980062##22##] (data not shown). These observations suggested that MEME was not sufficiently sensitive to identify all potential ZREs within a collection of target gene promoters. Therefore, we analyzed the promoters of the 53 candidate genes (Fig. ##FIG##0##1A##) using a more sensitive motif identification algorithm called Regulatory Sequence Analysis Tools or RSAT. With RSAT, a position-specific probability matrix was generated using the ZREs identified by MEME and a representation of that consensus sequence is provided in Fig. ##FIG##0##1B##. This matrix was then used to search for additional ZRE-like sequences in the promoters of these genes. Sequences were scored based on their similarity to the probability matrix with higher scores indicating greater similarity. An RSAT score of 4.5 was used as a minimum cut-off value in this analysis because this is the lowest score obtained for a bona fide ZRE found in the <italic>EKI1 </italic>promoter [##REF##16551612##21##]. Our reasoning was that sequences scoring ≥ 4.5 were strong candidates for functional ZREs. Among the 53 genes implicated to be Zap1 targets by the combination of E1, E2, and E3, the promoters of 49 genes contain one or more potential ZREs identified by RSAT and these are listed in Tables ##TAB##0##1## and ##TAB##1##2##. Table ##TAB##0##1## lists 33 genes that were identified as potential Zap1 targets in our previous experiments. While several of these have been demonstrated to be Zap1 targets by additional experiments (<italic>DPP1</italic>, <italic>IZH1</italic>, etc), several others (<italic>MOH1</italic>, <italic>TKL2</italic>, etc.) had not been further characterized since our initial analysis. For all genes listed, the new E3 analysis provided additional experimental support that these genes are indeed Zap1 targets.</p>", "<p>Table ##TAB##1##2## lists 16 new candidate Zap1 target genes identified in this study. These genes were not identified in our previous analysis due to the insensitivity of MEME in detecting ZRE-like sequences. This group includes <italic>HSP26</italic>, <italic>SED1</italic>, <italic>CTT1</italic>, and <italic>TSA1</italic>.</p>", "<p>We noted that 13 of the 46 Zap1 target genes previously identified were not clearly up-regulated in cells expressing the Zap1^TCallele (E3). The E1, E2, and E3 results for these genes are provided in Additional file ##SUPPL##0##1##. While several of these genes showed increased expression in the Zap1^TC-expressing cells, these increases did not satisfy our cut-off criteria. It is unclear at this time why these genes were less responsive to the constitutive Zap1 allele than other targets. We also found that 4 genes (<italic>ALD2</italic>, <italic>PIR3</italic>, <italic>YBR285W</italic>, <italic>YNR066C</italic>) showed increased expression in E1, E2, and E3 but did not contain ZREs in their promoters that were detectable by RSAT (Additional file ##SUPPL##1##2##). These genes may contain ZRE sequences that are more divergent from the consensus. Alternatively, Zap1 may alter their expression indirectly. It should be noted that <italic>ALD2 </italic>is closely related to <italic>ALD3</italic>, which was found to be a potential Zap1 target (Table ##TAB##2##3##). Therefore, <italic>ALD2 </italic>may have been detected due to cross-hybridization with <italic>ALD3 </italic>mRNA in the microarray experiments.</p>", "<title>Identification of Zap1 targets induced by severe zinc deficiency</title>", "<p>In our previous study, cells grown in the low zinc conditions used in E1 and E2 experiments were cultured in CSD medium from which zinc was removed with a metal-binding resin [##REF##10884426##3##]. This medium was chosen to specifically provide a zinc-limiting condition without the use of strong chelators that can bind other metal ions and alter their availability. However, because chelators are not included in CSD, this medium is not severely zinc limiting due to the presence of small amounts of contaminating zinc. We reasoned that some Zap1 targets might require extremely low zinc conditions, i.e. lower than that provided by CSD, to be induced. Therefore, to search for additional Zap1 target genes, microarray experiments were performed with RNA from cells grown in LZM, a low zinc medium containing EDTA and citrate as metal buffers. Because of these chelators, cells grown in LZM + 3 μM added ZnCl₂are more zinc-limited than cells grown in CSD with no added zinc. When comparing expression in cells grown in LZM + 3 μM ZnCl₂vs. LZM + 3 mM ZnCl₂, 182 genes were up-regulated ≥ 1.5-fold in duplicate arrays under this very low zinc condition. We refer to this experiment as \"E4.\" The overlap between E3 and E4 was 67 genes (Fig. ##FIG##0##1C##). Of these 67 genes, 50 contained ZRE-like sequences in their promoters detectable by RSAT. Fifteen of these 50 genes were not detected in the more mild E1/E2 growth conditions suggesting that they are only induced under severe zinc-limiting conditions. A list of these additional potential Zap1 target genes is provided in Table ##TAB##2##3##.</p>", "<p>To summarize this analysis, we have obtained additional evidence that 33 genes previously identified in our experiments are Zap1 targets (Table ##TAB##0##1##). In addition, we have identified a total of 31 new candidate Zap1 target genes (Tables ##TAB##1##2## and ##TAB##2##3##). The functions of many of these genes and their possible relevance to zinc deficiency will be discussed later in this report.</p>", "<title>Confirmation of microarray and RSAT results</title>", "<p>To confirm the effects of zinc status and Zap1 mutations on potential target gene expression, a subset of genes from Tables ##TAB##0##1##, ##TAB##1##2##, ##TAB##2##3## were selected for further analysis by S1 nuclease protection assay of RNA samples that were not used for the microarray analysis. The known Zap1 target <italic>ZRT1 </italic>and the constitutive calmodulin (<italic>CMD1</italic>) gene were used as positive and negative controls, respectively (Fig. ##FIG##1##2A##). Four genes from Table ##TAB##0##1## previously suggested to be Zap1 targets (<italic>TKL2, PST1</italic>, <italic>YJL132W</italic>, <italic>ICY2</italic>) were tested using cells grown under the same conditions as were used for microarray experiments E3 and E4. The increased expression of these genes in cells grown in low zinc or in cells expressing the Zap1^TCallele was confirmed by this analysis (Fig. ##FIG##1##2B##). Similarly, we tested 14 of the 31 new candidate Zap1 target genes, i.e. 7 from Table ##TAB##1##2## (Fig. ##FIG##1##2C##) and 7 from Table ##TAB##2##3## (Fig. ##FIG##1##2D##). The expression patterns of all 14 of these genes were confirmed by S1 nuclease protection assay. These data indicate that we have discovered several new genes directly regulated by Zap1.</p>", "<p>The RSAT algorithm identified several potential ZREs in addition to those recognized by MEME. Many of these sequences had low RSAT scores near the minimum cut-off value of 4.5 and were very divergent from the consensus ZRE sequence (ACCTTNAAGGT, RSAT score = 11.7) (Fig. ##FIG##0##1B##). To determine if Zap1 can bind to these candidate ZREs in a sequence-specific manner, we performed electrophoretic mobility shift assays (EMSA) (Fig. ##FIG##2##3A##). The Zap1 DNA binding domain (Zap1_DBD, residues 687–880) was purified from <italic>E. coli </italic>and used in these <italic>in vitro </italic>experiments. Double-stranded oligonucleotides were end-labeled with ³²P, mixed with the purified Zap1_DBD, and then fractionated by non-denaturing polyacrylamide gel electrophoresis. Binding of Zap1_DBDto the previously characterized <italic>TSA1 </italic>ZRE (RSAT score = 5.5) served as a positive control (Fig. ##FIG##2##3A##, lane 2). Zap1_DBDwas unable to bind to a <italic>TSA1 </italic>ZRE that had been mutagenized at each of its 11 base pair positions by transversion mutations (Fig. ##FIG##2##3A##, lane 3). This mutant ZRE was previously shown to be nonfunctional <italic>in vivo </italic>[##REF##17121842##23##]. Zap1_DBDbinding was detected with all of the candidate ZREs tested. These sequences ranged in RSAT scores from a low of 4.7 (<italic>SAM3</italic>, <italic>SED1</italic>) to a high of 6.9 (<italic>HNT1</italic>). The ability of Zap1_DBDto bind to these sequences <italic>in vitro </italic>suggests that they may be functional binding sites <italic>in vivo</italic>.</p>", "<p>Additional evidence that the identified ZREs are functional <italic>in vivo </italic>came from an analysis of the distribution of these sequences within the promoter regions of candidate Zap1 target genes. As shown in Fig. ##FIG##2##3B##, when the position of the RSAT-predicted ZREs were mapped relative to the open reading frame of each gene, a clear bias was apparent; most of these sequences were found between 100 and 500 base pairs upstream. This distribution correlates well with the position of regulatory elements in other yeast promoters [##REF##15534694##24##]. Chi-square analysis indicated that this distribution of ZRE sequences was not random (P &lt; 0.001). As an additional control, we identified 156 ZRE-like sequences in the promoters of yeast genes that do not show Zap1-dependent regulation and are therefore unlikely to be functional Zap1 binding sites (see Materials and Methods). The locations of these nonfunctional sequences were consistent with a random pattern of distribution (P &gt; 0.05). Taken together, these results suggest that RSAT is a sensitive method to detect regulatory motifs among the promoters of co-regulated genes that are likely to be functional.</p>", "<title>Differential regulation of Zap1 target genes</title>", "<p>To characterize how Zap1 target genes respond to a range of zinc availability, we performed additional microarray experiments in which we assayed their expression in wild type cells grown for 16 hours in batch cultures supplemented with various concentrations (i.e. 3, 10, 30, 100, or 300 μM) of zinc. RNA from cells grown in LZM + 3 mM ZnCl₂was used as the zinc-replete control for each of these comparisons and the data obtained are plotted in Fig. ##FIG##3##4A## (<italic>left </italic>panel). A narrow color scale was chosen for this panel to highlight the conditions under which detectable increases in gene expression occurred. Because this scale obscures the results obtained for the most highly induced genes (e.g. <italic>ZRT1, YOR387C</italic>), data for several genes are also plotted in Fig. ##FIG##3##4B## using a broader scale that allows for comparison of the expression levels across the full range of zinc conditions. The numerical data from the experiments shown in Figure 4 are provided in Additional file ##SUPPL##2##5##.</p>", "<p>From this dose-response analysis, it was apparent that Zap1 target genes show extremely varied responses to zinc deficiency. Some genes, such as <italic>ZRT1</italic>, <italic>FET4</italic>, <italic>ZPS1</italic>, and <italic>YOR387C </italic>responded to very mild conditions of zinc deficiency (i.e. LZM + 300 μM ZnCl₂) while the <italic>ZRT3 </italic>and <italic>IZH2 </italic>genes were induced by moderate conditions of zinc deficiency (LZM + 100 or 30 μM ZnCl₂). In contrast, most Zap1 target genes only responded to more severe deficiency conditions (LZM + 10 or 3 μM ZnCl₂). Among these was <italic>ADH4 </italic>whose induction by low zinc correlated closely with the Zap1-mediated repression of the <italic>ADH1 </italic>and <italic>ADH3 </italic>genes. Also among the genes only responding to severe zinc limitation were <italic>ZRG17 </italic>and <italic>TSA1</italic>, i.e. genes likely to be involved in adaptation to zinc-limiting conditions (see Discussion).</p>", "<p>Other patterns of expression were also observed. For example, induction of <italic>ZRT2 </italic>was observed in response to mild deficiency (LZM + 300 μM ZnCl₂) and its repression by Zap1 was readily apparent in cells grown in LZM + 10 or 3 μM ZnCl₂. No other Zap1 targets showed clear evidence for a similar combination of Zap1 induction and repression with the possible exceptions of <italic>IZH2 </italic>and <italic>PEP4</italic>. In contrast, several other genes (i.e. <italic>MCD4, UBX6, ICY2, HNT1, URA10</italic>) showed decreased expression under mild-to-moderate zinc deficiency but showed increased expression in severely zinc-limited cells. This profile may reflect the loss of activity of other transcription factors during moderate zinc deficiency that was compensated by up-regulation by Zap1 in severely zinc-limited cells.</p>", "<p>Regulation of Zap1 target genes was also assessed over time in cells undergoing zinc withdrawal. Wild type cells were transferred from a zinc-replete medium (LZM + 1 mM ZnCl₂) to a severely zinc-limiting condition (LZM + 1 μM ZnCl₂) and gene expression was monitored over an 8-hour period. We predicted that genes that respond to mild zinc deficiency under the more steady-state conditions of batch culturing would also respond quickly to the mild zinc deficiency that would occur shortly after zinc withdrawal commenced. Consistent with this expectation, several genes induced by mild or moderate zinc deficiency (e.g. <italic>ZRT1, ZPS1, YOR387C, ZRT3</italic>) were induced shortly (i.e. within 1 hour) after transition from high to low zinc (Fig. ##FIG##3##4A##, <italic>right </italic>panel). Similarly, genes that responded only to severe deficiency in batch culture usually required much longer periods of zinc withdrawal (8 hours) for induction to be observed. Many of these genes decreased in expression level soon after transition to zinc-limiting conditions and, in some cases, were not increased relative to zinc-replete cells even after 8 hours in zinc-limiting medium. Induction was apparent after 16 hours in zinc-limiting conditions (dose-response analysis, Fig. ##FIG##3##4A##, <italic>left </italic>panel) suggesting that these genes require more than 8 hours of zinc withdrawal for their mRNA levels to increase.</p>", "<p>The results in Fig. ##FIG##3##4## indicated that there is generally a good correlation between the severity of the zinc deficiency required for induction of a Zap1 target gene under steady state conditions and its timing of induction following zinc withdrawal. However, some striking exceptions to this correlation were observed. For example, while <italic>FET4 </italic>and <italic>ZRT2 </italic>responded to mild zinc deficiency, they responded only slowly to zinc withdrawal. Conversely, while <italic>ICY2 </italic>responded only to severe zinc deficiency, it was highly induced within 30 minutes of zinc withdrawal. The underlying mechanisms and physiological significance of these intriguing differences in expression patterns are unclear.</p>", "<title>One possible mechanism underlying differential Zap1 target gene expression</title>", "<p>The data in Figure ##FIG##3##4## indicate that some Zap1 target genes respond to mild zinc deficiency while others respond only to severe deficiency. While many factors may contribute to these patterns of expression, one simple hypothesis is that the quality of ZREs within a gene's promoter determines its expression pattern. Specifically, genes with ZREs closely matching the Zap1 consensus sequence would respond to mild zinc deficiency because Zap1 could bind to those high affinity sites under those conditions when Zap1 levels are low. To activate promoters with weaker binding sites, the increased expression of Zap1 that occurs in zinc-limited cells would be required. This concept is similar to the differential binding of Zap1 to the ZREs found in the <italic>ZRT2 </italic>promoter [##REF##14976557##17##]. Consistent with this hypothesis, we observed that the ZREs found in genes induced by mild deficiency (<italic>ZRT1</italic>, <italic>ZRT2</italic>, <italic>ZPS1</italic>, etc.) more closely match the consensus sequence (average RSAT score = 10.0) than do ZREs from genes that respond only to severe deficiency (average RSAT score = 7.1). This difference was even more striking when all of the ZREs were divided into quartiles based on RSAT score and the percentage of ZREs in each quartile were plotted. While ZREs among genes responding to only severe zinc limitation showed a fairly even distribution among quartiles, ZREs from genes responding to mild deficiency showed a clear bias toward the highest quartile (9.2–12.0) of RSAT scores (Fig. ##FIG##4##5##). Chi-square analysis confirmed that these are statistically significant differences (P &lt; 0.001). No significant differences in the number of ZREs per promoter or the distances of the ZREs from the open reading frame were observed between the two sets of genes. Thus, we suggest that ZRE affinity may play a major role in dictating expression patterns observed among Zap1 target genes. It should be noted, however, that there are clear exceptions to this rule. For example, the <italic>ZRG17 </italic>promoter has a high affinity ZRE that exactly matches the consensus sequence but this gene only responds to severe deficiency. Other factors, such as chromatin structure, may influence binding of Zap1 to the <italic>ZRG17 </italic>promoter.</p>" ]

[ "<title>Discussion</title>", "<p>Zinc deficiency causes drastic changes in yeast gene expression. We previously reported that ~15% (934) of all yeast genes increased or decreased in expression in zinc-limited vs. zinc-replete yeast grown in batch cultures [##REF##10884426##3##]. Similarly, De Nicola et al. reported that 381 genes were affected by zinc status in chemostat cultures [##REF##17933919##12##]. Because zinc plays so many functional roles, the majority of these effects are likely to be indirect responses to changes in cellular processes resulting from decreased activity of key zinc-dependent proteins. To define the direct responses to zinc deficiency, we are identifying genes under the control of the Zap1 transcription factor. In this way, we can learn how cells respond specifically to the stress of zinc-limiting conditions.</p>", "<p>We previously used a combination of microarray analysis and a motif identification algorithm to identify 46 potential Zap1 targets [##REF##10884426##3##]. In this current study, we made important modifications to our previous approach by using alternative growth conditions for the microarray experiments and a different motif analysis algorithm that was better able to detect potential Zap1 binding sites. Using these new tools, we have extended many aspects of our previous work. First, this study provides additional confirmation for many genes previously proposed to be Zap1 targets (Table ##TAB##0##1##). For example, genes such as <italic>MOH1</italic>, <italic>TKL2</italic>, etc. were suggested to be direct Zap1 targets based on their Zap1-dependent induction in zinc-limited cells [##REF##10884426##3##]. Their increased expression in cells expressing the Zap1^TCallele provides strong additional support for this hypothesis. In all, 33 of our original 46 potential targets were confirmed in this way. Several of the remaining 13 genes (e.g. <italic>YBL048W</italic>, <italic>COS4</italic>, <italic>COS8, RAD27</italic>) showed increased expression in Zap1^TC-expressing cells although these effects did not meet our minimum cut-off value of 1.5-fold changes (Additional file ##SUPPL##0##1##).</p>", "<p>In addition, we have added 31 potential new members to the Zap1 regulon. Sixteen of these genes were observed in our previous study to be zinc-responsive and Zap1-dependent [##REF##10884426##3##] but were not considered to be direct Zap1 targets because we were unable to identify potential ZREs in their promoters using MEME (Table ##TAB##1##2##). With RSAT, we identified their potential ZREs and went on to show that many of these sequences were specifically bound by Zap1 <italic>in vitro</italic>. The response of these genes to the Zap1^TCallele also provides additional evidence for their direct regulation by Zap1. The other 15 new target genes responded to more severe zinc-limiting conditions than that used in our previous study (Table ##TAB##2##3##). These genes, plus four additional genes identified as Zap1 targets using other methods, i.e. <italic>EKI1</italic>, <italic>PIS1, ZRR1</italic>, and <italic>ZRR2 </italic>[##REF##17139254##18##,##REF##16551612##21##,##REF##15980062##22##], brings the total of confirmed and potential Zap1 targets in the yeast genome to 81 genes.</p>", "<p>With the identification of many new targets of Zap1 activation, we are building a comprehensive picture of how yeast cells respond to zinc-limiting conditions. A summary of the functional roles of many of these genes is provided in Table ##TAB##3##4## and a figure showing the general relationship of these genes to their response over a range of zinc levels is provided in Fig. ##FIG##5##6##. We can separate these responses conceptually into first- and second-lines of defense against the stress of zinc deficiency. The first-line defense genes play key roles in zinc homeostasis while many second-line defense genes allow the cells to adapt to zinc-limiting conditions when they can no longer obtain sufficient zinc for optimal growth. One caveat to this analysis is that we are assuming that changes in transcript levels are accompanied by similar changes in protein abundance and this assumption is not necessarily true. Nonetheless, the following discussion provides a clear and testable framework for understanding the cellular responses to zinc deficiency.</p>", "<p>Among the first-line defense genes, <italic>ZRT1</italic>, <italic>ZRT2</italic>, and <italic>FET4 </italic>are induced by mild zinc deficiency to increase the ability of the cell to accumulate zinc from its environment. <italic>ZPS1 </italic>is also induced under these mild conditions and we have previously proposed that its product, a secreted protein related to metalloproteases, may also be involved in zinc acquisition by degrading extracellular proteins and releasing any bound metals [##REF##10884426##3##]. Similarly, <italic>ZRT3 </italic>is up-regulated by mild zinc deficiency and also responds rapidly to zinc withdrawal. Thus, mobilization of zinc stores from the vacuole is also a first-line response. Induction of the <italic>YOR387C </italic>gene in response to mild zinc deficiency suggests that this gene is also involved in the first-line defense against zinc limitation. The function of this protein is not yet known but its pattern of regulation by zinc suggests that it may play a role in zinc uptake or vacuolar zinc export. <italic>TIS11 (</italic>also known as <italic>CTH2</italic>) is also induced in LZM + 300 μM ZnCl₂. Tis11 binds to specific mRNAs and signals their degradation in response to iron limitation [##REF##15652485##25##]; its role in zinc deficiency is not yet determined.</p>", "<p>Other first-line defenses to zinc deficiency include an increase in <italic>ZAP1 </italic>expression mediated by Zap1 autoregulation [##REF##9271382##1##]. The resulting increase in Zap1 protein level may maximize target gene expression and is also likely to be responsible for repression of <italic>ZRT2 </italic>under these conditions [##REF##14976557##17##]. In support of this latter hypothesis, we note that <italic>ZRT2 </italic>repression correlates well with the observed increase in <italic>ZAP1 </italic>mRNA levels. In addition, the coordinated switch from <italic>ADH1/ADH3 </italic>expression to <italic>ADH4 </italic>expression likely represents a mechanism to conserve zinc for other uses [##REF##17139254##18##]. Finally, we observed an increase in <italic>ZRC1 </italic>expression in moderate-to-severely zinc-deficient cells. We showed previously that this induction is required for cells to survive the stress of zinc shock, i.e. when zinc-deficient cells are re-supplied with zinc [##REF##12556516##26##]. The high activity of zinc uptake transporters in these cells results in rapid zinc overload and the increased activity of Zrc1 is needed to sequester the excess zinc in the vacuole.</p>", "<p>Induction of many Zap1 target genes occurred only in response to the most severe zinc-limiting condition we tested (LZM + 3 μM ZnCl₂) and only slowly in response to zinc withdrawal. We consider these genes to be the second line of defense against zinc deficiency. Most of these second-line defense genes appear to be involved in the adaptation to zinc deficiency rather than playing a role in zinc homeostasis (see below). This transition from homeostatic to adaptive mechanisms correlates well with what we know about cell growth under these conditions. We previously showed that cells grow at their maximum growth rate in LZM supplemented with ≥ 10 μM ZnCl₂but their growth rate decreases in media containing less zinc [##REF##8637895##14##]. Thus, under conditions that induce the second-line defense genes, zinc homeostatic mechanisms are no longer capable of supplying sufficient zinc for optimal function of cellular processes. Our results indicate that it is under these conditions of suboptimal zinc that adaptive responses occur.</p>", "<p>One important functional category of second-line defense involves secretory pathway function (Table ##TAB##3##4##). The <italic>ZRG17 </italic>zinc transporter gene is up-regulated presumably to maintain the zinc status of the endoplasmic reticulum [##REF##15961382##27##,##REF##15277543##28##]. <italic>MCD4 </italic>and <italic>MNT2 </italic>encode enzymes required for protein modifications (GPI anchor synthesis and O-linked glycosylation), that occur in the ER and the Golgi, respectively [##REF##10069808##29##,##REF##10521541##30##]. <italic>YJR061W </italic>encodes an ER-localized protein [##REF##11914276##31##] related to Mnn4, which is involved in both N- and O-linked glycosylation [##REF##9813355##32##]. Increased expression of these proteins may help maintain the efficiency of these processes under the adverse conditions of severe zinc deficiency. Consistent with this hypothesis, Mcd4 is thought to be a zinc-dependent enzyme [##REF##11746679##33##].</p>", "<p>Another group of second-line defense genes are involved in cell wall function (Table ##TAB##3##4##). These include <italic>PST1</italic>, <italic>HSP12</italic>, <italic>SED1</italic>, and <italic>SCW4</italic>. While their specific functions are unclear, these proteins all reside in the cell wall suggesting that cell wall remodeling occurs under zinc-limiting conditions. These alterations may be important to maintain the structural integrity of the cell wall. Consistent with this hypothesis, disruption of zinc transport into the secretory pathway caused increased sensitivity to an inhibitor of cell wall function, calcofluor white [##REF##16760462##34##]. This observation suggests that defects in cell wall synthesis occur in zinc-limited cells.</p>", "<p>A third functional category of second-line defense genes are involved in stress resistance (Table ##TAB##3##4##). Our results implicate <italic>TSA1</italic>, <italic>CTT1</italic>, <italic>UTH1</italic>, and <italic>HSP26 </italic>as Zap1 targets along with previously identified targets <italic>RAD27 </italic>and <italic>GRE2</italic>. <italic>RAD27 </italic>encodes a nuclease that is required for the stability of minisatellite repeats in genomic DNA [##REF##12185499##35##,##REF##12065432##36##]. The recent observation that zinc deficiency in yeast destabilizes these DNA repeats [##REF##18073441##37##] is consistent with a role of Rad27 in promoting genome stability in zinc-limited cells. <italic>GRE2 </italic>encodes a methylglyoxal reductase [##REF##12722185##38##]. Methylglyoxal is a byproduct of glycolysis that can react with proteins to disrupt their function [##REF##18070066##39##]. We note with interest that two major targets of damage by methylglyoxal, the enolases encoded by <italic>ENO1 </italic>and <italic>ENO2 </italic>[##REF##18070066##39##], are also up-regulated by Zap1 (Table ##TAB##2##3##). <italic>TSA1</italic>, <italic>CTT1</italic>, and <italic>UTH1 </italic>are involved in resistance of the cell to oxidative stress. We recently showed that yeast cells grown under severe zinc-limiting conditions experience increased oxidative stress [##REF##17121842##23##]. In addition, we demonstrated that induction of <italic>TSA1</italic>, encoding the major cytosolic peroxiredoxin, in low zinc is required to resist that increased stress. <italic>CTT1</italic>, encoding the cytosolic form of catalase [##REF##6355826##40##], may be up-regulated for similar reasons. Uth1 is a mitochondrial protein of unknown function that is required for superoxide resistance [##REF##9799359##41##] so its induction by Zap1 may also be related to oxidative stress resistance. Lastly, Hsp26 is a member of the small heat shock protein family that has chaperone activity and protects proteins from misfolding [##REF##10581247##42##]. The regulation of these genes by Zap1 may help temper the various stresses experienced by zinc-limited cells. Other functional categories of Zap1 target genes induced by severe zinc deficiency include phospholipid synthesis, protein degradation, and carbohydrate, sulfur, and purine/pyrimidine metabolism (Table ##TAB##3##4##). On a final note, more than half of the genes now identified as potential Zap1 targets (47 of 81) have mammalian orthologs. Regulating the expression of these genes may also be important for cell growth under low zinc conditions in mammals.</p>" ]

[ "<title>Conclusion</title>", "<p>In this report, we have identified 31 new potential targets of Zap1 and have characterized the response of these and previously identified target genes to changes in zinc status and to time following zinc withdrawal. This analysis of Zap1 transcriptional regulation is providing unexpected and exciting new insights into the mechanisms of metal nutrient homeostasis in yeast and is telling us much about how these cells adapt to growth under the stress of zinc deficiency. Future studies will address the detailed roles of these various adaptive responses to cell growth under zinc-limiting conditions.</p>" ]

[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The Zap1 transcription factor is a central player in the response of yeast to changes in zinc status. We previously used transcriptome profiling with DNA microarrays to identify 46 potential Zap1 target genes in the yeast genome. In this new study, we used complementary methods to identify additional Zap1 target genes.</p>", "<title>Results</title>", "<p>With alternative growth conditions for the microarray experiments and a more sensitive motif identification algorithm, we identified 31 new potential targets of Zap1 activation. Moreover, an analysis of the response of Zap1 target genes to a range of zinc concentrations and to zinc withdrawal over time demonstrated that these genes respond differently to zinc deficiency. Some genes are induced under mild zinc deficiency and act as a first line of defense against this stress. First-line defense genes serve to maintain zinc homeostasis by increasing zinc uptake, and by mobilizing and conserving intracellular zinc pools. Other genes respond only to severe zinc limitation and act as a second line of defense. These second-line defense genes allow cells to adapt to conditions of zinc deficiency and include genes involved in maintaining secretory pathway and cell wall function, and stress responses.</p>", "<title>Conclusion</title>", "<p>We have identified several new targets of Zap1-mediated regulation. Furthermore, our results indicate that through the differential regulation of its target genes, Zap1 prioritizes mechanisms of zinc homeostasis and adaptive responses to zinc deficiency.</p>" ]

[ "<title>Abbreviations</title>", "<p>ZRE- Zinc-responsive domain, MEME- Multiple Expectation Maximization for Motif Elicitation, RSAT- Regulatory Sequence Analysis Tools, LZM- low zinc medium, SD- synthetic defined medium, DBD- DNA binding domain</p>", "<title>Availability and requirements</title>", "<p>Multiple Expectation Maximization for Motif Elicitation (MEME) program: <ext-link ext-link-type=\"uri\" xlink:href=\"http://meme.sdsc.edu/meme/\"/></p>", "<p>Regulatory Sequence Analysis Tools (RSAT): <ext-link ext-link-type=\"uri\" xlink:href=\"http://rsat.ulb.ac.be/rsat/\"/></p>", "<p>PatMatch: <ext-link ext-link-type=\"uri\" xlink:href=\"http://seq.yeastgenome.org/cgi-bin/PATMATCH/nph-patmatch\"/></p>", "<p>WebLogo: <ext-link ext-link-type=\"uri\" xlink:href=\"http://weblogo.berkeley.edu/\"/></p>", "<title>Authors' contributions</title>", "<p>C–YW performed the experiments and was the main author of the manuscript. AJB assisted in the microarray experiments and in the data analysis. LMC and MAN assisted in the statistical analysis of the array results. DRW provided critical intellectual contributions and assisted in writing the manuscript. DJE participated in experimental design and interpretation and assisted in writing the manuscript. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]

[ "<title>Acknowledgements</title>", "<p>This work was supported by Public Health Services grant GM-56285 from the National Institute of General Medical Sciences. The authors thank Joel Walker, Avery Frey, Yi-Hsuan Wu, and HuiChuan Lai for helpful discussions during the course of these studies.</p>" ]

[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>New strategies to identify genes regulated by Zap1.</bold> A) Identifying genes affected by moderate zinc deficiency. Experiments E1, E2, and E3 were combined to identify genes that showed increased expression in zinc-limited cells (E1), wild type cells vs. <italic>zap1Δ </italic>mutant cells in low zinc (E2), and Zap1^TC-expressing cells in high zinc (E3). B) The ZRE sequences in the promoters of the previously identified 46 Zap1 target genes [##REF##10884426##3##] were aligned and a logo was built using WebLogo. C) Experiments E3 and E4 were combined to identify Zap1 targets that respond to severe zinc deficiency. Regulatory Sequence Analysis Tools (RSAT) was used to identify potential ZREs in the promoters of co-regulated genes.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Confirmation of the microarray results for potential Zap1 target genes.</bold> S1 nuclease protection assays were performed using RNA isolated from cells grown under the same conditions as microarray experiments E3 and E4. A) <italic>ZRT1 </italic>and <italic>CMD1 </italic>were used as positive and loading controls, respectively. Results with candidate genes from Table 1 (B), Table 2 (C) and Table 3 (D) are shown. The band intensities were quantified and normalized to <italic>CMD1 </italic>levels, and the fold increase in E3 and E4 conditions is reported. These data confirmed the microarray results for these genes.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Evidence that ZRE sequences identified by RSAT are functional Zap1 binding sites.</bold> A) Electrophoretic mobility shift assay of candidate ZREs. Radiolabeled double-stranded oligonucleotides (0.5 pmol, 10,000 cpm) containing potential ZRE sequences from the indicated promoters were used as probes. The probes were mixed with 0 (-), 0.2 (±), 0.4 (+), or 0.8 (‡) μg per reaction of purified Zap1 DNA binding domain (Zap1_DBD). The <italic>arrow </italic>indicates the Zap1_DBD-DNA complex. The bona fide ZRE from <italic>TSA1 </italic>was used as a positive control and a mutant nonfunctional allele of that sequence (TSA1m) was used as a negative control. B) Nonrandom distribution of ZRE-like sequences in candidate Zap1 target gene promoters. In the <italic>upper </italic>panel, the positions of ZRE-like sequences in candidate Zap1 target promoters are plotted relative to the distance from the ATG start codon of the corresponding ORF. In the <italic>lower </italic>panel, the positions of ZRE-like sequences identified in the promoters of genes not showing zinc- and/or Zap1-responsive gene expression are plotted.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Differential regulation of Zap1 target genes in response to zinc.</bold> A) Microarray studies were performed with cells grown over a range of zinc (<italic>left </italic>panel) or over time after zinc withdrawal (<italic>right </italic>panel). For the dose-response analysis, cells were grown in LZM + 3 mM ZnCl₂, or LZM + 300, 100, 30, 10 or 3 μM ZnCl₂. Transcript levels were assayed using microarrays in which each sample was paired with the zinc-replete (LZM + 3 mM ZnCl₂) control. For the time-course studies, cells were grown to exponential phase in a zinc-replete medium (LZM + 1 mM ZnCl₂) and then transferred to a zinc-limiting medium (LZM + 1 μM ZnCl₂) for 8 hours. RNA was isolated at the indicated times and transcript levels were assayed using microarrays in which each sample was paired with the zinc-replete T_ocontrol. Genes were grouped by related function and the results are displayed using the Java Treeview program <ext-link ext-link-type=\"uri\" xlink:href=\"http://jtreeview.sourceforge.net\"/>. A narrow color intensity scale (<italic>yellow</italic>, increased expression relative to control; <italic>blue </italic>decreased expression) is used to show the conditions under which changes in gene expression were first detectable. B) The data for highly expressed genes in panel A were plotted with a broader scale to better show the differences in gene expression. The complete dose-response and time-course analyses were performed twice with similar results and the data, presented as the ratio relative to control, are provided in Additional file ##SUPPL##2##5##.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Differences in ZREs among Zap1 target genes.</bold> All ZREs from genes that respond to mild zinc deficiency and those that respond only to severe deficiency were divided into quartiles based on their RSAT scores. In the <italic>upper </italic>panel, the percentage of ZREs in each quartile for the genes responding to mild zinc deficiency are plotted. In the <italic>lower </italic>panel, the percentage of ZREs in each quartile for the genes responding to severe zinc deficiency are plotted. Chi-square analysis indicated that the different distributions among quartiles obtained with the two sets of ZREs are significant (P &lt; 0.001).</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>The relationship between gene function and regulation by Zap1 in response to zinc status.</bold> The top line indicates the range of zinc levels in our experiments ranging from mild (LZM + 300 μM ZnCl₂) to severe (LZM + 3 μM ZnCl₂) zinc deficiency. The bars below indicate the range of zinc over which each functional response occurs. Sample data from Figure 4A are included to illustrate the patterns of differential regulation for genes from different functional categories. See Table 4 for details regarding the various adaptive responses and the particular genes associated with both homeostatic and adaptive responses.</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Confirmation of previously identified Zap1 target genes by E1, E2, and E3 clustering.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>ORF^a</bold></td><td align=\"left\"><bold>Gene</bold></td><td align=\"left\"><bold>Function</bold></td><td align=\"center\" colspan=\"4\"><bold>Fold induction</bold></td><td align=\"center\" colspan=\"3\"><bold>ZRE</bold></td></tr><tr><td/><td/><td/><td colspan=\"4\"><hr/></td><td colspan=\"3\"><hr/></td></tr><tr><td/><td/><td/><td align=\"center\"><bold>E1^b</bold></td><td align=\"center\"><bold>E2^b</bold></td><td align=\"center\"><bold>E3-1^c</bold></td><td align=\"center\"><bold>E3-2^c</bold></td><td align=\"right\"><bold>start^d</bold></td><td align=\"right\"><bold>score^e</bold></td><td align=\"center\"><bold>sequence</bold></td></tr></thead><tbody><tr><td align=\"left\" colspan=\"10\"><bold>Experimentally tested Zap1 target genes</bold></td></tr><tr><td colspan=\"10\"><hr/></td></tr><tr><td align=\"left\">YDR284C</td><td align=\"left\"><italic>DPP1</italic></td><td align=\"left\">diacylglycerol pyrophosphate phosphatase</td><td align=\"center\">5.5</td><td align=\"center\">3.6</td><td align=\"center\">2.4</td><td align=\"center\">2.2</td><td align=\"right\">-452</td><td align=\"right\">9.6</td><td align=\"center\">ACCTGAAAGGT</td></tr><tr><td align=\"left\">YDR492W</td><td align=\"left\"><italic>IZH1</italic></td><td align=\"left\">membrane steroid hormone receptor ortholog</td><td align=\"center\">2.8</td><td align=\"center\">4.4</td><td align=\"center\">1.4</td><td align=\"center\">1.6</td><td align=\"right\">-914</td><td align=\"right\">5.0</td><td align=\"center\">TCCCTCAATGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-417</td><td align=\"right\">11.2</td><td align=\"center\">ACCCTAAAGGT</td></tr><tr><td align=\"left\">YGL255W</td><td align=\"left\"><italic>ZRT1</italic></td><td align=\"left\">plasma membrane zinc uptake transporter</td><td align=\"center\">24.2</td><td align=\"center\">18.9</td><td align=\"center\">38.8</td><td align=\"center\">34.4</td><td align=\"right\">-456</td><td align=\"right\">9.3</td><td align=\"center\">ACCTTTGGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-338</td><td align=\"right\">7.7</td><td align=\"center\">ACCTCGAAGGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-319</td><td align=\"right\">12.0</td><td align=\"center\">ACCTTGAGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-204</td><td align=\"right\">11.7</td><td align=\"center\">ACCTTGAAGGT</td></tr><tr><td align=\"left\">YGL256W</td><td align=\"left\"><italic>ADH4</italic></td><td align=\"left\">alcohol dehydrogenase IV</td><td align=\"center\">26.2</td><td align=\"center\">5.6</td><td align=\"center\">12.6</td><td align=\"center\">12.6</td><td align=\"right\">-269</td><td align=\"right\">9.4</td><td align=\"center\">ACCGTGAAGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-191</td><td align=\"right\">5.3</td><td align=\"center\">GCCTTCAATGA</td></tr><tr><td align=\"left\">YJL056C</td><td align=\"left\"><italic>ZAP1</italic></td><td align=\"left\">zinc-responsive transcriptional activator protein</td><td align=\"center\">7.5</td><td align=\"center\">16.9</td><td align=\"center\">96.5</td><td align=\"center\">79.7</td><td align=\"right\">-144</td><td align=\"right\">11.2</td><td align=\"center\">ACCCTAAAGGT</td></tr><tr><td align=\"left\">YKL175W</td><td align=\"left\"><italic>ZRT3</italic></td><td align=\"left\">vacuolar zinc export transporter</td><td align=\"center\">7.9</td><td align=\"center\">8.7</td><td align=\"center\">4.9</td><td align=\"center\">4.8</td><td align=\"right\">-155</td><td align=\"right\">11.6</td><td align=\"center\">ACCTTAAGGGT</td></tr><tr><td align=\"left\">YLR130C</td><td align=\"left\"><italic>ZRT2</italic></td><td align=\"left\">plasma membrane zinc uptake transporter</td><td align=\"center\">5.6</td><td align=\"center\">12.3</td><td align=\"center\">2.7</td><td align=\"center\">2.5</td><td align=\"right\">-941</td><td align=\"right\">6.1</td><td align=\"center\">ACCCTAACTGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-311</td><td align=\"right\">11.2</td><td align=\"center\">ACCCTAAAGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-262</td><td align=\"right\">11.2</td><td align=\"center\">ACCCTAAAGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-174</td><td align=\"right\">5.8</td><td align=\"center\">ACCTTTTGGGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-112</td><td align=\"right\">4.9</td><td align=\"center\">ACCAACAGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-41</td><td align=\"right\">5.3</td><td align=\"center\">GCCTGCAATGT</td></tr><tr><td align=\"left\">YMR243C</td><td align=\"left\"><italic>ZRC1</italic></td><td align=\"left\">vacuolar zinc import transporter</td><td align=\"center\">2.1</td><td align=\"center\">4.7</td><td align=\"center\">1.6</td><td align=\"center\">1.8</td><td align=\"right\">-174</td><td align=\"right\">9.6</td><td align=\"center\">GCCTTGAAGGT</td></tr><tr><td align=\"left\">YMR319C</td><td align=\"left\"><italic>FET4</italic></td><td align=\"left\">plasma membrane Fe/Cu/Zn uptake transporter</td><td align=\"center\">2.3</td><td align=\"center\">2.4</td><td align=\"center\">1.6</td><td align=\"center\">1.7</td><td align=\"right\">-384</td><td align=\"right\">7.8</td><td align=\"center\">ACCCCACGGGT</td></tr><tr><td align=\"left\">YNR039C</td><td align=\"left\"><italic>ZRG17</italic></td><td align=\"left\">endoplasmic reticulum zinc import transporter</td><td align=\"center\">3.2</td><td align=\"center\">3.5</td><td align=\"center\">2.3</td><td align=\"center\">1.9</td><td align=\"right\">-527</td><td align=\"right\">11.7</td><td align=\"center\">ACCTTGAAGGT</td></tr><tr><td align=\"left\">YOL002C</td><td align=\"left\"><italic>IZH2</italic></td><td align=\"left\">membrane steroid hormone receptor ortholog</td><td align=\"center\">1.9</td><td align=\"center\">2.0</td><td align=\"center\">3.4</td><td align=\"center\">3.3</td><td align=\"right\">-256</td><td align=\"right\">7.7</td><td align=\"center\">ACCCTAGAGGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-173</td><td align=\"right\">7.2</td><td align=\"center\">ACCCCGAGTGT</td></tr><tr><td align=\"left\">YOL154W</td><td align=\"left\"><italic>ZPS1</italic></td><td align=\"left\">cell wall protein, putative metalloprotease</td><td align=\"center\">13.9</td><td align=\"center\">10.1</td><td align=\"center\">78.6</td><td align=\"center\">57.9</td><td align=\"right\">-329</td><td align=\"right\">11.8</td><td align=\"center\">ACCTTCAGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-314</td><td align=\"right\">11.8</td><td align=\"center\">ACCTTCAGGGT</td></tr><tr><td colspan=\"10\"><hr/></td></tr><tr><td align=\"left\" colspan=\"10\"><bold>Likely Zap1 target genes</bold></td></tr><tr><td colspan=\"10\"><hr/></td></tr><tr><td align=\"left\">YBL049W</td><td align=\"left\"><italic>MOH1</italic></td><td align=\"left\">zinc-binding protein, function unknown</td><td align=\"center\">10.7</td><td align=\"center\">2.2</td><td align=\"center\">3.2</td><td align=\"center\">3.8</td><td align=\"right\">-387</td><td align=\"right\">8.7</td><td align=\"center\">CCCTTGAGGGA</td></tr><tr><td align=\"left\">YBR117C*</td><td align=\"left\"><italic>TKL2*</italic></td><td align=\"left\">transketolase, pentose phosphate pathway</td><td align=\"center\">5.4</td><td align=\"center\">2.0</td><td align=\"center\">1.9</td><td align=\"center\">2.1</td><td align=\"right\">-844</td><td align=\"right\">8.2</td><td align=\"center\">ACCTTATGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-524</td><td align=\"right\">5.3</td><td align=\"center\">ACCCAAAATGT</td></tr><tr><td align=\"left\">YDR055W*</td><td align=\"left\"><italic>PST1*</italic></td><td align=\"left\">cell wall protein</td><td align=\"center\">5.0</td><td align=\"center\">2.7</td><td align=\"center\">2.0</td><td align=\"center\">1.9</td><td align=\"right\">-886</td><td align=\"right\">7.4</td><td align=\"center\">ACCCCAAGGGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-405</td><td align=\"right\">8.0</td><td align=\"center\">TCCTTGAGGGA</td></tr><tr><td align=\"left\">YGL121C</td><td align=\"left\"><italic>GPG1</italic></td><td align=\"left\">putative gamma subunit of a heterotrimeric G protein</td><td align=\"center\">10.8</td><td align=\"center\">2.7</td><td align=\"center\">2.1</td><td align=\"center\">2.4</td><td align=\"right\">-287</td><td align=\"right\">8.3</td><td align=\"center\">CCCTTCGAGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-208</td><td align=\"right\">9.0</td><td align=\"center\">ACCGTAAAGGT</td></tr><tr><td align=\"left\">YGL257C</td><td align=\"left\"><italic>MNT2</italic></td><td align=\"left\">mannosyltransferase, O-linked glycosylation</td><td align=\"center\">2.3</td><td align=\"center\">4.4</td><td align=\"center\">4.0</td><td align=\"center\">3.7</td><td align=\"right\">-572</td><td align=\"right\">5.3</td><td align=\"center\">GCCTTCAATGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-494</td><td align=\"right\">9.4</td><td align=\"center\">ACCGTGAAGGT</td></tr><tr><td align=\"left\">YGL258W</td><td align=\"left\"><italic>VEL1</italic></td><td align=\"left\">function unknown</td><td align=\"center\">16.3</td><td align=\"center\">4.0</td><td align=\"center\">112.6</td><td align=\"center\">78.6</td><td align=\"right\">-763</td><td align=\"right\">6.3</td><td align=\"center\">ACCTTGCATGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-232</td><td align=\"right\">10.4</td><td align=\"center\">ACCCTGCGGGT</td></tr><tr><td align=\"left\">YGR295C</td><td align=\"left\"><italic>COS6</italic></td><td align=\"left\">function unknown</td><td align=\"center\">2.7</td><td align=\"center\">2.2</td><td align=\"center\">1.5</td><td align=\"center\">1.6</td><td align=\"right\">-875</td><td align=\"right\">5.0</td><td align=\"center\">AACTTAAATGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-309</td><td align=\"right\">9.1</td><td align=\"center\">ACCTTAAATGT</td></tr><tr><td align=\"left\">YJL132W*</td><td/><td align=\"left\">similar to phospholipase D</td><td align=\"center\">5.2</td><td align=\"center\">3.8</td><td align=\"center\">3.3</td><td align=\"center\">2.8</td><td align=\"right\">-154</td><td align=\"right\">7.8</td><td align=\"center\">ACCCAAAGGGT</td></tr><tr><td align=\"left\">YJR061W</td><td/><td align=\"left\">Mnn4-related, N-linked glycosylation</td><td align=\"center\">4.6</td><td align=\"center\">2.1</td><td align=\"center\">2.0</td><td align=\"center\">1.8</td><td align=\"right\">-278</td><td align=\"right\">9.8</td><td align=\"center\">ACCTTCAAGGA</td></tr><tr><td align=\"left\">YKL165C</td><td align=\"left\"><italic>MCD4</italic></td><td align=\"left\">glycosylphosphatidylinositol (GPI) anchor synthesis</td><td align=\"center\">4.1</td><td align=\"center\">2.5</td><td align=\"center\">1.8</td><td align=\"center\">1.8</td><td align=\"right\">-104</td><td align=\"right\">11.4</td><td align=\"center\">ACCTTAAAGGT</td></tr><tr><td align=\"left\">YLL020C</td><td/><td align=\"left\">function unknown</td><td align=\"center\">5.1</td><td align=\"center\">3.1</td><td align=\"center\">1.7</td><td align=\"center\">1.6</td><td align=\"right\">-933</td><td align=\"right\">12.0</td><td align=\"center\">ACCTTGAGGGT</td></tr><tr><td align=\"left\">YMR120C</td><td align=\"left\"><italic>ADE17</italic></td><td align=\"left\">AICAR transformylase, purine biosynthesis</td><td align=\"center\">5.2</td><td align=\"center\">3.7</td><td align=\"center\">1.9</td><td align=\"center\">2.2</td><td align=\"right\">-98</td><td align=\"right\">8.7</td><td align=\"center\">ACCTTTAGTGT</td></tr><tr><td align=\"left\">YMR271C</td><td align=\"left\"><italic>URA10</italic></td><td align=\"left\">orotate phosphoribosyltransferase, pyrimidine biosynthesis</td><td align=\"center\">6.1</td><td align=\"center\">2.5</td><td align=\"center\">4.9</td><td align=\"center\">5.2</td><td align=\"right\">-143</td><td align=\"right\">7.9</td><td align=\"center\">ACCTTTCGGGA</td></tr><tr><td align=\"left\">YMR297W</td><td align=\"left\"><italic>PRC1</italic></td><td align=\"left\">vacuolar carboxypeptidase Y</td><td align=\"center\">3.0</td><td align=\"center\">3.5</td><td align=\"center\">1.5</td><td align=\"center\">1.9</td><td align=\"right\">-182</td><td align=\"right\">8.1</td><td align=\"center\">ACCCCGCGGGT</td></tr><tr><td align=\"left\">YNL254C</td><td/><td align=\"left\">function unknown</td><td align=\"center\">9.7</td><td align=\"center\">12.0</td><td align=\"center\">2.3</td><td align=\"center\">2.0</td><td align=\"right\">-432</td><td align=\"right\">11.7</td><td align=\"center\">ACCTTGAAGGT</td></tr><tr><td align=\"left\">YNL336W</td><td align=\"left\"><italic>COS1</italic></td><td align=\"left\">function unknown</td><td align=\"center\">2.1</td><td align=\"center\">2.0</td><td align=\"center\">1.6</td><td align=\"center\">1.6</td><td align=\"right\">-871</td><td align=\"right\">5.0</td><td align=\"center\">AACTTAAATGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-591</td><td align=\"right\">5.3</td><td align=\"center\">AACCTAGAGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-314</td><td align=\"right\">9.1</td><td align=\"center\">ACCTTAAATGT</td></tr><tr><td align=\"left\">YOL084W</td><td align=\"left\"><italic>PHM7</italic></td><td align=\"left\">major facilitator superfamily member, function unknown</td><td align=\"center\">9.7</td><td align=\"center\">2.1</td><td align=\"center\">2.5</td><td align=\"center\">2.3</td><td align=\"right\">-766</td><td align=\"right\">11.7</td><td align=\"center\">ACCTTGAAGGT</td></tr><tr><td align=\"left\">YOL131W</td><td/><td align=\"left\">function unknown</td><td align=\"center\">5.0</td><td align=\"center\">2.3</td><td align=\"center\">2.5</td><td align=\"center\">2.2</td><td align=\"right\">-1000</td><td align=\"right\">7.6</td><td align=\"center\">AACTTCAGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-458</td><td align=\"right\">8.2</td><td align=\"center\">ACCTGGAAGGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-364</td><td align=\"right\">5.9</td><td align=\"center\">ACCCAAGAGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-122</td><td align=\"right\">5.5</td><td align=\"center\">TCCATTAGGGT</td></tr><tr><td align=\"left\">YOR387C</td><td/><td align=\"left\">function unknown</td><td align=\"center\">17.8</td><td align=\"center\">19.3</td><td align=\"center\">136.4</td><td align=\"center\">113.6</td><td align=\"right\">-763</td><td align=\"right\">6.3</td><td align=\"center\">ACCTTGCATGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-231</td><td align=\"right\">10.4</td><td align=\"center\">ACCCTGCGGGT</td></tr><tr><td align=\"left\">YOR134W</td><td align=\"left\"><italic>BAG7</italic></td><td align=\"left\">GTPase-activating protein, control of cell wall synthesis</td><td align=\"center\">5.9</td><td align=\"center\">2.0</td><td align=\"center\">1.7</td><td align=\"center\">1.6</td><td align=\"right\">-445</td><td align=\"right\">6.0</td><td align=\"center\">CCCTCCAAGGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-325</td><td align=\"right\">8.6</td><td align=\"center\">CCCCTGCAGGT</td></tr><tr><td align=\"left\">YPL250C*</td><td align=\"left\"><italic>ICY2*</italic></td><td align=\"left\">function unknown</td><td align=\"center\">2.1</td><td align=\"center\">2.3</td><td align=\"center\">3.1</td><td align=\"center\">3.1</td><td align=\"right\">-756</td><td align=\"right\">8.9</td><td align=\"center\">CCCTTCCGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-524</td><td align=\"right\">7.4</td><td align=\"center\">ACCCCAAGGGA</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-475</td><td align=\"right\">5.7</td><td align=\"center\">GCCCAAAGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-360</td><td align=\"right\">5.6</td><td align=\"center\">CCCGTCAGTGT</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>New candidate Zap1 target genes identified by E1, E2, and E3 clustering.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>ORF^a</bold></td><td align=\"left\"><bold>Gene</bold></td><td align=\"left\"><bold>Function</bold></td><td align=\"center\" colspan=\"4\"><bold>Fold induction</bold></td><td align=\"center\" colspan=\"3\"><bold>ZRE</bold></td></tr><tr><td/><td/><td/><td colspan=\"4\"><hr/></td><td colspan=\"3\"><hr/></td></tr><tr><td/><td/><td/><td align=\"center\"><bold>E1^b</bold></td><td align=\"center\"><bold>E2^b</bold></td><td align=\"center\"><bold>E3-1^c</bold></td><td align=\"center\"><bold>E3-2^c</bold></td><td align=\"right\"><bold>start^d</bold></td><td align=\"right\"><bold>score^e</bold></td><td align=\"center\"><bold>sequence^f</bold></td></tr></thead><tbody><tr><td align=\"left\">YBL029W</td><td/><td align=\"left\">function unknown</td><td align=\"center\">3.0</td><td align=\"center\">2.9</td><td align=\"center\">1.6</td><td align=\"center\">1.4</td><td align=\"right\">-995</td><td align=\"right\">5.8</td><td align=\"center\">CCCCTGCCGGT</td></tr><tr><td align=\"left\">YBR072W*</td><td align=\"left\"><italic>HSP26*</italic></td><td align=\"left\">small heat shock protein, protein folding</td><td align=\"center\">40.5</td><td align=\"center\">2.0</td><td align=\"center\">3.3</td><td align=\"center\">3.5</td><td align=\"right\">-451</td><td align=\"right\">5.3</td><td align=\"center\"><bold>ACCTTGCCTGT</bold></td></tr><tr><td align=\"left\">YDR077W*</td><td align=\"left\"><italic>SED1*</italic></td><td align=\"left\">cell wall protein</td><td align=\"center\">2.6</td><td align=\"center\">2.2</td><td align=\"center\">2.6</td><td align=\"center\">2.1</td><td align=\"right\">-896</td><td align=\"right\">4.7</td><td align=\"center\"><bold>CCCTTATAGGA</bold></td></tr><tr><td align=\"left\">YGR088W*</td><td align=\"left\"><italic>CTT1*</italic></td><td align=\"left\">catalase T, oxidative stress resistance</td><td align=\"center\">17.1</td><td align=\"center\">3.7</td><td align=\"center\">1.9</td><td align=\"center\">1.7</td><td align=\"right\">-337</td><td align=\"right\">5.7</td><td align=\"center\">CCCTTACCGGT</td></tr><tr><td align=\"left\">YGR243W</td><td align=\"left\"><italic>FMP43</italic></td><td align=\"left\">mitochondrial protein, function unknown</td><td align=\"center\">2.8</td><td align=\"center\">2.7</td><td align=\"center\">3.1</td><td align=\"center\">2.5</td><td align=\"right\">-552</td><td align=\"right\">4.9</td><td align=\"center\">TCCTCGAATGT</td></tr><tr><td align=\"left\">YHR214W-A</td><td/><td align=\"left\">function unknown</td><td align=\"center\">6.5</td><td align=\"center\">2.6</td><td align=\"center\">3.0</td><td align=\"center\">3.2</td><td align=\"right\">-274</td><td align=\"right\">4.8</td><td align=\"center\">ACCTCTGGTGT</td></tr><tr><td align=\"left\">YIL045W</td><td align=\"left\"><italic>PIG2</italic></td><td align=\"left\">putative protein phosphatase regulatory subunit</td><td align=\"center\">4.6</td><td align=\"center\">2.4</td><td align=\"center\">1.6</td><td align=\"center\">1.5</td><td align=\"right\">-859</td><td align=\"right\">4.7</td><td align=\"center\">ACCTTCCACGT</td></tr><tr><td align=\"left\">YJL048C*</td><td align=\"left\"><italic>UBX6*</italic></td><td align=\"left\">UBX (ubiquitin regulatory X) domain-containing protein</td><td align=\"center\">2.3</td><td align=\"center\">3.3</td><td align=\"center\">1.8</td><td align=\"center\">1.6</td><td align=\"right\">-198</td><td align=\"right\">5.2</td><td align=\"center\"><bold>TCCATTAAGGT</bold></td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-39</td><td align=\"right\">5.9</td><td align=\"center\">CCCTCAAAGGA</td></tr><tr><td align=\"left\">YKR046C</td><td align=\"left\"><italic>PET10</italic></td><td align=\"left\">lipid particle protein, unknown function</td><td align=\"center\">2.9</td><td align=\"center\">3.0</td><td align=\"center\">1.7</td><td align=\"center\">1.7</td><td align=\"right\">-929</td><td align=\"right\">4.8</td><td align=\"center\">ATCTTGCAGGT</td></tr><tr><td align=\"left\">YLR136C*</td><td align=\"left\"><italic>TIS11*</italic></td><td align=\"left\">tristetraproline homolog, control of mRNA stability</td><td align=\"center\">2.6</td><td align=\"center\">4.1</td><td align=\"center\">5.6</td><td align=\"center\">3.7</td><td align=\"right\">-821</td><td align=\"right\">4.9</td><td align=\"center\"><bold>GCCCGTGAGGT</bold></td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-768</td><td align=\"right\">6.3</td><td align=\"center\">AACCTGCGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-507</td><td align=\"right\">6.0</td><td align=\"center\">GCCCAGAGGGT</td></tr><tr><td align=\"left\">YML028W</td><td align=\"left\"><italic>TSA1</italic></td><td align=\"left\">peroxiredoxin, oxidative stress resistance</td><td align=\"center\">5.0</td><td align=\"center\">2.9</td><td align=\"center\">2.0</td><td align=\"center\">1.5</td><td align=\"right\">-195</td><td align=\"right\">5.5</td><td align=\"center\"><bold>GCCCGTCGGGT</bold></td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-170</td><td align=\"right\">7.2</td><td align=\"center\">TCCCTAAAGGA</td></tr><tr><td align=\"left\">YMR181C</td><td/><td align=\"left\">function unknown</td><td align=\"center\">5.9</td><td align=\"center\">2.2</td><td align=\"center\">2.6</td><td align=\"center\">2.7</td><td align=\"right\">-237</td><td align=\"right\">5.1</td><td align=\"center\">CCCTTCGAGGG</td></tr><tr><td align=\"left\">YNL239W*</td><td align=\"left\"><italic>LAP3*</italic></td><td align=\"left\">homocysteine thiolactonase</td><td align=\"center\">2.1</td><td align=\"center\">4.7</td><td align=\"center\">2.9</td><td align=\"center\">2.9</td><td align=\"right\">-947</td><td align=\"right\">4.8</td><td align=\"center\">GCCTCCAATGT</td></tr><tr><td align=\"left\">YOL082W</td><td align=\"left\"><italic>ATG19</italic></td><td align=\"left\">autophagy and cytoplasm-to-vacuole (CVT) targeting pathway</td><td align=\"center\">4.3</td><td align=\"center\">3.2</td><td align=\"center\">1.6</td><td align=\"center\">1.4</td><td align=\"right\">-281</td><td align=\"right\">5.9</td><td align=\"center\">ACCTTAAAAGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-998</td><td align=\"right\">4.9</td><td align=\"center\">ACCAACAGGGT</td></tr><tr><td align=\"left\">YOL155C*</td><td/><td align=\"left\">cell wall protein</td><td align=\"center\">11.8</td><td align=\"center\">3.5</td><td align=\"center\">2.3</td><td align=\"center\">2.9</td><td align=\"right\">-361</td><td align=\"right\">6.3</td><td align=\"center\"><bold>ACCGTGCAGGA</bold></td></tr><tr><td align=\"left\">YPL159C</td><td align=\"left\"><italic>PET20</italic></td><td align=\"left\">mitochondrial protein required for respiratory growth</td><td align=\"center\">9.1</td><td align=\"center\">5.2</td><td align=\"center\">4.5</td><td align=\"center\">4.0</td><td align=\"right\">-278</td><td align=\"right\">5.3</td><td align=\"center\">GCCATTAAGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-180</td><td align=\"right\">5.6</td><td align=\"center\">ACCCTTTGGGA</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>New candidate Zap1 target genes identified by E3 and E4 clustering.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>ORF^a</bold></td><td align=\"left\"><bold>Gene</bold></td><td align=\"left\"><bold>Function</bold></td><td align=\"center\" colspan=\"4\"><bold>Fold induction^b</bold></td><td align=\"center\" colspan=\"3\"><bold>ZRE</bold></td></tr><tr><td/><td/><td/><td colspan=\"4\"><hr/></td><td colspan=\"3\"><hr/></td></tr><tr><td/><td/><td/><td align=\"center\"><bold>E3-1</bold></td><td align=\"center\"><bold>E3-2</bold></td><td align=\"center\"><bold>E4-1</bold></td><td align=\"center\"><bold>E4-2</bold></td><td align=\"right\"><bold>start^c</bold></td><td align=\"right\"><bold>score^d</bold></td><td align=\"center\"><bold>sequence^e</bold></td></tr></thead><tbody><tr><td align=\"left\">YDL125C*</td><td align=\"left\"><italic>HNT1*</italic></td><td align=\"left\">adenosine 5'-monophosphoramidase</td><td align=\"center\">1.9</td><td align=\"center\">1.6</td><td align=\"center\">3.6</td><td align=\"center\">4.1</td><td align=\"right\">-279</td><td align=\"right\">6.9</td><td align=\"center\"><bold>GCCTCAAAGGT</bold></td></tr><tr><td align=\"left\">YEL060C*</td><td align=\"left\"><italic>PRB1*</italic></td><td align=\"left\">vacuolar proteinase B</td><td align=\"center\">2.6</td><td align=\"center\">2.8</td><td align=\"center\">2.0</td><td align=\"center\">2.1</td><td align=\"right\">-677</td><td align=\"right\">6.5</td><td align=\"center\"><bold>GCCATGAGGGT</bold></td></tr><tr><td align=\"left\">YFL014W</td><td align=\"left\"><italic>HSP12</italic></td><td align=\"left\">cell wall protein, stress resistance</td><td align=\"center\">1.5</td><td align=\"center\">1.5</td><td align=\"center\">4.4</td><td align=\"center\">3.7</td><td align=\"right\">-211</td><td align=\"right\">5.0</td><td align=\"center\">ACCTCAAAGTT</td></tr><tr><td align=\"left\">YGR254W</td><td align=\"left\"><italic>ENO1</italic></td><td align=\"left\">enolase I, glycolysis and gluconeogenesis</td><td align=\"center\">2.6</td><td align=\"center\">2.9</td><td align=\"center\">1.4</td><td align=\"center\">1.7</td><td align=\"right\">-927</td><td align=\"right\">9.4</td><td align=\"center\">ACCGTGAAGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-371</td><td align=\"right\">5.1</td><td align=\"center\">ACCTGAGCGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-216</td><td align=\"right\">5.6</td><td align=\"center\">CACCTCAAGGT</td></tr><tr><td align=\"left\">YGR279C*</td><td align=\"left\"><italic>SCW4*</italic></td><td align=\"left\">cell wall protein with similarity to glucanases</td><td align=\"center\">1.8</td><td align=\"center\">1.9</td><td align=\"center\">1.5</td><td align=\"center\">1.4</td><td align=\"right\">-481</td><td align=\"right\">5.4</td><td align=\"center\">CCCTGCACGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-466</td><td align=\"right\">6.3</td><td align=\"center\">ACCCTCTGGGA</td></tr><tr><td align=\"left\">YHR174W*</td><td align=\"left\"><italic>ENO2*</italic></td><td align=\"left\">enolase II, glycolysis and gluconeogenesis</td><td align=\"center\">2.5</td><td align=\"center\">2.8</td><td align=\"center\">1.3</td><td align=\"center\">1.6</td><td align=\"right\">-594</td><td align=\"right\">4.8</td><td align=\"center\"><bold>ACGCTGCGGGT</bold></td></tr><tr><td align=\"left\">YIL169C*</td><td/><td align=\"left\">cell wall potein</td><td align=\"center\">3.4</td><td align=\"center\">3.1</td><td align=\"center\">2.1</td><td align=\"center\">2.2</td><td align=\"right\">-742</td><td align=\"right\">10.8</td><td align=\"center\">ACCCGGAAGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-252</td><td align=\"right\">6.0</td><td align=\"center\">ACCTCGCAGGC</td></tr><tr><td align=\"left\">YJL052W</td><td align=\"left\"><italic>TDH1</italic></td><td align=\"left\">glyceraldehyde-3-phosphate dehydrogenase</td><td align=\"center\">1.4</td><td align=\"center\">1.6</td><td align=\"center\">1.6</td><td align=\"center\">1.6</td><td align=\"right\">-347</td><td align=\"right\">4.6</td><td align=\"center\">ACCTTCGGAGT</td></tr><tr><td align=\"left\">YJL171C</td><td/><td align=\"left\">cell wall protein</td><td align=\"center\">1.6</td><td align=\"center\">1.8</td><td align=\"center\">1.6</td><td align=\"center\">1.8</td><td align=\"right\">-573</td><td align=\"right\">4.9</td><td align=\"center\">CCCATAAAGGA</td></tr><tr><td align=\"left\">YKR042W*</td><td align=\"left\"><italic>UTH1*</italic></td><td align=\"left\">mitochondrial protein, oxidative stress resistance</td><td align=\"center\">3.5</td><td align=\"center\">4.0</td><td align=\"center\">1.4</td><td align=\"center\">1.5</td><td align=\"right\">-282</td><td align=\"right\">6.9</td><td align=\"center\">CCCTTCAATGT</td></tr><tr><td align=\"left\">YLL053C</td><td/><td align=\"left\">aquaporin</td><td align=\"center\">1.6</td><td align=\"center\">1.5</td><td align=\"center\">1.7</td><td align=\"center\">1.5</td><td align=\"right\">-354</td><td align=\"right\">5.5</td><td align=\"center\">ACCGGTCGGGT</td></tr><tr><td align=\"left\">YMR169C</td><td align=\"left\"><italic>ALD3</italic></td><td align=\"left\">aldehyde dehydrogenase</td><td align=\"center\">2.0</td><td align=\"center\">1.9</td><td align=\"center\">1.6</td><td align=\"center\">1.5</td><td align=\"right\">-214</td><td align=\"right\">9.1</td><td align=\"center\">TCCCTAAGGGT</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td align=\"right\">-79</td><td align=\"right\">4.6</td><td align=\"center\">ACCTGGCATGA</td></tr><tr><td align=\"left\">YOR348C</td><td align=\"left\"><italic>PUT4</italic></td><td align=\"left\">proline permease</td><td align=\"center\">1.8</td><td align=\"center\">1.8</td><td align=\"center\">1.9</td><td align=\"center\">1.8</td><td align=\"right\">-299</td><td align=\"right\">8.2</td><td align=\"center\">CCCTGCAAGGT</td></tr><tr><td align=\"left\">YPL274W*</td><td align=\"left\"><italic>SAM3*</italic></td><td align=\"left\">S-adenosylmethionine permease</td><td align=\"center\">1.9</td><td align=\"center\">1.7</td><td align=\"center\">2.1</td><td align=\"center\">2.2</td><td align=\"right\">-240</td><td align=\"right\">4.7</td><td align=\"center\"><bold>TCCCCTGCGGT</bold></td></tr><tr><td align=\"left\">YPR003C</td><td/><td align=\"left\">putative ER sulfate permease</td><td align=\"center\">2.2</td><td align=\"center\">2.1</td><td align=\"center\">1.5</td><td align=\"center\">1.5</td><td align=\"right\">-149</td><td align=\"right\">5.5</td><td align=\"center\">ACCGAAAAGGT</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Functional categories of genes regulated by the Zap1 transcriptional factor.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Functional Group</td><td align=\"left\">Zap1 Target Genes</td></tr></thead><tbody><tr><td align=\"left\">First-line defenses: Zinc homeostasis</td><td/></tr><tr><td align=\"left\"> Zinc uptake</td><td align=\"left\"><italic>ZRT1, ZRT2, FET4, ZPS1</italic></td></tr><tr><td align=\"left\"> Mobilize zinc stores</td><td align=\"left\"><italic>ZRT3</italic></td></tr><tr><td align=\"left\"> Increase transcription response</td><td align=\"left\"><italic>ZAP1</italic></td></tr><tr><td align=\"left\"> Zinc sparing</td><td align=\"left\"><italic>ZRR1 (ADH1), ZRR2 (ADH3), ADH4</italic></td></tr><tr><td align=\"left\"> Zinc shock resistance</td><td align=\"left\"><italic>ZRC1</italic></td></tr><tr><td align=\"left\"> Other zinc homeostasis genes</td><td align=\"left\"><italic>IZH1, IZH2</italic></td></tr><tr><td/><td/></tr><tr><td align=\"left\">Second-line defenses: Adaptive responses</td><td/></tr><tr><td/><td/></tr><tr><td align=\"left\"> Secretory pathway function</td><td align=\"left\"><italic>ZRG17, MNT2, MCD4, YJR061W</italic></td></tr><tr><td align=\"left\"> Cell wall function</td><td align=\"left\"><italic>PST1, BAG7</italic>, <bold><italic>HSP12, SED1, SCW4, YOL155C, YIL169C, YJL171C</italic></bold></td></tr><tr><td align=\"left\"> Stress resistance</td><td align=\"left\"><italic>GRE2, RAD27</italic>, <bold><italic>TSA1, CTT1, UTH1, HSP26</italic></bold></td></tr><tr><td align=\"left\"> Phospholipid metabolism</td><td align=\"left\"><italic>DPP1, EKI1, PIS1, YJL132W</italic></td></tr><tr><td align=\"left\"> Sulfur metabolism</td><td align=\"left\"><bold><italic>LAP3, SAM3, YPR003C</italic></bold></td></tr><tr><td align=\"left\"> Protein degradation</td><td align=\"left\"><italic>PRC1, PEP4</italic>, <bold><italic>PRB1, ATG19, UBX6</italic></bold></td></tr><tr><td align=\"left\"> Carbohydrate metabolism</td><td align=\"left\"><italic>NRG2, TKL2</italic>, <bold><italic>ENO1, ENO2, TDH1</italic></bold></td></tr><tr><td align=\"left\"> Purine/pyrimidine metabolism</td><td align=\"left\"><italic>URA10, ADE17</italic>, <bold><italic>HNT1</italic></bold></td></tr><tr><td align=\"left\"> Mitochondrial function</td><td align=\"left\"><bold><italic>FMP43, PET20, PET10</italic></bold></td></tr><tr><td/><td/></tr><tr><td align=\"left\">Other</td><td align=\"left\"><italic>VEL1, MOH1, GPG1, COS6, ICY2, COS1, PHM7, <bold>PIG2, TIS11, ALD3, PUT4</bold>, COS2, ZIP1, COS4, COS8, TPO5, COS3, YNL254C, YOR387C, YLL020C, YOL131W, <bold>YBL029W, YHR214W-A, YMR181C, YLL053C </bold>, YBL048C, YMR086W, YNL234W</italic></td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Microarray results of Zap1 target genes from Lyons et al. (ref. [##REF##10884426##3##]) not confirmed by Experiment E3.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p>Microarray results for potential Zap1 targets identified in this study that lack detectable ZREs.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional file 5</title><p>Microarray results for Zap1 targets from time course and dose response studies.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional file 3</title><p>Oligonucleotides used for S1 nuclease protection assays.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional file 4</title><p>Oligonucleotides used for electrophoretic mobility shift experiments.</p></caption></supplementary-material>" ]

[ "<table-wrap-foot><p>a) Results for genes marked with asterisks were confirmed independently by S1 nuclease protection assay in Figure 2.</p><p>b) Expression ratios are the average of two independent microarray experiments (Lyons et. al. 2000).</p><p>c) Results from two independent microarray experiments (E3-1 and E3-2) are shown.</p><p>d) Numbers indicate the distance from the ATG initiation codon.</p><p>e) Score calculated for each sequence with a position-specific scoring matrix generated by RSAT.</p></table-wrap-foot>", "<table-wrap-foot><p>a) Results for genes marked with asterisks were confirmed independently by S1 nuclease protection assay in Figure 2.</p><p>b) Expression ratios are the average of two independent microarray experiments (Lyons et. al. 2000).</p><p>c) Results from two independent microarray experiments (E3-1 and E3-2) are shown.</p><p>d) Numbers indicate the distance from the ATG initiation codon.</p><p>e) Score calculated for each sequence with a position-specific scoring matrix generated by RSAT.</p><p>f) ZREs shown in bold were tested for interaction with Zap1 as described in Figure 3A.</p></table-wrap-foot>", "<table-wrap-foot><p>a) Results for genes marked with asterisks were confirmed independently by S1 nuclease protection assay in Figure 2.</p><p>b) Results from two independent microarray experiments (E3-1, E3-2; E4-1, E4-2) for each condition are shown.</p><p>c) Numbers indicate the distance from the ATG initiation codon.</p><p>d) Score calculated for each sequence with a position-specific scoring matrix generated by RSAT.</p><p>e) ZREs shown in bold were tested for interaction with Zap1 as described in Figure 3A.</p></table-wrap-foot>", "<table-wrap-foot><p>Genes shown in bold are new targets in this study</p></table-wrap-foot>" ]

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[ "<media xlink:href=\"1471-2164-9-370-S1.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2164-9-370-S2.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2164-9-370-S5.xls\" mimetype=\"application\" mime-subtype=\"vnd.ms-excel\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2164-9-370-S3.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2164-9-370-S4.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>" ]

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[ "<title>Discussion</title>", "<p>Overall, the results from studies of gene and protein expression after exposure to RF fields from mobile phones are inconclusive to date. Two questions should be asked concerning the use of HTSTs for the detection of possible health effects from exposure to RF fields from mobile phones. First, would a response, if confirmed, give evidence in favor of a risk of toxicity? This might not be the case, in principle, as long as no phenotypic change could be confirmed either <italic>in vitro</italic> or <italic>in vivo</italic> after RF exposure at intensities relevant to human exposure. In this respect, some authors simultaneously studied genomics and/or proteomics together with other related biological end points. ##REF##14760712##Leszczynski et al. (2004)##, for example, observed an increase in HSP27 phosphorylation—which can be considered an antiapoptotic event (##REF##9869631##Morimoto 1998##)—and a concomitant increase in stability of stress fibers of cells and down-regulation of genes of the Fas/TNF-α apoptotic pathway. Other authors did not observe simultaneous change in gene expression and in related downstream events: ##REF##16802864##Natarajan et al. (2006)## reported increased DNA-binding activity of NF-kappaB in human monocytes but no transactivation of kappaB-dependent gene expression after exposure to pulsed ultra-wide-band electromagnetic fields. ##REF##16116041##Nikolova et al. (2005)## observed changes in gene expression but without detectable change in cell physiology. ##REF##16878295##Nylund and Leszczynski (2006)## observed simultaneous changes in gene expression and in protein expression, but the latter were not related to the former.</p>", "<p>Second, are HTSTs a relevant method here? In other words, could a specific gene or protein response pattern ever be established after exposure to RF fields from mobile phones? From the strict point of view of biophysics, an effect of RF fields emitted by current cellular phones on gene and/or protein expression is unlikely to occur as long as no mechanism can be identified for the interaction between these fields and living tissues, with the exception of the known conversion of electromagnetic energy into thermal energy. Yet, whatever the exposure intensity, the possibility exists that RF fields influence these biochemical processes to a larger extent than would be predicted from the temperature change alone. Two pilot studies indicate higher expression of heat shock genes after heating cells by MW exposure compared with conventional heating methods: one study in rats exposed to 1.7 W/cm² (##REF##9516193##Walters et al. 1998##) and the other in human glioma MO54 cells at an SAR of &gt; 20 W/kg (##REF##12020433##Tian et al. 2002##). These observations are in accordance with studies on MW-assisted chemistry, where the kinetics of chemical reactions is faster after exposure to high-power MWs compared with those observed when the same magnitude of heating is obtained by conventional methods (##UREF##2##Kappe 2004##; ##UREF##6##Stuerga 2006##). MW-assisted chemistry is now widely used for a variety of applications, notably in organic chemistry [reviewed by ##REF##17406708##Collins and Leadbeater (2007)##, ##UREF##2##Kappe (2004)##, and ##UREF##3##Lidström et al. (2001)##]; it has also been used for chemistry of nucleic acid in ionic buffers (##UREF##4##Orrling et al. 2004##). No mechanism has been identified to date for an MW-specific effect, if any, in solutions. Yet, the specificity of MW heating is its in-core volumetric nature, and most authors agree about a possible role for energy absorption at the precise place of the reacting molecules. When considering, for example, the local SAR value <italic>in vivo</italic> at the interface between free DNA and the surrounding solution, a very preferential MW energy absorption exists at that place because of the particularly high concentrations of bound water molecules and counterions at this interface (##REF##15197763##Vanderstraeten and Vander Vorst 2004##). As a possible explanation for the phenomenon of MW-assisted chemistry, and based on a quantum mechanical model of an S_N2 reaction, ##UREF##1##Kalhori et al. (2002)## suggested that bound water molecules could confer vibrational modes in the low-GHz frequency range to solvated reaction complexes. In addition, a local superheating effect (temperature hot spots) has been proposed as a mechanism (##UREF##6##Stuerga 2006##). However, using a standard heat-conduction model, ##REF##12732485##Laurence et al. (2003)##, estimated that space-averaged SAR of the order of hundreds of watts per kilogram would lead to a local temperature gradient of only femto degrees (°C) in DNA-sized structures where significant SAR hot spots yet exist, relative to the surrounding medium. Therefore, no precise mechanism has been identified to date. If a phenomenon of MW-assisted chemistry was established in <italic>in vitro</italic> or <italic>in vivo</italic> systems using a nonthermal regime, no precise response pattern, if any, could be predicted after MW exposure. This response would depend not only on the functional status of the exposed cell and, presumably, of the heat-sensitive nature of the biochemical processes in progress, but also on the value of the time-averaged SAR and, for the same SAR value, on the duty cycle, which reflects the part of total exposure time during which an effective energy supply takes place. Although threshold values of these last parameters remain to be determined, it is uncertain whether this hypothesis would apply to the particular exposure to RF fields from current cellular phones, because SAR values often do not exceed a few tenths of watts per kilogram for the GSM phones, for example (##UREF##8##Wiart et al. 2000##).</p>" ]

[ "<title>Conclusions</title>", "<p>Because the overall results from the currently available literature are inconclusive and, in particular, because most of the reported positive findings are flawed by methodologic imperfections or shortcomings, uncertainty still prevails about the possible influence on gene and protein expression from RF exposure at intensities relevant to usual human health. Yet, from theoretical as well as experimental arguments, it is uncertain whether any specific gene or protein response pattern could ever be established after exposure to RF fields from mobile phones. In any case, further studies using HTSTs in this field should meet criteria as much as possible, allowing for unequivocal interpretation of the results (##REF##16507466##Corvi et al. 2006##; ##REF##16802876##Mayo et al. 2006##). Also, because of possible different responses according to cell type, as suggested by ##REF##16878293##Remondini et al. (2006)## and ##REF##16878295##Nylund and Leszczynski (2006)##, studies should further compare different biosystems and in particular, steady-state systems with those where a constitutive and specific gene overexpression exists. Finally, following the example of ##REF##17902192##Chauhan et al. (2007)## and ##REF##16802863##Qutob et al. (2006)##, different exposure intensities should be further compared to assess a possible SAR threshold value, if any, above which a gene and/or protein response might be observed after exposure to RF fields in the frequency range relevant to human exposure. The biological relevancy of any response must then be confirmed by related phenotypic changes.</p>" ]

[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Since 1999, several articles have been published on genome-wide and/or proteome-wide response after exposure to radiofrequency (RF) fields whose signal and intensities were similar to or typical of those of currently used mobile telephones. These studies were performed using powerful high-throughput screening techniques (HTSTs) of transcriptomics and/or proteomics, which allow for the simultaneous screening of the expression of thousands of genes or proteins.</p>", "<title>Objectives</title>", "<p>We reviewed these HTST-based studies and compared the results with currently accepted concepts about the effects of RF fields on gene expression. In this article we also discuss these last in light of the recent concept of microwave-assisted chemistry.</p>", "<title>Discussion</title>", "<p>To date, the results of HTST-based studies of transcriptomics and/or proteomics after exposure to RF fields relevant to human exposure are still inconclusive, as most of the positive reports are flawed by methodologic imperfections or shortcomings. In addition, when positive findings were reported, no precise response pattern could be identified in a reproducible way. In particular, results from HTST studies tend to exclude the role of a cell stressor for exposure to RF fields at nonthermal intensities. However, on the basis of lessons from microwave-assisted chemistry, we can assume that RF fields might affect heat-sensitive gene or protein expression to an extent larger than would be predicted from temperature change only. But in all likelihood, this would concern intensities higher than those relevant to usual human exposure.</p>", "<title>Conclusions</title>", "<p>The precise role of transcriptomics and proteomics in the screening of bioeffects from exposure to RF fields from mobile phones is still uncertain in view of the lack of positively identified phenotypic change and the lack of theoretical, as well as experimental, arguments for specific gene and/or protein response patterns after this kind of exposure.</p>" ]

[ "<p>The widespread use of cellular telephones has aroused public concern with respect to potential health risks associated with the radio-frequency (RF) fields emitted by these devices and their base station antennas. Although it is still too soon to assess with certainty or to definitively rule out possible long-term effects, experimental as well as epidemiologic studies to date have not provided any solid indication in favor of the presence or absence of a mobile communication-related health problem (##REF##17636416##Cardis et al. 2007##; ##REF##17431492##Valberg et al. 2007##). The INTERPHONE study, a multinational case–control study, has raised concern about possible increased prevalence of acoustic neuroma among regular users of mobile phones for &gt; 10 years (##REF##17636416##Cardis et al. 2007##). However, questions have been raised about possible errors in the estimation of this risk due to recall and participation biases (##REF##17636416##Cardis et al. 2007##). Several <italic>in vivo</italic> studies addressed possible tumorigenicity in animals (mostly rats and mice) exposed for extended periods (up to lifetime exposures of 2 years) and at a variety of modulations and frequencies (435–9,400 MHz). Results were inconclusive. Only three tests (20%) in the 15 studies addressing continuous waves (CWs) and four tests (6%) in 63 studies addressing modulated fields were positive. Overall, consistency and reproducibility were lacking (##REF##17431492##Valberg et al. 2007##). To date, a number of <italic>in vitro</italic> investigations of RF-induced genetic effects in human and other cell types have been conducted, most of which have indicated no evidence of <italic>in vitro</italic> RF-induced genetic damage at nonthermal exposure regimes or marked synergistic or additive effect with another environmental agent (mutagen/carcinogen) (##REF##14628315##Meltz 2003##; ##UREF##7##Verschaeve 2005##; ##REF##15624303##Vijayalaxmi and Obe 2004##). In addition, some investigators have suggested that RF fields may act as a cancer promoter; however, the evidence for a cocarcinogenic effect or an effect on tumor promotion or progression is at most suggestive, not substantive (##REF##11835682##Bartsch et al. 2002##; ##REF##11577012##Mason et al. 2001##).</p>", "<p>The majority of the studies were devoted to genetic end points that correspond to genotoxic or mutational changes unlikely to occur at exposure intensities relevant to human exposure (##REF##17431492##Valberg et al. 2007##). The investigation of the possibility of more subtle or functional effects on the transcription of genes, for example, requires the use of more sensitive methods, including gene and protein expression studies.</p>", "<title>Gene expression studies</title>", "<p>If RF radiation at intensities relevant to human exposure produces any biological effect, this result must notably imply changes in cell behavior and changes in gene and protein expression in particular. Genes known to be stress-responsive (heat shock and immediate early genes) have been investigated most frequently since the first publications by ##REF##9635489##Daniells et al. (1998)## and ##REF##10839528##de Pomerai et al. (2000)## regarding heat shock gene expression in <italic>Caenorhabditis elegans</italic> after exposure to microwaves (MWs; RF fields &gt; 300 MHz) at intensities too low to elicit any measurable temperature change. ##REF##15680925##Cotgreave (2005)## recently reviewed the studies addressing the expression of specific genes after RF exposures relevant to human mobile phone use. The author considered the results to be inconclusive and found that most positive findings were flawed by inconsistencies and lack in reproducibility. Since then, a number of studies have failed to show consistent effect on expression of either heat shock or immediate early genes from RF exposure [brief review in ##REF##17902192##Chauhan et al. (2007)## and ##REF##16802863##Qutob et al. (2006)##].</p>", "<p>For almost a decade, high-throughput screening techniques (HTSTs) have been developed, allowing genome- and proteome-wide investigations of gene expression (functional genomics or transcriptomics) and protein expression (proteomics). These HTSTs are currently widely applied, notably in the field of environmental health sciences, with the objective of identifying possible markers of toxic exposure by discerning reproducible response patterns (##REF##15929880##Freeman 2005##). Since the first work by ##REF##10736198##Harvey and French (1999)##, several researchers have used HTSTs for investigation of gene and/or protein expression after RF exposure from mobile phones.</p>", "<title>Genome- and proteome-wide studies</title>", "<p>Most transcriptomics applied to RF exposure use a microarray technique based on mRNA extraction and subsequent hybridization to cDNA or oligonucleotide probes representing numerous well-characterized genes. Proteomics has been investigated by HTST with the use of two-dimensional gel electrophoresis. These HTSTs allow for the simultaneous screening of the expression of thousands of genes or proteins, but because of their high sensitivity, they are prone to false-positive results (##REF##16888768##Leszczynski and Meltz 2006##). It is important to take into account the statistical issues that are crucial for the interpretation of data (##REF##16802876##Mayo et al. 2006##) and the confirmation experiments with quantitative tests that are required to evaluate positive findings. This is especially true, for example, when determining the significance of small changes (&lt; 1.5- to 2.0-fold) in probed mRNA and protein levels. In addition, because of the variability of results between experiments related to experimental setup and methodology, another objective is the identification of biomarkers that are notably independent of technology platform (##REF##16507466##Corvi et al. 2006##).</p>", "<p>##TAB##0##Table 1## provides a summary of the HTST studies of gene and/or protein expression after RF exposure that were published between 1999 and November 2007 in English. The signals used were either CWs or pulse-modulated waves (PMWs) of commercially used signals. Fifteen of the 17 reviewed studies used exposure signals and intensities similar to or typical of human exposure to mobile phone radiation; these studies used the signals of the code-division or frequency-division multiple access (CDMA or FDMA, respectively) system, of the PMWs of the Canadian mobile phone system, or of the time-division multiple access of the global system for mobile communications (GSM). The specific absorption rate (SAR) was between 0.1 and 10 W/kg, and the temperature conditions were generally well controlled. Two other investigations addressed gene expression after exposure to different signals and intensities: ##REF##10736198##Harvey and French (1999)## reported changes in 3 of 558 screened genes under intermittent exposure to CWs at an SAR of 7.3 W/kg but in only twice-repeated experiments and without quantitative confirmation experiments such as reverse transcriptase polymerase chain reaction (RT-PCR). In contrast, ##REF##14703943##Port et al. (2003)## observed no change under exposure to a 400-MHz pulsed E-field of 50 kV/m, but they used only a 6-min exposure and they did not mention pulse duration or SAR value.</p>", "<p>From the 15 studies relevant to human exposure, 13 were related to gene expression. Of these, 4 studies that reported positive results were based only on single experiments. From these 4 studies, two reported positive findings that were not validated by confirmation experiments: ##REF##12201670##Pacini et al. (2002)## reported changes in the expression of 14 genes but with a rather imprecise exposure methodology, and ##REF##16107253##Lee et al. (2005)## reported changes in hundreds of genes after either 2-hr or 6-hr exposure (2.45 GHz PMW, 10 W/kg) but with no sham-controlled sample for the 6-hr exposure. Interestingly, and unlike all of the other researchers, ##REF##16107253##Lee et al. (2005)## used the serial analysis of gene expression (SAGE) technique for the screening of gene expression, and they fixed the cutoff value of fold change at a rather high value (4.0). With the use of oligonucleotide chip-based microarray, the two remaining studies reported positive findings that were further confirmed by RT-PCR: two responding genes among 8,400 screened (##REF##16877012##Gurisik et al. 2006##) and 34 responding genes but with a cutoff value of only 1.15-fold change (##REF##17449163##Zhao et al. 2007##).</p>", "<p>Using membrane-based cDNA micro-array, the Bio-NIR Research Group of STUK (Radiation and Nuclear Safety Authority) in Finland published a total of three studies of gene expression in human endothelial (EA.hy926) cells with positive findings not yet confirmed by further quantitative experiment: <italic>a</italic>) down-regulation of various genes from the Fas/tumor necrosis factor-α (Fas/TNF-α) aptoptotic pathway but without precision of the method followed for the data analysis and interpretation (##REF##14760712##Leszczynski et al. 2004##); <italic>b</italic>) 1 (EA.hy926 cells) and 13 (EA.hy926v1 cells) responding genes with a cutoff value of 2.0-fold change (##REF##16878295##Nylund and Leszczynski 2006##); and <italic>c</italic>) 32 differentially expressed genes after 900-MHz RF exposure (##REF##16878293##Remondini et al. 2006##). ##REF##16878293##Remondini et al. (2006)## reported the results of a multicentric study that was part of the REFLEX project (##UREF##5##Quality of Life and Management of Living Resources 2004##) of the European Union’s Fifth Framework Programme, and they also reported the results of five other research teams who studied different types of human cells after exposure to GSM 900 or 1,800 radiation with use of membrane-based cDNA microarray and with application of strict methods for false discovery rate (FDR) control. Although no change could be observed in three types of cells, the three other types (among which were EA.hy926 cells) showed consistent gene responses. However, the replicate experiments were technical ones (pooled RNAs), and no quantitative experiments were reported to validate the positive results (1.2-fold change considered). In addition, the interpretation of the positive results was rendered difficult by the variability in the gene response, notably in HL-60 cells under intermittent versus continuous exposure to 1,800-MHz fields or in EA.hy926 cells under exposure to 900-MHz versus 1,800-MHz fields. Two other studies also reported positive findings, which were not confirmed by quantitative experiments: 12 responding genes among 8,800 screened by oligonucleotide chip-based microarray (##REF##16511873##Belyaev et al. 2006##) and 68 responding genes among 30,000 screened by glass cDNA microarray, for a SAR value of 10 W/kg (##UREF##0##Huang et al. 2006##).</p>", "<p>The four remaining studies reported negative findings with use of a oligonucleotide chip-based microarray. Three of these studies used stringent methods for the preprocessing and analysis of data and for FDR control (they used positive and negative controls) (##REF##17902192##Chauhan et al. 2007##; ##REF##16802863##Qutob et al. 2006##; ##REF##16802862##Whitehead et al. 2006##). In constrast, after having conducted an RT-PCR test, ##REF##16888767##Zeng et al. (2006)## could not confirm the positive results they found in DNA microarray screening.</p>", "<p>Five articles addressed proteomic outcomes. The Bio-NIR Research Group of STUK reported positive findings in EA.hy926 cells in four different articles (##REF##12076339##Leszczynski et al. 2002##, ##REF##14760712##2004##; ##REF##15188403##Nylund and Leszczynski 2004##, ##REF##16878295##2006##). In the first study, ##REF##12076339##Leszczynski et al. (2002)## addressed protein phosphorylation status with observation of hundreds of differentially phosphorylated proteins, but no repeat experiments were conducted; however, confirmation by two independent tests was obtained with respect to the increased phosphorylation level of heat shock protein (HSP) 27. The three other studies (##REF##14760712##Leszczynski et al. 2004##; ##REF##15188403##Nylund and Leszczynski 2004##, ##REF##16878295##2006##) addressed protein expression, all reporting positive findings in as many as 10 repeated independent experiments. An isoform of vimentin, a cytoskeleton protein, and other proteins were validated by further confirmation experiments. The remaining study on proteomics from ##REF##16888767##Zeng et al. (2006)## reported findings whose inconsistencies led the authors to believe that the observed changes might have occurred by chance.</p>", "<p>In summary, the currently available results from studies of transcriptomics show a variability that must be due at least in part to the variability of the platform used and the methodology for data analysis and interpretation. This variability justifies the recommendations of ##REF##16507466##Corvi et al. (2006)## for standardization of methods. It cannot be ruled out that the variability of the results also reflects other specifics, such as the biosystem used or the characteristics of exposure, or the time point to assess bioeffect. However, most of the reported positive findings are flawed by methodologic imperfections or shortcomings and consequently need to be reproduced and validated by further confirmation experiments. Moreover, no specific pattern could be observed in a reproducible way in gene responses, even with the use of the same biosystems and/or experimental setup. In particular, results from HTST studies tend to exclude a role of cell stressor for exposure to RF fields at nonthermal intensities; with exposure to a cellular phone, an SAR of 1.6 W/kg causes a temperature elevation in head tissues of ≤ 0.2–0.3°C (##REF##10533916##Van Leeuwen et al. 1999##). Furthermore, no mechanism has been identified for the activation of the heat shock gene expression in mammals by RF exposure at nonthermal intensity, which depends on heat-activated conformational change of the heat shock factor (HSF) protein (##REF##9869631##Morimoto 1998##). ##REF##16038587##Laszlo et al. (2005)##, for example, did not observe HSF activation in mammalian cells after exposure to FDMA- or CDMA-modulated RF fields, even at 5 W/kg.</p>", "<p>From the few studies using HTSTs for proteomics, consistent protein responses were reported by ##REF##12076339##Leszczynski et al. (2002##, ##REF##14760712##2004)## and ##REF##15188403##Nylund and Leszczynski (2004##, ##REF##16878295##2006)##. However, these observations should still be independently reproduced.</p>" ]

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[ "<table-wrap id=\"t1-ehp-116-1131\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Studies addressing gene and protein expression with use of HTSTs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">Exposure\n<hr/></th><th colspan=\"4\" align=\"center\" rowspan=\"1\">Test\n<hr/></th><th colspan=\"3\" align=\"center\" rowspan=\"1\">Response\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Reference</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Cell or tissue<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1131\">a</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Signal<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1131\">b</xref> (MHz)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">SAR (W/kg)<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1131\">c</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Duration</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">End point (<italic>n</italic>)<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1131\">d</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Pl<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1131\">e</xref></th><th align=\"right\" rowspan=\"1\" colspan=\"1\">No.<xref ref-type=\"table-fn\" rid=\"tfn7-ehp-116-1131\">f</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Conf<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1131\">g</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Fold<xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1131\">h</xref></th><th align=\"right\" rowspan=\"1\" colspan=\"1\">No.<xref ref-type=\"table-fn\" rid=\"tfn10-ehp-116-1131\">i</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Type<xref ref-type=\"table-fn\" rid=\"tfn11-ehp-116-1131\">j</xref></th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##10736198##Harvey and French 1999##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">HMC-1 mast cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">CW 864</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3 × 20′/day\n<break/>7 days</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (558)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.25</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">c-kit, NDPK, DAD-1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##12076339##Leszczynski et al. 2002##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">EA hy926 endothelial cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PP (1,200)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes*</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">391</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Unspecified (^¬/HSP27*)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##12201670##Pacini et al. 2002##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Skin fibroblasts</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 902</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (91)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.5</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Stress, cc regulators</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##14703943##Port et al. 2003##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">HL-60 leukemia cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PMW 400</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50 kV/m</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6 min</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (1,176)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 2.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##14760712##Leszczynski et al. 2004##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">EA hy926 cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PE (1,300)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes*</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Various (cytoskeleton*)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (3,600)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">?</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Fas/TNF-α, apo</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##15188403##Nylund and Leszczynski 2004##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">EA.hy926 cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PE (1,300)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes*</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Various (cytoskeleton*)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##16107253##Lee et al. 2005##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">HL60 cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PMW 2,450</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2 hr\n<break/>6 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">SAGE</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 4.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">221\n<break/>759</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">met, apo, cc, tpt, RNAp-t, RNAp met, glia function</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##16511873##Belyaev et al. 2006##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Rat cerebellum (<italic>in vivo</italic>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 915</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (8,800)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Oc</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.3</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##16877012##Gurisik et al. 2006##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">SK-N-SH neuroblastoma cell</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (8,400)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Oc</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.3</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##UREF##0##Huang et al. 2006##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Jurkat T lymphocytes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">CDMA 1,763</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr/day\n<break/>7 days</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (30,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Gl</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">?</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">68</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">apo, met</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##16802863##Qutob et al. 2006##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">U87MG glioblastoma cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PMW 1,900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.1, 1, 10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (18,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Oc</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 2.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##16802862##Whitehead et al. 2006##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">C3H 10T (1/2) mouse cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">FDMA 836</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (9,200)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Oc</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.3</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">CDMA 848</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (9,200)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Oc</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.3</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##16888767##Zeng et al. 2006##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MCF-7 breast cancer cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 1,800</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0, 3.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24 hr\n<break/>5′ on/10′ off</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (14,500)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Oc</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 1,800</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 to 24 hr (int/cont)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PE (1,100)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 2.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##16878293##Remondini et al. 2006##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NB69 neuroblastoma cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 1,800</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24 hr\n<break/>5′on/5′off</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (75,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">T lymphocytes (quiescent)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 1,800</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44hr\n<break/>10′on/20′off</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (75,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">EA.hy926 cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8–2.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (75,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">met, stress, sign, diff</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 1,800</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8–2.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (75,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">HL-60 cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 1,800</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24 hr\n<break/>5′on/5′off</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (75,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">met, stress</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 1,800</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (75,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">U937 lymphoma monocytes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (75,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">met, signal, diff</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">CHME5 microglial cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (75,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##16878295##Nylund and Leszczynski 2006##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">EA hy926 cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (1,167)\n<break/>PE</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3\n<break/>10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 2.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1\n<break/>38</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Unspecified</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">EA hy926v1 cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (1,167)\n<break/>PE</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mb</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3\n<break/>10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 2.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">13\n<break/>45</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Various\n<break/>Unspecified</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##17449163##Zhao et al. 2007##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Rat neurons</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GSM 1,800</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24 hr\n<break/>5′on /10′off</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (&gt; 1,200)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Oc</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.15</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Various</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##17902192##Chauhan et al. 2007##</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">U87MG glioblastoma cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PMW 1,900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.1, 1.0, 10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24 hr</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (18,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Oc</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.35</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">MM6 monocytoid cells</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PMW 1,900</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0, 10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6 hr\n<break/>5′on/10′off</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">GE (18,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Oc</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">× 1.35</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>We thank D. Peeters (Quantum Chemistry Department of the Catholic University of Louvain) for his useful advice and information about microwave-assisted chemistry.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1131\"><p>Abbreviations: ‘, minutes; apo, apoptosis-related genes; cc, cell-cycle; DAD-1, defender against cell death 1; diff, differentiation; GE, gene expression; Gl, glass cDNA microarray; int/cont, intermittent/continuous; Mb, membrane-based cDNA microarray; met, metabolism; NDPK, nucleoside diphosphate kinase; Oc, oligonucleotide chip-based microarray; PE, protein expression; Pl, platform; PP, protein phosphorylation; RNAp, RNA processing; RNAt, RNA translation; sign, signaling; stress, stress response; tpt, transport.</p></fn><fn id=\"tfn2-ehp-116-1131\"><label>a</label><p>All cells are of human origin except where indicated.</p></fn><fn id=\"tfn3-ehp-116-1131\"><label>b</label><p>Mobile phone signal and carrier frequency.</p></fn><fn id=\"tfn4-ehp-116-1131\"><label>c</label><p>Space- and time-averaged value of the SAR.</p></fn><fn id=\"tfn5-ehp-116-1131\"><label>d</label><p>Number of screened genes or proteins.</p></fn><fn id=\"tfn6-ehp-116-1131\"><label>e</label><p>Platforms used for gene expression study; all reported studies on protein expression used two-dimensional gel electrophoresis.</p></fn><fn id=\"tfn7-ehp-116-1131\"><label>f</label><p>Number of independent experiments (all are sham-controlled with the exception of the 6-hr exposure reported by ##REF##16107253##Lee et al. (2005##).</p></fn><fn id=\"tfn8-ehp-116-1131\"><label>g</label><p>Confirmation experiment.</p></fn><fn id=\"tfn9-ehp-116-1131\"><label>h</label><p>Fold change cutoff value.</p></fn><fn id=\"tfn10-ehp-116-1131\"><label>i</label><p>Number of statistically responding genes.</p></fn><fn id=\"tfn11-ehp-116-1131\"><label>j</label><p>Predominant functional classification of affected genes as reported by the authors</p></fn><fn id=\"tfn12-ehp-116-1131\"><p>asterisks indicate confirmed proteins.</p></fn></table-wrap-foot>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>Animals</title>", "<p>We purchased 8-week-old male and female NC/Nga mice from Charles River Japan (Osaka, Japan). They were fed a commercial diet (CE-2; Japan Clea Co., Tokyo, Japan) and water <italic>ad libitum</italic> and housed in an animal facility maintained at 22–26°C with 40–69% humidity and a 12/12-hr light/dark cycle. All mice were treated humanely with regard for alleviation of suffering in accordance with guidelines of the National Institute for Environmental Studies for animal experiments. All protocols involving mice were approved by the institutional review board.</p>", "<title>Study protocols</title>", "<title>Experiment 1: maternal exposure to DEHP during fetal periods</title>", "<p>We divided male and female mice (8 weeks of age) into seven experimental groups and kept them under specific-pathogen-free (SPF) conditions in an animal facility for 1 week. We mated four male and five female mice in each cage under SPF conditions for 1 week (days 0–7) and then separated male and female mice. We administered DEHP by intraperitoneal injection at a dose of 0.8, 4, 20, or 100 μg dissolved in 0.1 mL olive oil (Wako Pure Chemical Industries, Ltd., Osaka, Japan) to the dams in four groups on days 0, 7, and 14 (##FIG##0##Figure 1##). These doses were equivalent to daily DEHP intake of 4.8, 24, 120, and 600 μg/kg body weight per day. Two groups of animals received only olive oil and served as vehicle controls, and one group received no treatment (untreated). We checked breeding cages daily for births.</p>", "<title>Experiment 2: maternal exposure to DEHP during neonatal periods</title>", "<p>We divided male and female mice (8 weeks of age) into seven experimental groups and kept them under SPF conditions in an animal facility for 1 week. We mated four male and five female mice in each cage under SPF conditions for 1 week and then separated male and female mice. We checked breeding cages daily for births. We then administered DEHP at a dose of 0.8, 4, 20, or 100 μg by intraperitoneal injection to dams in four groups on days 1, 8, and 15 after birth (##FIG##0##Figure 1##). Two groups of animals received only olive oil and served as vehicle controls. The untreated group received no treatment.</p>", "<title>Mite allergen treatment in pups</title>", "<p>In both experiments, at least one litter was included in each experimental group. We kept litters with dams until weaning at 4 weeks of age. While under anesthesia with 4% halothane (Takeda Chemical Industries, Ltd., Osaka, Japan), 8-week-old males (22–25 g) were injected intradermally with saline (one group) or 5 μg (10 μL) of mite allergen extract [<italic>Dermatophagoides pteronyssinus</italic> (Dp); Cosmo Bio LSL, Tokyo, Japan] dissolved in saline (five groups) on the ventral side of their right ears on treatment days 0, 2, 4, 7, 9, 11, 14, and 16 (##FIG##0##Figure 1##). Twenty-four hours after each intradermal injection, we measured ear thickness with a gauge (Ozaki Mfg. Co. Ltd., Osaka, Japan) and evaluated clinical scores for skin dryness, eruption, and wound, graded from 0 to 3 (0, no symptoms; 1, mild; 2, moderate; 3, severe). The untreated group received no Dp treatment.</p>", "<title>Histologic evaluation</title>", "<p>Animals were sacrificed by etherization 24 hr after the last intradermal injection (day 17). Right ears of males were removed and fixed in 10% phosphate-buffered formalin (pH 7.2), embedded in paraffin, cut into 3-μm sections, and stained with hematoxylin/eosin (H&amp;E) and toluidine blue (pH 4.0). We performed histologic analyses using an Olympus AX80 microscope (Olympus Corp., Tokyo, Japan) and measured the length of the cartilage in each specimen using an Olympus VM-30 video micrometer. The infiltration of eosinophils and mast cells were morphometrically evaluated as the number of cells per millimeter of cartilage. We also evaluated the degranulation of mast cells as not degranulated (0%), mildly degranulated (0–50%), and severely degranulated (&gt; 50%) (##REF##16882537##Takano et al. 2006##).</p>", "<title>ELISA</title>", "<p>Right ears of males were removed 24 hr after the last intradermal injection; ears were then homogenized and centrifuged as previously described by ##REF##9230723##Takano et al. (1997)##. We conducted enzyme-linked immunosorbent assays (ELISAs) for eotaxin (R&amp;D Systems, Minneapolis, MN, USA) in the ear tissue supernatants according to the manufacturer’s instructions. The detection limit for eotaxin was &lt; 3 pg/mL.</p>", "<title>Statistical analysis</title>", "<p>We analyzed differences between the groups using Dunnett’s or Steel multiple comparison tests using Excel Statistics 2006 statistical software (Social Survey Research Information Co., Ltd., Tokyo, Japan). We considered <italic>p</italic>-values &lt; 0.05 to be significant, and data are reported as mean ± SE.</p>" ]

[ "<title>Results</title>", "<title>Maternal DEHP exposure during fetal periods and atopic dermatitis-like skin lesions in offspring</title>", "<p>To evaluate the effects of maternal exposure to DEHP during fetal periods on atopic dermatitis-like skin lesions related to mite allergen in males, we examined clinical scores, including dryness, eruption, wound, edema, and ear thickening in males in the presence or absence of mite allergen. Treatment with mite allergen significantly enhanced ear thickening (<italic>p</italic> &lt; 0.01; ##FIG##1##Figure 2##) and clinical scores (<italic>p</italic> &lt; 0.01; see Supplemental Material, ##FIG##0##Figure 1##, available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11191/suppl.pdf\">http://www.ehponline.org/members/2008/11191/suppl.pdf</ext-link>) compared with untreated or saline groups from day 5. However, maternal exposure to DEHP during fetal periods did not show significant enhancing effects in the presence of allergen compared with exposure to vehicle alone. We found no change in the no treatment and saline treatment groups.</p>", "<title>Maternal DEHP exposure during neonatal periods and atopic dermatitis-like skin lesions in offspring</title>", "<p>To evaluate the effects of maternal exposure to DEHP during neonatal periods on atopic dermatitis-like skin lesions related to mite allergen in males, we examined ear thickening and clinical scores. Treatment with mite allergen significantly enhanced ear thickening (<italic>p</italic> &lt; 0.01; ##FIG##2##Figure 3A##) and clinical scores (<italic>p</italic> &lt; 0.01; see Supplemental Material, ##FIG##1##Figure 2##, available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11191/suppl.pdf\">http://www.ehponline.org/members/2008/11191/suppl.pdf</ext-link>) compared with untreated or saline groups from day 5. From day 12, maternal exposure to 100 μg DEHP significantly increased ear thickening (##FIG##2##Figure 3A##) and clinical scores (see Supplemental Material, ##FIG##1##Figure 2##, available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11191/suppl.pdf\">http://www.ehponline.org/members/2008/11191/suppl.pdf</ext-link>) in males compared with vehicle exposure in the presence of mite allergen. In macroscopic examination, we observed edema, dryness, excoriation, and crust at the dorsal site of allergen-injected ears in males (##FIG##2##Figure 3C##). Furthermore, these findings were prominent and were often accompanied by severe hemorrhage and erosion in the presence of maternal exposure to DEHP during neonatal periods (##FIG##2##Figure 3D##). We found no change in untreated (data not shown) or saline groups (##FIG##2##Figure 3B##).</p>", "<title>Maternal DEHP exposure during neonatal periods and histologic changes in the skin related to mite allergen in offspring</title>", "<p>To determine the effects of maternal exposure to DEHP during neonatal periods on histologic changes of the skin related to mite allergen in males, we evaluated the skin specimens stained with H&amp;E (##FIG##3##Figure 4A–D##) or toluidine blue (##FIG##3##Figure 4E–H##) 24 hr after the last intradermal inoculation. We found no pathologic alterations with no treatment or saline treatment (##FIG##3##Figure 4B,4F##). Treatment with mite allergen (##FIG##3##Figure 4A,C##) enhanced the infiltration of eosinophils into the skin lesions compared with saline treatment or no treatment (<italic>p</italic> &lt; 0.01). Further, maternal exposure to 100 μg DEHP (##FIG##3##Figure 4D##) during neonatal periods caused more prominent eosinophilic inflammation than did exposure to vehicle in the presence of allergen [##FIG##3##Figure 4A##; <italic>p</italic> &lt; 0.01 for Dp (+ DEHP 100 μg) group vs. Dp (+ vehicle) group]. In overall trend, these changes were consistent with the severity of mast cell degranulation [##FIG##3##Figure 4E–H##, <italic>p</italic> &lt; 0.01 for Dp (+ DEHP 100 μg) group vs. untreated group, saline (+ vehicle) group, and Dp (+ vehicle) group].</p>", "<title>Maternal DEHP exposure during neonatal periods and protein expression of eotaxin in the skin related to mite allergen in offspring</title>", "<p>We evaluated the protein expression of eotaxin in the ear 24 hr after the final intradermal inoculation to elucidate the mechanism of the enhancing effects of maternal exposure to DEHP during neonatal periods on atopic dermatitis-like skin lesions related to mite allergen in males. Treatment with mite allergen increased the expression of eotaxin compared with untreated or saline groups (##FIG##4##Figure 5##; <italic>p</italic> &lt; 0.05). Furthermore, in males treated with mite allergen, maternal exposure to 100 μg DEHP during neonatal periods markedly enhanced the protein expression of eotaxin compared with vehicle exposure (<italic>p</italic> &lt; 0.05). In addition, DEHP at a dose of 4 or 20 μg tended to enhance the local expression of eotaxin.</p>" ]

[ "<title>Discussion</title>", "<p>The present study shows that maternal exposure of mice to DEHP during neonatal periods aggravates atopic dermatitis-like skin lesions related to mite allergen in male offspring. The enhancing effects are nearly paralleled by those on eosinophilic inflammation, mast cell degranulation, and local expression of eotaxin. In contrast, we observed no significant enhancing effects after maternal exposure to DEHP during fetal periods.</p>", "<p>DEHP, the most commonly used plasticizer in flexible PVC formulations, is a ubiquitous environmental toxicant. Thus, the general population can be exposed to DEHP in food, water, and air via ingestion or inhalation. In particular, fetuses and infants can be maternally exposed to DEHP at early life stages (##UREF##0##Calafat et al. 2004##; ##REF##12566679##Latini et al. 2003a##, ##REF##14594632##2003b##; ##REF##16451866##Main et al. 2006##). Previous studies have suggested that maternal exposure to phthalates during fetal and/or neonatal periods can adversely affect reproduction and development by acting as EDCs in animals and humans (##REF##16690193##Borch et al. 2006##; ##REF##15513902##Kim et al. 2004##; ##REF##16759976##Marsee et al. 2006##). However, whether maternal exposure to DEHP and/or its metabolites during fetal and/or neonatal periods can affect allergic diseases, including atopic dermatitis in offspring, has not been elucidated.</p>", "<p>The developing immune system, which depends chiefly on mother-derived humoral immunity during embryonic/fetal periods and acquired cellular and humoral immunity during neonatal periods, can be vulnerable to environmental toxicants such as EDCs (##REF##10502532##Holladay 1999##; ##REF##10852846##Holladay and Smialowicz 2000##). Epidemiologic studies have indicated that exposure to EDCs, such as dioxins and polychlorinated biphenyls, during human development can affect the immune system (##REF##15471725##Dallaire et al. 2004##; ##REF##9828306##Nagayama et al. 1998##; ##REF##7494667##Weisglas-Kuperus et al. 1995##). In experimental studies, prenatal/perinatal exposure to 2,3,7,8-tetrachlorodibenzo-<italic>p</italic>-dioxin and bisphenol A can modulate immune responses in rodents (##REF##9328223##Gehrs et al. 1997##; ##REF##16574621##Vorderstrasse et al. 2006##; ##REF##15196218##Yoshino et al. 2004##). On the other hand, few studies have exposed experimental animals to environmental chemicals only during postnatal periods. In the present study, maternal exposure to DEHP only during neonatal periods enhanced atopic dermatitis-like skin lesions related to mite allergen in male offspring. In contrast, maternal exposure to DEHP during fetal periods did not affect the aggravation of atopic dermatitis-like skin lesions related to mite allergen. Together, these data indicate that maternal exposure to DEHP may contribute to the modulation of adaptive immunity in offspring rather than mother-derived immunity in our model. This hypothesis is supported by our previous study in which DEHP exposure in young subjects enhanced atopic dermatitis-like skin lesions related to mite allergen (##REF##16882537##Takano et al. 2006##). Our present and previous results thus implicate exposure to environmental chemicals during neonatal periods in the enhancement of allergen-related inflammation in offspring.</p>", "<p>DEHP reportedly induces reproductive and developmental problems predominantly in male rodents (##REF##16690193##Borch et al. 2006##; ##REF##15513902##Kim et al. 2004##). Also, a recent epidemiologic study has suggested that estimated daily phthalate exposure is associated with reduced anogenital distance in male infants (##REF##16759976##Marsee et al. 2006##). Our present experiments also showed that maternal exposure to DEHP during fetal and neonatal periods did not affect atopic dermatitis-like symptoms in females (data not shown). The present results may demonstrate the existence of sex differences in the sensitivity to DEHP during neonatal periods in the case of atopic dermatitis, similar to differences in aromatase activity described by ##REF##16949715##Andrade et al. (2006)##.</p>", "<p>In the present study, maternal exposure to DEHP during neonatal periods aggravates atopic dermatitis-like skin lesions in male offspring at a dose about 30-fold lower (100 μg/ animal/week ≈ 600 μg/kg/day) than the no observed adverse effect level (19 mg/kg/day), which was determined on the basis of histologic changes in the rodent liver (##REF##13079323##Carpenter et al. 1953##). Phthalate metabolites have been found in human breast milk (##UREF##0##Calafat et al. 2004##; ##REF##16451866##Main et al. 2006##) and in infant formula (##REF##15933851##Mortensen et al. 2005##; ##REF##16521163##Sorensen 2006##). Several studies have estimated the average daily intake of DEHP in infants 0–3 months of age at 13 μg/kg/day for infant formula and 21 μg/kg/day for breast milk (##REF##15336709##Latini et al. 2004##). On the other hand, there are differences in disposition kinetics of DEHP and its biological metabolite MEHP between intraperitoneal and oral administration (##REF##4002226##Pollack et al. 1985##). In addition, oral exposure of rat dams to DEHP at high doses (2 g/kg for 5 days) during lactation can lead to plasma levels of DEHP (216 ± 23 μg/mL, mean ± SE) and MEHP (25 ± 6 μg/mL) in suckling pups (##REF##2892284##Dostal et al. 1987##). To our knowledge, however, no previous studies have determined the differences in the plasma levels of suckling pups and/or breast milk levels of DEHP and its metabolites after maternal exposure to DEHP via different routes during neonatal periods. Thus, in the next stage of research, we should try to quantify the concentration of DEHP and/or its metabolites in the breast milk and/or plasma levels of suckling pups after intraperitoneal or oral administration and investigate the effects of oral administration at the level of human exposure of DEHP.</p>", "<p>In the present study we found that maternal exposure to DEHP at a dose of 100 μg during neonatal periods increased the expression of eotaxin in the ear tissue in the presence of allergen. The results were concomitant with the recruitment of eosinophils into skin lesions and with mast cell degranulation. Eosinophils play a prominent proinflammatory role in a broad range of allergic diseases, including atopic dermatitis. Eotaxin is important in the early recruitment of eosinophils after allergen challenge. Eotaxin is reportedly increased in the blood of patients with atopic dermatitis (##REF##11576081##Hossny et al. 2001##; ##REF##15813816##Jahnz-Rozyk et al. 2005##) and expressed in lesions of human atopic dermatitis (##REF##10417617##Yawalkar et al. 1999##). CCR3, a principal receptor for eotaxin, has been found to be essential for eosinophil recruitment into murine skin lesions caused by repeated allergen sensitization (##REF##11877470##Ma et al. 2002##). In addition, tryptase/chymase double-positive mast cells express CCR3 and are attracted by eotaxin (##REF##10432289##Ochi et al. 1999##; ##REF##10514402##Romagnani et al. 1999##). Also, mast cell–deficient mice have shown reduced lung eosinophilia after eotaxin administration compared with wild-type mice after allergen sensitization (##REF##16614169##Das et al. 2006##). Interestingly, the present study shows that DEHP at a dose of 4 or 20 μg tended to enhance the local expression of eotaxin. These findings indicate that maternal exposure to DEHP at doses &lt; 100 μg during neonatal periods can prompt the manifestation of atopic dermatitis in offspring at the molecular level. In contrast, mite allergen treatment induced the expression of T helper 2 (T_H2)-type cytokines, including IL-4, IL-5, IL-13, and RANTES, compared with no treatment or saline treatment, whereas maternal exposure to DEHP during neonatal periods did not significantly enhance the effects (data not shown). Thus, eotaxin might be a critical molecule in our model.</p>", "<p>In our present model using NC/Nga mice, mite allergen treatment significantly elevated the production of total IgE and allergen-specific IgG1 in serum, increased the number of submandibular lymph node cells, and enhanced cell proliferation of submandibular lymph node cells in the presence of allergen stimulation <italic>ex vivo</italic> (data not shown). These results suggest that allergen-specific responses play critical roles in the present model of atopic dermatitis-like skin lesions. In addition, maternal exposure to DEHP during neonatal periods enhanced effects related to mite allergen in offspring, including eotaxin expression, eosinophilic inflammation, and mast cell degranulation, all of which are typically shown in allergen-related T_H2-dominant responses and/or inflammation (##REF##14707467##Heishi et al. 2003##; ##REF##11752882##Sasakawa et al. 2001##; ##REF##11823539##Yagi et al. 2002##; ##REF##17336291##Yamashita et al. 2007##). Taken together, maternal exposure to DEHP during neonatal periods can accelerate allergen-related inflammation possibly via T_H2-dominant responses in our model.</p>" ]

[ "<title>Conclusion</title>", "<p>In the present study using a murine model, we have shown that maternal exposure to DEHP during neonatal periods aggravates atopic dermatitis-like skin lesions related to mite allergen in male offspring. The enhancing effects may be mediated through the enhanced expression of eotaxin. Our results support the novel hypothesis that maternal exposure to DEHP during neonatal periods via breast milk and/or infant formula may be responsible, at least in part, for the recent increase in atopic dermatitis in offspring. To further clarify this hypothesis, we should examine breast milk levels and/or suckling pup plasma levels of DEHP and/or its metabolites and the time course of cytokine expression in our model, as well as in a model using oral administration of DEHP. Furthermore, epidemiologic studies should determine the relationships between exposure to DEHP via breast milk and/or infant formula and the manifestation of atopic dermatitis in offspring.</p>" ]

[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Di-(2-ethylhexyl) phthalate (DEHP) has been widely used in polyvinyl chloride products and is ubiquitous in developed countries. Although maternal exposure to DEHP during fetal and/or neonatal periods reportedly affects reproductive and developmental systems, its effects on allergic diseases in offspring remain to be determined.</p>", "<title>Objectives</title>", "<p>In the present study, we examined whether maternal exposure to DEHP during fetal and/or neonatal periods in NC/Nga mice affects atopic dermatitis-like skin lesions related to mite allergen in offspring.</p>", "<title>Methods</title>", "<p>We administered DEHP at a dose of 0, 0.8, 4, 20, or 100 μg/animal/week by intraperitoneal injection into dams during pregnancy (gestation days 0, 7, and 14) and/or lactation (postnatal days 1, 8, and 15). Eight-week-old male offspring of these treated females were injected intradermally with mite allergen into their right ears. We then evaluated clinical scores, ear thickening, histologic findings, and protein expression of eotaxin in the ear.</p>", "<title>Results</title>", "<p>Maternal exposure to a 100-μg dose of DEHP during neonatal periods, but not during fetal periods, enhanced atopic dermatitis-like skin lesions related to mite allergen in males. The results were concomitant with the enhancement of eosinophilic inflammation, mast cell degranulation, and protein expression of eotaxin in overall trend.</p>", "<title>Conclusion</title>", "<p>Maternal exposure to DEHP during neonatal periods can accelerate atopic dermatitis-like skin lesions related to mite allergen in male offspring, possibly via T helper 2 (T_H2)-dominant responses, which can be responsible, at least in part, for the recent increase in atopic dermatitis.</p>" ]

[ "<p>Epidemiologic studies have shown that the prevalence of allergic diseases has increased at a great rate, mainly among children and juveniles, over the past several decades (##REF##12877235##Beasley et al. 2003##). The etiology of allergy includes genetic factors (e.g., sensitivity of hosts) and environmental factors (e.g., allergen load, environmental pollutants) (##REF##2369661##Burney et al. 1990##; ##REF##9893177##Peat and Li 1999##). In particular, environmental chemicals may increase the potency of allergens and thereby play a role in the development and/or enhancement of allergic diseases (##REF##10619333##Casillas et al. 1999##). In fact, we and other groups have previously reported that diesel exhaust particles, which are major environmental pollutants in urban areas and contain a variety of organic chemicals such as polyaromatic hydrocarbons, possess adjuvant activity (##REF##9208060##Diaz-Sanchez 1997##; ##REF##7523450##Diaz-Sanchez et al. 1994##; ##REF##11223170##Heo et al. 2001##) and aggravate allergic airway inflammation in murine models (##REF##14745849##Ichinose et al. 2004##; ##REF##17079263##Inoue et al. 2007##; ##REF##9551727##Miyabara et al. 1998##; ##REF##12119504##Sadakane et al. 2002##; ##REF##9230723##Takano et al. 1997##, ##REF##9653676##1998##; ##REF##16499651##Yanagisawa et al. 2006##).</p>", "<p>Di-(2-ethylhexyl) phthalate (DEHP), another environmental chemical, has become ubiquitous in developed countries. DEHP is the most abundant phthalate plasticizer in polyvinyl chloride (PVC) formulations, including vinyl flooring, wall covering, food containers, gloves, and infant toys. However, DEHP is not chemically bound to PVC and thus leaches out from the PVC items with time and use. An epidemiologic study has shown that DEHP in house dust is associated with allergic asthma in children (##REF##15471731##Bornehag et al. 2004##). In animal experiments, DEHP has displayed an adjuvant effect on allergen-related immunoglobulin production (##REF##11701218##Thor Larsen et al. 2001##) and enhanced allergic responses, including the production of interleukin-4 (IL-4) from CD4⁺ T cells (##REF##15178890##Lee et al. 2004##). More recently, we have shown that exposure to DEHP aggravates atopic dermatitis-like skin lesions related to mite allergen in young male mice, as evidenced by macroscopic and microscopic examinations. Furthermore, these changes were consistent with the protein expression of chemokines in the ear tissue in overall trend (##REF##16882537##Takano et al. 2006##).</p>", "<p>In contrast, fetuses and infants, who are believed to be one of the most sensitive populations to environmental chemicals (##REF##8080506##Colborn et al. 1993##; ##REF##10502532##Holladay 1999##; ##REF##10852846##Holladay and Smialowicz 2000##), can be maternally exposed to DEHP. In fact, DEHP and/or mono-(2-ethylhexyl) phthalate (MEHP), a metabolite of DEHP, have been detectable in human cord blood and maternal plasma (##REF##12566679##Latini et al. 2003a##, ##REF##14594632##2003b##). In addition, MEHP has been found in human breast milk (##UREF##0##Calafat et al. 2004##; ##REF##16451866##Main et al. 2006##) and in infant formula (##REF##15933851##Mortensen et al. 2005##; ##REF##16521163##Sorensen 2006##). Previous animal studies have suggested that maternal exposure to phthalates during fetal and/or neonatal periods may cause reproductive and developmental toxicities in offspring due to their actions as endocrine-disrupting chemicals (EDCs) (##REF##16690193##Borch et al. 2006##; ##REF##15513902##Kim et al. 2004##).</p>", "<p>In the present study, we examined whether maternal exposure to DEHP in NC/NgaTndCrj (NC/Nga) mice during fetal and/or neonatal periods affects atopic dermatitis-like skin lesions related to mite allergen in offspring.</p>" ]

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[ "<fig id=\"f1-ehp-116-1136\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Experimental protocol for maternal exposure to DEHP during fetal and/or neonatal periods and for allergen sensitization in offspring. Dp, <italic>Dermatophagoides pteronyssinus</italic> extract.</p></caption></fig>", "<fig id=\"f2-ehp-116-1136\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Effect of maternal exposure to DEHP during fetal periods on atopic dermatitis-like skin lesions in offspring shown by ear thickening 24 hr after each intradermal injection of Dp. Data shown are mean ± SE of 7–12 animals per group. </p><p>**<italic>p</italic> &lt; 0.01, Dp-treated groups compared with untreated group and saline (+ vehicle) groups.</p></caption></fig>", "<fig id=\"f3-ehp-116-1136\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Effect of maternal exposure to DEHP during neonatal periods on atopic dermatitis-like skin lesions in offspring shown by ear thickening 24 hr after each intradermal injection of Dp (<italic>A</italic>) and macroscopic features 24 hr after the last injection (<italic>B–D</italic>). (<italic>B</italic>) Saline (+ vehicle). (<italic>C</italic>) Dp (+ vehicle). (<italic>D</italic>) Dp (+ DEHP 100 μg). Data shown are mean ± SE of 12 animals per group. </p><p>**<italic>p</italic> &lt; 0.01, Dp-treated groups compared with untreated and saline (+ vehicle) groups. ^#<italic>p</italic> &lt; 0.01, Dp (+ DEHP 100 μg) group compared with Dp (+ vehicle) group. For B–D, bar = 5 mm.</p></caption></fig>", "<fig id=\"f4-ehp-116-1136\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Histologic changes in the ear 24 hr after the last intradermal injection of Dp. The infiltration of eosinophils (<italic>A</italic>) and mast cells (<italic>E</italic>) were morphometrically evaluated as the number of cells per millimeter of cartilage. We also evaluated the degranulation of mast cells as not degranulated (0%), mildly degranulated (0–50%), and severely degranulated (&gt; 50%). (<italic>B–D</italic>) and (<italic>F–H</italic>) show histologic findings of the saline (+ vehicle) (<italic>B</italic>, <italic>F</italic>), Dp (+ vehicle) (<italic>C</italic>, <italic>G</italic>), and Dp (+ DEHP 100 μg) (<italic>D</italic>, <italic>H</italic>) groups; tissues were stained with H&amp;E (<italic>B–D</italic>) or toluidine blue (<italic>F–H</italic>). In <italic>A</italic> and <italic>E,</italic> data shown are mean ± SE of four animals per group. </p><p>*<italic>p</italic> &lt; 0.05, Dp-treated groups compared with untreated and saline (+ vehicle) groups. **<italic>p</italic> &lt; 0.01, Dp-treated groups compared with untreated and saline (+ vehicle) groups. ^#<italic>p</italic> &lt; 0.01, Dp (+ DEHP 100 μg) group compared with Dp (+ vehicle) group. For <italic>B–D</italic>, bar = 100 μg; for inset in <italic>D</italic>, bar = 10 μm; for <italic>F–H</italic>, bar = 50 μm.</p></caption></fig>", "<fig id=\"f5-ehp-116-1136\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Effects of DEHP on local expression of eotaxin measured by ELISA 24 hr after the last intradermal injection of Dp. Data shown are mean ± SE of eight animals per group. </p><p>*<italic>p</italic> &lt; 0.05, Dp-treated groups compared with untreated and saline (+ vehicle) groups. ^#<italic>p</italic> &lt; 0.05, Dp (+ DEHP 100 μg) group compared with Dp (+vehicle) group.</p></caption></fig>" ]

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[ "<fn-group><fn><p>Supplemental Material is available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11191/suppl.pdf\">http://www.ehponline.org/members/2008/11191/suppl.pdf</ext-link></p></fn><fn><p>We thank M. Sakurai and N. Ueki for their technical assistance.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"ehp-116-1136f1\"/>", "<graphic xlink:href=\"ehp-116-1136f2\"/>", "<graphic xlink:href=\"ehp-116-1136f3\"/>", "<graphic xlink:href=\"ehp-116-1136f4\"/>", "<graphic xlink:href=\"ehp-116-1136f5\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>Production of stable transfectant</title>", "<p>pSEAP2-Control (Great EscAPe SEAP Reporter System 3) and pTK-Hygromycin (both from Clontech, Mountain View, CA, USA) were cotransfected into LS-8 cells using Lipofectamine-LTX reagent and Lipofectamine Plus Reagent (both from Invitrogen, Carlsbad, CA, USA). After 24 hr, cells were washed and grown in alpha-MEM medium (Invitrogen) containing 750 μg/mL Hygromycin B (Invitrogen). Positive clones, isolated using cloning rings, were assayed for SEAP activity using the Great EscAPe SEAP Chemiluminescence Kit 2.0 (Clontech). Clone 10 showed the highest activity and was used for all experiments. For negative control, LS-8 cells were stably transfected with pTK-Hygromycin and pSEAP2-Basic (Great EscAPe SEAP Reporter System 3; Clontech), that lacks the SV40 early promoter and enhancer sequences present in pSEAP2-Control. Cells were maintained in alpha-MEM containing 10% fetal bovine serum and 750 μg/mL Hygromycin B during all experiments.</p>", "<title>SEAP activity assay</title>", "<p>We measured SEAP activity using the Great EscAPe SEAP Chemiluminescence Kit 2.0 according to the manufacturer’s instructions. Briefly, 25 μL cell supernatant was mixed with 75 μL of 1×dilution buffer in 96-well plates and incubated at 65°C for 30 min. Plates were chilled for 3 min, and 100 μL SEAP substrate was added. After incubating at room temperature for 30 min, samples were measured for chemiluminescence on a Victor 1420 multilabel counter (PerkinElmer, Waltham, MA, USA); data are expressed in relative light units (RLU). All experiments were performed in triplicate and were repeated three times.</p>", "<title>Immunoblotting</title>", "<p>Cells were grown in 100-mm plates and treated with varying doses of sodium F⁻ for either 6 or 24 hr. Supernatant was collected for assessing the quantity and activity of SEAP released into the medium. To detect UPR proteins and intra-cellular SEAP, cells were washed twice with phosphate-buffered saline (PBS, pH 7.4) and lysed with Complete Lysis-M reagent (Roche Diagnostics, Mannheim, Germany) containing protease and phosphatase inhibitors (Roche Diagnostics). We determined the protein concentration using the BCA assay kit (Pierce, Rockford, IL, USA). A total of 10–30 μg protein was loaded per lane onto 4–20% polyacrylamide gels (Bio-Rad Laboratories, Hercules, CA, USA). Proteins were transferred onto nitrocellulose membranes (Schleicher and Schuell, Whatman, Germany), blocked with 5% nonfat dried milk, and probed with primary antibodies in blocking solution overnight at room temperature. Blots were washed with PBS containing 0.1% Tween-20 (PBST) and incubated with secondary antibodies conjugated to horseradish peroxidase for 1 hr at room temperature. After washing with PBST, bands were developed with ECL Advance Western Blotting kit (GE Healthcare, Piscataway, NJ, USA). We used the following primary antibodies: goat anti-PLAP (placental alkaline phosphatase; 1:1000) and goat anti-BiP (immunoglobulin heavy chain binding protein; 1:1000) from Santa Cruz Biotechnology Inc. (Santa Cruz, CA, USA); rabbit anti-eIF2α (1:1000, [pS52]; BioSource, Camarillo, CA, USA), and mouse anti-actin (1:500; Sigma, St. Louis, MO, USA). Secondary antibodies were purchased from Southern Biotech (Birmingham, AL, USA).</p>", "<title>Immunocytochemistry and immunohisto-chemistry</title>", "<p>LS8-SEAP cells were grown on four-chamber tissue culture-treated glass slides (BD Biosciences, Bedford, MA, USA) and treated with 0.125 mM (2.4 ppm) sodium fluoride (NaF) for 24 hr. Cells were fixed with 2% paraformaldehyde for 30 min and permeabilized with 0.2% Triton X-100 for 30 min. After washing with PBS (pH 7.4), cells were blocked with 10% goat serum in PBS for 1 hr and treated with primary antibody (Rb monoclonal anti-phospho-PERK, 1:100; Cell Signaling Technology, Danvers, MA, USA) in blocking solution overnight. After washing with PBS, secondary antibody (Alexa 488-conjugated goat anti-rabbit IgG, 1:2000; Invitrogen) in blocking solution was added for 1 hr. Antifade containing 2,4-diamino-diphenyl indole (DAPI) was then added, and slides were cover-slipped and sealed before imaging under a Leica DM RX 2 microscope (Leica Microsystems, Bannockburn, IL, USA) running Axiovision version 5.0 (Carl Zeiss Microimaging, Thornwood, NY, USA)</p>", "<p>Immunohistochemistry was performed as described previously by ##REF##15849362##Kubota et al. (2005)##. Briefly, 6-week-old C57BL/6J mice were given water containing different concentrations of F⁻ (0, 25, 50, and 100 ppm) <italic>ad libitum</italic> for 3–4 weeks. Mice were sacrificed, and incisors were formalin-fixed, paraffin-embedded, and sectioned. Sections were blocked with 10% goat serum in PBS and incubated overnight with rabbit anti-phospho-eIF2α (1:200, [pS⁵²]; BioSource) followed by incubation in peroxidase-conjugated antibody (Vectastain Elite Reagent, Vector Labs, Burlingame CA, USA) and in Sigma Fast 3,3’-diaminobenzidine substrate (Sigma). Sections were examined by light microscopy and photographed. All animals were treated humanely and with regard for alleviation of suffering according to institutional animal care and use committee guidelines.</p>", "<title>Cell proliferation assay</title>", "<p>LS8-SEAP cells were plated at a density of 2,500 cells in 96-well plates. After 18 hr, medium was changed and cells were incubated for either 6 or 24 hr in 100 μL medium containing varying doses of F⁻. For measuring cell proliferation, 10 μL WST-1 (Roche Diagnostics) was added, and the resulting absorbance was measured after 3 hr at 440 nm on an EL800 Universal Microplate Reader (Biotek Instruments, Inc, Winooski, VT, USA). All experiments were performed in triplicate and repeated three times.</p>", "<title>Statistics</title>", "<p>We performed one-way analysis of variance with Bonferroni’s posttest using GraphPad Prism, version 5.00 for Windows (GraphPad Software, San Diego CA, USA).</p>" ]

[ "<title>Results</title>", "<title>Dose-dependent decrease in SEAP secretion</title>", "<p>The ameloblast-derived LS8 cells were stably transfected with SEAP expression plasmid to generate cells (LS8-SEAP) that constitutively secrete SEAP. LS8-SEAP cells were treated with medium containing 0.0, 0.125, 0.25, 0.5, and 1.0 mM F⁻ (corresponding to 0, 2.4, 4.8, 9.5, and 19 ppm F⁻, respectively). Aliquots of cell supernatant were removed at 6 hr and assayed for SEAP activity. As a positive control, cells were treated with the N-linked glycosylation inhibitor tunicamycin. As shown in ##FIG##0##Figure 1A##, SEAP activity decreased in a linear, dose-dependent manner with increasing concentrations of F⁻. A significant decrease was observed within 6 hr with the lowest F⁻ dose tested (0.125 mM F⁻; <italic>p</italic> &lt; 0.01) and became highly significant (<italic>p</italic> &lt; 0.001) for each of the F⁻ concentrations &gt; 0.125 mM. Cell proliferation, quantified by the WST-1 assay, showed no significant difference at 6 hr (##FIG##0##Figure 1C##). Similarly, the assay for lactate dehydrogenase, a cytoplasmic enzyme that is released into the medium on cell death, showed no significant F⁻-induced cell cytotoxicity at 6 hr (data not shown). Thus, the observed decrease in SEAP activity did not correlate to cell proliferation or cell death. Treatment of cells with sodium chloride did not have any significant effect on SEAP activity, suggesting that the F⁻ ion was responsible for the observed effects (##FIG##0##Figure 1B##). Tunicamycin also decreased SEAP activity, confirming previous reports that SEAP can be used to detect ER stress (##REF##16877567##Hiramatsu et al. 2006b##). Therefore, F⁻ treatment attenuated the secretion of SEAP from LS8-SEAP cells.</p>", "<title>Lack of direct inhibition of SEAP activity</title>", "<p>Flouride, at a high concentration of 50 mM (950 ppm), is commonly used as a serine/threonine phosphatase inhibitor during cell lysis. Therefore, it is possible that F⁻ directly inhibits the alkaline phosphatase activity of SEAP without causing an ER stress-mediated decrease in SEAP secretion. To address this issue, we collected cell-free medium containing SEAP from LS8-SEAP cells grown in the absence of F⁻. Recombinant SEAP thus obtained was then incubated with different doses of F⁻ for 6 and 24 hr. As shown in ##FIG##1##Figure 2##, no significant decrease in SEAP activity was observed (<italic>p</italic> &gt; 0.05) until the F⁻concentration reached 50–100 mM. Thus, the decrease in the SEAP activity observed in our experiments can be attributed to reduced SEAP secretion and not to direct inhibition of phosphatase activity by F⁻.</p>", "<title>Intracellular accumulation of SEAP and activation of the UPR</title>", "<p>Any factor that disturbs ER homeostasis could lead to the intra-cellular accumulation of misfolded or unfolded proteins, causing ER stress. As shown in ##FIG##2##Figure 3A##, treatment of LS8-SEAP cells with F⁻ results in a dose-dependent increase in the intracellular accumulation of SEAP protein. Conversely, secretion of SEAP into the medium decreases (##FIG##2##Figure 3B##). These immunoblot results indicate that F⁻ interferes with the secretion of SEAP and, presumably, other endogenous secretory proteins. We also demonstrate a concurrent induction of the ER stress-induced UPR signaling pathway along with the observed decrease in SEAP secretion (##FIG##3##Figure 4##). The UPR proximal sensor, PERK, is a transmembrane serine/threonine kinase activated by autotransphosphorylation (##REF##10882126##Harding et al. 2000##). Activated PERK phosphorylates eIF2α (reviewed by ##REF##10216940##Kimball 1999##). This leads to transient translation attenuation, allowing the cells to cope with the proteins that have already accumulated in the ER. As shown in ##FIG##3##Figure 4A##, PERK is activated (phosphorylated) in LS8-SEAP cells treated with 0.125 mM (2.4 ppm) F⁻. Immunoblots show translational induction of the molecular chaperone BiP, as well as phosphorylation of the PERK target eIF2α (##FIG##3##Figure 4B##) within 6 hr of F⁻ treatment. BiP remains induced after 24 hr of treatment, especially in cells treated with higher doses of F⁻ (≥19 ppm).</p>", "<p>We next asked if maturation stage ameloblasts from mice drinking F⁻-treated water (0, 25, 50, or 100 ppm in drinking water for approximately 3–4 weeks) initiated phosphorylation of eIF2α<italic>in vivo</italic>. As shown in ##FIG##4##Figure 5##, eIF2αwas phosphorylated at the lowest dose tested (25 ppm). The amount of eIF2αphosphorylation <italic>in vivo</italic> correlated positively with F⁻ dose, suggesting an increase in the magnitude of ER stress with increasing doses of F⁻.</p>" ]

[ "<title>Discussion</title>", "<p>Fluorosed enamel is characterized by hypomineralization, increased protein content, and greater surface and subsurface porosity. The most significant characteristic of fluorosed enamel is its increased protein content (##REF##389448##Holland 1979a##). Several studies have pointed toward F⁻-mediated inhibition of protein secretion and/or synthesis (##REF##5969077##Conconi et al. 1966##; ##REF##1245473##Godchaux and Atwood 1976##; ##REF##1068504##Helgeland 1976##; ##REF##389448##Holland 1979a##, ##REF##40394##1979b##; ##REF##5969765##Lin et al. 1966##; ##REF##5418164##Vesco and Colombo 1970##; ##REF##9022911##Zhou et al. 1996##). For example, F⁻ in the drinking water of rats inhibited protein removal from early maturation-stage incisor enamel (##REF##3463596##DenBesten 1986##; ##REF##1607440##DenBesten and Thariani 1992##; ##REF##9022911##Zhou et al. 1996##). F⁻ has also been shown to inhibit insulin secretion in rats (##REF##16149713##Menoyo et al. 2005##; ##REF##2110856##Rigalli et al. 1990##). However, a direct mechanism for F⁻-induced inhibition of protein secretion remains to be elucidated.</p>", "<p>In this article, we show that NaF decreases secreted SEAP activity in a dose-dependent manner (##FIG##0##Figure 1A##). The effect is mediated only by NaF and not by NaCl (##FIG##0##Figure 1B##), suggesting that F⁻ is responsible for the observed decrease in protein secretion. We also demonstrate that the low concentrations of F⁻ used in our experiments do not directly inhibit recombinant SEAP activity (##FIG##1##Figure 2##). Thus, F⁻ does not interfere with our assay system.</p>", "<p>Accumulation of excess protein within the ER is a hallmark of ER stress. We found that with an increase in F⁻ dose, increasing quantities of SEAP accumulate intracellularly (##FIG##2##Figure 3##). Thus, the observed decrease in SEAP secretion is at least partially due to ER stress-mediated protein retention within the cells. Our results with F⁻ are similar to reports using the well-characterized ER-stress inducer thapsigargin. SEAP also accumulated in the ER after thapsigargin treatment (##REF##16904124##Hiramatsu et al. 2006a##).</p>", "<p>F⁻ induces ER stress and initiates the UPR, as demonstrated by the induction of the molecular chaperone BiP and by phosphorylation of PERK and eIF2α ⁽##FIG##3##Figure 4##). We also demonstrated phosphorylation of eIF2α<italic>in vivo</italic> in ameloblasts of mice treated <italic>ad libitum</italic> with F⁻ at doses of ≥ 25 ppm (##FIG##4##Figure 5##). It must be noted that a higher F⁻ dose is required to cause flourosis in a mouse (25 ppm) compared with a human (~ 2 ppm). This may be because the continuously erupting mouse incisor ameloblast progresses from the secretory stage to the final maturation stage in a matter of weeks, whereas this progression occurs over several years for human teeth. Thus, human ameloblasts have a much longer exposure to F⁻ present in drinking water than do mouse ameloblasts. Second, rodents do appear to more efficiently clear F⁻from their bodies compared with humans (##REF##6580951##Angmar-Mansson and Whitford 1984##). The immunostaining for phosphorylated eIF2αobserved in maturation stage ameloblasts exposed to 25 ppm F⁻ is highly significant, because 25 ppm F⁻ is the threshold concentration where F⁻-susceptible mice will have fluorosis (##REF##12407097##Everett et al. 2002##).</p>", "<p>F⁻-induced ER stress and subsequent inhibition of protein secretion is consistent with prior <italic>in vivo</italic> studies demonstrating F⁻-mediated disruption in the export of proteins from the ER (##REF##5251694##Kruger 1968##; ##REF##8740536##Matsuo et al. 1996##, ##UREF##3##2000##). Furthermore, 100 ppm Fin rat drinking water delays by as much as 30% the modulation cycle of the apical ends of ameloblasts between a ruffle-ended and smooth-ended morphology during the maturation stage of enamel development (##REF##8238976##Smith et al. 1993##). This modulation is thought to assist the ameloblasts in removing H⁺ ions from the enamel matrix, and its inhibition by F⁻ is consistent with a decrease in the translation of proteins required for the modulation to occur. Taken together, these observations support our results suggesting that F⁻ causes ER stress in ameloblasts and induces the UPR, which initiates eIF2α phosphorylation with subsequent attenuation of protein synthesis and secretion.</p>" ]

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[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Exposure to excessive amounts of fluoride (F⁻) causes dental fluorosis in susceptible individuals; however, the mechanism of F⁻-induced toxicity is unclear. Previously, we have shown that high-dose F⁻ activates the unfolded protein response (UPR) in ameloblasts that are responsible for dental enamel formation. The UPR is a signaling pathway responsible for either alleviating endoplasmic reticulum (ER) stress or for inducing apoptosis of the stressed cells.</p>", "<title>Objectives</title>", "<p>In this study we determined if low-dose F⁻ causes ER stress and activates the UPR, and we also determined whether F⁻ interferes with the secretion of proteins from the ER.</p>", "<title>Methods</title>", "<p>We stably transfected the ameloblast-derived LS8 cell line with secreted alkaline phosphatase (SEAP) and determined activity and localization of SEAP and F⁻-mediated induction of UPR proteins. Also, incisors from mice given drinking water containing various concentrations of F⁻ were examined for eucaryotic initiation factor-2, subunit alpha (eIF2α) phosphorylation.</p>", "<title>Results</title>", "<p>We found that F⁻ decreases the extracellular secretion of SEAP in a linear, dose-dependent manner. We also found a corresponding increase in the intracellular accumulation of SEAP after exposure to F⁻. These changes are associated with the induction of UPR proteins such as the molecular chaperone BiP and phosphorylation of the UPR sensor PKR-like ER kinase, and its substrate, eIF2α. Importantly, F⁻-induced phosphorylation of eIF2αwas confirmed <italic>in vivo</italic>.</p>", "<title>Conclusions</title>", "<p>These data suggest that F⁻ initiates an ER stress response in ameloblasts that interferes with protein synthesis and secretion. Consequently, ameloblast function during enamel development may be impaired, and this may culminate in dental fluorosis.</p>" ]

[ "<p>Fluoride is anticariogenic and is recommended by the U.S. Public Health Service for addition to drinking water at a concentration of 0.7–1.2 ppm, such that an average of 1 mg F⁻ is consumed per day [##UREF##0##Centers for Disease Control and Prevention (CDC) 1995##]. However, higher levels of F⁻ exposure can result in dental fluorosis, which is manifested as mottled, discolored, porous enamel that is susceptible to decay (##UREF##1##DenBesten 1999##). High F⁻ doses can cause skeletal fluorosis that may result in bone fracture (##REF##2765315##Boivin et al. 1989##). High F⁻ doses may also cause renal toxicity (##REF##9033286##Zager and Iwata 1997##), epithelial lung cell toxicity (##REF##11294978##Thrane et al. 2001##), and reproductive defects (##REF##12220599##Ghosh et al. 2002##). Among these, attention has been focused on the role of F⁻ in dental fluorosis, because the most apparent effects of excess F⁻ ingestion in an individual are white spots (mild fluorosis) or dark stains (moderate to severe fluorosis) on the teeth.</p>", "<p>Ameloblasts are epithelial cells that are responsible for enamel formation. The three major stages of the ameloblast life cycle, namely, secretory, transition, and maturation, correspond to distinct steps in enamel development. During the secretory stage, the ameloblasts are tall and columnar, and they secrete large amounts of proteins that form a matrix within which thin enamel ribbons of hydroxyapatite crystallize. Once the enamel ribbons attain their full length, ameloblasts enter the transition stage, when they decrease in height and experience a reduction in Golgi complex and rough endoplasmic reticulum (ER). During the maturation stage, the ameloblasts secrete KLK4 (##REF##9465170##Simmer et al. 1998##) to help degrade the enamel proteins, which are then resorbed from the maturing enamel. It is during the maturation stage that the enamel ribbons grow in width and thickness to form mature hardened enamel. Normal enamel is composed of about 96% mineral and 4% organic content (##REF##3178535##Robinson et al. 1988##). Excess F⁻ ingestion during tooth formation causes an increase in the protein content and a decrease in overall mineral content of the enamel (##REF##15153700##Robinson et al. 2004##; ##UREF##4##Robinson and Kirkham 1990##; ##REF##9033447##Wright et al. 1996##). F⁻ ions have been suggested to adversely affect the precipitation of hydroxyapatite that forms the mineralized enamel (##REF##12097358##Aoba and Fejerskov 2002##). However, two observations suggest that F⁻-mediated toxicity also involves genetic responses. First, different inbred strains of mice with similar overall levels of F⁻ in their enamel differ in their susceptibilities to fluorosis (##REF##12407097##Everett et al. 2002##). Second, no correlation was found between the concentration of F⁻ in enamel and the severity of dental fluorosis (##REF##14691118##Vieira et al. 2004##). These results clearly suggest a genetic basis for susceptibility to fluorosis (##REF##17936699##Yan et al. 2007##). We have previously shown that F⁻induces ER stress in ameloblasts, thereby compromising their function during enamel formation (##REF##15849362##Kubota et al. 2005##).</p>", "<p>Proteins to be secreted are translocated into the ER for posttranslational modification, folding, and assembly. The ER is a quality control organelle in which individual proteins must adopt a stable conformation; unfolded or misfolded proteins are prevented from traversing the secretory pathway (##UREF##2##Hammond and Helenius 1995##). Factors that compromise ER homeostasis initiate an ER-to-nucleus signaling pathway, termed the unfolded protein response (UPR). Activation of the UPR serves three major functions: <italic>a</italic>) it results in transcriptional up-regulation of molecular chaperones such as BiP/GRP78 that help augment the folding capacity of the ER; <italic>b</italic>) it transiently attenuates protein translation via phosphorylation of the translation initiation factor, eucaryotic initiation factor-2, subunit alpha (eIF2α), thereby allowing cells to cope with the existing protein load; and <italic>c</italic>) it activates components of the ER-associated degradative pathway (ERAD) to degrade the accumulated mis-folded proteins. If these pathways succeed in alleviating cell stress, the cell survives; if not, the cell undergoes apoptosis via caspase activation. Indeed, ER stress is associated with several diverse diseases, including diabetes, neurodegenerative disorders (##REF##14528054##Gow and Sharma 2003##), arsenite exposure (##REF##11689689##Lu et al. 2001##), and heavy metal–induced toxicity (##REF##17475259##Hiramatsu et al. 2007##).</p>", "<p>Recently, the reporter construct secreted alkaline phosphatase (SEAP) (##REF##3417148##Berger et al. 1988##) was used to detect and quantify ER stress in real time (##REF##16877567##Hiramatsu et al. 2006b##). SEAP traverses the secretory pathway (##REF##16608874##Lara-Lemus et al. 2006##), and its activity can be detected at very low levels (0.2 pg/mL). Because SEAP is a secreted protein, medium supernatant can be assayed for SEAP activity in a real-time fashion. Most importantly, transfection of cells with SEAP does not, by itself, cause ER stress (##REF##16877567##Hiramatsu et al. 2006b##). SEAP secretion is decreased only by ER stress-inducing agents such as tunicamycin that blocks N-linked glycosylation or thapsigargin that functions as an inhibitor of Ca^{2 +} ATPase (##REF##17475259##Hiramatsu et al. 2007##). Cytokines that do not cause ER stress, such as tumor necrosis factor-α,transforming growth factor-β, or interleukin-1β, do not decrease SEAP activity (##REF##16877567##Hiramatsu et al. 2006b##). Thus, SEAP is specifically sensitive to ER stress-inducing agents. SEAP has been used to detect ER stress induced by heavy metals such as nickel, cadmium, and cobalt in cell lines as well as in mice (##REF##17475259##Hiramatsu et al. 2007##).</p>", "<p>In this study, we demonstrate that F⁻ concentrations as low as 2.4 ppm can induce ER stress in LS8 cells, which results in the inhibition of protein secretion; we also identify PKR-like ER kinase (PERK)-mediated phosphorylation of eIF2α as a signaling pathway responsible for F⁻-mediated inhibition of protein synthesis.</p>" ]

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[ "<fig id=\"f1-ehp-116-1142\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Effect of F⁻ on SEAP secretion in LS8-SEAP cells treated with F⁻ for 6 hr. (<italic>A</italic>) Linear dose–dependent decrease in SEAP activity (<italic>p</italic> &lt; 0.001) after NaF treatment. (<italic>B</italic>) Decrease in SEAP activity after treatment with F⁻ or the ER stress-inducing agent tunicamycin (Tm); NaCl has no effect. (<italic>C</italic>) Relationship between the 6 hr F⁻-mediated decrease in SEAP activity and cell proliferation. All experiments were performed in triplicate and performed three times</p><p>*<italic>p</italic> &lt; 0.0001.</p></caption></fig>", "<fig id=\"f2-ehp-116-1142\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Effect of low-dose F⁻ on SEAP activity. Recombinant SEAP collected from untreated LS8-SEAP cells was directly treated with NaF for 6 hr (<italic>A</italic>) or 24 hr (<italic>B</italic>), and SEAP activity was measured. A significant decrease in SEAP activity was observed only with doses &gt; 50 mM NaF, suggesting that F⁻ does not directly inhibit SEAP activity. Data are averages of three separate experiments</p><p>*<italic>p</italic> &lt; 0.001.</p></caption></fig>", "<fig id=\"f3-ehp-116-1142\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Immunoblots showing the effect of F⁻treatment on intracellular SEAP accumulation in LS8-SEAP cells treated with NaF for 24 hr. (<italic>A</italic>) Cell lysates. (<italic>B</italic>) Medium supernatants. Note the increase in intracellular SEAP (<italic>A</italic>) and a corresponding decrease in extracellular SEAP (<italic>B</italic>), suggesting protein retention within the cell. Blots were stripped and probed for actin as a loading control.</p></caption></fig>", "<fig id=\"f4-ehp-116-1142\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Effect of F⁻ on activation or induction of the UPR markers PERK, eIF2α, and BiP. (<italic>A</italic>) PERK phosphorylation in LS8-SEAP cells treated with 0.125 mM (2.4 ppm) NaF for 18 hr. PERK is phosphorylated (green) in NaF-treated cells but not in control cells; nuclei were stained with DAPI (blue). Bar = 10 μm. (<italic>B</italic>) Immunoblots probed for BiP and for the phosphorylated form of eIF2α(eIF2α–P) in LS8-SEAP cells treated with NaF for 6 or 24 hr. Blots were stripped and probed for actin as a loading control.</p></caption></fig>", "<fig id=\"f5-ehp-116-1142\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Effect of F⁻ on phosphorylation of eIF2αin maturation stage ameloblasts in mice given drinking water containing either 0, 25, 50, or 100 ppm F⁻\n<italic>ad libitum.</italic> Immunohistochemistry was performed on incisor sections with antiserum specific for phosphorylated eIF2α. Note that significant staining occurred in ameloblasts (denoted by brackets) in the 25-ppm treatment group and that this staining intensified and spread to the papillary layer (beneath brackets) in the 100-ppm treatment group. Significant staining was not observed in identically treated secretory stage ameloblasts (not shown). Bar = 25 μm.</p></caption></fig>" ]

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[ "<fn-group><fn><p>We thank M. Snead, University of Southern California School of Dentistry, for providing us with LS8 cells.</p></fn><fn><p>This work was supported in part by grant DE016276 from the National Institute of Dental and Craniofacial Research to J.D.B.</p></fn></fn-group>" ]

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[ "<title>Materials and Methods</title>", "<title>Spatial–temporal model for risk of air pollution</title>", "<p>We selected the number of daily nonaccidental deaths as the response variable reflecting the adverse short-term health effects from air pollution. The association between short-term air pollution exposure and mortality is one of the most studied and best-characterized associations in air pollution epidemiology (##REF##15547165##Bell et al. 2004##; ##REF##11571608##Thurston and Ito 2001##). Further, mortality data are available on a national basis, so we can determine the indicator for each year.</p>", "<p>We propose two methods to modeling risk. In method A, we derive the annual estimate of risk from the time series of <italic>L</italic> years, including the current year of interest as well as <italic>L</italic> – 1 previous years. Thus, we obtain a unique estimate of risk for each calendar year. To identify changes in risk for more recent years, we weight the mortality counts so that we assign greater weight to more recent years. For this purpose, we use the tricubed function, which gives a much greater weight to nearby years, to generate appropriate weights that vary by calendar year. The tricubed function is a popular weight function used for locally weighted smoothers (##UREF##7##Hastie and Tibshirani 1990##). For example, if <italic>L</italic> = 10 for 1991–2000, data for 1991 will receive the smallest weight, 0.00316621, whereas data for 2000 will receive the largest weight, 0.15908596. We also update the terms for temporal trends in mortality, weather, and day of the week (dow) indicators in our model every <italic>L</italic>-year period. The greater the value of <italic>L</italic>, the smoother the temporal estimate of risk. We denote this estimation approach as our “multi-<italic>L</italic>-year estimate.”</p>", "<p>In method B, we based estimates of risk on data for each year separately and determine a weighted average of these annual estimates based on the previous <italic>L</italic> years. Again, we employ the tricubed function to generate weights, but this time on the regression coefficients. We denote this estimator as our “smoothed <italic>L</italic>-year estimate.”</p>", "<p>Both approaches employ a two-stage approach in which we estimate risk over time, <italic>t</italic>^, separately for each community, β<italic>_i</italic>(<italic>t</italic>) (stage 1), and then estimate a common risk among all cities, μ_β(<italic>t</italic>) (stage 2). We assumed the community-specific estimates to be normally distributed with a common mean and with the variance modeled by the sum of two variances: within-community variance, ν<italic>_i</italic>(<italic>t</italic>), and between-community variance indicating the heterogeneity of risk among cities, σ_β²(<italic>t</italic>). The estimator of the common risk among all cities, μ̂_β(<italic>t</italic>), is the weighted average of the city-specific risk estimates, β̂<italic>_i</italic>(<italic>t</italic>), where the weight is given by [σ̂_β²(<italic>t</italic>) + ν̂<italic>_i</italic>(<italic>t</italic>)]⁻¹. Our estimator of risk for community <italic>i</italic>^, β<italic>_i</italic>(<italic>t</italic>), is given by</p>", "<p>Here, β̃<italic>_i</italic>(<italic>t</italic>) will be always closer to μ̂β(<italic>t</italic>) than will β̂<italic>_i</italic>(<italic>t</italic>), so we termed β̃<italic>_i</italic>(<italic>t</italic>) “shrinkage” estimators. If σ̂_β²(<italic>t</italic>) = 0, then β̂<italic>_i</italic>(<italic>t</italic>) = μ̂_β(<italic>t</italic>), implying that the “best” estimate of risk for any community is the common risk estimate when there is evidence that the risks do not vary among communities. The larger the estimate of heterogeneity in risk [μ̂_β²(<italic>t</italic>)] compared with the within-community error estimate [ν̂<italic>_i</italic>(<italic>t</italic>)], the closer the shrinkage estimate of risk [β̂<italic>_i</italic>(<italic>t</italic>)] to the estimate based solely on a single community’s information [β̂<italic>_i</italic>(<italic>t</italic>)]. Although the shrinkage estimators are biased, they have smaller variances than do the community-specific estimators, thus providing more stable estimates of risk over time. We are thus borrowing strength from all the communities to estimate risk for each specific location. This is particularly useful when examining smaller communities that inherently have large uncertainties with respect to their risk estimates.</p>", "<p>The basic concept of modeling air pollution relative to time-series mortality risk in this article is similar to that used in a previous study (##UREF##1##Burnett et al. 2004a##). However, we have developed a new estimation approach that is supported by simulation studies, and the specific context of the estimator is also somewhat different from that of our earlier work. In this article, we focus on a measure that can be “naturally” updated over time such that when an additional year of mortality and air pollution information becomes available, it can be added to the historical data set, and a new risk estimate can be obtained for that year, without altering the risk estimates from previous years. Our previous estimation approach and that of another recent effort on this topic (##REF##17728271##Dominici et al. 2007##) did not specifically develop estimates to be used in this manner.</p>", "<title>Simulation study</title>", "<p>We evaluated the statistical properties of our two proposed estimators of spatial–temporal risk through a simulation study. To make the simulations as realistic as possible, we used data from an analysis of air pollution and mortality in 12 Canadian cities over the 20-year period 1981–2000. In particular, we incorporated the city-specific estimates of mortality risk, measurements of air pollutant, and the confounders into our simulation and generated daily counts of mortality by adding Poisson-distributed errors city by city. In this simulation, we focus on NO₂, a pollutant formed in the atmosphere mainly from transportation sources, with data obtained from the National Air Pollution Surveillance Network (##UREF##10##National Air Pollution Surveillance 2001##). We obtained data temperature from Environment Canada’s weather archive (##UREF##4##Environment Canada 2002##) and mortality data from the national mortality database (Vital Statistics—Death Database; ##UREF##12##Statistics Canada 2004##). We coded the mortality data by census boundaries and included only deaths from internal causes [<italic>International Classification of Diseases, 9th Revision</italic> (ICD-9; ##UREF##14##World Health Organization 1975##) codes &lt; 800 and ICD-10 (<italic>10th Revision</italic>; ##UREF##15##World Health Organization 1993##) codes A00–R00].</p>", "<p>For these simulations, we used daily death counts, daily 24-hr mean temperature, and a 2-day running average of the daily 24-hr mean concentrations of NO₂ from 1 January 1981 through 31 December 2000.</p>", "<p>A generalized additive Poisson regression model (##REF##12142253##Dominici et al. 2002##; ##UREF##7##Hastie and Tibshirani 1990##) applied to the daily death counts is</p>", "<p>where <italic>t</italic>, temp₁(<italic>t</italic>), temp₂(<italic>t</italic>), and dow(<italic>t</italic>) denote calendar time, temperature recorded on the day of and 1 day before death, and the days of the week, respectively; <italic>f</italic>₁<italic>_i</italic> , <italic>f</italic>₂<italic>_i</italic> , and <italic>f</italic>₃<italic>_i</italic> are nonlinear smoothing functions; and <italic>g</italic>₁<italic>_i</italic> is an indicator function (##REF##12142253##Dominici et al. 2002##). The three smoothing functions, <italic>f</italic>₁, <italic>f</italic>₂, and <italic>f</italic>₃, describe the potential nonlinear association between time (<italic>t</italic>) or weather variables (temp₁, temp₂) and mortality, respectively. Here, <italic>x</italic><italic>_i</italic>(<italic>t</italic>) represents the average of concentrations of NO₂ on the day of and day before death. We used natural cubic spline functions to estimate <italic>f</italic>₁, <italic>f</italic>₂, and <italic>f</italic>₃, which we specified by the number of knots or degrees of freedom (df). We explored the df of the natural splines, such as df = 4, 6, 8, 10, 12 for time; df = 3, 4, 5 for temperature; and lag = 0, 1, average of (0, 1)-day lagged, and average of (0, 1, 2)-day lagged air pollution. Here we used 6 df per year for time, 3 df for temperature for the entire time period, and the average of 0- and 1-day lagged air pollutions, because we found the least variation from these parameters. [For a further discussion on natural splines and choice of df, see ##REF##12500041##Ramsay et al. (2003)##.] We completed the model specification by assuming that the variance of the mortality counts is equal to the expected value.</p>", "<title>Three scenarios for the simulations</title>", "<p>The 12 Canadian cities revealed several patterns in the secular trend in associations for NO₂, and we considered three different scenarios, as described below. As stated above, we consider only temporal functions of risk that vary across years: constant risk over time, linearly increasing risk over time, and stepwise change in risk over time. These three scenarios are represented by solid lines in ##FIG##0##Figure 1A–C##; the broken lines are results from simulated data, discussed further below. Under each scenario, we compared results from the two estimators mentioned above with preassigned values for the three scenarios.</p>", "<title>Simulation process</title>", "<p>We summarize our simulation process as follows: <italic>a</italic>) generating daily mortality data; <italic>b</italic>) estimating the city-specific annual risks from the simulated data by method A (multiyear estimator) and method B (smoothed-annual estimator); <italic>c</italic>) estimating heterogeneity among the different cities for each year; and <italic>d</italic>) estimating the pooled risk for each year.</p>", "<p>More details on each step are described in the <xref ref-type=\"app\" rid=\"app1-ehp-116-1147\">Appendix</xref>.</p>" ]

[ "<title>Results</title>", "<title>Simulation results</title>", "<p>As described above, we used two methods to estimate pooled risks: multi-<italic>L</italic>-year estimator (method A) and smoothed <italic>L</italic>-annual estimator (method B) for L &gt; 1. As a baseline for comparison of the two proposed methods, we also considered a single annual estimator. We estimated the mortality risks for NO₂ from the simulated data by the proposed methods for <italic>L</italic> = 3, 5, 7, and 9 years and compared these with each other.</p>", "<p>First, we compared the risk estimates from the simulated data for the two methods for all scenarios. The solid lines in ##FIG##0##Figure 1## indicate the preassigned risk over 20 years, and all the other dotted lines indicate the averages of 1,000 estimated risks for each year. Three panels on the left show results from multiyear estimator, and those on the right from smoothed-annual estimator. We used the plots to visualize the performance of the two methods (dashed and dotted lines) in capturing the given time trends. Both methods captured the preassigned trends well overall for scenario 1, except for 1987. However, the multiyear estimator that used 3 years of data, labeled <italic>L</italic> = 3 in ##FIG##0##Figure 1A## (left), showed a considerable underestimate for year 2000. Both methods returned slightly better estimates as <italic>L</italic> became larger. For scenario 2, the two methods captured the linearly increasing trend but underestimated the effects because of the inclusion of previous years, where the risks were lower. Scenario 3 required more data to capture the stepwise increasing trend. After inspecting the findings, our best choice for the number of multiyears should be 5 or 7 years to avoid the apparent over- and underestimation associated with shorter or longer periods.</p>", "<p>Second, we compared the root mean squared errors (RMSE) of the two methods for <italic>L</italic> = 3, 5, 7, and 9 (##FIG##1##Figure 2##). A smaller RMSE indicates a better fit in terms of bias and variance. Overall, the multiyear estimator and smoothed-annual estimator yielded similar results, with slightly better fit with the multiyear method for scenario 3.</p>", "<p>Third, we compared model-based error with simulation error. The model-based error is the square root of the average of 1,000 squared estimated SEs of the pooled risk estimates, whereas the simulation error is the SD of 1,000 pooled risk estimates. ##TAB##0##Table 1## shows the results for scenario 1. As with the RMSE, the multiyear estimator provides slightly more consistent results than does the smoothed-annual estimator.</p>", "<p>Finally, we compared the parameter variance estimates from all methods. Given that the parameter variance (heterogeneity) is known, ##TAB##1##Table 2## shows the ratios of the estimated values to the preassigned values for the parameter variance. Ratios that are closer to unity reflect better estimates with less bias, and multiyear estimator with <italic>L</italic> = 5 years appears to be the optimal method for all scenarios together, even if not the best for each of the three scenarios. Although method B with <italic>L</italic> = 5 shows a good fit for scenario 1, its estimates for the other scenarios revealed considerable underestimation.</p>", "<p>In summary, considering both risk estimates and heterogeneity estimates, we conclude that the multi-<italic>L</italic>-year estimator is better than the smoothed <italic>L</italic>-annual estimator based on the RMSEs of the risk estimates and ratios to true values in heterogeneity estimates. Regarding the optimal value for <italic>L</italic>, the results for RMSE indicate <italic>L</italic> = 7 or 9, whereas those for parameter variance ratio indicate <italic>L</italic> = 5 or 7. It may depend on the time period for which data are available. For the given Canadian urban data for 20 years, choosing <italic>L</italic> = 7 seems to be reasonable based on the simulation results.</p>", "<title>Example</title>", "<p>Daily variations in nonaccidental mortality in Canadian cities have been shown to be related to daily variations in both O₃ and NO₂ (##REF##16201668##Burnett et al. 2004b##). We illustrate our temporal model of risk using these pollutants. We consider the daily 8-hr running maximum as the summary measure of population average exposure for O₃ because it is the metric employed for the Canada-wide ozone standard (##UREF##2##Canadian Council of Ministers of Environment 2000##). We used the daily average concentration for NO₂. We selected communities with a reasonably long time series of both pollutants, resulting in 24 cities having information from 1984 through 2000, the last year of nationally available mortality data. The time series models comprise natural spline terms in the model for time with 9 df/year, two natural spline terms for daily average temperature with 3 df recorded on the day of and the day before death, day of week indicator functions, and the 2-day average of pollution concentrations.</p>", "<p>We initially considered a static- or constant-risk model for each city with β<italic>_i</italic>(<italic>t</italic>) = β<italic>_i</italic>. For O₃, the pooled common risk is μ̂ = 7.42 × 10⁻⁴, which indicates the log relative rate of mortality associated with a unit (ppb) increase in O₃, with an SE of 1.46 × 10^−4. Here, σ̂_β = 2.23 × 10⁻⁴, implying that 95% of cities have risks that lie in the interval (0.59 × 10⁻⁴ to 9.33 × 10⁻⁴), assuming a normal distribution. For NO₂, the pooled common risk is μ̂ = 8.59 ×10⁻⁴, which denotes the log relative rate of mortality associated with a unit (parts per billion) increase in NO₂, with an SE of 1.66 × 10⁻⁴. However, there is no evidence of heterogeneity of risk among cities because the estimate of the heterogeneity is zero (σ̂ = 0). Therefore, our best estimate of risk for each city is the common risk (μ̂). Based on this analysis, assuming a constant risk over time, we conclude that there is sufficient evidence to suggest that a statistical association exists between daily variation in both O₃ and NO₂ and nonaccidental mortality.</p>", "<p>##FIG##2##Figure 3## presents the annual average daily concentrations of O₃ and NO₂. We obtained these values by weighting the ambient concentrations by city-specific daily average mortality counts, thus reflecting population average exposure with respect to the health outcome of interest. We applied the Mann–Kendall test (##UREF##5##Gilbert 1987##), a nonparametric test for monotonic trend, to these annual averages. Concentrations of O₃ appear to be increasing over the 17-year period, whereas those of NO₂ are decreasing. However, the temporal pattern in NO₂ is much clearer than that for O₃. Our graphical interpretation of trends is supported by the Mann–Kendall test results, with stronger evidence rejecting the null hypothesis of no trend and accepting the alternative hypothesis of some monotonic trend for NO₂ (<italic>p</italic> = 0.00005) than for O₃ (<italic>p</italic> = 0.0435). The increasing trend in levels of O₃ can be attributed to southern Ontario communities, which suffer from regional North American increases in O₃, even though the O₃ precursor pollutant, NO₂, is declining over time (##UREF##6##Government of Canada 2006##). The time trends in nonweighted O₃ and NO₂ show similar Mann–Kendall test results.</p>", "<p>##FIG##3##Figure 4## displays the estimates of the annual pooled or common risk. Evidence for supporting the alternative hypothesis of some monotonic increasing trend in the annual risks for O₃ is weak (<italic>p</italic> = 0.3870) but somewhat stronger for NO₂ (<italic>p</italic> = 0.1082). Only the 1998 risk for NO₂ (##FIG##3##Figure 4B##, red circle) is outside ±2 SD (blue lines) of the 17 annual risk estimates. To examine the sensitivity of the conclusion of the existence of a monotonic trend in annual risk, we applied the Mann–Kendall test 17 times to data sets, excluding a single year of data. For O₃, the <italic>p</italic>-value varied between 0.6853 when excluding 1984 and 0.2241 when excluding 1989. There is no strong evidence to reject the null hypothesis of no increasing risks based on exclusion of any single year of data. However, <italic>p</italic>-values varied for NO₂ from 0.2241 excluding 1999 to 0.0217 excluding 1998. This is consistent with the graphical information presented in ##FIG##3##Figure 4##, for which the 1998 annual risk is clearly different from those in the proximal years. The other year in which risk appeared to be somewhat unusual was 1992. Excluding this year resulted in a Mann–Kendall test with a <italic>p</italic>-value of 0.0647, the second lowest value examined. Thus, data for 1998 and 1992 have the most influence on the statistical test for linear time trend in NO₂.</p>", "<p>We examined in detail the temporal pattern of mortality, temperature, and air pollution in each city in an attempt to identify unusual patterns that might explain the relatively low risk for NO₂ in 1998. We observed no obvious patterns for mortality or air pollution. However, there was a clear increase in ambient temperature in southern Ontario in 1998 (data not shown). We then examined the sensitivity of the annual risk for NO₂ to model specification. To account for this, we varied the df of the natural splines, such as df = 6, 9, 12 for time and df = 3, 6, 9 for temperature, but this did not affect the temporal pattern of risk. We examined the sensitivity of the estimates of annual risk to singular exclusion of the three largest cities, Toronto, Montreal, and Vancouver. The pattern of annual risk estimates was unaltered. Next, we divided the 24 Canadian cities into four regions. We observed a relatively low risk estimate for 1998 in the region of western Canada. The reasons for this lower risk remain unclear and subject to further investigations, such as looking into demographic changes in the western Canada region. We applied the multi-7-year method to both O₃ and NO₂, which we plotted over time from 1990 through 2000 (##FIG##4##Figure 5##). The temporal estimator of risk follows the general pattern of the annual estimates. Risk increased slightly for O₃ from 1990 to 1996, followed by a slight decrease in risk for the next few years (##FIG##4##Figure 5A##). It is difficult to clearly distinguish a temporal pattern of risk different from a constant in ##FIG##4##Figure 5A##. However, the temporal pattern in risk is more distinct for NO₂ (##FIG##4##Figure 5B##), with a clear monotonic increase in risk from 1990 to 1997 and then a sharp decline in 1998. We examined the influence of the 1998 data on this pattern by removing this year and recalculating the temporal risk pattern (##FIG##4##Figure 5C##). Without 1998, a clear monotonic increase in risk is apparent.</p>" ]

[ "<title>Discussion</title>", "<p>Here we proposed new methods to estimate the association between daily variations in ambient air pollution and daily fluctuations in nonaccidental mortality over space and time. Spatial–temporal risk estimates, coupled with city-specific and national estimates in trends in air pollution, can be used to assess whether the adverse effect of air pollution related to mortalities has changed over time. Simulation methods show our estimator to have reasonable statistical properties for estimates of the common risk under various scenarios for changes in risk over time. However, estimates of the heterogeneity of risk among cities are unstable, with zero values frequently occurring, both in the simulation study and in the analysis of real data. In particular, estimates of heterogeneity in risk can vary considerably over time. This instability results in highly variable shrunken estimates of the city-specific risks, making it difficult to clearly identify temporal patterns. In the case of O₃, we used the estimate of heterogeneity of risk based on all 17 years of data to determine the city-specific shrunken risk. We frequently observed zero values for the variance estimate from our temporal model of risk. This is likely attributable to the much larger within-city estimation error of risk compared with the variation in risk among cities. Alternative estimation procedures such as Bayesian methods should be considered to improve estimation of the heterogeneity.</p>", "<p>We considered the temporal risks of NO₂ exposure on mortality for two reasons. First, NO₂ has been shown to be the strongest and most consistent predictor of mortality in Canadian studies (##UREF##0##Brook et al. 2007##). Second, it is not clear that NO₂ itself is the direct causal agent; it may be acting as a surrogate for combustion in general and traffic specifically. We can address an interesting question with NO₂: Are the Canadian government’s efforts to improve air quality by, in part, reducing NO₂ translating into improvements in mortality risk? Based on the present analysis, the answer is no. The same issue arises with particulate matter (PM): It is not likely that mass itself is the causal agent; rather, the shape, number, or chemistry of particles may be causing the observed statistical associations between PM and mortality. Canada has historically monitored PM only every sixth day and only in a few cities. The limited sample size generates a large amount of statistical uncertainty in the risk estimates. This limits our ability to detect time trends in a meaningful way.</p>", "<p>Risk per unit of air pollutant of interest may vary over time and space because the measured pollutant may act as a surrogate for the true toxic agent, the population or the monitoring sites may vary over time and space, or the association between exposure and death may not be linear.</p>", "<title>Monitored air pollutant may act as a surrogate for true toxic agent</title>", "<p>Although statistical associations can be observed between air pollution and mortality, whether the monitored pollutant is in fact the true causal agent is not known. Several pollutants are emitted from common sources, and daily variations in concentration can be affected by weather conditions, resulting in high correlations among pollutants. For example, NO₂ has been shown in Canadian cities to be a stronger predictor of mortality than is fine PM (##REF##16201668##Burnett et al. 2004b##). However, NO₂ has also been shown to be a stronger correlate than is fine PM with several pollutants generated from local mobile sources (##UREF##0##Brook et al. 2007##), so NO₂ may be acting as a marker for these pollutants. The increase in the risk of NO₂ over time (excluding 1998) is approximately the same rate as the decrease in annual average concentrations, suggesting that the attributable risk (product of risk and concentration) is stable over time. One explanation for this pattern is that NO₂ itself may not be causally linked to mortality and that reductions in ambient concentrations are not translating into improvements in population health. The truly toxic components of the urban atmosphere may not be changing over time, at least not at the same rate as NO₂. Daily PM measurements in Canada have not been collected historically. Size-fractionated mass and elemental concentrations have been collected in some cities since 1984 on a sampling schedule of every sixth day (##REF##12881885##Burnett et al. 2000##). Speciated PM data (elements, ions, carbon) have been collected on a sampling schedule of every third day from 2003 in several Canadian communities. The temporal pattern in the correlation between NO₂ and these PM pollutants can be examined in order to identify possible changes in the composition of the atmosphere and thus determine possible reasons for increasing NO₂ risks.</p>", "<title>The population most at risk for mortality related to air pollution may change over time and vary in composition across the nation</title>", "<p>It is well documented that the age distribution of the Canadian population has changed significantly over the last 20 years, such that there is an increasing proportion of elderly individuals at greater risk of death (##UREF##9##Hogan 2001##). The prevalence of cardiorespiratory disease has also increased (##UREF##8##Heart and Stroke Foundation of Canada 1999##). These factors could plausibly contribute to an increase in the mortality risk associated with air pollution over time.</p>", "<title>The adequacy of network air pollution monitoring sites as a surrogate for population average personal exposure could vary by community and time</title>", "<p>This misclassification of exposure can lead to underestimation of risk, with the amount of underestimation depending on both space and time. This issue could be examined by correlations among monitors within each community and the use of enhanced exposure assessment methods such as land-use regression models, spatial kriging methods, and population density measures.</p>", "<title>The shape of the association between exposure and death may not be linear</title>", "<p>In this simulation, we have assumed a linear association between concentration and mortality. If the association is nonlinear, then as pollution levels change over time, the number of deaths attributable to air pollution will vary depending on the level of exposure. Methods will need to be developed to incorporate nonlinear (threshold) models such as those developed by ##REF##10765418##Cakmak et al. (1999)##.</p>", "<p>In our study, the annual average daily concentrations of O₃ appeared to be increasing over the 17-year period, whereas those of NO₂ are decreasing. However, our proposed method returns different time trends in mortality risks. Evidence for some monotonic increasing time trends in the annual risks for O ₃ is weak ( <italic>p</italic> = 0.3870) but somewhat stronger for NO₂ (<italic>p</italic> = 0.1082). In particular, an increasing time trend becomes apparent when excluding year 1998, which reveals lower risk than proximal years, even though concentrations of NO₂ are decreasing.</p>", "<p>Despite decreasing ambient concentrations, mortality risks related to NO₂ appear to be increasing. Further investigations are necessary to understand why the concentrations and adverse effects of NO₂ show opposite time trends and why year 1998 is quite different from its proximal years.</p>" ]

[]

[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Countries worldwide are expending significant resources to improve air quality partly to improve the health of their citizens. Are these societal expenditures improving public health?</p>", "<title>Objectives</title>", "<p>We consider these issues by tracking the risk of death associated with outdoor air pollution over both space and time in Canadian cities.</p>", "<title>Materials and methods</title>", "<p>We propose two multi-year estimators that use current plus several previous years of data to estimate current year risk. The estimators are derived from sequential time series analyses using moving time windows. To evaluate the statistical properties of the proposed methods, a simulation study with three scenarios of changing risk was conducted based on 12 Canadian cities from 1981 to 2000. Then an optimal estimator was applied to 24 of Canada’s largest cities over the 17-year period from 1984 to 2000.</p>", "<title>Results</title>", "<p>The annual average daily concentrations of ozone appeared to be increasing over the time period, whereas those of nitrogen dioxide were decreasing. However, the proposed method returns different time trends in public health risks. Evidence for some monotonic increasing trends in the annual risks is weak for O₃ (<italic>p</italic> = 0.3870) but somewhat stronger for NO₂ (<italic>p</italic> = 0.1082). In particular, an increasing time trend becomes apparent when excluding year 1998, which reveals lower risk than proximal years, even though concentrations of NO₂ were decreasing. The simulation results validate our two proposed methods, producing estimates close to the preassigned values.</p>", "<title>Conclusions</title>", "<p>Despite decreasing ambient concentrations, public health risks related to NO₂ appear to be increasing. Further investigations are necessary to understand why the concentrations and adverse effects of NO₂ show opposite time trends.</p>" ]

[ "<p>Governments throughout the developed world have started to improve air quality by changing the ways their societies use and generate energy, altering industrial processes, and selectively altering emissions streams. Introduction of these new technologies and programs is expected to cost trillions of dollars worldwide, and these societal expenditures often contribute to an improvement in air quality. To what extent does improved air quality improve public health?</p>", "<p>In Canada, an annual reporting system has been developed in which time trends in the levels of outdoor air pollution are estimated annually, with each successive reporting year based on an additional year of monitoring data (##UREF##6##Government of Canada 2006##). The U.S. ##UREF##11##National Research Council (2004)## suggested that, in addition to reporting trends in outdoor concentrations of pollutants, health risks attributable to these exposures should also be monitored.</p>", "<p>Hundreds of studies throughout the world have linked daily variations in urban air pollution with daily variations in the number of deaths within cities (##UREF##3##Dominici et al. 2000##; ##UREF##13##Stieb et al. 2002##). Most countries maintain mortality records, thus providing a resource to routinely track an important aspect of adverse health risks associated with air pollution. We illustrate our approach to estimating risk over space and time with the case of the association between two pollutants, nitrogen dioxide and ground-level ozone, and nonaccidental mortality in 24 of Canada’s largest cities over the 17-year period from 1984 through 2000. We assessed statistical properties of our method using a simulation approach.</p>" ]

[ "<title>Appendix</title>", "<title>Generating daily mortality data <italic>(Y*</italic>\n<italic>_ij</italic>\n<italic>)</italic></title>", "<p>We designed this simulation to be as close to the real situation as possible. We generated daily mortality counts to be close to the expectation of the daily death counts for each of the 12 cities for 20 years using the model in Equation 2 (model 2), which required calendar time, temperature, day of week, and air pollutant level. To conduct a reliable simulation, we incorporated the contribution of those factors (potential confounding variables) in generating daily mortality counts. We gave only the relative risk for each city <italic>i</italic>^, β<italic>_i</italic> (<italic>t</italic>), preselected values that were close to the estimate from that city’s data.</p>", "<p>Suppose we estimate the expectation of the daily mortality from model 2 as follows. For city <italic>i</italic> and year <italic>j</italic>,</p>", "<p>The contribution of the potential confounding variables is</p>", "<p>Let β<italic>_ij</italic><italic>*</italic> represent the true values and replacing the estimated risk β̂<italic>_ij</italic> by the true risk β<italic>_ij</italic><italic>*</italic> ; we now have the new expectation of the daily mortality,</p>", "<p>Adding random errors (ɛ<italic>_ij</italic>) from a Poisson distribution, we generated daily deaths as</p>", "<p>We assumed the true risks, β<italic>_ij</italic><italic>*</italic> , to be different for each year within each city. For each scenario, we determined the risk for the first year, β<italic>_i</italic>₁^*, equal to the shrunken estimate from the Canadian 12-city data. The preassigned values for city-specific risks ranged from 0.096 × 10⁻³ to 1.677 × 10⁻³ for the estimated risks of NO₂</p>", "<title>Estimating city-specific annual risks using simulated data <italic>(</italic>β̂<italic>_ij</italic><italic>)</italic></title>", "<p>From 1,000 simulated daily mortality data sets, we obtained 1,000 estimates for the risk, β̂<italic>_ij</italic>, and its sampling variance, ν̂<italic>_ij</italic>, by applying model 2.</p>", "<title>Estimating heterogeneity among different cities for year j [σ̂_β²<italic>(j)</italic>]</title>", "<p>We obtained annual risk estimates for each of the 12 cities. Applying the random-effects model to these 12 city-specific risk estimates for each year, we estimated the parameter variances, σ̂_β²(<italic>j</italic>), which indicate heterogeneity among the 12 cities.</p>", "<title>Estimating the pooled health risk for year j [μ̂_β<italic>(j)</italic>]</title>", "<p>For any year <italic>j</italic>, we obtained the pooled risk estimate by combining the 12 city-specific risk estimates:</p>", "<p>where <italic>K</italic> is the number of cities and <italic>w</italic><italic>_ij</italic> = 1 / var(β̂<bold><italic>ij</italic></bold> ) = 1 / var(σ̂_β²_R,adj(<italic>j</italic>) + ν̂<italic>_ij</italic> ) for city <italic>i</italic>.</p>", "<p>The variance of this pooled risk estimate is</p>" ]

[ "<fig id=\"f1-ehp-116-1147\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Comparison of multi-<italic>L</italic>-year estimator [method A (<italic>A</italic>)] and smoothed <italic>L</italic>-annual estimator (method B (<italic>B</italic>)] applied to simulated data for scenario 1 (top), scenario 2 (center), and scenario 3 (bottom). The solid line of each panel indicates the preassigned risk. We considered methods closer to the solid line to be better methods.</p></caption></fig>", "<fig id=\"f2-ehp-116-1147\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Comparison of RMSE of Canada-wide risk estimates by multi-<italic>L</italic>-year estimator (method A) and smoothed <italic>L</italic>-annual estimator (method B): box plots of RMSE distribution of 1,000 runs for scenario 1 (<italic>A</italic>), scenario 2 (<italic>B</italic>), and scenario 3 (<italic>C</italic>). Baseline indicates nonsmoothed annual estimator. S<italic>m</italic>A<italic>n</italic> and S<italic>m</italic>B<italic>n</italic> indicate scenario <italic>m</italic>, method A or method B, for <italic>L</italic> = <italic>n</italic> years. The red dashed line indicates the median of nonsmoothed annual estimator as the worst fit; blue dashed line indicates the median of multiyear estimator as the best fit. The solid horizontal line in each box indicates the median of the distribution of RMSE for each method. The box, whiskers, and dots represent the interquartile range, smallest and largest non-outliers, and outliers, respectively.</p></caption></fig>", "<fig id=\"f3-ehp-116-1147\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Trend in annual mortality-weighted averages of O₃ (<italic>A</italic>) and NO₂ (<italic>B</italic>) concentrations (ppb) from 24 Canadian cities. The curve represents time trends in concentrations smoothed by locally weighted scatterplot smoothing (LOWESS).</p></caption></fig>", "<fig id=\"f4-ehp-116-1147\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Annual pooled risk estimates for O₃ (<italic>A</italic>) and NO₂ (<italic>B</italic>) from 24 Canadian cities. Blue lines indicate ±2 SD of the 17 annual estimates of risk. The red circle indicates the risk outside the SD, for 1998.</p></caption></fig>", "<fig id=\"f5-ehp-116-1147\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Time trend in pooled risks of O₃ (<italic>A</italic>) and NO₂ (all years, <italic>B</italic>; 1998 excluded, <italic>C</italic>) from 24 Canadian cities. Black lines indicate the pooled risk; blue lines indicate 95% confidence intervals.</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1147\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Comparison of consistency of estimates for scenario 1: multi-<italic>L</italic>-year estimator versus smoothed <italic>L</italic>-annual estimator for <italic>L</italic> = 3, 5, 7, and 9 years.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Method (estimator)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Model.SE<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1147\">a</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Simul.SE<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1147\">b</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Difference<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1147\">c</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Ratio<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1147\">d</xref></th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Baseline<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1147\">e</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.73 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.39 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.42 × 10⁻⁵</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.63 × 10⁻²</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Multi-3-year</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.59 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.43 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.64 × 10⁻⁵</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.70 × 10⁻²</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Multi-5-year</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.76 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.63 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.34 × 10⁻⁵</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.70 × 10⁻²</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Multi-7-year</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.29 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.16 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.35 × 10⁻⁵</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.28 × 10⁻²</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Multi-9-year</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.95 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.86 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.13 × 10⁻⁶</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.19 × 10⁻²</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Smoothed 3-annual</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.96 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.70 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.66 × 10⁻⁵</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.66 × 10⁻²</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Smoothed 5-annual</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.11 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.86 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.47 × 10⁻⁵</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.39 × 10⁻²</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Smoothed 7-annual</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.60 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.37 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.31 × 10⁻⁵</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.86 × 10⁻²</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Smoothed 9-annual</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.26 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.03 × 10⁻⁴</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.32 × 10⁻⁵</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.67 × 10⁻²</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1147\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Comparison of bias in heterogeneity (difference among the 12 Canadian cities) estimates for all scenarios during 12 year time period, 1989–2000.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">Ratio<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1147\">a</xref>\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Method (estimator)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Scenario 1</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Scenario 2</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Scenario 3</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Baseline<xref ref-type=\"table-fn\" rid=\"tfn7-ehp-116-1147\">b</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.72</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.10</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Multi-3-year</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.87</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.88</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Multi-5-year</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.82<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1147\">c</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.92<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1147\">c</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Multi-7-year</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.80</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.88</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Multi-9-year</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.42</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.82</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Smoothed 3-annual</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.58</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.65</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Smoothed 5-annual</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.04<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1147\">c</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.57</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Smoothed 7-annual</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.61</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.53</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Smoothed 9-annual</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.51</td></tr></tbody></table></table-wrap>" ]

[ "<disp-formula></disp-formula>", "<disp-formula></disp-formula>", "<disp-formula></disp-formula>", "<disp-formula></disp-formula>", "<disp-formula></disp-formula>", "<disp-formula></disp-formula>", "<disp-formula></disp-formula>", "<disp-formula></disp-formula>" ]

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[ "<fn-group><fn><p>M.S.G. gratefully acknowledges receipt of an Investigator Award from the Canadian Institutes of Health Research.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1147\"><label>a</label><p>Model.SE is the square root of the average of 1,000 squared SEs of the pooled risk estimates.</p></fn><fn id=\"tfn2-ehp-116-1147\"><label>b</label><p>Simul.SE is the SD of the 1,000 pooled risk estimates.</p></fn><fn id=\"tfn3-ehp-116-1147\"><label>c</label><p>Difference = model.SE – simul.SE.</p></fn><fn id=\"tfn4-ehp-116-1147\"><label>d</label><p>Ratio = (model.SE – simul.SE)/simul.SE.</p></fn><fn id=\"tfn5-ehp-116-1147\"><label>e</label><p>Baseline is the nonsmoothed annual estimator.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn6-ehp-116-1147\"><label>a</label><p>Ratio = (parameter variance estimate)/(preassigned value for parameter variance).</p></fn><fn id=\"tfn7-ehp-116-1147\"><label>b</label><p>Baseline = nonsmoothed annual estimator.</p></fn><fn id=\"tfn8-ehp-116-1147\"><label>c</label><p>Ratios indicate the best results; the closer to 1, the better estimate.</p></fn></table-wrap-foot>" ]

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[ "<title>Materials and Methods</title>", "<title>Chemicals</title>", "<p>Most chemicals, including sodium arsenite, tBHQ, and Hoechst 33258, were from Sigma Chemical Co. (St. Louis, MO, USA). Rubescensin A (oridonin) was purchased from LKT Laboratories Inc. (St. Paul, MN, USA).</p>", "<title>Cell cultures</title>", "<p>We obtained human MDA-MB-231 breast carcinoma cells from American Type Culture Collection (Manassas, VA, USA). Cells were cultured in Eagle’s minimal essential medium (MEM) supplemented with 10% fetal bovine serum (FBS), 2 mM HEPES, and 6 ng/mL bovine insulin from Sigma Chemical Co. UROtsa cells were generously provided by M.A. Sens and D. Sens (University of North Dakota). UROtsa cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) enriched with 5% FBS. All mammalian cells were incubated at 37°C in a humidified incubator containing 5% CO₂.</p>", "<title>Establishment of a reporter cell line</title>", "<p>We purchased the luciferase plasmid, pGL4.22[luc2CP/Puro] from Promega (Madison, WI, USA). A 39-bp antioxidant responsive element (ARE)-containing sequence from the promoter region of the human NAD(P)H quinone oxidoreductase 1 (<italic>NQO1</italic>) gene was inserted into the cloning site of the luciferase plasmid. The ARE-luciferase plasmid was transfected into MDA-MB-231 cells using Lipofectamine Plus from Invitrogen (Grand Island, NY, USA) according to the manufacturer’s instructions. At 48 hr posttransfection, cells were grown in medium containing 3 μg/mL puromycin for selection. Stable cell lines were considered established once all the cells in the negative control plate were killed. Stable cell lines were continuously grown in the MEM containing 3 μg/mL puromycin. For the reporter gene assay, the ARE-luciferase stable reporter cells were seeded the day before and treated with different doses of test compounds for 24 hr. Cells were lysed in extraction buffer [0.1 M potassium phosphate and 1 mM dithiothreitol (DTT)] by freezing and thawing three times, and luciferase activities were measured in an assay buffer (25 mM glycylglycine, 15 mM magnesium sulfate, 500 μM ATP, 250 μM luciferin, and 250 μM coenzyme A) using a BioTek Synergy 2 microplate reader (Winooski, VT, USA). We performed the reporter gene assay in triplicate and calculated the mean ± SD.</p>", "<title>Luciferase reporter gene assay</title>", "<p>For the dual luciferase reporter gene assay, MDA-MB-231 cells were transfected with the same ARE-luciferase plasmid along with the renilla luciferase expression plasmid, pGL4.74[hRluc/TK], from Promega. At 24 hr posttransfection, the transfected cells were treated with compounds for 24 hr, and both firefly and renilla luciferase activities were measured with the dual luciferase reporter assay system from Promega. Firefly luciferase activity was normalized to renilla luciferase activity. The experiment was carried out in triplicate and expressed as the mean ± SD.</p>", "<title>mRNA extraction</title>", "<p>Total mRNA was extracted from cells using TRIZOL reagent (Invitrogen), and equal amounts of RNA were reverse-transcripted to cDNA using the Transcriptor First Strand cDNA synthesis Kit (Roche, Indianapolis, IN, USA). The PCR condition, as well as Taqman probes and primers for <italic>Nrf2, NQO1</italic>, heme oxygenase-1 (<italic>HO-1</italic>), and <italic>GAPDH</italic> were reported previously (##REF##17765279##Wang et al. 2007##). Briefly, we obtained the following Taqman probes from the universal probe library (Roche): <italic>hNrf2</italic> (#70), <italic>hNQO1</italic> (#87), <italic>hHO-1</italic> (#25), and <italic>hGAPDH</italic> (#25). The following primers were synthesized by Integrated DNA Technologies (Coralville, IA, USA): <italic>hNrf2</italic>: forward (acacggtccacagctcatc) and reverse (tgtcaatcaaatccatgtcctg); <italic>hNQO1</italic>: forward (atgtatgacaaaggacccttcc) and reverse (tcccttgcagagagtacatgg); <italic>hHO-1</italic>: forward (aactttcagaagggccaggt) and reverse (ctgggctctccttgttgc); and <italic>hGAPDH</italic>: forward (ctgacttcaacagcgacacc) and reverse (tgctgtagccaaattcgttgt).</p>", "<title>Real-time reverse transcriptase-polymerase chain reaction (RT-PCR)</title>", "<p>The real-time PCR condition was as follows: one cycle of initial denaturation (95°C for 10 min), 40 cycles of amplification (95°C for 10 sec and 60°C for 20 sec), and a cooling period (50°C for 5 sec). The data presented are relative mRNA levels normalized to <italic>GAPDH</italic>, and the value from the untreated cells was set as 1. We used triplicate samples to determine the mean ± SD.</p>", "<title>Antibodies and immunoblot analysis</title>", "<p>We purchased the antibodies for Nrf2, Keap1, and β-actin from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Cells were lysed in a sample buffer [50 mM Tris-HCl (pH 6.8), 2% SDS, 10% glycerol, 100 mM DTT, 0.1% bromophenol blue]. After sonication, cell lysates were electrophoresed through an SDS-polyacrylamide gel and subjected to immunoblot analysis. For detection of the ubiquitinated Nrf2 <italic>in vivo</italic>, cells were transfected with expression vectors for hemagglutinin (HA)-ubiquitin, Keap1, and Gal4-Neh2 (the N-terminal domain of Nrf2 containing the ubiquitin conjugating sites). The transfected cells were either left untreated or treated with chemicals along with 10 μM MG132 (Sigma Chemical Co.) for 4 hr. Cells were lysed by boiling in a buffer containing 2% SDS, 150 mM NaCl, 10 mM Tris-HCl, and 1 mM DTT. These lysates were then diluted 5-fold in buffer lacking SDS and incubated with anti-Nrf2 or anti-Keap1 antibodies. Immunoprecipitated proteins were analyzed by immunoblot with antibodies directed against the HA epitope (##REF##14585973##Zhang and Hannink 2003##).</p>", "<title>Ubiquitination assay</title>", "<p>To detect endogenous Nrf2 ubiquitination, the UROtsa cells were treated with 10 μM MG132 and lysed and diluted in the same way. Nrf2 was immunoprecipitated with an anti-Nrf2 antibody and subjected to immunoblot analysis with an antiubiquitin antibody (Sigma Chemical Co.).</p>", "<title>Protein half-life measurement</title>", "<p>To measure the half-life of Nrf2, cells were either left untreated or treated with oridonin for 4 hr. To block protein synthesis, we added 50 μM cycloheximide. Total cell lysates were collected at different time points and subjected to immunoblot analysis with an anti-Nrf2 antibody. The relative intensity of bands was quantified by the ChemiDoc CRS gel documentation system and Quantity One software from BioRad (Hercules, CA, USA).</p>", "<title>Transient transfection of siRNA and measurement of glutathione concentration</title>", "<p>We purchased Nrf2-siRNA and the control siRNA from Qiagen (Valencia, CA, USA). Transient transfection of siRNA was performed using HiPerFect Transfection Reagent according to the manufacturer’s protocol (Qiagen). Intracellular glutathione concentration was measured using the QuantiChrom glutathione assay kit from BioAssay Systems (Hayward, CA, USA). All the procedures were carried out according to the manufacturer’s instructions.</p>", "<title>ROS detection</title>", "<p>For detection of ROS, we pretreated cells with 1.4 μM oridonin for 24 hr, followed by As(III) treatment or As(III) plus oridonin cotreatment for another 24 hr. ROS levels were measured using dichlorofluorescein (Sigma Chemical Co., 10 μg/mL final concentration) and flow cytometry.</p>", "<title>Detection of cell viability</title>", "<p>Cell viability was measured by 3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) (##REF##17765279##Wang et al. 2007##) and by colony formation assays. We performed colony formation assays in 35-mm plates with 200 UROtsa cells. Attached cells were left untreated or were treated with oridonin for 24 hr, followed by treatment with different doses of As(III) for another 48 hr. After exposure, the medium was replaced with fresh medium, and cells were incubated for 12–14 days. The cells were then fixed and stained with crystal violet (0.5% in 95% ethanol), and the colonies in each plate were counted.</p>", "<title>Detection of cell death</title>", "<p>For detection of apoptotic cell death, we used two different methods: <italic>a</italic>) Annexin V-FITC apoptosis detection (Sigma Chemical Co.), and <italic>b</italic>) Hoechst staining (1 μg/mL) for detection of the condensed nuclei. All experiments were conducted in triplicate and expressed as mean ± SD. The statistical significance was determined by the Student’s <italic>t</italic>-test.</p>" ]

[ "<title>Results</title>", "<title>Identification of oridonin as an Nrf2 activator</title>", "<p>Using the stable ARE luciferase reporter cell line derived from MDA-MB-231 cells combined with a 96-well high-throughput screening system established in our laboratory, we identified a novel Nrf2 activator that belongs to the class of diterpenoids (##FIG##0##Figure 1A##). The MDA-MB-231 cell line was used to show Nrf2 activation for two reasons: first, MDA-MB-231 cells can be easily transfected; and second, the Nrf2 pathway is most sensitive in this cell line in response to Nrf2-inducers. Oridonin induced transcription of the ARE-dependent luciferase gene in a dose-dependent manner in this stable cell line (##FIG##0##Figure 1B##). To confirm oridonin activation of Nrf2 using the high-throughput screening method, we also performed a dual luciferase reporter gene assay in which we included a renilla luciferase gene as an internal control for transfection efficiency and for toxicity induced during oridonin exposure. Consistent with the data obtained from the high-throughput screening, oridonin induced the ARE-dependent luciferase activity in a dose-dependent manner (##FIG##0##Figure 1C##). Slight induction (1.5-fold) was observed at as low as 1.4 μM and reached maximum induction (11.3-fold) at 14 μM. There was no obvious toxicity at 14 μM, as judged by cell morphology and renilla luciferase activity.</p>", "<title>Oridonin activated the ARE-dependent response primarily through up-regulation of the Nrf2 protein level</title>", "<p>Previous studies have demonstrated that ARE-dependent reporter gene activity correlated very well with the protein level of Nrf2. Therefore, we used the same cell lysates from the dual luciferase reporter gene assay for immunoblot analysis for detection of Nrf2, Keap1, and β-actin. Although the Keap1 levels remained constant, oridonin enhanced the levels of Nrf2 protein in a dose-dependent manner, with the highest induction at 14 μM (##FIG##1##Figure 2A##). During the reporter gene assay, any doses &gt; 14 μM caused marked toxicity, as indicated by an increased number of rounded and floating cells. A large body of literature indicates that the antitumor activity of oridonin relies on its ability to inhibit cell growth and to induce cell death (##REF##17426700##Zhou et al. 2007a##). Because Nrf2 regulates a cellular survival response, we envisioned that treatment with high doses of oridonin could inhibit Nrf2, allowing cells to undergo cell death. Therefore, we tested Nrf2 protein levels in response to high doses of oridonin. After treatment of MDA-MB-231 cells with different doses of oridonin for 24 hr, we collected all cells, including floating cells. Equal amounts of proteins were subjected to immunoblot analysis with Nrf2, Keap1, and β-actin antibodies. Interestingly, at doses &gt; 28 μM, Nrf2 protein levels decreased in a dose-dependent manner, whereas the expression of Keap1 and β-actin had no significant change (##FIG##1##Figure 2B##, lanes 7–9). Previously, it has been demonstrated that Nrf2 activators, including tBHQ, induce the Nrf2 signaling pathway primarily through stabilization of the Nrf2 protein, rather than up-regulation of its mRNA (##REF##12446695##Nguyen et al. 2003##).</p>", "<p>Next, we measured mRNA expression of <italic>Nrf2</italic> and its target genes, <italic>NQO1</italic> and <italic>HO-1</italic>, in response to oridonin using real-time RT-PCR. Nrf2 mRNA increased slightly in a dose-dependent manner in response to oridonin, whereas tBHQ had no effect (##FIG##1##Figure 2C##, top panel). As expected, mRNA of <italic>NQO1</italic> and <italic>HO-1</italic> were induced significantly by oridonin in a dose-dependent manner (##FIG##1##Figure 2C##, center and bottom panels). These results demonstrate that oridonin is able to induce the Nrf2 signaling pathway mainly through up-regulation of Nrf2 at the protein level.</p>", "<title>Oridonin blocked Nrf2 ubiquitination and enhanced Keap1 ubiquitination</title>", "<p>tBHQ enhances the Nrf2 protein level by interfering with the Keap1-dependent ubiquitin conjugation process. Therefore, we tested the ability of oridonin in modulating Nrf2 ubiquitination. For this assay, we used Gal4-Neh2, a model fusion protein previously used for the Nrf2 ubiquitination test (##REF##14585973##Zhang and Hannink 2003##). In a manner similar to tBHQ, oridonin suppressed Nrf2 ubiquitination (##FIG##2##Figure 3A##, left panel).</p>", "<p>Furthermore, ##REF##15983046##Zhang et al. (2005)## showed that tBHQ caused a shift of ubiquitination from the substrate Nrf2 to the substrate adaptor Keap1. As with tBHQ, oridonin treatment was also effective in enhancing ubiquitination of Keap1 (##FIG##2##Figure 3A##, center panel). These results demonstrate that oridonin can induce a shift of ubiquitination from Nrf2 to Keap1. One of the major roles for ubiquitin conjugation onto a protein is to target the protein for 26S proteasome-mediated degradation. Next, we measured the half-life of Nrf2 in the absence or presence of oridonin. Half-life of the endogenous Nrf2 protein in MDA-MB-231 cells was 19 min, whereas treatment with oridonin increased the half-life to 51 min (##FIG##2##Figure 3B##, left panel). Taken together, these results indicate that oridonin activates the Nrf2 pathway by inhibiting ubiquitination and degradation of Nrf2, leading to an increase in Nrf2 protein level and activation of the Nrf2-dependent response.</p>", "<title>Efficacy of oridonin in protecting against As(III)-induced toxicity</title>", "<p>To test the feasibility of using oridonin as a chemopreventive compound to elicit the Nrf2-mediated protective response to defend against environmental insults, we used the UROtsa cell line, an established model system for arsenic toxicity. First, we determined activation of the Nrf2 pathway by oridonin in this cell line. Ubiquitination of endogenous Nrf2 in UROtsa cells was blocked by oridonin or tBHQ treatment (##FIG##2##Figure 3A##, right panel). Consistent with a decrease in ubiquitination of Nrf2 in response to oridonin, the half life of Nrf2 was increased from 10 min in the untreated condition to 16 min in response to oridonin treatment (##FIG##2##Figure 3B##, right panel).</p>", "<p>Next, we determined the oridonin dose range that induces Nrf2 protein in UROtsa cells. Compared with MDA-MB-231 cells (##FIG##1##Figure 2B##, lanes 2–7), UROtsa cells had a narrow range of Nrf2 induction, from 1.4 to 14μM (##FIG##3##Figure 4A##, lanes 3–6). Doses &gt; 14 μM appeared to be toxic, and induction of Nrf2 was decreased (##FIG##3##Figure 4A##, lanes 7–9). At 56 or 112 μg/mL, there was a decrease even in the level of β-actin due to cytotoxicity (##FIG##3##Figure 4A##, lanes 8 and 9). Nevertheless, reduction of the Nrf2 protein was significantly more substantial, indicating that reduction of Nrf2 at high doses may not be due solely to reduced cell number. Based on this result, we chose a low dose of oridonin (0.5 μg/mL) for the protection assays.</p>", "<p>One of the major functions of Nrf2 is to regulate an antioxidant response by up-regulating intracellular antioxidants and genes such as <italic>GCS</italic> and the <italic>xCT</italic> cysteine antiporter that encode key enzymes in the synthesis of glutathione. To confirm activation of the Nrf2-dependent response by 1.4 μM oridonin, we compared the intracellular glutathione level in the oridonin-treated cells with that in untreated cells. Oridonin treatment resulted in a significant increase in the glutathione level (##FIG##3##Figure 4B##). Thus, oridonin is able to augment the cellular redox capacity, which is the key mechanism in suppressing oxidative stress–induced damage by environmental insults. In the protection assays, we used sodium arsenite [As(III)] to treat UROtsa cells. We measured the ability of oridonin to alleviate As(III)-induced ROS (##FIG##3##Figure 4C##). Treatment with 30 μM As(III) for 24 hr increased the level of ROS significantly, whereas 5.6 μM oridonin itself had no effect. Pretreatment of cells with several doses of oridonin for 24 hr and further cotreatment with As(III) for an additional 24 hr resulted in a significant reduction of ROS levels, especially with 5.6 μM oridonin. These data clearly demonstrate the efficacy of oridonin in suppressing oxidative stress imposed by As(III) exposure.</p>", "<p>Finally, we assessed the effectiveness of oridonin in protecting cells from acute cell death in response to As(III). UROtsa cells were left untreated or were pretreated with 1.4 μM oridonin. After a 24-hr pretreatment period, several doses of As(III) were added to both groups and incubated for an additional 48 hr before measuring total cell death using both MTT and colony formation assays. Pretreatment followed by cotreatment with oridonin significantly improved cell survival as judged by the MTT assay (##FIG##3##Figure 4D##, top panel) and the colony formation assay (##FIG##3##Figure 4D##, bottom panel). To confirm that protection against As(III)-induced cell death was attributed to the activation of Nrf2 by oridonin, the MTT assay was performed in UROtsa cells that were treated with Nrf2-siRNA for 48 hr. Immunoblot analysis confirmed the effectiveness of Nrf2-siRNA in reducing Nrf2 expression (##FIG##3##Figure 4D##, center panel). Inhibition of Nrf2 expression in UROtsa cells reverted the MTT curve [i.e., oridonin lost its protection against As(III) toxicity] and aggravated As(III)-induced cell death (##FIG##3##Figure 4D##, center panel). This result demonstrates that oridonin-mediated protection requires activation of the Nrf2 pathway.</p>", "<p>Apoptotic cell death was quantified using Annexin V-FITC/flow cytometry. Treatment with 30 μM As(III) for 48 hr increased the percentage of apoptotic cells, whereas pre-treatment followed by cotreatment with 1.4 μM oridonin reduced apoptotic cell death to a level comparable to the untreated cells (##FIG##3##Figure 4E##, top and center panels). Apoptotic cell death was not increased by 1.4 μM oridonin alone (data not shown). We used Hoechst staining to detect condensed chromosomes in the apoptotic cells. The number of positive-stained cells increased after treatment with 30 μM As(III), whereas pre-treatment followed by cotreatment with oridonin markedly reduced the number of apoptotic cells (##FIG##3##Figure 4E##, bottom panel). Together, these results demonstrate that a low dose of oridonin is able to protect cells from As(III)-induced damage, as illustrated by reduced ROS and increased survival in response to As(III).</p>" ]

[ "<title>Discussion</title>", "<p>The pivotal role of Nrf2 in chemoprevention has clearly been shown in Nrf2 null mice. These mice express lower basal levels of the Nrf2 target genes such as <italic>NQO1</italic>, <italic>GST</italic>, <italic>GCS</italic>, UDP-glucuronosyltransferase, glutathione peroxidase-2, and <italic>HO-1</italic> (##REF##11118612##Chan and Kwong 2000##; ##REF##11804867##Cho et al. 2002##; ##REF##10816095##Hayes et al. 2000##; ##REF##11471548##Kwak et al. 2001##; ##REF##11309284##McMahon et al. 2001##). As a consequence, these mice are more susceptible to toxic and carcinogenic challenges such as butylated hydroxytoluene, benzo[<italic>a</italic>]pyrene, diesel exhaust, aflatoxin B₁, <italic>N</italic>-nitrosobutyl (4-hydroxybutyl) amine, pentachlorophenol, acetaminophen, ovalbumin, cigarette smoke, and 4-vinyl cyclohexene diepoxide (##REF##11437637##Aoki et al. 2001##; ##REF##11287661##Chan et al. 2001##; ##REF##10535991##Chan and Kan 1999##; ##REF##11134556##Enomoto et al. 2001##; ##REF##16428448##Hu et al. 2006##; ##REF##15374950##Iida et al. 2004##; ##REF##16324149##Iizuka et al. 2005##; ##REF##11248092##Ramos-Gomez et al. 2001##; ##REF##15998787##Rangasamy et al. 2005##; ##REF##16352618##Umemura et al. 2006##). These results provide the basis for chemopreventive intervention targeting the Nrf2 signaling pathway. Many previously indentified, naturally occurring compounds, including sulforaphane, epigallocatechin-3-gallate, caffeic acid phenethyl ester, and curcumin, have proved to be Nrf2 activators, which further implies the importance of Nrf2 in chemoprevention (##REF##16487042##Jeong et al. 2006##; ##REF##17449203##Nishinaka et al. 2007##; ##REF##17145701##Zhang 2006##). Identification, validation, and optimization of new Nrf2 activators are essential for the development of effective dietary supplements or therapeutic drugs that can be used to boost the Nrf2-dependent adaptive system to protect humans from various environmental insults.</p>", "<p>Oridonin represents a novel class of Nrf2 activators that has not been demonstrated previously. Mechanistic studies presented here indicate that oridonin induced the Nrf2-dependent response primarily by enhancing the Nrf2 protein level. The increase in the Nrf2 protein level in response to oridonin is attributed mainly to the stabilization of Nrf2, with minor contribution from increased mRNA. Similar to tBHQ, oridonin is able to block ubiquitination and degradation of Nrf2, resulting in the prolonged half-life of Nrf2. Furthermore, we demonstrated the effectiveness of a low dose of oridonin (1.4 μM) in eliciting the Nrf2-dependent cytoprotective response in an As(III)-toxicity model. Low doses of oridonin are able to enhance the cellular reducing capacity by significantly elevating the reduced glutathione level, thus inhibiting the formation of ROS, resulting in increased survival in response to As(III) exposure. Furthermore, glutathione is able to conjugate arsenic to facilitate arsenic excretion, thereby reducing As(III) toxicity (##REF##16516206##Shinkai et al. 2006##). In addition to <italic>GCS</italic>, which modulates intracellular glutathione levels, other Nrf2 downstream genes, including <italic>GST</italic>, UDP-glucuronosyl transferase, and multidrug resistance proteins, also contribute to the Nrf2-mediated protection against arsenic toxicity (##REF##14550278##Hayashi et al. 2003##; ##REF##16887173##Kobayashi and Yamamoto 2006##; ##REF##15833929##Maher et al. 2005##; ##REF##11408547##Vernhet et al. 2001##; ##REF##15832810##Xu et al. 2005##; ##REF##17145701##Zhang 2006##). Although the present study shows only the protection of oridonin against acute As(III) toxicity, oridonin certainly can be applied to other toxic and carcinogenic chemicals, because oridonin induces the well-characterized Nrf2-dependent defensive response.</p>", "<p>This cell-based study provides evidence that low-dose oridonin can be used as a chemopreventive compound that specifically targets Nrf2. Further studies on the chemo-preventive activity of oridonin in animal models are needed. If oridonin is shown to have great chemopreventive potential, then it has a great economic advantage because it can easily be extracted from <italic>Rabdosia rubescens</italic> “the Chinese grass.” In addition, identification of diterpenoids as a new class of Nrf2 activators will broaden the choice for new chemopreventive compounds. Moreover, the diterpenoid structure can serve as a scaffold for the development of chemopreventive drugs. Identification of naturally occurring diterpenoids or synthetic optimization of the diterpenoid oridonin, which potently and specifically induce the Nrf2 signaling pathway, will greatly improve the efficacy of chemopreventive drugs and decrease side effects, which will have a profound impact on human health.</p>", "<p>High doses of oridonin promote anti-cancer activity by causing cell cycle arrest, inhibiting proliferation, and inducing apoptotic cell death. The dose range needed for oridonin to exhibit anticancer activities in these studies, conducted by different laboratories with a variety of cancer cell lines, is very broad, with 100-fold differences. Although this may be due partially to the purity of oridonin used among groups, it largely indicates a difference in sensitivity of cancer cells to the oridonin-induced apoptotic response. In the present study, the effect of oridonin in inducing the Nrf2 protein level was assessed in two different cell lines, breast carcinoma MDA-MB-231 cells and immortalized but non-transformed bladder urothelium UROtsa cells. UROtsa cells showed a narrower window of Nrf2 induction in response to different doses of oridonin. It is interesting to note that oridonin induced Nrf2 protein and reporter gene activity in a dose-dependent manner to a certain point at which the Nrf2 protein level and the reporter gene activity dropped suddenly. We observed an initial decrease in Nrf2 protein in MDA-MB-231 cells and UROtsa cells at 56 μM and 28 μM oridonin, respectively. This decrease in Nrf2 protein level in response to high doses of oridonin is not due solely to cell toxicity because Keap1 or β-actin levels decreased only slightly. Based on the important role of Nrf2 in cell survival, it is conceivable that Nrf2 has to be repressed before the execution of cell death. In support of this notion, Nrf2 has been reported as a substrate of caspase 3 (##REF##10510468##Ohtsubo et al. 1999##). The cleavage sites in Nrf2 for caspase 3 have been identified. Based on the Nrf2 induction profile, it is remarkable that oridonin functions as a chemopreventive compound at low doses by activating the Nrf2 cytoprotective pathway, whereas at high doses, it activates apoptotic cell death and concomitantly inhibits the Nrf2-dependent survival pathway. Further studies are needed to understand the molecular events that cause the switch between life and death.</p>" ]

[]

[ "<p>Y.D. and N.F.V. contributed equally to this work.</p>", "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Groundwater contaminated with arsenic imposes a big challenge to human health worldwide. Using natural compounds to subvert the detrimental effects of arsenic represents an attractive strategy. The transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) is a critical regulator of the cellular antioxidant response and xenobiotic metabolism. Recently, activation of the Nrf2 signaling pathway has been reported to confer protection against arsenic-induced toxicity in a cell culture model.</p>", "<title>Objectives</title>", "<p>The goal of the present work was to identify a potent Nrf2 activator from plants as a chemopreventive compound and to demonstrate the efficacy of the compound in battling arsenic-induced toxicity.</p>", "<title>Results</title>", "<p>Oridonin activated the Nrf2 signaling pathway at a low subtoxic dose and was able to stabilize Nrf2 by blocking Nrf2 ubiquitination and degradation, leading to accumulation of the Nrf2 protein and activation of the Nrf2-dependent cytoprotective response. Pretreatment of UROtsa cells with 1.4 μM oridonin significantly enhanced the cellular redox capacity, reduced formation of reactive oxygen species (ROS), and improved cell survival after arsenic challenge.</p>", "<title>Conclusions</title>", "<p>We identified oridonin as representing a novel class of Nrf2 activators and illustrated the mechanism by which the Nrf2 pathway is activated. Furthermore, we demonstrated the feasibility of using natural compounds targeting Nrf2 as a therapeutic approach to protect humans from various environmental insults that may occur daily.</p>" ]

[ "<p>Arsenic is a major environmental pollutant that exists in soil and minerals; it readily enters the groundwater system, contaminating drinking water. The concentration of arsenic in ground-water varies significantly in different geographic areas. Arsenic concentrations are highest in East Asia, including Bangladesh; West Bengal, India; and China (##REF##17002598##Kumagai and Sumi 2007##; ##REF##11019458##Smith et al. 2000##; ##REF##14585726##Tchounwou et al. 2003##). Many efforts have been made to reduce arsenic damage as exemplified by the guideline for arsenic in drinking water set by the World Health Organization (##REF##15138028##Smith and Smith 2004##) and by local governments. Nevertheless, a large number of populations are still at risk of arsenic exposure and are suffering from arsenic-induced adverse effects, such as hypertension, arteriosclerosis, diabetes, hyperkeratosis, neuropathy, and skin, liver, bladder, and lung cancer (##REF##17002598##Kumagai and Sumi 2007##; ##REF##16882542##Smith et al. 2006##; ##REF##10705880##Steinmaus et al. 2000##; ##REF##12365859##Tseng 2002##). Clearly, the best way to protect humans from arsenic-induced damage is to reduce arsenic intake. However, it is not always practical because many people have no choice but to consume drinking water and rice heavily contaminated with arsenic, as these are their only sources of food and water. Therefore, an alternative choice, of equal importance, is to subvert the detrimental effects of arsenic by modulating the body’s defense system.</p>", "<p>Nuclear factor erythroid 2-related factor 2 (Nrf2) is a critical transcription factor that regulates a cytoprotective response. Many of its downstream target genes are important in maintaining the cellular antioxidant response and xenobiotic metabolism. For example, γ-glutamylcysteine synthetase (GCS) and the xCT cysteine antiporter are the key enzymes for synthesis of glutathione and maintenance of cellular redox homeostasis (##REF##11118612##Chan and Kwong 2000##; ##REF##12235164##Sasaki et al. 2002##; ##REF##10559251##Wild et al. 1999##). Conjugating enzymes, such as glutathione <italic>S</italic>-transferases (GSTs) and UDP-glucuronosyl transferase, facilitate the removal of toxic and carcinogenic chemicals by increasing their solubility and excretion (##REF##16887173##Kobayashi and Yamamoto 2006##; ##REF##17145701##Zhang 2006##). Many transporters such as multidrug resistance proteins and p-glycoprotein are important in uptake and removal of xenobiotics (##REF##14550278##Hayashi et al. 2003##; ##REF##15833929##Maher et al. 2005##; ##REF##11408547##Vernhet et al. 2001##; ##REF##15832810##Xu et al. 2005##). Activation of the Nrf2 signaling pathway is tightly regulated by Kelch-like ECH-associated protein 1 (Keap1) according to changes in the intracellular redox state when cells are exposed to exogenous stimuli. Under normal conditions, cells maintain low constitutive levels of Nrf2-target genes through constant ubiquitination and degradation of Nrf2, which is accomplished by the Keap1-dependent E3 ubiquitin ligase complex. Upon induction, Nrf2 is stabilized because of impaired Keap1-E3 ubiquitin ligase activity, which results in activation of the Nrf2 signaling pathway (##REF##15367669##Cullinan et al. 2004##; ##REF##15601839##Furukawa and Xiong 2005##; ##REF##15282312##Kobayashi et al. 2004##; ##REF##17636022##Sun et al. 2007##; ##REF##15572695##Zhang et al. 2004b##). Chemopreventive compounds are able to activate the Nrf2-dependent adaptive response and thus confer protection against subsequent toxic or carcinogenic damage (##REF##16487042##Jeong et al. 2006##; ##REF##17723167##Yates and Kensler 2007##).</p>", "<p>In addition to the beneficial antioxidants and many chemopreventive compounds, the Nrf2 signaling pathway can also be induced by many harmful chemicals such as arsenic, hydrogen peroxide, and even anticancer drugs including cisplatin (##REF##12745069##Aono et al. 2003##; ##REF##16785233##He et al. 2006##; ##REF##16487037##Massrieh et al. 2006##; ##REF##14567983##Pi et al. 2003##; ##REF##17081560##Purdom-Dickinson et al. 2007##; ##REF##17108137##Wang et al. 2006##). This paradox may be explained by the balance between the induction of the Nrf2 defensive response and the toxic outcome elicited by a particular compound. The most attractive chemopreventive compounds are those that potentially induce the Nrf2-dependent defensive response without eliciting toxic effects, that is, those that tip the balance toward the Nrf2-dependent beneficial response. In accordance with this notion, many chemopreventive compounds extracted from dietary sources or plants activate the Nrf2-dependent response at low doses and do not elicit detectable toxic effects. Nrf2 activators identified so far can be classified into categories that include phenolic antioxidants (caffeic acid, epigallocatechin-3-gallate, butylated hydroxyanisole), dithiolethiones (oltipraz, 3H-1,2-dithiole-3-thione), isothiocyanates (sulforaphane), and triterpenoids [1-(2-cyano-3,12-dioxooleane-1,9[11]-dien-28-oyl)imidozole] (##REF##17723167##Yates and Kensler 2007##; ##REF##17145701##Zhang 2006##). Up-regulation of the Nrf2-dependent defense response has proved to be beneficial in reducing arsenic-induced toxicity in a cell culture model (##REF##17765279##Wang et al. 2007##). Stable knockdown of endogenous Nrf2 using Nrf2-shRNA rendered cells more sensitive to arsenic-induced cell death. On the other hand, pretreatment with chemicals that activate Nrf2 enhanced cell resistance to arsenic-induced cell death. The present study provides the framework of using natural compounds to activate the Nrf2-dependent protective pathway to counteract arsenic-induced damage.</p>", "<p>In this article we report the identification of a novel class of Nrf2 activators. Oridonin, also known as rubesecensin A, is a diterpenoid purified from the Chinese medicinal herb <italic>Rabdosia rubescens.</italic> As one of the important traditional Chinese medicines, <italic>R. rubescens</italic> has been used by Chinese doctors to treat swelling of the throat, insect bites, snake bites, inflammation of the tonsils, and cancer of the esophagus, stomach, liver, prostate, and breast (##REF##17426700##Zhou et al. 2007a##). The active ingredients in <italic>R. rubescens</italic> are rubesecensin A (oridonin) and rubesecensin B. Currently the major research focus on oridonin is in its antiproliferation and antitumor activities. The anticancer activity of oridonin is thought to rely on its ability to inhibit cell growth, reduce angiogenesis, and enhance apoptosis (##REF##15703811##Chen et al. 2005##; ##REF##12964003##Ikezoe et al. 2003##; ##REF##15250252##Liu et al. 2004##, ##REF##16432862##2006##; ##REF##14738375##Meade-Tollin et al. 2004##; ##REF##15008459##Zhang et al. 2004a##). Oridonin inhibits cell growth and induces apoptotic cell death in many cancer cell lines, including leukemia (NB4, HL-60, HPB-ALL, Kasumi-1), glioblastoma (U118, U138), melanoma (A375-S2), cervical carcinoma (HeLa), ovarian carcinoma (A2780, PTX10), prostate carcinoma (LNCap, Du145, PC3), breast carcinoma (MCF-7, MDA-MB231), murine fibrosarcoma (L929), and non–small-cell lung carcinoma (NCI-H520, NCI-H460, NCI-H1299) (##REF##15703811##Chen et al. 2005##; ##REF##12964003##Ikezoe et al. 2003##; ##REF##15250252##Liu et al. 2004##, ##REF##16432862##2006##; ##REF##15008459##Zhang et al. 2004a##). The reported doses needed for growth inhibition and apoptosis vary significantly among different groups using different cell lines, ranging from 0.5 μM (0.18 μg/mL) in Kasumi-1 cells to 56 μM (20.4 μg/mL) in HPB-ALL cells (##REF##16432862##Liu et al. 2006##; ##REF##17197433##Zhou et al. 2007b##). In addition, oridonin enhances the efficacy of the cancer drug cisplatin in mouse sarcoma cells (##REF##8010060##Gao et al. 1993##).</p>", "<p>Mechanistic studies have provided a molecular basis by which oridonin inhibits cell growth and induces apoptosis. Oridonin induced p21 expression, resulting in cell cycle arrest in LNCaP and NCI-H520 cells (##REF##12964003##Ikezoe et al. 2003##). Oridonin activated the caspase 3–dependent apoptotic pathway through up-regulation of Bax and down-regulation of Bcl-2, which promotes release of cytochrome c (##REF##15703811##Chen et al. 2005##; ##REF##16432862##Liu et al. 2006##). Inhibition of telomerase activity has been reported to be another mechanism that contributes to the anticancer function of oridonin (##REF##15250252##Liu et al. 2004##). Because telomerase activity is absent in normal somatic cells but is up-regulated in cancer cells or tumor tissues, this allows oridonin to specifically target abnormal tissue. In addition, total tyrosine kinase activity was reduced in response to oridonin treatment (##REF##17268061##Li et al. 2007##). In addition to cancer cell lines, the efficacy of oridonin <italic>in vivo</italic> has been demonstrated in a colorectal carcinoma cell HT29-inoculated mouse model (##REF##17663191##Zhu et al. 2007##). More significantly, a recent study using both cell culture and mouse models demonstrated that oridonin displayed a great antitumor activity specifically in acute myeloid leukemia with the t(8;21) translocation between <italic>AML1</italic> and <italic>ETO</italic> genes. Mechanistically, oridonin induced the caspase 3–dependent cleavage of the AML1–ETO fusion protein, leading to an accelerated apoptotic response (##REF##17197433##Zhou et al. 2007b##).</p>", "<p>Here, we report that oridonin belongs to a novel class of Nrf2 activators. Similar to <italic>tert</italic>-butylhydroquinone (tBHQ), it inhibits ubiquitination and degradation of Nrf2, resulting in stabilization of Nrf2 and activation of the Nrf2 signaling pathway. Furthermore, the chemopreventive activity of oridonin was demonstrated using a previously established arsenic-UROtsa cell model. Pretreatment of UROtsa cells with 1.4 μM oridonin significantly enhanced the cellular redox capacity, reduced formation of reactive oxygen species (ROS), and improved survival of UROtsa cells after arsenic exposure.</p>" ]

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[ "<fig id=\"f1-ehp-116-1154\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>(<italic>A</italic>) Structure of the diterpenoid oridonin. (<italic>B, C</italic>) Luciferase reporter gene assays in MDA-MB-231 cells expressing ARE-luciferase. (<italic>B</italic>) Luciferase activity showing oridonin as an Nrf2 activator using a high-throughput screening system. The stable MDA-MB-231 cells expressing ARE-luciferase were seeded in 96-well plates; cells were grown to 90% confluence and treated with oridonin for 24 hr before analysis of luciferase activity. (<italic>C</italic>) Luciferase activity in MDA-MB-231 cells cotransfected with a plasmid containing an ARE-luciferase reporter gene and a plasmid encoding renilla luciferase driven by the herpes simplex virus thymidine kinase promoter. The transfected cells were treated with oridonin for 24 hr prior to measurement of firefly and renilla luciferase activities in cell lysates. All luciferase reporter gene assays were run in triplicate and expressed as mean ± SD.</p></caption></fig>", "<fig id=\"f2-ehp-116-1154\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Effects of oridonin on MDA-MB-231 cells. (<italic>A</italic>) An aliquot of cell lysates from the dual luciferase reporter gene assay was subjected to immunoblot analysis with anti-Nrf2, anti-Keap1, and anti-β-actin. (<italic>B</italic>) Total cell lysates from MDA-MB-231 cells treated with oridonin for 24 hr were subjected to immunoblot analysis with anti-Nrf2, anti-Keap1, and anti-β-actin antibodies. (<italic>C</italic>) mRNA from similarly treated cells was extracted and reverse transcribed into cDNA prior to real-time PCR analysis for detection of mRNA for <italic>Nrf2</italic> (top), <italic>NQO1</italic> (center), and <italic>HO-1</italic> (bottom).</p></caption></fig>", "<fig id=\"f3-ehp-116-1154\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Effects of oridonin on Nrf2 and Keap1 ubiquitination and protein half-life in MDA-MB-231 cells or UROtsa cells. Abbreviations: IB, immunoblot; IP, immunoprecipitation; <italic>t</italic>_1/2, half-life; Ub, ubiquitin. (<italic>A</italic>) MDA-MB-231 cells were cotransfected with expression vectors for HA-ubiquitin, a Gal4–Neh2 fusion protein, and Keap1; the transfected cells were left untreated or treated with 8.4 μM oridonin or 100 μM tBHQ for 4 hr, along with 10 μM MG132. Cells were lysed in 2% SDS and immediately heated. Anti-Gal4 (left) and anti-Keap1 (center) immunoprecipitates analyzed by immunoblot with anti-HA antibodies for detection of the ubiquitin-conjugated Neh2 or Keap1. (Right) Ubiquitination of endogenous Nrf2 assessed in UROtsa cells treated with DMSO, 8.4 μM oridonin, or 100 μM tBHQ for 4 hr, along with 10 μM MG132; Nrf2 was immunoprecipitated with an anti-Nrf2 antibody, and ubiquitinated Nrf2 was detected with an anti-ubiquitin antibody. (<italic>B</italic>) Protein half-life in MDA-MB-231 cells (left) and UROtsa cells (right) left untreated or treated with 8.4 μM oridonin for 4 hr. Cycloheximide (50 μM) was added to block protein synthesis. Cells were lysed at the indicated time points, and lysates were subjected for immunoblot analysis with anti-Nrf2 and anti-β-actin antibodies (top). Intensity of the bands was quantified using Quantity One software (bottom). In <italic>B</italic>, the bottom left and right graphs represent the bands in the top left and right, respectively.</p></caption></fig>", "<fig id=\"f4-ehp-116-1154\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Effect of oridonin on UROtsa cells. Abbreviations: AnnV, Annexin V; DCF, 2′,7′-dichlorodihydrofluorescein; PI, propidium iodide. (<italic>A</italic>) Immunoblot showing Nrf2, Keap1, and β-actin in UROtsa cells treated with oridonin for 24 hr; cell lysates were collected and subjected to immunoblot analysis with anti-Nrf2, anti-Keap1, and anti-β-actin antibodies. (<italic>B</italic>) Intracellular glutathione concentrations in UROtsa cells either untreated (control) or treated with 1.4 μM oridonin. Concentrations were measured using the QuantiChrom glutathione assay kit; values shown are the mean ± SD of experiments run in triplicate. (<italic>C</italic>) ROS analysis in UROtsa cells untreated or pretreated with oridonin for 24 hr and then further treated with As(III) or As(III) plus oridonin, respectively, for another 24 hr. ROS were measured by dichlorofluorescein/flow cytometry; values shown are the mean ± SD of experiments run in triplicate. (<italic>D</italic>) Cell survival in UROtsa cells untreated or pretreated with 1.4 μM oridonin for 24 hr and then treated with As(III) in the absence or presence of 1.4 μM oridonin for another 48 hr; values shown are the mean ± SD of experiments run in triplicate. (<italic>D,</italic> top) Cell survival measured by the MTT assay. (<italic>D,</italic> center, at right) Immunoblot analysis showing Nrf2 protein levels in UROtsa cells transfected with control siRNA or Nrf2-siRNA for 48 hr; Nrf2 protein levels were assessed by immunoblot analysis with an anti-Nrf2 antibody to con-firm knockdown of Nrf2 expression. (<italic>D,</italic> center) Cell survival in Nrf2-siRNA transfected cells at 48 hr posttransfection measured by the MTT assay; 200 cells in 35-mm plates were pretreated and cotreated in the same manner as in the MTT assay. (<italic>D</italic>, bottom) Cell survival measured by the colony formation assay. Values shown are the mean ± SD of experiments run in triplicate. (<italic>E</italic>) Apoptotic cell death in UROtsa cells untreated or pretreated with 1.4 μM oridonin for 24 hr and then treated with As(III) in the absence or presence of 1.4 μM oridonin for another 48 hr. Apoptotic cell death was detected using Annexin V-FITC and flow cytometry; the mean ± SD was calculated from experiments run in triplicate (center). (<italic>E</italic>, bottom) Apoptois in UROtsa cells grown on cover slides were pretreated and cotreated in the same way. Apoptotic cells were visualized by condensed nuclei using Hoechst staining; bars = 25 μm. The experiment was repeated, and similar results were obtained.</p><p>*<italic>p</italic> &lt; 0.05. **<italic>p</italic> &lt; 0.01.</p></caption></fig>" ]

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[ "<fn-group><fn><p>This work was supported by research grants from the National Institute of Environmental Health Sciences (1 R01 ES015010-01) and the American Cancer Society (RSG-07-154-01 -CNE) to D.D.Z. and by the China State Scholarship Fund to Y.D.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"ehp-116-1154f1\"/>", "<graphic xlink:href=\"ehp-116-1154f2\"/>", "<graphic xlink:href=\"ehp-116-1154f3\"/>", "<graphic xlink:href=\"ehp-116-1154f4\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<p>Full details of the OSCAR study population can be found elsewhere (##REF##12117647##Alfven et al. 2002##). In brief, subjects 16–80 years of age who had resided near a nickel-cadmium battery plant for at least 5 years between 1910 and 1992 were invited to participate in the OSCAR study; 904 (62%) agreed to participate. In addition, workers with previous or current occupational exposure were invited to participate, and 117 (48%) agreed to take part. In total, 1,021 individuals (60%) agreed to participate in the study. The OSCAR participants provided information on employment, residence, smoking, diet, medical history (especially regarding kidney diseases and diseases related to osteoporosis). Specially trained nurses collected urine and blood samples and measured height and weight. Forearm BMD was measured with an ambulant instrument (Osteometer DTX-200; Meditech A/S, Rødovre, Denmark), using dual energy X-ray absorptiometry, which is commonly used to evaluate BMD (##REF##10934657##Alfven et al. 2000##; ##REF##11256895##Glynn et al. 2000a##; ##REF##16234998##Wallin et al. 2005##). The distal site in the nondominant forearm was measured with the patient in a supine position. This site includes both the radius and the ulna from the 8-mm point (the point where the radius and ulna are separated by 8 mm) and 24 mm proximally, and contains 10–20% trabecular bone (##UREF##5##Schlenker and VonSeggen 1976##). Internal variation was checked by daily calibration using a phantom, and the measurements from the ambulant instrument were validated against a stationary hospital-based instrument (##REF##9816375##Jarup et al. 1998##).</p>", "<p>In addition to investigating BMD as a continuous variable, we created a dichotomous variable representing presence or absence of low BMD for use in logistic regression analyses. Low BMD was assessed by computing an age- and sex-standardized <italic>Z</italic>-score. A common definition of low BMD is <italic>Z</italic>-score less than −1 (##REF##9373575##Kanis et al. 1997##), which indicates 1 standard deviation below a sex- and age-standardized mean obtained from a reference population provided by the instrument supplier.</p>", "<p>Blood samples from a subset of the OSCAR cohort consisting of participants ≥ ⁶⁰ years of age (<italic>n</italic> = 325) were included in this study [insufficient sample remained and/or labeling was unclear in 23 out of 348 samples (6.6%), meaning that we could assess only 325 out of 348 eligible participants]. Samples were analyzed for five mono-<italic>ortho</italic> chlorine substituted congeners (CBs 105, 118, 156, 157, and 167), expressed in terms of toxic equivalency (TEQ_mono-<italic>_ortho</italic>), and individual CB-118 levels, to assess the effect of the dioxin-like activity. The sum of three most abundant non-dioxin-like (or di-<italic>ortho</italic> chlorine substituted) CBs 138, 153, and 180 (Σ_3PCB), and individual CB-153 levels were also analyzed. We measured CB-153 because the concentration of this congener correlates well with total and dioxin-like PCB concentrations in plasma and serum (##REF##11194153##Glynn et al. 2000b##). Σ_3PCB is considered to be an indicator of the content of total PCBs in human samples; this measure represented, on average, 61% of the human PCB body burden (##UREF##3##European Food Safety Authority 2005##). Finally, we also analyzed <italic>p,p</italic>′-DDE, the persistent metabolite of DDT (dichlorodiphenyltrichloroethane) because this organochlorine has been measured in other studies exploring the effects of organochlorines on BMD (##REF##10908100##Beard et al. 2000##; ##REF##11256895##Glynn et al. 2000a##; ##REF##16234998##Wallin et al. 2005##). Ethical approval for this study was obtained from the Karolinska Institutet ethics committee, and all participants gave their written, informed consent before the OSCAR study.</p>", "<p>The analytical method for the measurement of PCBs was initially developed in serum (##UREF##4##Mariani et al. 2002##; ##REF##14966849##Turci et al. 2004##), but has been extended to plasma and whole blood. Before the analysis of the OSCAR samples for this study, analyses of PCBs in whole blood, serum, and plasma derived from the same blood sample were performed, and results showed no major differences between these matrices in the distribution of the congeners, and the coefficients were close to 1 [see Supplemental Material, Figure 1 (online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11107/suppl.pdf\">http://www.ehponline.org/members/2008/11107/suppl.pdf</ext-link>)]. Analysis of whole blood, however, has been suggested to better reflect the PCB body burden (##REF##10595720##Janak et al. 1999##). Briefly, after thawing, aliquots of 0.5 mL of blood samples were spiked with a mixture of the eight PCB congeners (¹³C₁₂ labeled; Wellington Laboratories, Guelph, Ontario, Canada) and <italic>p,p</italic>′-DDE. Sulfuric acid was added to a final volume of 2.5 mL. After cleanup overnight on an Extrelut column (Merck, Darmstadt, Germany), samples were concentrated to a small volume and analyzed by high-resolution gas chromatography (CG)–high resolution mass spectrometry using a trace CG (Finnigan, Bremen, Germany) provided with a GC PAL autosampler (CTC-Analytics, Zwingen, Switzerland) coupled to a Thermofinnigan MAT95XP mass spectrometer. The mass spectrometer was operated in electron ionization mode, using selected ion monitoring at a mass resolution of 10,000. Quantification was done by the isotopic dilution method. We carried out blank analyses routinely for every 20 samples, and for the definition of the limit of detection (LOD), a signal-to-noise ratio of 3:1 was chosen. For the TEQ_mono-<italic>_ortho</italic> calculation the latest toxic equivalency factors (TEFs) were used (##REF##16829543##Van den Berg et al. 2006##) and when the CB concentration values were below the LOD, they were entered as 0 (lower bound method).</p>", "<p>We evaluated associations between organochlorine levels and BMD in SPSS 12.0 for Windows (SPSS Inc., Chicago, IL, USA) using univariate and multivariate linear regression and logistic regression analyses. Given the probable hormonal action of organochlorines on bone, and given the very large difference in osteoporosis incidence in males and females after 60 years of age, it seemed most appropriate to assess the relationships between organo-chlorines and BMD measures in males and females separately. Information on potentially confounding lifestyle factors and food consumption habits collected for the OSCAR cohort were included in the linear regression model if they fulfilled the stepwise criteria (probability-of-<italic>F</italic>-to-enter ≤ 0.10, probability-of-<italic>F</italic>-to-remove ≥ 0.15) to aid the interpretation of the results. Because the OSCAR cohort was originally set up to explore the effect of cadmium on BMD, blood cadmium was included as a potential confounding variable in the linear and logistic regression analyses presented.</p>" ]

[ "<title>Results</title>", "<p>The blood concentration levels ranged from 0.002 to 0.067 pg TEQ_mono-<italic>_ortho</italic>/mL for the dioxin-like PCBs, from 438 to 8,960 pg/mL for Σ_3PCB and from 16.4 to 17,500 pg/mL for <italic>p,p</italic>′-DDE (##TAB##0##Table 1##). Women had significantly higher levels of CB-118 and <italic>p,p</italic>′-DDE than did men.</p>", "<p>In unadjusted analyses, significant negative correlations were seen between CB-118 and BMD (Pearson correlation coefficient = −0.130; <italic>p</italic> = 0.020) when males and females were investigated as one group. When males and females were investigated separately, none of the individual CB congeners was significantly correlated with BMD. There were significant positive correlations between each of the five organochlorine markers of interest. The dioxin-like organochlorines CBs 105, 118, 156, and 167 were all strongly correlated with TEQ_mono-<italic>_ortho</italic> in males, females, and the whole group (Pearson correlation coefficient &gt; 0.6); CB-157 showed far weaker, even non-significant correlations with the other organo-chlorines. CB-118 showed consistently strong correlations with the TEQ_mono-<italic>_ortho</italic> measure (correlation coefficient &gt; 0.9) and with the Σ_3PCB measure (correlation coefficient &gt; 0.6). The abundant non-dioxin-like congeners were always strongly correlated with each other (correlation coefficient &gt; 0.6), and CB-153 was always very strongly correlated with the Σ_3PCB measure (correlation coefficient &gt; 0.9). In males, <italic>p,p</italic>′-DDE showed relatively strong correlations with TEQ_mono-<italic>_ortho</italic>, CB-118, Σ_3PCB, and CB153 (correlation coefficient ~ 0.5), though these correlations were weaker in women (correlation ~ 0.4).</p>", "<p>As anticipated, in bivariate analyses, age was negatively correlated with BMD, and BMI was positively correlated with BMD (data not shown). In males, alcohol was negatively correlated and milk consumption was positively correlated with BMD. In females, age at menopause and number of reproductive years (age at menopause minus age at menstruation) were both positively correlated with BMD; age at menstruation was negatively correlated with BMD. Contraceptive pill use was associated with an increased BMD, as was ever having been pregnant.</p>", "<p>##TAB##1##Table 2## shows the results from multivariate linear regression analysis in males and females. In males, multivariate linear regression analysis indicated that age, BMI, blood cadmium, and milk consumption explained 25% of the variability in BMD; none of the organochlorines was significantly associated with BMD when entered into the model individually. The organochlorine variables were then entered stepwise into the model, sequentially, in order of the smallest probability of <italic>F</italic> (if <italic>F</italic> ≤ 0.10), until no more variables were eligible for inclusion or removal (<italic>F</italic> ≥ 0.15). In this model CB-118 was negatively associated (B = −0.00024; <italic>p</italic> = 0.002) and Σ_3PCB positively associated (B = 0.00002; <italic>p</italic> = 0.003) with BMD in males, explaining an additional 6% of the variability in BMD. In females, multivariate linear regression analysis indicated that age, BMI, blood cadmium, age at menstruation, and ever having being pregnant explained 39.5% of the variability in BMD. When organochlorines were entered individually into this model, CB-118 was significantly positively associated with BMD (B = 0.00008; <italic>p</italic> = 0.045), explaining a further 2% of the variability in BMD. No additional organochlorines contributed significantly to the model after stepwise entry. Variables representing interactions between the organochlorines and cadmium did not explain any further variability in BMD.</p>", "<p>A dichotomous variable representing presence or absence of low BMD (defined as <italic>Z</italic>-score less than −1) was used in logistic regression analyses; 47 of 154 (30.5%) of the males, and 31 of 167 (18.6%) of the females had low BMD. Those organochlorines that were found to significantly explain variability in BMD after stepwise entry into the multivariate linear regression models (CB-118 and Σ_3PCB in males and CB-118 in females) were included in the multivariate logistic regression as categorical variables (tertiles). Where a linear dose–response relationship seemed apparent by tertiles, these variables were reentered into the model as continuous variables to maximize power in assessing the dose–response relationship. ##TAB##2##Table 3## shows the results from logistic regression analysis in males and females. In males, the odds ratio (OR) for low BMD decreased with increasing BMI and milk consumption and increased with increasing blood cadmium levels. The risk of low BMD increased by tertile of CB-118, and when analyzed as a continuous variable, the OR was 1.06 (95% confidence interval 1.01–1.12) for every 10 pg/mL CB-118. The risk of low BMD did not show a clear trend across tertiles of Σ_3PCB. In women, the ORs for low BMD decreased with increasing BMI and increased with increasing age at menstruation; however, the risk of low BMD did not show a consistent trend with tertiles of CB-118.</p>" ]

[ "<title>Discussion</title>", "<p>The present study is one of the largest to date to assess the relationship between organochlorine levels in blood and BMD in an environmentally exposed population. The findings of this study suggest that even at relatively low levels of organochlorine exposure, BMD might be affected, at least in males, after controlling for major confounding variables. Despite the large variability of the individual values, spanning over three orders of magnitude, the blood concentration levels of these pollutants in this population are in the range of the values detected in human serum and/or blood samples for the general population (##REF##15261782##Botella et al. 2004##; ##REF##14966849##Turci et al. 2004##; ##REF##12872532##Wilhelm et al. 2003##). Nonetheless, there was a significant positive dose–response relationship in men between the dioxin-like congener CB-118 and BMD (OR = 1.06 per 10 pg/mL; 95% confidence interval 1.01–1.12). In females, after finding a small but significant positive association between CB-118 and BMD in the linear regression analysis, we anticipated a decrease in risk of low BMD with increasing levels of CB-118; however, the risk of low BMD did not show a consistent trend with tertile of CB-118. This contradictory finding, in conjunction with the small change in proportion of BMD variability explained by this variable in the linear regression suggests that CB-118 is unlikely to exert an important influence on BMD in females in this population sample.</p>", "<p>In this study, women had significantly higher levels of CB congener 118 and <italic>p,p</italic>′-DDE than did men. Consistently higher levels of CB-118 in women were also reported in Native Americans in the United States (##REF##15935445##Schaeffer et al. 2006##), but higher levels of <italic>p,p</italic>′-DDE in women have not been reported elsewhere. In fact, other studies have reported no difference in <italic>p,p</italic>′-DDE levels by sex (##REF##16688357##Sandanger et al. 2006##) or reported higher <italic>p,p</italic>′-DDE levels in males than in females (##REF##11209822##Bjerregaard et al. 2001##; ##REF##16283941##Jonsson et al. 2005##). Lower levels of <italic>p,p</italic>′-DDE in younger females are likely attributable to breast-feeding (which may reduce plasma levels of persistent organic pollutants) and lower dietary exposure; as such, this sex difference is likely to be less evident in populations &gt; 40 years of age, by which age the sex imbalance in organochlorine accumulation due to breast-feeding will be reduced (##REF##12948250##Sandanger et al. 2003##).</p>", "<p>These sex differences in organochlorine levels are unlikely to be explained by age, which was similar for the males and females in this study. The main source of exposure to organochlorines for this population is anticipated to be dietary, mainly via fish, to some extent caused by intake of salmon from the Emån river (a well-known salmon-fishing river), which has been polluted with PCBs from an upstream paper mill. More important, the local population may be exposed to PCBs by eating fish from the nearby Baltic Sea. Farmed salmon and wild herring in the Baltic Sea are known to have high concentrations of PCBs. Although individual-level data on fish consumption were not available for analysis, it has been reported by the Swedish National Food Administration that older women eat more fish than older men (##UREF##1##Becker et al. 2007##). The difference observed in levels of organochlorines in this study could therefore be attributable to a combination of factors including sex-specific differences in consumption patterns, differential metabolism (##REF##9520360##Landi et al. 1998##), or elimination by lactation (##REF##12948250##Sandanger et al. 2003##), although these mechanisms have not been shown to be specific to CB-118 or to <italic>p,p</italic>′-DDE.</p>", "<p>No other epidemiologic studies have reported similar sex differences with respect to BMD to those found in this study, although most studies on this topic have focussed on single-sex populations. Similarly the literature on organochlorine exposure and sex-specific effects on BMD in experimental animals is very limited (##REF##16984958##van der Ven et al. 2006##), although sex differences with respect to other outcomes have been observed, possibly indicating a greater sensitivity of males to reproductive system and neurobehavorial effects (##REF##9299592##Gray et al. 1997a##, ##REF##9344891##1997b##). Whether the likely hormonal action of organochlorines could explain or contribute to the different effects on BMD seen in males and females in this study is not clear.</p>", "<p>The findings from this study suggest an effect of organochlorine exposure on BMD at levels of exposure experienced in a population in southern Sweden, but there are several limitations of the study that need to be considered. Although this is one of the largest studies to date, when males and females were split into separate groups for analysis, the power of the study to detect statistically significant relationships between organochlorine exposure and BMD was limited. In this study we had data on blood levels of organochlorines in adults, but we do not know about early life exposures, which may be more important than adult exposures in determining the effects of organochlorines on bone. Although it was possible to control for several potentially confounding variables, the linear regression analyses indicate that more than half the variability in BMD is likely to be explained by other, unknown variables that could not be taken into account in this study. Further work should be directed toward establishing whether the sex-specific effects observed in this study are also evident in other environmentally exposed populations. More detailed lifestyle data could be used to elucidate whether any sex-specific effects are caused by a hormonal action of the organochlorines (or the hormonal status of the individuals at the time of exposure), or whether these observations are due to other sex-specific differences related to lifestyle, diet, or occupation.</p>", "<p>The purpose of this study was to investigate the relationship between environmental organochlorine exposure and BMD in a population potentially exposed to PCBs from the environment to add to the sparse and inconsistent literature on this important topic. The findings of this study indicate that exposure to some CB congeners, even at relatively low levels, may influence BMD and that this effect may be sex specific.</p>" ]

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[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Bone toxicity has been linked to organochlorine exposure following a few notable poisoning incidents, but epidemiologic studies in populations with environmental organochlorine exposure have yielded inconsistent results.</p>", "<title>Objectives</title>", "<p>The aim of this study was to investigate whether organochlorine exposure was associated with bone mineral density (BMD) in a population 60–81 years of age (154 males, 167 females) living near the Baltic coast, close to a river contaminated by polychlorinated biphenyls (PCBs).</p>", "<title>Methods</title>", "<p>We measured forearm BMD in participants using dual energy X-ray absorptiometry; and we assessed low BMD using age- and sex-standardized <italic>Z</italic>-scores. We analyzed blood samples for five dioxin-like PCBs, the three most abundant non-dioxin-like PCBs, and <italic>p,p</italic>′-dichloro-phenyldichloroethylene (<italic>p,p</italic>′-DDE).</p>", "<title>Results</title>", "<p>In males, dioxin-like chlorobiphenyl (CB)-118 was negatively associated with BMD; the odds ratio for low BMD (<italic>Z</italic>-score less than −1) was 1.06 (95% confidence interval, 1.01–1.12) per 10 pg/mL CB-118. The sum of the three most abundant non-dioxin-like PCBs was positively associated with BMD, but not with a decreased risk of low BMD. In females, CB-118 was positively associated with BMD, but this congener did not influence the risk of low BMD in women.</p>", "<title>Conclusions</title>", "<p>Environmental organochlorine exposures experienced by this population sample since the 1930s in Sweden may have been sufficient to result in sex-specific changes in BMD.</p>" ]

[ "<p>It is well known that hormones, vitamins, pharmaceuticals, and metals can have adverse effects on bone. Bone effects, mainly congenital, have also been linked to persistent organochlorine exposure following a few notable poisoning incidents. High-level, accidental dietary exposure to hexachlorobenzene resulted in severe osteoporosis (##REF##6487543##Cripps et al. 1984##; ##REF##2590490##Gocmen et al. 1989##), and infants exposed <italic>in utero</italic> to high concentrations of polychlorinated biphenyls (PCBs) and poly-chlorinated dibenzofurans developed irregular calcification of their skull bones (##REF##3928346##Miller 1985##).</p>", "<p>Recent epidemiologic studies in populations with environmental organochlorine exposure have yielded inconsistent results regarding whether organochlorine exposure has an effect on bone properties. Several single-sex studies have indicated a possible association between organochlorine exposure and bone quality. In 115 Swedish men from the general population, a significant association was seen between serum concentrations of the organochlorines chlorobiphenyl (CB)-28 and γ-hexachlorocyclohexane and broadband ultrasound attenuation [an indirect estimation of bone mineral density (BMD)] of the left os calcis, and between CB-167 and total-body BMD after adjustment for confounding variables (##REF##11256895##Glynn et al. 2000a##). In 68 sedentary Australian women, a negative correlation between <italic>p,p</italic>′-dichlorodiphenyldichloroethylene (<italic>p,p</italic>′-DDE) levels and BMD was observed, suggesting that past exposure to DDT may be associated with reduced BMD in women (##REF##10908100##Beard et al. 2000##). Other studies have not found organochlorine exposure to be associated with bone effects. In 103 peri- and post-menopausal women from the United States, no correlation was found between <italic>p,p</italic>′-DDE concentration and bone density or rate of bone loss at the spine or radius (##REF##11128875##Bohannon et al. 2000##). In 153 peri- and postmenopausal Inuit women with high organochlorine exposure from their seafood diet, concentrations of CB-153 were inversely correlated with bone parameters in univariate analyses, but, again, this relationship was no longer evident after adjustment for potential confounding variables (##REF##17184534##Cote et al. 2006##).</p>", "<p>One study investigated bone effects in males and females from Sweden. In the initial study, the risk of hospitalization from fracture in fishermen and their wives was compared between those living on the east coast of Sweden (near the Baltic sea, where environmental organochlorine exposure is likely attributable to consumption of contaminated fatty fish) and those living on the west coast (##REF##12690500##Alveblom et al. 2003##). There was a significantly increased incidence rate ratio for vertebral fractures among east coast fisherman’s wives, with a similar (nonsignificant) tendency in east coast fishermen after adjustment for age and calendar year, compared with those dwelling on the west coast. It should be noted that this exposure assessment is rather crude and that other fracture types did not show a similar trend. Following from this study, the relationship between serum levels of CB-153 and <italic>p,p</italic>′-DDE and BMD in a subset of the Baltic coast fishermen (<italic>n</italic> = 196) and their wives (<italic>n</italic> = 184) was further studied (##REF##16234998##Wallin et al. 2005##). Univariate analyses revealed significant negative associations between CB-153 and BMD in males and females, but after adjustment for age and body mass index (BMI), these relationships did not persist.</p>", "<p>Although the epidemiologic evidence is inconsistent, an increasing number of experimental studies lend biological plausibility to organochlorine-induced bone effects (##REF##2501909##Andrews 1989##; ##UREF##0##Badraoui et al. 2007##). Animal studies also have demonstrated that the timing of exposure can be critical, with effects being observed at lower dose levels when exposure occurs during earlier life stages; furthermore, the same compound seems to have the potential to modulate bone quality differently depending on the developmental stage at exposure (##REF##11585345##Jamsa et al. 2001##; ##REF##15746008##Miettinen et al. 2005##). The estrogen status of the exposed individual has also been shown to influence the toxicity of organochlorines on bone. Studies in rats exposed to the dioxin-like CB-126 showed that bone strength and composition are impaired and that estrogen can modulate the induced effects depending on the estrogen status of the individual (##REF##9931283##Lind et al. 1999##, ##REF##10996662##2000##, ##REF##15147787##2004##). CB-126 exposure did not affect bone mineral density or trabecular bone volume of tibia in sham-operated rats. In contrast, in estrogen-deprived ovariectomized rats, CB-126 exposure resulted in a decreased length and increased bone mineral density of tibia. Furthermore, estrogen supplementation modulated CB-126-induced effects on bone tissue in rats (##REF##9931283##Lind et al. 1999##, ##REF##10996662##2000##, ##REF##15147787##2004##). Because of these various influences, the effect of exposure to a number of different organochlorines is likely to be difficult to predict; nonetheless, these animal studies suggest a possible causal relationship between organochlorine exposure and adverse bone effects. Such a relationship, if applicable to humans, could play a role in the observed increase in osteoporosis and osteoporotic fractures in the western world (##REF##10692972##Genant et al. 1999##; ##REF##12111017##Ismail et al. 2002##).</p>", "<p>The purpose of the present study was to investigate the relationship between organo-chlorine exposure and BMD in a subset of the Osteoporosis Cadmium as Risk Factor (OSCAR) cohort. The OSCAR cohort was established to investigate the effect of low-level cadmium exposure on bone and kidneys (##REF##12117647##Alfven et al. 2002##, ##REF##15125789##2004##). Because many of the examined individuals live close to the Baltic coast, these people might also have experienced elevated dietary PCB exposure from consuming contaminated fatty fish. In addition, there was potential for PCB exposure from a nearby contaminated river, which was polluted with PCB containing paper pulp from an upstream paper mill (##UREF##2##Bremle et al. 1998##).</p>" ]

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[ "<table-wrap id=\"t1-ehp-116-1162\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Characteristics of study participants, including mean, minimum, and maximum blood levels of measured organochlorines, and percent of samples below the LOD.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"3\" align=\"left\" rowspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Males (<italic>n</italic> = 154)\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Females (<italic>n</italic> = 167)\n<hr/></th></tr><tr><th colspan=\"3\" align=\"left\" rowspan=\"1\">Continuous variables</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Range</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Range</th></tr></thead><tbody><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Age</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">70.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">60–81</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">69.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">60–81</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">BMI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.1–34.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.5–46.3</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Blood cadmium (nmol/L)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.4<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0–84.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.4<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0–45.9</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Alcohol (g/week)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.1<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0–181</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.9<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0–44</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Milk (dL/week)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36.1<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0–140</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26.7<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0–70</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Age at menstruation (<italic>n</italic> = 135)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10–18</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Age at menopause (<italic>n</italic> = 146)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">48.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">30–85</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Reproductive years (<italic>n</italic> = 123)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15–75</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">BMD (g/cm²)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.51<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27–0.71</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.38<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.13–0.64</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Dichotomous variables</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Yes (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No (%)</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">\n<hr/></td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Low BMD (<italic>Z</italic>-score less than −1)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">47 (30.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">107 (69.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31 (18.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">136 (81.4)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Cortisone (&gt; 3 months)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18 (12)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">132 (88)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16 (9.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">150 (90.4)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Ever pregnant</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">142 (85.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24 (14.5)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Contraceptive pill use (<italic>n</italic> = 156)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26 (16.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">130 (86.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Organochlorines (pg/mL)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">LOD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">% &lt; LOD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Range</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Range</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">\n<hr/></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-105</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–421</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">49.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–366</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-118</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">184<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–1,359</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">236<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–1,145</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-156</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">116<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–450</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">98.5<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–243</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-157</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–211</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–64.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-167</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–172</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–127</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TEQ_mono-<italic>_ortho</italic></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.012</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.002–0.067</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.0130</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.003–0.053</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-138</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">561</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.0–2,239</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">582</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">121–1,461</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-153</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,290</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">196–4,360</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,260</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">328–4,587</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-180</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">819<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">117–3,309</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">706^{<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref>}</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; LOD–1,966</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Σ_3PCB</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,670</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">438–8,957</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,548</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">717–7,010</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>p,p</italic>′-DDE</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,405<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16–14,268</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3,126<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1162\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">259–17,519</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1162\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Multivariate linear regression analyses showing associations between BMD (g/cm²) and explanatory variables.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">Males<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1162\">a</xref>\n<hr/></th><th colspan=\"3\" align=\"center\" rowspan=\"1\">Females<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1162\">b</xref>\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Coefficient (B)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">SE</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Coefficient (B)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">SE</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.004009</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.001123</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.005635</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000953</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">BMI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.008255</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.002006</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.006300</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.001209</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ln blood-Cd (nmol/L)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.013383</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.005946</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.026</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.010184</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.008257</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.220</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Milk (dL/week)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000544</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000245</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Age at menstruation</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.009020</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.003598</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.013</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ever pregnant</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.037579</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.015894</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.020</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-118</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000110</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000062</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.079</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000080</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000039</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.045</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TEQ_mono-<italic>_ortho</italic></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.225387</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.156013</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.846</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.652927</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.861054</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.057</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CB-153</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000011</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000011</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.331</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000009</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000010</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.385</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Σ_3PCB</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000008</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000006</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.154</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000007</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000006</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.223</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>p,p</italic>′-DDE</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000003</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000003</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.323</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000003</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.000002</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.184</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1162\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Logistic regression model for low BMD (<italic>Z</italic>-score less than −1) in males and females.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Males (<italic>n</italic> = 150)<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1162\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> BMI</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.83 (0.72–0.95)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.009</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ln Blood cadmium (nmol/L)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.76 (1.19–2.59)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Milk consumption (dL/week)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.97 (0.95–0.99)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> CB-118 (&lt; 33rd percentile)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.00 (referent)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> CB-118 (33rd–67th percentile)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.48 (0.55–3.97)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.437</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> CB-118 (&gt; 67th percentile)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.11 (0.63–7.12)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.227</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> CB-118 (continuous variable)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.06 (1.01–1.12)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.027</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Σ_3PCB (&lt; 33rd percentile)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.00 (referent)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Σ_3PCB (33rd–67th percentile)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.17 (0.44–3.16)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.753</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Σ_3PCB (&gt; 67th percentile)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.91 (0.27–3.02)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.879</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Σ_3PCB (continuous variable)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.00 (0.99–1.00)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.101</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Females (<italic>n</italic> = 134)<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1162\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> BMI</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.80 (0.70–0.93)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ln Blood cadmium (nmol/L)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.88 (0.40–1.92)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.745</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Age at menstruation</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.48 (1.07–2.03)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.016</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Ever pregnant</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.47 (0.13–1.70)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.251</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> CB-118 (&lt; 33rd percentile)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.00 (referent)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> CB-118 (33rd–67th percentile)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.29 (0.66–7.92)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.191</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> CB-118 (&gt; 67th percentile)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.13 (0.58–7.84)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.255</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>Supplemental Material is available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11107/suppl.pdf\">http://www.ehponline.org/members/2008/11107/suppl.pdf</ext-link></p></fn><fn><p>We thank the European Commission (EU-QLK-CT-2002-02528) and the Swedish Environmental Protection Agency for financial support. Ethical approval for this study was obtained from the Karolinska Institutet ethics committee.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1162\"><label>*</label><p>Significant difference between males and females (<italic>p</italic> &lt; 0.05).</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn2-ehp-116-1162\"><p>Age, BMI, blood cadmium, and milk (males) or age at menstruation and ever pregnant (females) entered simultaneously; organochlorines then entered individually into the model.</p></fn><fn id=\"tfn3-ehp-116-1162\"><label>a</label><p>Adjusted for age, BMI, blood-cadmium, and milk consumption (final sample size = 150).</p></fn><fn id=\"tfn4-ehp-116-1162\"><label>b</label><p>Adjusted for age, BMI, blood-cadmium, age at menstruation, and ever pregnant (final sample size = 134).</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn5-ehp-116-1162\"><p>CI, confidence interval. In males, BMI, blood cadmium, milk, CB-118 (tertiles) and Σ_3PCB (tertiles) were entered simultaneously; tertiles of CB-118 and Σ_3PCB were then replaced with the continuous variables, also entered simultaneously. In females, BMI, blood cadmium, age at menstruation, ever pregnant, and CB-118 (tertiles) were entered simultaneously.</p></fn><fn id=\"tfn6-ehp-116-1162\"><label>a</label><p>Final sample size.</p></fn></table-wrap-foot>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>Study population and outcomes</title>", "<p>We studied class III HF patients who were participating in the COMPASS-HF (Chronicle Offers Management to Patients with Advanced Signs and Symptoms of Heart Failure) trial and were implanted with the Chronicle at either Robert Wood Johnson University Hospital in New Brunswick, New Jersey, or Beth Israel Medical Center in Newark, New Jersey (<italic>n</italic> = 13 subjects). In this trial, the Chronicle continuously measured and stored intracardiac pressure, body temperature, physical activity, and heart rate. This device has been described in detail previously (##REF##18342224##Bourge et al. 2008##). Briefly, the device includes a programmable unit similar to the pulse generator of a pacemaker, and a transvenous lead with a sensor near its tip to measure intracardiac pressures. The device is positioned subcutaneously in the pectoral area. The lead is placed transvenously with its tip located in the RV outflow tract or high septum. Approximately every 8 min, the device stores the medians of several pressure metrics, including estimated PA diastolic pressure (ePAD), RV diastolic pressure, RV systolic pressure, mean PA pressure (MPAP), physical activity, and heart rate (beats per minute). ePAD is defined as the RV pressure at the time of pulmonary valve opening and is correlated (<italic>r</italic> = 0.84) with actual PA pressure (##UREF##1##Magalski et al. 2002##; ##UREF##2##Ohlsson et al. 1995##; ##REF##11914262##Zile and Brutsaert 2002##). Physical activity level was measured by an accelerometer in the chest wall device. Any physical activity sensed by the accelerometer is measured and averaged over the programmed storage interval (i.e., 8 min). A physical activity of 0 means that the sensor did not detect any activity during that interval.</p>", "<p>Each week, these measurements were transmitted by patients via telephone line and subsequently downloaded to each patient’s cardiologist using the Internet. As part of the COMPASS-HF trial, subjects were randomized subjects to either “total clinician access,” which meant the cardiologist could view these data weekly and, if necessary, prescribe changes in medications or therapy, or “blocked clinician access,” which meant the cardiologist could not view these pressure data and could monitor the subject only during regularly scheduled clinic visits (i.e., the normal standard of care). Details of this trial have been published previously (##REF##18342224##Bourge et al. 2008##). A barometric pressure monitor (Chronicle Tracker, Medtronic, Inc.) worn by the patient also made continuous measurements and stored values for the same time intervals as the heart pressure readings. As part of the COMPASS-HF trial, ejection fraction was obtained by standard clinical echocardiography. Heart failure etiology was based on clinical criteria. If there was known coronary artery disease or history of myocardial infarction, patients were categorized as ischemic. Patients without these conditions were categorized as non ischemic.</p>", "<p>Of the original 13 study subjects implanted with the Chronicle, we excluded one patient who lived in New York because we could not match a New Jersey air pollution monitor to that person. We also excluded a second subject who died of septic shock 24 days after device implantation, leaving 11 subjects for analysis. For each subject, we included all pressure data from the date of implant through the date of death or last data transmittal before 11 January 2007 [range, 358–1,090 days; median, 846 days (2.32 years)]. For our primary analysis examining changes in daily pressure and daily PM_2.5 concentration, we excluded all intervals where the subject’s physical activity was &gt; 0 and then calculated mean PA and RV pressures for each day of observation, leaving <italic>n</italic> = 5,807 person-days (<italic>n</italic> = 11 subjects) for analysis. The University of Medicine and Dentistry of New Jersey Institutional Review Board (New Brunswick and Piscataway campuses) approved this study.</p>", "<title>PM and weather measurements</title>", "<p>PM_2.5 was measured continuously in New Brunswick, Camden, Elizabeth, Jersey City, and Rahway, New Jersey, during the study period by the New Jersey Department of Environmental Protection (Trenton, NJ) using a tapered element oscillating microbalance. We retrieved hourly concentrations at these stations from the U.S. Environmental Protection Agency (EPA) website (##UREF##5##U.S. EPA 2007##). For each subject, we calculated the distance between each PM_2.5 monitor and the subject’s residence, assigning PM_2.5 measurements from the closest monitor to their residence. We assigned all subjects PM_2.5 measurements from either the New Brunswick or Elizabeth monitoring sites (median distance, 13.4 km; range, 7.0–34.2 km).</p>", "<p>Temperature and dew point were measured hourly at the Newark, Caldwell, Somerset, and Trenton airports during the study period. We used the airport monitor closest to each subject’s residence to provide the weather observations for that subject during the study period. For each day, we calculated apparent temperature (##UREF##0##Kalkstein and Valimont 1986##; ##REF##17182639##O’Neill et al. 2007##; ##UREF##4##Steadman 1979##; ##REF##16079066##Zanobetti and Schwartz 2005##) as a measure of each subject’s perceived air temperature given the humidity [apparent temperature = –2.653 + (0.994 × air temperature) + (0.0153 × dewpoint2)] and used these values in all analyses.</p>", "<title>Statistical analysis</title>", "<p>We used a two-step modeling process to estimate the change in daily mean ePAD associated with each incremental increase in same-day (i.e., lag 0) mean PM_2.5 concentration. In the first step, each subject appeared to have different long-term patterns in ePAD during the follow-up period (data not shown). Therefore, we removed long-term trends in ePAD separately for each person via spline-based nonparametric regression. We regressed a natural cubic spline (3 degrees of freedom) of time against ePAD and obtained residuals from this model to be used as the outcome in the second step of the analysis. We found that higher degrees of freedom were not necessary for modeling the long-term trends and did not substantially alter the results in the second step of the analysis (data not shown). In the second step, focusing on average short-term pressure changes associated with same-day PM_2.5 concentration, we used a repeated-measures model with these residuals as the response, combining all subjects’ data into a single model. We regressed fixed factors of same-day PM_2.5 concentration, day of week, and month, as well as quadratic effects of apparent temperature and barometric pressure, against these residuals, using a first-order autocorrelation structure, such that we modeled measurements separated by <italic>d</italic> days to have a correlation of ρ<italic>_d</italic>, where ρ represents the correlation between measurements from 2 successive days. We then repeated this procedure to examine separately the changes in daily RV diastolic pressure, RV systolic pressure, and MPAP associated with each 11.62 μg/m³ increase (the interquartile range observed during the study period) in same-day mean PM_2.5 concentration. Relative to compound symmetry, we found the first-order autocorrelation structure provided a better fit for the data as determined by Akaike’s information criterion.</p>", "<p>Next, to evaluate whether the assumption of a linear exposure response function was justified, we examined the concentration–response using quintiles of PM_2.5 and performed a test of trend. Last, to evaluate whether any association between daily right heart pressure and same-day PM_2.5 concentration was independent of previous days’ mean PM_2.5 concentrations, we repeated this two-stage modeling process including the mean PM_2.5 concentrations from lag days 0–6, rather than just lag 0, in the same model. For each analysis, we present the unit change in right heart pressure associated with each 11.62 μg/m³ increase in PM_2.5 concentration, and its 95% CI.</p>", "<p>We also evaluated effect modification of any pressure–PM_2.5 association by randomization group (total clinician access vs. blocked clinician access), left ventricular ejection fraction (LVEF) at implantation (≥ 45% vs. &lt; 45%), season (winter vs. summer), and body mass index (BMI) at implantation (obese, BMI ≥ 30, vs. nonobese, BMI &lt; 30), by adding an interaction term (effect modifier ×PM_2.5) to the mixed model described above. We present the unit change in right heart pressure associated with each 11.62 μg/m³ increase in PM_2.5 concentration for each category of each potential effect modifier. We conducted all analyses using SAS/STAT software, version 9.1.3 for Windows (SAS Institute Inc., Cary, NC).</p>" ]

[ "<title>Results</title>", "<p>Subjects were predominantly white (73%), and obese (73% had BMI ≥ 30) and ranged in age from 25 to 68 years (median, 57 years). All but two subjects had LVEFs &lt; 45% at Chronicle implantation, and seven (64%) also had an implantable cardioverter defibrillator (ICD) or biventricular ICD (Bi-V ICD) (##TAB##0##Table 1##). All subjects (<italic>n</italic> = 11) were taking beta-blockers and diuretics during the study period, with lower but substantial proportions taking angiotensin converting enzyme inhibitors (<italic>n</italic> = 9), amiodarone (<italic>n</italic> = 4), digoxin (<italic>n</italic> = 8), anticoagulants (<italic>n</italic> = 6 ), aspirin (<italic>n</italic> = 6), and antihyperlipidemia medications (<italic>n</italic> = 4).</p>", "<p>We found significant increases in daily mean ePAD and RV diastolic pressure, and nonsignificant increases in daily mean RV systolic pressure and MPAP, associated with each 11.62-μg/m³ increase in same-day mean PM_2.5 concentration, after adjusting for long-term time trends, calendar month, weekday, and apparent temperature (##TAB##1##Table 2##). Because we found larger, significant associations with ePAD and RV diastolic pressure, we restricted all further analyses to these pressure measurements.</p>", "<p>Changes in RV diastolic pressure and ePAD generally increased with quintiles of PM_2.5 (##FIG##0##Figure 1##), and the tests for trend were statistically significant for both pressures (RV diastolic pressure, <italic>p</italic> &lt; 0.001; ePAD, <italic>p</italic> = 0.006). When we included the mean PM_2.5 concentrations for lag days 1–6 in the same model, ePAD and RV diastolic pressure increases associated with each 11.62-μg/m³ increase in same-day (lag 0) mean PM_2.5 concentration were not substantially attenuated and remained statistically significant (ePAD: 0.17 mmHg; 95% CI, 0.02–0.32; RV diastolic pressure: 0.23 mmHg; 95% CI, 0.10–0.35) (##FIG##1##Figure 2##).</p>", "<p>We found no statistically significant effect modification of the PM_2.5–RV diastolic pressure association by access randomization group, LVEF at implantation, season, or BMI at implantation (##TAB##2##Table 3##). However, the unit change in RV diastolic pressure was largest for the blocked clinician access randomization group compared with the total clinician access group, subjects with LVEF ≥ 45% at implantation compared with subjects with LVEF &lt; 45%, winter versus summer person-days, and obese subjects compared with nonobese subjects (##TAB##2##Table 3##).</p>" ]

[ "<title>Discussion</title>", "<p>In a pilot study using continuous PA and RV pressures in 11 class III HF patients and ambient measurements of PM_2.5 at nearby monitoring stations, we observed small, but statistically significant increases in mean daily ePAD and RV diastolic pressure (0.19 and 0.23 mmHg, respectively) associated with each 11.62-μg/m³ increase in mean PM_2.5 concentration on the same day. These increases were not attenuated when controlling for the previous 6 lag days of mean PM_2.5 concentration. Further, the concentration–response functions were generally linear. However, we found no statistically significant effect modification by access randomization group, LVEF at implantation, obesity, or season. These findings add mechanistic plausibility to previous studies’ reports of increased hospital admissions for HF associated with ambient PM concentrations in the previous few days (##REF##10094292##Burnett et al. 1999##; ##REF##7573618##Morris et al. 1995##; ##REF##7785670##Schwartz and Morris 1995##; ##REF##16793861##Symons et al. 2006##; ##UREF##6##Wellenius et al. 2005a##, ##REF##16442405##2006##).</p>", "<p>The observed pattern of lag day response (strongest effect on lag day 0, a negative effect on lag day 1–3, and little change in pressure for lag days 4–6) is a pattern similar to that observed by ##REF##16442405##Wellenius et al. (2006)##. In their study of Medicare hospital admissions for HF, they reported a 0.72% increase in the risk of hospital admission for HF associated with each 10-μg/m³ increase in PM₁₀ on lag day 0, a negative association on lag day 1, and no association on lag days 2 and 3.</p>", "<p>Ambient PM may trigger pathophysiologic mechanisms by several means, which ultimately lead to acute exacerbation of HF. These include direct depressant effects on the myocardium secondary to ischemia (##REF##12186796##Pekkanen et al. 2002##; ##REF##12676590##Wellenius et al. 2003##), arrhythmia (##REF##15937021##Rich et al. 2005##, ##REF##16698809##2006a##), PA vasoconstriction (observed in rodent models; ##REF##12460797##Batalha et al. 2002##), and reduced alveolar fluid clearance (##REF##16860038##Mutlu et al. 2006##). These nonischemic mechanisms may be of particular importance because most of our subjects did not have an underlying ischemic mechanism for their HF (<italic>n</italic> = 7). The ultimate effect of any of those pathways would be an increase in right heart pressures, leading to hospitalization if it is amplified and prolonged without compensation or treatment. The pressure changes resulting in a hospitalization for HF are certainly much greater than those observed on average for these 11 subjects, perhaps between one and two orders of magnitude. One possible explanation for the muted pressure response to ambient PM_2.5 in these 11 subjects is better management, with state-of-the-art medical therapy (a requirement of the COMPASS-HF trial), than that received by the population of Medicare patients examined by ##REF##16442405##Wellenius et al. (2006)##. Thus, our lack of dramatic pressure changes does not negate the likelihood that the small changes we observed could trigger larger changes in an HF patient more particularly prone to decompensation. Ultimately, the small changes we observed and the limited number of subjects in our analysis suggest the need for further study in larger panels/cohorts to validate RV pressure increases as a mechanism for acute air pollution effects on HF exacerbations.</p>", "<p>Although our study had strengths, including passive continuous monitoring of PA and RV pressures in moderate to severe HF patients, it also had several limitations. First, exposure mis-classification was likely because we used ambient measurements of PM_2.5 to approximate each subject’s exposure, rather than personal measurements. However, because this exposure error is likely nondifferential with respect to heart pressure measurements, our reported effect estimates are likely underestimates.</p>", "<p>Second, it is possible that residual confounding by meteorology conditions or an unmeasured confounder may be responsible for the effects observed. However, that factor would have to both be correlated with daily mean PM_2.5 concentrations measured at the monitoring stations and increases in daily mean PA and RV pressures in these 11 subjects. Thus, it seems unlikely that other meteorologic conditions not correlated with those included in our analysis (temperature, relative humidity, and barometric pressure) or other acutely time-varying, non-weather-related factors could explain the results observed.</p>", "<p>Third, although we retained 5,807 person-days in our analysis, these were among only 11 subjects, resulting in reduced statistical power in our main analyses and even less power in our assessment of effect modification by subject specific characteristics (e.g., access randomization group, obesity, and LVEF at implantation) and season. However, although we found no statistically significant effect modification by these characteristics, there were considerable differences in the magnitude of response between each level of these characteristics (e.g., total clinician access vs. blocked clinician access, obese vs. nonobese, LVEF ≥ 45% vs. LVEF &lt; 45%, winter vs. summer), suggesting that with a larger sample size these differences may have been statistically significant. The previously reported associations between obesity and susceptibility to air pollution (##REF##17637913##Chen et al. 2007##; ##REF##16844771##Zeka et al. 2006##) as well as increased PM-related health effects in winter time (##REF##16079075##Becker et al. 2005##) are consistent with our findings.</p>", "<p>In this pilot study of the acute effects of ambient PM_2.5 concentrations on a likely precursor of hospital admissions for HF exacerbation (PA and RV pressure increases), we demonstrated the feasibility of linking ambient air pollution data to continuously measured right heart pressures in HF patients. Further, we found significant increases in daily ePAD and RV diastolic pressure associated with increases in same-day ambient PM pollution concentrations. We have planned confirmation in a larger sample with more varied geography and therefore likely different sources and composition of PM.</p>" ]

[]

[ "<p>R.S.F. has previously received grant support from and served as a consultant to Medtronic, Inc. Y.C. is an employee of Medtronic, Inc. The remaining authors declare they have no competing financial interests.</p>", "<title>Objectives</title>", "<p>We explored the association between acute changes in daily mean pulmonary artery (PA) and right ventricular (RV) pressures and concentrations of ambient fine particulate matter [PM with aerodynamic diameter ≤2.5 μm (PM_2.5)] as an explanation for previous associations between congestive heart failure (HF) hospital admissions and PM.</p>", "<title>Materials and methods</title>", "<p>In the Chronicle Offers Management to Patients with Advanced Signs and Symptoms of Heart Failure (COMPASS-HF) trial, to see whether management of ambulatory HF could be improved by providing continuous right heart pressure monitoring to physicians, the Chronicle Implantable Hemodynamic Monitor (Medtronic, Inc., Minneapolis, MN, USA) continuously measured multiple right heart hemodynamic parameters, heart rate, and activity trends in subjects with moderate/severe HF. Using these trial data, we calculated daily mean pressures, using only those time intervals where the subject was not physically active (<italic>n</italic> = 5,807 person-days; <italic>n</italic> = 11 subjects). We then studied the association between mean daily PA/RV pressures and mean ambient PM_2.5 concentrations on the same day and previous 6 days.</p>", "<title>Results</title>", "<p>Each 11.62-μg/m³ increase in same-day mean PM_2.5 concentration was associated with small but significant increases in estimated PA diastolic pressure [0.19 mmHg; 95% confidence interval (CI), 0.05–0.33] and RV diastolic pressure (0.23 mmHg; 95% CI, 0.11–0.34). Although we saw considerable differences in the magnitude of response by COMPASS-HF randomization group (total data access for physicians vs. blocked clinician access), season, left ventricular ejection fraction, and obesity, these effects were not significantly different.</p>", "<title>Conclusions</title>", "<p>These pilot study findings provide a potential mechanism for previous findings of increased risk of HF associated with ambient PM. However, because of the small number of subjects, a larger study is needed for confirmation.</p>" ]

[ "<p>Increased incidence of specific acute cardiovascular outcomes, including myocardial infarction, ventricular arrhythmia, episodes of atrial fibrillation, ischemic stroke, and heart failure (HF), have been reported to occur immediately after as little as 1–2 hr of increased particulate matter (PM) concentration (##REF##11401937##Peters et al. 2001##; ##REF##16393668##Rich et al. 2006b##), as well as 1–2 days later (##REF##10094292##Burnett et al. 1999##; ##REF##14501267##D’Ippoliti et al. 2003##; ##REF##9755140##Morris and Naumova 1998##; ##REF##7573618##Morris et al. 1995##; ##REF##10615837##Peters et al. 2000##, ##UREF##3##2005##; ##REF##15937021##Rich et al. 2005##, ##REF##16698809##2006a##; ##REF##7785670##Schwartz and Morris 1995##; ##REF##16793861##Symons et al. 2006##; ##UREF##6##Wellenius et al. 2005a##, ##REF##16254223##2005b##, ##REF##16442405##2006##; ##REF##10658547##Wong et al. 1999##; ##REF##16079066##Zanobetti and Schwartz 2005##). However, not all studies have reported these acute to subacute responses (##REF##11246580##Levy et al. 2001##; ##REF##15204752##Rich et al. 2004##; ##REF##15613944##Sullivan et al. 2005##; ##REF##15204751##Vedal et al. 2004##). Recent epidemiologic, animal, and controlled human exposure studies have suggested that PM air pollution may elicit changes in subclinical indices, such as autonomic function, inflammation, endothelial dysfunction, and thrombotic tendency within hours after air pollution exposures, which may contribute to the increased incidence of cardiovascular disease observed (##REF##11927516##Brook et al. 2002##; ##REF##10725286##Gold et al. 2000##; ##REF##10378998##Liao et al. 1999##; ##REF##11524390##Magari et al. 2001##, ##REF##12204821##2002##; ##REF##16365212##Mills et al. 2005##, ##REF##17855668##2007##; ##REF##12359661##Nemmar et al. 2002##, ##REF##12615802##2003##; ##REF##14962820##Riediker et al. 2004##; ##REF##16293802##Ruckerl et al. 2006##; ##REF##17446340##Tornqvist et al. 2007##; ##REF##11401052##Tunnicliffe et al. 2001##; ##REF##12676590##Wellenius et al. 2003##).</p>", "<p>##UREF##6##Wellenius et al. (2005a)## reported small but statistically significant increases in the risk of HF hospital admissions associated with increases in same-day PM₁₀ and nitrogen dioxide concentration in Pittsburgh, Pennsylvania. In a later multicity study, these same investigators reported a 0.72% [95% confidence interval (CI), 0.35–1.10%] increase in risk of HF admission for each 10-μg/m³ increase in same-day ambient PM ₁₀ concentration (##REF##16442405##Wellenius et al. 2006##). These Medicare database admission studies demonstrated acute cardiovascular responses to air pollution that require an exploration of possible physiologic correlates both for verification and for clues as to mechanism. We hypothesized that hospital admissions for decompensation of HF that were triggered by air pollution would be associated with more frequent subclinical increases in pulmonary arterial (PA) diastolic and right ventricular (RV) pressures. Such pressure increases could ultimately lead to emergency department/hospital admission when individuals are vulnerable due to the presence of other established HF exacerbation triggers (e.g., volume overload, electrolyte status, decreased oxygenation, rhythm disturbance, exertion, decreased myocardial contractility, suboptimal medical therapy). If PM has a causal role in HF admissions because of acute pressure increases, then direct passive measurement of these specific pressures should be a much more sensitive and upstream measure of stress on the cardiopulmonary system than hospital admissions for HF, which presumably depend on a confluence of exacerbating factors.</p>", "<p>Therefore, we coupled passively monitored data of continuous PA diastolic and RV pressure data in HF patients implanted with the Chronicle Implantable Hemodynamic Monitor (Medtronic, Inc., Minneapolis, MN) and ambient fine PM [aerodynamic diameter ≤ 2.5 μm (PM_2.5)] measurements made at monitoring stations throughout New Jersey. In this pilot study to investigate the feasibility of such an approach to examine mechanisms underlying previously reported associations between air pollution and HF, we examined the association between daily mean PM_2.5 concentrations and daily mean PA and RV pressures.</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1167\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Change in mean daily ePAD (mmHg) and RV diastolic pressure (mmHg) per quintile of mean daily PM_2.5 concentration on lag day 0 (<italic>n</italic> = 11 subjects). Error bars are 95% CIs. Tests for trend were statistically significant: RV diastolic pressure, <italic>p</italic> &lt; 0.001; ePAD, <italic>p</italic> = 0.006.</p></caption></fig>", "<fig id=\"f2-ehp-116-1167\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Change in mean daily ePAD (mmHg) and RV diastolic pressure (mmHg) per 11.62-μg/m³ increase in mean daily PM_2.5 concentration on lag days 0–6 (<italic>n</italic> = 11 subjects). Error bars are 95% CIs</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1167\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Characteristics of study population (<italic>n</italic> = 11).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">ID no.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Sex</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Race</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Height (m)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Weight (kg)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">BMI (kg/m²)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Age at implant (years)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">HF etiology</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Heart rate at implant (bpm)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">LVEF at implant</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Other implanted device</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">01</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">30.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Ischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">02</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.68</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">67.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Nonischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">03</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">97.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Nonischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">ICD</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">04</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Hispanic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">92.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Nonischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">05</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Black</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.55</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">74.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Nonischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">06</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.83</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">88.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Nonischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Bi-V ICD</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">07</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.70</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">92.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">67</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Ischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">ICD</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">08</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">117.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">61</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Nonischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Bi-V ICD</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">09</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Black</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">130.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">47.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Nonischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">ICD</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Ischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">ICD</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">100.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">68</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Ischemic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">ICD</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1167\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Change in mean daily pressure (mmHg) per 11.62-μg/m³ increase in mean PM_2.5 concentration on the same day (<italic>n</italic> = 11 subjects).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Outcome</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. of person-days</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Change in pressure (mmHg)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ePAD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5,807</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.05 to 0.33</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">RV diastolic pressure</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5,807</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11 to 0.34</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">RV systolic pressure</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5,807</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.07 to 0.31</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">MPAP</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5,667</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.05 to 0.28</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1167\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Change in mean daily RV diastolic pressure (mmHg) per 11.62-μg/m³ increase in mean PM_2.5 concentration on the same day, by level of effect modifier (<italic>n</italic> = 11 subjects; <italic>n</italic> = 5,807 person-days).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Effect modifier</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. of person-days</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Change in pressure (mmHg)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Interaction term <italic>p</italic>-value</th></tr></thead><tbody><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">LVEF (%)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,006</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.28</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.05 to 0.51</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.61</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4,801</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.10 to 0.34</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Obesity</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Obese (BMI ≥ 30)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3,838</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.14 to 0.40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Nonobese (BMI &lt; 30)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,969</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.02 to 0.32</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Access randomization group</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Total clinician access</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,305</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.00 to 0.32</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Blocked clinician access</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3,502</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.28</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.14 to 0.41</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Season<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1167\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Winter</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,297</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.31</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.09 to 0.54</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.33</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Summer</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,374</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.09 to 0.39</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>This research was supported, in part, by the National Institute of Environmental Health Sciences (NIEHS)–sponsored University of Medicine and Dentistry of New Jersey’s Center for Environmental Exposures and Disease, grant NIEHS P30ES005022, U.S. Environmental Protection Agency (EPA) Star Grant R832144, and American Heart Association (AHA) grant 0735287N.</p></fn><fn><p>The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS, U.S. EPA, or AHA.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1167\"><label>a</label><p>Winter, December–February. Summer, June–August. We excluded person-days occurring in the spring or autumn from this analysis.</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1167f1\"/>", "<graphic xlink:href=\"ehp-116-1167f2\"/>" ]

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[ "<title>Methods</title>", "<title>Study area and population</title>", "<p>Wuhan is the capital of Hubei Province, which is located in the middle of the Yangzi River delta, at 29°58′ −31°22′ north latitude and 113°41′ −115°05′ east longitude. Its population is approximately 7.5 million people, of whom approximately 4.5 million reside in nine urban core districts within an area of 201 km². Wuhan has a subtropical, humid, monsoon climate with a distinct pattern of four seasons. Its average daily temperature in July is 37.2°C, and the maximum daily temperature often exceeds 40°C. The major industries in Wuhan include ferrous smelters, chemical plants, power plants, and machinery plants. The major sources of air pollution in the city are motor vehicles and the burning of coal for domestic cooking, heating, and industrial processes.</p>", "<title>Data sources</title>", "<p>Mortality data from 1 July 2000 to 30 June 2004 were obtained from the Wuhan Centres for Disease Prevention and Control (WCDC). The government requires that a decedent’s family obtain a death certificate from a hospital or a local community clinic to remove the deceased person from the government-controlled household registration. The local WCDC issues two copies of the death certificate according to the certificate from the hospital or the clinic. One copy is submitted to the public safety department to stop the decedent’s address registration, and the other copy is used for the cremation.</p>", "<p>The WCDC electronically archives all death certificates. In 1992, the WCDC became the first center in China to standardize its system for mortality data collection. The system’s requirements are as follows: <italic>a</italic>) mortality data must be validated four times per year; <italic>b</italic>) death events collected from the WCDC must conform with those collected from the Wuhan Police Department; <italic>c</italic>) no data may be missing from any death certificate; <italic>d</italic>) unclear causes and diagnosis may not constitute &gt; 2% of deaths in urban districts; and <italic>e</italic>) a correct coding rate of &gt; 98% must be achieved for cause-specific deaths. For deaths that occurred before 1 January 2003, the <italic>International Classification of Disease, Ninth Revision</italic> [ICD-9; ##UREF##6##World Health Organization (WHO) 1978##] codes were applied; for deaths that occurred after 31 December 2002, ICD-10 (##UREF##7##WHO 1993##) codes were applied. Total mortality was divided into the following major causes: non-accidental mortality (ICD-9 codes 1–799; ICD-10 codes A00–R99), cardiovascular diseases (ICD-9 codes 390–459; ICD-10 codes I00–I99), stroke (ICD-9 codes 430–438; ICD-10 codes I60–I69), cardiac diseases (ICD-9 codes 390–398 and 410–429; ICD-10 codes I00–I09 and I20–I52), respiratory diseases (ICD-9 codes 460–519; ICD-10 codes J00–J98), and cardiopulmonary diseases (ICD-9 codes 390–459 and 460–519; ICD-10 codes I00–I99 and ICD-10 J00–J98). The Human Subject Protection Office of the Penn State College of Medicine approved the current study protocol.</p>", "<p>Pollution data were collected by the Wuhan Environmental Monitoring Center (WEMC) and certified by the U.S. Environmental Protection Agency. Daily concentrations of PM₁₀, sulfur dioxide, nitrogen dioxide, and ozone (8-hr mean concentrations, 1000–1800 hours) were collected for the study period. The monitoring system strictly followed the quality assurance/quality control procedure set by the ##UREF##5##State Environmental Protection Administration of China (1992)##. Briefly, the WEMC conducts regularly scheduled performance audits and precision checks on the air-monitoring equipment. Quarterly performance audits are also conducted to assess data accuracy. PM₁₀ measurements were collected using PM₁₀ beta attenuation mass monitors, (model 7001); SO₂ measurements were collected using an ultraviolet fluorescence SO₂ analyzer (model 4108); NO₂ measurements were collected using a chemiluminescent NO₂ analyzer (model 2108); and O₃ measurements were collected using an ultraviolet photometry O₃ analyzer (model 1008), all from Dasibi Environmental Corporation (Glendale, CA, USA). Meteorologic data were provided by the Wuhan Meteorological Administration.</p>", "<title>Statistical methods</title>", "<p>We used quasi-likelihood estimation within the context of the generalized additive models (GAMs) to model the natural logarithm of the expected daily death counts as a function of the predictor variables (##UREF##3##Hastie and Tibshirani 1990##). We examined the effect estimates for each pollutant at 0-, 1-, 2-, 3-, and 4-day lags, and at lag 0–1 day and lag 0–4 day average concentrations prior to the death events. In general, the largest pollutant effects were observed at the lag 0–1, where pollution concentrations were evaluated at the average of the day of death (lag 0) and 1 day before death (lag 1). Therefore, for purposes of this study we focused on the results of the lag 0–1 model. All model analyses were performed using R, version 2.5.0, using the mgcv package, 1.3–24 (##UREF##4##The R Foundation for Statistical Computing 2007##).</p>", "<p>There were two steps in the model building and fit: development of the best base model (without a pollutant) and development of the main model (with a pollutant). The latter was achieved by adding the air pollution variable(s) to the final and best cause-specific base model, assuming a linear relationship between the logarithmic mortality count and the air pollutant concentration. To obtain the best base model, the GAM analyses were performed covering two major areas. First, we controlled for potential confounding of yearly, seasonal, and subseasonal variations and for other time-varying influences on mortality. To begin, we included indicators for days of the week to take into account the change in traffic volume between workdays and weekends. We then regressed the natural logarithm of the daily death counts on a day sequence to adjust for time trends using either natural splines (ns) or penalized splines (ps). Furthermore, visual inspection of the mortality time-series showed two peaks of death counts over the two periods 28 July–3 August 2003 (sum03) and 1 December–31 December 2003 (win03). We added a factor variable for the three periods (sum03, win03, and others) and performed local smoothing by specifying the “by” option for these three periods to control for the extreme peaks of death counts. Second, we controlled for potential confounding of relevant weather variables, which is important during unusually high and low temperatures in Wuhan. We controlled for weather variables using <italic>a</italic>) indicator variables for extremely hot days, cold days, and humid days; and <italic>b</italic>) ns or ps for the temperature and humidity, respectively. The extremely hot and cold days were defined as those days on which the highest or lowest daily average temperatures were &gt; 95th percentile or &lt; 5th percentile of the 4 years of data, respectively (##REF##1464289##Dockery et al. 1992##). The 5th and 95th percentiles for temperature were 3.6 and 31.7°C.. Similarly, the extremely humid days were those days with daily average relative humidity &gt; 95th percentile of the 4 years of data. The goal in the previous two steps was to obtain conservative estimates on the subsequent pollution mortality associations.</p>", "<p>Taking into account the literature review and the common protocol of the Health Effects Institute’s program of the Public Health and Air Pollution in Asia, we used four competing approaches to determine the appropriate degrees of freedom (df) for the time and weather in developing the best base model for each cause-specific mortality model (##REF##11772788##Curriero et al. 2002##; ##UREF##2##Dominici et al. 2003##). These include two ns methods that used the fixed df, the sequential ns method, and the ps method, where the former three ns methods were parametric-based regression splines and always used 2 df and 3 df for the local smoothers for sum03 and win03, respectively. For the two fixed-df models, we considered 6 and 8 df/year for time, 3 and 4 df for temperature, and 3 and 4 df for humidity over the entire 4-year study period. For the sequential method, we started with a reduced model (only days of week, extreme weather indicators, and local smoothing terms). We tried 3–8 df/year for the time and then chose the df that had the smallest sum of the absolute partial autocorrelation values over a 30-day lag period. Next we added temperature to the above model using 2–4 df. We repeated this process for relative humidity after including temperature, time trend, days of week, and extreme weather indicators. We ran the ps model to select the optimal df for overall time trend, local time intervals, temperature, and relative humidity. We initialized the df as 8 df/year for time, 3 df for sum03, 3 df for win03, and 3 df for both temperature and relative humidity. We observed that the local smoothing df remains the same or within 1–2 df differences from the dfs used in the sequential method for various cause-specific mortality. The criteria for selecting the best-fitting model are as follows: <italic>a</italic>) the absolute value of the partial autocorrelation &lt; 0.1 for all 30-day lags; and <italic>b</italic>) the smallest sum of the absolute partial autocorrelation values over a 30-day lag period.</p>", "<p>To address whether estimated effects are valid and whether they are strongly influenced by different model specifications during the modeling process, we conducted a series of sensitivity analyses in two areas. The first area concerns different smoothing approaches for time, temperature, and humidity. These included <italic>a</italic>) alternating smoothing order in the sequential method from time, temperature, and humidity to temperature, humidity, and time; <italic>b</italic>) using fixed df for time, temperature, and humidity (e.g., 6 df for time/year, 3 df for temperature, and 3 df for humidity; and 8 df for time/year, 4 df for temperature, and 4 df for humidity); and <italic>c</italic>) using the ps approach. The second area concerns model specifications, where the best main models were fitted alternatively by <italic>a</italic>) adding influenza epidemics; <italic>b</italic>) adding an indicator for the period of ICD-10 use; <italic>c</italic>) removing Wuhan, the most industrialized district; <italic>d</italic>) removing extreme temperature data; <italic>e</italic>) redefining extreme temperature; and <italic>f</italic>) adding the lag climate variable</p>", "<p>Last, we redefined the temperature groups using different percentile cutoffs of the temperature ranges (3rd, 7th, 10th, and 15th percentiles) to determine whether the effects observed using the 5th percentiles were significantly changed.</p>", "<p>We used several approaches to investigate the validity of the linearity assumption for each air pollutant. First, we replaced the linear term of the pollutant concentrations with a smooth function with 3 df using ns. Both the likelihood ratio test with 2 df (which compares the original main model with the smoothed model) and the visual inspection approach were used to assess whether the smoothed exposure–response curve resembles a straight line. Next, we performed piecewise regression models by allowing different slopes of pollutant concentrations before and after a cutoff point. The cutoff points of PM₁₀ were tested from zero to 150 μg/m³ in 25-μg/m³ increments. The best piecewise regression model was the one in which the cutoff point minimized the generalized cross-validation value. In general, assuming the linearity of air pollution effects on the logarithm of mortality appears to be appropriate.</p>", "<p>To investigate the synergetic effects between air pollution and temperature, our main models were built to include additional season indicators and two interaction terms between a linear term of air pollution and an indicator of either extreme high temperature or extreme low temperature (the normal temperature serves as the reference). The effect estimates were expressed using a percentage change in the mean number of daily deaths per 10-μg/m³ increments in 24-hr mean concentrations of a pollutant (8-hr mean concentrations for O₃). The associated upper and lower 95% confidence limits by weather condition were obtained by taking the exponential of the upper and lower 95% confidence limits of the estimated βs. The overall test of the interaction effects between extreme high and low temperatures and air pollution was performed using the likelihood ratio test with 2 df.</p>" ]

[ "<title>Results</title>", "<p>The daily mean concentrations of PM₁₀, SO₂, and NO₂ were much lower during high-temperature days than during low-temperature and normal-temperature days (##TAB##0##Table 1##). The 8-hr mean concentrations of O₃, as expected, were highest during the high-temperature days. There was great variation in the daily average temperature (33.1°C vs. 2.2°C) but small variation in the daily average relative humidity among the three temperature groups.</p>", "<p>There were considerable variations in mean daily levels of pollutants (##TAB##1##Table 2##). The mean daily concentrations of SO₂ and NO₂ generally increased during the study period across the three temperature groups. Despite spatial variations in the daily mean concentrations, which were mainly driven by the highest PM₁₀ and SO₂ concentrations measured at the Wugan station located near a smelter, we found that the distributions of PM₁₀ over distances were fairly homogeneous, as shown by the high Pearson correlation coefficients between measurements from the monitoring stations (0.50–0.97). SO₂ and NO₂ were similarly homogeneously distributed except during the high-temperature days.</p>", "<p>We collected information on a total of 89,131 nonaccidental death cases. The daily mean number of nonaccidental deaths was 61, with a maximum of 213 and with a main contribution of cardiopulmonary mortality (daily mean of 35). The majority of individuals died when they were ≥ 65 years of age (71.9%). The mean age of nonaccidental deaths was 69 years, with a range of 0–106 years. Persons ≥ 65 years of age contributed to more than half of the daily deaths for each of the underlying causes of death. The percentage of deaths in the 0–4 year age group was 1.5%. There were only 11 no-death days, all with normal temperature (##TAB##2##Table 3##). Each variance was greater than the mean, indicating that the mortality data followed the overdispersed Poisson distributions across the three temperature groups, which warrant additional control for weather and temporal trends in the data.</p>", "<p>We observed consistent associations between daily mortality and PM₁₀, NO₂, and SO₂ (##REF##17604019##Qian et al. 2007a##, ##REF##17687993##2007b##). In general, using different smoothing approaches did not change the effect estimates significantly, nor did using different model specifications. We also observed a consistent interaction of PM₁₀ with temperature (##TAB##3##Table 4##). The PM₁₀ effects were strongest on extremely high-temperature days (daily average temperature, 33.1°C), less strong on extremely low-temperature days (2.2°C), and weakest on normal-temperature days (18.0°C). The estimates of the mean percentage of change in daily mortality per 10-μg/m³ increase in PM₁₀ concentrations at the average of lags 0 and 1 day during high temperature were 2.20% [95% confidence interval (CI), 0.74–3.68] for nonaccidental; 3.28% (1.24–5.37) for cardiovascular; 2.35% (−0.03 to 4.78) for stroke; 3.31% (−0.22 to 6.97) for cardiac; 1.15% (−3.54 to 6.07) for respiratory; and 3.02% (1.03–5.04) for cardio-pulmonary mortality. Interestingly, we did not observe consistent stronger temperature effects of modification for the majority of outcomes in the elderly (##TAB##4##Table 5##). One possible explanation might be that the elderly were more likely to stay inside the house on hot days, avoiding exposure to extreme temperature. For the gaseous pollutants, the only interaction observed was that of O₃ on nonaccidental mortality. We found that the estimated PM₁₀ effects using the 5th percentile cutoff were generally similar to the effects estimated using the 3rd percentile (##FIG##0##Figure 1##). Except for respiratory mortality, we observed that the estimated PM₁₀ effect decreased with increasing percentile on the high-temperature days. ##FIG##0##Figure 1## also shows that the relationship of daily mortality with temperature is U-shaped, which is consistent with other studies (##REF##12777425##Gouveia et al. 2003##).</p>", "<p>The estimated PM₁₀ effects were attenuated in the two pollutant models (##TAB##5##Table 6##). For example, inclusion of NO₂ in the model substantially reduced the PM₁₀ effect for non-accidental mortality at normal temperature, whereas the inclusion of SO₂ had less influence. These relationships were also present at low temperatures. Conversely, at high temperatures, the inclusion of either NO₂ or SO₂ had little influence on the association of PM₁₀ with nonaccidental mortality. Although PM₁₀ was correlated with both NO₂ and SO₂ (##TAB##6##Table 7##), the attenuation of the estimated effects in two-pollutant models might not be due simply to confounding, but rather an indicator of the source-related component of PM responsible for the adverse health effect. The sources and composition of PM₁₀, and hence the toxicity, vary with temperature. Thus, temperature may be serving as an indicator of PM₁₀ composition. The interaction of O₃ on nonaccidental mortality was attenuated but remained significant after controlling for PM₁₀ and SO₂ in the copollutant models (##TAB##7##Table 8##). Because temperature was positively correlated with O₃ (<italic>r</italic> = 0.52), part of the interaction between PM₁₀ and high temperature might be due to O₃.</p>" ]

[ "<title>Discussion and Conclusion</title>", "<p>We observed that high temperatures enhanced PM₁₀ mortality effects, even though PM₁₀ daily concentrations were lower on the extremely high-temperature days than on the normal-temperature and low-temperature days.</p>", "<p>The small number of previous relevant studies reported conflicting results on this interaction. ##REF##9593623##Samet et al. (1998)## found no significant evidence that weather variables modified the pollution–mortality relationship. However, ##REF##8357272##Katsouyanni et al. (1993)## found a significant effect of the interaction between SO₂ and high temperature on total mortality but no significant interactions between high temperature and either smoke or O₃. We speculate that the following environmental features are related to the significant synergistic effects of PM₁₀ and high temperature in Wuhan. First, the maximum summer temperature often exceeded 40°C and lasted about 2 weeks. Wuhan’s special topography causes narrow differences in daily high and low temperatures. Even around midnight in the summer, indoor air temperatures &gt; 32°C are not uncommon. Thus, the city residents were exposed to high temperatures for longer periods than residents of many other cities. Second, few residences in Wuhan were built with energy conservation in mind; a vast amount of radiant energy can easily infiltrate buildings and be absorbed, even when all windows are closed. The temperature inside is commonly comparable to the temperature in the shade outside. In addition, air conditioners have seldom been used because of the high cost of electricity. Third, the most commonly used means for cooling are fans, which can be effective in protecting against heat stress in areas without extremely high temperatures. However, with the temperatures in Wuhan, the use of fans could contribute to heat stress by exacerbating dehydration (##REF##7623759##Centers for Disease Control and Prevention 1995##). Finally, approximately 4.5 million permanent residents plus approximately 1 million transients live in the urban core districts with an area of 201 km². This high population density adds to the urban “heat island” effect, which would make the temperature somewhat higher in the urban core areas than in the suburban areas.</p>", "<p>The mechanism underlying the synergistic effects of ambient particle pollution and extremely high temperatures on daily mortality is not yet clear. Some potential explanations have been proposed, especially for the elderly (##REF##11000103##Easterling et al. 2000##). ##REF##12401268##Brunekreef and Holgate (2002)## hypothesized that air particles increase the risk of cardiopulmonary mortality through direct and indirect pathophysiologic mechanisms, including pulmonary and systemic inflammation, accelerated atherosclerosis, altered cardiac autonomic function, and increase of inflammatory cytokines in the heart. Many studies have addressed the mechanisms by which high temperature is associated with increased mortality. In animal studies, ##REF##3776986##Keatinge et al. (1986)## observed dehydration, increased intracranial and arterial hypertension, endothelial cell damage, and cerebral ischemia during the onset of heat stroke in animals exposed to high temperatures. In a clinical trial study, ##REF##3294350##Gordon et al. (1988)## found that exposure to high temperatures increased plasma viscosity and serum cholesterol level. ##REF##14551399##Tsai et al. (2003)## suggested that high temperature may help precipitate coronary artery disease and cerebral infarction. ##REF##15728404##Flynn et al. (2005)## observed that many of the elderly who died in the heat wave in France during the first 2 weeks of August 2003 were dehydrated, hypernatremic, and hyperkalemic, with evidence of renal failure (##REF##14627799##Vanhems et al. 2003##). The investigators postulated that the most probable causes of death during the heat wave were thromboembolic disease and malignant cardiac arrhythmias as well as heat-induced sepsislike shock (##REF##15728404##Flynn et al. 2005##).</p>", "<p>Our study has several limitations. First, both ICD-9 and ICD-10 codes were used. The change in ICD coding might produce misclassification in cause-specific mortality. To address this uncertainty, we examined daily death counts between ICD-9 and ICD-10 mortality data in 2002. We found high concordance rates between the two-coded mortality data, and the maximum change in the estimated pollution mortality effect was 0.09%. These results support our contention that the change in the ICD coding system did not significantly affect the associations identified in this study. Second, there might be other important unknown and unmeasured factors. For example, socioeconomic status can play an important role as an effect modifier. Unfortunately, we do not currently have data on hand to explore the effects of these factors. Third, interpretation of the effects of interaction between O₃ and temperature requires caution, because O₃ data were obtained from only one monitoring station. The limited O₃ data may also restrict our ability to reach any reliable conclusion. Last, measurement errors in exposure are clearly applicable to this study. However, this measurement error generally belongs to the Berkson type and thus is nondifferential in nature, which is likely to cause a bias toward the null and lead to underestimated associations (##UREF##0##Armstrong 1998##).</p>", "<p>In conclusion, we found synergistic effects between PM₁₀ and extremely high temperature on daily mortality in this highly polluted city. Further studies are needed to confirm these findings.</p>" ]

[ "<title>Discussion and Conclusion</title>", "<p>We observed that high temperatures enhanced PM₁₀ mortality effects, even though PM₁₀ daily concentrations were lower on the extremely high-temperature days than on the normal-temperature and low-temperature days.</p>", "<p>The small number of previous relevant studies reported conflicting results on this interaction. ##REF##9593623##Samet et al. (1998)## found no significant evidence that weather variables modified the pollution–mortality relationship. However, ##REF##8357272##Katsouyanni et al. (1993)## found a significant effect of the interaction between SO₂ and high temperature on total mortality but no significant interactions between high temperature and either smoke or O₃. We speculate that the following environmental features are related to the significant synergistic effects of PM₁₀ and high temperature in Wuhan. First, the maximum summer temperature often exceeded 40°C and lasted about 2 weeks. Wuhan’s special topography causes narrow differences in daily high and low temperatures. Even around midnight in the summer, indoor air temperatures &gt; 32°C are not uncommon. Thus, the city residents were exposed to high temperatures for longer periods than residents of many other cities. Second, few residences in Wuhan were built with energy conservation in mind; a vast amount of radiant energy can easily infiltrate buildings and be absorbed, even when all windows are closed. The temperature inside is commonly comparable to the temperature in the shade outside. In addition, air conditioners have seldom been used because of the high cost of electricity. Third, the most commonly used means for cooling are fans, which can be effective in protecting against heat stress in areas without extremely high temperatures. However, with the temperatures in Wuhan, the use of fans could contribute to heat stress by exacerbating dehydration (##REF##7623759##Centers for Disease Control and Prevention 1995##). Finally, approximately 4.5 million permanent residents plus approximately 1 million transients live in the urban core districts with an area of 201 km². This high population density adds to the urban “heat island” effect, which would make the temperature somewhat higher in the urban core areas than in the suburban areas.</p>", "<p>The mechanism underlying the synergistic effects of ambient particle pollution and extremely high temperatures on daily mortality is not yet clear. Some potential explanations have been proposed, especially for the elderly (##REF##11000103##Easterling et al. 2000##). ##REF##12401268##Brunekreef and Holgate (2002)## hypothesized that air particles increase the risk of cardiopulmonary mortality through direct and indirect pathophysiologic mechanisms, including pulmonary and systemic inflammation, accelerated atherosclerosis, altered cardiac autonomic function, and increase of inflammatory cytokines in the heart. Many studies have addressed the mechanisms by which high temperature is associated with increased mortality. In animal studies, ##REF##3776986##Keatinge et al. (1986)## observed dehydration, increased intracranial and arterial hypertension, endothelial cell damage, and cerebral ischemia during the onset of heat stroke in animals exposed to high temperatures. In a clinical trial study, ##REF##3294350##Gordon et al. (1988)## found that exposure to high temperatures increased plasma viscosity and serum cholesterol level. ##REF##14551399##Tsai et al. (2003)## suggested that high temperature may help precipitate coronary artery disease and cerebral infarction. ##REF##15728404##Flynn et al. (2005)## observed that many of the elderly who died in the heat wave in France during the first 2 weeks of August 2003 were dehydrated, hypernatremic, and hyperkalemic, with evidence of renal failure (##REF##14627799##Vanhems et al. 2003##). The investigators postulated that the most probable causes of death during the heat wave were thromboembolic disease and malignant cardiac arrhythmias as well as heat-induced sepsislike shock (##REF##15728404##Flynn et al. 2005##).</p>", "<p>Our study has several limitations. First, both ICD-9 and ICD-10 codes were used. The change in ICD coding might produce misclassification in cause-specific mortality. To address this uncertainty, we examined daily death counts between ICD-9 and ICD-10 mortality data in 2002. We found high concordance rates between the two-coded mortality data, and the maximum change in the estimated pollution mortality effect was 0.09%. These results support our contention that the change in the ICD coding system did not significantly affect the associations identified in this study. Second, there might be other important unknown and unmeasured factors. For example, socioeconomic status can play an important role as an effect modifier. Unfortunately, we do not currently have data on hand to explore the effects of these factors. Third, interpretation of the effects of interaction between O₃ and temperature requires caution, because O₃ data were obtained from only one monitoring station. The limited O₃ data may also restrict our ability to reach any reliable conclusion. Last, measurement errors in exposure are clearly applicable to this study. However, this measurement error generally belongs to the Berkson type and thus is nondifferential in nature, which is likely to cause a bias toward the null and lead to underestimated associations (##UREF##0##Armstrong 1998##).</p>", "<p>In conclusion, we found synergistic effects between PM₁₀ and extremely high temperature on daily mortality in this highly polluted city. Further studies are needed to confirm these findings.</p>" ]

[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>We investigated whether the effect of air pollution on daily mortality is enhanced by high temperatures in Wuhan, China, using data from 2001 to 2004. Wuhan has been called an “oven” city because of its hot summers. Approximately 4.5 million permanent residents live in the 201-km² core area of the city.</p>", "<title>Method</title>", "<p>We used a generalized additive model to analyze pollution, mortality, and covariate data. The estimates of the interaction between high temperature and air pollution were obtained from the main effects and pollutant–temperature interaction models.</p>", "<title>Results</title>", "<p>We observed effects of consistently and statistically significant interactions between particulate matter ≤ 10 μm (PM₁₀) and temperature on daily nonaccidental (<italic>p</italic> = 0.014), cardiovascular (<italic>p</italic> = 0.007), and cardiopulmonary (<italic>p</italic> = 0.014) mortality. The PM₁₀ effects were strongest on extremely high-temperature days (daily average temperature, 33.1°C), less strong on extremely low-temperature days (2.2°C), and weakest on normal-temperature days (18.0°C). The estimates of the mean percentage of change in daily mortality per 10-μg/m³ increase in PM₁₀ concentrations at the average of lags 0 and 1 day during hot temperature were 2.20% (95% confidence interval), 0.74–3.68) for nonaccidental, 3.28% (1.24–5.37) for cardiovascular, 2.35% (−0.03 to 4.78) for stroke, 3.31% (−0.22 to 6.97) for cardiac, 1.15% (−3.54% to 6.07) for respiratory, and 3.02% (1.03–5.04) for cardiopulmonary mortality.</p>", "<title>Conclusions</title>", "<p>We found synergistic effects of PM₁₀ and high temperatures on daily nonaccidental, cardiovascular, and cardiopulmonary mortality in Wuhan.</p>" ]

[ "<p>Extreme temperatures are associated with increased daily mortality in many regions of the world (##REF##11980532##Patz and Khaliq 2002##). Because human activity is likely to increase overall global average temperatures, research efforts have focused on the health effects of exposure to high temperatures and heat waves in summer. In the United States, increased mortality during high-temperature days has been extensively investigated. ##REF##8649494##Semenza et al. (1996)## reported that a heat wave in Chicago, Illinois, in 1995 was associated with an increase in the death rate among socially isolated people who had no air conditioning. In studies of multiple U.S. cities, similar results were reported (##REF##11772788##Curriero et al. 2002##). In Europe, excess mortality during high-temperature days has also been noted. ##REF##16357598##Le Tertre et al. (2006)## also reported an association between the 2003 heat wave in France and increases in all causes of mortality in nine French cities. ##REF##16570026##Stafoggia et al. (2006)## explored vulnerability to heat-related mortality in four Italian cities: Bologna, Milan, Rome, and Turin. The populations particularly vulnerable to high summer temperatures were the elderly, women, widows and widowers, those with particular medical conditions, and those in nursing homes and health care facilities.</p>", "<p>Air pollution is also associated with increased daily mortality (##REF##10981452##Pope 2000##). A large number of daily mortality time-series analyses have provided sufficiently convincing evidence that nonaccidental mortality, including cardio-pulmonary mortality, is associated with ambient particulate matter (PM) exposure in the United States (##REF##17366813##Ostro et al. 2007##), Canada (##UREF##1##Burnett et al. 2000##), Rome (##REF##16847936##Forastiere et al. 2007##), China (##REF##17229464##Kan et al. 2007##), Korea (##REF##11097798##Lee et al. 2000##), Greece (##REF##9180068##Katsouyanni et al. 1997##), and Chile (##REF##17450219##Cakmak et al. 2007##). The estimated effect is generally in the range of 1.0–8.0% excess deaths per 50-μg/m³ increments in 24-hr average concentrations of particulate matter ≤ 10 μm in aerodynamic diameter (PM₁₀) (##REF##11055627##Schwartz and Zanobetti 2000##).</p>", "<p>Although the independent impacts of high temperature and air pollution on daily mortality have been widely explored, few studies have examined the interaction between high temperature and air pollution (##REF##9593623##Samet et al. 1998##). Investigating the effects of the synergy between air pollution and high temperature on mortality, although desirable, is difficult, because a suitable study site is not easily available. The Chinese city of Wuhan, however, provides an opportunity to examine these synergistic effects; it has been called an “oven” city because of its extremely hot summers. Previous studies in Wuhan (##REF##8250589##He et al. 1993##; ##REF##15051237##Qian et al. 2004##) have shown high air pollution levels, with concentration ranges wider than those reported in the published literature for other locations. Therefore, we tested the hypothesis that temperature extremes modify the mortality effects of air pollution.</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1172\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Cause-specific mortality plots for PM₁₀ stratified by varying percentiles of temperature cutoff points (3, 5, 7, 10, 15) at lag 0–1 day. Values shown are the mean percentage of change in daily mortality per 10-μg/m³ increase in PM₁₀ concentration and 95% CI.</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1172\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Distributions of mean daily ambient air pollutants (μg/m³) and weather variables by temperature<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1172\">a</xref> in Wuhan, China, July 2001–June 2004.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Normal temperature\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Low temperature\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">High temperature\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Pollutant</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Days (<italic>n</italic>)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Days (<italic>n</italic>)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Days (<italic>n</italic>)</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,312</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">145.7 ± 64.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">117.3 ± 49.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">96.3 ± 27.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,265</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">87.4 ± 47.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51.5 ± 24.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">91.9 ± 41.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,311</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.4 ± 25.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.3 ± 26.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">23.8 ± 10.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,311</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">52.9 ± 18.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51.2 ± 17.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">32.5 ± 6.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Daily mean temperature (°C)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,315</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.0 ± 8.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.2 ± 1.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33.1 ± 0.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Daily mean relative humidity (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,315</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">74.4 ± 12.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">75.3 ± 16.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">64.7 ± 5.6</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1172\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Correlations and trends in measured ambient air pollutants by temperature in Wuhan, China, July 2001–June 2004.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Mean of daily means\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Pollutant (μg/m³)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. of monitoring stations</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Range of mean values between stations</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Coefficient of variation of daily mean (%)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Range of Pearson correlation coefficients between monitoring stations</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Average annual change<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1172\">a</xref></th></tr></thead><tbody><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">PM₁₀</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">116.9–166.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.83–0.97</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">145.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−4.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">95.5–126.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.76–0.97</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">117.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72.7–118.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">28.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.50–0.93</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">96.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−1.5</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">O₃</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">54.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">87.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−2.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">47.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">45.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">91.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−3.0</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">SO₂</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32.8–45.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">64.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.64–0.84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">41.3–58.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">53.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.61–0.87</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.4–28.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27–0.78</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.6</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">NO₂</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36.3–64.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.57–0.84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">52.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">37.6–61.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.69–0.86</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">22.3–43.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11–0.66</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1172\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Daily mortality in Wuhan, China, by cause of death and temperature, July 2001–June 2004.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"5\" align=\"center\" rowspan=\"1\">Percentile\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Underlying cause of death</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Total no.of deaths</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. of days with no deaths</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Variance</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Variance/mean</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Minimum</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Maximum</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">25th</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">50th</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">75th</th></tr></thead><tbody><tr><td colspan=\"11\" align=\"left\" rowspan=\"1\">Nonaccidental</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78,666</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">59.82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">216.23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.61</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">213</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">58</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">67</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5,839</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">79.99</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">142.96</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.79</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">107</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">88</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4,626</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">63.37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">562.10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.87</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">156</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">68</td></tr><tr><td colspan=\"11\" align=\"left\" rowspan=\"1\">Cardiovascular</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35,684</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.14</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">65.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.42</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">67</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,815</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38.56</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">56.78</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.47</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">43</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,124</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29.10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">194.73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.69</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32</td></tr><tr><td colspan=\"11\" align=\"left\" rowspan=\"1\">Stroke</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">22,544</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.14</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.24</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">21</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,713</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.47</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25.97</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,300</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.81</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">71.27</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20</td></tr><tr><td colspan=\"11\" align=\"left\" rowspan=\"1\">Cardiac</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10,634</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.09</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.09</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">898</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.30</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.88</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">634</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.68</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25.11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.89</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11</td></tr><tr><td colspan=\"11\" align=\"left\" rowspan=\"1\">Respiratory</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8,894</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.76</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32.14</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">125</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">894</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.86</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">499</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">46.50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.80</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8</td></tr><tr><td colspan=\"11\" align=\"left\" rowspan=\"1\">Cardiopulmonary</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Normal temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44,578</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.90</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">137.88</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.07</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">185</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3,709</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.81</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">87.88</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">56</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High temperature</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,623</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35.93</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">345.09</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">111</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4-ehp-116-1172\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>Estimates of the mean percentage of change (95% CI) in daily mortality per 10-μg/m³ increase in pollutants by cause of death and temperature, lag 0–1 day, in Wuhan, China, July 2001–June 2004.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">Temperature\n<hr/></th><th align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Cause of death</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Normal</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">High</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Nonaccidental</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.36 (0.17 to 0.56)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.62 (−0.09 to 1.34)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.20 (0.74 to 3.68)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.014</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.89 (1.22 to 2.57)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.22 (0.16 to 4.32)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.59 (−1.78 to 11.36)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.613</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.10 (0.55 to 1.66)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.74 (0.25 to 3.26)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.56 (−2.11 to 7.45)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.505</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19 (−0.15 to 0.54)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.68 (−0.83 to 2.21)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.41 (0.23 to 2.61)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.049</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Cardiovascular</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.39 (0.11 to 0.66)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.72 (−0.25 to 1.70)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.28 (1.24 to 5.37)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.007</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.89 (0.95 to 2.84)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.03 (−0.78 to 4.92)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.23 (−3.71 to 15.00)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.727</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.36 (0.57 to 2.15)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.81 (−0.24 to 3.91)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.35 (−6.18 to 7.32)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.840</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.25 (−0.72 to 0.22)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.09 (−1.94 to 2.15)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.39 (−0.25 to 3.06)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.092</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Stroke</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.38 (0.06 to 0.70)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.67 (−0.50 to 1.85)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.35 (−0.03 to 4.78)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.222</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.94 (0.82 to 3.06)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.02 (−1.35 to 5.50)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.42 (−5.96 to 15.95)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.895</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.99 (0.06 to 1.92)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.32 (−1.12 to 3.82)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.26 (−8.01 to 8.14)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.913</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.27 (−0.81 to 0.28)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.57 (−1.91 to 3.10)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.09 (−0.77 to 2.98)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.275</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Cardiac</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.32 (−0.14 to 0.79)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.50 (−1.10 to 2.13)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.31 (−0.22 to 6.97)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.229</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.92 (0.31 to 3.55)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.17 (−3.44 to 6.00)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.31 (−14.58 to 16.35)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.911</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.04 (0.70 to 3.39)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.90 (−1.50 to 5.41)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−1.99 (−12.65 to 9.98)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.771</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.64 (−1.44 to 0.16)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.04 (−3.39 to 3.42)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.45 (−1.47 to 4.46)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.332</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Respiratory</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.80 (0.25 to 1.35)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.07 (−0.76 to 2.95)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.15 (−3.54 to 6.07)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.931</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.64 (1.69 to 5.63)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.17 (−2.13 to 8.75)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.68 (−12.36 to 32.30)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.896</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.84 (0.29 to 3.41)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.84 (−0.99 to 6.82)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.75 (−2.59 to 30.51)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.253</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06 (−1.09 to 0.99)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.14 (−2.88 to 5.33)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.98 (−0.79 to 6.90)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.160</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Cardiopulmonary</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.45 (0.19 to 0.70)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.69 (−0.22 to 1.61)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.02 (1.03 to 5.04)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.014</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.13 (1.24 to 3.03)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.98 (−0.65 to 4.68)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.31 (−4.32 to 13.72)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.852</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.28 (0.56 to 2.01)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.43 (−0.46 to 3.36)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.26 (−4.05 to 8.98)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.930</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.04 (−0.42 to 0.50)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.01 (−1.89 to 1.92)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.51 (−0.11 to 3.16)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.123</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t5-ehp-116-1172\" orientation=\"portrait\" position=\"float\"><label>Table 5</label><caption><p>Estimates of the mean percentage of change (95% CI) in daily mortality per 10-μg/m³ increase in PM₁₀ concentration by cause of death, temperature, and age, lag 0–1 day, in Wuhan, China, July 2001–June 2004.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">Temperature\n<hr/></th><th align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Cause of death, age (years)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Normal</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">High</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Nonaccidental</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23 (−0.10 to 0.56)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.78 (0.52 to 3.05)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.34 (−0.09 to 4.83)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.010</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.41 (0.18 to 0.64)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22 (−0.61 to 1.05)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.14 (0.42 to 3.89)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.071</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Cardiovascular</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.17 (−0.40 to 0.73)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.63 (0.67 to 4.63)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.32 (0.10 to 8.71)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.007</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.44 (0.14 to 0.74)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.24 (−0.84 to 1.32)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.03 (0.77 to 5.34)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.043</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Stroke</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.17 (−0.53 to 0.88)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.85 (0.34 to 5.42)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.54 (−0.79 to 10.16)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.031</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.43 (0.07 to 0.79)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11 (−1.22 to 1.45)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.83 (−0.83 to 4.57)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.489</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Cardiac</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.04 (−1.07 to 1.01)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.79 (−1.65 to 5.35)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.71 (−4.58 to 10.56)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.458</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.40 (−0.10 to 0.91)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19 (−1.55 to 1.95)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.45 (−0.41 to 7.46)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.292</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Respiratory</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.35 (−1.85 to 1.18)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−1.13 (−6.33 to 4.35)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−3.42 (−15.82 to 10.80)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.856</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.93 (0.38 to 1.50)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.30 (−0.57 to 3.20)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.76 (−3.03 to 6.78)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.852</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Cardiopulmonary</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.07 (−0.47 to 0.61)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.95 (0.04 to 3.90)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.49 (−0.66 to 7.81)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.040</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.53 (0.25 to 0.81)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.43 (−0.57 to 1.44)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.91 (0.74 to 5.12)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.052</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t6-ehp-116-1172\" orientation=\"portrait\" position=\"float\"><label>Table 6</label><caption><p>Copollutant regression estimates of the mean percentage of change (95% CI) in daily mortality per 10-μg/m³ increase in PM₁₀ concentration by temperature, lag 0–1 day, in Wuhan, China, July 2001–June 2004.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">Temperature\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Cause of death, pollutant</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Normal</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">High</th></tr></thead><tbody><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Nonaccidental</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.36 (0.17 to 0.56)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.62 (−0.09 to 1.34)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.20 (0.74 to 3.68)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + NO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.07 (−0.17 to 0.30)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.24 (−0.49 to 0.97)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.87 (0.42 to 3.35)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + SO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.27 (0.06 to 0.47)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.45 (−0.27 to 1.17)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.12 (0.67 to 3.60)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + O₃</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.38 (0.18 to 0.58)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.72 (0.00 to 1.44)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.15 (0.55 to 3.77)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Cardiovascular</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.39 (0.11 to 0.66)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.72 (−0.25 to 1.70)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.28 (1.24 to 5.37)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + NO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.11 (−0.23 to 0.45)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.37 (−0.62 to 1.38)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.00 (0.95 to 5.09)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + SO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.27 (−0.02 to 0.55)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.50 (−0.47 to 1.49)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.20 (1.16 to 5.29)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + O₃</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.42 (0.15 to 0.70)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.82 (−0.16 to 1.80)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.71 (1.50 to 5.96)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Stroke</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.38 (0.06 to 0.70)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.67 (−0.50 to 1.85)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.35 (−0.03 to 4.78)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + NO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.09 (−0.31 to 0.49)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.29 (−0.90 to 1.51)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.05 (−0.34 to 4.49)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + SO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.31 (−0.03 to 0.64)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.53 (−0.65 to 1.73)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.31 (−0.07 to 4.74)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + O₃</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.38 (0.05 to 0.71)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.69 (−0.48 to 1.87)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.77 (0.25 to 5.35)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Cardiac</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.32 (−0.14 to 0.79)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.50 (−1.10 to 2.13)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.31 (−0.22 to 6.97)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + NO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.02 (−0.57 to 0.60)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.12 (−1.53 to 1.80)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.01 (−0.54 to 6.69)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + SO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.11 (−0.38 to 0.61)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.14 (−1.48 to 1.78)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.17 (−0.37 to 6.84)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + O₃</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.41 (−0.06 to 0.89)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.72 (−0.90 to 2.37)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.92 (0.96 to 9.03)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Respiratory</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.80 (0.25 to 1.35)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.07 (−0.76 to 2.95)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.15 (−3.54 to 6.07)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + NO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.30 (−0.39 to 0.99)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.44 (−1.46 to 2.36)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.63 (−4.07 to 5.55)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + SO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.64 (0.07 to 1.22)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.80 (−1.05 to 2.69)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.03 (−3.66 to 5.94)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + O₃</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.84 (0.28 to 1.41)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.11 (−0.73 to 2.99)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.66 (−2.44 to 8.02)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Cardiopulmonary</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.45 (0.19 to 0.70)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.69 (−0.22 to 1.61)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.02 (1.03 to 5.04)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + NO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.15 (−0.17 to 0.47)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.33 (−0.61 to 1.27)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.70 (0.72 to 4.73)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + SO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.34 (0.07 to 0.61)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.50 (−0.42 to 1.43)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.95 (0.96 to 4.97)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀ + O₃</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.43 (0.17 to 0.70)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.76 (−0.16 to 1.68)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.32 (1.16 to 5.53)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t7-ehp-116-1172\" orientation=\"portrait\" position=\"float\"><label>Table 7</label><caption><p>Pearson correlations between daily measurements of pollutants in Wuhan, China, stratified by temperature, July 2001–June 2004.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Temperature, pollutant</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">SO₂</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">O₃</th></tr></thead><tbody><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Normal</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.72</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.59</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Low</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.31</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.33</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">High</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.65</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.65</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.42</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t8-ehp-116-1172\" orientation=\"portrait\" position=\"float\"><label>Table 8</label><caption><p>Copollutant regression estimates of the mean percentage of change (95% CI) in daily mortality per 10<italic>-</italic>μg/m³ increase in O₃ concentrations by temperature, lag 0–1 day mean, in Wuhan, China, July2001–June 2004.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">Temperature\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Cause of death, pollutant</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Normal</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">High</th></tr></thead><tbody><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Nonaccidental</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19 (−0.15 to 0.54)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.68 (−0.83 to 2.21)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.41 (0.23 to 2.61)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.16 (−0.18 to 0.50)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.52 (−0.98 to 2.04)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.20 (0.02 to 2.39)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.02 (−0.33 to 0.36)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.33 (−1.16 to 1.85)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.10 (−0.07 to 2.29)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06 (−0.29 to 0.41)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.38 (−1.12 to 1.90)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.25 (0.07 to 2.44)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Cardiovascular</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.25 (−0.72 to 0.22)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.09 (−1.94 to 2.15)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.39 (−0.25 to 3.06)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.25 (−0.71 to 0.22)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.00 (−2.01 to 2.06)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.16 (−0.47 to 2.82)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.39 (−0.86 to 0.08)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.20 (−2.22 to 1.85)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.09 (−0.54 to 2.74)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.37 (−0.84 to 0.10)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.21 (−2.23 to 1.85)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.22 (−0.41 to 2.88)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Stroke</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.27 (−0.81 to 0.28)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.57 (−1.91 to 3.10)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.09 (−0.77 to 2.98)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.28 (−0.82 to 0.26)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.48 (−1.99 to 3.01)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.87 (−0.98 to 2.76)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.42 (−0.97 to 0.13)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27 (−2.19 to 2.80)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.78 (−1.07 to 2.66)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.37 (−0.92 to 0.18)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.37 (−2.11 to 2.90)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.96 (−0.89 to 2.85)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Cardiac</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.64 (−1.44 to 0.16)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.04 (−3.39 to 3.42)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.45 (−1.47 to 4.46)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.61 (−1.41 to 0.19)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.17 (−3.51 to 3.28)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.26 (−1.66 to 4.27)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.77 (−1.57 to 0.04)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.40 (−3.74 to 3.06)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.16 (−1.76 to 4.16)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.82 (−1.62 to –0.01)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.58 (−3.91 to 2.86)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.20 (−1.71 to 4.19)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Respiratory</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06 (−1.09 to 0.99)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.14 (−2.88 to 5.33)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.98 (−0.79 to 6.90)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06 (−1.09 to 0.98)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.84 (−3.16 to 5.02)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.57 (−1.19 to 6.48)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.37 (−1.41 to 0.67)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.53 (−3.48 to 4.71)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.41 (−1.34 to 6.31)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.27 (−1.31 to 0.79)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.65 (−3.37 to 4.83)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.72 (−1.04 to 6.63)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Cardiopulmonary</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.04 (−0.42 to 0.50)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.01 (−1.89 to 1.92)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.51 (−0.11 to 3.16)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.01 (−0.46 to 0.45)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.22 (−2.10 to 1.69)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.37 (−0.24 to 3.00)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.18 (−0.63 to 0.29)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.45 (−2.32 to 1.46)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.26 (−0.34 to 2.89)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.13 (−0.60 to 0.34)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.38 (−2.26 to 1.54)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.45 (−0.16 to 3.08)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Noncardiopulmonary</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22 (−0.22 to 0.66)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.39 (−0.74 to 3.57)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.50 (−1.01 to 2.02)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.21 (−0.23 to 0.65)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.26 (−0.87 to 3.42)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.37 (−1.13 to 1.90)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.09 (−0.35 to 0.54)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.10 (−1.03 to 3.26)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.30 (−1.20 to 1.82)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃ + SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.14 (−0.31 to 0.58)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.12 (−1.01 to 3.29)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.41 (−1.10 to 1.94)</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>We thank E. Lehman and D. Liao for their assistance and support.</p></fn><fn><p>This research was conducted under contract to the Health Effects Institute (4710-RFIQ03-3/04-6).</p></fn><fn><p>The contents of this article do not necessarily reflect the views of the funding agencies.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1172\"><label>a</label><p>Normal temperature ≥ 5th percentile and ≤ 95th percentile of daily average temperatures during the 4-year study period; low temperature &lt; 5th percentile; high temperature &gt; 95th percentile.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn2-ehp-116-1172\"><p>NA, not applicable.</p></fn><fn id=\"tfn3-ehp-116-1172\"><label>a</label><p>Calculated from a linear regression model.</p></fn></table-wrap-foot>" ]

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[ "<title>Methods</title>", "<title>Data</title>", "<p>Our study period extended from 1999 through 2003. We obtained daily mortality data from the Ministry of Public Health, which currently uses the <italic>International Classification of Diseases, 10th Revision</italic> (ICD-10) to categorize cause of death (##UREF##9##WHO 1992##). For all ages, we abstracted those with “non-accidental” mortality (i.e., total mortality minus accidents and homicides), respiratory-specific mortality, cardiovascular-specific mortality, and mortality for some additional subcategories including ischemic heart disease, stroke, conduction disorders, respiratory mortality for those &lt; 1 year of age, lower respiratory infection (LRI) for those &lt; 5 years of age, chronic obstructive pulmonary disease (COPD), asthma, and senility. The latter was included as an end point because our preliminary analysis showed a relatively low number of daily deaths from cardiovascular diseases and a high number from senility. We speculated that the high apparent mortality from senility might have been the result of mislabeling the cause of death from cardiovascular diseases to senility, especially among the elderly dying outside the hospitals. We also classified nonaccidental mortality by various age groups and by sex.</p>", "<p>In Bangkok, five ambient and seven roadside monitoring stations have been measuring hourly ambient levels of PM₁₀ since 1996; ten stations measure hourly ambient nitrogen dioxide, sulfure dioxide, and nitric oxide; and eight stations measure hourly ambient ozone. Because of road traffic congestion, we used PM₁₀ data from the five ambient monitoring stations to represent general population exposure. Based on the common protocol, days with &lt; 18 hourly readings were considered missing. We calculated 24-hr averages for NO₂, NO (using the difference between NO_x and NO₂), SO₂, and PM₁₀, with the requirement that at least 75% of 1-hr values be available on that particular day. For the 8-hr average value of O₃, at least six hourly values from 0100 to 1800 hours had to be available, because the maximum O₃ levels always occur during daylight. We calculated the daily concentrations for each pollutant in the analysis by taking the mean of all available monitoring stations. We used only the stations that provided at least 75% completeness of the measurements over the study period.</p>", "<p>Daily weather data are available at two locations (the airport and city center) and are highly correlated (##REF##11002832##Ostro et al. 1999##). Therefore, we used data from the metropolitan weather station in the center of Bangkok, because there were no missing values. The data obtained included average daily temperature and average daily relative humidity.</p>", "<title>Statistical approach</title>", "<p>To assess the short-term effects of PM₁₀ on daily mortality, we followed a common protocol developed by participants in the Public Health and Air Pollution Project in Asia (PAPA project), which included research teams representing Bangkok and Hong Kong, Shanghai, and Wuhan, China. We used Poisson regression, conditional on several independent variables, to control for temporal trends and meteorologic conditions. For the basic model, we used natural cubic spline models with smoothing for time and weather, using R software (version 2.5 with mgcv 1.3–24; R ##UREF##6##Development Core Team 2007##). The natural spline model is a parametric approach that fits cubic functions joined at knots, which are typically placed evenly throughout the distribution of the variable of concern, such as time. The number of knots determines the overall smoothness of the fit. We determined the “best” core model for all nonaccidental cause mortality, controlling for time trend, seasonality, temperature, relative humidity, day of week, and public holidays, before entering an air pollutant into the model. In developing the core model, all PAPA cities examined 4–6 degrees of freedom (df) per year for the smoothing of time trend and 3 df for the smoothing of same-day lag of daily mean temperature and daily mean relative humidity. Preliminary analysis indicated that models with 4 or 5 df for time had mild autocorrelation, which would bias the standard errors. In contrast, a model with 6 df for the smoothing of time and first- and second-order autocorrelation terms resulted in no remaining serial correlation. Therefore, all subsequent models used this specification, although the results were very similar to those derived from the model unadjusted for autocorrelation. Based on the agreed-upon PAPA protocol, our core model used a lag of zero and 1 day (lag01) (i.e., the average of current day’s and previous day’s values), but single-day lags up to 5 days and moving averages of up to 5 days were also examined.</p>", "<p>We conducted several sensitivity analyses to assess the impacts of different model specifications in our results. This included models with <italic>a</italic>) different lags of PM₁₀, <italic>b</italic>) various sets of degrees of freedom for time and weather, <italic>c</italic>) different lags of temperature and relative humidity, and <italic>d</italic>) penalized splines for time and weather in place of natural splines. We also fitted co-pollutant models assessing the effects of PM₁₀ with adjustment for gaseous pollutants. An influenza epidemic could be a potential confounder of the associations, a possibility we assessed in the sensitivity analysis. Unfortunately, daily death counts for influenza in Bangkok were likely to be under-reported, so we defined influenza epidemic according to whether the weekly respiratory mortality count was greater than the 90th percentile of each year.</p>", "<p>All results are presented in terms of the excess risk (ER) per 10 μg/m³ of PM₁₀, which was calculated from the relative risk (RR) as ER = (RR –1) ×100.</p>" ]

[ "<title>Results</title>", "<title>Descriptive analysis</title>", "<p>##TAB##0##Table 1## summarizes the daily mortality data in Bangkok from 1 January 1999 to 31 December 2003. There was an average of 95 deaths per day from nonaccidental mortality. About 8% and 14% of the total consisted of mortality from respiratory and cardiovascular diseases, respectively, and about half of the total deaths were among those ≥65 years of age. Males make up about 64% of the total mortality in Bangkok. This may be attributable simply to the higher numbers of males in the city, possibly because of employment opportunities. We observed slightly increasing trends without apparent seasonal patterns in mortality data for Bangkok, suggesting that trend and seasonality may not be the strong confounding factors for the acute effects of PM₁₀ on mortality.</p>", "<p>##TAB##1##Table 2## provides the statistical distributions of the air pollutants and weather data used in this analysis, which were 100% complete over the study period except for PM₁₀, which had 4 missing days. Mean PM₁₀ was 52 μg/m³, with a maximum value of 169.2 μg/m³, higher than in most cities in NAWE. We observed a high correlation between PM₁₀ and both NO₂ (<italic>r</italic> = 0.78) and O₃ (<italic>r</italic> = 0.59). The weather in Bangkok was generally hot and humid. The median 24-hr temperature was 29.9°C and the median daily average humidity was 73.1%.</p>", "<title>Analytical results</title>", "<p>##TAB##2##Table 3## summarizes the results of pollutant models per 10-μg/m³ increase in PM₁₀ for various disease-specific causes of mortality as well as age- and sex-specific mortality using lag01. We observed statistically significant associations with most of the outcomes including nonaccidental and cardiovascular mortality, and we observed a positive but nonsignificant association for this lag for respiratory mortality. The ER for nonaccidental mortality was 1.3% [95% confidence interval (CI), 0.8–1.7] for a 10-μg/m³ increase in PM₁₀, with ER for cardiovascular and respiratory mortality of 1.9% (95% CI, 0.8–3.0) and 1.0% (95% CI, –0.4 to 2.4), respectively. With respect to subclassifications of cardiovascular disease, many were associated with PM₁₀, with mortality from stroke demonstrating a particularly elevated risk. Among the subgroups of respiratory mortality, we observed elevated excess risks for young children, especially among infants with respiratory causes, and asthma. Some of these estimates had very wide CIs, likely due to the small number of mortality counts for these outcomes. As indicated above, we also examined death from senility and found an excess risk of 1.8% (95% CI, 0.7–2.8) which was similar to that of cardiovascular at ≥ 65 years of age.</p>", "<p>Analysis of nonaccidental mortality by age group indicated that the effects of PM₁₀ increased with age, with the strongest effects for ages ≥ 75 years. However, associations were observed for all of the other age groups and, as indicated above, for respiratory mortality for children &lt; 1 year of age. Our analysis by sex demonstrated relatively similar effects for males and females.</p>", "<p>##TAB##3##Table 4## summarizes the effects of different lags of PM₁₀ on several mortality outcomes. For nonaccidental and ≥ 65 mortality, of the single-day lags, unlagged PM₁₀ provided the highest ER. For cardiovascular and respiratory mortality, the highest ER was observed for single-day lags of 1 and 3 days, respectively. However, for all end points, cumulative averages of 5 days of pollution generated the highest risk estimates.</p>", "<p>##TAB##4##Table 5## summarizes the results of the sensitivity analysis, with a focus on all-cause and cardiovascular mortality. The table indicates the effects on the ER for different df in the smoothing of time, and for multipollutant models. We examined models with 3 to 15 df per year for time, and the results were generally insensitive to the number of df specified. In addition, the inclusion of SO₂, NO₂, or O ₃ in the model had either no effect or slightly attenuated the estimated effect of PM₁₀. Finally, the results were generally insensitive to different lags and df for smoothing for temperature and humidity (however, overall, a lag0 temperature and humidity smooth term provided the best model fit, based on the percent of the explained deviation), use of penalized spline models, and inclusion of a term for influenza epidemics. In addition, the results for senility and for cardiovascular together with senility were similar and generally insensitive to the model specifications indicated above.</p>" ]

[ "<title>Discussion</title>", "<p>The results of our analysis of 5 years of data from Bangkok, Thailand, indicate a statistically significant association between daily mortality and daily concentrations of PM₁₀. For PM₁₀, the effect estimates for nonaccidental, cardiovascular, respiratory, and age ≥ 65 (nonaccidental) mortality are generally similar to (but in the high range) of those found elsewhere (##UREF##7##U.S. EPA 2004##). A 10-μg/m³ increase in lag01 PM₁₀ was associated with an excess risk in nonaccidental, cardiovascular, respiratory, and age ≥ 65 mortality of 1.3, 1.9, 1.0, and 1.5%, respectively. These estimates are generally similar to those reported by ##UREF##5##Ostro et al. (1998##, ##REF##11002832##1999)## and ##UREF##8##Vajanapoom et al. (2002)## in studies of Bangkok covering earlier years. However, these studies largely used PM₁₀ data estimated from airport visibility rather than the direct measurements of PM₁₀ used here.</p>", "<p>Excess risks from PM₁₀ were observed for many of the cardiovascular- and respiratory-disease specific subclasses of mortality, with particularly high risks related to respiratory diseases for those &lt; 1 year of age, asthma, LRI, stroke, and senility. The similar magnitudes of the excess risks on cardiovascular age ≥ 65 years and senility suggested that the latter probably includes cardiovascular mortality that has been incorrectly classified, especially for the elderly dying outside of hospitals, where the cause of death is often diagnosed as senility by a nonphysician coroner. Analysis by age indicated associations with PM₁₀ for all of the subgroups, and our examination of lags indicated that multiday averages of 5 days generated the largest effect estimates. In addition, many of the PM₁₀ associations were retained in multipollutant models. The results of the sensitivity analyses indicate that our core model was generally robust to choices of model specifications, spline model used, degrees of freedom of time smoothers, lags for temperature, adjustment for autocorrelation, and adjustment for influenza epidemics.</p>", "<p>Generally our analysis of PM₁₀ per 10 μg/m³ in Bangkok generated effect estimates that are higher than most previously reported. For example, our estimate for nonaccidental mortality is 1.3% (95% CI, 0.8–1.7%). In comparison, an analysis of 75 single-city time-series analyses from around the world generated an estimate of 0.6% (95% CI, 0.5–0.7%) (##REF##15703529##Anderson et al. 2005##). A study of the 90 largest cities in the United States gave an estimate of 0.2% (95% CI, 0.1–0.4%) (##UREF##2##Dominici et al. 2003##), whereas a study of 29 European cities yielded an estimate of 0.6% (95% CI, 0.4–0.7%) (##UREF##4##Katsouyanni et al. 2003##). A study of 14 cities in the United States using a case–crossover approach generated an estimate of 0.35% (95% CI, 0.2–0.5%) (##REF##15550600##Schwartz 2004##). A meta-analysis of Asian studies using a random-effects estimate gave an estimate of 0.49% (95% CI, 0.23–0.76%) based on four cities: Bangkok; Seoul and Inchon, South Korea; and Hong Kong (##UREF##3##HEI 2004##). Thus, it is clear that the results for Bangkok are at the upper end of the range of estimates. It is also significant that some high estimates have been reported in other less-developed countries. For example, a study in Mexico City reported an excess risk of 1.8% (95% CI, 0.9–2.7%), whereas analysis of Santiago, Chile, found an excess risk of 1.1% (95% CI, 0.9–1.4%) (##UREF##0##Castillejos et al. 2000##; ##REF##8777376##Ostro et al. 1996##).</p>", "<p>We can speculate on several possible reasons for our findings, including <italic>a</italic>) differences in particle chemistry in Bangkok; <italic>b</italic>) the proximity of a large proportion of the population to roads and traffic congestion; <italic>c</italic>) the likely high penetration rates due to low prevalence of home air conditioning in favor of open ventilation between indoors and outdoors (##REF##10703844##Tsai et al. 2000##); <italic>d</italic>) the greater duration of exposure due to the amount of time spent outdoors, because many Thais work and eat outdoors; <italic>e</italic>) factors related to lower economic development and socioeconomic status, such as lower background health status and use of health care, and higher smoking rates and co-morbidity; <italic>f</italic> ) greater exposure to indoor sources such as incense and cooking; and <italic>g</italic>) stochastic variability. Because of several of these factors (although only anecdotal in nature), it is likely that the effective inhaled dose of any given concentration measured from a fixed site outdoor monitor is greater in Bangkok than in Western industrialized countries.</p>", "<p>To date, few studies that relate mortality to air pollution have been conducted in Asia. Studies of daily mortality have been conducted in Inchon (##REF##10544154##Hong et al. 1999##), Seoul, and Ulsan, South Korea (##REF##11416779##Kwon et al. 2001##; ##REF##10417360##Lee and Schwartz 1999##; ##REF##9924011##Lee et al. 1999##); Shenyang, China (##REF##10821512##Xu et al. 2000##); seven cities in South Korea (##REF##11097798##Lee et al. 2000##); and New Delhi, India (##UREF##1##Cropper et al. 1997##). For the most part, policy makers in Asia have had to draw from studies conducted in North America and Western Europe. Although it may be reasonable to extrapolate the findings from the NAWE region to other parts of the world, our study also suggests that the per-unit effects may be higher in certain developing countries. Additional studies undertaken in developing countries in Asia and other parts of the world can validate our findings and help determine the factors that might modify the effect estimate.</p>", "<p>Finally, our analysis demonstrated an association between air pollution and mortality in a region that would not be confounded by cold weather and associated respiratory infections. As such, it supports the likelihood of a causal association in studies in NAWE, which experience greater seasonality and colder temperatures.</p>" ]

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[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Air pollution data in Bangkok, Thailand, indicate that levels of particulate matter with aerodynamic diameter ≤10 μm (PM₁₀) are significantly higher than in most cities in North America and Western Europe, where the health effects of PM₁₀ are well documented. However, the pollution mix, seasonality, and demographics are different from those in developed Western countries. It is important, therefore, to determine whether the large metropolitan area of Bangkok is subject to similar effects of PM₁₀.</p>", "<title>Objectives</title>", "<p>This study was designed to investigate the mortality risk from air pollution in Bangkok, Thailand.</p>", "<title>Methods</title>", "<p>The study period extended from 1999 to 2003, for which the Ministry of Public Health provided the mortality data. Measures of air pollution were derived from air monitoring stations, and information on temperature and relative humidity was obtained from the weather station in central Bangkok. The statistical analysis followed the common protocol for the multicity PAPA (Public Health and Air Pollution Project in Asia) project in using a natural cubic spline model with smooths of time and weather.</p>", "<title>Results</title>", "<p>The excess risk for non-accidental mortality was 1.3% [95% confidence interval (CI), 0.8–1.7] per 10 μg/m³ of PM₁₀, with higher excess risks for cardiovascular and above age 65 mortality of 1.9% (95% CI, 0.8–3.0) and 1.5% (95% CI, 0.9–2.1), respectively. In addition, the effects from PM₁₀ appear to be consistent in multipollutant models.</p>", "<title>Conclusions</title>", "<p>The results suggest strong associations between several different mortality outcomes and PM₁₀. In many cases, the effect estimates were higher than those typically reported in Western industrialized nations.</p>" ]

[ "<p>Compelling epidemiologic evidence indicates that current ambient levels of airborne particulate matter (PM) in North American and Western European (NAWE) cities are associated with premature mortality and a wide range of morbidity outcomes [##UREF##7##U.S. Environmental Protection Agency (EPA) 2004##; ##UREF##10##World Health Organization (WHO) 2000##]. Existing air pollution monitoring information and recent exposure assessments suggest that 6 to 10 million residents of Bangkok, Thailand, are exposed to levels of particulate matter with aerodynamic diameter ≤10 μm (PM₁₀) that are as high as or higher than those in NAWE cities. A recent review of Asian cities, mostly in more developed countries, suggests that PM may also be associated with both mortality and morbidity [##UREF##3##Health Effects Institute (HEI) 2004##]. However, PM chemical composition and relevant population characteristics, such as activity patterns, background health status, and other factors related to socioeconomic status, may all contribute to differential risks in developing countries such as Thailand. In addition, studies of mortality and air pollution in cities like Bangkok, which have seasonal patterns dramatically different from those of NAWE, provide an opportunity to assess the potentially confounding aspects of seasonality. Bangkok’s climate is hot and humid throughout the year, with 24-hr average temperatures almost always above 80°F. Therefore, with the lack of a cold season, the seasonal weather patterns are very different from those observed in most previous studies.</p>", "<p>The question remains whether residents of cities in developing countries are adversely affected by the existing levels of PM₁₀ and whether the impacts per unit are similar to those experienced in developed Western countries. Improvements in the mortality data collection system and air monitoring program in Bangkok provide an excellent opportunity to examine the effects of PM₁₀ and several gaseous pollutants on daily mortality for the years 1997 through 2003.</p>" ]

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[ "<table-wrap id=\"t1-ehp-116-1179\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Average daily mortality in Bangkok, 1 January 1999 to 31 December 2003.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ICD-10 codes</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Deaths/day ± SD</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Nonaccidental (age, years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">A00–R99</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">95.0 ± 12.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.0 ± 1.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 4–44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">29.0 ± 5.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 18–50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.0 ± 6.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 45–64</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.0 ± 5.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &gt; 50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">66.0 ± 9.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">45.0 ± 7.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">21.0 ± 5.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">61.0 ± 8.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">43.0 ± 7.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cardiovascular</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">I00–I99</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.0 ± 4.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ischemic heart diseases</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">I20–I25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.0 ± 2.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Stroke</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">I60–I69</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0 ± 2.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Conduction disorder</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">I44–I49</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cardiovascular ≥age 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">I00–I99</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.7 ± 3.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Respiratory</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">J00–J98</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0 ± 3.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Respiratory &lt; age 1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">J00–J98</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.1 ± 0.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">LRI &lt; 5 years</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">J10–J22</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">COPD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">J40–J47</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0 ± 1.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Asthma</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">J45–J46</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.2 ± 0.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Respiratory &gt; age 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">J00–J98</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.5 ± 2.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Senility</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">R54</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.0 ± 4.2</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1179\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Distribution of air pollutants and meteorology data in Bangkok, 1 January 1999 to 31 December 2003.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"5\" align=\"center\" rowspan=\"1\">Percentiles\n<hr/></th><th align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Pollutants and meteorology</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Min</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Max</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">SD</th><th align=\"right\" rowspan=\"1\" colspan=\"1\">5th</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">25th</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">50th</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">75th</th><th align=\"right\" rowspan=\"1\" colspan=\"1\">95th</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. of days</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀ (μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">52.1</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">21.3</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">169.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">20.1</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">29.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">46.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">59.9</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">93.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,822</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">SO₂ (μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">61.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.6</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">21.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,826</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂ (μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.7</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">15.8</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">139.6</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">17.3</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">24.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">54.8</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">79.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,826</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃ (μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">59.4</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">8.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">180.6</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">26.4</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">25.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">59.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">75.3</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">109.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,826</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO (μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">28.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">126.9</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">14.2</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">11.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">28.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.9</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">56.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,826</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Temperature (°C)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">28.9</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">18.7</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">33.6</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1.7</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">25.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">28.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29.9</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">31.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,826</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Relative humidity (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72.8</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">41.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">95.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">8.3</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">58.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">67.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78.3</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">86.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,826</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1179\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Percent ER in mortality (95% CI) for a 10-μg/m³ increase in lag01 PM₁₀\n<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1179\">a</xref>.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">%ER (95% CI)</th></tr></thead><tbody><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Cause-specific</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Nonaccidental</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3 (0.8 to 1.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Cardiovascular</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.9 (0.8 to 3.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Ischemic heart disease</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.5 (−0.4 to 3.5)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Stroke</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.3 (0.6 to 4.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Conduction disorders</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.3 (−5.9 to 5.6)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Cardiovascular ≥age 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8 (0.2 to 3.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Respiratory</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 (−0.4 to 2.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Respiratory ≤age 1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.6 (2.9 to 27.6)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  LRI &lt; age 5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.7 (−3.6 to 20.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  COPD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3 (−1.8 to 4.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Asthma</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.4 (1.1 to 14.1)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Respiratory ≥age 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3 (−0.8 to 3.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Senility</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8 (0.7 to 2.8)</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Age-specific for nonaccidental (years)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 0–4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.2 (−2.0 to 2.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 5–44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.9 (0.2 to 1.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 18–50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.2 (0.5 to 1.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 45–64</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.1 (0.4 to 1.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.4 (0.9 to 1.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.5 (0.9 to 2.1)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.2 (1.3 to 3.0)</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Sex-specific for nonaccidental</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.2 (0.7 to 1.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3 (0.7 to 1.9)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4-ehp-116-1179\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>Lag effects of PM₁₀ for major causes of mortality [percent ER (95% CI)].</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Lag days</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Nonaccidental</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Cardiovascular</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Respiratory</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Age ≥65</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Lag0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.2 (0.8 to 1.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.5 (0.5 to 2.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 (−0.3 to 2.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.5 (0.9 to 2.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Lag1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.9 (0.6 to 1.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.7 (0.7 to 2.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.8 (−0.5 to 2.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.1 (0.6 to 1.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Lag2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.9 (0.5 to 1.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.6 (0.6 to 2.6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1 (−0.1 to 2.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.1 (0.6 to 1.6)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Lag3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.8 (0.4 to 1.2)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.8 (−0.1 to 1.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.3 (0.1 to 2.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.2 (0.6 to 1.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Lag4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3 (−0.1 to 0.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.1 (−1.1 to 0.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.7 (−0.6 to 1.9)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.7 (0.2 to 1.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">0–1 mean</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.3 (0.8 to 1.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.9 (0.8 to 3.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 (−0.4 to 2.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.5 (0.9 to 2.1)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">0–4 mean</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.4 (0.9 to 1.9)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.9 (0.6 to 3.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.9 (1.2 to 2.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.9 (1.2 to 2.6)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t5-ehp-116-1179\" orientation=\"portrait\" position=\"float\"><label>Table 5</label><caption><p>Percent ER (95% CI) in mortality for a 10-μg/m³ increase in PM₁₀ with alternative degrees of freedom for smoothing of time and with adjustment for gaseous pollutants.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Model specification</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">%ER (95% CI)</th></tr></thead><tbody><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Nonaccidental (df)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3 (0.9 to 1.8)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.2 (0.8 to 1.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3 (0.8 to 1.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 6, with SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.2 (0.8 to 1.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 6, with NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 (0.2 to 1.8)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 6, with O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.1 (0.6 to 1.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.1 (0.7 to 1.6)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.1 (0.6 to 1.5)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.2 (0.7 to 1.6)</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Cardiovascular (df)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8 (0.8 to 2.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.6 (0.7 to 2.6)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.7 (0.7 to 2.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 6, with SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0 (0.9 to 3.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 6, with NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.3 (0.2 to 4.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 6, with O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8 (0.5 to 3.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.7 (0.6 to 2.8)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8 (0.7 to 3.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.2 (0.9 to 3.4)</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>We especially thank S. Wangwongwatana, Director of the Pollution Control Department, for his support of this project. We also thank W. Huang and S. Mehta of the Health Effects Institute (HEI) for their assistance on this project. We acknowledge the HEI for helpful comments from their International Scientific Oversight Committee. We also acknowledge the cooperation of the Thai Ministry of Public Health, the Pollution Control Department, and the Meteorological Department.</p></fn><fn><p>This study was supported by grant 4714-RFIQ03-3/04-10 from the HEI.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1179\"><p>Max, maximum; Min, minimum.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn2-ehp-116-1179\"><label>a</label><p>Model covariates include smooth of time with 6 df, smooth of unlagged temperature and humidity with 3 df, and day of week.</p></fn></table-wrap-foot>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>Data</title>", "<p>Shanghai, the most populous city in China, comprises urban/suburban districts and counties, with a total area of 6,341 km² and had a population of 13.1 million by the end of 2004. Our study area was limited to the traditional nine urban districts of Shanghai (289 km²). The target population includes all permanent residents living in the area—around 6.3 million in 2004. In the target population, the male/female ratio was 100.9%, and the elderly (&gt; 65 years of age) accounted for 11.9% of the total population.</p>", "<p>Daily nonaccidental mortality data from 1 January 2001 to 31 December 2004 were collected from the database of the Shanghai Municipal Center of Disease Control and Prevention (SMCDCP). Death certificates are completed either by community doctors for deaths at home or by hospital doctors for deaths in hospitals. The information on the certificates is then sent to the SMCDCP through their internal computer network. In Shanghai, all deaths must be reported to appropriate authorities before cremation. The database for 2001 and 2002–2004 was coded according to the <italic>International Classification of Diseases</italic>, <italic>Revision 9</italic> [ICD-9; ##UREF##7##World Health Organization (WHO) 1978##] and <italic>Revision 10</italic> (ICD-10; ##UREF##8##WHO 1993##), respectively. The mortality data were classified into deaths due to all nonaccidental causes (ICD-9 codes &lt; 800; ICD-10 codes A00–R99), cardiovascular diseases (ICD-9 codes 390–459; ICD-10 codes I00–I99), and respiratory diseases (ICD-9 codes 460–519; ICD-10 codes J00–J98). The data were also classified by sex and age (0–4, 5–44, 45–64, and ≥ 65 years) for all-cause deaths. Educational attainment has often been used as a surrogate indicator of SES in time-series studies (##REF##16404215##Cakmak et al. 2006##; ##REF##14684724##Jerrett et al. 2004##; ##REF##10824299##Zanobetti and Schwartz 2000##; ##REF##16554348##Zeka et al. 2006##). We therefore classified all-cause, cardiovascular, and respiratory deaths by educational attainment (low, illiterate or primary school; high, middle school or above).</p>", "<p>Daily air pollution data, including PM₁₀, SO₂, NO₂, and O₃, were retrieved from the database of the Shanghai Environmental Monitoring Center, the government agency in charge of collection of air pollution data in Shanghai. The daily concentrations for each pollutant were averaged from the available monitoring results of six fixed-site stations in the nine urban districts and covered by China National Quality Control. These stations are mandated to be located away from major roads, industrial sources, buildings, or residential sources of emissions from the burning of coal, waste, or oil; thus, our monitoring results reflect the background urban air pollution level in Shanghai rather than local sources such as traffic or industrial combustion.</p>", "<p>We abstracted the daily 24-hr mean concentrations for PM₁₀, SO₂, and NO₂, and maximal 8-hr mean concentrations for O₃. The maximal 8-hr mean was used because the ##UREF##9##WHO (2000)## recommended that the 8-hr mean reflects the most health-relevant exposure to O_3. For the calculation of both 24-hr mean concentrations of PM₁₀, SO₂, and NO₂, as well as maximal 8-hr mean O₃ concentrations, at least 75% of the 1-hr values must have been available on that particular day.</p>", "<p>To allow adjustment for the effect of weather conditions on mortality, we obtained daily mean temperature and humidity data from the Shanghai Meteorological Bureau database. The weather data were measured at a single fixed-site station in the Xuhui District of Shanghai.</p>", "<p>All of the mortality, weather, and air pollution data were validated by an independent auditing team assigned by the HEI. The team checked a sample of the original death certificates and monitoring records and validated the generation process of mortality, weather, and air pollution data used for the time-series analysis.</p>", "<title>Statistical methods</title>", "<p>Our statistical analysis followed the Common Protocol of the PAPA program. We used a generalized linear model (GLM) with natural splines (ns) to analyze the data. First, we built the basic models for various mortality outcomes excluding the air pollution variables. We incorporated the ns functions of time and weather conditions, which can accommodate nonlinear and non-monotonic relationships of mortality with time and weather variables, offering a flexible modeling tool (##UREF##0##Hastie and Tibshirani 1990##). We used the partial autocorrelation function (PACF) to guide the selection of degrees of freedom (df) for time trend (##REF##11505171##Katsouyanni et al. 2001##; ##UREF##6##Touloumi et al. 2004##, ##REF##16991105##2006##). Specifically, we used 4–6 df per year for time trend. When the absolute magnitude of the PACF plot was &lt; 0.1 for the first two lag days, the basic model was regarded as adequate; if this criterion was not met, autoregression terms for lag up to 7 days were introduced to improve the model. In this way, 4, 4, and 5 df per year for time trend, as well as 3, 2, and 4 lag-day autoregression terms, were used in our basic models for total, cardiovascular, and respiratory mortality, respectively. In addition, we used 3 df (whole period of study) for temperature and humidity because this has been shown to control well for their effects on mortality (##REF##16522832##Dominici et al. 2006##; ##REF##11114312##Samet et al. 2000a##). Day of the week was included as a dummy variable in the basic models. We examined residuals of the basic models to determine whether there were discernable patterns and autocorrelation by means of residual plots and PACF plots. After we established the basic models, we introduced the pollutant variables and analyzed their effects on mortality outcomes.</p>", "<p>Briefly, we fit the following log-linear GLM to obtain the estimated pollution log-relative rate β in Shanghai:</p>", "<p>where <italic>E(Y</italic><italic>_t</italic><italic>)</italic> represents the expected number of deaths at day <italic>t</italic>; β represents the log-relative rate of mortality associated with a unit increase of air pollutants; <italic>Z</italic><italic>_t</italic> indicates the pollutant concentrations at day <italic>t; DOW</italic> is dummy variable for day of the week; <italic>ns(time,df)</italic> is the ns function of calendar time; and <italic>ns(temperature/humidity, 3)</italic> is the ns function for temperature and humidity with 3 df. Current-day temperature and humidity (lag 0) and 2-day moving average of air pollutant concentrations (lag 01) were used in our analyses.</p>", "<p>We assessed both total nonaccidental and cause-specific mortality. We were able to stratify by sex and age only for total mortality. We analyzed effects of air pollution separately for the warm season (April–September) and the cool season (October–March) as well as for the entire year (##REF##15746475##Peng et al. 2005##; ##REF##16991105##Touloumi et al. 2006##). The basic models of seasonal analyses were different from those of whole-period analyses, using various dfs for time trend. Analyses by educational attainment were conducted for total, cardiovascular, and respiratory mortality. We tested the statistical significance of differences between effect estimates of the strata of a potential effect modifier (e.g., the difference between females and males) by calculating the 95% confidence interval (CI) as</p>", "<p>where Q̂₁ and Q̂₂ are the estimates for the two categories, and <italic>S</italic>Ê₁ and <italic>S</italic>Ê₂ are their respective SEs (##REF##16554348##Zeka et al. 2006##). Regardless of significance, we considered modification of effect by a factor of ≥ 2 to be important and worthy of attention (##REF##16554348##Zeka et al. 2006##).</p>", "<p>As a sensitivity analysis, we also examined the impact of model specifications such as lag structure and df selection on the effects of air pollutants (##REF##15961590##Welty and Zeger 2005##). We did not find substantial differences using alternative specifications.</p>", "<p>All analyses were conducted in R, version 2.5.1, using the mgcv package (R ##UREF##4##Development Core Team 2007##). The results are presented as the percent change in daily mortality per 10-μg/m³ increase of air pollutants.</p>" ]

[ "<title>Results</title>", "<title>Data description</title>", "<p>From 2001 to 2004 (1,461 days), a total of 173,911 deaths (82,597 females and 91,314 males) were registered in the study population. The percentages of total deaths by age group were 0.3% for 0–4 years, 3.2% for 5–44 years, 13.0% for 45–64 years, and 83.5% for ≥ 65 years. On average, there were approximately 119 nonaccidental deaths per day, including 44 from cardiovascular diseases and 14 from respiratory diseases (##TAB##0##Table 1##). Cardiorespiratory disease accounted for 49.1% of total nonaccidental deaths.</p>", "<p>During our study period, the mean daily average concentrations of PM₁₀, SO₂, NO₂, and O₃ were 102.0, 44.7, 66.6, and 63.4 μg/m³, respectively. There were two missing value days for O₃ and none for the other three pollutants. The mean daily average temperature and humidity were 17.7°C and 72.9%, respectively, reflecting the subtropical climate in Shanghai.</p>", "<p>Generally, PM₁₀, SO₂, and NO₂ were relatively highly correlated with each other (Pearson correlation coefficients ranged from 0.64 to 0.73). PM₁₀/SO₂/NO₂ concentrations were negatively correlated with temperature and humidity. Maximal 8-hr mean O₃ was weakly correlated with PM₁₀, SO₂, and NO₂ (Pearson correlation coefficients ranged from 0.01 to 0.19) and moderately correlated with temperature level (Pearson correlation coefficient, 0.48).</p>", "<title>Effects by season</title>", "<p>In the whole-period analyses, outdoor air pollution was associated with mortality from all causes and from cardiopulmonary diseases in Shanghai (##TAB##1##Table 2##). An increase of 10 μg/m³ of 2-day average concentrations of PM₁₀, SO₂, NO₂, and O₃ corresponds to 0.25% (95% CI, 0.14–0.37), 0.95% (95% CI, 0.62–1.28), 0.97% (95% CI, 0.66–1.27), and 0.31% (95% CI, 0.04–0.58) increase of all-cause mortality, respectively.</p>", "<p>There were more deaths, higher concentrations of pollutants (except for O₃, which had higher concentrations in the warm season), and drier weather conditions in the cool season than in the warm season (##TAB##0##Table 1##).</p>", "<p>The effect estimates of PM₁₀ on total mortality were similar in both seasons. Effect estimates were approximately 2–3 times higher for SO₂ and NO₂ in the cool season compared with the warm season. The effect estimate of O₃ was significant in both cool and warm seasons, and the magnitude of the O₃-associated increase in total mortality was approximately 5-fold higher in the cool season than in the warm season. Between-season differences in total mortality were significant for NO₂ and O₃ but not for PM₁₀ or SO₂ (##TAB##1##Table 2##).</p>", "<p>For cardiovascular mortality, the effect estimate of PM₁₀ was similar in both seasons. For SO₂, NO₂, and O₃, the effect estimate in the cool season were approximately 3–4 times higher than in the warm season. Between-season differences in cardiovascular mortality were insignificant for all four pollutants.</p>", "<p>For the smaller category of respiratory mortality, the effect estimates of PM₁₀, SO₂, and NO₂ were significant only in the cool season, and their between-season differences were significant. The effect effect estimate of O₃ on respiratory mortality was insignificant in either season.</p>", "<title>Effects by sex and age</title>", "<p>The percent increase associated with higher concentration levels of air pollutants varied by sex or age group (##TAB##2##Table 3##). The effect estimates of PM₁₀ and O₃ among females were approximately twice those among males, although their between-sex differences were insignificant. The effect estimates of SO₂ and NO₂ on total mortality in females were slightly higher than in males.</p>", "<p>The number of deaths for residents under 5 years of age was very low and therefore was excluded from our analysis. We did not observe significant effects of air pollution in residents 5–44 years of age or 45–64 years of age. Among those ≥ 65 years of age, the effect estimates of all four pollutants were significant, and approximately 2–5 times higher than among people 5–44 years of age or 45–64 years of age, although the between-age differences among all three groups were insignificant.</p>", "<title>Effects by education</title>", "<p>Generally, residents with low educational attainment (illiterate or primary school) had a higher number of deaths from air pollution–related effects than those with high educational attainment (middle school or above) (##TAB##3##Table 4##).</p>", "<p>For total mortality, the effect estimates of PM₁₀, SO₂, and NO₂ were significant in both education groups. The effect estimates of these three pollutants were 1–2 times larger among the low-education group compared with the high-education group, although the educational differences were significant only for NO₂ for total mortality. The effect estimate of O₃ of total mortality were similar and insignificant in both groups.</p>", "<p>For cardiovascular mortality, the effect estimates of PM₁₀ and NO₂ were significant or marginally significant in both education groups; the effect estimate of SO₂ was significant only in the low-education group; no significant effect of O₃ was seen in either group. The effect estimates of all four pollutants were 1–2 times larger among the low-education group compared with the high-education group. The educational differences in cardiovascular mortality were not significant for any pollutants.</p>", "<p>For respiratory mortality, the effect estimates of PM₁₀, SO₂, and NO₂ were significant only among those with low education, whereas the effect estimate of O₃ on respiratory mortality was not significant in either group. The effect estimates of PM₁₀, SO₂, and NO₂ were several times larger among the low-education group compared with the high-education group. The educational differences in respiratory mortality were not significant for any pollutants.</p>" ]

[ "<title>Discussion</title>", "<p>Although the associations between outdoor air pollution and daily mortality have been well established in developed countries, the question of the potential modifiers remains inconclusive. As the U.S. ##UREF##3##National Research Council (1998)## pointed out, it is important to understand the characteristics of individuals who are at increased risk of adverse events due to outdoor air pollution. Our results suggest that season and individual sociodemographic factors (e.g., sex, age, SES) may modify the health effects of air pollution in Shanghai. Specifically, the association between air pollution and daily mortality was generally more evident for the cool season than the warm season; females and the elderly (≥65 years of age) appeared to be more vulnerable to air pollution than males and younger people; and disadvantaged SES may intensify the adverse health effects of outdoor air pollution.</p>", "<p>Our finding of a stronger association between air pollution and daily mortality in the cool season is consistent with several prior studies in Hong Kong (##REF##10562884##Wong et al. 1999##, ##REF##11335180##2001##) and Athens, Greece (##REF##8758224##Touloumi et al. 1996##), but in contrast with others reporting greater effects in the warm season (##REF##8597732##Anderson et al. 1996##; ##REF##15951661##Bell et al. 2005##; ##REF##17234874##Nawrot et al. 2007##). In Shanghai, the concentrations of PM₁₀, SO₂, and NO₂ were higher and more variable in the cool season than in the warm season (##TAB##0##Table 1##). Because these three pollutants were highly correlated, greater effects observed during the cool season may also be due to other pollutants that were also at higher levels during that season. In contrast, the O₃ level was higher in the warm season than in the cool season, and our exposure–response relationship also revealed a flatter slope at higher concentrations of O₃ for both sexes (data not shown). At higher concentrations, the risks of death could be reduced because vulnerable subjects may have died before the concentration reached the maximum level (##REF##11335180##Wong et al. 2001##).</p>", "<p>Exposure patterns may contribute to our season-specific observation. During the warm season, Shanghai residents tend to use air conditioning more frequently because of the relatively higher temperature and humidity, thus reducing their exposure. For example, in a survey of 1,106 families in Shanghai, 32.7% of the families never turn on air conditioners in the winter compared with 3.7% in the summer (##UREF##2##Long et al. 2007##). Heavy rain in the warm season may reduce time outdoors, thus reducing personal exposure. In contrast, the cool season in Shanghai is drier and less variable, so people are more likely to go outdoors and open the windows. Nevertheless, the fact that a consistently significant health effect of air pollution was observed only in the cool season in two subtropical Asian cities [Shanghai (present study) and Hong Kong (##REF##10562884##Wong et al. 1999##, ##REF##11335180##2001##)] suggests that the interaction of air pollution exposure and season may vary by location.</p>", "<p>Unlike the gaseous pollutants, the constituents of the complex mix of PM₁₀ may vary by season. Therefore, another potential explanation for the seasonal difference in the effects of PM₁₀ is that the most toxic particles may have a cool-season maximum in Shanghai.</p>", "<p>We found a greater effect of ambient air pollution on total mortality in females than in males. Results of prior studies on sex-specific acute effects of outdoor air pollution were discordant. For example, ##REF##8777375##Ito and Thurston (1996)## found the highest risk of mortality related with air pollution exposure among black women. ##REF##11836148##Hong et al. (2002)## found that elderly women were most susceptible to the adverse effects of PM₁₀ on the risk of acute mortality from stroke. However, ##REF##16404215##Cakmak et al. (2006)## found that sex did not modify the hospitalization risk of cardiac diseases due to air pollution exposure.</p>", "<p>The reasons for our sex-specific observations are unclear and deserve further investigation. In Shanghai, females have a much lower smoking rate than males (0.6% in females vs. 50.6% in males) (##UREF##10##Xu 2005##). One study suggested that effects of air pollution may be stronger in nonsmokers than in smokers (##REF##15687058##Künzli et al. 2005##). Oxidative and inflammatory effects of smoking may dominate to such an extent that the additional exposure to air pollutants may not further enhance effects along the same pathways in males. In addition, females have slightly greater airway reactivity than males, as well as smaller airways (##REF##1416415##Yunginger et al. 1992##); therefore, dose–response relations might be detected more easily in females than in males. Deposition of particles in the lung varies by sex, with greater lung deposition fractions of 1-μM particles in all regions for females (##REF##9609774##Kim and Hu 1998##; ##REF##10051270##Kohlhaufl et al. 1999##). ##REF##10625173##Sunyer et al. (2000)## suggested that differing particulate deposition patterns between females and males may partly explain the difference between the sexes. Moreover, compared with males, females in Shanghai had a lower education level (73.9% in females vs. 41.0% in males); thus, lower SES might contribute to the observed larger effects of air pollution in females.</p>", "<p>As in a few other studies (##REF##10990478##Gouveia and Fletcher 2000##; ##REF##11505171##Katsouyanni et al. 2001##), we found the elderly were most vulnerable to the effects of air pollution. Low numbers of deaths in the 0- to 4-year age group limited our power to detect the effects of air pollution on mortality, even if they exist. Two groups, the elderly and the very young, are presumed to be at greater risk for air pollution–related effects (##REF##10990478##Gouveia and Fletcher 2000##; ##REF##15060197##Schwartz 2004##). For the elderly, preexisting respiratory or cardiovascular conditions are more prevalent than in younger age groups; thus, there is some overlap between potentially susceptible groups of older adults and people with heart or lung diseases.</p>", "<p>It has long been known that SES can affect health indicators such as mortality (##REF##9186383##Mackenbach et al. 1997##). Recently, studies have started to examine the role of SES in the vulnerability of subpopulations to outdoor air pollution, especially for particles and O₃, although the results remain inconsistent (##REF##14644658##O’Neill et al. 2003##). For example, ##REF##16554348##Zeka et al. (2006)## found that individual-level education was inversely related to the risk of mortality associated with PM _{10 .}Another cohort study with small-area measures of SES in Hamilton, Ontario, Canada, found important modification of the particle effects by social class (##REF##12952800##Finkelstein et al. 2003##; ##REF##14684724##Jerrett et al. 2004##). In contrast, ##REF##10990478##Gouveia and Fletcher (2000)## observed a larger effect of air pollution in areas of higher SES level; ##REF##15127905##Bateson and Schwartz (2004)## found no indication that susceptibility to air pollution varied by group-level SES measures. In the present study, using individual-level education as a measure of SES, we found that residents with low educational attainment were more sensitive to air pollution exposure than those with high educational attainment. Our results provide the first evidence in Mainland China that lower SES may compose a risk factor for air pollution–related health effects.</p>", "<p>SES factors such as educational attainment may modify the health effects of outdoor air pollution in several pathways. People with lower SES may be more sensitive to air pollution–related health hazards because they have a higher prevalence of preexisting diseases that confer a greater risk of dying associated with air pollution exposure, and they may also receive inferior medical treatment for preexisting diseases. Disadvantaged living conditions may contribute to the modification effect; people with lower SES may have more limited access to fish, fresh fruits, and vegetables, resulting in reduced intake of antioxidant polyunsaturated fatty acids and vitamins that may protect against adverse consequences of particle exposure (##REF##16210665##Romieu et al. 2005##). Additionally, exposure patterns may contribute to effect modification by SES. Persons with lower SES are less likely to have air conditioning (##UREF##2##Long et al. 2007##) and more likely to live near busy roadways and have coexposures due to either poor housing or occupation. For example, disadvantaged groups have been found to be more highly exposed to some air pollutants (##REF##8184446##Sexton et al. 1993##). Scandinavian studies have shown differential personal exposures to particles and other pollutants by education and occupation (##REF##10981732##Rotko et al. 2000##, ##REF##11477519##2001##), and a study in the U.S. Great Lakes region indicates differences in exposure to gaseous pollutants by occupation and education, minority status, and income (##REF##10189626##Pellizzari et al. 1999##). Finally, as ##REF##14684724##Jerrett et al. (2004)## pointed out, persons with lower education are less mobile and experience less exposure measurement error, thereby reducing bias toward the null.</p>", "<p>The limitations of our analysis should be noted. As in other studies in this field, we used available outdoor monitoring data to represent the population exposure to air pollutants. Our assessment of weather conditions was derived entirely from one monitoring station. Measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly for the time-series design (##REF##10811568##Zeger et al. 2000##). It is possible that this type of error may introduce bias to the results of our analysis; however, because of lack of available information on personal exposure to air pollutants, we could not quantify such a bias. Compared with other studies in Europe and North America, the data we collected were limited in being only one city, in sample size, and in duration. In addition, high correlation between particulate matter and gaseous pollutants in Shanghai limited our ability to separate the independent effect for each pollutant.</p>", "<p>In summary, in this time-series analysis, we found that outdoor air pollution was associated with mortality from all causes and from cardiopulmonary diseases in Shanghai during 2001–2004. Furthermore, our results suggest that season and sociodemographic factors (e.g., sex, age, SES) may modify the acute health effects of air pollution. These findings provide new information about the effects of modifiers on the relationship between daily mortality and air pollution in developing countries and may have implications for local environmental and social policies.</p>" ]

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[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Various factors can modify the health effects of outdoor air pollution. Prior findings about modifiers are inconsistent, and most of these studies were conducted in developed countries.</p>", "<title>Objectives</title>", "<p>We conducted a time-series analysis to examine the modifying effect of season, sex, age, and education on the association between outdoor air pollutants [particulate matter &lt; 10 μm in aerodynamic diameter (PM₁₀), sulfur dioxide, nitrogen dioxide, and ozone] and daily mortality in Shanghai, China, using 4 years of daily data (2001–2004).</p>", "<title>Methods</title>", "<p>Using a natural spline model to analyze the data, we examined effects of air pollution for the warm season (April–September) and cool season (October–March) separately. For total mortality, we examined the association stratified by sex and age. Stratified analysis by educational attainment was conducted for total, cardiovascular, and respiratory mortality.</p>", "<title>Results</title>", "<p>Outdoor air pollution was associated with mortality from all causes and from cardiorespiratory diseases in Shanghai. An increase of 10 μg/m³ in a 2-day average concentration of PM₁₀, SO₂, NO₂, and O₃ corresponds to increases in all-cause mortality of 0.25% [95% confidence interval (CI), 0.14–0.37), 0.95% (95% CI, 0.62–1.28), 0.97% (95% CI, 0.66–1.27), and 0.31% (95% CI, 0.04–0.58), respectively. The effects of air pollutants were more evident in the cool season than in the warm season, and females and the elderly were more vulnerable to outdoor air pollution. Effects of air pollution were generally greater in residents with low educational attainment (illiterate or primary school) compared with those with high educational attainment (middle school or above).</p>", "<title>Conclusions</title>", "<p>Season, sex, age, and education may modify the health effects of outdoor air pollution in Shanghai. These findings provide new information about the effects of modifiers on the relationship between daily mortality and air pollution in developing countries and may have implications for local environmental and social policies.</p>" ]

[ "<p>Epidemiologic studies have reported associations of outdoor air pollution with daily mortality and morbidity from cardiorespiratory diseases (##REF##15025190##Goldberg et al. 2003##). Multicity analyses conducted in the United States, Canada, and Europe provide further evidence supporting coherence and plausibility of the associations (##REF##12881885##Burnett et al. 2000##; ##REF##16522832##Dominici et al. 2006##; ##REF##9180068##Katsouyanni et al. 1997##, ##REF##11505171##2001##; ##REF##11114312##Samet et al. 2000a##). Recently, interest has been focused on the possible modifying effect of season (##REF##15746475##Peng et al. 2005##; ##REF##16991105##Touloumi et al. 2006##; ##REF##16554348##Zeka et al. 2006##), preexisting health status (##REF##15127905##Bateson and Schwartz 2004##; ##REF##11544152##Goldberg et al. 2001##; ##REF##11505171##Katsouyanni et al. 2001##), and population demographic characteristics such as sex and age (##REF##11734437##Atkinson et al. 2001##; ##REF##15127905##Bateson and Schwartz 2004##; ##REF##16404215##Cakmak et al. 2006##; ##REF##11505171##Katsouyanni et al. 2001##) on the relation between air pollution and daily mortality. It is also hypothesized that the effects of air pollution exposure on health are greater in people with lower socioeconomic status (SES) (##REF##14644658##O’Neill et al. 2003##). However, prior findings about the modifying effect of SES remain inconsistent: some studies found evidence of modification (##REF##12952800##Finkelstein et al. 2003##; ##REF##14684724##Jerrett et al. 2004##; ##REF##16020033##Krewski et al. 2005##; ##REF##16554348##Zeka et al. 2006##), but others did not (##REF##15127905##Bateson and Schwartz 2004##; ##REF##16404215##Cakmak et al. 2006##; ##UREF##5##Samet et al. 2000b##; ##REF##10824299##Zanobetti and Schwartz 2000##). Moreover, most of these studies were conducted in developed countries, and only a small number of studies have been conducted in Asia (##UREF##1##Health Effects Institute 2004##). The need remains for studies of cities in developing countries, where characteristics of outdoor air pollution (e.g., air pollution level and mixture, transport of pollutants), meteorological conditions, and sociodemographic patterns may differ from those in North America and Europe.</p>", "<p>Better knowledge of these modifying factors will help in public policy making, risk assessment, and standard setting, especially in cities of developing countries with fewer existing studies. In the present study, we conducted a time-series analysis to examine the modifying effect of season, sex, age, and education on the association between outdoor air pollutants [particulate matter &lt; 10 μm in diameter (PM₁₀), sulfur dioxide, nitrogen dioxide, and ozone] and daily mortality in Shanghai, China. This study is a part of the joint Public Health and Air Pollution in Asia (PAPA) program supported by the Health Effects Institute (HEI).</p>" ]

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[ "<table-wrap id=\"t1-ehp-116-1183\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Daily deaths, air pollutant concentrations, and weather conditions (mean ± SE) in Shanghai, China, 2001–2004.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Warm season (<italic>n</italic> = 729)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Cool season (<italic>n</italic> = 732)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Entire period (<italic>n</italic> = 1,461)</th></tr></thead><tbody><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">No. of daily deaths</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Total (nonaccident)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">106.1 ± 0.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">132.0 ± 0.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">119.0 ± 0.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Cardiovascular</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">37.9 ± 0.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.5 ± 0.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.2 ± 0.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Respiratory</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.4 ± 0.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.2 ± 0.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.3 ± 0.2</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Air pollutant concentration (μg/m³)<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1183\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">87.4 ± 1.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">116.7 ± 2.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">102.0 ± 1.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.4 ± 0.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.1 ± 1.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.7 ± 0.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">57.3 ± 0.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">76.0 ± 1.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">66.6 ± 0.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78.4 ± 1.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">48.3 ± 0.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">63.3 ± 1.0</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Meteorological measures</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Temperature (°C)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24.3 ± 0.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.2 ± 0.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.7 ± 0.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Humidity (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">75.1 ± 0.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">70.6 ± 0.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72.9 ± 0.3</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1183\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Percent increase [mean (95% CI)] of mortality outcomes of Shanghai residents associated with 10-μg/m³ increase in air pollutant concentrations by season, 2001–2004.<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1183\">a</xref></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Pollutant</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Warm season</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Cool season</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Entire period</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.21 (0.09 to 0.33)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.26 (0.22 to 0.30)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.25 (0.14 to 0.37)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.57 (−0.03 to 1.18)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.10 (0.66 to 1.53)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.95 (0.62 to 1.28)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.46 (−0.07 to 0.98)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.24 (0.84 to 1.64)<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1183\">*</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.97 (0.66 to 1.27)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22 (0.03 to 0.41)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.19 (0.56 to 1.83)<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1183\">*</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.31 (0.04 to 0.58)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cardiovascular</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22 (−0.14 to 0.58)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.25 (0.05 to 0.45)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.27 (0.10 to 0.44)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.31 (−0.65 to 1.29)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.02 (0.40 to 1.65)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.91 (0.42 to 1.41)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.30 (−0.54 to 1.14)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.26 (0.68 to 1.84)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.01 (0.55 to 1.47)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.32 (−0.05 to 0.69)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.42 (0.51 to 2.33)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.38 (−0.03 to 0.80)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Respiratory</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.28 (−0.93 to 0.38)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.58 (0.25 to 0.92)<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1183\">*</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.27 (−0.01 to 0.56)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−1.13 (−2.86 to 0.62)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.47 (1.41 to 3.54)<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1183\">*</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.37 (0.51 to 2.23)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−1.37 (−2.86 to 0.15)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.66 (1.67 to 3.65)<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1183\">*</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.22 (0.42 to 2.01)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.12 (−0.72 to 0.98)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.94 (−0.60 to 2.50)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.29 (−0.44 to 1.03)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1183\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Percent increase [mean (95% CI)] in total mortality of Shanghai residents associated with a 10-μg/m³ increase in air pollutant concentrations by sex and age.<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1183\">a</xref></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"4\" align=\"center\" rowspan=\"1\">Pollutant\n<hr/></th></tr><tr><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean daily deaths (<italic>n</italic>)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">PM₁₀</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">SO₂</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">O₃</th></tr></thead><tbody><tr><td colspan=\"6\" align=\"left\" rowspan=\"1\">Sex</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">56.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.33 (0.18 to 0.48)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.06 (0.62 to 1.51)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.10 (0.69 to 1.51)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.40 (0.03 to 0.76)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">62.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.17 (0.03 to 0.32)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.85 (0.43 to 1.28)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.88 (0.49 to 1.28)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19 (−0.16 to 0.55)</td></tr><tr><td colspan=\"6\" align=\"left\" rowspan=\"1\">Age (years)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 5–44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.04 (−0.52 to 0.59)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.21 (−0.47 to 2.91)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.52 (−1.01 to 2.08)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.08 (−1.38 to 1.25)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 45–64</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.17 (−0.11 to 0.45)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.22 (−0.60 to 1.04)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.64 (−0.11 to 1.40)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.47 (−0.19 to 1.12)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">99.6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.26 (0.15 to 0.38)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.01 (0.65 to 1.36)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.01 (0.69 to 1.34)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.32 (0.03 to 0.61)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4-ehp-116-1183\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>Percent increase in number of deaths due to total, cardiovascular, and respiratory causes associated with a 10-μg/m³ increase in air pollutants by educational attainment.<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1183\">a</xref></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"4\" align=\"center\" rowspan=\"1\">Pollutant\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Educational attainment</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean daily deaths (<italic>n</italic>)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">PM₁₀</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">SO₂</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">O₃</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">67.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.33 (0.19 to 0.47)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.19 (0.77 to 1.61)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.27<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1183\">*</xref> (0.89 to 1.66)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26 (−0.09 to 0.60)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.18 (0.01 to 0.36)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.66 (0.16 to 1.17)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.62 (0.15 to 1.09)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.30 (−0.11 to 0.71)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cardiovascular</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.30 (0.10 to 0.51)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.08 (0.47 to 1.69)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.15 (0.58 to 1.72)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.39 (−0.13 to 0.90)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.23 (−0.03 to 0.50)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.57 (−0.20 to 1.35)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.73 (0.01 to 1.45)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26 (−0.38 to 0.91)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Respiratory</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.36 (0.00 to 0.72)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.54 (0.43 to 2.66)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.59 (0.57 to 2.62)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.20 (−0.74 to 1.16)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.02 (−0.43 to 0.47)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.73 (−0.61 to 2.09)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.34 (−0.89 to 1.60)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27 (−0.86 to 1.41)</td></tr></tbody></table></table-wrap>" ]

[ "<disp-formula></disp-formula>", "<disp-formula></disp-formula>" ]

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[ "<fn-group><fn><p>This study was funded by the Health Effects Institute through grant 4717-RFIQ03-3/04-13. The research was also supported by the Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services.</p></fn><fn><p>The views expressed in this article are those of the authors and do not necessarily reflect the views of the Health Effects Institute or its sponsors.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1183\"><label>a</label><p>Twenty-four-hour average for PM₁₀, SO₂, and NO₂; 8-hr (1000–1800 hours) average for O₃.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn2-ehp-116-1183\"><label>a</label><p>We used current day temperature and humidity (lag 0) and 2-day moving average of air pollutant concentrations (lag 01), and applied 3 df to temperature and humidity.</p></fn><fn id=\"tfn3-ehp-116-1183\"><label>*</label><p>Significantly different from the warm season (p &lt; 0.05).</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn4-ehp-116-1183\"><label>a</label><p>We used current day temperature and humidity (lag 0) and 2-day moving average of air pollutant concentrations (lag 01), and applied 3 df to temperature and humidity.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn5-ehp-116-1183\"><label>a</label><p>We used current day temperature and humidity (lag 0) and 2-day moving average of air pollutants concentrations (lag 01) and we applied 3 df to temperature and humidity.</p></fn><fn id=\"tfn6-ehp-116-1183\"><label>*</label><p>Significantly different from high educational attainment (p &lt; 0.05).</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1183e1.jpg\" position=\"float\" orientation=\"portrait\"/>", "<graphic xlink:href=\"ehp-116-1183e2.jpg\" position=\"float\" orientation=\"portrait\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>Tertiary planning units (TPUs)</title>", "<p>The TPU system was devised by the Hong Kong Planning Department for town planning purposes. In 2001, the whole land area of Hong Kong was divided into 276 TPUs. Our analysis included all TPUs except for suburban TPUs (<italic>n</italic> = 67) in the New Territories and outer islands of Hong Kong, which are remote and have population densities lower than the lowest quartile (533/km²) of the whole territory. People residing in these sparsely populated areas account for about 1.5% of the total population and are usually exposed to sources and levels of air pollution different from those in urban areas. Because air pollution exposure measurements were based on data from monitoring stations located in urban areas, exclusion of nonurban areas would reduce exposure measurement errors.</p>", "<title>Measures of social deprivation</title>", "<p>The Census and Statistics Department of Hong Kong conducts a population census every 10 years and a by-census every intermediate 5 years. TPUs are the smallest units in the population census report. The 2001 census report contains 44 statistics of the Hong Kong population measured at TPU level. We performed factor analysis on 18 socioeconomic and demographic variables related to social deprivation available in this population census database. Six factors accounting for 69% of the variation were extracted from principal-component analysis. Based on the distribution of factor loadings, we chose six variables to describe the conditions of social deprivation for each TPU: the proportions of the population with <italic>a</italic>) unemployment, <italic>b</italic> ) monthly household income &lt; US$250, <italic>c</italic>) no schooling at all, <italic>d</italic> ) one-person household, <italic>e</italic>) never-married status, and <italic>f</italic>) subtenancy. Each of these six variables had significant factor loading for a specific principal factor, and all of them are deemed to be representative indicators of social disadvantage in the published literature and in the setting of the Hong Kong population. The first four conditions are more or less related to a lack of material resources. Being unmarried in Chinese society would have been regarded previously as undesirable in a social and family context. In Hong Kong, people who cannot afford to rent a whole flat may rent a part (usually a small room) of a flat from another tenant. The six selected variables in this study are similar to those used in other well-known social deprivation indices in other countries such as Index of Local Conditions (##UREF##1##Department of Environment 1994##) and the Jarman (##REF##6405943##Jarman 1983##), and Townsend (##REF##11238578##Benach et al. 2001##; ##UREF##7##Payne et al. 1996##; ##UREF##12##Townsend et al. 1988##) indices. For example, the “unemployment proportion” is similar to “unemployment rate”; “subtenancy” is similar to “not owner-occupier households”; “never married” is a dimension similar to “lone parent household”; “one-person household” could indicate partly “lone pensioner”; and “no school” is broadly similar to “low secondary education attainment” (##REF##11238578##Benach et al. 2001##; ##UREF##7##Payne et al. 1996##).</p>", "<p>The social deprivation index (SDI) for each TPU was calculated by taking the average of these six selected variables. A detailed description of the development of SDI is given in one of our previous studies (##UREF##15##Wong et al. 1999##), which showed that each of these six measures was correlated with standard mortality rate at TPU level and mortality was high in TPUs with high SDI. Based on tertiles of SDI, all TPUs were classified into one of three SDI groups: low (less than the lowest tertile of SDI), middle (the lowest tertile to the middle tertile), and high (greater than the highest tertile). ##TAB##0##Table 1## shows a summary of basic characteristics for the 209 urban TPUs by SDI level.</p>", "<title>Health outcomes</title>", "<p>The Census and Statistics Department of Hong Kong provided mortality data for all registered deaths from January 1996 to December 2002, including age, sex, date of death, TPU of residence, and the code of underlying cause of death, which is classified according to the <italic>International Classification of Diseases, 9th Revision</italic> (ICD-9), 1996–1999 and <italic>10th Revision</italic> (ICD-10), 2000–2002 (##UREF##13##World Health Organization 1977##, ##UREF##14##1992##). For each SDI group, we aggregated daily numbers of deaths due to all nonaccidental causes (ICD-9 codes 001-799; ICD-10 codes A00-T99, Z00-Z99), cardiovascular (ICD-9 390-459; ICD-10 I00-I99) and respiratory (ICD-9 460-519; ICD-10 J00-J98) diseases, respectively.</p>", "<title>Air pollution and meteorologic data</title>", "<p>Hourly concentrations of nitrogen dioxide, sulfur dioxide, particulate matter with aero-dynamic diameter &lt; 10 μm (PM₁₀), and ozone were derived from eight fixed-site general monitoring stations operated by the Environmental Protection Department (##UREF##5##HK EPD 2007##). The measurement methods for NO₂, SO₂, PM₁₀, and O₃ were chemiluminescence, fluorescence, tapered element oscillating microbalance, and ultraviolet absorption, respectively. NO₂, SO₂, and O₃ were also measured by differential optical absorption spectroscopy in some monitoring stations. Daily concentrations of air pollutants for each monitoring station were taken to be the average of the 24-hr concentrations of NO₂, SO₂, and PM₁₀ and of 8-hr (0100–1800 hours) concentrations of O₃. Daily concentrations of air pollutants for the whole territory of Hong Kong were evaluated by averaging the daily concentrations across all monitoring stations using the method of centering (##REF##11335180##Wong et al. 2001##). In calculating the daily data there should be at least 75% 1-hr values of that particular day, and for each monitoring station there should be at least 75% of daily data complete for the whole study period. Meteorologic data, including daily temperature and relative humidity, were provided by the ##UREF##5##Hong Kong Observatory (2007)##.</p>", "<title>Statistical methods</title>", "<p>We used generalized linear modeling to obtain the most adequate core models for each health outcome. We used Poisson regression with quasi-likelihood method to model mortality and hospital admission counts with adjustment for over-dispersion (##UREF##6##McCullagh and Nelder 1989##). To control for systematic variation over time, we introduced a trend and seasonality term and dummy variables for day of the week and public holidays. Other covariates considered and adjusted for were daily mean temperature and relative humidity. The trend and seasonality term was defined by fitting a natural smoothing spline with 4–6 degrees of freedom (dfs) per year. Additional smoothing splines with 3 dfs were included to adjust for the effects of temperature and 3 dfs to adjust for relative humidity. The choice of the number of dfs for each smoothing function was made on the basis of observed autocorrelations for the residuals using partial autocorrelation function plots. Partial autocorrelation coefficient (##UREF##2##Hastie and Tibshirani 1990##) of |ρ| &lt; 0.1 for the first 2 lag days was used as a criterion for a minimally adequate model. Randomness of residuals and autoregressive terms were also considered in selecting the most appropriate models. If the above criteria were met, the variable for the air pollutant concentrations was entered into the core model for assessment of percentage excess risk (ER) per 10-μg/m³ increase of an air pollutant at single lag 0–4 days and at average lag 0 and lag 1 day. We performed Poisson regression analysis and assessed the ER for each level of social deprivation in the data set stratified by level of social deprivation. All analyses under Poisson regression were performed using the statistical software package R version 2.5.1 (R Development Core team 2006) with mgcv package version 1.3-25.</p>", "<p>In addition, we used a case-only approach in a combined data set to assess potential interaction between social deprivation level and ambient air pollution on mortality. The case-only approach with logistic regression was originally proposed for studying the gene–environment interaction and has been widely used in this field of study (##REF##12916025##Fallin et al. 2003##; ##REF##15894495##Fracanzani et al. 2005##). ##REF##12843773##Armstrong (2003)## has pointed out that this method can be extended for evaluating the interaction between time-varying variables and individual factors. Subsequently, ##REF##15613947##Schwartz (2005)## gave a more detailed description of this method and applied it to examine whether medical conditions modify the mortality effects of extreme temperature. We used this method recently to examine the effect modification of air pollution by individual smoking status and physical activity (##REF##17700248##Wong et al. 2007a##, ##REF##17291575##2007b##). In the present study, we assume that the risk of dying associated with temporary increase in air pollution level is modified by residence in different social deprivation areas. For example, people who died on days with high levels of air pollution would be more likely to reside in a high SDI area than those who died on days with low levels of air pollution, and therefore the air pollution level at the date of death could be a predictor of neighborhood SDI level of the deceased using logistic regression. The difference in relative risk of mortality associated with air pollution between SDI levels was calculated based on the relationship between SDI and the levels of ambient air pollution using multinomial logistic regression. Furthermore, an ordinal logit model was fitted to determine whether there was a trend in the health effects of air pollution increasing from low to middle and then to high SDI levels.</p>" ]

[ "<title>Results</title>", "<p>##FIG##0##Figure 1## shows the geographic variations in social deprivation in the whole of Hong Kong excluding suburban areas. Most of the areas with high SDI levels were in the northern territories bordering mainland China and in the outer islands. There were also a few highly deprived areas in the inner city.</p>", "<title>Health outcomes and covariates</title>", "<p>Our study included a total of 215,240 nonaccidental deaths (males: 120,262; females: 94,978) from 1996 to 2002, with an average of 30,749 deaths per year. Summary statistics were compiled for daily counts of deaths from nonaccidental causes and from cardiovascular and respiratory diseases as well as daily meteorologic conditions and concentrations of the four air pollutants under study (##TAB##0##Table 1##). On each day there were, on average, 19, 36, and 17 deaths from non-accidental causes in the TPUs among low, middle, and high SDI levels, respectively.</p>", "<title>Effects of air pollution for all areas</title>", "<p>In all areas, for nonaccidental and subcategory cardiovascular causes of mortality, the biggest single-day associations with all air pollutants occurred at either lag 0 or lag 1 day (##TAB##1##Tables 2## and ##TAB##2##3##), but for subcategory respiratory mortality, they occurred at lag 2 day except with SO₂, which occurred at lag 0 day (##TAB##3##Table 4##). There were statistically significant (<italic>p</italic> &lt; 0.05) ERs for all the pollutants except O₃ on all the three mortality outcomes.</p>", "<title>Separate effects of air pollution for each SDI group</title>", "<p>The lag patterns of ER were comparable in the high, middle, and low SDI groups (##TAB##1##Table 2##). At average 0–1 lag—that is, with average pollutant concentration measured in the lag 0–1 day period—for NO₂ and SO₂, the point estimates of ER were higher in the middle SDI than in the low SDI group, except for SO₂ for cardiovascular mortality, and were the highest in the high SDI group, except for NO₂ for nonaccidental mortality (##FIG##1##Figure 2##). At average 0–1 lag, for PM₁₀ and O₃ the point estimates of ER were higher in the middle SDI than in the low SDI group (data not shown). Those in the high SDI group were higher than in the low SDI group (except the effect of PM₁₀ on nonaccidental mortality). For respiratory mortality, at average 0–1 lag, for NO₂ and SO₂ the point estimates of ER increased from low to high SDI groups (##FIG##1##Figures 2## and ##FIG##2##3##), with ER increasing from 0.76 to 1.44% for NO₂ (##FIG##1##Figure 2##), and from 0.90 to 2.27% for SO₂ (##FIG##2##Figure 3##). However, for PM₁₀ and O₃, the point estimates of ER varied from low to high SDI groups by only a small magnitude (0.82 to 0.70% for PM₁₀; 0.23 to 0.0% for O₃) (data not shown).</p>", "<title>Differences in effects of air pollution between SDI groups</title>", "<p>The biggest difference in ER between SDI groups generally occurred at lag 1 day (data not shown). For nonaccidental mortality and for the subcategory cardiovascular mortality, the ER due to NO₂ and SO₂ at lag 1 day was significantly higher (<italic>p</italic> &lt; 0.05) in the high SDI group than in the middle or low SDI groups; and the trends from low to high SDI groups were significant (<italic>p</italic> &lt; 0.05) (data not shown). At the average 0–1 lag of a pollutant per 10 μg/m³, significantly (<italic>p</italic> &lt; 0.05) greater ER for nonaccidental mortality, between high and middle SDI groups [change in ER 1.15%; 95% confidence interval (CI) 0.06–2.26] and between high and low (change in ER 1.38%; 95% CI, 0.13–2.63) SDI groups were shown (##TAB##4##Table 5##). Significant trend (change in ER 0.45%; 95% CI, 0.03–0.87) with change between middle and low or between high and middle SDI groups were found for an increase in concentrations of SO₂, but not in concentrations of the other pollutants, although the differences in ER were in the same direction as that for SO₂. For effects on cardiovascular mortality, significant increases (<italic>p</italic> &lt; 0.05) in ER were found for SO₂ (between high and middle SDI groups) and for NO₂ (between high and low SDI groups); and significant trend (<italic>p</italic> &lt; 0.05) was found for NO₂. The magnitude of the difference and trend between SDI groups in effects of all pollutants on respiratory mortality were similar to those on all nonaccidental mortality but were statistically not significant (<italic>p</italic> &gt; 0.05).</p>" ]

[ "<title>Discussion</title>", "<p>In Hong Kong, we found that air pollution mortality effects for SO₂ were stronger in high compared with low SDI areas. Some previous studies in Hong Kong (##REF##11335180##Wong et al. 2001##, ##REF##11781167##2002a##) and Mainland China (##REF##14646303##Kan and Chen 2003##; ##REF##12676616##Venners et al. 2003##; ##REF##8031176##Xu et al. 1994##, ##REF##7618954##1995##) showed the gaseous pollutants NO₂ and SO₂ had stronger effects on morbidity and mortality compared with particulate air pollution in contrast to the findings in the United States (##UREF##10##Samet et al. 2000##). In this study, in addition to SO₂ we found those residing in high SDI areas had higher ERs of death also associated with NO₂, particularly for cardiovascular disease, than those in low SDI areas. A possible explanation is that socially deprived subgroups are more likely to have poorer health care and nutrition and other increased health risks, resulting in increased susceptibility to the adverse effects of air pollution. A meta-analysis of short-term health effects of air pollution (SO₂, NO₂, CO, PM₁₀, and O₃) in eight Italian cities showed that the ERs for hospital admission were modified by deprivation score and by NO₂/PM₁₀ ratio (##REF##15859200##Biggeri et al. 2005##). Another explanation is that those residing in higher SDI areas may be exposed to higher levels of NO₂ and SO₂. A study in the Hamilton Census Metropolitan Area, Canada (##REF##15911644##Finkelstein et al. 2005##), showed that subjects in the more deprived neighborhoods were exposed to higher levels of ambient particulates and gaseous pollutants. At least some of the observed social gradients associated with circulatory mortality arise from inequalities in environmental factors in terms of exposure to background and traffic-related pollutants. In Hong Kong, the daily levels of PM₁₀ with correlations (<italic>r</italic>) between the eight monitoring stations ranged from 0.9 to 1.0 and annual average concentration from 42 to 55 μg/m³, indicating the homogeneity of PM₁₀ exposure between SDI areas. However, the corresponding levels for NO₂ ranged from 45 to 67 μg/m³ (<italic>r</italic> = 0.5–0.9), and 8–16 μg/m³ for SO₂ (<italic>r</italic> = 0.4–0.8). The difference in the levels of NO₂ and SO₂ across geographic areas may partly explain the significant differences in their effects between SDI areas. On the other hand, in Hong Kong a large proportion of ambient air pollution is attributable to pollution emissions from road traffic (##UREF##16##Wong et al. 2002b##). Many deprived areas are located in the inner city on multiple busy traffic routes. Most of the population live next to roads and are affected by street canyon effects commonly formed by continuous building blocks in Hong Kong (##UREF##0##Chan and Kwok 2000##). In another study, high exposure to carbon monoxide was found to have a significant effect on asthma admissions for children 1–18 years of age, and the effect was greater for children with lower socioeconomic status (##REF##15556243##Neidell 2004##).</p>", "<p>In six regions of São Paulo City, Brazil, PM₁₀ effects on daily respiratory deaths at the region level were negatively correlated with both the percentage of people with college education and high family income and were positively associated with the percentage of people living in slums, suggesting that social deprivation represents an effect modifier of the association between air pollution and respiratory deaths (##REF##14684725##Martins et al. 2004##). In the city of Hamilton, Ontario, Canada, which was divided into five zones based on proximity to fixed-site air pollution monitors, SO₂ and coefficient of haze (as a measure of particulate pollution) were associated with increased mortality, and the effects were higher among those zones with lower socioeconomic characteristics, lower educational attainment, and higher manufacturing employment (##REF##14684724##Jerrett et al. 2004##).</p>", "<p>There are several limitations to our study. First, we are aware that the SDI we defined may not reflect the whole profile of deprivation, although all of the information available from the census is included in the computation. Second, there may be heterogeneity within areas having the same SDI levels that have not been accounted for. However, we classified SDI levels into three broad categories, which should help reduce misclassification of deprivation. Third, population-level exposures using average concentrations from a limited number of air pollution monitors as a proxy for each individual may be subject to some measurement errors, and consequently we cannot determine whether the increased pollution-related mortality risk in high SDI areas is due mainly to greater pollutant exposure or increased biologic susceptibility. However, the population density in Hong Kong is very high (about 6,200/km²), and the daily air pollution levels among eight monitoring stations included in the study were highly correlated. This justifies our use of the average air pollution concentrations over all monitoring stations as daily concentrations for the whole territory. The aggregated daily concentrations derived for the whole of Hong Kong should be at least as reliable as measurements used in other daily time-series air pollution studies. In this study, we used PM₁₀ to assess the effect of particulate pollution, because the measurements of PM_2.5 were not available in all the stations under study during the period of the study. However, based on the available data from two stations, the Spearman correlation coefficient between daily levels of the two measures was 0.89, and PM_2.5 constituted a high proportion of PM₁₀ (around 70%); therefore, it is unlikely that estimates using the two measures would differ to a great extent in Hong Kong. Unlike specific gaseous pollutants that are comparable from place to place, the potency of PM₁₀ will depend on the composition of the particulates, which may vary greatly in different geographic locations. The comparability of air pollution studies on health effects of particulates may be related more to specific subspecies than the particle size measured. Finally, the mechanisms underlying why some population groups with high SDI experienced higher adverse effects of air pollution are still unclear, and research on specific protective interventions is needed.</p>" ]

[ "<title>Conclusions</title>", "<p>This study provides evidence that neighborhood socioeconomic status plays a role in the association between ambient air pollution and mortality. Residence in areas of high social deprivation may increase the mortality risks associated with air pollution. These findings should promote discussion among scientists, policy makers, and the public about social inequities in health when considering environmental protection and management in the context of economic, urban, and infrastructural development.</p>" ]

[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Poverty is a major determinant of population health, but little is known about its role in modifying air pollution effects.</p>", "<title>Objectives</title>", "<p>We set out to examine whether people residing in socially deprived communities are at higher mortality risk from ambient air pollution.</p>", "<title>Methods</title>", "<p>This study included 209 tertiary planning units (TPUs), the smallest units for town planning in the Special Administrative Region of Hong Kong, China. The socioeconomic status of each TPU was measured by a social deprivation index (SDI) derived from the proportions of the population with <italic>a</italic>) unemployment, <italic>b</italic>) monthly household income &lt; US$250, <italic>c</italic>) no schooling at all, <italic>d</italic>) one-person household, <italic>e</italic>) never-married status, and <italic>f</italic> ) subtenancy, from the 2001 Population Census. TPUs were classified into three levels of SDI: low, middle, and high. We performed time-series analysis with Poisson regression to examine the association between changes in daily concentrations of ambient air pollution and daily number of deaths in each SDI group for the period from January 1996 to December 2002. We evaluated the differences in pollution effects between different SDI groups using a case-only approach with logistic regression.</p>", "<title>Results</title>", "<p>We found significant associations of nitrogen dioxide, sulfur dioxide, particulate matter with aerodynamic diameter &lt; 10 μm, and ozone with all nonaccidental and cardiovascular mortality in areas of middle or high SDI (<italic>p</italic> &lt; 0.05). Health outcomes, measured as all nonaccidental, cardiovascular, and respiratory mortality, in people residing in high SDI areas were more strongly associated with SO₂ and NO₂ compared with those in middle or low SDI areas.</p>", "<title>Conclusions</title>", "<p>Neighborhood socioeconomic deprivation increases mortality risks associated with air pollution.</p>" ]

[ "<p>There is ample evidence that air pollution is a health hazard both in developed (##UREF##10##Samet et al. 2000##) and developing countries [##UREF##4##Health Effects Institute (HEI) 2004##]. Although all individuals are exposed to some level of air pollution, those who are already in poor health (##REF##15127905##Bateson and Schwartz 2004##; ##REF##10625173##Sunyer et al. 2000##) and those who are socially disadvantaged (##REF##16847936##Forastiere et al. 2006##; ##REF##14684724##Jerrett et al. 2004##; ##REF##15556243##Neidell 2004##) are most strongly affected. Globalization has resulted in the shifting of industries notorious for their pollution from wealthier to poorer areas, where costs of production are cheaper and environmental regulations are less stringent (##UREF##8##Pulido 2000##). Disparities in environmental health hazards among countries have become greater. In areas near sources of pollution, particularly those with mixed residential and industrial activity and an economically disadvantaged population, residents are exposed to higher levels of air pollution (##REF##15911644##Finkelstein et al. 2005##). This situation has aroused concerns about social injustice, and governments have been urged to take social inequality into account when considering air quality interventions. Studies in Europe and the United States have indicated a link between air pollution and poverty in terms of health impacts (##REF##15534498##Filleul et al. 2004##; ##REF##10856032##Schwartz 2000##; ##REF##10824299##Zanobetti and Schwartz 2000##). In the Asia Pacific region, where air pollution and the burden of potentially avoidable morbidity and mortality are increasing (##UREF##4##HEI 2004##), no study has examined the interaction between socioeconomic status and pollution-related health outcomes.</p>", "<p>The biologic mechanisms underlying the health effects of air pollution can be explained in terms of oxidative stress and immune system damage after both long- and short-term exposures. There are two main hypotheses regarding the possible effect of the interactions between air pollution and socioeconomic status on health. First, people of lower socioeconomic status are more likely to live and work in places with more toxic pollution. An alternative hypothesis is that because of inadequate access to medical care, lack of material resources, poorer nutrition, and higher smoking prevalence, those of lower socioeconomic status may be more susceptible to the adverse effects of air pollution than those in higher socioeconomic groups (##REF##14644658##O’Neill et al. 2003##).</p>", "<p>Health effects associated with socioeconomic factors can be assessed at both the individual and neighborhood levels according to an individual’s area of residence. The effect modification of air pollution by socioeconomic status measured at the individual level has been demonstrated in several epidemiologic studies (##REF##15534498##Filleul et al. 2004##; ##UREF##3##HEI 2000##; ##REF##16020033##Krewski et al. 2005##). However, the possible modification of air pollution effects associated with socioeconomic status, assessed at the neighborhood level, has not been well studied, and findings are still controversial (##REF##14644658##O’Neill et al. 2003##). Whether residence in socially deprived areas is a greater environmental health hazard compared with residence in better-off areas is an important public health issue, and the possible effects need to be examined through appropriately designed studies.</p>", "<p>Hong Kong is an affluent area in the Asia Pacific region, but poverty is still a problem among some subgroups of the population, resulting in serious social inequity. Socially deprived areas should be identified for additional community environmental protection and health resource allocation. Socioeconomic factors are usually multidimensional, and some of them, such as low income and low education, may be correlated with each other. Instead of studying several factors individually, we used a deprivation score at a specific community planning unit level to estimate neighborhood social deprivation for each of the subjects based on geographic code of their residency at the time of death, then assessed whether residents in poorer areas were subject to greater risk of mortality from ambient air pollution.</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1189\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>SDI in three levels for Hong Kong, 2001, excluding suburban areas.</p></caption></fig>", "<fig id=\"f2-ehp-116-1189\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>ER of mortality from nonaccidental, cardiovascular, and respiratory per 10-μg/m³ increase in NO₂ concentration by three levels [low (L), middle (M), and high (H)] of social deprivation at average 0–1 lag day. Error bars indicate 95% CIs of estimates of ER.</p><p>*<italic>p</italic> &lt; 0.05.</p></caption></fig>", "<fig id=\"f3-ehp-116-1189\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>ER of mortality from nonaccidental, cardiovascular, and respiratory per 10 μg/m³ increase in SO₂ concentration by three levels [low (L), middle (M), and high (H)] of social deprivation at average 0–1 lag day. Error bars indicate 95% CIs of estimates of ER.</p><p>*<italic>p</italic> &lt; 0.05.</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1189\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Summary statistics for TPUs by three levels of social deprivation, air pollution, and meteorologic variables for whole territories.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Min</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">1st Quartile</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Median</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">3rd Quartile</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Max</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">SD</th></tr></thead><tbody><tr><td colspan=\"8\" align=\"left\" rowspan=\"1\">Population size (× 10,000)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.19</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.32</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.99</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.22</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.19</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Middle SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.86</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20.36</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.86</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.76</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.42</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.52</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.07</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.99</td></tr><tr><td colspan=\"8\" align=\"left\" rowspan=\"1\">Area (km²)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.54</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.08</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.33</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.45</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Middle SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.81</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.62</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.05</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35.61</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.43</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.37</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.38</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.79</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.43</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.30</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.56</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.00</td></tr><tr><td colspan=\"8\" align=\"left\" rowspan=\"1\">Population density (× 10,000/km²)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.09</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.55</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.68</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.80</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.49</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.76</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Middle SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.06</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.48</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.03</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.28</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.52</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.02</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.95</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.14</td></tr><tr><td colspan=\"8\" align=\"left\" rowspan=\"1\">Mortality (daily count)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">46.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Middle SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">66.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High SDI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">21.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.4</td></tr><tr><td colspan=\"8\" align=\"left\" rowspan=\"1\">Air pollutants (μg/m³)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">45.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">56.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">69.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">168.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">58.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">22.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">109.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">45.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">66.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">188.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−8.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">196.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Temperature (°C)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Relative humidity (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">74.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">79.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">84.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">97.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">77.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.0</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1189\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Excess risk (%) of nonaccidental mortality per 10-μg/m³ increase in pollutant concentration by three levels of social deprivation at lag 0, 1, 2, 3, and 4 days.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Lag</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Low SDI ER (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Middle SDI ER (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">High SDI ER (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">All areas ER (95% CI)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.55 (0.00 to 1.11)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.07 (0.65 to 1.50)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.53 (−0.06 to 1.13)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.75 (0.45 to 1.06)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.40 (−0.15 to 0.95)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.04 (0.61 to 1.46)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.07 (0.48 to 1.66)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.79 (0.49 to 1.10)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.16 (−0.37 to 0.70)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.62 (0.21 to 1.04)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.52 (−0.05 to 1.10)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.37 (0.07 to 0.67)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.29 (−0.24 to 0.82)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.39 (−0.03 to 0.80)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.12 (−0.45 to 0.70)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.20 (−0.10 to 0.50)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.30 (−0.82 to 0.24)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.12 (−0.29 to 0.53)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.22 (−0.79 to 0.36)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.12 (−0.41 to 0.18)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.64 (−0.16 to 1.44)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.76 (0.14 to 1.38)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.81 (−0.05 to 1.68)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.68 (0.24 to 1.12)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.21 (−0.57 to 1.00)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.62 (0.02 to 1.23)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.44 (0.60 to 2.29)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.62 (0.19 to 1.06)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23 (−0.53 to 1.01)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.34 (−0.26 to 0.93)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.33 (−0.50 to 1.17)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.20 (−0.23 to 0.63)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.15 (−0.61 to 0.92)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.14 (−0.45 to 0.74)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.45 (−1.28 to 0.38)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.10 (−0.53 to 0.32)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.70 (−1.46 to 0.07)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.18 (−0.41 to 0.77)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.55 (−1.38 to 0.28)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.24 (−0.66 to 0.18)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.37 (−0.10 to 0.84)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.70 (0.34 to 1.07)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22 (−0.29 to 0.73)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.45 (0.19 to 0.72)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.40 (−0.04 to 0.84)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.48 (0.14 to 0.82)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.46 (−0.01 to 0.94)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.40 (0.15 to 0.64)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.14 (−0.28 to 0.57)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.35 (0.02 to 0.68)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.29 (−0.17 to 0.75)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22 (−0.02 to 0.45)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.12 (−0.55 to 0.30)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.18 (−0.14 to 0.51)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.05 (−0.51 to 0.40)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.00 (−0.24 to 0.23)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.14 (−0.56 to 0.28)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.17 (−0.16 to 0.50)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06 (−0.51 to 0.40)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.03 (−0.20 to 0.26)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.20 (−0.73 to 0.34)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.41 (0.00 to 0.82)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.53 (−0.04 to 1.11)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23 (−0.07 to 0.52)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22 (−0.26 to 0.70)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.46 (0.09 to 0.83)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.02 (−0.49 to 0.54)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27 (0.00 to 0.53)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.20 (−0.25 to 0.65)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.23 (−0.12 to 0.58)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19 (−0.30 to 0.68)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.18 (−0.07 to 0.43)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.00 (−0.44 to 0.45)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.21 (−0.14 to 0.55)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.18 (−0.30 to 0.66)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.13 (−0.11 to 0.38)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.17 (−0.60 to 0.27)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.04 (−0.29 to 0.38)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.03 (−0.50 to 0.45)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.02 (−0.27 to 0.22)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1189\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Excess risk (%) of cardiovascular mortality per 10-μg/m³ increase in pollutant concentration by three levels of social deprivation at lag 0, 1, 2, 3, and 4 days.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Lag</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Low SDI ER (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Middle SDI ER (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">High SDI ER (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">All areas ER (95% CI)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.82 (−0.25 to 1.90)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.24 (0.45 to 2.03)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.45 (0.37 to 2.53)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.17 (0.61 to 1.73)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.76 (−0.30 to 1.83)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00 (0.22 to 1.78)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.14 (1.07 to 3.21)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.08 (0.53 to 1.64)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.34 (−0.70 to 1.39)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.85 (0.08 to 1.61)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.95 (−0.09 to 2.00)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.53 (−0.02 to 1.08)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27 (−0.76 to 1.31)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.46 (−0.30 to 1.23)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.28 (−1.32 to 0.77)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.09 (−0.45 to 0.63)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.51 (−1.54 to 0.52)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.08 (−0.67 to 0.84)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.02 (−1.01 to 1.06)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.13 (−0.66 to 0.41)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.10 (−0.45 to 2.68)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.71 (−0.44 to 1.87)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.85 (0.28 to 3.44)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.03 (0.21 to 1.85)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.89 (−0.64 to 2.44)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.30 (−0.83 to 1.45)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.88 (1.35 to 4.43)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.93 (0.13 to 1.74)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.38 (−1.12 to 1.90)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.36 (−0.75 to 1.48)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.28 (−0.22 to 2.81)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.42 (−0.37 to 1.21)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26 (−1.23 to 1.77)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.25 (−0.85 to 1.37)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06 (−1.45 to 1.58)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.10 (−0.69 to 0.89)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.75 (−2.24 to 0.76)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.27 (−1.36 to 0.85)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.66 (−0.84 to 2.19)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.21 (−1.00 to 0.58)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.14 (−0.77 to 1.06)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.66 (0.00 to 1.34)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.83 (−0.08 to 1.75)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.52 (0.05 to 1.00)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.64 (−0.21 to 1.49)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.49 (−0.13 to 1.12)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.89 (0.04 to 1.75)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.58 (0.14 to 1.03)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.24 (−0.58 to 1.07)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.80 (0.20 to 1.40)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.12 (−0.70 to 0.95)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.43 (0.00 to 0.86)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.27 (−1.09 to 0.55)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.65 (0.06 to 1.25)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.09 (−0.91 to 0.73)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.14 (−0.28 to 0.57)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.01 (−0.80 to 0.83)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.52 (−0.07 to 1.12)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.04 (−0.77 to 0.86)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23 (−0.20 to 0.65)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23 (−0.81 to 1.29)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.57 (−0.19 to 1.35)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.66 (−0.39 to 1.72)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.42 (−0.12 to 0.97)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.41 (−0.53 to 1.35)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.65 (−0.04 to 1.34)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23 (−0.71 to 1.18)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.45 (−0.04 to 0.94)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.51 (−0.37 to 1.40)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.52 (−0.13 to 1.17)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23 (−0.66 to 1.13)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.38 (−0.08 to 0.84)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.51 (−0.35 to 1.39)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.55 (−0.09 to 1.19)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.17 (−1.04 to 0.71)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.28 (−0.17 to 0.74)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.29 (−1.15 to 0.58)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.02 (−0.61 to 0.66)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.51 (−1.37 to 0.37)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.23 (−0.68 to 0.22)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4-ehp-116-1189\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>Excess risk (%) of respiratory mortality per 10-μg/m³ increase in pollutant concentration by three levels of social deprivation at lag 0, 1, 2, 3, and 4 days.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Lag</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Low SDI ER (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Middle SDI ER (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">High SDI ER (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">All areas ER (95% CI)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.02 (−0.31 to 2.36)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.76 (−0.20 to 1.72)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.97 (−0.34 to 2.30)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.88 (0.19 to 1.58)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.16 (−1.16 to 1.49)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.07 (0.13 to 2.03)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.26 (−0.04 to 2.57)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.90 (0.22 to 1.60)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.05 (−1.34 to 1.26)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.02 (0.10 to 1.96)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.62 (0.35 to 2.91)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.92 (0.25 to 1.60)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.13 (−1.16 to 1.43)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.94 (0.02 to 1.87)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.95 (−0.32 to 2.23)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.75 (0.08 to 1.42)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.53 (−1.81 to 0.77)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.51 (−0.40 to 1.44)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.30 (−1.56 to 0.98)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.05 (−0.62 to 0.72)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.21 (−0.70 to 3.16)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.57 (−0.80 to 1.95)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.84 (−0.04 to 3.76)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.06 (0.06 to 2.06)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06 (−1.83 to 1.98)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.33 (−0.01 to 2.68)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.32 (−0.53 to 3.20)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.02 (0.04 to 2.01)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.45 (−1.40 to 2.33)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.01 (−0.31 to 2.34)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.47 (−0.34 to 3.32)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.99 (0.03 to 1.96)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.32 (−1.53 to 2.20)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.30 (−0.01 to 2.62)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.67 (−2.48 to 1.18)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.56 (−0.40 to 1.52)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−1.36 (−3.21 to 0.53)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.77 (−0.54 to 2.10)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−1.05 (−2.87 to 0.81)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.21 (−1.17 to 0.76)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.69 (−0.44 to 1.82)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.31 (−0.50 to 1.13)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27 (−0.85 to 1.40)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.39 (−0.20 to 0.99)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.55 (−0.50 to 1.61)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.77 (0.01 to 1.53)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.72 (−0.32 to 1.78)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.70 (0.15 to 1.26)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.36 (−0.66 to 1.39)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.85 (0.12 to 1.59)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.46 (0.45 to 2.47)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.89 (0.36 to 1.42)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.24 (−1.25 to 0.78)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.66 (−0.07 to 1.39)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.70 (−0.30 to 1.71)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.45 (−0.08 to 0.98)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.17 (−1.17 to 0.85)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.69 (−0.03 to 1.42)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.48 (−0.52 to 1.48)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.43 (−0.10 to 0.96)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.22 (−1.50 to 1.07)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.02 (−0.90 to 0.94)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.60 (−0.66 to 1.88)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11 (−0.55 to 0.79)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.46 (−0.68 to 1.61)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.26 (−0.56 to 1.09)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.51 (−1.65 to 0.64)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11 (−0.48 to 0.72)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.01 (−1.09 to 1.09)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.50 (−0.28 to 1.28)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.42 (−0.65 to 1.51)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.36 (−0.21 to 0.93)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.31 (−1.38 to 0.77)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.24 (−0.52 to 1.01)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.55 (−0.50 to 1.62)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19 (−0.37 to 0.75)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.01 (−1.06 to 1.06)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.04 (−0.71 to 0.80)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.88 (−0.16 to 1.93)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.25 (−0.30 to 0.80)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t5-ehp-116-1189\" orientation=\"portrait\" position=\"float\"><label>Table 5</label><caption><p>Difference in ER [% (95% CI)] of mortality between areas with different SDI levels associated with air pollutants per 10-μg/m³ increase at average lag 0–1 day.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Pollutant</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Nonaccidental causes</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Cardiovascular disease</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Respiratory disease</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">High vs. middle</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.45 (−0.16 to 1.06)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.03 (−0.11 to 2.18)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.94 (−0.41 to 2.31)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.15 (0.06 to 2.26)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.74 (0.66 to 4.85)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.62 (−0.83 to 4.12)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.23 (−0.25 to 0.72)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.49 (−0.40 to 1.40)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.49 (−0.58 to 1.58)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.14 (−0.41 to 0.70)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.09 (−0.95 to 1.14)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.75 (−0.50 to 2.01)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">High vs. low</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.51 (−0.18 to 1.20)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.35 (0.49 to 2.67)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.59 (−0.98 to 2.18)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.38 (0.13 to 2.63)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.16 (−0.19 to 4.57)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.42 (−0.47 to 5.38)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.12 (−0.42 to 0.67)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.82 (−0.20 to 1.86)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.15 (−1.39 to 1.10)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.14 (−0.48 to 0.76)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.13 (−1.06 to 1.33)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.33 (−1.12 to 1.79)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Trend test</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.16 (−0.07 to 0.39)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.45 (0.01 to 0.88)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.21 (−0.32 to 0.73)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.45 (0.03 to 0.87)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.71 (−0.08 to 1.51)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.81 (−0.15 to 1.71)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.04 (−0.15 to 0.22)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.27 (−0.07 to 0.61)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.04 (−0.46 to 0.37)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.05 (−0.16 to 0.25)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.04 (−0.35 to 0.44)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.12 (−0.37 to 0.60)</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>We thank P.C. Lai, Department of Geography, The University of Hong Kong, for the Hong Kong map with the geographic distribution of social deprivation index.</p></fn><fn><p>Research described in this article was conducted under contract to the Health Effects Institute (HEI), an organization jointly funded by the U.S. Environmental Protection Agency (EPA) (Assistance Agreement R82811201) and automobile manufacturers.</p></fn><fn><p>The contents of this article do not necessarily reflect the views of HEI, nor do they necessarily reflect the views and policies of the U.S. EPA or of motor vehicle and engine manufacturers.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1189\"><p>Abbreviations: Max, maximum; Min, minimum.</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1189f1\"/>", "<graphic xlink:href=\"ehp-116-1189f2\"/>", "<graphic xlink:href=\"ehp-116-1189f3\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>Mortality data</title>", "<p>We focused on mortality from all natural causes in all ages, ≥ 65 years, and ≥ 75 years, and for cardiovascular and respiratory disease at all ages. The <italic>International Classification of Disease</italic>, <italic>Ninth Revision</italic> [ICD-9; ##UREF##9##World Health Organization (WHO) 1977##] and <italic>Tenth Revision</italic> (ICD-10; ##UREF##10##WHO 1992##) rubrics of the health outcomes were as follows: all natural causes, ICD-9 codes 001–799 or ICD-10 codes A00–R99; cardiovascular, ICD-9 codes 390–459 or ICD-10 codes I00–I99; and respiratory, ICD-9 codes 460–519 or ICD-10 codes J00–J98.</p>", "<p>The sources of health data were the Ministry of Public Health, Bangkok; the Census and Statistics Department, Hong Kong; the Shanghai Municipal Center of Disease Control and Prevention, Shanghai; and the Wuhan Centre for Disease Prevention and Control, Wuhan.</p>", "<title>Air pollutant and meteorological data</title>", "<p>Air quality indicators included nitrogen dioxide, sulfur dioxide, PM₁₀, and ozone. For NO₂, SO₂, and PM₁₀, daily data were 24-hr averages and an 8-hr average was used for O₃ (1000–1800 hours). Each city maintains several fixed-site air monitoring stations—dispersed throughout the metropolitan areas—that met the quality assurance and quality control procedures of local governments. The air pollutant concentrations were measured in Bangkok by the Pollution Control Department, Ministry of Natural Resources and Environment (<italic>n</italic> = 10 air monitoring stations); in Hong Kong by the Environmental Protection Department (<italic>n</italic> = 8); in Shanghai by the Shanghai Environmental Monitoring Center (<italic>n</italic> = 6); and in Wuhan by the Wuhan Environmental Monitoring Center (<italic>n</italic> = 6). The measurement methods for NO₂, SO₂, and O₃ were similar for the four cities based on chemiluminescence, fluorescence, and ultraviolet absorption, respectively, whereas for PM₁₀, the Chinese cities used tapered element oscillating microbalance and Bangkok used beta gauge monitors.</p>", "<p>The calculation of 24-hr average concentrations of NO₂, SO₂, and PM₁₀, and 8-hr average concentrations of O₃ required at least 75% of the 1-hr values on that particular day. If &gt; 25% of the daily values were missing for the whole period of analysis, the entire station was not included for that particular pollutant. Missing data were not imputed.</p>", "<title>Statistical analysis</title>", "<p>The analytical methods were developed and adopted by all four teams in a common protocol. The protocol includes the specifications for selection of monitoring stations, as well as quality assurance and quality control procedures for data collection and for health outcomes and air pollutants to be included in the analysis. Generalized linear modeling was used to model daily health outcomes, with natural spline smoothers (##REF##16201668##Burnett et al. 2004##; ##UREF##12##Wood 2006##) for filtering out seasonal patterns and long-term trends in daily mortality, as well as temperature and relative humidity. We also included an adjustment for the day of the week and dichotomous variables relevant to individual cities if available, such as public holidays (Hong Kong) and extreme weather conditions (Wuhan). In an attempt to minimize autocorrelation, which would bias the standard errors, the aim of the core model was for partial autocorrelation function plots to have coefficients in absolute values &lt; 0.1 for the first 2 lag days. Randomness of residuals was also considered in selecting the most appropriate models. If these criteria were not met, other methods were used to reduce autocorrelation, such as the inclusion of explanatory variables to model influenza epidemics and the addition of autoregression terms. If there were special periods with extra variations for which the core model could not account, an additional spline smoother was included. Air pollutant concentrations were entered into the core model to assess the health effects of specific pollutants. Exposure at the current day (lag 0), a 2-day average of lag 0 and lag 1 days (lag 0–1), and a 5-day average of lag 0 to lag 4 days (lag 0–4) were examined. For each pollutant, the excess risk of mortality with the 95% confidence interval (CI) per 10-μg/m³ increase in average concentration at lag 0–1 was calculated. However, for brevity’s sake, point estimates with <italic>p</italic>-values could be used to describe sets of effects.</p>", "<p>Because several differences were observed in effect estimates among cities, we conducted additional sensitivity analyses to attempt to explain these differences and to determine the robustness of the initial findings. We focused on PM₁₀, given the wealth of worldwide findings of effects from this pollutant, and used the average concentration of lag 0–1 days. In these analyses we aimed to explore the impact of the following: higher concentrations of PM₁₀ that might be dominated by the coarse fraction and therefore have differential toxicity; monitors that might be overly affected by proximity to traffic; effects of different seasonality patterns among the cities; different controls for temperature; and different ways in aggregating daily concentration data and differences in spline models. We regarded a change of excess risk &gt; 20% from that of the analysis as an indication of sensitive results. Specifically, the sensitivity analysis included the following items:</p>", "<p>Exclude the daily concentration of PM₁₀ &gt; 95th percentile</p>", "<p>Exclude the daily concentration of PM₁₀ &gt; 75th percentile</p>", "<p>Exclude the daily concentration of PM₁₀ &gt; 180 μg/m³</p>", "<p>Exclude monitoring stations with high traffic sources (highest nitric oxide/nitrogen oxides ratio)</p>", "<p>Assess warm season effect with dummy variables of seasons in the core model</p>", "<p>Add temperature at average lag 1–2 days or 3–7 days into the model</p>", "<p>Use a centered daily concentration of PM₁₀ (##REF##11335180##Wong et al. 2001##)</p>", "<p>Use natural spline with degrees of freedom (df) of time trend per year, temperature, and humidity fixed at 8, 4, and 4, respectively</p>", "<p>Use penalized spline instead of natural spline.</p>", "<p>Combined estimates of excess risk of mortality and their standard errors were calculated using a random-effects model. Estimates were weighted by the inverse of the sum of within-and between-study variance.</p>", "<p>Concentration–response curves for the effect of each pollutant on each mortality outcome in the four cities were plotted. We applied a natural spline smoother with 3 df on the pollutant term. We assessed nonlinearity by testing the change of deviance between a nonlinear pollutant (smoothed) model with 3 df and linear pollutant (unsmoothed) model with 1 df.</p>", "<p>The main analyses and the combined analysis were performed using R, version 2.5.1 (R ##UREF##7##Development Core Team 2007##). We also used mgcv, a package in R.</p>" ]

[ "<title>Results</title>", "<p>##TAB##0##Table 1## summarizes the mortality data for the four cities, and ##TAB##1##Table 2## summarizes the pollution and meteorological variables. The daily mortality counts for all natural causes at all ages for each city showed more marked seasonal variations in the cities farther north. Shanghai (mean daily deaths, 119; population, 7.0 million) and Bangkok (95; 6.8 million) had higher daily numbers of deaths than Hong Kong (84; 6.7 million) and Wuhan (61; 4.2 million). The ratios for causes of death due to cardiovascular disease relative to respiratory disease were the highest in Wuhan (4:1) followed by Shanghai (3:1), Bangkok (2:1), and Hong Kong (1.5:1). The proportion of total cardiorespiratory mortality was also the highest in Wuhan (57%) followed by Shanghai (49%), Hong Kong (48%), and Bangkok (23%) [##TAB##0##Table 1##; Supplemental Material, Table 1 (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11257/suppl.pdf\">http://www.ehponline.org/members/2008/11257/suppl.pdf</ext-link>)]. Deaths occurring at ≥ 65 years of age were less frequent in Bangkok (36%) than in the three Chinese cities (72–84%).</p>", "<p>As indicated in ##TAB##1##Table 2## and ##FIG##1##Figure 2##, Wuhan showed the highest concentrations of PM₁₀ and O₃, whereas Shanghai had the highest concentrations of NO₂ and SO₂. The latter was probably due to the significant local contribution of power plants in Shanghai’s metropolitan area. To provide an indication of the relative magnitude of the pollution concentrations in these four large Asian cities, we compared them to the 20 largest cities in the United States using data from 1987 to 1994 from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) (##UREF##8##Samet et al. 2000##). Generally, in the PAPA cities, the concentrations of PM₁₀ and SO₂ were much higher than those reported in the United States (PM₁₀ means of 52–142 μg/m3 in the cities of the PAPA study vs. 33 μg/m³ in NMMAPS, and SO₂ means of 13–45 μg/m3 vs. 14 μg/m³); comparisons of NO₂ and O₃ showed a fairly similar pattern.</p>", "<p>We demonstrated the adequacy of the core models with partial autocorrelation function plots of the residuals in the previous 2 days, all within |0.1| [Supplemental Material, Figure 1 (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11257/suppl.pdf\">http://www.ehponline.org/members/2008/11257/suppl.pdf</ext-link>)].</p>", "<p>In individual cities, for all natural causes at all ages (##TAB##2##Table 3##) the percentage of excess risk per 10-μg/m³ associated with NO₂ ranged from 0.90 to 1.97 (all <italic>p</italic>-values ≤ 0.001); with SO₂, from 0.87 to 1.61 (all <italic>p</italic>-values ≤ 0.05); with PM₁₀, from 0.26 to 1.25 (all <italic>p</italic>-values ≤ 0.001); and with O₃, from 0.31 to 0.63 (all <italic>p</italic>-values ≤ 0.05), but the effect in Wuhan was not significant. The excess risk showed trends of increasing risk with increasing age for all four pollutants. The trends for the age-specific effects were the strongest in Bangkok, less strong in Hong Kong and Wuhan, but absent in Shanghai (##FIG##2##Figure 3##). For all four pollutants, the excess risk in Bangkok was higher than those in the three Chinese cities. When the pollutant concentrations were expressed as the interquartile range (IQR; i.e., 75th percentile–25th percentile), Bangkok estimates were comparable to those of the three Chinese cities, particularly in all ages. Within cities, the effect estimates of different pollutants were also comparable to each other (data not shown).</p>", "<p>In all cities, there was heterogeneity in effect estimates for NO₂ and PM₁₀ on all natural-cause mortality and for PM₁₀ on cardiovascular mortality (##TAB##2##Table 3##). For all natural-cause mortality, the combined random effects excess risk were 1.23, 1.00, 0.55, and 0.38% for NO₂, SO₂, PM₁₀, and O₃, respectively (all <italic>p-</italic>values ≤ 0.05). The results for cardiovascular mortality (##TAB##2##Table 3##) followed a generally similar pattern, with the highest excess risk per 10-μg/m³ in Bangkok for PM₁₀ and O₃, and in Wuhan for NO₂ and SO₂. All of the cities demonstrated significant associations for each pollutant except SO₂ in Bangkok and O₃ in Wuhan, whereas all of the combined estimates were statistically significant. A similar pattern was shown for respiratory mortality, for which the highest estimates were found in Wuhan for NO₂ and SO₂ and in Bangkok for PM₁₀ and O₃. All the random effects estimates were statistically significant at the 5% level except for O₃.</p>", "<p>For the lag effects in the three Chinese cities, with a few exceptions, the average lag 0–1 days usually generated the highest excess risk. However, for Bangkok the longer cumulative average of lag 0–4 days generated the highest excess risk for all of the pollutants except SO₂. For the combined estimates, effects at the lag 0–1 days showed the highest excess risk, except O₃, for which the effect at lag 0–4 days was the greatest (data not shown).</p>", "<p>Sensitivity analyses for PM₁₀ showed that, in general, the results were fairly robust for various concentrations, monitors, specifications for temperature, methods of aggregating daily data, df used in the smoothers, and alternative spline models. In all cases, the effect estimates were statistically significant. In all cities, the effect estimates for PM₁₀ were sensitive to exclusion of the higher concentrations. For the Chinese cities, this increased the excess risk &gt; 20% for PM₁₀, but in Bangkok the effect estimate decreased, with the excess risk changing from 1.25% to 0.73% per 10-μg/m³ increase in average concentration of lag 0–1 days (##TAB##3##Table 4##). Examination of the warm season (which varied for each city) resulted in significant increases in effect estimates for Bangkok and Wuhan but decreases in Hong Kong and, to a lesser extent, in Shanghai (excess risk changed from 0.26% to 0.24%). Adjusting for temperature through use of longer-term cumulative averages tended to decrease the PM₁₀ effect.</p>", "<p>The smoothed concentration-response (CR) relationship, between all natural-cause mortality and concentration of each pollutant, appeared to be positive. Most CR curves showed linear relationships over the IQR of the concentrations (##FIG##3##Figure 4##). At all ages, tests for nonlinearity for the entire curve showed that linearity could not be rejected at the 5% level for most of the associations between air pollution and mortality (data not shown).</p>" ]

[ "<title>Discussion</title>", "<title>Review of PAPA project results</title>", "<p>In the city-specific main effects for the five main health outcomes under study, there were variations in effect estimates between cities. For NO₂ the estimates were similar in magnitude and precision for Bangkok and Wuhan, and for Hong Kong and Shanghai. The effects for Bangkok and Wuhan were higher but less precise (as reflected by a wider 95% CI) than for Shanghai and Hong Kong. For SO₂ the estimates for Bangkok were higher but less precise than for the three Chinese cities. For PM₁₀ the estimates in the three Chinese cities were very similar, but estimates were higher and less precise in Bangkok. For O₃ the effect estimates and the precision among the four cities were similar, although estimates in Bangkok were higher. However, when expressed by IQR increase in concentrations, the effect estimates for each pollutant were similar in the four cities.</p>", "<p>In the combined four-city analysis, the excess risks per 10-μg/m³ increase in NO₂ were 2–3 times greater than those derived from the APHEA (Air Pollution and Health: A European Approach) project (##REF##16540496##Samoli et al. 2006##) for mortality at all ages due to all natural causes, cardiovascular disease, and respiratory disease (1.23% vs. 0.3%, 1.36% vs. 0.4%, and 1.48% vs. 0.38%, respectively). For SO₂, the estimate (random effects) of 1.00% for mortality due to all natural causes derived from the present study was higher than the 0.52% previously reported from the other Asian cities studied (##UREF##1##HEI 2004##) and higher than the 0.40% from the APHEA project (Katsouyani et al. 1997) [Supplemental Material, Table 2 (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11257/suppl.pdf\">http://www.ehponline.org/members/2008/11257/suppl.pdf</ext-link>)]. For PM₁₀, the excess risk of 0.55% for all natural causes of death at all ages was comparable to 0.49% from all Asian cities (##UREF##1##HEI 2004##), 0.5% from NMMAPS (##UREF##8##Samet et al. 2000##), and 0.6% from the APHEA project (##UREF##0##Anderson et al. 2004##). A meta-analysis of Chinese studies found that each 10-μg/m³ increase in PM₁₀ concentration was significantly associated with 0.3% increase in all natural-cause mortality, 0.4% increase in cardiovascular mortality, and 0.6% increase in respiratory mortality (##REF##15262154##Aunan and Pan 2004##). For O₃, the estimate from the present study was significant and higher than that from APHEA (##UREF##0##Anderson et al. 2004##) and NMMAPS (##REF##15547165##Bell et al. 2004##) for all natural causes (0.38 vs. 0.20 and 0.26, respectively) and similar for cardiovascular causes (0.37 vs. 0.4 and 0.32); however, the estimates for respiratory disease (0.34 vs. −0.1 and 0.32%) were similar to those of the NMMAPS, but negative and statistically not significant (<italic>p</italic> &gt; 0.05) in APHEA [Supplemental Material, Table 2).</p>", "<title>Review of estimates from previous Asian studies</title>", "<p>For NO₂, we found few time-series studies, and these were mainly from South Korea (##REF##10544154##Hong et al. 1999##) and Hong Kong (##REF##11335180##Wong et al. 2001##). The variation of effects was large compared with other pollutants for all natural-cause mortality, respiratory mortality, and cardiovascular mortality. For SO₂, most time-series studies in China showed significant association with all natural-cause mortality, even at levels below the current WHO Air Quality Guideline (##REF##15138055##Chen et al. 2004##; ##UREF##11##WHO 2005##). A review of Asian studies (##UREF##1##HEI 2004##) also found that SO₂ was associated with all natural-cause mortality either from random-effects models or fixed-effects models. For PM₁₀, although fewer time-series studies were published from Asia than from other regions, most studies found a significant association with all natural-cause mortality, but only respiratory and cardiovascular mortality were examined in Bangkok (##UREF##6##Ostro et al. 1999##). However, significant associations with respiratory and cardiovascular mortality were not found in Seoul, Korea (##REF##10544154##Hong et al. 1999##), or Hong Kong studies (##REF##11335180##Wong et al. 2001##). For O₃ studies using different time-average concentrations such as 1, 8, and 24 hr, the estimates varied greatly between studies (##UREF##1##HEI 2004##).</p>", "<p>In the four individual cities included in the PAPA project, consistent with other studies for Asia, air pollution effects were found in each city and for all the disease-specific outcomes under consideration. The results provide important information on air pollution–related health effects in Asia, especially for areas known to have high exposures but are under-represented in the literature.</p>", "<title>Robustness of the results</title>", "<p>Our sensitivity analyses indicated that most of the PM₁₀ effect estimates did not deviate from the main analysis &gt; 20%. The PM₁₀ effect estimates were insensitive to different methods adopted, the use of higher df, and the replacement of the smoothing function by the penalized spline. However, across the four cities, additional adjustment for the average temperature at 3–7 lag days showed that the estimates for effects of PM₁₀ were attenuated, indicating possible residual confounding due to uncontrolled lag effects of temperature. Studies (##REF##15475726##Schwartz et al. 2004##; ##UREF##4##Medina-Ramón and Schwartz 2007##) show that different cumulative lag days of temperature have effects on both morbidity and mortality estimates. However, in the present study, current day temperature was specified <italic>a priori</italic> in the core model and was determined to be sufficient to adjust for temperature effects at the beginning of the study. On the other hand, we found high correlations between temperatures at each lag 1–7 days and at the current day, which suggest problems of multicollinearity if we make further adjustment to these lag temperature effects in the model of the main analysis.</p>", "<title>Scientific issues derived from PAPA study results</title>", "<p>For all natural-cause, cardiovascular, and respiratory mortality, the effect estimates of PM₁₀ and O₃ are relatively similar among the three Chinese cities. However, there are some differences in the PM₁₀ effect estimates in that Shanghai is consistently lower, by almost half, than Hong Kong and Wuhan. These differences in effect estimates may be related to differences in the location of the monitoring stations and differences in the actual ambient levels of exposure of the population.</p>", "<p>Estimates for PM₁₀ in Bangkok were higher, and the effect estimates much higher, than those of the three Chinese cities (1.25 vs. 0.26–0.53; 1.90 vs. 0.27–0.61; and 1.01 vs. 0.27–0.87). The reasons might be related to consistently higher temperature, a population that spends a longer time outdoors, and less availablity and use of air conditioning in Bangkok than in the other cities (##UREF##6##Ostro et al. 1999##). With relatively higher mortality due to infectious diseases [Supplemental Material, Table 1 (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11257/suppl.pdf\">http://www.ehponline.org/members/2008/11257/suppl.pdf</ext-link>)] and with more deaths at younger ages, it is also likely that the Bangkok population is exposed to a larger number of other risk factors and may be more susceptible to the risks associated with air pollution. ##REF##10703844##Tsai et al. (2000)## reported that exposure levels for indoor and outdoor particulates in shopping areas were underestimated by the ambient monitoring stations in Bangkok, and therefore that the excess risk per air pollutant concentration would be higher than if it were a well-calibrated measurement. The higher ratio of PM_2.5 (PM ≤ 2.5 μm in aerodynamic diameter) to PM₁₀ may suggest that the proportion of smaller particles in the PM₁₀ composition in Bangkok is more important and might be more strongly related to adverse health effects than in the other cities (##REF##12269671##Jinsart et al. 2002##).</p>", "<p>In all the three Chinese cities, the maximum effects always occurred at lag 0–1 days, except for O₃ in Shanghai, where maximum effects were recorded at longer lags. The lag pattern is consistent with other reports in demonstrating a maximum at lag 1 day for most pollutants (##REF##15626653##Samoli et al. 2005##, ##REF##16540496##2006##). However, for O₃, the effect estimates are maximal at longer lags, showing that the pattern is also consistent with the literature (##REF##11682364##Goldberg et al. 2001##; ##REF##11335180##Wong et al. 2001##). The lag patterns of SO₂ and O₃ in Bangkok are consistent with those of the three Chinese cities; however, the Bangkok lag patterns for NO₂ and PM₁₀, with greater effects at longer lags, are different from those of the three Chinese cities. For the traffic-related pollutants NO₂ and PM₁₀, the effects appear to be stronger, and they also seem to last longer in Bangkok than in the three Chinese cities.</p>", "<p>In all cities in the PAPA study, the effects of air pollution are stronger for cardiopulmonary causes than for all natural causes. This is consistent with results from most North American and Western European studies (##UREF##0##Anderson et al. 2004##; ##UREF##8##Samet et al. 2000##) and supports the validity of the estimates from the present study. In addition, the effects of the four single pollutants appear to be stronger at older ages than at younger ages, particularly in Bangkok, which may have a more susceptible population than the three Chinese cities. The stronger effects at older ages for these pollutants support the validity of our estimates.</p>", "<p>As expected, the exclusion of high levels of PM₁₀ concentrations from the analysis affects the effect estimates. In the present study, consistent with the literature from North America and Western Europe, exclusion of PM₁₀ concentrations greater than the 75th or 95th percentile produces larger estimates in all three Chinese cities. These results suggest that the CR curves might be curvilinear, with the slope less steep at higher concentrations. We cannot explain the opposite findings noted in Bangkok; however, they may be related to the exclusion of readings from one monitor located in a region with both high particulate levels and a fairly susceptible population.</p>", "<p>The health effects estimates during the warm season are higher than those with all seasons combined in both Bangkok (excess risk 2.16 vs. 1.25%) and Wuhan (0.81 vs. 0.43%), but those in Hong Kong (0.37 vs. 0.53%) and Shanghai (0.24 vs. 0.26%) were similar or lower. These observations support the hypothesis that the populations in Bangkok and Wuhan, which are less affluent than the other two cities, may be more exposed and susceptible because of less use of air conditioning in summer; this may also explain the generally higher air pollution effects observed in Bangkok and Wuhan than in the other two cities (##UREF##3##Long et al. 2007##). The lower effect in Hong Kong may also be explained by air mass movements and southerly winds prevalent in the summer. In Wuhan the higher effect may be due to extremely high temperatures in summer. There may also be synergistic effects between PM₁₀ and extremely high temperatures on mortality. Nevertheless, further study will be important in understanding how results derived from hotter climates could be extrapolated to cooler climates.</p>", "<p>Understanding the shapes of the CR curves is important for environmental public health policy decision making and setting of air quality standards. Comparison across geographic regions is also important in demonstrating causality and how effects estimated from one location can be generalized to others. The CR curves for PM₁₀ effects on all natural-cause mortality derived from the present study clearly show that the relationship is linear without a threshold in most of the cities studied, although some nonlinear relationships appear in Shanghai. Thus our estimates are consistent with a linear model without threshold, a finding in most North American and Western European studies (##REF##10981451##Daniels et al. 2000##; ##REF##17063860##Pope and Dockery 2006##; ##REF##15626653##Samoli et al. 2005##). The CR relation of a pollutant would be affected by the method used, the susceptibility of the population being investigated, the toxicity of the pollutant, and the weather and social conditions with which the pollutant may interact.</p>", "<p>In the present study, effect estimates for PM₁₀ are comparable, whereas those for gaseous pollutants, particularly for NO₂, are higher than those in the West. One postulation for the higher effect estimates may be related to their correlation with particulate pollutant [correlation between PM₁₀ and NO₂ ranging from 0.71 to 0.85; Supplemental Material, Table 3 (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11257/suppl.pdf\">http://www.ehponline.org/members/2008/11257/suppl.pdf</ext-link>)]. However for the three Chinese cities, the estimates for effects of NO₂ remain robust after adjustment for PM₁₀ (Supplemental Material, Figure 2A); whereas those of the PM₁₀ effects were attenuated (Supplemental Material, Figure 2B). But for Bangkok, the change in effect estimates for the two pollutants after adjustment for the other as a copollutant are opposite of those for the three Chinese cities. Thus in Asian cities, the observed effects of gaseous pollutants may not necessarily be related to their covariation with a particulate pollutant. Further research is needed to clarify the effects of copollutants.</p>", "<title>Limitations</title>", "<p>Among the major limitations of our study was the difference in monitoring locations among the cities. In densely populated cities such as Hong Kong and Shanghai, the monitors tend to be close to major roadways, whereas in Bangkok and Wuhan the monitors are located farther from major pollutant sources. Thus, it is difficult to determine the true effects and to compare our results both within the PAPA cities and with previous studies. In addition, the specific components of particulate responsible for the observed health effects have not been elucidated. Such identification will aid in targeting and prioritizing future pollution control efforts. Also, information about potential effect modifiers (e.g., time spent outdoors, use of air conditioning, residential distance to roadways, housing construction, comorbidity in the population) varied in its availability and quality among the cities, making it difficult to explain quantitative differences among the PAPA cities.</p>" ]

[ "<title>Conclusion</title>", "<p>Effects of particulate pollutants in Asian cities are similar to or greater than those observed in most North American and Western European cities in spite of large differences in concentrations; similarly, effects of gaseous pollutants in Asian cities are as high or higher. The methodology adopted and developed in the PAPA study could be used for other countries preparing to conduct air pollution studies. In addition, results from PAPA studies can be used in Asian and other cities for health impact assessment. Finally, further efforts are needed to understand the socioeconomic and demographic factors that might modify the effects of air pollution.</p>" ]

[ "<p>Project Teams by location: Bangkok—N. Vichit-Vadakan and N. Vajanapoom (Faculty of Public Health, Thammasat University); and B. Ostro (California Environmental Protection Agency). Hong Kong—C.M. Wong, T.Q. Thach, P.Y.K. Chau, K.P. Chan, R.Y. Chung, C.Q. Ou, L. Yang, G.N. Thomas, T.H. Lam, and A.J. Hedley (Department of Community Medicine, School of Public Health, The University of Hong Kong); J.S.M. Peiris (Department of Microbiology; The University of Hong Kong); and T.W. Wong (Department of Community and Family Medicine, Chinese University of Hong Kong). Shanghai—H. Kan, B. Chen, N. Zhao, and Y. Zhang (School of Public Health, Fudan University); H. Kan and S.J. London (Epidemiology Branch, National Institute of Environmental Health Sciences); G. Song and L. Jiang (Shanghai Municipal Center of Disease Control and Prevention); G. Chen (Shanghai Environmental Monitoring Center). Wuhan—Z. Qian, H.M. Lin, C.M. Bentley (Pennsylvania State University College of Medicine); H.M. Lin (Mount Sinai School of Medicine); Q. He and L. Kong (Wuhan Academy of Environmental Science); N. Yang and D. Zhou (Wuhan Centres for Disease Prevention and Control); and S. Xu and W. Liu (Wuhan Center of Environmental Monitoring).</p>", "<p>The authors declare they have no competing financial interests.</p>", "<title>Background and objectives</title>", "<p>Although the deleterious effects of air pollution from fossil fuel combustion have been demonstrated in many Western nations, fewer studies have been conducted in Asia. The Public Health and Air Pollution in Asia (PAPA) project assessed the effects of short-term exposure to air pollution on daily mortality in Bangkok, Thailand, and in three cities in China: Hong Kong, Shanghai, and Wuhan.</p>", "<title>Methods</title>", "<p>Poisson regression models incorporating natural spline smoothing functions were used to adjust for seasonality and other time-varying covariates that might confound the association between air pollution and mortality. Effect estimates were determined for each city and then for the cities combined using a random effects method.</p>", "<title>Results</title>", "<p>In individual cities, associations were detected between most of the pollutants [nitrogen dioxide, sulfur dioxide, particulate matter ≤ 10 μm in aerodynamic diameter (PM₁₀), and ozone] and most health outcomes under study (i.e., all natural-cause, cardiovascular, and respiratory mortality). The city-combined effects of the four pollutants tended to be equal or greater than those identified in studies conducted in Western industrial nations. In addition, residents of Asian cities are likely to have higher exposures to air pollution than those in Western industrial nations because they spend more time outdoors and less time in air conditioning.</p>", "<title>Conclusions</title>", "<p>Although the social and environmental conditions may be quite different, it is reasonable to apply estimates derived from previous health effect of air pollution studies in the West to Asia.</p>" ]

[ "<p>Time-series studies of daily mortality in several Asian cities can contribute significantly to the world’s literature on the health effects of air pollution. First, they provide direct evidence of air pollution effects in areas for which there are few studies. Second, because they involve different exposure conditions and populations, mortality studies of Asian cities can shed light on factors that may modify the effects of air pollution on health. In addition, multicity collaborative studies conducted within Asia, especially when analyzed using a common protocol, can generate more robust air pollution effect estimates for the region than those from individual studies and provide relevant and supportable estimates of the local impacts of environmental conditions for decision makers. Finally, they can determine the appropriateness of applying the results of health effects of air pollution studies conducted in North America and Western Europe to regions where few studies, if any, have been conducted.</p>", "<p>Recent reviews (##UREF##0##Anderson et al. 2004##; ##UREF##5##Ostro 2004##) suggest that proportional increases in daily mortality per 10-μg/m³ increase in PM₁₀ (particulate matter ≤ 10 μm in aerodynamic diameter) are generally similar among North American and Western European regions and the few developing countries where studies have been undertaken. However, the relatively few studies that have been conducted in Asia are not geographically representative and have used different methodologies, making it difficult to compare results in Asian cities with each other or with the broader literature. In addition, the worldwide data have not been appropriately analyzed for real differences in the magnitude of the effects of short-term exposure and the possible reasons for such differences, such as sources of air pollution or population characteristics.</p>", "<p>Efforts to bring the world’s data together for such analyses are under way with funding from the Health Effects Institute (HEI) in the PAPA (Public Health and Air Pollution in Asia) project and the APHENA (Air Pollution and Health: A European and North American Approach) project. These efforts can provide important insights to the time-series literature in terms of variability in air pollution, climate, population, and city characteristics involved.</p>", "<p>The first phase of the PAPA study was carried out using data from Bangkok, Thailand, from 1999 to 2003, Hong Kong, China, from 1996 to 2002, and Shanghai and Wuhan, China, both from 2001 to 2004 (##FIG##0##Figure 1##) (##UREF##2##HEI 2008##). A common protocol (available from the authors) for the design and analysis of data from multiple Asian cities and a management framework to conduct the coordinated analysis were established. These were designed to provide a basis for combining estimates and for isolating important independent factors that might explain effect modification in the city-specific estimates. It is anticipated that the results will not only contribute to the international scientific discussion on the conduct and interpretation of time-series studies of the health effects of air pollution but will also stimulate the development of routine systems for recording daily deaths and hospital admissions for time-series analysis.</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1195\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Bangkok, Hong Kong, Shanghai, and Wuhan. Numbers in parentheses indicate the number of monitoring stations used in each city.</p></caption></fig>", "<fig id=\"f2-ehp-116-1195\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Box plots of the air pollutants for the four cities. Boxes indicate the interquartile range (25th percentile–75th percentile); lines within boxes indicate medians; whiskers and circles below boxes represent minimum values; and circles above boxes indicate maximum values.</p></caption></fig>", "<fig id=\"f3-ehp-116-1195\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Excess risk (%) of mortality [point estimates (95% CIs)] for a 10-μg/m³ increase in average concentration of lag 0–1 days for three age groups.</p></caption></fig>", "<fig id=\"f4-ehp-116-1195\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>CR curves for all natural-cause mortality at all ages in all four cities for the average concentration of lag 0–1 days for NO₂ (<italic>A</italic>), SO₂ (<italic>B</italic>), PM₁₀ (<italic>C</italic>), and O₃ (<italic>D</italic>). The thin vertical lines represent the IQR of pollutant concentrations. The thick lines represent the WHO guidelines (##UREF##11##WHO 2005##) of 40 μg/m³ for 1-year averaging time for NO₂ (<italic>A</italic>), 20 μg/m³ for 24-hr averaging time for SO₂ (<italic>B</italic>), 20 μg/m³ for 1-year averaging time for PM₁₀ (<italic>C</italic>), and 100 μg/m³ for daily maximum 8-hr mean for O₃ (<italic>D</italic>).</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1195\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Summary statistics of daily mortality counts.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"4\" align=\"center\" rowspan=\"1\">Mean ± SD\n<hr/></th><th colspan=\"4\" align=\"center\" rowspan=\"1\">Minimum, maximum\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Bangkok</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Hong Kong</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Shanghai</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Wuhan</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Bangkok</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Hong Kong</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Shanghai</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Wuhan</th></tr></thead><tbody><tr><td colspan=\"9\" align=\"left\" rowspan=\"1\">All natural causes</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> All ages</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">94.8 ± 12.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">84.2 ± 12.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">119.0 ± 22.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">61.0 ± 15.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29, 147</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">48, 135</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51, 198</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25, 213</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 65 years</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.3 ± 6.7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">65.4 ± 11.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">99.6 ± 20.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">43.8 ± 13.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13, 63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34, 113</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">46, 175</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18, 159</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 75 years</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">21.3 ± 5.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">43.6 ± 9.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">71.5 ± 16.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25.7 ± 9.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6, 50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17, 82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33, 129</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6, 106</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cardiovascular</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.4 ± 4.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">23.8 ± 6.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.2 ± 11.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.8 ± 8.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1, 28</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6, 54</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11, 85</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8, 94</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Respiratory</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.1 ± 3.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">16.2 ± 5.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.3 ± 6.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.0 ± 5.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1, 20</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3, 34</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3, 45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0, 125</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1195\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Summary statistics of air pollutant concentrations and meteorological conditions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"4\" align=\"center\" rowspan=\"1\">Mean\n<hr/></th><th colspan=\"4\" align=\"center\" rowspan=\"1\">Median\n<hr/></th><th colspan=\"4\" align=\"center\" rowspan=\"1\">IQR\n<hr/></th><th colspan=\"4\" align=\"center\" rowspan=\"1\">Minimum, maximum\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Bangkok</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Hong Kong</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Shanghai</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Wuhan</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Bangkok</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Hong Kong</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Shanghai</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Wuhan</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Bangkok</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Hong Kong</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Shanghai</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Wuhan</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Bangkok</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Hong Kong</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Shanghai</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Wuhan</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂ (μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">58.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">66.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">56.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">62.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">47.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.8, 139.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.3, 167.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.6, 253.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.2, 127.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">SO₂ (μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">28.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">30.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.5, 61.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.4, 109.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.4, 183.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.3, 187.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀ (μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">52.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">102.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">141.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">46.8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">45.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">84.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">130.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">80.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">21.3, 169.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.7, 189.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.0, 566.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24.8, 477.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃ (μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">59.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">63.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">85.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">54.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">56.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">81.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">45.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">67.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2, 180.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.7, 195.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.3, 251.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0, 258.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Temperature (°C)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">28.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">24.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.7, 33.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.9, 33.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−2.4, 34.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−2.5, 35.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">RH (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">77.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">74.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73.0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">73.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">74.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">41.0, 95.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27, 97.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.0, 97.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35.0, 99.0</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1195\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Excess risk (ER; %) of mortality (95% CI) for a 10-μg/m³ increase in the average concentration of lag 0–1 days by main effect estimates of individual cities and combined random effects.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Bangkok\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Hong Kong\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Shanghai\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Wuhan\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Random effects (4 cities)\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Random effects (3 Chinese cities)\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Pollutant</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ER</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ER</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ER</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ER</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ER</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ER</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">All natural causes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.41</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.89 to 1.95</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.58 to 1.23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.97</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.66 to 1.27</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.97</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.31 to 2.63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.84 to 1.62<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1195\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.19</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.71 to 1.66<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1195\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">(all ages)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.61</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.08 to 3.16</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.38 to 1.36</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.62 to 1.28</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.19</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.65 to 1.74</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.75 to 1.24</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.74 to 1.23</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.82 to 1.69</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.53</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26 to 0.81</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.14 to 0.37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.43</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.24 to 0.62</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.55</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26 to 0.85<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1195\">#</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.21 to 0.54</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.30 to 0.95</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.32</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.01 to 0.62</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.31</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.04 to 0.58</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.05 to 0.63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.38</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23 to 0.53</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.31</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.13 to 0.48</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cardiovascular</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.78</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.47 to 3.10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.64 to 1.82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.01</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.55 to 1.47</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.18 to 3.06</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.36</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.89 to 1.82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.32</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.79 to 1.86</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.77</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−2.98 to 4.67</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.19</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.29 to 2.10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.91</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.42 to 1.41</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.47</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.70 to 2.25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.09</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.71 to 1.47</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.09</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.72 to 1.47</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.90</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.80 to 3.01</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.61</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11 to 1.10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.10 to 0.44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.57</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.31 to 0.84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.58</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22 to 0.93<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1195\">**</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19 to 0.68</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">O₃</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.03 to 1.63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.62</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06 to 1.19</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.38</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.03 to 0.80</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.07</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.53 to 0.39</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.01 to 0.73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.09 to 0.68</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Respiratory</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.60 to 2.72</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.42 to 1.88</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.22</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.42 to 2.01</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.68</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.77 to 5.63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.48</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.68 to 2.28</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.63</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.62 to 2.64<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1195\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">SO₂</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.66</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−3.09 to 6.64</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.28</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.19 to 2.39</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.51 to 2.23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.60 to 3.65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.47</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.85 to 2.08</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.46</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.84 to 2.08</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">PM₁₀</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.01</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.36 to 2.40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23 to 1.44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.01 to 0.56</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.34 to 1.41</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.62</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22 to 1.02</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.16 to 1.04</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">O3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.89</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.10 to 1.90</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.22</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.46 to 0.91</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.44 to 1.03</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.89 to 1.15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.34</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.07 to 0.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.22 to 0.68</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4-ehp-116-1195\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>Excess risk (ER; %) of mortality (95% CI) for a 10-μg/m³ increase in the average concentration of lag 0–1 days by sensitivity analysis for PM₁₀ effects with variation in concentration levels, stations, seasons and methods.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"4\" align=\"center\" rowspan=\"1\">ER\n<hr/></th><th colspan=\"3\" align=\"center\" rowspan=\"1\">Random effect (4 cities)\n<hr/></th><th colspan=\"3\" align=\"center\" rowspan=\"1\">Random effect (3 Chinese cities)\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">All natural causes, all ages</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Bangkok</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Hong Kong</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Shanghai</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Wuhan</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ER</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>Value</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ER</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>Value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Main analysis</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.53</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.26</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.43</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.55</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26–0.85</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">_#</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.21–0.54</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Omit PM₁₀ &gt; 95 percentile</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.82<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.75<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.28</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.52<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.53</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27–0.78</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"><xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1195\">*</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.47<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.21–0.73</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"><xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1195\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Omit PM₁₀ &gt; 75 percentile</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.73<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.89<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.36<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.70<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.53</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.29–0.78</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.55<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.24–0.85</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Omit PM₁₀ &gt; 180 μg/m³</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.22</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.73<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.24–1.06</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">_#</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.46<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.15–0.76</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"><xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1195\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Omit stations with high traffic source</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.18</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.55</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26–0.85</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">_#</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.38</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.20–0.57</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Warm season defined by simple dichotomous variables</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.16<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.37<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.81<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.86<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11–1.60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">_#</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.43</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.10–0.76</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Add temperature at lag 1–2 days</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.43</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.23</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.48</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.51</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23–0.79</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">_#</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.36</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.18–0.53</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Add temperature at lag 3–7 days</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.96<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.36<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.15<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.34<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.35<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.14–0.57</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"><xref ref-type=\"table-fn\" rid=\"tfn10-ehp-116-1195\">**</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.25<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1195\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.10–0.40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Daily PM₁₀ defined by centering</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.53</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.26</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.42</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26–0.82</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">_#</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.21–0.53</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Natural spline with (8, 4, 4) df</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.23</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.28</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.38</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26–0.81</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">_#</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.36</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23–0.49</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Penalized spline</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.20</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.48</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.28</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.39</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.52</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.26–0.77</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">_#</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.34</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.23–0.45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>Supplemental Material is available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11257/suppl.pdf\">http://www.ehponline.org/members/2008/11257/suppl.pdf</ext-link></p></fn><fn><p>We thank F. Speizer (Harvard School of Public Health) for his advice on the manuscript.</p></fn><fn><p>Research described in this article was conducted under contract to the Health Effects Institute (HEI), an organization jointly funded by the U.S. Environmental Protection Agency (EPA; Assistance Agreement R82811201) and automobile manufacturers. The contents of this article do not necessarily reflect the views of HEI, nor do they necessarily reflect the views and policies of the U.S. EPA or of motor vehicle and engine manufacturers.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1195\"><p>Study period: Bangkok, 1999–2003; Hong Kong, 1996–2002; and Shanghai and Wuhan, both 2001–2004.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn2-ehp-116-1195\"><p>Abbreviations: IQR, interquartile range; RH, relative humidity. NO₂, SO₂, and PM₁₀ are expressed as 24-hr averages, and O₃ is an 8-hr average.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn3-ehp-116-1195\"><p><italic>p</italic>-Values (homogeneity test):</p></fn><fn id=\"tfn4-ehp-116-1195\"><label>*</label><p>0.01 &lt; <italic>p</italic> ≤ 0.05;</p></fn><fn id=\"tfn5-ehp-116-1195\"><label>**</label><p>0.001 &lt; <italic>p</italic> ≤ 0.01; and</p></fn><fn id=\"tfn6-ehp-116-1195\"><label>#</label><p><italic>p</italic> ≤ 0.001.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn7-ehp-116-1195\"><p>NS, not significant.</p></fn><fn id=\"tfn8-ehp-116-1195\"><label>a</label><p>ER changed &gt; 20% from the main analysis. <italic>p</italic>-Values (homogeneity test):</p></fn><fn id=\"tfn9-ehp-116-1195\"><label>*</label><p>0.01 &lt; <italic>p</italic> ≤ 0.05;</p></fn><fn id=\"tfn10-ehp-116-1195\"><label>**</label><p>0.001 &lt; <italic>p</italic> ≤ 0.01;</p></fn><fn id=\"tfn11-ehp-116-1195\"><label>#</label><p><italic>p</italic> ≤ 0.001.</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1195f1\"/>", "<graphic xlink:href=\"ehp-116-1195f2\"/>", "<graphic xlink:href=\"ehp-116-1195f3\"/>", "<graphic xlink:href=\"ehp-116-1195f4\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>Chemicals</title>", "<p>Compounds, identified by Roman numerals in the text, are listed in ##FIG##0##Figure 1##; compounds were either purchased or synthesized in this laboratory. The carbanilide compounds, carbanilide (I; reported purity, 99.9%), TCC (III; reported purity, 99.3%), and 1,3-dicyclohexylurea (VI; reported purity, &gt;98%) were purchased from Aldrich (St. Louis, MO); 3-trifluoromethyl-4,4′-dichloro-carbanilide (VII; reported purity, &gt; 99%) was purchased from Chembridge (San Diego, CA). We synthesized structurally related carbanilide compounds (II, IV, and V; purity, &gt; 99%) by condensing the appropriate isocyanate and amine according to previously published reports (##REF##10430859##Morisseau et al. 1999##; ##REF##11171526##Newman et al. 2001##). We purchased TCS (VIII; reported purity of 99.8%) from Fluka (St. Louis, MO). The commercial TCC (III) and TCS (VIII) compounds were further purified to approximately 100% purity by recrystallization three times from ethanol and petroleum ether, respectively. We obtained TCDD from S. Safe (Texas A&amp;M University, College Station, TX). We purchased dimethyl sulfoxide (DMSO), 17β-E₂, and phenol red-free Dulbecco’s modified Eagle medium (DMEM) from Sigma Chemical Co. (St. Louis, MO); cell culture reagents and media from Gibco/BRL (Grand Island, NY); and 17β-testosterone from Alltech (State College, PA). All test compounds, except for the steroids, were dissolved in DMSO; steroids were dissolved in absolute ethanol. We purchased [³H]Ry (60–90 Ci/mmol; &gt; 99% pure) from Perkin-Elmer New England Nuclear (Wilmington, DE) and unlabeled Ry (&gt; 99% by ultraviolet-HPLC from Calbiochem (San Diego, CA).</p>", "<title>Cell-based AhR-mediated bioassay</title>", "<p>Recombinant rat hepatoma (H4L1.1c4) cells were grown and maintained as previously described (##REF##8812265##Garrison et al. 1996##). These cells contain the stably integrated, dioxin-responsive–element (DRE)-driven firefly luciferase reporter gene plasmid pGudLuc1.1. Transcriptional activation of the plasmid occurs in a ligand-, dose-, and AhR-dependent manner. Cells were plated into white, clear-bottomed 96-well tissue culture dishes at 75,000 cells/well and allowed to attach for 24 hr. Cells were incubated with carrier solvent DMSO (1% final solvent concentration), TCDD (1 nM), the indicated compound (for measurement of agonist activity), or the indicated compound plus 1 nM TCDD (for measurement of antagonist activity) for 4 hr at 37°C. For luciferase measurement, sample wells were washed twice with phosphate-buffered saline, followed by addition of cell lysis buffer (Promega, Madison, WI); the plates were then shaken for 20 min at room temperature to allow cell lysis. We measured luciferase activity in each well using a Lucy2 microplate luminometer (Anthos, Durham, NC) with automatic injection of Promega stabilized luciferase reagent. Luciferase activity in each well was expressed relative to that maximally induced by TCDD.</p>", "<title>Cell-based ER-mediated bioassay</title>", "<p>Recombinant human ovarian cancer cells (BG1Luc4E₂, ER-α–positive) were grown and maintained as previously described (##REF##10900408##Rogers and Denison 2000##). These cells contain a stably integrated, ER-responsive firefly luciferase reporter plasmid, pGudLuc7ERE. Cells were maintained in estrogen-stripped media for 5 days before they were plated into white, clear-bottomed 96-well tissue culture dishes at 75,000 cells/well and allowed to attach for 24 hr. Cells were then incubated with carrier solvent (ethanol; 1% final solvent concentration), E₂ (1 nM), the indicated compound (for measurement of agonist activity), or the indicated compound plus 1 nM E₂ (for measurement of antagonist activity) for 24 hr at 37°C. We measured luciferase activity as described above; activity is expressed relative to that maximally induced by E₂.</p>", "<title>Cell-based AR-mediated bioassays</title>", "<p>For the cell-based human AR-responsive bioassay, recombinant human cells [T47D-androgen-responsive element (ARE)] were grown and maintained as described above for H4L1.1c4 cells. The T47D-ARE cells contain a stably integrated AR-responsive firefly luciferase reporter gene plasmid, pGudLuc7ARE (##REF##12021401##Rogers and Denison 2002##). Cells were plated into white, clear-bottomed 96-well tissue culture dishes at 75,000 cells/well and allowed to attach for 24 hr. Cells were incubated with carrier solvent (ethanol; 1% final solvent concentration), 10 μM testosterone, the indicated compound (for measurement of agonist activity), or the indicated compound plus the indicated concentration of testosterone (for measurement of antagonist activity) for 24 hr at 37°C. We measured luciferase activity as described above; activity in each well is expressed relative to that maximally induced by testosterone. For comparison, we present data from a previously published study on the effect of these chemicals on AR in human embryonic kidney (HEK) 2933Y cells that lack key steroid-metabolizing enzymes (##REF##18048496##Chen et al. 2008##).</p>", "<title>RyR1-mediated bioassay</title>", "<p>Preparation of primary cultures of skeletal myotubes from wild-type mice has been previously described (##REF##8207057##Rando and Blau 1994##). Wild-type myoblasts were cultured in treated dishes coated with calf skin collagen with F-10 nutrient medium containing 20% (vol/vol) fetal bovine serum, 2 mM <sc>l</sc>-glutamine, 4 ng/mL fibroblast growth factor, 100 units/mL penicillin-G, and 100 μg/mL streptomycin sulfate at 37°C in 10% CO₂/5% O₂. For fura-2 imaging, cells were plated onto 96-well μ-clear plates (Greiner Bio-One GmbH, Monroe, NC) coated with Matrigel (Becton Dickinson, Franklin Lakes, NJ). Upon reaching ~ 80% confluency, the cells were differentiated into multinucleated myotubes over a period of 3 days using DMEM containing 5% (vol/vol) heat-inactivated horse serum, 2 mM <sc>l</sc>-glutamine, penicillin-G, and streptomycin sulfate at 37°C in 10% CO₂/10% O₂. Intracellular Ca²⁺ signals were recorded as previously described (##REF##11053126##Fessenden et al. 2000##). Briefly, differentiated primary wild-type myotubes were loaded with the calcium indicator dye fura-2, and the cells were imaged using an intensified charge-coupled device camera.</p>", "<p>The [³H]Ry-binding assay was performed as previously described (##REF##2436032##Pessah et al. 1987##). Observation of [³H]Ry binding to the isolated sarcoplasmic reticulum preparation was performed in the presence of 1 nM [³H]Ry and 10 μM Ca²⁺. The binding reaction was carried out at 37°C for 3 hr in 0.5 mL containing 50 μg sarcoplasmic reticulum protein. We assessed nonspecific binding in the presence of 10 μM unlabeled Ry. We separated bound and free ligand by rapid filtration through Whatman GF/B glass fiber filters, using a Brandel (Gaithersburg, MD) cell harvester. Filters were washed with 2 volumes of 5 mL ice-cold wash buffer containing 20 mM Tris-HCl, 250 mM KCl, 15 mM NaCl, and 50μM CaCl₂ (pH 7.1) and placed in vials with 5 mL scintillation cocktail (Ready Safe, Beckman Instruments, Fullerton, CA). We quantified [³H]Ry remaining on the filters by liquid scintillation spectrometry.</p>", "<title>Data analysis</title>", "<p>We measured luciferase activity per well as relative light units. We calculated luciferase induction as a percentage of TCDD (AhR bioassay), E₂ (ER bioassay), or testosterone (AR bioassay) activity by setting the maximal induction by 1 nM TCDD, 1 nM E₂, or 10 μM testosterone at 100%. Background activity present in the vehicle control was subtracted from treated cells. For antagonistic effects in the AhR bioassay, the induction at 1 nM concentration of TCDD was set at 100%. When evaluating for enhancement effects of the hormone receptors in the CALUX bioassays, the induction at a concentration of 1 nM E₂ (ER bioassay) or 10 μM testosterone (AR bioassay) was set at 100%, and the degree of enhancement by each compound tested was calculated as the ratio of the luciferase reporter gene induction value of each compound when combined with E₂ or testosterone relative to that of the hormone alone. We calculated the fold increase of [³H]Ry binding by TCS in skeletal muscle sarcoplasmic reticulum vesicles by dividing the mean value of [³H]Ry binding triggered by TCS with that triggered by the solvent control (DMSO); the solvent had none of the effects seen with TCS. Values shown are mean ± SD from three independent experiments for each dose tested, with vehicle control values subtracted. We analyzed data by one-way analysis of variance, followed by a multiple comparisons test when appropriate, using SigmaStat version 3.5 (Systat Software, San Jose, CA). We set the level of significance at <italic>p</italic> &lt; 0.05.</p>", "<title>Molecular modeling of TCS</title>", "<p>We performed molecular modeling using the CS ChemOffice 2005 software package (CambridgeSoft Corp., Cambridge, MA). We compared the optimized geometries of 2,2′,3,5′,6-pentachlorobiphenyl (PCB-95) and TCS at their minimum energy levels with a minimum root mean square gradient of 0.1 computed by MM2 force fields. We measured the dihedral angles formed by two phenyl rings in the structures of PCB-95 and TCS after molecular modeling; three-dimensional projections of the structures of TCS and PCB-95 were simulated using ChemIDplus (##UREF##2##National Library of Medicine 2008##).</p>" ]

[ "<title>Results</title>", "<title>Cell-based AhR-mediated bioassay</title>", "<p>We evaluated the activity of AhR-mediated cells by measuring luciferase activity induced by test compounds compared with that of the solvent control (DMSO) or TCDD as positive control. As shown in ##TAB##0##Table 1##, no carbanilide compounds tested (I–VII) exhibited induction except 1,3-dicyclohexylurea (VI), which induced reporter gene activity to 51% of that induced by TCDD. Interestingly, induction by compound VI was lower at the higher concentration, suggesting that it may be toxic to the cells, although we observed no overt cell toxicity by visual inspection. Except for compound VI, all carbanilides at higher concentrations (10 μM) inhibited TCDD-dependent luciferase gene expression between 20% and 70%, suggesting that these chemicals may act as weak AhR antagonists.</p>", "<p>We tested TCS (compound VIII) in the AhR bioassay because of its structural similarity to hydroxylated metabolites of the polybrominated diphenyl ethers 2,4,4′-tribromodiphenyl ether [bromodiphenyl ether-28 (BDE-28)], and 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47). TCS, at 10 μM, not only induced luciferase expression to 40% of that of TCDD induction but also inhibited the induction of luciferase expression by TCDD by approximately 30%. These agonist/antagonist results are consistent with TCS being a partial agonist of the AhR.</p>", "<title>Cell-based ER- or AR-mediated bioassay</title>", "<p>We evaluated activity of the recombinant ER-or AR-responsive cells by measuring luciferase activity induced by E₂ or testosterone, respectively, and compared results from the carbanilide compounds with solvent controls or positive controls (E₂, testosterone). Coincubation of E₂ and TCC resulted in enhanced E₂-dependent induction of luciferase gene expression, with significant increases observed at 1–10 nM E₂ (##FIG##1##Figure 2A##). We also examined the effect of TCC on the ability of testosterone to induce AR-mediated reporter gene activity; similar to results with the ER-reporter system, TCC enhanced testosterone-dependent induction of luciferase gene expression in T47D-ARE cells, but only at the highest concentration (10 μM) of testosterone (##FIG##1##Figure 2B##). Amplification of testosterone-dependent induction of ARE-linked luciferase reporter gene in a stably transfected HEK 293-ARE cell line has been previously published (##REF##18048496##Chen et al. 2008##), although that study reported the enhancement effect to occur at testosterone concentrations as low as 0.1 nM. Together, these results demonstrate that TCC can exert an enhancing effect on at least two members of the steroid hormone receptor family of transcription factors. Whether other related receptors will be similarly affected remains to be determined.</p>", "<p>The activity of TCC in the ER- and AR-responsive cells provides an interesting mechanism to enhance the endocrine-disrupting activity of chemicals. To determine whether other carbanilides also exert similar hormone-enhancing activity and whether they have any estrogenic or androgenic activity, we examined the ability of these chemicals to induce ER- or AR-dependent luciferase reporter gene activity and to enhance/inhibit hormone (E₂/T)-dependent reporter gene induction in the cell bioassays. As shown in ##FIG##2##Figure 3A##, TCC and its analogs, at concentrations of 1 or 10 μM, exhibited weak ER activity, &lt; 30% of maximal E₂-induced reporter gene induction; dicyclohexylurea (VI) induced ER-dependent gene expression only at 10 μM. Interestingly, compound VI at 10 μM induced ER-dependent reporter gene expression to a level significantly greater than that of a maximally inducing concentration of E₂. The results of the combined treatment of the carbanilides and E₂ (##FIG##2##Figure 3B##) revealed an enhancement of E₂-dependent gene expression by several compounds, with some being more effective enhancers at the lower concentration (I, III, and V) and one (VI) being a more effective E₂ enhancer at the higher concentration, increasing maximal E₂-dependent induction by 2.5-fold. The dramatic reduction in E₂-dependent induction of luciferase by compounds III, IV, and VII at 10 μM resulted from cell toxicity, as determined by visual inspection.</p>", "<p>Examination of the ability of the carbanilide compounds to induce AR-dependent reporter gene activity (##FIG##2##Figure 3C##) revealed that most compounds either were inactive or had very low agonist activity (inducing luciferase activity to &lt; 10% of a maximal inducing concentration of testosterone). When combined with testosterone (10 μM), the four carbanilide compounds (I, II, III, and V) at 1 μM enhanced testosterone-dependent gene expression, whereas compounds I, II, V, and VI at 10 μM enhanced luciferase gene induction to a significantly greater degree than observed with 1 μM (##FIG##2##Figure 3D##). The reduction in testosterone-dependent induction of luciferase activity by compounds III, IV, and VII at 10 μM resulted from cell toxicity as determined by microscopic examination. At 1 μM, compounds IV and VII did not enhance testosterone-dependent reporter gene expression. As shown in ##FIG##3##Figure 4A##, all test compounds were inactive in HEK 2933Y-ARE cells; however, when combined with testosterone at a physiologically relevant concentration of 0.1 nM in HEK 2933Y-ARE cells (##FIG##3##Figure 4B##), the 1-μM concentration of the carbanilide compounds, except for TFC (VII), exhibited a range of 20–100% amplification of testosterone-induced AR activity. Carbanilide VI and VII did not alter the signal significantly. Taken together, the results observed in the two cell lines strongly support the ability of a number of the carbanilide compounds to interact with the AR or AR-signal transduction pathway, leading to enhancement of AR-dependent gene expression.</p>", "<p>The above results indicate the ability of TCC and other compounds to enhance hormone-, ER-, and AR-dependent reporter gene expression. Given the environmental and exposure relevance of TCC, we examined the concentration-dependent nature of the TCC-dependent enhancing effect in both cell bio-assay systems. We examined the effect of increased concentrations of TCC in the cell incubation medium (up to a maximum of 1 μM; we observed toxicity at higher concentrations) on ER- and AR-dependent gene expression levels using maximally inducing concentrations of E₂ or testosterone, respectively. These analyses reveal a TCC concentration-dependent enhancement of E₂- and testosterone-dependent reporter gene expression (##FIG##4##Figure 5##). In contrast, coincubation with TCS (VIII) resulted in a TCS concentration-dependent decrease of E₂-dependent reporter gene expression, with 50% inhibition observed at a concentration of 1 μM TCS (##FIG##5##Figure 6##). TCS did not exhibit estrogenic activity at any concentration shown in the absence of E₂.</p>", "<title>RyR-responsive bioassay</title>", "<p>TCS significantly increased the amount of [³H]Ry binding to microsomes enriched in RyR1 from skeletal muscle (##FIG##6##Figure 7##). We measured net changes in intracellular Ca²⁺ concentration in myotubes with the fluorometric dye fura-2. The initial rate of Ca²⁺ increase in the cytoplasm in resting myotubes depends on the concentration of TCS (0.5–10 μM) perfused in the medium (##FIG##7##Figure 8A##). As shown in ##FIG##7##Figure 8B##, TCS increased cytosolic Ca²⁺ concentrations even in the presence of a buffer containing nominally free extracellular Ca²⁺ (~ 7 μM), indicating that TCS can mobilize Ca²⁺ to the cytoplasm from endoplasmic reticulum/sarcoplasmic reticulum and/or mitochondrial intracellular stores (##FIG##7##Figure 8B##). The carbanilide compounds, including TCC, showed no significant perturbation of resting Ca²⁺ concentration when perfused at ≤ 10 μM (data not shown). TCS has a noncoplanar configuration and substitutions at the <italic>ortho</italic> position similar to PCB-95, one of the most active congeners toward RyR1 (##REF##16411661##Pessah et al. 2006##; ##REF##8609904##Wong and Pessah 1996##) (##FIG##8##Figure 9##). However, TCS is significantly less hydrophobic than PCB-95 (log <italic>p</italic> = 4.76 vs. 6.55, respectively). Also, because of the ether linkage, TCS is significantly longer and more flexible than is PCB-95. TCS activity at RyR1 may therefore reflect its ability to assume a conformation like those of noncoplanar PCBs.</p>", "<p>Results from molecular modeling indicate that TCS has around 67° and 100° dihedral angles formed by the two phenyl rings, similar to the configuration of PCB-95, which has around 78° and 113° dihedral angles, supporting the importance of noncoplanarity for RyR activity.</p>" ]

[ "<title>Discussion</title>", "<p>We have examined the biological activity of the antimicrobial TCC, its carbanilide analogs, and TCS in <italic>in vitro</italic> and cell-based AhR, ER, AR, and RyR bioassays. Although TCC and other carbanilide compounds exhibited no significant or weak agonist activity in the ER and AR cell bioassays, most of the compounds enhanced the ability of the steroid hormones E₂ and testosterone to induce ER-and AR-dependent reporter gene expression in recombinant cell bioassays. In contrast, although TCS and most carbanilides, with the exception of compound VI, exhibited weak agonistic and/or antagonistic activity in the AhR-responsive cell bioassay (##TAB##0##Table 1##), none of these compounds significantly enhanced the ability of TCDD to induce reporter gene expression. These results indicate that these chemicals exhibit distinct mechanisms of action on these distinctly different ligand-dependent nuclear receptor signaling pathways.</p>", "<p>The signal enhancement activities by the carbanilide compounds were not involved in cell proliferation. At concentrations &lt; 1 μM, TCC, its analogs, and TCS showed no significant effect on ATP levels for cell proliferation/ cytotoxicity by the ViaLight kit in HEK-293 cells relative to the solvent control (data not shown). ViaLight cell proliferation and cytotoxicity assay was performed according to manufacturer instructions (Cambrex, Inc., East Rutherford, NJ). Similarly, &lt; 1 μM, TCC showed no significant effect on methylthiazol tetrazolium activity in the HEK 2933Y-ARE cell proliferation assay (##REF##18048496##Chen et al. 2008##). Some compounds exhibited cytotoxic effects at concentrations &gt; 10 μM in HEK-2933Y-ARE, T47D-ARE, and BG1-ERE cell lines.</p>", "<p>Because relatively small quantities of impurities may obscure the results for TCC, we further purified commercial TCC purchased at a purity of 99.8% by recrystallization, and we estimated higher purity (~ 100%) of TCC at 270 nm by HPLC. We evaluated the recrystallized TCC for the enhancement effect in the ER- and AR-mediated bioassays. The results were not significantly different from those in the bioassays using the commercial TCC (data not shown), so we conclude that the steroid-enhancing activity is mediated by the chemicals themselves and not by a contaminant(s).</p>", "<p>Although the trend of the enhancing activity produced by several carbanilides in the presence of testosterone in androgen-responsive recombinant T47D breast cancer cells and HEK-2933Y cells is similar, we demonstrated the testosterone concentrations at which we observed enhanced induction to be distinctly different. The different outcomes may result from a cell-type–specific biological response for the testosterone-induced AR-dependent luciferase reporter gene expression and from differences between the endogenous AR in T47D cancer cells and the exogenous transformed AR in HEK-2933Y cells, as well as differences in the specific interlaboratory protocols.</p>", "<p>Enhanced gene expression by carbanilide compounds in the presence of endogenous steroids in the AR- and ER-mediated reporter gene bioassays (present study) and the increased expression of AR protein by TCC and testosterone in MDA-kb2 human breast cancer cells (##REF##18048496##Chen et al. 2008##) suggest that the carbanilides may sensitize the complex of receptor-associating proteins similar to cofactors or coactivators common in cells containing ER and AR (##REF##15860367##Chang and McDonnell 2005##; ##REF##12040178##McDonnell and Norris 2002##). Such an interaction would allow the DNA-bound receptor to enhance the overall rate of transcription of the target gene as evidenced by the similar response in both ER- and AR-responsive systems. An <italic>in vitro</italic> AR binding assay using the rat AR ligand-binding domain and competitive fluorescence polarization measurement showed that TCC did not directly compete with testosterone for AR binding (##REF##18048496##Chen et al. 2008##). However, further investigation is needed to determine whether TCC increases the activity of sex steroid hormones by binding to the receptors or to receptor coactivators.</p>", "<p>Among the carbanilide compounds that showed gene induction enhancement, II, III (TCC), and IV are found in effluent from wastewater treatment plants; they likely result from use of personal care products or from synthesis and production of TCC or the phenylurea herbicide diuron (##REF##16678153##Sapkota et al. 2007##). Carbanilide (compound I) without elemental chlorine can be obtained as a byproduct in the synthesis of the phenylurea herbicide siduron when it is prepared from phenyl isocyanate. 1,3-Dicyclohexylurea (VI) has saturated cyclohexyl rings rather than phenyl rings of compound I, and compound VI exhibited both strong agonistic and amplification activities, particularly in the ER-mediated bioassay. It is obtained in an equal molar ratio as an industrial waste by-product, along with the desired product, by dicyclohexylcarbodiimide-mediated coupling reactions for common peptide synthesis.</p>", "<p>Hard bar soaps containing TCC, added at concentrations from about 0.6% up to 1.5%, are normally used by consumers for personal care purposes. The physicochemical properties of TCC suggest that it will penetrate the skin poorly, and this prediction is supported by limited experimental data (##REF##15819193##Halden and Paull 2005##). However, soaps provide good emollients to accelerate dermal penetration of TCC. The increasing use of TCC in personal care products results in increased human exposure. Also of concern are TCC’s recalcitrance to environmental degradation and its tendency to bind tightly to organic materials such as bio-solids from sewage sludge that are reapplied to land for agriculture and other uses. Taken together, these data suggest caution in its continued use.</p>", "<p>Possibly of broader significance is the illustration of a new mechanism of action for TCC as an EDC. The enhancement characteristics of TCC and its carbanilide analogs on endogenous/exogenous androgens and estrogens, in contrast to antagonist activity of TCS, may have an effect on possible normal physiology and/or reproduction in both males and females. Consider that synthetic and natural hormones used as growth promoters in cattle, which are eaten by women during pregnancy, have been shown to interfere with reproduction and may be involved in reduced sperm quality in their sons (##REF##17392290##Swan et al. 2007##). Exposure to TCC together with the steroid hormones in women may result in similar poor sperm quality because testicular development may be altered <italic>in utero</italic>. An animal study has indicated that chronic oral administration of high doses of TCC to male rats resulted in testicular degeneration (##UREF##3##Scientific Committee on Consumer Products 2005##). The Hershberger assay for <italic>in vivo</italic> screening to evaluate the synergistic effect of testosterone by TCC demonstrated that the treatment of 0.25% (wt/wt) TCC mixed in rat chow in the presence of testosterone propionate (0.2 mg/kg by subcutaneous injection) significantly increased the weight of accessory sex organs and tissues of reproductive tracts of castrated male Sprague Dawley rats, compared with rats given testosterone treatment alone (##REF##18048496##Chen et al. 2008##). Human exposure to TCC contained in commercial personal care soaps that are frequently used may enhance the activity of endogenous sex steroid hormones, suggesting that TCC as an EDC may affect male reproductive systems. In females, because the breast can be exposed to antimicrobial TCC-containing products such as soap and deodorants applied to the underarm and breast area, TCC amplification of E₂-induced ER activity may harm patients with ER-positive breast cancer. These studies suggest the importance of evaluating the relative benefits and risks of TCC found in personal care products.</p>", "<p>The content of TCS ranges from 0.1% to 1% in personal care products, and may reach 1% in medicated materials such as gloves/ scrubs used in hospitals where strong germicidal activity is needed. Because of its popular use, environmental residues have been detected at microgram per liter levels, suggesting extensive contamination of aquatic ecosystems and bioaccumulation in biota. Contrary to studies showing that TCS provided little or no estrogenic activity in the estrogen-dependent systems (##REF##11460682##Foran et al. 2000##; ##REF##15597899##Houtman et al. 2004##; ##REF##15003701##Ishibashi et al. 2004##), we found that TCS exhibited antagonist activity in the ER-mediated bioassay (##FIG##5##Figure 6##), as it did in the AR-mediated assay (##REF##17481686##Chen et al. 2007##; ##REF##16876421##Tamura et al. 2006##). Its phenolic structural relationship to nonsteroidal estrogens such as diethylstilbestrol and bisphenol A also raises concerns. Furthermore, TCS is structurally similar to noncoplanar <italic>ortho</italic>-substituted PCBs (##FIG##8##Figure 9##) and sensitizes the channel of an RyR found in various forms of muscle and other excitable animal tissue. Our results (##FIG##6##Figures 7## and ##FIG##7##8##) are the first to identify RyRs as sensitive targets of TCS and provide a potent tool for understanding mechanisms regulating the structure and function of Ca²⁺ release complexes. These new results provide additional concern for TCS as an EDC and concern that it has potential neurotoxicity, which taken together with a finding that TCS disrupts thyroid action for normal growth and development of humans and wildlife (##REF##17011055##Veldhoen et al. 2006##), suggests reevaluation of its use in consumer products.</p>", "<p>In conclusion, we used a battery of mechanism-based screening bioassays developed by the University of California Superfund Basic Research Program for detection of toxicants and assessment of exposure to toxic substances with a selected series of carbanilides and TCS. As summarized in ##TAB##1##Table 2##, we found that a group of carbanilides, including the antimicrobial TCC, enhances effects of steroid hormones in ER- and AR-dependent gene expression bioassays, although the compounds have minor endocrine activity on their own, suggesting a new class of EDC. In general, the carbanilide compounds except for dicyclohexylurea (VI), which acted as an agonist, provided antagonistic activity in the AhR-responsive cell bioassay. Similar to noncoplanar <italic>ortho</italic>-substituted PCBs, the noncoplanar antimicrobial compound TCS exhibited weak AhR activity but was a potent antagonist in both ER-mediated and AR-mediated bioassays and a potent channel sensitizer in an RyR-mediated bioassay and dysregulator of cell Ca²⁺ homeostasis. These observations have potentially significant implications regarding human and animal health because exposure may be directly through dermal contact or indirectly through the food chain. These screening studies revealed that further investigations into the biological and toxicologic effects of TCC, its carbanilide analogs, and TCS are urgently needed.</p>" ]

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[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Concerns have been raised about the biological and toxicologic effects of the antimicrobials triclocarban (TCC) and triclosan (TCS) in personal care products. Few studies have evaluated their biological activities in mammalian cells to assess their potential for adverse effects.</p>", "<title>Objectives</title>", "<p>In this study, we assessed the activity of TCC, its analogs, and TCS in <italic>in vitro</italic> nuclear-receptor–responsive and calcium signaling bioassays.</p>", "<title>Materials and methods</title>", "<p>We determined the biological activities of the compounds in <italic>in vitro</italic>, cell-based, and nuclear-receptor–responsive bioassays for receptors for aryl hydrocarbon (AhR), estrogen (ER), androgen (AR), and ryanodine (RyR1).</p>", "<title>Results</title>", "<p>Some carbanilide compounds, including TCC (1–10 μM), enhanced estradiol (E₂)-dependent or testosterone-dependent activation of ER- and AR-responsive gene expression up to 2.5-fold but exhibited little or no agonistic activity alone. Some carbanilides and TCS exhibited weak agonistic and/or antagonistic activity in the AhR-responsive bioassay. TCS exhibited antagonistic activity in both ER- and AR-responsive bioassays. TCS (0.1–10 μM) significantly enhanced the binding of [³H]ryanodine to RyR1 and caused elevation of resting cytosolic [Ca²⁺] in primary skeletal myotubes, but carbanilides had no effect.</p>", "<title>Conclusions</title>", "<p>Carbanilides, including TCC, enhanced hormone-dependent induction of ER- and AR-dependent gene expression but had little agonist activity, suggesting a new mechanism of action of endocrine-disrupting compounds. TCS, structurally similar to noncoplanar <italic>ortho</italic>-substituted poly-chlorinated biphenyls, exhibited weak AhR activity but interacted with RyR1 and stimulated Ca²⁺ mobilization. These observations have potential implications for human and animal health. Further investigations are needed into the biological and toxicologic effects of TCC, its analogs, and TCS.</p>" ]

[ "<p>The antimicrobial agents triclosan (TCS; 2,4,4′-trichloro-2′-hydroxydiphenyl ether) and triclocarban (TCC; 3,4,4′-trichloro-carbanilide) are high-production-volume chemicals that are widely used as “value added” chemicals in personal care products. In a limited retail survey, approximately 45% of liquid and bar soaps on the market contained these antimicrobials; TCS and TCC were the predominant antimicrobials in liquid soaps and bar soaps, respectively (##REF##11584251##Perencevich et al. 2001##). According to the ##UREF##4##U.S. Environmental Protection Agency (2007)##, U.S. consumers spend nearly $1 billion/year on these products. TCS is a broad-spectrum bacteriostatic germicide that is now used in consumer products such as liquid hand soap, toothpaste, mouth rinse, cosmetics, pharmaceutical products, fabrics, plastics, textiles, and plastic kitchenware since its original introduction as an active ingredient in a surgical scrub for professional health care in 1972 (##REF##9949382##Tierno 1999##). It is a powerful antibacterial agent that inhibits the activity of the enzyme enoylacyl carrier-protein reductase, which catalyzes an essential step in membranes of many bacteria and fungi (##REF##10196195##Heath et al. 1999##; ##REF##9707111##McMurry et al. 1998b##). TCC, another antimicrobial agent, is more often added to consumer bar soaps and deodorants, and it is active predominantly against gram-positive bacteria (##REF##9880479##McDonnell and Russell 1999##). The carbanilide analog 3-trifluoromethyl-4,4′-dichlorocarbanilide (TFC) is also used as an antibacterial agent (##REF##15808##Jeffcoat et al. 1977##).</p>", "<p>Although these compounds are broadly classified as halogenated aromatic hydrocarbons, TCS has functional moieties representative of phenols, diphenyl ethers, and polychlorinated biphenyls (PCBs), whereas TCC is structurally related to carbanilide compounds, including some drugs and pesticides, and sterically and electronically related to a variety of other chemicals (##FIG##0##Figure 1##). Both TCS and TCC have been detected at microgram per liter levels in waterways in the United States and Switzerland, indicating extensive contamination of aquatic ecosystems (##REF##15819193##Halden and Paull 2005##; ##UREF##0##Kolpin et al. 2002##; ##UREF##1##Lindstrom et al. 2002##). The potential of these compounds for bioaccumulation has raised public concern regarding their possible effects on human health (##REF##17275881##Coogan et al. 2007##; ##REF##16522524##Darbre 2006##; ##REF##10592150##Daughton and Ternes 1999##) and microbial resistance (##REF##9770288##McMurry et al. 1998a##). Recent reports note that TCS levels as high as 2,000 μg/kg lipid have been detected in human breast milk (##REF##17011099##Dayan 2007##), and concentrations in human fluids such as plasma and milk are positively correlated to levels of exposure (##REF##12002480##Adolfsson-Erici et al. 2002##; ##REF##17007908##Allmyr et al. 2006##; ##REF##17011099##Dayan 2007##; ##REF##11706375##Hovander et al. 2002##).</p>", "<p>Because of these concerns, we screened TCS, TCC, and a series of TCC analogs for biological activity in several mechanistically derived cell-based assay systems. Mammalian ligand-dependent nuclear receptors serve as biomarkers that evaluate the potential of an environmental toxicant to affect endocrine and non–endocrine-signaling systems. One set of assays used in the present study is based on the chemically activated luciferase gene expression (CALUX) bioassays. The recombinant cells used in the CALUX bioassays include a stably transfected aryl hydrocarbon receptor (AhR)-, androgen receptor (AR)-, or estrogen receptor (ER)-responsive firefly luciferase reporter gene that responds to chemicals that can bind to and/or activate the respective receptor, leading to the induction of luciferase reporter gene expression.</p>", "<p>AhR is a transcription factor that activates gene expression in a ligand-dependent manner. Exposure to the most potent ligand, 2,3,7,8-tetrachlorodibenzo-<italic>p</italic>-dioxin (TCDD), results in a variety of toxic and biological responses, most of which are AhR dependent, such as birth defects, immunotoxicity, tumor production, changes in metabolism, and lethality. Dioxin-like PCBs, polychlorinated dibenzofurans, and related chemicals that mimic the action of TCDD at the level of the AhR are detected by measurement of their ability to stimulate AhR-dependent gene expression in the CALUX bioassay (##REF##8812265##Garrison et al. 1996##; ##REF##15096657##Han et al. 2004##).</p>", "<p>Steroid hormones control reproduction, metabolism, and ion balance in vertebrates. ER and AR are nuclear receptors for estrogenic and androgenic chemicals such as estradiol (E₂) and testosterone, respectively, that function as transcription factors to regulate female and male reproduction, sexual development, and bone structure. Chemicals acting as endocrine-disrupting compounds (EDCs) affect these receptors and lead to activation/ inhibition of hormone-dependent gene expression. However, EDCs may also alter hormone receptor function simply by changing phosphorylation of the receptors (activating them) without the responsible chemical or natural ligand ever binding to the receptor (##REF##17536004##Weigel and Moore 2007##). Recently, ##REF##18048496##Chen et al. (2008)## reported data indicating a mechanism of endocrine disruption that involves the receptor but that does not appear to act through competition with the receptor’s primary binding site. Instead, they observed an amplification or enhancement of the ability of the chemical to stimulate gene expression in concert with the natural ligand, possibly indicating a new type of EDC that may not share the basic qualities of previously defined EDCs.</p>", "<p>An increasing number of studies report that chemicals in the environment, mimicking natural estrogen, interact with or affect the ER in cells and thereby disrupt normal endocrine function, raising public concerns about the biological/reproductive effects of these chemicals (##REF##16522524##Darbre 2006##). Reproductive health concerns regarding androgenic EDCs that may reduce sperm production, alter genital development, and contribute to neurologic syndromes in males have been proposed (##REF##17481686##Chen et al. 2007##; ##REF##17149866##Larsson et al. 2006##; ##REF##15483189##Sonneveld et al. 2005##). In relation to the work reported here, TCS may have cytotoxic effects on breast cancer cells (##REF##11935210##Liu et al. 2002##) and have endocrine-disrupting properties (antiandrogenic activity and thyroid-hormone-like activity) in aquatic species and human recombinant cells in culture (##REF##17481686##Chen et al. 2007##; ##REF##11460682##Foran et al. 2000##; ##REF##17011055##Veldhoen et al. 2006##).</p>", "<p>The ryanodine (Ry) receptor type 1 (RyR1)-based bioassay is used for screening potential compounds exhibiting biological activity that alters Ca²⁺ homeostasis (##REF##16411661##Pessah et al. 2006##). RyRs function as high-conductance Ca²⁺ channels broadly expressed in animal cells, including muscle (skeletal, cardiac, smooth), neurons, and immune cells. Chemicals that enhance or inhibit RyR channel activity, such as caffeine and Ry, have been demonstrated to influence Ca²⁺ signaling events and Ca²⁺-dependent processes in a number of cell types. Noncoplanar, <italic>ortho</italic>-substituted PCB congeners that exhibit weak AhR activity enhance the sensitivity of RyRs to activation by endogenous ligands in a manner requiring the immunophilin FKBP12–RyR complex (##REF##9182535##Wong et al. 1997##). Disruption of Ca²⁺ homeostasis in the affected regions of the brain by compounds altering RyR may contribute to alteration of neurodevelopment and neuroplasticity function (##REF##14600284##Gafni et al. 2004##; ##REF##11208908##Wong et al. 2001##).</p>", "<p>The screening assays used in this study are part of a library of techniques developed by the University of California, Davis Superfund Basic Research Program, whose aim is to identify biomarkers of exposure and effect of toxic substances. The goal of this study was to demonstrate that such mechanistic, nuclear-receptor–based screening assays can rapidly provide useful information on environmental chemicals, and to assess the potential for the antimicrobials TCC, its analogs, and TCS to produce specific toxic effects that would warrant further study.</p>", "<title>C<sc>orrection</sc></title>", "<p>In ##FIG##1##Figure 2## of the original manuscript published online, the concentration of TCC was incorrect; it has been corrected here.</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Chemical structures and use of TCC, its analogs, and TCS.</p></caption></fig>", "<fig id=\"f2-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Results of ER- and AR-mediated bioassays showing the effects of 1 μM TCC on gene expression of ER (<italic>A</italic>) and AR (<italic>B</italic>) induced by E₂ in BG1-ERE cells and testosterone (T) in T47D-ARE cells, respectively. Luciferase activity (mean ± SD) is expressed relative to that maximally induced by E₂ and T in (<italic>A</italic>) and (<italic>B</italic>), respectively.</p><p>*Significantly greater than E₂ or T positive control groups (<italic>p</italic> &lt; 0.05).</p></caption></fig>", "<fig id=\"f3-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Effects of carbanilide compounds at 1 (10⁻⁶ M) and 10 μM (10⁻⁵ M) on ER- and AR-mediated activity in the absence (<italic>A</italic> and <italic>C</italic>) and presence (<italic>B</italic> and <italic>D</italic>) of E₂ (1 nM; <italic>A</italic> and <italic>B</italic>) or testosterone (T; 10 μM; <italic>C</italic> and <italic>D</italic>) in ER-responsive (BG1-ERE cells) or AR-responsive (T47D-ARE cells) bioassay.</p><p>*Significantly greater than the solvent control (<italic>A</italic> and <italic>C</italic>) or E₂ or T positive controls (<italic>B</italic> and <italic>D</italic>) at <italic>p</italic> &lt; 0.05 for agonist/amplification evaluation. ^#Cell toxicity at 10-μM concentration.</p></caption></fig>", "<fig id=\"f4-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Effects of 1-μM carbanilide compounds on the AR-mediated activity in the absence (<italic>A</italic>) and the presence (<italic>B</italic>) of testosterone (T; 0.1 nM) in the HEK-2933Y AR-responsive cell system. The activity by carbanilide compounds I–V was reported by ##REF##18048496##Chen et al. (2008)##.</p><p>*Significantly greater than the solvent control (<italic>A</italic>) or T positive control (<italic>B</italic>) at <italic>p</italic> &lt; 0.05 for agonist/amplification evaluation.</p></caption></fig>", "<fig id=\"f5-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>The effects of TCC (III) tested at different concentrations on ER (<italic>A</italic>) or AR (<italic>B</italic>) gene expression in the presence of steroid hormone E₂ (1 nM) or testosterone (T; 10 μM) at a constant concentration in the ER-responsive (BG1-ERE; <italic>A</italic>) or AR-responsive (T47D-ARE; <italic>B</italic>) bioassay.</p><p>*Significantly greater than the E₂ or T positive control at <italic>p</italic> &lt; 0.05.</p></caption></fig>", "<fig id=\"f6-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Activity of TCS in the ER-mediated bioassay.</p><p>*Significantly different from the control.</p></caption></fig>", "<fig id=\"f7-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>[³H]Ry binding with or without 1.2 μM TCS in skeletal muscle sarcoplasmic reticulum vesicles.</p><p>*Significantly greater than the control at <italic>p</italic> &lt; 0.05.</p></caption></fig>", "<fig id=\"f8-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Effect of TCS on cytosolic Ca²⁺ concentration. (<italic>A</italic>) Cytosolic Ca²⁺ concentration in resting myotubes increased in a dose-dependent manner after TCS treatment; each trace is an average of <italic>n</italic> ≥ 5 cells in separate cell cultures in Ca²⁺-replete (1.8 mM) buffer. (<italic>B</italic>) TCS 1 μM triggered an increase in the cytosolic Ca²⁺ concentration even in nominally Ca²⁺-free (~ 7 μM) extracellular buffer.</p></caption></fig>", "<fig id=\"f9-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Figure 9</label><caption><p>Three-dimensional projection of TCS and PCB-95 generated by ChemIDplus (##UREF##2##National Library of Medicine 2008##).</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Induction or inhibition of AhR-dependent luciferase reporter gene expression in H4L1.1c4 cells.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Luciferase activity (percent of TCDD)\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Compound</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">1 μM of compound</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">10 μM of compound</th></tr></thead><tbody><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Induction of luciferase</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> TCDD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">100 ± 8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">100 ± 8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> I</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0 ± 0.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.8 ± 0.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> II</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.1 ± 1.7<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.6 ± 0.1<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> III (TCC)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.8 ± 0.4<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.2 ± 0.3<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> IV</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.1 ± 0.2<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.1 ± 0.5<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> V</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.5 ± 0.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3 ± 0.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> VI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.9 ± 1.5<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.6 ± 1.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> VII</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.5 ± 0.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.6 ± 0.2<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> VIII (TCS)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.0 ± 1.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40.6 ± 6.1<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Inhibition of TCDD induction of luciferase</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> DMSO</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">100 ± 8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">100 ± 8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> I</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">85.9 ± 4.4<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">80.3 ± 0.6<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref><xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> II</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">61.4 ± 0.7<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref><xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">81.3 ± 4.4<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref><xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> III (TCC)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">64.6 ± 3.2<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref><xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.7 ± 2.8<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref><xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> IV</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">75.1 ± 2.5<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref><xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.4 ± 4.1<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1203\">a</xref><xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> V</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">98.2 ± 3.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">79.3 ± 4.6<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> VI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">103 ± 2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">112 ± 3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> VII</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">97.1 ± 2.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32.5 ± 1.6<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> VIII (TCS)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">98.3 ± 5.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">70.4 ± 2.1<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1203\">*</xref></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1203\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>The biological activity of TCC, its analogs, and TCS in the receptor bioassay screens.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">AhR (H4L1.1c4-DRE cells)\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">ER (BG1-ERE cells)\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">AR (T47D-ARE or HEK2933Y-ARE cells)\n<hr/></th><th align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Compound</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Compound alone</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">With 1 nM TCDD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Compound alone</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">With 1 nM E2</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Compound alone</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">With 0.1 nM or 10 μM T</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">RyR ([³H]Ry binding)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">I</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ant)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1203\">a</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1203\">a</xref>,<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1203\">b</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">II</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ant)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1203\">a</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1203\">a</xref>,<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1203\">b</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">III (TCC)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ant)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1203\">a</xref>,<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1203\">b</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IV</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ant)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1203\">a</xref>,<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1203\">b</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">V</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ant)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">− (ag<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1203\">a</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1203\">b</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">VI</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1203\">a</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">VII</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ant)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (amp)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">VIII (TCS)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ag)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ant)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ant)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (ant<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1203\">b</xref>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">+ (sensitive)</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>We thank T.A. Ta for some of the [³H]Ry binding analysis.</p></fn><fn><p>The Research Translation Core of the University of California, Davis Superfund Basic Research Program was instrumental in coordinating the studies.</p></fn><fn><p>This research was supported by the National Institute of Environmental Health Sciences (NIEHS) Superfund Basic Research Program (5P42 ES04699); NIEHS Center for Environmental Health Sciences (P30 ES05707); NIEHS grant R37 ES02710; the University of California, Davis Center for Children’s Environmental Health and Disease Prevention (1PO1 ES11269); the U.S. Environmental Protection Agency through the Science to Achieve Results (STAR) program (R829388); and National Institute of Occupational Safety and Health Center for Agricultural Disease and Research, Education and Prevention grant 1 U50 OH07550.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1203\"><p>Values are expressed as a percentage of that induced by 1 nM TCDD and represent the mean ± SD of triplicate determinations of luciferase activity.</p></fn><fn id=\"tfn2-ehp-116-1203\"><label>a</label><p>Data from ##REF##16788953##Zhao et al. (2006)##.</p></fn><fn id=\"tfn3-ehp-116-1203\"><label>*</label><p>Significantly different from the DMSO-treated controls or TCDD-treated samples (<italic>p</italic> &lt; 0.05).</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn4-ehp-116-1203\"><p>Abbreviations: –, no effect; +, positive effect; ag, agonistic; amp, amplification; ant, antagonistic.</p></fn><fn id=\"tfn5-ehp-116-1203\"><label>a</label><p>Data from T47D-ARE bioassays.</p></fn><fn id=\"tfn6-ehp-116-1203\"><label>b</label><p>Data from HEK 2933Y-ARE bioassay.</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1203f1\"/>", "<graphic xlink:href=\"ehp-116-1203f2\"/>", "<graphic xlink:href=\"ehp-116-1203f3\"/>", "<graphic xlink:href=\"ehp-116-1203f4\"/>", "<graphic xlink:href=\"ehp-116-1203f5\"/>", "<graphic xlink:href=\"ehp-116-1203f6\"/>", "<graphic xlink:href=\"ehp-116-1203f7\"/>", "<graphic xlink:href=\"ehp-116-1203f8\"/>", "<graphic xlink:href=\"ehp-116-1203f9\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>SWCNT particles</title>", "<p>We obtained commercially manufactured raw SWCNTs through collaboration with the National Institute of Standards and Technology (Gaithersburg, MD); a more detailed description is presented in the Supplemental Material available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>. Reported impurities include nickel and yttrium, which are encapsulated in carbon shells (see Supplemental Material, Table 1). Detailed high-resolution transmission electron microscopy revealed that the diameters ranged from 0.8 to 2.0 nm (see Supplemental Material, Figure 1C).</p>", "<title>Mesothelial cell culture</title>", "<p>Exposure to crocidolite is well documented to cause mesothelioma in humans and animals, and cellular studies using mesothelial cells are reported to mimic important biologic responses involved in mesothelioma development. Therefore, in the present study we used normal human NM and malignant MM mesothelial cells that we maintained as described in the Supplemental Material (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>).</p>", "<title>Electron spin resonance (ESR) assay</title>", "<p>We determined the production of reactive oxygen species (ROS) caused by exposing NM and MM cells to SWCNTs as described in the Supplemental Material (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>).</p>", "<title>Intracellular detection of O₂^•− and H₂O₂ in intact cells by confocal microscopy</title>", "<p>We investigated intracellular production of ROS generation in mesothelial cells exposed to SWCNTs, crocidolite, or vehicle [RPMI-1640 medium containing 0.1% fetal bovine serum (FBS)]. We used the dyes dihydro-ethidium (DHE) and dichlorodihydro-fluorescein diacetate (H₂DCFDA) for the intracellular localization of O₂^•−and H₂O₂ and the intracellular detection of ROS as described in the Supplemental Material (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>).</p>", "<title>Cell viability assay</title>", "<p>We seeded the NM and MM cells (5 × 104) overnight and treated them with 12.5, 25, or 125 μg/cm² SWCNTs or with vehicle alone for 24 hr. We evaluated cell viability using 3-[4,5-dimethylthiazolyl-2]-2,5-diphenyltetrazolium bromide (MTT) assay kit according to the manufacturer’s instructions (Roche Molecular Biochemicals, Indianapolis, IN), and as described in the Supplemental Material (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>).</p>", "<title>DNA damage by comet assay</title>", "<p>We seeded the NM and MM cells (10⁵) overnight and treated them with vehicle or 25 or 50 μg/cm² SWCNTs for 24 hr. We assessed DNA damage using a commercially available comet assay according to the manufacturer’s instructions (Trevigen, Gaitherburg, MD) and as described in the Supplemental Material (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>).</p>", "<title>Histone H2AX phosphorylation of DNA double-strand breaks</title>", "<p>We exposed NM and MM cells cultured in black-wall/clear-bottom microplates to 25 or 50 μg/cm² SWCNTs or crocidolite for 24 hr. We detected H2AX phosphorylation according to the manufacturer’s protocol (Millipore, Billerica, MA) and as described in the Supplemental Material (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>).</p>", "<title>Western blot analysis of cleaved poly(ADP-ribose) polymerase (PARP)</title>", "<p>We subcultured NM and MM cells and maintained them overnight in 10% FBS growth medium. We then replaced the standard growth medium with 0.1% FBS-containing medium; exposed the cells to 50 μg/cm² SWCNTs or crocidolite for 0, 6, or 18 hr; and analyzed PARP activation as described in the Supplemental Material (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>).</p>", "<title>Protein kinase phosphorylation assay</title>", "<p>We treated NM and MM cells (10⁶) seeded overnight with 25 μg/cm² SWCNTs or vehicle for varying times up to 120 min and assayed phosphorylation as described in the Supplemental Material (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>).</p>", "<title>Activation of activator protein-1 (AP-1) and nuclear factor κB (NF-kB)</title>", "<p>We seeded the NM and MM cells (10⁶) in six-well plates overnight and treated them with 25 μg/cm² SWCNTs or vehicle for 1, 2, or 4 hr. We prepared nuclear extractions using a nuclear extraction kit and determined activation of AP-1 and NF-κB using an enzyme-linked immunosorbent assay (ELISA) kit (Panomics Inc., Redwood City, CA) according to the manufacturer’s instructions.</p>", "<title>Western blot analysis of protein serine-threonine kinase (Akt)</title>", "<p>We cultured NM and MM cells (2 × 105) in six-well plates with 10% FBS medium. We then washed the cells and exposed them to 0, 25, 75, or 125 μg/cm² SWCNTs in 0.1% FBS for 30 or 60 min. We analyzed Akt phosphorylation as described in the Supplemental Material (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>).</p>", "<title>Statistics</title>", "<p>Data presented are mean ± SE of values compared and analyzed using one-way analysis of variance. We considered <italic>p</italic> ≤ 0.05 statistically significant.</p>" ]

[ "<title>Results</title>", "<p>Although SWCNTs are not water soluble, in the present investigation we used a vehicle containing 1% FBS and ultrasonication to suspend the SWCNTs for cell exposure studies. Light microscopy, scanning electron microscopy, and transmission electron microscopy studies of the suspended samples showed that this technique produced homogeneous dispersion of SWCNTs with small agglomerates of nanoropes and mats of SWCNTs [see Supplemental Material, Figure 1A–C (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>)].</p>", "<title>Trace metals in SWCNTs</title>", "<p>Among the 31 metals analyzed in three different SWCNT samples, we identified metals suspected of having toxic biological effects [see Supplemental Material, Table 1 (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>)]. Two redox-sensitive trace metals (iron, 0.07%; nickel, 20.6%)—in appreciable concentrations—and yttrium (6.2%) were present in raw SWCNTs.</p>", "<title>ROS generation by SWCNT-exposed mesothelial cells</title>", "<p>Generation of ROS monitored by ESR in SWCNT-exposed NM and MM cells revealed that the NM cells generated more ROS than did MM cells [see Supplemental Material, Figure 2A, center panel (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>)]. The reaction mixture with the cells in the absence of SWCNTs did not produce any detectable ESR signals, whereas addition of SWCNTs produced a distinct ESR signal spectrum with cells [see Supplemental Material, Figure 2A, center panel). The hyper-fine splitting of the spin adduct produced by SWCNTs were characteristic evidence of hydroxyl radical (•OH) generation.</p>", "<p>Exposure of NM and MM cells to 500 μg/mL SWCNTs significantly increased •OH radical generation [see Supplemental Material, Figure 2B (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>)], which was higher in NM cells than in MM cells (see Supplemental Material, Figure 2B). When catalase, a decomposing enzyme of H₂O₂, was present, the SWCNT-induced ESR signal 5,5-dimethyl-1-pyrroline-1-oxide (DMPO)-•OH was inhibited by 39% in NM cells (<italic>p</italic> &lt; 0.05) and by 43% in MM cells [see Supplemental Material, Figure 2A, right panel; ##FIG##1##Figure 2B##). Deferoxamine, a metal iron chelator, produced a similar inhibition pattern in both cell types. The SWCNT-induced ESR signal intensity decreased 38% in NM cells and 44% in MM cells (see Supplemental Material, Figure 2B). This indicates that some chelatable metals are partially involved in the generation of SWCNT-induced •OH radicals. However, because the chelation did not completely nullify the generation of ROS, the cell stimulation by SWCNTs could be considered a potential source of ROS generation. We present data on semiquantitative measurement of differential DMPO-•OH signal intensities and inhibition induced by catalase and deferoxamine in the Supplemental Material (##FIG##1##Figure 2B##).</p>", "<title>Intracellular detection of ROS generation in intact cells</title>", "<p>To further confirm the ability of NM and MM cells to generate ROS after exposure to SWCNTs and crocidolite, we analyzed the cells treated with particles by intracellular staining for O₂^•− and H₂O₂. DHE (a specific fluorescent dye for O₂^•−), and H₂DCFDA (a fluorescent dye specific for H₂O₂) were used to monitor ROS generation. In the presence of 150 μg/mL SWCNTs for 90 min, the fluorescence for O₂^•− and H₂O₂ were increased in both NM and MM cells. ROS generation was much greater in NM cells than in MM cells, confirming the ESR studies (Figure1). Fluorescence for O₂^•− and H₂O₂ was significantly greater with crocidolite than with SWCNTs in both cell types (##FIG##0##Figure 1##). Catalase or superoxide dismutase (SOD) pre-treatment abolished most of the particle-induced generation of ROS (data not shown).</p>", "<title>Effects of SWCNTs on cell viability</title>", "<p>We evaluated the effects of SWCNTs and crocidolite on cell viability of NM and MM cells using MTT, lactate dehydrogenase (LDH), and trypan blue assays. However, LDH enzyme molecules were adsorbed by the SWCNTs, and only crocidolite caused a strong cytotoxicity in increasing doses (data not shown). Therefore, only MTT and trypan blue viability assay results are presented for comparison of cytotoxicity [see Supplemental Material, Figure 3A,B (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>)]. Cell viability studies using the MTT assay indicated that SWCNTs in increasing mass concentrations of 12.5, 25, and 125 μg/cm² (incubated for 24 hr at 37°C) caused a dose-dependent decline in cell viability in both NM and MM cells. The SWCNT-dependent decrease in cell viability was significant compared with control samples in both cell types (see Supplemental Material, Figure 3A). The trypan blue exclusion assay showed that exposure of both cell types to mass doses similar to those used for the MTT assay also caused decreasing cell viability with increasing doses. However, cell viability by trypan blue assay was significantly decreased only with the two higher doses of 50 and 125 μg/cm² SWCNTs (see Supplemental Material, Figure 3B). Exposure of NM and MM cells to 12.5, 25, and 50 μg/cm² crocidolite for time points similar to those for SWCNTs resulted in a dose-dependent decrease in cell viability, and the decrease was significantly greater compared with SWCNT samples over the tested doses. A concentration of 50 μg/cm² crocidolite decreased cell viability by 75–77%, whereas SWCNTs at the same concentration and exposure time decreased cell viability by only 30% and 27%, for NM and MM cells, respectively (see Supplemental Material, Figure 3C). We also obtained similar results for crocidolite in the MTT cell viability assay (data not shown).</p>", "<title>DNA damage induced by SWCNTs</title>", "<p>Because SWCNT exposure caused generation of ROS, we investigated whether raw SWCNT-induced oxidative stress resulted in DNA damage. DNA damage, investigated using a comet assay in NM and MM cells exposed to 25 or 50 μg/cm² SWCNTs or vehicle for 24 hr, showed that the SWCNTs induced DNA damage in both cell types (##FIG##1##Figure 2A##). ##FIG##1##Figure 2B## shows semiquantitative measurements of SWCNT-induced dose-dependent DNA tail migration, demonstrating significant DNA damage at both doses in NM and MM cells (##FIG##1##Figure 2B##). Exposure of NM cells to 25 or 50 μg/cm² SWCNTs for 24 hr resulted in a 5.2- and 6.6-fold increase in DNA tail length migration, respectively. In contrast, exposure of MM cells to the same mass concentrations of crocidolite for 24 hr caused an increased DNA tail migration of 7.9- and 11.1-fold, respectively. Coincubation of MM cells with SWCNTs (25 μg/cm²) and catalase (100 U/mL), SOD (100 U/mL), or deferoxamine (1 mM; an iron chelator) for 24 hr resulted in a 35%, 30%, and 32% decrease in DNA damage, respectively.</p>", "<title>H2AX phosphorylation by SWCNTs and crocidolite</title>", "<p>Exposure of NM and MM cells to 25 or 50 μg/cm² SWCNTs resulted in a nominal increase in phosphorylation of H2AX on Ser139, which was moderately higher in MM cells. The same concentrations of crocidolite induced a significantly greater phosphorylation in both cell types (##FIG##2##Figure 3##).</p>", "<title>Effects of SWCNTs and crocidolite on PARP</title>", "<p>Apoptosis is often associated with PARP cleavage, leading to the activation of caspase; therefore, we investigated the effects of exposure to SWCNTs or crocidolite in NM and MM cells. PARP, a chromatin-bound enzyme activated by DNA strand breaks, may alter the chromosomal proteins to facilitate DNA repair. Our studies with SWCNTs and crocidolite show time-dependent activation of cleaved PARP in NM cells. Crocidolite and SWCNTs induced significantly greater activation of PARP in NM cells compared with MM cells (##FIG##3##Figure 4##). MM cells showed moderate activation of cleaved PARP after 18 hr exposure to SWCNTs or to crocidolite. SWCNTs caused only a 2-fold activation after 6 hr compared with 2.8-fold activation by crocidolite (##FIG##3##Figure 4##). These results indicate that enhancement of DNA repair is significantly impaired in MM cells.</p>", "<title>Effect of SWCNT exposure on AP-1 and NF-κB activation</title>", "<p>We examined effects of SWCNT-induced ROS on the activation of redox-sensitive signaling pathways, especially the activation of two important transcription factors, AP-1 and NF-κB. AP-1 was activated in NM cells incubated with 25 μg/cm² SWCNTs in the first 1–2 hr and then declined after 4 hr (##FIG##4##Figure 5A##). On the other hand, in MM cells, the same mass concentration of SWCNTs induced a smaller early response, with an activation similar to that seen in NM cells only after 4 hr (##FIG##4##Figure 5A##).</p>", "<p>Exposure of NM and MM cells to 25 μg/cm² SWCNTs resulted in a similar response in the activation of NF-κB (##FIG##4##Figure 5B##). In NM cells, the NF-κB activation was maximal at 1–2 hr and then declined by 4 hr. In MM cells, a time-dependent peak response of NF-κB activation was achieved only after 4 hr. This delayed activation of NF-κB in MM cells was 2-fold greater than the basal level at 4 hr.</p>", "<title>Effect of SWCNT exposure on mitogen-activated protein kinase (MAPK) phosphorylation</title>", "<p>Because MAPKs are the upstream kinases responsible for c-Jun phosphorylation and AP-1 and NF-κB activation, we investigated which classes of MAPKs are activated by the SWCNTs. We examined the effects of SWCNTs on phosphorylation of extracellular signal-regulated kinase 1/2 (ERK1/2), Jun <italic>N</italic>-terminal kinases (JNKs), and protein p38 kinase in NM and MM cells. Treatment of MM cells with SWCNTs led to increased phosphorylation of ERKs and p38 (##FIG##5##Figure 6A##) but not JNKs (data not shown). Alterations in the phosphorylation of ERKs and p38 in NM cells were very minimal and occurred only at the 15-min time point, where both p38 and phosphorylated ERK1/2 (p-ERK1/2) showed significant phosphorylation in MM cells compared with NM cells (##FIG##5##Figure 6B,C##). The studies using Western blot and densitometry clearly demonstrate the significant difference in response of MM cells at 60 and 120 min (##FIG##5##Figure 6B,C##). These results indicate that activation of AP-1 and NF-κB by SWCNTs may be mediated through the induction of ERKs or p38 signaling in NM and MM cells.</p>", "<title>Effect of SWCNTs on Akt</title>", "<p>Involvement of Akt, a signal transduction protein regulated by downstream signaling by phosphoinositide 3-kinase (PI-3K), is reported to play a major role in lung tumor and mesothelioma genesis. Because of this important role of Akt in tumorogenesis, we examined the association between SWCNT exposure and activation of Akt in NM and MM cells. Western blot analysis of Akt (Thr308) after 30 and 60 min of exposure of NM and MM cells to SWCNTs induced activation of phosphorylated Akt (p-Akt) only in MM cells to the level slightly lower than the positive control, epidermal growth factor (EGF) (40 ng/mL) (##FIG##6##Figure 7A##). This activation was dose and time dependent at 30 min of SWCNT exposure and then remained at the same level after 60 min (##FIG##6##Figure 7A##). Densitometric analysis indicates a 1.6-fold increase by 125 μg/cm² SWCNTs compared with 1.7-fold increase by EGF at 60 min in MM cells (##FIG##6##Figure 7B##). In NM cells, p-Akt was not expressed after exposure to the same or higher concentrations of SWCNT.</p>" ]

[ "<title>Discussion</title>", "<p>Several studies have shown that occupational and environmental exposures to particulate matter with mean diameters of &lt; 10 μm or nanoparticles with a size dimension &lt; 100 nm are associated with respiratory diseases, including cancer (##REF##15027112##Knaapen et al. 2004##). The present study focused on raw SWCNTs, which represent one of the most widely investigated nanoparticles with enormous potential for industrial, technologic, and medical applications. SWCNTs have been shown to translocate to subpleural areas in the lung, and therefore may have the potential to cross the cell membrane to the mesothelial layer (##REF##18024722##Mercer et al. 2008##). Although SWCNTs have been the subject of extensive research over the last few years, their potential interactions with human mesothelial cells have not been reported. Because they behave like asbestos, with biopersistence and ability to generate ROS, the potential human health impacts and risks compel us to understand the toxic and molecular interactions reported here. These studies are further justified by a recent report on the induction of mesothelioma in p53^+/−mice by multiwall carbon nanotubes (##REF##18303189##Takagi et al. 2008##). The data presented in the present study provide basic information regarding the potential health hazard of SWCNTs and support the bioactivity of SWCNTs on mesothelial cells <italic>in vitro</italic>, although with lower levels of activity compared with crocidolite.</p>", "<p>Asbestos fibers have high aspect ratios, are biopersistent, and contain high concentrations of iron with the potential to generate ROS. These characteristic fiber features, combined with the ability to translocate to mesothelium, are well documented as major contributing factors triggering the development of mesothelioma by asbestos, leading to the activation of cell signaling pathways, early response genes, and carcinogenesis (##REF##11811924##Manning et al. 2002##). Because raw SWCNTs used in this study also have a high aspect ratio, have high metal contamination (nickel, yttrium, iron), are biopersistent, and are reported to translocate to subpleural areas in the lungs, the present investigation is warranted. SWCNT exposures to animals indicate that SWCNTs are not well recognized by macrophages and that the pulmonary inflammatory response to SWCNTs is not persistent, yet progressive interstitial fibrotic response has been noted (##REF##16527436##Kagan et al. 2006##; ##REF##14514958##Lam et al. 2004##; ##REF##15951334##Shvedova et al. 2005##). ##REF##18024722##Mercer et al. (2008)## reported deposition of labeled SWCNTs to the distal alveolar interstitium including subpleural areas and mesothelium. Therefore, transport of SWCNTs from the distal airspaces to the pleura and/or extrapulmonary locations, including mesothelium, is a possibility. The present study indicates that SWCNTs are toxic to NM and MM cells. However, the degree of toxicity of SWCNTs was one-third that of crocidolite in NM and MM cells [see Supplemental Material, Figure 3B,C (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link>)].</p>", "<p>Oxidative molecular mechanisms triggered by the persistent ability of asbestos fibers to cause injury to the mesothelial cells have been reported to be features involved in asbestos-fiber–induced mesothelioma development (##REF##8386370##Heintz et al. 1993##). Cellular reactions observed in animal models, mesothelial cell lines, and patients with crocidolite-induced mesothelioma are reported to be similar (##REF##16166281##Altomare et al. 2005a##; ##REF##16920675##Kane 2006##; ##REF##16795078##Ramos-Nino et al. 2006##; ##REF##12151629##Vaslet et al. 2002##). Therefore, recapitulation of biochemical and molecular events observed in human mesothelial cells exposed to raw SWCNTs reported in the present study may provide a functional basis to explore the potential of SWCNTs to induce mesothelioma in animal models.</p>", "<p>The ability of engineered nanomaterials to interact with biological tissues and generate ROS has been proposed as possible mechanisms involved in the toxicity (##REF##16456071##Nel et al. 2006##). ROS are well known to play both a deleterious and a beneficial role in biological interactions. Oxidative damage due to ROS results in damage to DNA, proteins, and lipids and in the activation of cell signaling pathways that are associated with loss of cell growth regulation, leading to carcinogenesis (##REF##16430879##Valko et al. 2006##). In the present study, raw SWCNTs upon interaction with NM and MM cells induced the formation of ROS, as demonstrated by ESR as well as <italic>in situ</italic> localization. The level of raw SWCNT-dependent •OH radicals generated was approximately 1.6-fold higher in NM cells than in MM cells. <italic>In situ</italic> localization of ROS confirmed the ESR data and provided parallel results indicating greater generation of ROS in NM cells by the interaction of both SWCNTs and crocidolite. The increased generation of ROS caused by exposure to particles has been shown for many different forms of fine, ultrafine, and nanoscale particles, including SWCNTs, to be associated with minimal metal contamination (##REF##17663266##Sharma et al. 2007##; ##REF##14514433##Shvedova et al. 2003##).</p>", "<p>Nanoparticles, because of their physical and chemical properties, are unique compared with known fine-sized parent compounds that behave differently in toxicity and DNA damage (Knaappen et al. 2004). Also, the mechanism of particle-induced DNA damage could be direct or indirect and is not fully understood. Genotoxic effects may be produced either by direct interaction of particles with genetic material or by secondary damage from particle-induced ROS. Biopersistence of particles and the potential to translocate through the lung to the mesothelium is a major contributing factor involved in sustained ROS generation and DNA damage of mesothelial cells. In the present study, addition of ROS scavengers resulted in a moderate reduction of the extent of DNA damage and not in a complete abrogation of the damage. These results suggest that genotoxic effects of SWCNTs occur in part through direct damage to DNA and not solely through oxidative stress. In addition, asbestos-like fibrous characteristics of SWCNTs are likely to contribute to the mechanisms involved in a sustained level of ROS generation inducing DNA damage. In support of this potential of SWCNTs to generate ROS, ##REF##18049996##Kisin et al. (2007)## demonstrated that exposure of lung fibroblast V79 to acid-purified SWCNTs also resulted in DNA damage. The present results and the existing literature therefore suggest that the genotoxic effects of SWCNTs result from a combination of direct effects of crocidolite-like behavior and the potential of metal catalysts associated with SWCNTs to induce oxidative stress on a sustained basis. However, the exact genotoxic mechanism of SWCNTs <italic>in vitro</italic> remains to be elucidated. ROS in the absence of anti-oxidant protection can directly interact or modify cellular proteins, lipids, and DNA, which in turn may alter cellular functions and predispose the cells to impaired apoptosis and abnormal cell growth. Continued oxidative stress induced by these mechanisms may disrupt DNA repair, cause mutations, and change growth patterns and gene expression. These events are well documented in animals exposed by intraperitoneal injections of crocidolite and have been linked to the development of malignant mesothelioma (##REF##9437800##Goodglick et al. 1997##; ##REF##12151629##Vaslet et al. 2002##). Although ROS have been linked to the development of mesothelioma, the exact mechanisms by which mesothelial cells are transformed to malignant cells by asbestos are not fully understood. Animal and cellular studies show that asbestos fibers induce mesothelioma by direct interaction of the fibers with the mesothelial cells and the generation of ROS, which in turn promotes signaling and activation of cascades of events that may finally induce cancer.</p>", "<p>H2AX phosphorylation is a very rapid and sensitive response to DNA damage and occurs within a short time after exposure to ionizing radiation and environmental stress (##REF##11893489##Redon et al. 2002##). As a result of the DNA double-strand breaks, the histone H2AX protein can be distinguished from other histones by a unique carboxy-terminal sequence that is rapidly phosphorylated at the fourth residue (Ser139) in response to DNA damage (##REF##9488723##Rogakou et al. 1998##). H2AX phosphorylation occurs rapidly irrespective of the type of DNA damage, resulting in the phosphorylation of thousands of H2AX molecules (##REF##12897845##Pilch et al. 2003##). The detection of H2AX phosphorylation and the induction of double-strand breaks induced by SWCNTs and crocidolite complement other molecular evidence supporting potential carcinogenic activity. However, H2AX phosphorylation by SWCNTs was less than that observed with crocidolite exposure.</p>", "<p>In this study, we observed a dramatic time-dependent increase in cleaved PARP after exposure to SWCNTs and crocidolite at 6 and 18 hr in NM cells. This high activation of PARP can lead to depletion of ATP and nicotinamide adenine dinucleotide levels and cell death in NM cells. Similar concentrations of SWCNTs or crocidolite in MM cells caused relatively lesser activation of cleaved PARP at 6 and 18 hr. The enzymatic activation of PARP is induced when DNA damage occurs and PARP protein is cleaved during apoptosis to signal the repair pathways that contribute to posttranslational modification of histones and nuclear proteins (##REF##15279798##Huber et al. 2004##). The higher levels of ROS seen by ESR and <italic>in situ</italic> localization, as well as the increased activation of PARP in NM cells by SWCNTs, support the potential to induce transformation of NM cells.</p>", "<p>A growing body of evidence suggests that EGF, platelet-derived growth factor, and the insulin signal transduction pathway mediated by PI-3Ks play important roles in the activation of Akt. PI-3K/Akt has been shown to be associated with carcinogenesis, and this signal pathway is important in cell survival and proliferation (##REF##11882383##Nicholson and Anderson 2002##). The PI-3K phosphorylated membrane phospholipid products induce the activation of Akt. Akt-1 is an important modulator of insulin signaling and cell proliferation, and Akt-2 plays a major role in cell survival (##REF##16982699##Heron-Milhavet et al. 2006##). In malignant mesothelioma cells and many other types of human cancers, Akt is constitutively activated and is reported to play important roles in the development and aggressiveness of mesothelioma (##REF##15897870##Altomare et al. 2005b##; ##REF##11882383##Nicholson and Anderson 2002##). Akt is also a redox-sensitive target for oxidant and growth factor stimulation, such as hepatocyte growth factor and its receptor tyrosine kinase, c-Met, which are highly expressed in mesotheliomas through the activation of PI-3K/Akt pathway (##REF##17872495##Ramos-Nino et al. 2008##). Akt activation may also be involved with the functional changes in tumor suppressor genes involved in the pathogenesis of mesothelioma. Elevated levels of Akt trigger antiapoptotic events and activation of NF-κB, angiogenesis, telomerase activity, and tumor metastasis. Current evidence also suggests that growth-promoting genes, such as c-<italic>fos</italic> and c-<italic>jun</italic>, are activated by these signaling mechanisms that are involved in the development of mesothelioma (##REF##8386370##Heintz et al. 1993##).</p>", "<p>Induction of oxidative stress by raw SWCNTs containing redox active iron has been reported in human keratinocyte (HaCaT) cells (##REF##14514433##Shvedova et al. 2003##). This oxidative assault to cells could lead to cytotoxic responses or even cell death via an apoptotic pathway or by necrosis (##REF##15601574##Higuchi 2004##). ROS are involved in activation of AP-1 and NF-κB, and both transcription factors play an important role in carcinogenesis (##REF##11096084##Ding et al. 2001##). AP-1 is one of the transcription factors involved in the oxidative stress response after changes in cellular oxidative status. In this respect, AP-1 has been identified as a target of the MAPK family, including ERKs, JNKs, and p38 kinase (##REF##9069263##Karin et al. 1997##). In addition, the activation of NF-κB has been shown to be regulated by some upstream MAPKs that regulate JNK activation in the cells (##REF##11096084##Ding et al. 2001##).</p>", "<p>In summary, the cellular studies reported here clearly demonstrate that NM cells are more susceptible to raw SWCNT-induced injury. Exposure of mesothelial cells to raw SWCNTs resulted in the generation of •OH, leading to several molecular alterations, activation of important signaling pathways, and transcription factors. Such responses are similar to reported asbestos-induced changes common in animal and human mesothelioma development. In this study, we found that <italic>in vitro</italic> exposure of NM and MM cells to SWCNTs altered molecular pathways associated with carcinogenesis. However, uncertainty lingers as to whether SWCNT exposure is a risk for mesothelioma development in humans. To address this question, <italic>in vivo</italic> animal studies are warranted. In human mesotheliomas, deletions of the <italic>Cdkn2a/Arf</italic> and <italic>Cdkn2b</italic> gene loci associated with hypermethylation are reported to be common at the <italic>NF2</italic> gene locus (##REF##16920675##Kane 2006##). Because heterozygous <italic>Nf2</italic>+/− mice exposed to crocidolite develop malignant mesothelioma at a faster rate than wild-type littermates, this mouse model could be used as an ideal and relatively rapid animal model to study the potential of SWCNTs to cause mesothelioma.</p>" ]

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[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Single-wall carbon nanotubes (SWCNTs), with their unique physicochemical and mechanical properties, have many potential new applications in medicine and industry. There has been great concern subsequent to preliminary investigations of the toxicity, biopersistence, pathogenicity, and ability of SWCNTs to translocate to subpleural areas. These results compel studies of potential interactions of SWCNTs with mesothelial cells.</p>", "<title>Objective</title>", "<p>Exposure to asbestos is the primary cause of malignant mesothelioma in 80–90% of individuals who develop the disease. Because the mesothelial cells are the primary target cells of asbestos-induced molecular changes mediated through an oxidant-linked mechanism, we used normal mesothelial and malignant mesothelial cells to investigate alterations in molecular signaling in response to a commercially manufactured SWCNT.</p>", "<title>Methods</title>", "<p>In the present study, we exposed mesothelial cells to SWCNTs and investigated reactive oxygen species (ROS) generation, cell viability, DNA damage, histone H2AX phosphorylation, activation of poly(ADP-ribose) polymerase 1 (PARP-1), stimulation of extracellular signal-regulated kinase (ERKs), Jun <italic>N</italic>-terminal kinases (JNKs), protein p38, and activation of activator protein-1 (AP-1), nuclear factor κB (NF-κB), and protein serine-threonine kinase (Akt).</p>", "<title>Results</title>", "<p>Exposure to SWCNTs induced ROS generation, increased cell death, enhanced DNA damage and H2AX phosphorylation, and activated PARP, AP-1, NF-κB, p38, and Akt in a dose-dependent manner. These events recapitulate some of the key molecular events involved in mesothelioma development associated with asbestos exposure.</p>", "<title>Conclusions</title>", "<p>The cellular and molecular findings reported here do suggest that SWCNTs can cause potentially adverse cellular responses in mesothelial cells through activation of molecular signaling associated with oxidative stress, which is of sufficient significance to warrant <italic>in vivo</italic> animal exposure studies.</p>" ]

[ "<p>By 2015, the worldwide market for products with nanotechnology components will reach an estimated $1 trillion (##UREF##1##Roco 2005##). The unique behavior and properties of nanoscale materials have revolutionized technology, producing an estimated 1,300 materials either in use or being tested for potential commercial applications. Enhanced physical and chemical properties associated with the nanosize of these materials have been exploited to produce a wide variety of new products. In addition, nanoparticles are being explored for several treatment modalities, including early detection of tumors and other clinical applications (##REF##17185269##Gwinn and Vallyathan 2006##). With these applications come unprecedented avenues of human exposure to nanomaterials. Engineered single-wall carbon nanotubes (SWCNTs) are a class of nanoparticles being actively evaluated for myriad industrial and biomedical applications (##UREF##0##Dresselhaus et al. 2004##). Exponential growth in the use of SWCNTs potentially can cause exposure to a large number of workers (##REF##17041243##Maynard 2007##).</p>", "<p>SWCNTs have been reported to have many adverse cellular and animal toxicity reactions, which may be predictive of detrimental human health effects upon exposures (##REF##14514958##Lam et al. 2004##; ##REF##15951334##Shvedova et al. 2005##). The likely widespread industrial application of SWCNTs in several consumer products and medical applications may pose an emerging human health concern (##REF##16484287##Donaldson et al. 2006##; ##REF##17041243##Maynard 2007##). It has been suggested that inhaled SWCNTs and other nanoparticles are likely to evade phagocytosis, penetrate lung tissue, and translocate to other organs to cause systemic cell toxicity and injury (##REF##17185269##Gwinn and Vallyathan 2006##; ##REF##16002369##Oberdörster et al. 2005##). Therefore, toxicity studies of nanoparticles should not be limited to a single lung cell or only to the lung, but should involve other systemic targets.</p>", "<p>Preliminary cellular and animal exposure investigations on toxicity and pathogenicity of SWCNTs have demonstrated biological interactions, including toxicity, inflammatory reactions, oxidative stress, and fibroproliferative response (##REF##14514958##Lam et al. 2004##; ##REF##18024722##Mercer et al. 2008##; ##REF##15951334##Shvedova et al. 2005##). SWCNTs are biopersistent and have the ability to distribute to subpleural areas after pharyngeal aspiration (##REF##18024722##Mercer et al. 2008##). These earlier investigations compelled the present studies of potential interactions of SWCNTs with mesothelial cells.</p>", "<p>Epidemiologic, animal, and cellular studies indicate that exposure to crocidolite asbestos (crocidolite) can cause pulmonary fibrosis, lung cancer, and malignant mesothelioma (##REF##11811924##Manning et al. 2002##). Data indicate that mesothelioma in 80–90% of individuals is associated with crocidolite as the primary cause. Because mesothelial cells are the primary target cells of asbestos-induced molecular changes mediated through an oxidant-linked mechanism, we used well-characterized SWCNTs with a known concentration of metal catalyst contamination to investigate the alterations in molecular signaling in response to SWCNT exposure in normal mesothelial (NM) and malignant mesothelial (MM) cells. We used this form of SWCNTs because SWCNTs with different redox-sensitive iron contents have displayed diverse redox potentials, with iron-rich SWCNTs causing a significant loss of glutathione and increased lipid peroxidation in alveolar macrophages (##REF##16527436##Kagan et al. 2006##).</p>", "<p>In this study we examined the toxicity and alterations in molecular signaling pathways in mesothelial cells exposed to raw SWCNTs with significant metal contamination. We compared some results with known effects of crocidolite in cultured mesothelial cells.</p>" ]

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[ "<fig id=\"f1-ehp-116-1211\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Confocal micrographs of O₂^•− and H₂O₂ generation in intact NM cells (top) and MM cells (bottom) treated with SWCNTs and crocidolite. a, control; b and c, SWCNT; d and e, crocidolite; b and d, DHE staining; c and e, H₂DCFDA staining. Red, localization of O₂^•−; green, localization of H₂O_2; blue, diamidinophenylindole. Bars = 20 μm.</p></caption></fig>", "<fig id=\"f2-ehp-116-1211\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Effect of SWCNTs (25 or 50 μg/cm²) on DNA migration in NM and MM cells using the comet assay. (<italic>A</italic>) Micrographs of NM (a,b) and MM (c,d) cells treated with vehicle (control; a,c) or 50 μg/cm² SWCNTs (b,d) for 24 hr: (<italic>B</italic>) Semiquantitative analysis of concentration-dependent effects of SWCNTs on DNA migration in NM and MM cells using the comet assay. Data are presented as the mean ± SE of three experiments. Bars = 25 μm</p><p>*Significant increase from control (<italic>p</italic> &lt; 0.05).</p></caption></fig>", "<fig id=\"f3-ehp-116-1211\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Effect of 24-hr exposure to SWCNTs or crocidolite (25 or 50 μg/mL) on the activation of γ-H2AX (Ser139) in NM and MM cells. Absorbance values were normalized to control values; data are presented as mean ± SE of three experiments</p><p>*Significantly different from control (<italic>p</italic> ≤0.05).</p></caption></fig>", "<fig id=\"f4-ehp-116-1211\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Effects of SWCNTs and crocidolite on the activation of PARP in NM and MM cells. (<italic>A</italic>) Cells were exposed to 50 μg/cm² SWCNTs or crocidolite for 6 or 18 hr and then examined by Western blot analysis for cleaved PARP (each blot is from one representative experiment per treatment). Detection of full PARP and β-actin of the same membrane ensured equal sample loading. (<italic>B</italic>) Results of densitometric analysis of PARP activation to total PARP; the fold activation is relative to normalized values of unstimulated control specimens. Data shown are mean ± SE of three experiments</p><p>*Significantly different from control (<italic>p</italic> ≤ 0.05).</p></caption></fig>", "<fig id=\"f5-ehp-116-1211\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>ELISA assay results showing the effect of 1-, 2-, or 4-hr treatment with 25 μg/cm² SWCNTs on the activation of AP-1 (<italic>A</italic>) and NF-κB (<italic>B</italic>) in NM and MM cells. Data shown are the mean ± SE of three experiments</p><p>*Significant increase from controls (<italic>p</italic> &lt; 0.05).</p></caption></fig>", "<fig id=\"f6-ehp-116-1211\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Effect of SWCNTs on the activation of MAPKs in NM and MM cells. Abbreviations: p-p38, phosphorylated p38. (<italic>A</italic>) Western blot analysis of NM and MM cells treated with vehicle or 25 μg/cm² SWCNTs for 5–120 min showing phosphorylated and nonphosphorylated ERK1/2 and p38. (<italic>B</italic>) Densitometric analysis of Western blots of p-ERK1/2 signal normalized to total ERK1/2. (<italic>C</italic>) Densitometric analysis of Western blots of p-p38 signal normalized to total p38. Fold activations are relative to normalized values of unstimulated cells; data shown are mean ± SE of three experiments.</p></caption></fig>", "<fig id=\"f7-ehp-116-1211\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Activation of p-Akt (Thr308) induced by SWCNTs. Abbreviations: C, control; PC, positive control (EGF, 40 ng/mL). (<italic>A</italic>) Western blot analysis of Akt in MM cells treated with 25, 75, or 125 μg/cm² SWCNTs for 30 or 60 min in medium containing 0.1% FBS; detection of total Akt of the same membrane was used to ensure equal sample loading per lane. (<italic>B</italic>) Densitometric analysis of Western blots showing p-Akt signal normalized to total Akt (t-Akt). The fold activation is relative to normalized values of unstimulated control specimens; data shown are mean ± SE of four experiments.</p></caption></fig>" ]

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[ "<fn-group><fn><p>Supplemental Material is available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10924/suppl.pdf\">http://www.ehponline.org/members/2008/10924/suppl.pdf</ext-link></p></fn><fn><p>We thank M. Postek (Precision Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD) for providing the single-wall carbon nanotube samples used in this study.</p></fn><fn><p>This study was supported by the National Institute for Occupational Safety and Health.</p></fn><fn><p>The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health.</p></fn></fn-group>" ]

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[ "<title>Materials and Methods</title>", "<title>Animals</title>", "<p>We maintained male and female breeding C57BL/6, ApoE^−/−, and CD4^−/−mice, purchased from Jackson Laboratories (Bar Harbor, ME), by brother/sister matings at UVM. We used the B6.129S2-Cd4^tm1Mak strain of CD4^−/− mice, the same background as the ApoE^−/− mice used in these experiments. Female offspring were used in our experiments. Double knockout (DKO) animals (ApoE^−/− and CD4^−/−) were generated by producing F₂ generation mice between ApoE^−/− and CD4^−/− and using polymerase chain reaction (PCR) to select mice that were homozygous for both gene knockouts. We then maintained homozygous ApoE^−/−/CD4^−/−DKO mice by brother/sister matings. Animals homozygous for both genes were used in the experiments. The DKO mice were selected in the F₂ offspring by genotyping using PCR protocols and were backcrossed into the C57BL/6 background for more than eight generations. We maintained both strains as colonies at UVM and fed them normal chow. All procedures were approved by the UVM Institutional Committee on Use and Care of Animals. We treated mice humanely and with regard for the attenuation of suffering. We weighed all animals after anesthesia and found no differences between groups and treatments (data not shown).</p>", "<title>Inhalation procedures and sample collection</title>", "<p>ApoE^−/− or DKO mice at 4–6 weeks of age were exposed to chrysotile asbestos [Mg₃Si₂O₅(OH)₄; National Institute of Environmental Health Sciences (NIEHS) reference sample at approximately 5 mg/m³ air; range, 4.7–5.7 mg/m³ air] for 6 hr/day, 5 days/week. This concentration is equivalent historically to concentrations of chrysotile asbestos in unregulated workplaces and levels that caused lung diseases, and in air during the World Trade Center disaster (##UREF##0##CDC 2003##). The size dimensions (mean aerodynamic diameter, 0.34 μm) of aerosolized NIEHS chrysotile asbestos have been reported previously (##REF##8607143##BéruBé et al. 1996##).</p>", "<p>We conducted whole-body inhalation exposures within our inhalation facility (accredited by the Association for Assessment of Laboratory Animal Care) as described previously (##REF##16251409##Sabo-Attwood et al. 2005##). We exposed control animals (sham groups) to clean, ambient air. We studied younger animals with early lesion development to optimize the likelihood of detecting differences in lesion size as a consequence of exposure. If we had used older animals with larger lesions, lengths of exposure to asbestos fibers may not have elicited easily detectable differences in lesion size.</p>", "<p>Because the schedule of exposure was 5 days/week, simulating a workplace setting, 30 days of exposure is equivalent to 6 weeks. We used shorter exposures to determine whether differences in early responses to asbestos might reveal potential mechanisms responsible for triggering differential lesion sizes at the end of 30 days of exposure, a time point previously shown to be associated with lung fibrogenesis (##REF##16251409##Sabo-Attwood et al. 2005##).</p>", "<p>We used fine TiO₂ (0.2–2.5 μm diameter; Fisher Scientific, Pittsburgh, PA) as a non-pathogenic particle control at surface area concentrations approximately equal to those of asbestos (~ 28 mg/m³ air) in some of the experiments using ApoE^−/− mice. After 3, 9, or 30 days of exposure, we collected bronchoalveolar lavage fluid (BALF) and blood, aorta, and lung tissues and prepared them for analyses. We performed surgical procedures after injecting the mice intraperitoneally with sodium pentobarbital. We collected blood via cardiac puncture into tubes with sodium EDTA as the anticoagulant, and saved the plasma for chemokine/cytokine assays. Tracheas of mice were cannulated with polyethylene tubing, and the lungs lavaged with sterile calcium- and magnesium-free phosphate-buffered saline (PBS) in a total volume of 1 mL.</p>", "<p>Mouse lung, heart, and aorta were dissected and immersion fixed overnight in 3% paraformaldehyde/PBS at 4°C as previously described (##REF##10817670##Taatjes et al. 2000##; ##REF##12122448##Wadsworth et al. 2002##). During fixation, we bisected the hearts with a cut parallel to both atria. The hearts and lungs were immersed in optimal cutting temperature compound (OCT; Tissue-Tek, Torrance, CA) in labeled embedding molds (hearts were oriented cut side up so that the first sections taken would reveal the sinus area), snap-frozen in liquid-nitrogen–cooled 2-methyl butane, and stored at −80°C until the time of cryostat sectioning, as previously described (##REF##3426656##Paigen et al. 1987##).</p>", "<p>We defined the area for sectioning by three prominent valve cusps at the juncture of the aortic sinus region to the end of the valve region, when the valves disappeared and the aorta became more rounded in appearance.The sections on the slides were air-dried for 30 min to ensure proper adhesion before being stored in a slide box at −80°C. <italic>En face</italic> preparations were not done in these experiments because the thoracic and abdominal aortas were used for nuclear and cytoplasmic protein extraction for analyses of transcription factors.</p>", "<title>Aortic lesion quantitation</title>", "<p>We examined oil red O–stained sections (Pearse method) using an Olympus BX50 upright light microscope (Olympus America, Inc., Lake Success, NY) with an attached Optronics MagnaFire digital camera (Optical Analysis Corp., Nashua, NH). The sections were imaged with a 4 × objective lens, and we used MagnaFire software (version 2.0) to capture 1,280 × 1,024 pixel RGB digital images. We performed computer-assisted image analysis using MetaMorph software (Universal Imaging Corp., Downingtown, PA) essentially according to previously published protocols (##REF##12122448##Wadsworth et al. 2002##). We opened cropped digital images and set the appropriate (precalibrated) objective calibration by choosing “Measure/Calibrate Distance/Apply.” This allowed area measurements to be expressed in calibrated square micrometer values; pixel values were displayed at the bottom of the screen. The images were then assigned threshold values for pixel measurements. Once this was done, the “Integrated Morphometry” feature measured the thresholded area and logged the area values onto a Microsoft Excel spreadsheet (Microsoft Corp., Redmond, WA). The logged values were then converted into an Excel chart for presentation. Animal comparisons were calculated and expressed as area values.</p>", "<title>Fiber deposition studies in lung and aorta</title>", "<p>To compare the fiber burden in lung with that in aorta, we digested the left lobes and aortas of two control and two asbestos-exposed mice in hypochlorite, a process that does not affect fiber integrity, as previously described (##REF##8607143##BéruBé et al. 1996##). We transferred the digest to Nuclepore filters and examined them by scanning electron microscopy and X-ray energy-dispersive spectroscopy at 5,000× magnification. We evaluated 10 random fields per filter.</p>", "<title>Cholesterol measurement</title>", "<p>We measured total cholesterol in mouse plasma in the early experiments using a standard commercial cholesterol esterase enzymatic assay (Cayman Chemical Co., Ann Arbor, MI). No differences in cholesterol levels were found between ApoE^−/− and DKO mice or in the different treatment groups.</p>", "<title>Lung histopathology for inflammation and fibrosis</title>", "<p>To determine whether the extent of lung inflammation and fibrosis in lungs correlated with the extent of atherogenesis, lungs were inflated after collection of BALF with a 1:1 mixture of OCT and PBS and fixed in 4% paraformaldehyde. We stained paraffin sections of lung (5 μm thickness) with hematoxylin and eosin (H&amp;E) to quantify inflammation or Masson’s trichrome technique for detection of collagen (##REF##16251409##Sabo-Attwood et al. 2005##). A board-certified pathologist (K.J.B.) evaluated sections using a blind coding system, as previously described (##UREF##1##Craighead 1982##; ##REF##17237430##Haegens et al. 2007##).</p>", "<p>We scored inflammation on a scale from 1 to 4: 1, no inflammation; 2, mild inflammation that was rarely peribronchiolar and consisted primarily of lymphocytes; 3, moderate inflammation with peribronchiolar neutrophils, eosinophils, lymphocytes, and abundant macrophages; and 4, severe inflammation with peribronchiolar neutrophils, eosinophils, and lymphocytes that extend to involve adjacent alveolar septa and abundant bronchiolar and intraalveolar macrophages. We also scored fibrosis on a scale from 1 to 4: 1, no fibrosis; 2, focal fibrosis; 3, moderate fibrosis; 4, severe fibrosis.</p>", "<title>BALF differential cell counts</title>", "<p>We enumerated total cells in BALF and centrifuged 2 × 10⁴ cells onto glass slides at 600 rpm. We stained cytospins using the Hema3 kit (Biochemical Sciences, Swedesboro, NJ), and performed differential cell counts on 500 cells/mouse (##REF##17237430##Haegens et al. 2007##).</p>", "<title>Cytokine concentrations in BALF and plasma</title>", "<p>To determine whether altered cytokine profiles occurred in the lung and the systemic circulation, we initially measured cytokines using an enzyme-linked immunosorbent assay (ELISA). We used commercial kits for interleukin-6 (IL-6), MCP-1, macrophage inflammatory protein-2 (MIP-2), and interferon-γ (IFN-γ) (Endogen, Woburn, MA) according to the manufacturer’s directions. Briefly, we aliquoted 2-fold dilutions of plasma or BALF into 96-well microplates coated with antibody to the indicated cytokines for 2 hr at 25°C. Plates were then washed, incubated with biotin-conjugated secondary antibody, washed, incubated with streptavidin–horseradish peroxidase conjugate, washed again, and incubated with 3,3′,5,5′-tetramethylbenzidine substrate. The reaction was halted by adding the stop solution in the plate reader at 450 nm and analyzed using a Biotech EL808 ELISA Reader (BioTek, Inc., Winooski, VT). We determined cytokine concentrations from a standard curve using reference standards supplied in the kit.</p>", "<p>In subsequent studies, we also measured cytokine and chemokine levels in BALF using the Bio-Plex Protein Array System and a mouse cytokine 22-plex panel (Bio-Rad, Hercules, CA), as previously described (##REF##16251409##Sabo-Attwood et al. 2005##). This method of analysis is based on Luminex technology and simultaneously measures IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-17, tumor necrosis factor-α (TNF-α), regulated in activation, normal T expressed and secreted (RANTES; CCL5), MIP-1α, MIP-1β, MCP-1, CXCR2 ligand KC/GRO-α (KC), granulocytecolony–stimulating factor (G-CSF), granulocyte/monocyte-colony–stimulating factor (GM-CSF), IFN-γ, and eotaxin protein. We determined concentrations of each cytokine and chemokine using Bio-Plex Manager software, Version 3.0 (Bio-Rad).</p>", "<title>Electrophoretic mobility shift assay (EMSA) and Western blot analyses for AP-1 and NF-κB in aortas</title>", "<p>We used the EMSA to determine whether inhaled particulates affected the DNA binding of oxidant-associated transcription factors in aortic tissue. Isolated aorta was minced using sterile scissors and then homogenized in a prechilled homogenizer. Nuclear proteins were extracted as previously described (##REF##9250152##Janssen et al. 1997##; ##REF##2771659##Schreiber et al. 1989##). Briefly, after homogenization, the cells were lysed in hypotonic buffer and 0.6% Nonidet P-40 (Sigma, St. Louis, MO). After centrifugation, the supernatant containing cytoplasmic proteins was collected and stored at −80°C for later analysis. Nuclear proteins were then extracted from the pelleted nuclei (##REF##9250152##Janssen et al. 1997##; ##REF##2771659##Schreiber et al. 1989##).</p>", "<p>Protein concentrations were measured using the Bio-rad Protein Assay (Bio-Rad). The AP-1 and NF-κB probes containing the consensus sequences of AP-1 and NF-κB binding sites, respectively, were synthesized commercially (Promega, Madison, WI). The oligos were then labeled with γ-³²P-ATP (NEN Life Science, Boston, MA) by T₄ polynucleotide kinase (GIBCO-BRL, Gaithersburg, MD). We incubated 20 μL of each binding mixture, composed of 7 μg nuclear protein in binding buffer with 40 mM HEPES (pH 7.8), 4% Ficoll 400, 200 μg/mL poly(dI:dC) [poly(deoxy-inosinic:deoxycytidylic acid); Amersham Pharmacia, Piscataway, NJ], 1 mM MgCl₂, 0.1 mM dithiothreitol, and 1 μL of labeled probe (0.02 pmol), at room temperature for 15 min and then loaded it onto a 5% sodium dodecyl sulfate (SDS) polyacrylamide gel (nondenaturing), as previously described (##REF##9250152##Janssen et al. 1997##; ##UREF##3##Mossman et al. 2000##). The gel was run in 0.25 × Tris/borate/EDTA buffer at 120 V for 2.5 hr, then dried and exposed to X-ray film overnight at −80°C. The autoradiographic films were scanned by densitometry and analyzed with Quantity One Software, Version 4.2 (Bio-Rad).</p>", "<p>We used cytoplasmic protein extracted from the aortas during the preparation of nuclear extracts for Western blot analysis to confirm the DNA binding of NF-κB by using an antibody to phosphorylated Kappa B inhibitor (IκB; Cell Signaling Technology, Inc., Danvers, MA). Briefly, 20 μg protein from each sample was electrophoresed on a 10% SDS-PAGE and electroblotted onto a nitrocellulose membrane, which was then incubated with the antibody overnight with shaking at 4°C. After incubation, the protein bands were visualized using a SuperSignal West Pico Trial Kit (Pierce, Rockford, IL) and exposed to radiographic film. The blots were reprobed with a β-actin antibody (Abcam, Cambridge, MA) to detect cytoplasmic β-actin as the loading control. The images and densities were captured with a GS-700 Imaging Densitometer (Bio-Rad), analyzed with Quantity One Software, and presented as the ratio of IκB to β-actin.</p>", "<title>Statistics</title>", "<p>We used Student’s two-sample <italic>t</italic>-tests to compare groups when the data were normally distributed. We used the Mann-Whitney test to compare groups using transformed data to stabilize the variance or when results were not normally distributed. Two-way analysis of variance (ANOVA) was used to analyze transformed cytokine/chemokine data for each time point, with post hoc analysis to determine genotype effects within treatment or treatment effects within genotype. We used chi-square analysis to compare the histologic scores for fibrosis and inflammation. Nonparametric Kendall tau correlation coefficients were derived for the relationships between lesion size and MCP-1 concentrations in ApoE^−/− mice. All analyses were performed using SPSS, version 14, and data are presented as mean ± SE.</p>" ]

[ "<title>Results</title>", "<title>Aortic responses</title>", "<p>We examined the effect of inhaled chrysotile asbestos fibers on the development of atherosclerotic lesions in three separate but identical inhalation experiments (<italic>n</italic> = 6–10 mice per group per time point per experiment). We observed no detectable differences in lesion size after 3- or 9-day exposures to asbestos. However, as determined by lipid staining with oil red O, lesions in the region of the aortic sinus to the aortic valve in 30-day asbestos-exposed ApoE^−/− mice, were approximately three times larger (<italic>p</italic> &lt; 0.001 by ANOVA) than lesions in control, TiO₂-exposed ApoE^−/− mice or in asbestos-exposed DKO animals (##FIG##0##Figure 1##). Fiber analyses showed only one or two small fibers in two fields of an aorta preparation from a clean-air control animal, which we concluded was likely caused by contamination from the previous processing of a lung sample. The rest of the aorta samples were free of fibers. These data suggest that asbestos-induced aortic effects are not due to direct translocation of fibers to aortic tissue.</p>", "<title>Lung responses</title>", "<p>To determine whether the degree of lung inflammation and fibrosis in response to particulates correlated with the amount of atherosclerosis observed in the different groups of mice, we examined lung sections and graded them using an established blind coding system developed for asbestosis in human lung (##UREF##1##Craighead 1982##; ##REF##17237430##Haegens et al. 2007##). ##FIG##1##Figure 2## shows representative lung sections from a clean-air–exposed ApoE^−/− mouse (##FIG##1##Figure 2A##), a TiO₂-exposed ApoE^−/− mouse (##FIG##1##Figure 2B##), and a clean-air–exposed DKO mouse (##FIG##1##Figure 2C##) after 30 days and demonstrates the absence of inflammation and fibrosis. In contrast, histologic analysis of lungs from both the ApoE^−/− (##FIG##1##Figure 2D##) and DKO (##FIG##1##Figure 2E##) mice showed striking peribronchiolar inflammation and fibrosis originating at distal bronchioles and alveolar duct regions after 30 days of asbestos exposure. As shown in ##TAB##0##Table 1## (which summarizes the grading of inflammatory cell infiltration) and ##TAB##1##Table 2## (which summarizes peribronchiolar/perivascular fibrosis) in H&amp;E-stained lung sections, both ApoE^−/− and DKO mice had significantly greater immune cell infiltration after 3, 9, and 30 days of exposure to asbestos compared with exposure to clean air (<italic>p</italic> &lt; 0.001). Fibrosis was not elevated until 30 days, and peribronchiolar fibrosis was significantly greater in asbestos-exposed ApoE^−/− and DKO mice compared with the respective clean-air controls (<italic>p</italic> &lt; 0.001). We detected no differences in the fibrotic or inflammatory scores for lungs between asbestos-exposed ApoE^−/− and DKO mice at any time point.</p>", "<p>##FIG##2##Figure 3## shows differential cell counts in BALF and also demonstrates similar changes in the inflammatory responses of ApoE^−/− and DKO mice after 3, 9, and 30 days of asbestos exposure compared with respective clean-air control animals. Total cell numbers and numbers of macrophages were higher in 9-day clean-air–exposed DKO mice, but the overall response to asbestos was similar to that in the 9-day ApoE^{− /−} mice. Asbestos exposure significantly increased total neutrophils and eosinophils in both ApoE^−/− and DKO mice at all time points.</p>", "<title>Cytokine and chemokine concentrations in BALF and plasma by ELISA</title>", "<p>In initial experiments to determine whether cytokine and chemokine profiles in BALF or plasma correlated with atherogenic responses, we measured concentrations of four inflammatory cytokines (IL-6, IFN-γ, MCP-1, and MIP-2) previously associated with lung inflammation after asbestos exposure (##REF##17237430##Haegens et al. 2007##; ##REF##16251409##Sabo-Attwood et al. 2005##), as well as the appearance of atherosclerosis (##REF##11742859##Hansson 2001##) in BALF and in plasma obtained from ApoE^−/− mice exposed to clean air or chrysotile asbestos fibers for 30 days. In BALF, levels of MCP-1, IL-6, and MIP-2, a potent chemotactic factor for monocyte and neutrophil recruitment, were significantly increased (<italic>p</italic> &lt; 0.04) in asbestos-exposed ApoE^−/− animals, whereas levels of IFN-γ were comparable in clean-air–exposed and asbestos-exposed groups (##FIG##3##Figure 4A##). We were also interested in whether plasma levels of these cytokines were elevated at 30 days, suggesting either systemic signaling to aortic tissue or their production by circulating inflammatory cells. In contrast to the other cytokines measured, only MCP-1 concentrations in plasma were significantly higher (<italic>p</italic> &lt; 0.04) in asbestos-exposed compared with clean-air–exposed ApoE^−/− mice at 30 days (##FIG##3##Figure 4B##). Furthermore, plasma levels of MCP-1 significantly correlated with athero-sclerotic lesion sizes in asbestos-exposed ApoE^−/− mice (<italic>p</italic> &lt; 0.04).</p>", "<title>Cytokine and chemokine levels in BALF and plasma using the Bio-Plex assay</title>", "<p>In later, more comprehensive experiments, we used the Bio-Plex assay for analysis of cytokines/ chemokines in BALF of asbestos-exposed ApoE^{− / −} and DKO mice after 3, 9, and 30 days of exposure. At 3 days, both ApoE^−/−and DKO mice exposed to asbestos showed increased levels of MCP-1 (<italic>p</italic> &lt; 0.01), G-CSF (<italic>p</italic> &lt; 0.001), IL-5 (<italic>p</italic> &lt; 0.01), IL-4 (<italic>p</italic> &lt; 0.05), KC (<italic>p</italic> &lt; 0.05), and IL-6 (<italic>p</italic> &lt; 0.001) in BALF (##FIG##4##Figure 5A##). TNF-α in BALF was increased significantly only in ApoE^−/− mice exposed to asbestos (<italic>p</italic> &lt; 0.05); levels were significantly higher in ApoE^−/− mice than in DKO mice (<italic>p</italic> &lt; 0.02). The magnitudes of MCP-1 and IL-6 concentrations in BALF were similar in both ApoE^−/− and DKO mice after 3 days of asbestos exposure. However, none of the other cytokines on the panel were significantly altered at this time point.</p>", "<p>After 9 days of asbestos exposure, which has previously been shown to be the time point of the maximum inflammatory response in lung after inhalation of chrysotile asbestos fibers (##REF##17237430##Haegens et al. 2007##; ##REF##16251409##Sabo-Attwood et al. 2005##), MCP-1 in BALF remained elevated, with ApoE^−/− mice having an approximately 2-fold greater response than the DKO mice (<italic>p</italic> &lt; 0.03) (##FIG##4##Figure 5B##). We observed increased BALF levels of G-CSF (<italic>p</italic> &lt; 0.01), IL-5 (<italic>p</italic> &lt; 0.05), IL-4 (<italic>p</italic> &lt; 0.05), KC (<italic>p</italic> &lt; 0.05), and IL-6 (<italic>p</italic> &lt; 0.01) in both ApoE^−/− and DKO mice exposed to asbestos. Interestingly, the DKO mice had relatively lower TNF-α levels in BALF compared with the ApoE^−/− animals after either 3 or 9 days of exposure.</p>", "<p>As shown in ##FIG##4##Figure 5C##, significant elevations in BALF cytokines in both asbestos-exposed ApoE^−/− and DKO animals were sustained at 30 days for MCP-1 (<italic>p</italic> &lt; 0.001), IL-4 (<italic>p</italic> &lt; 0.05), and KC (<italic>p</italic> &lt; 0.01). In contrast, only G-CSF (<italic>p</italic> &lt; 0.05) and IL-6 (<italic>p</italic> &lt; 0.01) levels remained significantly elevated in asbestos-exposed ApoE^−/− mice compared with clean-air–exposed animals. After asbestos exposure for 30 days, differences between ApoE^−/−and DKO animals were found in BALF for MCP-1 (<italic>p</italic> &lt; 0.03; mean ± SE, 491 ± 417 pg/mL in ApoE^−/− mice vs. 176 ± 107 pg/mL for DKO mice) and IL-6 (<italic>p</italic> &lt; 0.03; 45 ± 46 pg/mL in ApoE^−/− vs. 7.4 ± 7.6 pg/mL in DKO mice) (##FIG##4##Figure 5C##).</p>", "<p>Using the Bio-Plex assay on a subset of available ApoE^−/− and DKO plasma samples obtained in one 30-day exposure experiment, we found an approximately 49% increase in MCP-1 levels in the ApoE^−/− animals (clean air, 86 ± 30 pg/mL, <italic>n</italic> = 4; asbestos, 128 ± 51, <italic>n</italic> = 5). The magnitude of the change was similar to that found when using the ELISA assay. In contrast, we found no difference in MCP-1 concentrations between the clean-air–exposed and asbestos-exposed DKO mice (180 ± 9 pg/mL, <italic>n</italic> = 3, vs. 177 ± 70 pg/mL, <italic>n</italic> = 6, respectively). The levels in DKO mice were higher than those measured in ApoE^−/−mice using both assays, but asbestos exposure did not elicit a change in the concentrations in plasma as it did in the ApoE^−/− mice. The Bio-Plex data followed patterns similar to those shown in ##FIG##3##Figure 4B## for the ApoE^−/− mice. We found no differences in the clean-air–exposed versus asbestos-exposed DKO mice for IL-6 (0.7 ± 0.4 vs. 0.7 ± 1.5 pg/mL, respectively), TNF-α (29 ± 32 vs. 42 ± 54 pg/mL), or IFN-γ (0.2 ± 0.2 vs. 0.4 ± 0.6 pg/mL).</p>", "<title>AP-1 and NF-κB activation in the aorta</title>", "<p>Binding sites for both AP-1 and NF-κB (oxidant-induced transcription factors associated with inflammation) are found in the promoter region of the <italic>MCP-1</italic> gene, and their cooperative action is important in the induction of <italic>MCP-1</italic> gene expression (##REF##9174597##Martin et al. 1997##). Although it is well known that both AP-1 and NF-κB are activated in the lung after asbestos exposure (##REF##17237430##Haegens et al. 2007##; ##REF##9250152##Janssen et al. 1997##; ##UREF##3##Mossman et al. 2000##) and that MCP-1 is important in the initiation of atherosclerosis (##REF##10079097##Gosling et al. 1999##), distal changes in AP-1 and NF-κB binding to DNA in the aorta after inhalation of asbestos are novel findings. Data in ##FIG##5##Figure 6A and B## demonstrate significant activation of both AP-1 and NF-κB, respectively, in the aorta from ApoE^−/− mice after 9 days of asbestos exposure, consistent both with patterns of early and significant inflammatory responses in lung tissue (##TAB##0##Table 1##) and with rises in MCP-1 levels in BALF (##FIG##3##Figure 4##). ##FIG##5##Figure 6C and D## shows no AP-1 or NF-κB DNA binding, respectively, in aortas isolated from DKO mice exposed to clean air or asbestos. Western blot analyses for phosphorylated IκB in cytoplasmic protein extracted from the aortas also revealed increased levels relative to levels of β-actin, a loading control protein, after 9 days of asbestos exposure in ApoE^−/− mice (##FIG##6##Figure 7A##) but not in DKO mice (##FIG##6##Figure 7B##). Phosphorylated IκB releases the NF-κB complex for translocation to the nucleus; therefore, the increase in phosphorylated IκB found in the 9-day samples is consistent with the increased DNA binding activity of NF-κB in ApoE^−/− mice at this point as shown by EMSA (##FIG##5##Figure 6##).</p>" ]

[ "<title>Discussion</title>", "<p>These data are the first to show that inhaled chrysotile asbestos, a documented pathogenic airborne fiber, exacerbates atherosclerotic lesions in chow-fed ApoE^−/− mice. It should be noted that concentrations of airborne asbestos fibers generated in this study were high, as encountered episodically during the World Trade Center disaster, in contrast to mean ambient levels in buildings (0.00004 and 0.00243 fibers/mL air in rural and urban samples, respectively) or outdoor air (0.00001 and 0.0001 fibers/mL air in rural and urban samples, respectively) (##UREF##2##Health Effects Institute 1991##). Atherosclerotic effects were attenuated significantly when animals also lack CD4⁺ T cells, showing their critical importance in the pathogenesis of disease. The fact that DNA binding of both AP-1 and NF-κB were increased in nuclear extracts from aortas of ApoE^−/− mice after 9 days of asbestos exposure, in the absence of asbestos fibers in the aortas, indicates that distal signaling mechanisms involving these two transcription factors are activated in atherogenesis. We found no detectable differences in AP-1 and NF-κB DNA binding activity in extracts from aortas of DKO mice exposed to either clean air or asbestos, which provides additional support for the important interactions among asbestos exposure, CD4⁺ T cells, and lesion development through redox-sensitive signaling pathways. Furthermore, our studies suggest that MCP-1, which has binding sites for both AP-1 and NF-κB in its promoter region, is an important chemokine in the signaling events leading to atherosclerosis. Recently, ##REF##16414948##Sun et al. (2005)## reported that ApoE^−/− mice fed a high-fat diet and exposed to inhaled particulate matter had increased atherosclerotic lesions that were accompanied by increased macrophage infiltration and evidence of oxidative stress in the aorta. However, they did not identify the link between lung effects and these findings in aortic tissue and mechanisms of particulate matter–induced atherosclerosis.</p>", "<p>Atherosclerosis is a complex disease with etiologies that involve both genetic and environmental factors. The present study confirms the importance of inflammation in the athero-genic process and supports a role for the CD4⁺ T cell, shown to be present in atherosclerotic lesions (##REF##8701976##Zhou et al. 1996##). Although we did not stain for the presence of CD4⁺ T cells in lesions in these experiments, we did find more circulating CD4⁺ T cells in the blood of a small subset of ApoE^−/− mice exposed to asbestos compared with a subset exposed to clean air for 30 days, although the differences were not significant (data not shown). Early work using ApoE^−/− mice crossed with IFN-γ receptor–deficient mice suggested that IFN-γ promoted atherosclerosis and was critical in modulating the balance between T_H1 (cellular immunity) and T_H2 (humoral immunity) sub-sets of T cells (##REF##9169506##Gupta et al. 1997##). Exogenous IFN-γ has also been shown to increase atherosclerosis in ApoE^−/− mice (##REF##11106554##Whitman et al. 2000##). IL-4 is reportedly necessary for invoking a T_H2 response (##REF##15961574##Feili-Hariri et al. 2005##) and has been shown to play a role in the progression of atherosclerosis (##REF##12937153##Davenport and Tipping 2003##). IL-4 levels were increased similarly in BALF of both ApoE^−/− and DKO mice exposed to asbestos for 3, 9, or 30 days, but levels were not detectable in plasma.</p>", "<p>##REF##15579444##Elhage et al. (2004)## reported that DKO mice had increased lesions in the descending and abdominal aorta, but they found similar lesions in the aortic sinuses of ApoE^−/− mice. We recognize that the lesions observed in the present study are larger than reported in the literature for ApoE^−/− mice of similar age. The reasons for the differences between the present work and previous reports are unclear but may relate to the study design and housing conditions. We chose to start the exposures to asbestos at 4–6 weeks of age and to terminate the experiments after 30 days of exposure (5 days/week, for a total of 6 weeks) when animals were at most 12 weeks of age. Because we examined only the aortic sinus, extending the exposures to 18 weeks or 1 year or doing <italic>en face</italic> analysis may have yielded different results. However, our objective was to determine whether the length of exposure to asbestos necessary to develop lung inflammation and early fibrosis was sufficient to elicit differences in aortic responses between ApoE^−/− and DKO mice. Of major significance here is that both ApoE^−/− and DKO mice appeared to respond similarly to asbestos exposure with respect to lung inflammation, fibrosis, and selected cytokine concentrations in BALF but differed significantly in their responses in plasma concentrations of MCP-1 after 30 days of exposure to asbestos. ApoE^−/− mice exposed to asbestos had an increase in plasma MCP-1 levels compared with clean-air–exposed animals. In contrast, MCP-1 concentrations in DKO mice, although constitutively higher, remained unchanged after clean air and asbestos exposure. In BALF, genotype differences in response became apparent at 9 and 30 days of exposure to asbestos in that MCP-1 and IL-6 were higher in ApoE^−/− than in DKO mice. The source of these cytokines remains unknown, and future studies will examine changes induced by asbestos in MCP-1–defi-cient mice crossed with ApoE^−/− mice.</p>", "<p>Our data showing elevated levels of plasma MCP-1 in asbestos-exposed ApoE^−/− mice are consistent with growing evidence that cytokines participate as autocrine and paracrine mediators in the pathogenesis of atherosclerosis (##REF##11742859##Hansson 2001##; ##REF##15156262##Upadhya et al. 2004##). Because elevated levels of MCP-1 are known to occur in BALF after inhalation of chrysotile asbestos in C57BL/6 mice (##REF##17237430##Haegens et al. 2007##), neutrophils elaborating MCP-1 may enter the systemic circulation. Alternatively, cells activated in the lung by asbestos [e.g., macrophages, dendritic cells (DCs), neutrophils, lymphocytes] may migrate to pulmonary lymph nodes, where they activate CD4⁺ T cells that are released into the circulation and contribute to atherogenesis. This latter explanation could account for the remarkably similar inflammatory and fibrotic patterns in the lungs of ApoE^−/− and DKO mice, but the dramatically different extent of atherosclerotic lesions in their aortas. The elevation of systemic levels of MCP-1 and the significant relationship between plasma MCP-1 concentrations and lesion size in the ApoE^−/− mice provide support for previous work showing the importance of MCP-1 in atherogenesis (##REF##10079097##Gosling et al. 1999##; ##REF##11742859##Hansson 2001##). DKO mice had higher plasma MCP-1 levels than did ApoE^−/− mice, but asbestos had no effect compared with clean air in these mice. This suggests that constitutive levels may be higher in DKO mice but that higher levels are not necessarily associated with larger lesions. It appears that the induced change in concentrations is of greater importance in the pathogenesis of the disease, highlighting the complex interrelationships.</p>", "<p>Our work also demonstrates that many classically defined inflammatory cytokines/ chemokines (e.g., KC, MIP-1α, MIP-1β^, IL-6) are elevated in BALF in this model, but these differences appear to be dissociated from effects seen in the aorta. Previous studies have revealed that MCP-1 is necessary for the activation of T lymphocytes (##REF##7883984##Taub et al. 1995##) and that MCP-1 produced by lung fibroblasts exerts an immunomodulatory effect on CD4⁺ T cells <italic>in vitro</italic> (##REF##9574568##Hogaboam et al. 1998##). G-CSF and KC, both important in neutrophil recruitment, were elevated in our studies, but not GM-CSF, which is an important growth factor for lung DC maturation and proliferation (##REF##15879415##Vermaelen and Pauwels 2005##). However, because DCs are central to the initiation and orchestration of immunity and tolerance, we suggest that asbestos fibers initiate an inflammatory cascade in lung epithelial cells and macrophages, cells first encountering inhaled asbestos, thus leading to secretion of cytokines and chemokines with DC-attracting potential. DCs then could migrate to lymph nodes, where they activate T cells, which enter the systemic circulation and come into contact with a vulnerable aorta, an observation consistent with the detection of macrophages and CD4⁺ T cells in early aortic lesions (##REF##8701976##Zhou et al. 1996##). Further inflammatory cell infiltration would then exacerbate lesion development. Investigation is ongoing to document a role for DCs in the translation of asbestos-induced pulmonary inflammation to systemic responses.</p>", "<p>In summary, exposure to inhaled chrysotile asbestos fibers was associated with an exacerbation of the development of atherosclerotic lesions in ApoE^−/− mice. The induction of atherosclerosis appears to be CD4⁺ T cell dependent and dissociated from the magnitude of lung inflammation or fibrosis associated with inhaled asbestos. More important, our data also suggest the importance of a change in systemic MCP-1 in atherogenesis associated with altered signaling through the transcription factors AP-1 and NF-κB, and that the effects of inhaled asbestos extend beyond the lung. The data also raise the possibility of constitutive differences in MCP-1 levels between the different genotypes that warrant further investigation.</p>" ]

[]

[ "<p>Current address: Waters, Inc., Woburn, MA.</p>", "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Associations between air pollution and morbidity/mortality from cardiovascular disease are recognized in epidemiologic and clinical studies, but the mechanisms by which inhaled fibers or particles mediate the exacerbation of atherosclerosis are unclear.</p>", "<title>Objective and methods</title>", "<p>To determine whether lung inflammation after inhalation of a well-characterized pathogenic particulate, chrysotile asbestos, is directly linked to exacerbation of atherosclerosis and the mechanisms involved, we exposed apolipoprotein E–deficient (ApoE^−/−) mice and ApoE^−/− mice crossed with CD4^−/− mice to ambient air, NIEHS (National Institute of Environmental Health Sciences) reference sample of chrysotile asbestos, or fine titanium dioxide (TiO₂), a nonpathogenic control particle, for 3, 9, or 30 days.</p>", "<title>Results</title>", "<p>ApoE^−/− mice exposed to inhaled asbestos fibers had approximately 3-fold larger atherosclerotic lesions than did TiO₂-exposed ApoE^−/− mice or asbestos-exposed ApoE^−/−/CD4^−/− double-knockout (DKO) mice. Lung inflammation and the magnitude of lung fibrosis assessed histologically were similar in asbestos-exposed ApoE^−/− and DKO mice. Monocyte chemoattractant protein-1 (MCP-1) levels were increased in bronchoalveolar lavage fluid and plasma, and plasma concentrations correlated with lesion size (<italic>p</italic> &lt; 0.04) in asbestos-exposed ApoE^−/− mice. At 9 days, activator protein-1 (AP-1) and nuclear factor-κB (NF-κB), transcription factors linked to inflammation and found in the promoter region of the <italic>MCP-1</italic> gene, were increased in aortas of asbestos-exposed ApoE^−/− but not DKO mice.</p>", "<title>Conclusion</title>", "<p>Our findings show that the degree of lung inflammation and fibrosis does not correlate directly with cardiovascular effects of inhaled asbestos fibers and support a critical role of CD4⁺ T cells in linking fiber-induced pulmonary signaling to consequent activation of AP-1– and NF-κB–regulated genes in atherogenesis.</p>" ]

[ "<p>Cardiovascular disease is the leading cause of death and hospitalization among adults. An association between air pollution and morbidity/mortality from cardiovascular disease has been recognized in epidemiologic and clinical studies (##REF##15173049##Brook et al. 2004##; ##REF##16079061##Delfino et al. 2005##; ##REF##16522832##Dominici et al. 2006##; ##REF##11879110##Pope et al. 2002##; ##REF##16414948##Sun et al. 2005##), but the mechanisms by which inhaled fibers or particles mediate the exacerbation of atherosclerosis are unclear. Moreover, elucidating the <italic>in vivo</italic> mechanisms of action of the multiple gaseous and particulate components of air pollution is challenging because of their physical and chemical complexity and dynamic nature of reactivity. Current theories purport that fine (≤ 2.5 μm in diameter) or ultrafine (≤ 0.1 μm in diameter) particles or their metal components (e.g., iron, vanadium) preferentially induce cardiovascular disease via nonspecific inflammation in lung, direct translocation to heart tissue, and site-specific effects or altered autonomic cardiac responses (##REF##15879201##Nel 2005##; ##REF##12153769##Oberdörster and Utell 2002##; ##REF##11879110##Pope et al. 2002##; ##UREF##5##Pope and Dockery 2006##).</p>", "<p>The mechanisms of lung inflammation and disease by asbestos have been widely studied (##REF##17237430##Haegens et al. 2007##; ##REF##9250152##Janssen et al. 1997##; ##UREF##3##Mossman et al. 2000##) and its association with cardiovascular disease reported in occupational cohorts (##REF##6846339##Dement et al. 1983##; ##REF##8280638##McDonald et al. 1993##; ##REF##8398871##Sanden et al. 1993##; ##REF##9282121##Sjogren 1997##, ##REF##11800330##2001##). However, to our knowledge, no studies examining the mechanisms of the exacerbation of atherosclerosis by inhaled asbestos have been performed. Therefore, to test the hypothesis that airborne fibers exacerbate atherosclerosis and to elucidate the mechanisms involved, we used chrysotile asbestos fibers in a murine model of inhalation. Chrysotile asbestos is ubiquitous in the northern hemisphere and is the asbestos type historically used worldwide in &gt; 95% of asbestos-containing products (##REF##2153315##Mossman et al. 1990##). Although significant efforts have been made to reduce occupational and environmental exposure to amphibole types of asbestos (crocidolite, amosite) that may be more pathogenic in mesothelioma (##REF##2659987##Mossman and Gee 1989##), the use of chrysotile asbestos continues worldwide (##UREF##4##Nicholson 1997##). Moreover, analysis of airborne surface dust in residential areas of Lower Manhattan after the collapse of the World Trade Center in New York City revealed increased chrysotile asbestos [##UREF##0##Centers for Disease Control and Prevention (CDC) 2003##], raising the concern about its short- and long-term pathogenic effects after inhalation by the general population.</p>", "<p>In these experiments, we used the atherosclerosis-prone apolipoprotein E–deficient (ApoE^−/−) mouse and ApoE^−/− mice crossed with CD4^−/− [double-knockout (DKO)] mice to test the hypothesis that inhaled asbestos fibers exacerbate atherosclerosis and that the mechanism involves CD4⁺ T cells that are increased in lung after inhalation of asbestos (##REF##17200189##Shukla et al. 2007##). We exposed the mice to ambient air, fine titanium dioxide (TiO₂; a nonpathogenic control particle), or chrysotile asbestos in the University of Vermont (UVM) Inhalation Facility (##REF##16251409##Sabo-Attwood et al. 2005##). Our data are the first to show a direct relationship between inhaled pathogenic fibers and the exacerbation of atherosclerosis via a CD4⁺ T cell–dependent process. Moreover, they suggest an important role for activation of monocyte chemoattractant protein-1 (MCP-1) and early transcription factors activator protein-1 (AP-1) and nuclear factor-κB (NF-κB) in the development of atherosclerosis by inhaled pathogenic pollutants.</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1218\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Size (mean ± SE) of atherosclerotic lesions after 30-day exposures of ApoE^−/− mice to clean air (<italic>n</italic> = 11), asbestos (<italic>n</italic> = 11), or TiO₂ (<italic>n</italic> = 6), and of DKO mice to clean air (<italic>n</italic> = 8) or asbestos (<italic>n</italic> = 8). Error bars represent the variance in the average lesion area per animal. </p><p>*<italic>p</italic> &lt; 0.001.</p></caption></fig>", "<fig id=\"f2-ehp-116-1218\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Representative lung sections stained with Masson’s trichrome after 30-day exposures, showing inflammatory cell infiltration and fibrosis in asbestos-exposed animals. Blue indicates collagen associated with fibrosis. (<italic>A</italic>) ApoE^−/− mice exposed to clean air. (<italic>B</italic>) ApoE^−/− mice exposed to TiO₂. (<italic>C</italic>) DKO mice exposed to clean air. (<italic>D</italic>) ApoE^−/− mice exposed to asbestos. (<italic>E</italic>) DKO mice exposed to asbestos.</p></caption></fig>", "<fig id=\"f3-ehp-116-1218\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Differential cell counts (mean ± SE) in BALF from ApoE^−/− and DKO mice after exposure for 3 days (<italic>A</italic>), 9 days (<italic>B</italic>), or 30 days (<italic>C</italic>) to clean air or asbestos. Error bars represent the variance of the averages of the respective cell counts per animal. Eos, eosinophils; Lymph, lymphocytes; Mac, macrophages; Neutro, neutrophils.</p><p>*<italic>p</italic> &lt; 0.05, **<italic>p</italic> &lt; 0.01, and #<italic>p</italic> &lt; 0.001, compared with respective clean-air–exposed animals (<italic>n</italic> = 5–10 animals per group per treatment).</p></caption></fig>", "<fig id=\"f4-ehp-116-1218\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Cytokine concentrations (mean ± SE) in BALF (<italic>A</italic>) and plasma (<italic>B</italic>) obtained from ApoE^−/− mice exposed to clean air or asbestos for 30 days (<italic>n</italic> =11 per group). Error bars represent the variance of the averages of values obtained in each animal. </p><p>*<italic>p</italic> &lt; 0.04 compared with respective clean-air–exposed animals.</p></caption></fig>", "<fig id=\"f5-ehp-116-1218\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Cytokine and chemokine concentrations (mean ± SE) in BALF from ApoE^−/− and DKO mice exposed to clean air or chrysotile asbestos for 3 days (<italic>A</italic>), 9 days (<italic>B</italic>), or 30 days (<italic>C</italic>), as analyzed by the Bio-Plex assay. Error bars represent the variance of the averages of respective values from each animal. </p><p>*<italic>p</italic> &lt; 0.05, **<italic>p</italic> &lt; 0.01, and #<italic>p</italic> &lt; 0.001, compared with respective clean-air–exposed animals.</p></caption></fig>", "<fig id=\"f6-ehp-116-1218\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Results of EMSA for NF-κB and AP-1 in aortic extracts. (<italic>A,B</italic>) DNA binding of AP-1 (<italic>A</italic>) and NF-κB (<italic>B</italic>) in aortic extracts from ApoE^−/− mice exposed to clean air (control; lanes 1–4) or to chrysotile asbestos for 3 days (lanes 5–8), 9 days (lanes 9–12 ), or 30 days (lanes 13–16). Data are expressed relative to OCT-1 compound as a loading control. Values in top portions of <italic>A</italic> and <italic>B</italic> are mean ± SE; error bars represent the variance of the averages of values obtained for each animal. (<italic>C,D</italic>) DNA binding of AP-1 (<italic>C</italic>) and NF-κB (<italic>D</italic>) in aortic extracts from DKO mice exposed to clean air (lanes 1–4) or to asbestos for 3 days (lanes 5–7), 9 days (lanes 8–11), or 30 days (lanes 12–14). Lane 15 represents a positive control. Arrows denote bands for respective transcription factor binding.</p><p>**<italic>p</italic> &lt; 0.01 compared with control (<italic>n</italic> = 4 per group).</p></caption></fig>", "<fig id=\"f7-ehp-116-1218\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Results of Western blot analyses for phosphorylated IκB (p-IκB). (<italic>A</italic>) Western blot analyses (mean ± SE; <italic>n</italic> = 4 per group) for p-IκB/β-actin ratios in cytoplasmic extracts from aortas of ApoE^−/− mice exposed to clean air (control) or chrysotile asbestos for 3, 9, or 30 days (left), and an example of a Western blot comparing clean air with 9-day exposures (right). (<italic>B</italic>) Western blot analyses (mean ± SE; <italic>n</italic> = 4 per group) for p-IκB/β-actin ratios in cytoplasmic extracts from aortas of DKO mice exposed to clean air (control) or chrysotile asbestos for 3, 9, or 30 days (left), and an example of the Western blot comparing clean air with 9-day exposures (right). Blots are not shown for 3- and 30-day exposures because there were no significant changes.</p><p>*<italic>p</italic> &lt; 0.02 compared with clean air exposure.</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1218\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Immune cell infiltration in lung tissue after asbestos exposure.<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1218\">a</xref></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">ApoE^−/−\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">DKO\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Exposure time (days)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Control</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Asbestos</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Control</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Asbestos</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.0 ± 0<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1218\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.1 ± 0.2<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1218\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.8 ± 0.3<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1218\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.5 ± 0.5<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1218\">*</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.5 ± 0.2<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1218\">*</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.2 ± 0.2<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1218\">*</xref></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1218\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Fibrosis in lung tissue after asbestos exposure.<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1218\">a</xref></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">ApoE^−/−\n<hr/></th><th colspan=\"3\" align=\"center\" rowspan=\"1\">DKO\n<hr/></th></tr><tr><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Asbestos\n<hr/></th><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Asbestos\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Exposure time (days)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Control</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Peribronchiolar</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Perivascular</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Control</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Peribronchiolar</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Perivascular</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.5 ± 0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.5 ± 0.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.5 ± 0.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.8 ± 0.2<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1218\">*</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.3 ± 0.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.3 ± 0.3<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1218\">*</xref></td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 ± 0</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>We thank T. Barrett, J. Levis, E. Parker, J. Robbins, D. Sartini, M. von Turkovich, and M. Wadsworth, for expert technical assistance.</p></fn><fn><p>This work was supported by grants from the National Institute on Aging (K01-AG00947 and R01-AG21106); the National Heart, Lung, and Blood Institute (P01-HL067004); and the National Institute of Environmental Health Sciences (T32 ES0071).</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1218\"><label>a</label><p>Evaluated on H&amp;E-stained sections using a scoring system where 1 indicates no infiltration; 2, mild infiltration; 3, moderate infiltration; and 4, severe infiltration.</p></fn><fn id=\"tfn2-ehp-116-1218\"><label>*</label><p><italic>p</italic> &lt; 0.001 compared with control.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn3-ehp-116-1218\"><label>a</label><p>Evaluated on lung sections stained with the Masson’s trichrome method to detect collagen, using a scoring system where 1 indicates no fibrosis; 2, focal fibrosis; 3, moderate fibrosis; and 4, severe fibrosis.</p></fn><fn id=\"tfn4-ehp-116-1218\"><label>*</label><p><italic>p</italic> &lt; 0.001 compared with control.</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1218f1\"/>", "<graphic xlink:href=\"ehp-116-1218f2\"/>", "<graphic xlink:href=\"ehp-116-1218f3\"/>", "<graphic xlink:href=\"ehp-116-1218f4\"/>", "<graphic xlink:href=\"ehp-116-1218f5\"/>", "<graphic xlink:href=\"ehp-116-1218f6\"/>", "<graphic xlink:href=\"ehp-116-1218f7\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Methods</title>", "<title>Study population</title>", "<p>The NAS consists of a cohort of 2,280 healthy male volunteers from Boston, Massachusetts. Participants were mostly white, 21–80 years of age, and free of heart disease, hypertension, diabetes mellitus, cancer, peptic ulcer, gout, recurrent asthma, bronchitis, or sinusitus at the beginning of the study in 1961 (##UREF##1##Bell et al. 1972##). Since entry, participants have had regular medical screenings every 3–5 years consisting of a physical examination, blood and urine tests, and pulmonary function testing (##REF##16291079##Litonjua et al. 2005##). In this study we analyzed a randomly selected subcohort of 96 NAS participants whose urinary cadmium was measured for a separate study assessing the relationship between urinary cadmium and dietary intake. All 96 participants had a 24-hr urinary specimen (collected between March 1994 and November 1995) and met the following criteria: <italic>a</italic>) subject’s 24-hr urine volume collected was ≥ 3,000 mL, <italic>b</italic>) no urine was spilled during collection, <italic>c</italic>) subject’s creatinine clearance was ≥ 50 in order to exclude subjects with poor renal function, and <italic>d</italic>) the subject had complete information on dietary intake.</p>", "<p>Participants visited the study center in the morning after abstaining from smoking. Physical examinations included measurement of height, weight, and lung function (FVC, FEV₁) testing. We assessed smoking status and intensity (pack-years of cigarettes) using the American Thoracic Society Division of Lung Diseases 1978 questionnaire (##REF##742764##Ferris 1978##). All participants provided written informed consent. This study was reviewed and approved by the institutional review boards of all participating institutions.</p>", "<title>Pulmonary function testing</title>", "<p>All pulmonary tests were performed according to American Thoracic Society standard methodology (##REF##3674589##ATS 1987##). Each subject made up to eight FVC maneuvers to obtain three acceptable curves according to predefined criteria, and standard methods were used to obtain FVC and FEV₁ (##REF##16291079##Litonjua et al. 2005##). We used the largest value of the three maneuvers for either measure (not necessarily from the same curve) for this analysis, and all values were corrected to body temperature and pressure saturated with water vapor (##REF##16291079##Litonjua et al. 2005##). Pulmonary function data including FEV₁ and FVC measurements between 1994 and 2002 were available for all 96 subjects, for a total of 222 observations (3 measurements for 46 subjects; 2 measurements for 34 subjects; 1 measurement for 16 subjects). The mean duration between successive pulmonary tests was approximately 3.1 years.</p>", "<p>We calculated FEV₁ and FVC as the percentage of predicted values using the prediction equations from NHANES III (##REF##9872837##Hankinson et al. 1999##). For white males ≥ 20 year of age, the following equations are suggested:</p>", "<title>Urinary cadmium analysis</title>", "<p>For each subject, urinary cadmium samples and pulmonary function tests were given on the same date during between March 1994 and November 1995. All urine samples were handled in a class-100 clean hood. All glassware and plastics were cleaned by soaking in 10% nitric acid (HNO₃) for 24 hr and rinsing several times with deionized water. Reagents used in this study were HNO₃ (Optima; Seastar Chemical Co., Pittsburgh, PA); National Institute of Standard and Technology Standard Reference Material (NIST SRM) 1643d (Gaithersburg, MD); enriched isotope spike ¹¹³Cd (Oak Ridge National Laboratory, Oak Ridge, TN). We mixed 5 mL of urine sample with 1 mL of isotope dilution spike (40 ppb ¹¹³Cd-enriched isotope dilution spike) and 20 μL of chelating resin [10% SPR-IDA (suspended particulate reagent–iminodiacetate), 10 μL; CETAC Technologies (Omaha, NE)] in a plastic centrifuge tube. After adjusting the pH of the solution to 8, samples were centrifuged at 5,000 rpm for 20 min. Supernatant was discarded and resin was washed with deionized water adjusted to a pH of 8. Then 2 mL of 5% HNO₃ was added to the precipitate, and it was centrifuged at 5,000 rpm for 20 min. The cadmium-containing supernatant was then removed and analyzed by inductively coupled plasma mass spectrometry (Sciex Elan 5000; PerkinElmer, Norwalk, CT), with standard instrument operating and data collection parameters, using the isotope dilution procedure. Quality control and quality assurance procedures included analyses of procedural blanks, duplicate analysis, spiked samples, and analysis of NIST SRM 2670 (toxic metals in freeze-dried urine), and NIST SRM 1643d (trace elements in water) to monitor the accuracy and recovery rates of the procedure for each analytic batch. We report results as the average of five replicate measurements. Recoveries of these quality control standards were between 90% and 110% in our laboratory. The calculated detection limit for Cd analysis by this procedure was 0.02 ng/mL of sample (urinary Cd for all 96 samples &gt; 0.02 ng/mL). Urinary cadmium measurements used in this study were adjusted for individual 24-hr urine output.</p>", "<title>Statistical analysis</title>", "<p>Statistical analysis was carried out using SAS (version 9.1; SAS Institute Inc., Cary, NC), and the primary outcome measures were FEV₁ percent predicted, FVC percent predicted, and FEV₁/FVC ratio. We log-transformed 24-hr urinary cadmium outputs (U-Cd) to normalize the distribution. Because our pulmonary function outcomes were measured repeatedly, we fit a mixed-effects models using the PROC MIXED procedure in SAS to deal with lack of independence of observations. For FEV₁/FVC ratio, we considered the following covariates: baseline age (years), baseline height (cm), time elapsed from the baseline (years), and smoking status [never smokers, former smokers with low pack-years (≤ 30), former smokers with high pack-years (&gt; 30), and current smokers], where baseline is defined as the time point of urinary cadmium and pulmonary function measurements during the period between March 1994 and November 1995. For FEV₁ percent predicted and FVC percent predicted, baseline age and height were not included because the percentage of predicted value already accounts for age and height. Because smoking status is the only time-dependent variable, the interaction between time and smoking status was included. We also included an interaction between time and urinary cadmium in the model to test whether the association between urinary cadmium and lung functions changes over time. A random slope for the time elapsed from the baseline was initially considered to account for subject-specific variability of lung function over time, but a random intercept-only model was preferred based on the likelihood ratio test comparing the two models. The following equation describes the structure of our fitted models:</p>", "<p>where <italic>Y</italic><italic>_ij</italic> is the lung function in subject <italic>i</italic> at time <italic>j</italic>, <italic>b</italic>₀ is the overall intercept, <italic>u</italic><italic>_i</italic> is the separate random intercept for subject <italic>i</italic>, <italic>b</italic>₁ is the slope representing the overall effect of time, <italic>b</italic>₂ is the slope representing the overall effect of log-transformed U-Cd, and <italic>b</italic>₃ is the slope for the interaction between time and U-Cd. As a sensitivity analysis, we also re-ran the same mixed models using raw lung function measures (FEV₁ and FVC, in liters) further adjusted for baseline age and height.</p>", "<p>To evaluate effect modification by smoking status, we introduced an interaction term between categories of smoking status and log-transformed U-Cd, and tests for linear trend of U-Cd across the four categories of smoking status were computed.</p>" ]

[ "<title>Results</title>", "<p>Geometric mean 24-hr urinary cadmium output and mean pulmonary function measurements at baseline stratified by age, height, and smoking status are presented in ##TAB##0##Table 1##. Current smokers had a higher geometric mean U-Cd and a lower mean FEV₁/FVC ratio than both former smokers and never smokers, and former smokers who reported smoking &gt; 30 pack-years had a higher mean U-Cd and a lower mean FEV₁/FVC ratio than both former smokers reporting ≤ 30 pack-years and never smokers (<italic>p</italic>-values for trend &lt; 0.05).</p>", "<p>##TAB##1##Table 2## shows the adjusted regression results of the main effects models of U-Cd with FEV₁ percent predicted, FVC percent predicted, or FEV₁/FVC ratio as the primary outcome measurement. Among all subjects, a single log-unit increase in U-Cd was inversely associated with FEV₁ percent predicted [β = −7.56%; 95% confidence interval (CI), −13.59 to −1.53%], FVC percent predicted (β = −2.70%; 95% CI, −7.39 to 1.99%), and FEV₁/FVC ratio (β = −4.13%; 95% CI, −7.61 to −0.66%) at baseline after adjusting for potential confounders. No statistically significant interactions between log U-Cd and time were found. The main-effects models also showed that current smokers had borderline significantly lower FEV₁ percent predicted (β = −10.70%; 95% CI, −21.40 to 0.00) compared with never smokers, and there was a gradual reduction in FEV₁ percent predicted across categories of smoking status (<italic>p</italic>-value for trend = 0.07). Additionally, there was a marginally significant increasing trend in FEV₁/FVC ratio decline over time across categories of smoking status (<italic>p</italic>-value for trend = 0.08). The models using raw FEV₁ and FVC measured in liters additionally adjusted for baseline age and height gave similar results to those found using the percentage of predicted values (data not shown).</p>", "<p>We assessed whether smoking status modified the effect of U-Cd on lung function (##FIG##0##Figure 1##). Because the interaction between log U-Cd and time was not statistically significant, this term was excluded when we tested the interaction between U-Cd and smoking status. A log-unit increase in urinary cadmium output was associated with −0.90% (95% CI, −5.37 to 3.58%), −6.22% (95% CI, −13.87 to 1.44%), −10.06% (95% CI, −16.49 to −3.64%), and −9.97% (95% CI, −19.60 to −0.34%) in FEV ₁ /FVC ratio among never smoker, former smokers with low pack-years, former smokers with high pack-years, and current smokers, respectively.For FEV₁/FVC ratio, there was a trend of increasing magnitude of effect of U-Cd on pulmonary function with increasing smoking status category (<italic>p</italic>-value for trend = 0.03). The same trend was seen for FEV₁ percent predicted, with the largest effect of U-Cd among former smokers with greater than 30 pack-years. The effect of U-Cd among current smokers was comparable but slightly weaker (<italic>p</italic>-value for trend = 0.28). In contrast, U-Cd was not significantly associated with FVC percent predicted, and smoking status did not seem to modify the association between U-Cd and FVC in our cohort.</p>" ]

[ "<title>Discussion</title>", "<p>In this study cohort, U-Cd was significantly associated with lower FEV₁ as the percentage of predicted values and FEV₁/FVC ratio after adjustment for potential confounders. We also observed a significant trend of increased effect of U-Cd on pulmonary function, especially FEV₁/FVC ratio, with increasing smoking status category. However, U-Cd was not associated with the rate of change in lung function over the period of observation.</p>", "<p>This finding is consistent with a cross-sectional study on urinary cadmium body burden and pulmonary function using data from NHANES III (##REF##14985551##Mannino et al. 2004##). Other studies that have examined this association in human populations have been primarily concerned with occupational exposure, and the results are somewhat inconsistent. For example, inverse associations between occupational exposure to cadmium and pulmonary function have been suggested in several studies (##REF##2895211##Davison et al. 1988##; ##REF##3778835##Edling et al. 1986##; ##REF##15683157##Jakubowski et al. 2004##; ##REF##15688851##Nordberg 2004##; ##REF##6980459##Sakurai et al. 1982##; ##REF##937833##Smith et al. 1976##), but the effect estimates of cadmium exposure on pulmonary function and the methods used among these studies varied widely, making it difficult to confirm a definitive dose–response relationship. In addition, other occupational studies of cadmium exposure and pulmonary function have found no such association (##REF##1303942##Cortona et al. 1992##; ##REF##226353##Lauwerys et al. 1979##; ##REF##204000##Stanescu et al. 1977##), although some of these latter studies did not control for smoking status among their study groups, raising methodological issues (##UREF##2##Elinder 1986##). Despite these uncertainties, it remains generally accepted that chronic exposure to cadmium fumes in the workplace is a risk factor for reduced pulmonary function (##UREF##2##Elinder 1986##; ##UREF##4##Occupational Safety and Health Administration 1993##), supporting the hypothesis that environmental cadmium exposure may be a significant contributor to reduced pulmonary function among smokers.</p>", "<p>Animal studies also support a link between cadmium exposure and reduced pulmonary function. For example, rats exposed to cadmium aerosol at a concentration of 1.6 mg/m³ for 2 weeks had increased leukocyte concentrations and alveolar thickening in the lung (##REF##3484847##Hart 1986##). The administration of three intratracheal injections of cadmium chloride over a 5-day period was associated with decreased lung capacity and increased alveolar wall thickness in rats (##REF##1728666##Lai and Diamond 1992##). Furthermore, repeated 1-hr exposures to 0.1% cadmium chloride for 3 weeks led to higher bronchoalveolar lavage fluid concentrations of macrophages and neutrophils in rats, as well as to decreased levels of glutathione during the first week of exposure (##REF##16484040##Kirschvink et al. 2006##). These results suggest that cadmium exposure can lead to an acute inflammatory reaction accompanied by a buildup of free radicals (evidenced by decreased glutathione levels), which can consequently lead to lung inflammation (##REF##16484040##Kirschvink et al. 2006##). Thus, in animals cadmium is associated with physiological changes indicative of reduced pulmonary function.</p>", "<p>We observed a dose-dependent relationship between urinary cadmium and FEV₁/FVC ratio with increasing smoking status category. It has previously been shown that smokers have higher body burdens of cadmium compared with nonsmokers, which may explain why the strength of the association between urinary cadmium and pulmonary function depends on smoking intensity (##REF##4109933##Lewis et al. 1972##). However, because urinary cadmium was associated with pulmonary function even after adjusting for smoking status, smoking factors alone cannot explain the association. The cadmium in cigarette smoke may exert a toxic effect that is independent of the combined effects of the non-cadmium elements of cigarette smoke.</p>", "<p>It was also surprising that we did not find a strong association between 24-hr U-Cd and FVC. Although the NHANES III study did find an inverse association between creatinine-adjusted urinary cadmium and FVC (##REF##14985551##Mannino et al. 2004##), the association was weaker than that between urinary cadmium and FEV₁. It is possible that, due to sample size (<italic>n</italic> = 96), we did not have the statistical power to detect the association between urinary cadmium and FVC.</p>", "<p>We also did not find any suggestion of an effect among nonsmokers. One potential explanation is that cadmium’s effect on lung function requires airborne exposure and pulmonary deposition, a possibility also noted by ##REF##14985546##Hendrick (2004)## when commenting on the same phenomenon in ##REF##14985551##Mannino et al.’s (2004)## study of cadmium and lung function in NHANES III data. An alternative explanation, as Hendrick points out, is that cadmium body burden may simply be a passive marker of exposure to cigarette smoke and unrelated to the decrements in lung function seen among current and former smokers in our study. However, because there was a significant effect of cadmium on lung function even after controlling for smoking status, the results are consistent with an independent effect of cadmium. In addition, although smoking would clearly be the dominant source of airborne cadmium exposure to any given individual, it is well known that cadmium content varies widely among cigarettes (depending on cadmium levels in soil used to grow tobacco, methods of processing, and the like) (##REF##1586467##Yue 1992##), which may explain why we would see an effect of cadmium on lung function that is independent from an effect of smoking. Unfortunately, our study does not answer the question of whether other metals that occur in cigarette smoke, such as lead and aluminum, may enhance the effect of cadmium on lung function. ##REF##16685021##Mutti et al. (2006)## demonstrated that these elements are abundant along with cadmium in the exhaled breath condensate of current smokers. Cadmium and lead both have mechanisms of toxicity that could result in the physiological changes that cause reduced lung function: Cadmium inhibits the production of connective tissue in the lungs, and lead may cause glutathione depletion in the lungs (##REF##16685021##Mutti et al. 2006##). It cannot be ruled out, therefore, that these two effects (as well as others) may be acting in tandem to result in the decrements in lung function seen among the subjects in this study.</p>", "<p>In the NHANES III study, urinary cadmium concentrations were adjusted for creatinine concentration (##REF##14985551##Mannino et al. 2004##). In this study, creatinine-adjusted urine concentrations were not used because we had more complete data on 24-hr urinary output and thus we adjusted urinary cadmium for total urinary output instead. Adjustment for urinary creatinine is required for spot samples because of concentration dilution of urine, but creatinine adjustment may not be necessary for 24-hr urine samples (##REF##3988354##Berlin et al. 1985##). The use of certain methods for the adjustment of urinary cadmium output for hydration status may also influence the effect of smoking status on urinary cadmium and pulmonary function.</p>", "<p>In this study, most subjects had multiple time points of pulmonary function data, which allowed us to reduce the effect of within-subject variability in pulmonary function over time. It also helped us to explore time-dependent behavior of smoking status in the model and eliminate any trend effects due to time that are not accounted for by the other variables. However, there were some limitations to this study. First, our study cohort was small (<italic>n</italic> = 96) compared with similar analyses drawn from large-scale cohort studies such as NAS and NHANES III, and thus our statistical power is comparatively low. Second, although we had repeated pulmonary function data, we only had one valid time point (1994–1995) of urinary cadmium data per subject. Thus, we had to assume that the 24-hr urinary cadmium outputs from 1994–1995 were representative of the actual 24-hr urinary cadmium outputs for the entire time (1994–2002) during which pulmonary data were collected. This might be a reason that we did not find a significant association of urinary cadmium with the rate of change in lung function. In addition, we had only at most three measurements of lung function per subject, so we may not have the power to detect such an association. Third, we cannot say whether high urinary cadmium output physiologically precedes decrements in pulmonary function over time because there was not a long enough follow-up period between urinary cadmium measurement and pulmonary testing to make that kind of causal inference. Fourth, we lacked data on urinary cotinine levels and therefore we did not have an objective measurement of smoking intensity. Finally, we lacked precise data on the duration of time since quitting among the cohort of former smokers in our study, so we were unable to take into consideration that former smokers may differ in susceptibility to the effects of cadmium exposure depending on how long they have abstained from smoking.</p>", "<p>In conclusion, we found significant evidence that there exists an inverse association between baseline cadmium body burden and pulmonary function, but the association does not change over time. Our results also suggest that cigarette smoking modifies this association. However, the present study should be interpreted with caution because of the small sample size; further studies with a larger sample size as well as a longitudinal design are needed to confirm our findings. The availability of more complete information on cigarette smoking, such as plasma cotinine levels, may also help elucidate the effect of smoking status on the association between cadmium exposure and pulmonary function.</p>" ]

[]

[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>High levels of cadmium exposure are known to cause emphysema in occupationally exposed workers, but little has been reported to date on the association between chronic environmental cadmium exposure and pulmonary function.</p>", "<title>Objective</title>", "<p>In this study we examined the association between pulmonary function and cadmium body burden in a subcohort of the Normative Aging Study, a community-based study of aging.</p>", "<title>Methods</title>", "<p>We examined 96 men who had cadmium measured in single 24-hr urinary specimens collected in 1994–1995 and who had one to three tests of pulmonary function between 1994 and 2002 (a total of 222 observations). We used mixed-effect models to predict pulmonary function based on individual 24-hr urinary cadmium output, adjusted for age, height, time elapsed from the baseline, and smoking status. We assessed effect modification by smoking status.</p>", "<title>Results</title>", "<p>Among all subjects, a single log-unit increase in baseline urinary cadmium was inversely associated with forced expiratory volume in 1 sec (FEV₁) percent predicted [β = −7.56%; 95% confidence interval (CI) −13.59% to −1.53%]; forced vital capacity (FVC) percent predicted (β = −2.70%; 95% CI −7.39% to 1.99%), and FEV₁/FVC ratio (β = −4.13%; 95% CI −7.61% to −0.66%). In models including an interaction between urinary cadmium and smoking status, there was a graded, statistically significant reduction in FEV₁/FVC ratio across smoking status in association with urinary cadmium.</p>", "<title>Conclusions</title>", "<p>This study suggests that chronic cadmium exposure is associated with reduced pulmonary function, and cigarette smoking modifies this association. These results should be interpreted with caution because the sample size is small, and further studies are needed to confirm our findings.</p>" ]

[ "<p>According to the World Health Organization (##UREF##5##WHO 2007##), 80 million people worldwide are afflicted with chronic obstructive pulmonary disease (COPD), and 3 million died from COPD in 2005. The WHO also predicts that COPD will be the fourth leading cause of death globally by 2030. Recent attention has focused on evaluating the relationship between pulmonary function and cadmium body burden and the possible role of cadmium in the development of pulmonary diseases such as COPD and emphysema.</p>", "<p>Cadmium is a trace element that has no nutritive function in humans (##UREF##3##Newman-Taylor 1998##), and it is a probable lung carcinogen in humans according to the Agency for Toxic Substances and Disease Registry (##UREF##0##ATSDR 1999##) <italic>Toxicological Profile for Cadmium</italic>. The toxicity of cadmium in the lungs has been well documented in animal studies. For example, cadmium inhalation produces a pulmonary inflammatory response in mammals (##REF##16484040##Kirschvink et al. 2006##), and daily doses of 1.6 mg/m³ cadmium aerosol over a 6-week period were associated with acute pulmonary damage and emphysema in rats (##REF##3484847##Hart 1986##). Thus, pulmonary toxicity studies in animals support the hypothesis that reduced lung function among smokers may be partially attributable to cadmium in cigarette smoke. Primary nonoccupational sources of cadmium exposure within the general population include ingestion of contaminated food (##REF##9569444##Järup et al. 1998##; ##REF##1596102##Kido et al. 1992##) and inhalation of cigarette smoke (##REF##9569444##Järup et al. 1998##; ##REF##15238284##Satarug and Moore 2004##), and smokers have higher body burdens of cadmium than nonsmokers (##REF##16468007##Erzen and Kragelj 2006##; ##REF##14605069##Grasseschi et al. 2003##; ##REF##9569444##Järup et al. 1998##; ##REF##16685021##Mutti et al. 2006##).</p>", "<p>Although previous studies have examined the association between pulmonary function and cadmium exposure in animals (##REF##3484847##Hart 1986##; ##REF##16484040##Kirschvink et al. 2006##; ##REF##1728666##Lai and Diamond 1992##) and in occupationally exposed cohorts (##REF##1303942##Cortona et al. 1992##; ##REF##2895211##Davison et al. 1988##; ##REF##3778835##Edling et al. 1986##; ##UREF##2##Elinder 1986##; ##REF##15683157##Jakubowski et al. 2004##; ##REF##226353##Lauwerys et al. 1979##; ##REF##15688851##Nordberg 2004##; ##UREF##4##OSHA 1993##; ##REF##6980459##Sakurai et al. 1982##; ##REF##937833##Smith et al. 1976##; ##REF##204000##Stanescu et al. 1977##), few studies have evaluated the association between cadmium exposure and pulmonary function in the general population.</p>", "<p>A recent study evaluated the effects of cadmium exposure (measured by individual spot urinary concentrations) on pulmonary function among participants in the Third National Health Examination and Nutrition Examination Survey (NHANES III) (##REF##14985551##Mannino et al. 2004##). Forced vital capacity (FVC), forced expiratory volume in 1 sec (FEV₁), FEV₁/FVC ratio, and a diagnosis of the Global Initiative on Obstructive Lung Disease (GOLD) stage II or higher COPD (defined as FEV₁/FVC ratio &lt; 0.7 and FEV₁ &lt; 80% predicted) were all inversely correlated with urinary cadmium concentrations in current and former smokers but not in nonsmokers (##REF##14985551##Mannino et al. 2004##). The Mannino study was crucial in that it was the first to evaluate the relationship between cadmium body burden and pulmonary function using a quantifiable biomarker of exposure. Urinary cadmium is influenced by body burden and increases with age (##REF##9569444##Järup et al. 1998##) and in proportion to the accumulated amount in the body (##UREF##2##Elinder 1986##), and urinary cadium is therefore generally recognized as an effective exposure surrogate.</p>", "<p>The aim of this study was to examine the association between pulmonary function and cadmium body burden in a subcohort of the Normative Aging Study (NAS) while using multiple time points of pulmonary function measurements to control for subject-specific variation and to reduce residual confounding associated with subject age and time.</p>" ]

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[ "<fig id=\"f1-ehp-116-1226\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Estimated change (95% confidence interval) in FEV₁ percent predicted, FVC percent predicted, and FEV₁/FVC ratio for every log-unit increase in 24-hr urinary cadmium output by smoking status. pkyr, pack-years. Each model was adjusted for smoking status, time elapsed from the baseline, and the interaction between smoking status and time. FEV₁/FVC ratio model was also adjusted for baseline age and height. The <italic>p</italic>-values above each pulmonary function are for tests for trend (<italic>n</italic> = 96 subjects, 222 pulmonary function observations, with a maximum of 3 pulmonary function observations per subject).</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1226\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Frequency distributions, geometric mean 24-hr urinary cadmium outputs, and mean pulmonary function measurements at baseline stratified by age, height, and smoking status (<italic>n</italic> = 96).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. (%)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">24-hr U-Cd (ng)<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1226\">a</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">FEV₁ (% predicted)<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1226\">b</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">FVC (% predicted)<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1226\">b</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">FEV₁/FVC ratio (%)<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1226\">b</xref></th></tr></thead><tbody><tr><td colspan=\"6\" align=\"left\" rowspan=\"1\">Baseline age (years)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 65</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36 (37.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">615.5 (1.9)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">95.3 (14.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">91.9 (12.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78.3 (7.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 65–70</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26 (27.1)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">566.9 (2.2)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">91.2 (17.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">90.8 (13.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">74.2 (8.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &gt; 70</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34 (35.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">577.2 (1.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">92.3 (20.9)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">87.0 (12.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">76.4 (12.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>p</italic> for trend</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.48</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.39</td></tr><tr><td colspan=\"6\" align=\"left\" rowspan=\"1\">Baseline height (cm)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 170</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">28 (29.2)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">573.1 (2.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">98.4 (14.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">91.9 (10.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78.9 (8.6)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 170–180</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">26 (27.1)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">585.9 (2.1)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">91.3 (18.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">90.1 (13.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72.5 (11.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &gt; 180</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42 (43.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">600.4 (1.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">89.6 (19.2)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">85.0 (13.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">77.4 (8.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>p</italic> for trend</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.93</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.43</td></tr><tr><td colspan=\"6\" align=\"left\" rowspan=\"1\">Smoking status</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  Never</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35 (36.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">394.3 (2.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">97.5 (16.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">91.8 (12.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78.6 (8.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Former (0–30 pack-years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">32 (33.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">621.5 (1.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">97.7 (15.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">92.8 (11.2)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">77.9 (8.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Former (&gt; 30 pack-years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18 (18.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">766.0 (1.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">86.3 (18.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">83.8 (12.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">75.3 (11.8)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Current</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11 (11.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1165.0 (1.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">77.5 (15.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">85.3 (15.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">67.8 (6.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>p</italic> for trend</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.027</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1226\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Adjusted estimates (95% confidence intervals) for FEV₁ percent predicted, FVC percent predicted, and FEV₁/FVC ratio from the main effects models of urinary cadmium <xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1226\">a</xref>.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Characteristic</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">FEV₁ (% predicted)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">FVC (% predicted)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">FEV₁/FVC ratio (%)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Intercept</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">142.2 (105.7 to 178.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">107.6 (79.28 to 136.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">130.0 (67.50 to 192.6)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Baseline age (years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.03 (−0.33 to 0.26)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Baseline height (cm)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.14 (−0.45 to 0.16)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Smoking status</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Never</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Referent</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Referent</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Referent</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Former (0–30 pack-years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.63 (−4.77 to 12.04)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.01 (−4.49 to 8.52)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.54 (−3.30 to 6.39)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Former (&gt; 30 pack-years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−6.83 (−16.58 to 2.92)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−5.33 (−13.03 to 2.37)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−1.30 (−7.03 to 4.42)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Current</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−10.70 (−21.40 to 0.00)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−7.00 (−16.07 to 2.06)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−3.17 (−9.64 to 3.31)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Time elapsed from the baseline (years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00 (−1.90 to 3.90)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.12 (−3.74 to 3.51)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.94 (−1.29 to 3.17)</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Smoking × time</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Never</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Referent</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Referent</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Referent</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Former (0–30 pack-years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.49 (−0.19 to 1.17)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.29 (−0.56 to 1.14)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06 (−0.46 to 0.58)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Former (&gt; 30 pack-years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.55 (−1.46 to 0.36)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.15 (−0.98 to 1.28)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.47 (−1.16 to 0.23)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Current</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.32 (−1.76 to 1.11)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.15 (−0.62 to 2.92)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−1.07 (−2.16 to 0.03)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">LogU-Cd (ng)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−7.56 (−13.59 to −1.53)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−2.70 (−7.39 to 1.99)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−4.13 (−7.61 to −0.66)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">LogU-Cd × time</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.19 (−0.67 to 0.29)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.10 (−0.70 to 0.50)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.11 (−0.47 to 0.26)</td></tr></tbody></table></table-wrap>" ]

[ "<disp-formula></disp-formula>", "<disp-formula></disp-formula>", "<disp-formula></disp-formula>" ]

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[ "<fn-group><fn><p>We thank N. Lupoli for his assistance with the cadmium measurements.</p></fn><fn><p>This research was supported primarily by the National Institute of Environmental Health Sciences (NIEHS R01-ES05257, P42-ES05947, and NIEHS Center grant P30-ES00002). B.M. was partially supported by the National Cancer Institute (R03-CA130045). The core data were collected under the auspices of the VA Normative Aging Study with support from the Research Services and the Cooperative Studies Program/ERIC of the U.S. Department of Veterans Affairs and the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC).</p></fn><fn><p>The views expressed in this article are those of the authors and do not necessarily represent the views of the U.S. Department of Veterans Affairs.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1226\"><label>a</label><p>Geometric mean (geometric SD).</p></fn><fn id=\"tfn2-ehp-116-1226\"><label>b</label><p>Arithmetic mean (SD).</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn3-ehp-116-1226\"><label>a</label><p>Sample size = 96 subjects, 222 pulmonary function observations, with a maximum of 3 pulmonary function observations per subject.</p></fn></table-wrap-foot>" ]

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[ "<title>Materials and Methods</title>", "<title>Chemicals</title>", "<p>We purchased T₃ from Sigma Chemical Co. (St. Louis, MO, USA), and TCDD and PCDFs [2,3,4,7,8-pentachloro-dibenzofuran (pentaCDF), 2,8-diCDF, and 2-monoCDF] from Cambridge Isotope Laboratory (Andover, MA, USA); all congeners were &gt; 98% pure. We purchased all PCB congeners [3,3′,4,4′-tetraCB (PCB-77), 3,3′,4,4′,5-pentaCB (PCB-126), 2,3,4,4′,5-pentaCB (PCB-114); 2,3′,4,4′,5-pentaCB (PCB-118); and 2,2′,4,4′,5,5′-hexaCB (PCB-153)] and OH-PCBs [4-OH-2′,3,3′,4′,5′-pentaCB (4-OH-PCB-106); 4-OH-2,3,3′,4,5,5′-hexaCB (4-OH-PCB-159); 4-OH-2,3,3′,5,5′,6-hexaCB (4-OH-PCB-165); and 4-OH-2,2′,3,4′,5,5′,6-heptaCB (4-OH-PCB-187)] from AccuStandard Chemicals (New Haven, CT, USA). All PCB congeners were &gt; 99% pure, and OH-PCBs were &gt; 98% pure.</p>", "<title>Plasmids</title>", "<p>The expression vectors for human TRβ1, GR, and mouse retinoid X receptor β (RXRβ), as well as the 2× glucocorticoid response element-luciferase (LUC) reporter in pTAL-LUC (BD Biosciences Clontech, Palo Alto, CA, USA) are described elsewhere (##REF##11443112##Iwasaki et al. 2001##). The LUC reporter constructs, the chick lysozyme (F2)–thymidine kinase (TK)-LUC, and the artificial direct repeat TRE, direct repeat 4 (DR4)-TK-LUC, in the PT109 vector are also described elsewhere (##REF##10067863##Koibuchi et al. 1999##).</p>", "<p>We subcloned restriction enzyme fragments of the cDNA inserts of human TRβ1 and GR into the <italic>Kpn</italic>I and <italic>Xba</italic>I sites of pcDNA3. To create human TRβ1 with <italic>Not</italic>I and <italic>Xho</italic>I sites, we changed the oligo-nucleotides used to create the <italic>Not</italic>I site from Asp-97 to Arg, from Lys-98 to Pro, and from Asp-99 to Pro. The oligonucleotides we used to create the <italic>Xho</italic>I site were changed from Thr-171 to Leu and from Asp-172 to Gly (##REF##2726731##Thompson and Evans 1989##). The creation of the <italic>Not</italic>I site on GR changed Pro-416 to Arg, whereas the creation of the <italic>Xho</italic>I site did not alter the GR amino acid sequence.</p>", "<p>We constructed chimeric receptors by exchanging <italic>Kpn</italic>I<italic>-Not</italic>I, <italic>Not</italic>I<italic>-Xho</italic>I, or <italic>Xho</italic>I<italic>-Xba</italic>I restriction fragments of human TRβ1 and GR with <italic>Not</italic>I and <italic>Xho</italic>I sites.</p>", "<title>Cell culture</title>", "<p>We maintained CV-1 and TE671 cells in Dulbecco’s modified Eagle’s medium supplemented with 5 μg/mL penicillin/streptomycin and 10% fetal bovine serum deprived of small lipophilic hormone at 37°C under a 5% CO₂ atmosphere as previously described (##REF##12445811##Iwasaki et al. 2002##).</p>", "<title>Transient transfection-based reporter gene assays</title>", "<p>We plated cells in 24-well plates 2 days before transfection by calcium phosphate coprecipitation (##REF##12445811##Iwasaki et al. 2002##). The internal control was a cytomegalovirus–β-galactosidase plasmid. Sixteen to 24 hr later, the cells were incubated for 24 hr in fresh medium containing T₃ and/or PCBs/dioxins. We then harvested the cells to measure the LUC activities as previously described (##REF##12445811##Iwasaki et al. 2002##). We balanced total amounts of DNA per well by adding pcDNA3 plasmids (Invitrogen, San Diego, CA, USA). LUC activities were normalized to that of β-galactosidase and then calculated as relative LUC activity. We repeated all transfection studies at least twice in triplicate. Data represent means ± SEs of triplicates. We analyzed the data by analysis of variance (ANOVA) and by post hoc comparisons using Bonferroni’s multiple range test.</p>", "<title>Electrophoretic mobility shift assay</title>", "<p>The methods for the electrophoretic mobility shift assay (EMSA) have been previously described (##REF##8910392##Satoh et al. 1996##). Briefly, we labeled double-stranded oligonucleotides using the Klenow fragment with [α-32P]-dCTP. We incubated <italic>in vitro</italic> transcribed and translated human TRβ1, mouse RXRβ, and 1 × 104 counts per minute labeled nucleotides in binding buffer [25 mM HEPES (pH 7.6), 5 mM MgCl₂, 4 mM EDTA, 110 mM NaCl, 5 μg/μL bovine serum albumin, 1 μg/μL of poly(deoxyinosinic-deoxycytidylic) acid sodium salt, 20% glycerol, and 2 mM dithiothreitol] with or without 10⁻⁶ M T₃ and/or 10⁻⁸ M PCBs/dioxins for 30 min on ice. We added various amounts of control reticulocyte lysate to some samples to render a consistent total volume of lysate. After incubation, the samples were resolved by electrophoresis and visualized by autoradiography.</p>" ]

[ "<title>Results</title>", "<title>Effects of PCBs/dioxins on TR-mediated transcription</title>", "<p>We previously reported that several PCB congeners, including OH metabolites such as 4-OH-PCB-106 and a PCB mixture (Aroclor 1254), suppress TR-mediated transcription (##REF##12445811##Iwasaki et al. 2002##; ##REF##14985366##Miyazaki et al. 2004##). Here, we further investigated the effects of dioxins on TR-mediated transcription in monkey fibroblast-derived CV-1 and human medulloblastoma-derived TE671 cells using transient cotransfection experiments (##FIG##0##Figure 1##). The most toxic congener, TCDD (TEF = 1) (##REF##16829543##Van den Berg et al. 2006##), in a range of concentrations from 10⁻¹⁶ to 10⁻⁶ M, did not suppress TR-mediated transcription activated by 10⁻⁷ M T₃ (##FIG##0##Figure 1A##). TR-mediated transcription was weakly suppressed by 10⁻⁹ M pentaCDF (TEF = 0.3), which is relatively higher than the effective dose of 4-OH-PCB-106, in TE671 but not in CV-1 cells. Among the PCDF congeners, 2,8-diCDF and 2-monoCDF did not affect TR-mediated transcription (##FIG##1##Figure 2##).</p>", "<p>We also examined the effects of several representative PCB congeners, including coplanar PCBs (##FIG##0##Figure 1C##), noncoplanar PCBs (##FIG##0##Figure 1D##), and OH-PCBs (##FIG##0##Figure 1E,F##). We selected these congeners on the basis of their <italic>ortho</italic>-substitution profiles and 4-OH-PCB metabolites, which had significant effects on TR (##REF##12445811##Iwasaki et al. 2002##; ##REF##14985366##Miyazaki et al. 2004##). Both PCB-77 (non-<italic>ortho</italic>; ##FIG##1##Figure 2##), a coplanar PCB congener, and PCB-153 (di-<italic>ortho</italic>; ##FIG##0##Figure 1D##), a noncoplanar PCB, slightly suppressed TR-mediated transcription at 10⁻¹⁰ M and 10⁻¹¹ M, respectively, whereas PCB-114 (mono-<italic>ortho</italic>; ##FIG##0##Figure 1C##) and 4-OH-PCB-165 (##FIG##0##Figure 1E##) had no effects. On the other hand, 4-OH-PCB-106 (##FIG##0##Figure 1F##) effectively suppressed TR-mediated transcription. ##FIG##1##Figure 2## summarizes the results of the effects of PCB/dioxin congeners on TR, including those from our previous studies under the same experimental conditions using CV-1 and TE671 cell lines (##REF##12445811##Iwasaki et al. 2002##; ##REF##14985366##Miyazaki et al. 2004##), with a distinct difference among these compounds. Hydroxylation, degree of chlorination, and structural coplanarity do not correlate with the magnitude of suppression of TR-mediated transcription.</p>", "<title>Correlation of suppression of TR action and TR-TRE dissociation</title>", "<p>We previously reported that suppression of TR-mediated transcription by 4-OH-PCB-106 is induced by partial dissociation of TR from TRE (##REF##14985366##Miyazaki et al. 2004##). We examined the effects of PCBs/dioxins on TR-TRE binding using EMSA. Some of the PCB/dioxin congeners neither altered TR-mediated transcription nor affected TR-TRE binding. On the other hand, the PCB congeners that suppressed TR-mediated transcription effectively dissociated TR-TRE binding, as shown as a representative data in ##FIG##2##Figure 3##. These results suggest that the magnitude of suppression of TR-mediated transcription by PCBs/dioxins correlates with those of the dissociation of TR-TRE binding, and that the site of action of PCBs in TR may be located within the DNA-binding domain (DBD).</p>", "<title>PCBs alter TR-mediated transcription through the DBD</title>", "<p>Because several PCB congeners and their OH metabolites affect TR-mediated transcription by partially dissociating TR from TRE, we determined which functional domain of TR is affected by using 4-OH-PCB-106, which had the most suppressive effect among the PCB congeners and their OH metabolites. We previously showed that 4-OH-PCB-106 did not affect GR-mediated transcription (##REF##12445811##Iwasaki et al. 2002##). We therefore constructed a series of chimeric receptors containing TR and GR functional domains (##FIG##3##Figure 4A##). Transcription of chimeric receptors containing TR-DBD was suppressed by 4-OH-PCB-106 (##FIG##3##Figure 4B,C##), whereas 4-OH-PCB-106 was not significantly suppressive when the chimeric receptors contained GR-DBD (##FIG##3##Figure 4B,C##). These results indicate that PCB congeners act on the TR through DBD rather than on the TR N-terminus or TR-ligand binding domain (LBD).</p>" ]

[ "<title>Discussion</title>", "<p>In the present study, we examined how PCBs/dioxins affect TR-mediated transcription and found distinct effects of several PCB congeners on TR. For example, several PCB congeners exerted significant suppression, whereas TCDD, the most toxic congener, did not. The magnitude of suppression was correlated with that of TR dissociation from TRE. Furthermore, we showed that these chemicals might act on TR-DBD.</p>", "<p>We previously reported that the transcription mediated by TR is suppressed by 4-OH-PCB-106 (##REF##12445811##Iwasaki et al. 2002##), and others have shown that dioxins and coplanar PCBs may disrupt the TH system (##REF##862558##Bastomsky 1977##; ##REF##3554617##Gorski and Rozman 1987##; ##REF##3111013##Henry and Gasiewicz 1987##; ##REF##8560478##Kohn et al. 1996##; ##REF##11836014##Nishimura et al. 2002##; ##REF##3715868##Potter et al. 1986##; ##REF##7672011##Van Birgelen et al. 1995##). Thus, we initially postulated that dioxins, coplanar PCBs, and OH-PCB compound suppress TR-mediated transcription. Although TCDD did not exert any effects, one PCDF congener and several PCB congeners, including mono-<italic>ortho–</italic>substituted congeners, suppressed TR action. On the other hand, 4-OH-PCB-165 had no suppressive effects. These results indicate that 4-hydroxylation and coplanarity are not essential for inducing the suppression.</p>", "<p>We investigated which functional domain of TR is responsible for PCB action in a system using a series of chimeric receptors (##FIG##3##Figure 4##) and found that PCBs act on TR-DBD, but not on LBD. Although PCBs do not have high affinity for TR-LBD (##REF##10090705##Cheek et al. 1999##), there are several possibilities for the interaction of PCBs and TR-DBD. It is conceivable that PCBs bind to and change the conformation of TR-DBD because we found that PCBs dissociate TR-coactivator complexes from TRE but not from TR-corepressor complexes (##REF##14985366##Miyazaki et al. 2004##) and alter the binding between coactivators or corepressors and TR (##REF##14985366##Miyazaki et al. 2004##). Coactivators bind to the activation function-2 domain of TR, which is located at the C-terminus of the LBD. In contrast, corepressors bind to a broad region of TRs, including the hinge region that is located immediately adjacent to the DBD. These observations are consistent with the notion that PCBs bind to the DBD and subsequently change the conformation of the domain and its surrounding region to induce the dissociation from TRE. Other possible interactions of PCBs and TR-DBD would be masking of the PCB-binding region of TR by corepressors and/or alteration of the TR-DBD conformation by PCBs binding or recruitment of a “PCB-responsive TR-binding protein.”</p>", "<p>Although the present study revealed that PCBs suppress TR action, ##REF##18007995##Gauger et al. (2007)## found that some PCB congeners, such as PCB-105 and/or PCB-118 (mono-<italic>ortho</italic> PCB), may exert agonistic action toward TR-mediated transcription in rat somatomam-motroph-derived GH3 cells. Their study suggested that OH-PCB-105 or OH-PCB-118 may be responsible for this agonistic action toward TR because this action occurred when cytochrome P450 (CYP) expression was induced by PCB-126. Thus, a mixture of coplanar and noncoplanar PCBs might result in an agonistic effect on TR-mediated transcription. On the other hand, we confirmed by semiquantitative reverse transcriptase-polymerase chain reaction that CYP1A1 is not expressed in CV-1 cells and that various PCB congeners do not induce CYP1A1 expression [Supplemental Material, Figure 1S (available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11176/suppl.pdf\">http://www.ehponline.org/members/2008/11176/suppl.pdf</ext-link>)]. Thus, we could refute the possible involvement of CYP1A1 and AhR in our experimental system, and the PCB congeners each might directly act on TR.</p>", "<p>Dioxins/PCBs cause learning and memory impairment in children (##REF##7816522##Koopman-Esseboom et al. 1994##) and in laboratory animals (##UREF##0##Hojo et al. 2008##; ##REF##8725643##Schantz et al. 1996##; ##REF##10386826##Seo et al. 1999##, ##REF##10974589##2000##). However, the molecular mechanisms of PCB action in the brain have not been clarified. Because the amounts of AhR expressed in the brain are limited, AhR’s involvement in PCB/dioxin actions in the brain might be less than that in other organs where it is abundant, such as the liver and reproductive organs. Thus, other signaling pathways may be involved in the neurotoxic manifestations. A possible mechanism is the disruption of intracellular signaling pathways that depend on Ca²⁺ homeostasis (##REF##18648619##Kodavanti 2005##; ##REF##17475378##Simon et al. 2007##). We have also shown that PCB congeners alter the intracellular Ca²⁺ levels in cultured neurons (##REF##15833269##Okada et al. 2005##), which may be relevant to the altered expression of Ca²⁺ sensitive genes, such as c-<italic>Jun</italic> (##REF##16300829##Shimokawa et al. 2006##). Another possible mechanism of neurotoxicity may be relevant to a decreasing trend in total T₄ levels in people living in the general environment (##REF##7816522##Koopman-Esseboom et al. 1994##; ##REF##9828307##Nagayama et al. 1998##). <italic>In utero</italic> and lactational exposures to TCDD have been reported to induce thyroid gland hyperplasia (##REF##12697716##Nishimura et al. 2003##) or to induce the liver uridine diphosphate glucuronosyltransferase 1 family that catalyzes TH (##REF##15716479##Nishimura et al. 2005##), which could induce hypothyroidism. However, the magnitude of decrease in brain TH levels might not be sufficient to induce hypothyroidism in the brain (##REF##12151632##Meerts et al. 2002##). Instead, PCBs might suppress TR action in the brain by dissociating TR from TRE as shown in the present study, or act as agonists of TR in cells that express AhR and CYP1A1, as noted above (##REF##18007995##Gauger et al. 2007##). Thus, we consider multiple pathways of PCB action to be involved in the induction of learning and memory disorders. Further analysis is required to clarify the distinct role of each of these possible signaling pathways.</p>", "<p>In summary, we examined the effects of several representative PCB/dioxin congeners on TR-mediated transcription. We also generated chimeric receptors from TR and GR to identify the functional domain responsible for PCB action. Under our experimental conditions, PCBs apparently acted on the DBD of TR. We propose that several pathways should be considered to determine how PCB and its related compounds exert their toxic effects.</p>" ]

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[ "<p>T.I. was previously employed by Eli Lilly and Company. The other authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Polychlorinated biphenyls (PCBs), polychlorinated dibenzo-<italic>p</italic>-dioxins, and poly-chlorinated dibenzofurans adversely affect the health of humans and various animals. Such effects might be partially exerted through the thyroid hormone (TH) system. We previously reported that one of the hydroxylated PCB congeners suppresses TH receptor (TR)-mediated transcription by dissociating TR from the TH response element (TRE). However, the binding site of PCB within TR has not yet been identified.</p>", "<title>Objectives</title>", "<p>We aimed to identify the functional TR domain responsible for the PCB-mediated suppression of TR action by comparing the magnitude of suppression using several representative PCB/dioxin congeners.</p>", "<title>Materials and methods</title>", "<p>We generated chimeric receptors by combining TR and glucocorticoid receptor (GR) and determined receptor-mediated transcription using transient transfection-based reporter gene assays, and TR-TRE binding using electrophoretic mobility shift assays.</p>", "<title>Results</title>", "<p>Although several PCB congeners, including the hydroxylated forms, suppressed TR-mediated transcription to various degrees, 2,3,7,8-tetrachlorodibenzo-<italic>p</italic>-dioxin did not alter TR action, but 2,3,4,7,8-pentachlorodibenzofuran weakly suppressed it. The magnitude of suppression correlated with that of TR–TRE dissociation. The suppression by PCB congeners was evident from experiments using chimeric receptors containing a TR DNA-binding domain (DBD) but not a GR-DBD.</p>", "<title>Conclusions</title>", "<p>Several nondioxin-like PCB congeners and hydroxylated PCB compounds suppress TR action by dissociating TR from TRE through interaction with TR-DBD.</p>" ]

[ "<p>Polychlorinated biphenyls (PCBs), poly-chlorinated dibenzo-<italic>p</italic>-dioxins (PCDDs), and polychlorinated dibenzofurans (PCDFs) are extremely persistent environmental compounds that adversely affect the health of humans and other animals. These compounds are toxic to the fetal and early post-natal developing brain, which is exposed via the placenta and breast milk as a result of maternal exposure (##REF##1906100##Gladen and Rogan 1991##; ##REF##8037844##Safe 1994##; ##REF##2115098##Tilson et al. 1990##; ##REF##9339820##Tilson and Kodavanti 1997##), even if the exposure level is too low to induce maternal toxicity (##REF##1360299##Chen et al. 1992##; ##REF##8843560##Jacobson and Jacobson 1996a##; ##REF##3930167##Jacobson et al. 1985##, ##UREF##1##1990##). ##REF##8703183##Jacobson and Jacobson (1996b)## suggested that exposure to PCBs <italic>in utero</italic> induced intellectual impairment in children born to mothers who consumed excessive amounts of sport fish obtained from the Great Lakes area in the United States. Disruption of cognitive development among children exposed to dioxins and PCBs has been documented in accidental human exposures, such as in the Yusho and Yu-cheng incidences (##REF##11386736##Aoki 2001##), and confirmed in experimental animals (##REF##9861526##Giesy and Kannan 1998##; ##REF##9291491##Jacobson and Jacobson 1997##). In addition, exposure to PCBs may alter dendrito-genesis in several brain regions during development (##UREF##1##Kimura-Kuroda et al. 2005##; ##REF##17450224##Lein et al. 2007##).</p>", "<p>The effects of PCBs/dioxins on the brain have been interpreted in several ways. First, dioxin-like PCB congeners are able to bind to and activate aryl hydrocarbon receptors (AhRs), exerting various toxic effects. The degree of such effects is numerically expressed as the toxicity equivalency factor (TEF) and is standardized to 2,3,7,8-tetrachlorodibenzo-<italic>p</italic>-dioxin (TCDD; TEF = 1) (##REF##16829543##Van den Berg et al. 2006##). However, the TEF concept might not fully encompass the developmental neurotoxicity of PCBs, because AhR expression in the brain may be regional (##REF##11097868##Huang et al. 2000##; ##REF##11054704##Petersen et al. 2000##) and because PCB congeners are considered to have neuro-toxicities via both AhR-dependent and AhR-independent mechanisms (##REF##9861526##Giesy and Kannan 1998##; ##REF##9339820##Tilson and Kodavanti 1997##). Second, PCBs may disrupt intracellular signaling pathways that are essential not only for brain function but also for brain development (reviewed by ##REF##18648619##Kodavanti 2005##), which will disturb intra-cellular calcium homeostasis (##REF##8236268##Kodavanti et al. 1993##). For example, <italic>ortho</italic>-substituted non-coplanar congeners might alter protein kinase C translocation, cellular dopamine uptake, and the formation of reactive oxygen species. Such toxicity is referred to as the neurotoxicity equivalent (reviewed by ##REF##18648619##Kodavanti 2005##; ##REF##17475378##Simon et al. 2007##).</p>", "<p>In addition to these signaling pathways, we and several other groups have focused on the possible interactions of these chemicals with the thyroid hormone (TH) system (##REF##14759069##Bogazzi et al. 2003##; ##REF##12445811##Iwasaki et al. 2002##; ##REF##15695023##Kitamura et al. 2005##; ##REF##14985366##Miyazaki et al. 2004##). TH is crucial for brain development, and TH deficiency during the critical perinatal period has been reported to cause cretinism, with severe cognitive and/or mental disorders in the offspring (##REF##10754532##Koibuchi and Chin 2000##; ##REF##9267760##Oppenheimer and Schwartz 1997##; ##REF##8491157##Porterfield and Hendrich 1993##). PCB/dioxin congeners are considered to cause neurotoxicity by altering TH homeo-stasis in the developing brain. Some researchers have reported that exposure to PCB congeners results in thyroid enlargement and reduced serum total thyroxine (T₄) levels with normal levels of triiodothyronine (T₃), an active compound of TH (##REF##9460170##Brouwer et al. 1998##; ##REF##9460171##Hauser et al. 1998##; ##REF##10852841##Porterfield 2000##), during a possible critical period of TH action. Certain PCB congeners were reported to induce the expression of a microsomal enzyme, uridine diphosphate glucuronosyl-transferase, which glucuronizes T₄ to facilitate excretion (##REF##12668117##Hood et al. 2003##; ##REF##7752103##Liu et al. 1995##). Exposure to TCDD also results in morphologic and functional alterations in the thyroid of adult rodents (##REF##3554617##Gorski and Rozman 1987##; ##REF##3111013##Henry and Gasiewicz 1987##; ##REF##3715868##Potter et al. 1986##; ##REF##7672011##Van Birgelen et al. 1995##). Such exposure induces not only an increase in the volume of thyroid follicular cells, followed by hyperplasia, but also follicular thyroid tumors in rats (##REF##2054688##Huff et al. 1991##; ##REF##7540335##Sewall et al. 1995##). Both PCDD and PCDF induce T₃ and T₄ excretion, thereby decreasing plasma T₃ and T₄ levels (##REF##862558##Bastomsky 1977##; ##REF##8560478##Kohn et al. 1996##; ##REF##11836014##Nishimura et al. 2002##). These results indicate that PCBs/dioxins disrupt the TH system by decreasing blood TH levels, which in turn induces hypothyroidism in various organs.</p>", "<p>Perinatal exposure to PCBs/dioxins in laboratory animals induces a decrease in plasma T₄ levels without significantly altering the growth (##REF##17450224##Lein et al. 2007##; ##REF##10614638##Zoeller et al. 2000##), and T₃ levels remain within the normal range (##REF##10852841##Porterfield 2000##), indicating that the toxicity of these chemicals does not manifest by altering blood TH levels. On the other hand, because the molecular structures of PCBs/dioxins are similar to those of TH, these chemicals are considered to act via TH receptors (TRs) (##REF##2551666##McKinney 1989##). Furthermore, these compounds can be transferred across the blood–brain barrier and accumulate in the brain (##REF##3094194##Brouwer and van den Berg 1986##; ##REF##10090705##Cheek et al. 1999##; ##REF##8571380##Darnerud et al. 1996##; ##REF##2551666##McKinney 1989##). These findings suggest that PCBs/dioxins induce abnormal brain development by directly acting on TR.</p>", "<p>We therefore performed a series of experiments and found that a hydroxylated (OH) PCB compound [4-OH-2′,3,3′,4′,5′-penta-chlorobiphenyl (pentaCB); 4-OH-PCB-106] at a concentration of 10⁻¹⁰ M suppresses TR-mediated transcription induced by TH (##REF##12445811##Iwasaki et al. 2002##). The magnitude of suppression induced by 4-OH-PCB-106 was cell-type dependent and most obvious in clonal TE671 cells derived from human cerebellar granule cells (##REF##12445811##Iwasaki et al. 2002##), and this suppression was due to the partial dissociation of TR from the TH response element (TRE) (##REF##14985366##Miyazaki et al. 2004##). These results suggest that PCBs directly act on TR, although the TR functional domain responsible for PCB action remains obscure.</p>", "<p>Here, we report that the magnitude of the suppression of TR-mediated transcription differs among a variety of congeners of PCBs/dioxins. In addition, we identified the functional domain responsible for PCB action using chimeric receptors generated from TR and the glucocorticoid receptor (GR).</p>" ]

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[ "<fig id=\"f1-ehp-116-1231\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Effects of PCBs/dioxins on TR-mediated transcription (data represent mean ± SE of triplicates). (<italic>A</italic> and <italic>B</italic>) Expression plasmids encoding TRβ1 (10 ng) were cotransfected with F2-TK-LUC reporter plasmid (100 ng) into CV-1 and TE671 cells, and cells were incubated with or without 10⁻⁷ M T₃ and indicated concentrations of TCDD (<italic>A</italic>) or pentaCDF (<italic>B</italic>). (<italic>C</italic>–<italic>F</italic>) Expression plasmids encoding TRβ1 (10 ng) were cotransfected with F2-TK-LUC reporter plasmid (100 ng) into CV-1 cells, and cells were incubated with or without T₃ (10⁻⁷ M) and with indicated concentrations of PCB-114 (coplanar type; <italic>C</italic>), PCB-153 (<italic>D</italic>), 4-OH-PCB-165 (<italic>E</italic>), or 4-OH-PCB-106 (<italic>F</italic>).</p><p>*<italic>p</italic> &lt; 0.01, **<italic>p</italic> &lt; 0.02, and ^#<italic>p</italic> &lt; 0.05 by ANOVA, compared with TRβ1 (+), T₃ (+), and TCDD (−) in <italic>A</italic>, PCDF (−) in <italic>B</italic>, and PCB (−) in <italic>C–F</italic>.</p></caption></fig>", "<fig id=\"f2-ehp-116-1231\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Effects of PCBs/dioxins on TR-mediated transcription in the presence of T₃ (10⁻⁷ M) in CV-1 and TE671 cells. Down arrows indicate suppression &gt; 50% at 10⁻⁸ M PCBs/dioxins; diagonal arrows indicate mild suppression (significant, but &lt; 50% at 10⁻⁸ M); and right arrows represent no effect. TEF = 1 for the most toxic congener, TCDD. Congeners without TEF are indicated as ND (##REF##16829543##Van den Berg et al. 2006##).</p></caption></fig>", "<fig id=\"f3-ehp-116-1231\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Magnitude of TR dissociation from TRE correlated with that of suppression by PCBs/dioxins. NS, nonspecific. (<italic>A</italic>) <italic>In vitro</italic> translated TRβ1 (1.5 μL) and/or RXRβ (3 μL) incubated with [³²P]-labeled F2-TRE with or without 10⁻⁶ M T₃ and 10⁻⁸ M 4-OH-PCB-106 (4OH-P106), 4-OH-PCB-165 (4OH-P165), or 3-methyl coranthrene (3MC). The results were similar in three independent repeats of the same experiment, and in experiments using DR4-TRE. (<italic>B</italic>) Histogram of relative intensity of dissociated TRβ1 from TRE by adding PCBs and 3MC. The intensity values of bands are ratios of intensity values with T₃ without PCB/3MC. Results are mean ± SE of three independent experiments.</p><p>*<italic>p</italic> &lt; 0.01 by ANOVA and post hoc comparison using Bonferroni’s multiple range test compared with TR/RXR (+), T₃ (+), and PCB (−).</p></caption></fig>", "<fig id=\"f4-ehp-116-1231\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>TR-mediated transcription altered by 4-OH-PCB-106 through TR-DBD. (<italic>A</italic>) Schematic structures and chimeric proteins used in the present study. Abbreviations: G, glucocorticoid receptor; N, N-terminal domain; NR, nuclear hormone receptor; T, thyroid hormone receptor. (<italic>B</italic>) Representative examples of PCB actions on chimeric receptor-induced transcription. Chimeric receptors (10 ng) were cotransfected with F2-TK-LUC or GRE-LUC reporter plasmid (100 ng) into CV-1 cells and incubated with or without T₃ (10⁻⁷ M) or dexamethasone (DEX; 10⁻⁷ M) and 10⁻¹¹–10⁻⁷ M 4-OH-PCB-106. (<italic>C</italic>) Effect of PCB on transcription through chimeric receptors containing TR-DBD or GR-DBD. Chimeric receptors (10 ng) were cotransfected with F2-TRE-LUC or GRE-LUC reporter plasmid (100 ng) into CV-1 cells and incubated with or without T₃ (10⁻⁷ M) or DEX (10⁻⁷ M) and 10⁻¹¹–10⁻⁷ M 4-OH-PCB-106. For B and C, data represent mean ± SE of triplicates.</p><p>*<italic>p</italic> &lt; 0.01, and ^#<italic>p</italic> &lt; 0.05 by ANOVA; for <italic>B,</italic> compared with GTG (+), DEX (+), and PCB (−).</p></caption></fig>" ]

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[ "<fn-group><fn><p>Supplemental Material is available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/11176/suppl.pdf\">http://www.ehponline.org/members/2008/11176/suppl.pdf</ext-link></p></fn><fn><p>This study was supported in part by a Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (17510039 to T.I.; 17390060 to N.K.; 187859 to W.M.) and a grant for Long-Range Research Initiation from the Japan Chemical Industry Association (T.I. and N.K.).</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"ehp-116-1231f1\"/>", "<graphic xlink:href=\"ehp-116-1231f2\"/>", "<graphic xlink:href=\"ehp-116-1231f3\"/>", "<graphic xlink:href=\"ehp-116-1231f4\"/>" ]

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[ "<title>Materials and Methods</title>", "<title>Study population and design</title>", "<p>We recruited residents from a nursing home in Mexico City who were chronically exposed to PM_2.5 and followed them from 26 September 2001 to 10 April 2002. We randomly assigned subjects in a double-blind fashion to receive either fish oil (n-3 PUFA) or soy oil. We conducted the study in two phases: a presupplementation phase of 3 months (27 September to 17 December 2001) and a supplementation phase of 4 months (9 January to 5 April 2002) (##REF##15821181##Holguin et al. 2005##). Before starting supplementation, participants provided a blood sample. During the supplementation phase, we asked subjects to provide three blood samples at 1 month, 3 months, and at the end of the study; seven participants provided only two samples during the supplementation phase. In addition, we measured indoor and ambient PM_2.5 levels in the nursing home, and each participant kept a diary of daily activities.</p>", "<p>The eligibility criteria were age &gt; 60 years, absence of cardiac arrhythmias, no cardiac pacemaker, no allergies to n-3 PUFA or fish, no treatment with oral anticoagulants, no history of bleeding diathesis, and ability to undergo HRV measurements in the supine position. Among 63 residents invited to the study, 52 agreed to participate and provided written informed consent for both randomization and supplementation (for study design, see ##FIG##0##Figure 1##). At baseline, all participants answered to a general purpose questionnaire and a validated food frequency questionnaire administered by a nutritionist (##REF##9617194##Hernandez-Avila et al. 1998##; ##REF##10427874##Romieu et al. 1999##). We extracted past medical histories from the medical files. The trial protocol was approved by the Institutional Research Board and Ethical Committee of the National Institute of Public Health in Mexico.</p>", "<title>Randomization to n-3 fish oil or soy oil</title>", "<p>We randomly assigned participants to two groups at baseline using a random-number table. Compliance was determined by directly observed supplement intake. Patients in the fish oil group received 2 g/day in divided doses. Each capsule contained 83.2% n-3 PUFA [52.4% docosahexanoic acid (C22:6 n-3 DHA), 25.0% eicosopentanoic acid (C20:5 n-3 EPA) and 5.8% docosapentaenoic acid (c22:5n-3 DPA)]. The soy oil capsules were identical, and each capsule contained 6.78% α-linolenic acid (ALA), a plant n-3 PUFA (C18:3 n-3), 16.3% saturated fat, 52.7% linoleic acid (C18:2 n-6), and 22.5% oleic acid (C18:1 n-9). Neither the participants nor the study personal were aware of the randomization group.</p>", "<title>Pollutants and temperature measurements</title>", "<p>We determined daily 24-hr measurements of PM_2.5 by gravimetric analysis using Mini-Vol portable air samplers (version 4.2; Air Metrics, Eugene, OR, USA), with 47 mm Teflon filters (Pall Gelman Laboratory, Ann Arbor, MI, USA) and flows set at 4 L/min, located within the living room of the nursing home during the follow-up period. Filter gravimetric analysis was performed at the air laboratory of the National Center for Environmental Research and Training in Mexico City under controlled climatic and temperature conditions. Filter weights were obtained by a micrometric scale (Cahn C-35; Thermo Electron Corp., Round Rock, TX, USA) under laminar flow.</p>", "<p>We obtained ambient levels of ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide as well as climatic variables from an automated monitoring station located 3 km upwind from the study site (in Tacuba, to the northeast). Given the low concentrations observed for SO₂, NO₂, and CO, we excluded these pollutants from further analysis.</p>", "<title>Laboratory analysis</title>", "<p>We immediately centrifuged blood samples, protected plasma aliquots with aluminum foil, and stored them at −70°C until analysis. Laboratory measurements were conducted in duplicate at the laboratory of the National Institute of Neurology in Mexico City. We used the mean of the duplicate measurements as the biomarker values for each individual at each sampling time.</p>", "<title>Cu/Zn SOD activity</title>", "<p>We assayed SOD activity by the xanthine/xanthine oxidase method as modified by ##REF##11470327##Alcaraz-Zubeldia et al. (2001)## from the method described by ##REF##9602043##Schwartz et al. (1998)##. We diluted plasma samples in a buffer consisting of 20 mM sodium bicarbonate and 0.02% Triton X-100 (pH 10.2), centrifuged them at 4,000 × <italic>g</italic> for 10 min, and collected the supernatants. We then added 50 μL of the clarified supernatant to 950 μL of the reaction mixture consisting of 10 μM sodium azide, 100 μM xanthine, 10 μM reduced cytochrome c, and 1 mM EDTA in 20 mM sodium bicarbonate, 0.02% Triton X-100 (pH 10.2). We initiated the assay by adding xanthine oxidase and monitored it by measuring the change in absorbance at 560 nm in a Lambda-20 ultraviolet/visible spectrophotometer (Perkin Elmer, Waltham, MA, USA). We carried out the analysis of samples in duplicate and calculated the participation of each SOD type as total activity minus the activity inhibited by the addition of 5 mM sodium cyanide, because cyanide selectively inhibits the Cu/Zn SOD isoform. The results are expressed as international units per milliliter. Measurements of Cu/Zn SOD were available for 20 subjects assigned to the soy oil group and 24 subjects assigned to the fish oil group. The coefficient of variation (mean) of duplicate measurements was 1.6%.</p>", "<title>LPO in plasma</title>", "<p>We monitored lipid fluorescence product formation as an index of LPO in plasma samples using the method described by ##REF##6699647##Triggs and Willmore (1984)##. We separated 500 μL of each plasma sample into a glass tube, covered from light, and added 4 mL of a chloroform-methanol 2:1 mixture. We measured fluorescence in a Perkin Elmer LS50B luminescence spectrophotometer at 370 nm excitation and 430 nm emission. We adjusted sensitivity of the spectrophotometer to 140 fluorescence units with a 0.1-mg/L quinine standard, prepared in 0.05 M aqueous sulfuric acid solution, before the measurement of the samples. We used pooled plasma samples obtained from healthy controls as an internal quality control. We obtained reference values for pooled plasma samples from repeated analyses to obtain quality control charts. Each time we carried out the measurements, we determined the pooled samples in the same sample batch to assess the reproducibility of the measurements. Results are expressed as fluorescence units (FU) per milliliter. We ran all samples in duplicate. Measurements of LPO products were available for 21 subjects assigned to the soy oil group and 21 subjects assigned to the fish oil group. The coefficient of variation (mean) of the duplicate measurements was 10.9%.</p>", "<title>GSH levels in plasma</title>", "<p>We assayed GSH using the method described by ##REF##8015473##Hu (1994)##. We diluted plasma samples 1:1 with a 10% (wt/vol) trichloroacetic solution and placed tubes on ice for 10 min. We measured fluorescence in a Perkin Elmer LS50B luminescence spectrophotometer at 350 nm excitation and 420 nm emission. For blank sample analysis, we substituted plasma samples with deionized water and treated these as regular samples. We performed internal quality control for GSH measurements as described for the lipid fluorescence product assay. We constructed calibration curves for glutathione and obtained the concentrations by interpolation in the standard curve. We express our results as micromolar concentrations of GSH. Measurements of GSH were available for 20 subjects assigned to the soy oil group and 23 subjects assigned to the fish oil group. The coefficient of variation (mean) of the duplicate measurements was 1.8%.</p>", "<title>Statistical analysis</title>", "<p>We compared the mean Cu/Zn SOD activity, LPO products, and GSH levels in plasma at baseline between the soy oil and the fish oil groups using a <italic>t</italic>-test. To normalize the distribution, we square-root–transformed the measure of LPO and log-transformed GSH. We determined the effect of supplementation by soil oil and fish oil and of PM_2.5 exposure on Cu/Zn SOD activity, LPO products, and GSH levels in plasma using linear mixed models (##REF##12018779##Xu and Hedeker 2001##) treating patient as random effect. Linear mixed models account for repeated measurements within the same individual and allow for variation of effect among individuals. We assigned the same-day indoor PM_2.5 level measured (24-hr average) in the nursing home area as PM_2.5 exposure for subjects tested on a given day (12 subjects/day). We adjusted regression models only for time, because characteristics of the general population were equally distributed between groups and did not confound the association. We also tested for interaction between PM_2.5 levels and supplementation phase for both the soy and fish oil supplementation groups.</p>", "<p>We assessed the possible nonlinearity of PM_2.5 using generalized additive mixed models (GAMM) with p-splines (##UREF##3##Greven et al. 2006##; ##UREF##4##Hastie and Tibshirani 1990##), modeling the dose–response relationship between bio-markers of response to oxidative stimuli and pollutant as a smooth function. Because we observed nonlinear associations between PM_2.5 and these biomarkers in the fish oil group, we included a quadratic term of PM_2.5 in our mixed linear model. We assigned the same-day indoor PM_2.5 level measured in the nursing home area as PM_2.5 exposure for subjects tested on a given day (12 subjects per day). We used the chi-square or Fisher’s exact test for discrete variables and frequencies, and considered a <italic>p</italic>-value of &lt; 0.05 significant. We conducted statistical analyses using Stata 8.2 (StataCorp., College Station, TX, USA) and GAMM using a SAS macro (version 9.1; SAS Institute Inc., Cary, NC, USA).</p>" ]

[ "<title>Results</title>", "<title>Study sample</title>", "<p>##TAB##0##Table 1## presents the general characteristics of participants according to their assignment to either the fish oil or soy oil supplementation groups. Most of the general characteristics were similar between groups. Ischemic cardiac disease was more frequent in the fish oil group (<italic>n</italic> = 0 vs. <italic>n</italic> = 4), and body mass index (BMI) was slightly lower in the fish oil group (27.6 vs. 24.6). Only three participants in the fish oil group and three in the soy oil group reported smoking outside of the residential home (##TAB##0##Table 1##). At baseline, participants from both the fish oil and soy oil groups reported low dietary intakes of foods rich in n-3 PUFA.</p>", "<title>Environmental exposure data</title>", "<p>Participants in both groups spent on average 93% of their time indoors. The mean daily PM_2.5 ambient level in the living room of the residence was 38.7 μg/m³ (14.7 SD), the median was 35.11 μg/m³, and the 25th and 75th percentiles were 30.62 μg/m³ and 41.10 μg/m³, respectively (range, 14.8–70.9 μg/m³). Indoor and outdoor PM_2.5 measurements were highly correlated (<italic>r</italic> = 0.95). We obtained O₃, PM₁₀ outdoor levels, and climatic data from the closest monitoring station. During the study phase, the O₃ 1-hr maximum ranged from 11.6 to 57.5 ppb, and the PM₁₀ 24-hr average ranged from 39.5 to 109.9 μg/m³.</p>", "<title>Fatty acids in erythrocytes</title>", "<p>In this study, we were not able to measure erythrocyte n-3 PUFA levels. However, a study conducted in a similar population with the same dose of fish and soy oil showed an important increase of EPA (eicosopentanoic acid) and DHA (docosahexanoic acid) in the fish oil group after 5 months of supplementation (394% and 140%, respectively), and a moderate increase in EPA (87%) and no significant changes in DHA in the soy oil group; EPA and DHA were significantly higher in the fish oil group (<italic>p</italic> &lt; 0.01) (##REF##16210665##Romieu et al. 2005##) [Supplemental Material, Table 1 (online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10578/suppl.pdf\">http://www.ehponline.org/members/2008/10578/suppl.pdf</ext-link>)].</p>", "<title>Biomarkers of responses to oxidative stimuli</title>", "<p>Characteristics of subjects who provided blood samples in the two phases of the study (presupplementation and supplementation phases) (<italic>n</italic> = 45) and those who did not (<italic>n</italic> = 7) were similar. At baseline (presupplementation phase), Cu/Zn SOD activity, LPO products, and GSH levels in plasma were similar in both supplementation groups (##TAB##0##Table 1##).</p>", "<p>We first modeled the data from participants in both supplementation groups and evaluated the impact of supplementation and exposure to PM _2.5 on these biomarkers (##TAB##1##Table 2##). Supplementation significantly increased the Cu/Zn SOD activity (β = 0.27, <italic>p</italic> = 0.002), significantly decreased levels of LPO products (β = −4.52, <italic>p</italic> = 0.03), and significantly increased levels of GSH in plasma (β = 0.95, <italic>p</italic> &lt; 0.001). Indoor PM_2.5 levels were inversely related to Cu/Zn SOD activity (β = −0.05, <italic>p</italic> = 0.001). The relation between GSH and PM_2.5 levels was nonlinear (significant quadratic term for PM_2.5 levels) (##TAB##1##Table 2##).</p>", "<p>We further stratified data by supplementation group (##TAB##2##Table 3##). As observed in the overall analysis of both supplementation groups, supplementation appeared to have a protective effect in the fish oil group (significant positive association with Cu/Zn SOD activity, β = 0.29, <italic>p</italic> = 0.04; inverse association with LPO products, β = −3.72, <italic>p</italic> = 0.002; positive association with GSH levels, β = 1.0, <italic>p</italic> &lt; 0.001); in the soy oil group, supplementation was positively related to Cu/Zn SOD activity (β = 0.17, <italic>p</italic> = 0.05) and GSH levels (β = 0.70, <italic>p</italic> = 0.003). We observed no effect of soy oil supplementation on LPO products (##TAB##2##Table 3##). Based on models presented in ##TAB##2##Table 3##, we calculated that, compared with baseline, supplementation with fish oil led to an approximate increase in Cu/Zn SOD activity of 49.1% [95% confidence interval (CI), 2.4–81.5] and in GSH levels of 62% (95% CI, 43.3–89.0%), and an approximate decrease in LPO products of 72.5% (95% CI, 15–172%). For the soy oil group, we calculated an increase of 22.6% (95% CI, 0.7–44.0%) in Cu/Zn SOD activity and of 55.3% (95% CI, 36.3–85.1%) in GSH levels compared with baseline.</p>", "<p>PM_2.5 levels were inversely related to Cu/Zn SOD activity in the soy oil group (β = −0.06, <italic>p</italic> &lt; 0.001) (##TAB##2##Table 3##). In the fish oil group, the relations between these biomarkers of response to oxidative stimuli and PM_2.5 levels were nonlinear. Both a linear term and a quadratic term for PM_2.5 levels were significant predictors of these biomarkers (##TAB##2##Table 3##).</p>", "<p>To better evaluate the shape of the association between PM_2.5 levels and the biomarkers of interest, we used GAM models. In the soy oil group, we observed that Cu/Zn SOD activity decreased linearly with increased levels of PM_2.5. We observed no significant effect on LPO products and GSH levels (##FIG##1##Figure 2A#x02013;C##); in the fish oil group, we observed that Cu/Zn SOD activity tended to decrease with exposure to PM_2.5 until levels came close to 40–45 μg/m³, and then the effect leveled off (##FIG##2##Figure 3A##). LPO products tended to decrease at low levels of PM_2.5 and start increasing after PM_2.5 levels of 40–45 μg/m³ (##FIG##2##Figure 3B##). For GSH levels, we observed little change until around 40 μg/m³ PM_2.5, after which we observed a significant decrease (##FIG##2##Figure 3C##). However, because almost 75% of our PM_2.5 measurements were &lt; 45 μg/m³, our estimates at higher PM_2.5 levels derive from sparse data.</p>" ]

[ "<title>Discussion</title>", "<p>In this randomized controlled trial, we observed that supplementation with both soy oil and fish oil appeared to modulate the plasma levels of biomarkers of response to oxidative stimuli by increasing Cu/Zn SOD activity and GSH levels. In addition, fish oil supplementation reduced LPO products in plasma. Fish oil appeared to modulate the adverse effect of PM_2.5 in a nonlinear manner.</p>", "<p>This is the first study to evaluate the impact of supplementation with n-3 PUFA on biomarkers of response to oxidative stimuli related to air pollution exposure among individuals in a noncontrolled environment. The mechanisms of PM-induced health effects are believed to involve inflammation and oxidative stress. The oxidative stress mediated by PM may arise from direct generation of ROS or be related to altered function of mitochondria or NADPH-oxidase (nicotinamide adenine dinucleotide phosphate-oxidase) and activation of inflammatory cells capable of generating ROS, reactive nitrogen species, and oxidative DNA damage (##REF##15051325##Gonzalez-Flecha 2004##; ##REF##16085126##Risom et al. 2005##). <italic>In vitro</italic> studies have shown that PM exposure increases the expression of NF-κB–related genes and activation of oxidant-dependent NF-κB factors, such as tumor necrosis factor-α, and inter-leukin-8 and -6 (##REF##15051325##Gonzalez-Flecha 2004##; ##REF##10896851##Jimenez et al. 2000##; ##REF##16085126##Risom et al. 2005##; ##REF##10919984##Shukla et al. 2000##). In animal experiments, airborne PM_2.5 have been shown to increase levels of LPO and alter intracellular redox status in multiple organs with decreased SOD, catalase, and GSH-Px activities and depleted GSH levels (##REF##16020041##Liu and Meng 2005##). Transgenic mice with overexpression of extracellular SOD (EC-SOD) had lower concentrations of oxidized glutathione in the lung after exposure to residual oily fly ash (ROFA), suggesting that enhanced EC-SOD expression decreased both lung inflammation and damage after exposure to ROFA (##REF##12060579##Ghio et al. 2002##). A recent enzyme activity assay showed that the activity of Cu/Zn SOD is reduced by PM, particularly ROFA and urban PM (##REF##16683627##Hatzis et al. 2006##). The impact of PM_2.5 observed in our study is in line with those results. This impairment of defense against oxidative stress could be responsible for the decrease in HRV observed in the elderly because alteration of autonomic function related to PM_2.5 exposure appears to be partly associated with oxidative stress (##REF##12530933##Brook et al. 2003##; ##REF##18007994##Chahine et al. 2007##).</p>", "<p>The major source of PM_2.5 in Mexico City is related to vehicular traffic, and a large proportion of vehicles use diesel fuel (##UREF##6##Secretaría de Medio Ambiente y Recursos Naturales 2003##; ##UREF##7##Secretaría de Transporte y Vialidad 2006##). Because smoking was not allowed in the nursing home, the main source of PM_2.5 measured indoors came from outdoor sources, with a correlation of <italic>r</italic> = 0.95 between indoor and outdoor PM_2.5 levels.</p>", "<p>Supplementation by both fish oil containing EPA and DHA (83.2%/g) and soy oil containing ALA (6.7%), a plant-derived n-3 PUFA, appears to enhance the response to oxidative stress by increasing Cu/Zn SOD activity and GSH. The antioxidant enzyme Cu/Zn SOD appears to play an important role in response to oxidative stress, catalyzing the formation of hydrogen peroxide from superoxide anion (##REF##16816350##Rahman and Adcock 2006##). GSH is a major intracellular and extracellular redox buffer and acts as a direct free radical scavenger. In animal experiments, supplementation with n-3 PUFA has been shown to have a protective effect against the toxicity of formaldehyde (FA) on the kidney. Rats administered n-3 PUFA while exposed to FA showed increased SOD and GSH-Px enzyme activities and decreased levels of malondialdehyde, a marker of LPO (##REF##16898265##Zararsiz et al. 2006##). A recent study reported a roughly linear relation between DHA in human fibroblast culture and a large increase in intracellular GSH content contributing to decrease in ROS levels (##REF##16441913##Arab et al. 2006##). Although in our study we measured these bio-markers in plasma, which may not parallel intracellular levels, their increase after supplementation reflects greater protection against oxidative stress. Long-chain n-3 PUFAs has been shown to act both directly (e.g., by replacing arachidonic acid as an eicosanoid substrate and inhibiting arachidonic acid metabolism) and indirectly by altering the expression of inflammatory genes through effects on transcription factor activation (##REF##16841861##Calder 2006##; ##REF##12616644##Maritim et al. 2003##; ##REF##16430879##Valko et al. 2006##). The inverse association we observed with LPO products suggests also that the substitution of n-3 PUFA in the membrane could play a role in decreasing LPO of PUFA in the membranes.</p>", "<p>Several factors need to be considered in interpreting our results. Exposure assessment was limited to a stationary 24-hr gravimetric analysis of PM_2.5 indoors; however, the diary of daily activities kept by each participant allowed us to assess that participants spent &gt; 93% of their time indoors and justify the use of indoor PM_2.5 levels to determine participants’ exposure. Information about other pollutants was available through a nearby automated monitoring station, situated 3 km upwind from the study site, allowing a reasonable estimation of the nursing home air pollution atmosphere. However, we did not find an association between biomarkers of response to oxidative stimuli and other air pollutants, and these pollutants did not appear to modify the association between PM_2.5 and biomarker levels.</p>", "<p>Our sample size limited the detailed exploration of interactions among supplementation groups and PM_2.5 effect. However, repeated measures in the same subject, with subjects serving as their own controls (comparing the presupplementation and supplementation phase), increased our power and accounted for slight differences in subject characteristics (##REF##10070672##Schouten 1999##). The fact that the sample size and the design of the study revealed statistical evidence for an effect of supplementation with either soy oil or fish oil on Cu/Zn SOD and GSH, and with fish oil on LPO, means that the power of the study was adequate to detect these effects. The lack of significant effect of soy oil supplementation on LPO could be attributable to a real lack of effect, or to the fact that the relation of PM_2.5 with LPO was weaker than with Cu/Zn SOD and GSH, or because the potential effect of soy oil was harder to find and therefore would have required a larger sample size. In addition, neither the laboratory technician nor the participants were aware of the randomization group minimizing the likelihood of information bias.</p>", "<p>We based compliance with the supplementation on direct observation from the medical team, because supplements were provided to the residents of the nursing home in the morning. However, in a study conducted in a similar population with a similar design, supplementation with fish oil led to a significant increase of EPA, DHA, and n-3/n-6 PUFA ratio and a decrease of arachidonic acid whereas supplementation with soy oil led to significant increase of EPA and a marginal increase of ALA, DHA, and n-3/n-6 PUFA ratio in erythrocyte membranes (##REF##16210665##Romieu et al. 2005##).</p>", "<p>A major concern with dietary supplementation is the effective dose. The fact that fish oil appears to be more effective against oxidative stress related to PM_2.5 exposure than is soy oil suggests that the small amount of ALA—further elongated in EPA and DHA—in soy oil might be insufficient to protect against the adverse effects of PM_2.5 exposure. We based our results on a limited sample size but suggest that essential fatty acids might play an important role in modulating the impact of PM on health, which warrants further investigation in larger populations.</p>" ]

[]

[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>The mechanisms of particulate matter (PM)-induced health effects are believed to involve inflammation and oxidative stress. Increased intake of omega-3 polyunsaturated fatty acids (n-3 PUFA) appears to have anti-inflammatory effects.</p>", "<title>Objective</title>", "<p>As part of a trial to evaluate whether n-3 PUFA supplementation could protect against the cardiac alterations linked to PM exposure, we measured biomarkers of response to oxidative stimuli [copper/zinc (Cu/Zn) superoxide dismutase (SOD) activity, lipoperoxidation (LPO) products, and reduced glutathione (GSH)] and evaluated the impact of supplementation on plasma levels.</p>", "<title>Methods</title>", "<p>We recruited residents from a nursing home in Mexico City chronically exposed to PM ≤2.5 μm in aerodynamic diameter (PM_2.5) and followed them from 26 September 2001 to 10 April 2002. We randomly assigned subjects in a double-blind fashion to receive either fish oil (n-3 PUFA) or soy oil. We measured PM_2.5 levels indoors at the nursing home, and measured Cu/Zn SOD activity, LPO products, and GSH at different times during presupplementation and supplementation phases.</p>", "<title>Results</title>", "<p>Supplementation with either fish or soy oil was related to an increase of Cu/Zn SOD activity and an increase in GSH plasma levels, whereas exposure to indoor PM_2.5 levels was related to a decrease in Cu/Zn SOD activity and GSH plasma levels.</p>", "<title>Conclusion</title>", "<p>Supplementation with n-3 PUFA appeared to modulate the adverse effects of PM_2.5 on these biomarkers, particularly in the fish oil group. Supplementation with n-3 PUFA could modulate oxidative response to PM_2.5 exposure.</p>" ]

[ "<p>Environmental exposure to particulate matter (PM) has been associated with increased cardiovascular mortality in the elderly (##UREF##0##Aga et al. 2003##; ##UREF##2##Devlin et al. 2003##; ##UREF##5##Samet et al. 2000##) and reductions in heart rate variability (HRV), a measure of cardiac autonomic regulation (##REF##10725286##Gold et al. 2000##; ##REF##14501266##Holguin et al. 2003##; ##REF##15743719##Park et al. 2005##). Although not well understood, the mechanisms of PM-induced health effects are believed to involve inflammation and oxidative stress initiated by the formation of reactive oxygen species (ROS) within affected cells (##REF##16053926##Cho et al. 2005##; ##REF##15051325##Gonzalez-Flecha 2004##). <italic>In vitro</italic> studies have shown that inhaled PM causes expression of nuclear factor kappa-B (NF-κB)-related genes and oxidant-dependent NF-κB activation (##REF##10896851##Jimenez et al. 2000##; ##REF##10919984##Shukla et al. 2000##). To defend against the oxidative damage, cells use up their stores of a key antioxidant, glutathione; glutathione depletion can induce a state of cellular stress and the activation of additional intracellular signaling cascades that regulate the expression of cytokine and chemokine genes and widespread proinflammatory effects remote from the site of damage (##REF##15716966##Saxon and Diaz-Sanchez 2005##). Data from PM samples of 20 European cities have shown that PM with an aerodynamic diameter ≤2.5 μm (PM_2.5) has strong redox activity and is able to deplete artificial respiratory lining fluid of reduced glutathione (GSH) and ascorbate (##REF##16675421##Künzli et al. 2006##). PM, depending on its toxicity, seems also to inhibit protective enzymes involved in oxidative stress responses [copper/zinc (Cu/Zn) superoxide dismutase (SOD), manganese SOD, glutathione peroxidase (GSH-Px), and glutathione reductase] (##REF##16683627##Hatzis et al. 2006##). Alteration of autonomic function related to PM_2.5 exposure appears to be partly associated with oxidative stress (##REF##12530933##Brook et al. 2003##; ##REF##18007994##Chahine et al. 2007##).</p>", "<p>Increased intake of omega-3 polyunsaturated fatty acids (n-3 PUFA) has been shown to decrease the risk of cardiovascular events (##REF##10465168##GISSI Prevenzione Investigators 1999##; ##REF##12438303##Kris-Etherton et al. 2002##; ##REF##12782616##Leaf et al. 2003##). The protective effect of n-3 PUFA seems to be linked in part to its cardiac antiarrhythmic properties (##REF##11422661##Christensen et al. 2001##; ##REF##15821181##Holguin et al. 2005##; ##REF##12438303##Kris-Etherton et al. 2002##; ##REF##11837982##Leaf 2001##) and its anti-inflammatory effects (##REF##16841861##Calder 2006##). Long-chain n-3 PUFA appears to act both directly (by replacing arachidonic acid as an eicosanoid substrate and inhibiting arachidonic acid metabolism) and indirectly by altering the expression of inflammatory genes through effects on transcription factor activation (##REF##16841861##Calder 2006##). Fish oil has been shown to modulate endothelial activation possibly by reducing ROS and therefore leading to the subsequent inactivation of the NF-κB system of gene transcription. This role of oxygen scavenging would lead to the prevention of O₂-generating hydrogen peroxide (H₂O₂) and thus prevent cell activation (##UREF##1##De Caterina et al. 2000##).</p>", "<p>We have previously shown that exposure to PM_2.5 is related to a decrease in HRV in elderly residents of a nursing home in Mexico City (##REF##14501266##Holguin et al. 2003##) and that fish oil supplementation could increase HRV (##REF##15821181##Holguin et al. 2005##) and modulate the adverse effects of PM_2.5 on HRV (##REF##16210665##Romieu et al. 2005##). We hypothesized that one of the mechanisms by which fish oil and, to a lesser extent, soy oil supplementation would modu late the adverse effects of PM_2.5 on HRV would be by acting on the oxidative stress response, reducing the generation of ROS, modulating the use of GSH as part of the oxidative stress response, and increasing the activity of enzymes involved in response to oxidative stimuli. We tested this hypothesis in a randomized trial of fish oil versus soy oil supplementation to prevent reductions in cardiac autonomic function associated with PM exposure. During the trial, we measured Cu/Zn SOD activity, GSH, and LPO levels in plasma to evaluate the impact of fish oil and soy oil supplementation and exposure to PM_2.5 on these biomarkers.</p>", "<title>C<sc>orrection</sc></title>", "<p>In ##TAB##1##Table 2## of the manuscript originally published online, the intercept for LPO was 34.54; it has been corrected here.</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1237\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Study design.</p></caption></fig>", "<fig id=\"f2-ehp-116-1237\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Cu/Zn SOD (<italic>A</italic>), LPO (<italic>B</italic>), and GSH (<italic>C</italic>) according to PM_2.5 exposure (μg/m³; based on our GAMMs) among patients in the fish oil group. Solid line, estimated smooth function; dashed lines, pointwise 95% CIs; circles, partial residuals. We suppressed CIs in cases where we estimated one smooth function to be linear or any variance component to be (almost) zero, because the covariance matrix is then singular and cannot be inverted for construction of the variability bands.</p></caption></fig>", "<fig id=\"f3-ehp-116-1237\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Cu/Zn SOD (<italic>A</italic>), LPO (<italic>B</italic>), and GSH (<italic>C</italic>) according to PM_2.5 exposure (μg/m³; based on our GAMMs) among patients in the soy oil group. Solid line, estimated smooth function; dashed lines, pointwise 95% CIs; circles, partial residuals. We suppressed CIs in cases where we estimated one smooth function to be linear or any variance component to be (almost) zero, because the covariance matrix is then singular and cannot be inverted for construction of the variability bands.</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1237\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Characteristics of the study population at baseline.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Soy oil (<italic>n</italic> = 26)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Fish oil (<italic>n</italic> = 26)</th><th align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Age [mean (range)]</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">77 (60–88)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">76 (60–96)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.491</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Male sex [no. (%)]</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12 (46.2)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12 (46.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Smokers [no. (%)]</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3 (11.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3 (11.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Medical conditions [no. (%)]</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Diabetes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2 (7.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6 (23.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.124</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Hypertension</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8 (34.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10 (38.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.773</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Ischemic cardiac disease</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4 (15.4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.037</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> COPD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2 (7.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3 (11.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.638</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Medical treatment [no. (%)]</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  β-Blockers</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1 (3.9)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2 (7.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.552</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Angiotensin-converting enzyme inhibitors</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2 (7.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3 (11.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.638</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Calcium-channel blockers</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3 (11.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2 (7.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.638</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Diuretics</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3 (11.5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.298</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">BMI [mean (range)]</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.6 (20.1–37.2)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24.6 (16.6–35.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.023</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Oxidative stress biomarkers<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1237\">a</xref> (mean ± SD)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Cu/Zn SOD (IU/mL)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.76 ± 0.04</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.68 ± 0.05</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.22</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> LPO (FU/mL)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.62 ± 9.88</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.08 ± 7.90</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.85</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> GSH (μM)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.66 ± 1.39</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.38 ± 1.70</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.31</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1237\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Effects of supplementation and exposure to PM_2.5 on biomarkers of oxidative stress.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Cu/Zn SOD\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">LPO<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1237\">a</xref>\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">GSH<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1237\">b</xref>\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">β (SE)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">β (SE)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">β (SE)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Intercept</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.73 (0.04)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.54 (0.52)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.32 (0.08)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Supplementation<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1237\">c</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.27 (0.08)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−4.52 (2.00)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.029</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.95 (0.14)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Fish oil<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1237\">d</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.04 (0.04)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.245</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.03 (0.19)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.897</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.10 (0.09)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.274</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Indoor PM_2.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.05 (0.02)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.08 (0.09)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.381</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06 (0.05)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.176</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Indoor (PM_2.5)²<xref ref-type=\"table-fn\" rid=\"tfn7-ehp-116-1237\">e</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.05 (0.01)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1237\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Effects of PM_2.5 exposure on biomarkers of oxidative stress by supplementation groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Soy oil\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Fish oil\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">β (SE)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">β (SE)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Cu/Zn SOD</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Intercept</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.75 (0.03)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.60 (0.06)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Supplementation<xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1237\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.17 (0.09)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.050</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.29 (0.14)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.042</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Indoor PM_2.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06 (0.02)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.17 (0.05)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Indoor (PM_2.5)²<xref ref-type=\"table-fn\" rid=\"tfn10-ehp-116-1237\">b</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.04 (0.02)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.009</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">LPO<xref ref-type=\"table-fn\" rid=\"tfn11-ehp-116-1237\">c</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Intercept</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.15 (0.23)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.96 (0.21)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Supplementation<xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1237\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.49 (1.13)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.669</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−3.72 (1.04)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Indoor PM_2.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.02 (0.14)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.904</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.35 (0.19)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.080</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Indoor (PM_2.5)²</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.16 (0.07)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.024</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">GSH<xref ref-type=\"table-fn\" rid=\"tfn12-ehp-116-1237\">d</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Intercept</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.21 (0.10)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.44 (0.08)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Supplementation<xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1237\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.70 (0.22)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00 (0.19)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Indoor PM_2.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.03 (0.04)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.406</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.09 (0.04)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.017</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>Supplemental Material is available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10578/suppl.pdf\">http://www.ehponline.org/members/2008/10578/suppl.pdf</ext-link></p></fn><fn><p>We thank R. Nadif (INSERM U780, Villejuif, France) and P. Sly (University of Western Australia, Perth), for their useful comments.</p></fn><fn><p>This work was supported by research grant 34483-M from the Mexican Consejo Nacional de Ciencia y Tecnología and by the Mexican Ministry of Health. I.R. was supported in part by the U.S. National Center for Environmental Health, Centers for Disease Control and Prevention (Atlanta, GA, USA); the GA²LEN project (European Union contract FOODCT-2004-506378); and the Spanish Ministry of Education and Science (SAB2004-0192).</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1237\"><label>a</label><p>Reference values: Cu/Zn SOD, ≥ 0.75 IU/mL; LPO, 25.76–32.08 FU/mL; GSH, ≥ 5 μM.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn2-ehp-116-1237\"><p>Data are regression coefficients; we adjusted models for date. For PM_2.5, the coefficients correspond to a change in 10 μg/m³. The regression model for Cu/Zn SOD includes 44 subjects and 164 observations; for LPO, 42 subjects and 106 observations; for GSH, 43 subjects and 162 observations.</p></fn><fn id=\"tfn3-ehp-116-1237\"><label>a</label><p>Square root transformed.</p></fn><fn id=\"tfn4-ehp-116-1237\"><label>b</label><p>Log transformed.</p></fn><fn id=\"tfn5-ehp-116-1237\"><label>c</label><p>Reference category: presupplementation.</p></fn><fn id=\"tfn6-ehp-116-1237\"><label>d</label><p>Reference category: soy oil.</p></fn><fn id=\"tfn7-ehp-116-1237\"><label>e</label><p>Linear relationship; indoor (PM_2.5)² does not apply.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn8-ehp-116-1237\"><p>Data are regression coefficients; we adjusted models for date. For PM_2.5, the coefficients correspond to a change in 10 μg/m³. Regression models for Cu/Zn SOD include 20 subjects and 74 observations in the soy oil group and 24 subjects and 90 observations in the fish oil group; for LPO, 21 subjects and 51 observations in the soy oil group and 21 subjects and 55 observations in the fish oil group; for GSH, 20 subjects and 76 observations in the soy oil group and 23 subjects and 86 observations in the fish oil group.</p></fn><fn id=\"tfn9-ehp-116-1237\"><label>a</label><p>Reference category: presupplementation.</p></fn><fn id=\"tfn10-ehp-116-1237\"><label>b</label><p>Linear relationship; indoor (PM_2.5)² does not apply.</p></fn><fn id=\"tfn11-ehp-116-1237\"><label>c</label><p>Square root transformed.</p></fn><fn id=\"tfn12-ehp-116-1237\"><label>d</label><p>Log transformed.</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1237f1\"/>", "<graphic xlink:href=\"ehp-116-1237f2\"/>", "<graphic xlink:href=\"ehp-116-1237f3\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Material and Methods</title>", "<title>Animals</title>", "<p>For these experiments, we used immature female Sprague-Dawley rats raised in our colony at the Texas A&amp;M University Department of Comparative Medicine. The animals were housed under controlled conditions of photoperiod (lights on at 0600 hours; lights off at 1800 hours) and temperature (23°C), with <italic>ad libitum</italic> access to food and water. All procedures used were approved by the University Animal Care and Use Committee and in accordance with the National Institutes of Health <italic>Guide for the Care and Use of Laboratory Animals</italic> (##UREF##5##Institute of Laboratory Animal Resources 1996##). Animals were treated humanely and with regard for alleviation of suffering. Rats were bred and allowed to deliver their pups normally. On postnatal day (PND) 2, pups were culled from individual litters, if necessary, so that the litter size for each dam was 10–12 pups total, with at least 5–6 females per litter. We used female pups from 50 separate litters for the experiments. For all experiments, we randomly assigned littermates to treatment groups.</p>", "<title>Pesticide</title>", "<p>We purchased ESF (Asana 98%, lot 306-118A, expiration date July 2009; and lot 328-113B, expiration date September 2010) from Chem Service, Inc. (West Chester, PA) and dissolved it in corn oil (Sigma Chemical Co., St. Louis, MO) for the dosing studies. We mixed dosing solution every other day and stored it at room temperature protected from light. Control animals received an equal volume of corn oil.</p>", "<title>Effect of ESF exposure on puberty-related hormones and the onset of puberty</title>", "<p>For the first experiment, we administered 0.5, 1.0, or 5.0 mg/kg/day ESF by gastric gavage to female pups beginning on PND22 and continuing until vaginal opening (VO) occurred. We used a random-block experimental design. We randomly assigned pups from multiple litters to either a control or treatment group, so that each ESF treatment group had its own control group. Dosing occurred in the morning, and dosing volume was 10.0 mL/kg (0.1 mL/ 10.0 g) body weight. Once VO occurred, we performed vaginal lavage daily and observed cytology until first diestrus (D1), which indicates sexual maturity (##REF##8413822##Nyberg et al. 1993##). In the second experiment, we randomly assigned PND22 female pups from multiple litters to 0, 0.5, 1.0, or 5.0 mg/kg ESF, gavaged them as previously described until PND29. All animals were killed by decapitation between 0900 and 1100 hours in a random order and were confirmed to be prepubertal by well-established criteria (##REF##2106089##Dees and Skelley 1990##). Briefly, all animals had small uteri, no intraluminal fluid, and closed vaginas. Trunk blood was collected, centrifuged it at 4°C, and stored at −80°C until assayed for luteinizing hormone (LH), follicle-stimulating hormone (FSH), and E₂.</p>", "<title>Effect of ESF on morning and afternoon basal levels of LH and hypothalamic responsiveness to N-methyl-d,l-aspartic acid (NMA)</title>", "<p>Animals were dosed with 0, 0.5, or 1.0 mg/kg ESF from PND22 to PND29. The 29-day-old female rats were then anesthetized with 2.5% tribromoethanol (Aldrich, Milwaukee, WI) and Silastic cannulas were inserted into the right external jugular vein of each rat (##UREF##4##Harms and Ojeda 1974##). The next day, an extension of tubing was attached to each cannula and flushed with heparinized saline (100 IU/mL). After a 1-hr acclimation period, three basal blood samples (200 μL) were drawn at 15-min intervals from each freely moving animal, beginning at 1000 hours. All animals received an equal volume of heparinized saline to replace blood volume. The animals were left undisturbed until 1500 hours, at which time three more samples were taken at 15-min intervals. After the third afternoon sample, we conducted a hypothalamic response test by administering a single intravenous injection of NMA (Sigma) at a dose of 40 mg/kg (##REF##8757241##Gore et al. 1996##; ##REF##8413822##Nyberg et al. 1993##). NMA is known to induce release of luteinizing hormone–releasing hormone (LHRH) in rats (##REF##2164923##Bourguignon et al. 1990##; ##REF##8413822##Nyberg et al. 1993##; ##REF##7920593##Smyth and Wilkinson 1994##; ##REF##3309706##Urbanski and Ojeda 1987##) and primates (##REF##10614642##Claypool et al. 2000##; ##REF##15217984##Dissen et al. 2004##; ##REF##3106017##Gay and Plant 1987##) after binding to the hypothalamic NMDA receptors. We collected two additional blood samples at 15-min intervals after the NMA injection. After the experiment, animals were euthanized with an overdose of tribromoethanol. Animals were confirmed to be anestrus, and serum was stored as described above until assayed for LH.</p>", "<title>LHRH stimulation in pesticide-treated juvenile females</title>", "<p>We treated a separate group of animals as described for the NMA stimulation experiment except after the third afternoon sample, we intravenously administered LHRH (25 ng). Again, two samples were collected at 15-min intervals after LHRH treatment; samples were stored as described above.</p>", "<title>Hormone analysis</title>", "<p>We measured rat LH and FSH using radioimmunoassay (RIA) procedures as previously described (##REF##8756538##Hiney et al. 1996##). Rat LH antiserum (NIDDK-anti-rLH-S-II), antigen (NIDDK-rLH-I-9), and reference preparation (NIDDK-rLH-RP-3) and FSH antiserum (NIDDK-rFSH-I-9) and reference preparation (NIDDK-rFSH-RP-2) were purchased from the National Institutes of Health Pituitary Hormones and Antisera Center (Harbor-UCLA Medical Center, Torrance, CA). The LH assay had a sensitivity of 0.07 ng/mL, and the FSH assay had a sensitivity of 0.4 ng/mL. We measured serum E₂ using an RIA kit purchased from Diagnostic Products Corp. (Los Angeles, CA) as previously described (##REF##8756538##Hiney et al. 1996##). The E₂ assay sensitivity was 8.0 pg/mL. All assays had inter- and intra-assay coefficients of variation of &lt; 10%.</p>", "<title>Statistical analysis</title>", "<p>Values are expressed as the mean ± SE. We analyzed differences between treatment groups in timing of puberty using the Student’s paired <italic>t</italic>-test; hormone levels (morning FSH, LH, and E₂) were analyzed by analysis of variance (ANOVA) followed by post hoc testing using Student-Newman-Keuls multiple-range test. We analyzed differences in LH serum levels among treatment groups comparing morning, afternoon, and poststimulation values by the Kruskal-Wallis test (nonparamteric ANOVA) followed by Dunn’s multiple comparisons test. We considered probability values of <italic>p</italic> &lt; 0.05 to be statistically significant. We used INSTAT and PRISM software, version 3.0, for personal computer (GraphPad, San Diego, CA) to calculate and graph results.</p>" ]

[ "<title>Results</title>", "<title>Effect of ESF exposure on puberty-related hormones and the onset of puberty</title>", "<p>Short-term exposure to ESF during juvenile development did not alter mean (± SE) daily weight gain at any dose administered. Controls gained 4.2 ± 0.11 g, whereas animals receiving 5.0, 1.0, and 0.5 mg/kg gained 4.07 ± 0.15, 4.11 ± 0.15, and 4.25 ± 0.22 g, respectively. However, the 5.0 mg/kg and 1.0 mg/kg doses of the pesticide delayed (<italic>p</italic> &lt; 0.01 and <italic>p</italic> &lt; 0.05, respectively) the age at VO compared with corn oil controls (##FIG##0##Figure 1##). ESF delayed the onset of puberty by 2 days in animals dosed with 5.0 mg/kg and by 1 day in females dosed with the 1.0 mg/kg compared with control animals.</p>", "<p>We performed cytologic evaluations on each rat on their respective day of VO and determined the stage of the estrous cycle. The interval from VO to D1 was the same for pesticide-treated and control animals in which the smear on the day of VO was either proestrus or estrus (1.25 days).</p>", "<p>We then compared serum levels of the puberty-related hormones LH, FSH, and E₂ from PND29 pesticide-treated and control animals. Animals exposed to the two highest doses of ESF (1.0 and 5.0 mg/kg) exhibited a 1.3- and 2-fold decrease in serum E₂, respectively, compared with controls (##FIG##1##Figure 2##). Interestingly, the morning serum levels of both LH and FSH were unchanged at any dose of ESF (##FIG##2##Figure 3##).</p>", "<title>Effect of ESF on morning-to-afternoon basal levels of LH and hypothalamic responsiveness to NMA</title>", "<p>Because morning LH levels were unaltered in pesticide-treated animals, we assessed whether ESF affected the morning-to-afternoon pattern of LH secretion. During peripubertal development, the release of LH becomes more prominent in the afternoon, which is a centrally mediated event (##REF##3090657##Ojeda et al. 1986##). To assess whether ESF affects this pattern of LH secretion, we dosed animals from PND22 with either the pesticide (0.5 and 1.0 mg/kg) or corn oil and took morning and afternoon blood samples on PND30. Control animals exhibited a 2-fold rise in basal afternoon LH levels compared with the morning levels (<italic>p</italic> &lt; 0.01). The mean afternoon LH levels in control animals were higher (<italic>p</italic> &lt; 0.05) than those of animals treated with 1.0 mg/kg ESF (##FIG##3##Figure 4##).</p>", "<p>To assess hypothalamic responsiveness, animals were injected with NMA after the third afternoon blood sample to induce LH release. NMA stimulated marked increases in LH secretion compared with their respective afternoon levels in all three groups of animals (<italic>p</italic> &lt; 0.05), but we noted no differences between either ESF treatment group compared with controls. These data demonstrate that hypothalamic responsiveness to NMA stimulation was not altered by the pesticide (##FIG##3##Figure 4##).</p>", "<p>We conducted a final experiment to assess pituitary responsiveness after ESF exposure. We dosed animals with ESF and collected serial blood samples as described above, except that instead of NMA administration, we challenged these animals with LHRH (25 ng) (##REF##3933526##Dees et al. 1985##). LHRH markedly (<italic>p</italic> &lt; 0.001) stimulated LH release over afternoon basal levels in control and both ESF treatment groups, demonstrating that pituitary responsiveness was not affected by the pesticide (data not shown).</p>" ]

[ "<title>Discussion</title>", "<p>This is the first study to show an inhibitory action of a type II pyrethroid pesticide, ESF, on the hypothalamic control of prepubertal gonadotropin secretion. This study is also the first to show that short-term administration of ESF to juvenile animals significantly delays the onset of female puberty. The dose of 1.0 mg/kg used for our short-term puberty studies was two times lower than the stated no observable effect level (NOEL) of 2.0 mg/kg/day used for the dietary developmental study in rats (##UREF##10##U.S. EPA 1998##).</p>", "<p>We found only one <italic>in vivo</italic> study that evaluated puberty in female animals exposed to a type II pyrethroid. ##REF##16005607##Moniz et al. (2005)## dosed pregnant animals with a commercial-grade formulation (Bernardi MM, personal communication) of an unknown purity of fenvalerate. ##REF##16005607##Moniz et al. (2005)## found age at VO to be delayed in exposed offspring. However, commercial-grade formulations typically contain other compounds, such as solvents and petroleum distillates (##UREF##6##Meister 1997##). No conclusions can be made as to whether the effects found by ##REF##16005607##Moniz et al. (2005)## were due to fenvalerate or the other components in the formulation, so we cannot directly compare their results with ours.</p>", "<p>In the present study, we dosed immature animals 1 day postweaning until just before the onset of puberty, at which time we measured morning serum levels of LH, FSH, and E_2. Females dosed with 5.0 and 1.0 mg/kg ESF had significantly suppressed E₂ levels.</p>", "<p>Interestingly, in our study, ESF did not affect the morning basal secretion of FSH or LH. If ESF were acting only at the ovarian level to suppress serum E₂ levels, a compensatory increase in LH would be expected because of the removal of the negative feedback effect at the hypothalamus. However, the morning LH levels in the pesticide-treated animals were the same as those in controls. This atypical response to the low E₂ suggested a hypothalamic deficit.</p>", "<p>However, our results for afternoon LH levels showed an inhibition in the afternoon hormonal rise in animals treated with even the lowest dose (0.5 mg/kg) of pesticide. The physiologic pattern of both LH and FSH secretion is periodic and intermittent, although more so for LH. Before the onset of puberty, the amplitude of LH release is low; however, once puberty begins, the release of LH becomes more prominent in the afternoon. Both rats and humans exhibit a similar release pattern (##UREF##1##Delemarre-Van De Waal et al. 1991##; ##REF##3309706##Urbanski and Ojeda 1987##), which is centrally driven (##REF##6774388##Knobil 1980##).</p>", "<p>Delayed puberty most commonly occurs because of decreased E₂ resulting from either hypothalamic and/or pituitary dysfunction or from a direct effect on the ovary. Many toxicants can perturb multiple systems, and ESF may also be interfering with ovarian steroid hormone biosynthesis as well. However, if ESF acted solely on the ovary to suppress E₂ synthesis or release, the expected response would be a compensatory increase in LH levels because of the feedback mechanism from the ovary to the hypothalamus and pituitary. Morning LH levels in the pesticide-treated animals were the same as those in control animals, indicating an abnormal feedback response.</p>", "<p>When we measured LH in serial morning and afternoon blood samples, the afternoon rise in LH was suppressed in females dosed at even the lowest ESF concentration. Based on the findings of low serum E₂ and low afternoon LH, we expected the ESF-treated animals to release less LH than controls after NMA stimulation. However, the ESF-treated animals responded to NMA stimulation as well as the controls.</p>", "<p>The toxic effects of pyrethroids are caused by the prolongation of the open state of voltage-dependent sodium channels, resulting in repetitive firing of the neurons. If the delay in puberty was due to the action of the pesticide on the sodium channels of the LHRH neurons, the expected result would be an initial increase in LH due to the rapid release of LHRH, followed by a decrease in LH levels after the releasable pool of LHRH was exhausted.</p>", "<p>One possible site of pyrethroid action that could potentially cause the delay in puberty is the NMDA receptor; activation of the NMDA receptor has been suggested as a mediator of pyrethroid neurotoxicity (##REF##1359079##Chugh et al. 1992##). Furthermore, ##REF##12573543##Wu and Liu (2003)## found that both c-fos and c-jun were expressed in adult male rats exposed to deltamethrin. Blocking the NMDA receptor with the antagonist MK-801 eliminated c-fos and c-jun expression, suggesting that the pesticide can act through this excitatory amino acid (EAA) pathway. A possible explanation of our data is that ESF may be interfering with hypothalamic storage, release, and/or transport of the EAAs.</p>", "<p>Any or all of the EAAs, such as glutamate or aspartate, may not be present in high enough levels to fully activate the NMDA receptor. Thus, when we administered the NMDA receptor agonist, more unoccupied receptor sites were available for binding and subsequent stimulation of LHRH release. Other neurotransmitters, such as norepinephrine, may also be adversely affected by the pesticide.</p>", "<p>Although the exact mechanism of action is unknown at this time, we observed the effects at dosage levels below the NOEL established through chronic dietary exposure studies in rats. The ##UREF##10##U.S. EPA (1998)## stated that “There is no evidence of additional sensitivity to young rats or rabbits following pre- or post-natal exposure to esfenvalerate.” The present study shows that immature female rats exposed to 1.0 mg/kg/day are sensitive to this pesticide, as evidenced by their delay in the onset of puberty. Delayed pubertal onset in humans has been associated with low bone mass density (##REF##16261454##Ho and Kung 2005##), and estrogen is necessary for bone mineral acquisition in both girls and boys (##REF##16261455##Yilmaz et al. 2005##). Importantly, a lowered endogenous estrogen level in females is one factor associated with bone fragility (##REF##14724768##Hoffman and Bradshaw 2003##).</p>", "<p>This could potentially affect current established exposure levels for humans, because the reference dose for ESF of 0.02 mg/kg/day is based directly on the rodent NOEL of 2.0 mg/kg/day. Obviously, more basic research is needed in this area; because of the worldwide use of this class of pesticide, further studies are warranted.</p>" ]

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[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>One of the most widely used classes of insecticides is the synthetic pyrethroids. Although pyrethroids are less acutely toxic to humans than to insects, <italic>in vitro</italic> studies have suggested that pyrethroids may be estrogenic.</p>", "<title>Objectives</title>", "<p>We assessed pubertal effects by orally administering 0.5, 1.0, and 5.0 mg/kg/day of the type II pyrethroid esfenvalerate (ESF) to female rats beginning on postnatal day (PND) 22 until vaginal opening. ESF administration suppresses serum estradiol and delays pubertal onset.</p>", "<title>Materials and methods</title>", "<p>To assess possible hypothalamic and/or pituitary effects, animals received 0.5 or 1.0 mg/kg ESF or corn oil on PNDs 22–29. On PND30, we drew three blood samples (200 μL) from each rat at 15-min intervals beginning at 1000 hours, and again at 1500 hours. To test hypothalamic responsiveness, after the third afternoon sample, all animals received an intravenous injection of <italic>N</italic>-methyl-d,l-aspartic acid (NMA; 40 mg/kg), and then we drew two more samples. We performed a second experiment as above except that animals received luteinizing hormone–releasing hormone (LHRH; 25 ng/rat) to test pituitary responsiveness.</p>", "<title>Results</title>", "<p>Basal levels of luteinizing hormone (LH) in the afternoon hours were higher in control animals than in animals treated with 1.0 mg/kg ESF (<italic>p</italic> &lt; 0.05). Furthermore, NMA- and LHRH-stimulated LH release was similar in control and ESF-treated animals, indicating that both hypothalamic and pituitary responsiveness, respectively, were unaffected.</p>", "<title>Conclusions</title>", "<p>Although the hypothalamus is able to respond to exogenous stimuli, absence of a normal afternoon rise in LH would indicate a hypothalamic deficit in ESF-treated animals.</p>" ]

[ "<p>Pesticides are used worldwide to control both agricultural and household pests. In 2001, the United States alone used approximately 122 million pounds of insecticides, and 12% of those compounds were for home and garden use [##UREF##11##U.S. Environmental Protection Agency (EPA) 2001##]. One of the most frequently used classes of pesticides is the synthetic pyrethroids (##UREF##8##Roberts and Hutson 1999##). They represented approximately one-fourth of the worldwide market for insecticides in 1998 (##REF##9444749##Casida and Quistad 1998##), and their use has continued to grow.</p>", "<p>Pyrethroid pesticides are the synthetic analogs of the naturally occurring toxin pyrethrin, which is derived from the flowers of <italic>Chrysanthemum cinerariaefolium</italic>. Pyrethroids exert their toxic action by binding to the voltage-dependent sodium channel in nervous tissue and prolonging the open phase (##REF##11812616##Soderlund et al. 2002##; ##REF##1964560##Vijverberg and van den Bercken 1990##). Although these pesticides have been modified to be more photostable, more lipophilic, and more toxic than pyrethrin, they are considerably less toxic to mammals than are other classes of insecticides, such as organochlorines, organophosphates, and carbamates. Because of their low acute human toxicity, pyrethroids are widely used to control insects in and around homes (##REF##15026777##Freeman et al. 2004##) and day care facilities. ##REF##17120552##Tulve et al. (2006)## detected two permethrin isomers and 13 different pyrethroids, including esfenvalerate (ESF), in floor wipe samples taken from 168 day care centers in the United States.</p>", "<p>In addition to household exposures, children may also consume pyrethroids in their diet. Permethrin and fenvalerate residues have been detected in selected baby food [Food and Drug Administration (FDA) 2005). Additionally, permethrin, cyfluthrin, cypermethrin, and deltamethrin have been detected in human breast milk in women living in an area of South Africa in which pyrethroids were used for malaria control. (##REF##16564119##Bouwman et al. 2006##). Pyrethroid metabolites have also been measured in urine samples taken from children with no known previous exposure (##REF##11333180##Heudorf and Angerer 2001##). However, that study found no correlation between levels of permethrin (the only pyrethroid found in analyzed household dust samples) and levels of pyrethroid metabolites in the urine, leading those researchers to conclude that the exposure route was primarily dietary. However, in a study measuring pyrethroid metabolites in the urine of children 3–11 years of age, ##REF##16966099##Lu et al. (2006)## found that self-reported residential pyrethroid use was significantly associated with metabolite levels, whereas changing the diet from conventional to organic was not. Thus, pyrethroid exposure in this group of children is likely to have occurred primarily through the environment.</p>", "<p>Only a few published reports of dietary intake levels of the different pyrethroids are available. The average daily intake for permethrin for a man weighing 70 kg was estimated at 3.2 μg/day, which is less than the acceptable daily intake of 50 μg/kg/day [##UREF##0##Centers for Disease Control and Prevention (CDC) 2005##], but no average daily intakes for children were listed. However, in a recent study ##REF##16564119##Bouwman et al. (2006)## calculated daily intakes of permethrin (13.6 μg/kg), cyfluthrin (73.4 μg/kg), and deltamethrin (13.3 μg/kg) for infants based on levels measured in breast milk of women living in three separate regions of Africa. Although calculated intakes of permethrin and cyfluthrin were below the currently established acceptable daily intake levels, those of deltamethrin exceeded the acceptable daily intake by 3.3 μg/kg.</p>", "<p>Although the overall incidence of fatalities and severe poisonings due to pyrethroids is lower than that of the organophosphates, several <italic>in vitro</italic> studies have indicated that pyrethroids may have estrogenic activity, causing them to be placed on the U.S. EPA’s list of possible endocrine disruptors (##REF##8080506##Colborn et al. 1993##; ##UREF##9##U.S. EPA 1997##). Fenvalerate has been shown to induce proliferation and increase the expression of the estradiol (E₂)-inducible gene <italic>pS2</italic> and the proto-oncogene <italic>Wnt10B</italic> in MCF-7 breast cancer cells (##REF##12396874##Chen et al. 2002##; ##REF##10064545##Go et al. 1999##; ##REF##12437293##Kasat et al. 2002##). ##REF##16626760##Lemaire et al. (2006)## showed that fenvalerate was able to increase transcription via estrogen receptor-α (ER-α ) in stably transfected HELN cells. Other studies have shown conflicting data in which fenvalerate had no effect on pS2 mRNA expression, ER binding, or ER expression (##REF##15118252##Kim et al. 2004##). Furthermore, fenvalerate has been shown to inhibit MCF-7 BUS (a variant MCF-7 cell line) proliferation in the presence of 17β-E₂, leading to speculation that it is a possible antiestrogen (##REF##15118252##Kim et al. 2004##).</p>", "<p>Most of the data suggesting the estrogenic action of the type II pyrethroids have come primarily from <italic>in vitro</italic> studies using human cancer cell lines. When synthetic pyrethroids, including fenvalerate, were tested by other screening assays, such as the luciferase reporter gene assay (##REF##11884232##Andersen et al. 2002##), yeast two-hybrid assay, and competitive ligand-binding assay using fluoromone ES1, the results were negative for estrogenic activity (##REF##10966511##Saito et al. 2000##). Additionally, <italic>in vivo</italic> screening tests [##UREF##7##Organisation for Economic Co-operation and Development (OECD) 1997##] that assessed the estrogenic and androgenic effects of the pyrethroids fenvalerate, ESF, and permethrin found no significant changes in the accessory sex glands or uterine weights of castrated adult male and female rats, respectively, after treatment with the pesticides (##REF##12052007##Kunimatsu et al. 2002##), thus calling into question the conclusions of estrogenic activity drawn from the <italic>in vitro</italic> data.</p>", "<p>However, none of the <italic>in vivo</italic> studies has evaluated the neuroendocrine effects of oral exposure to low doses of type II pyrethroids in immature animals. Because children and adolescents are exposed to pyrethroids and because of the conflicting data regarding the possible endocrine-disrupting capability of these pesticides, we wanted to assess the effects of short-term oral administration of a low dose of a type II pyrethroid on the onset of female puberty and on the levels of pubertal hormones <italic>in vivo</italic>.</p>", "<p>We chose to evaluate the pyrethroid pesticide ESF [benzeneacetic acid, 4-chloro-α-(1-methylethyl)-, cyano(3-phenoxyphenyl)methyl ester] because it is used both in the home (Ortho Bug-B-Gon; Scotts Miracle-Gro Company, Marysville, OH) and on a wide variety of crops, including fruits, vegetables, and nuts [##UREF##2##Extension Toxicology Network (EXTOXNET) 1996##].</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1243\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Effect of short-term oral administration of ESF at 5 mg/kg (<italic>A</italic>), 1 mg/kg (<italic>B</italic>), and 0.5 mg/kg (<italic>C</italic>) on the age at VO. VO was delayed by 2 days in animals treated with 5.0 mg/kg ESF (<italic>A</italic>) and by 1 day in animals treated with 1.0 mg/kg ESF (<italic>B</italic>). Values represent mean ± SE; the number within each bar indicates the number of animals.</p><p>*<italic>p</italic> &lt; 0.05, and **<italic>p</italic> &lt; 0.01 compared with control.</p></caption></fig>", "<fig id=\"f2-ehp-116-1243\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Effect of short-term oral administration of ESF on serum E₂ in female rats on PND29. The 1.0-mg/kg dose caused a 1.3-fold decrease in serum E₂, whereas the 5.0 mg/kg dose caused a 2-fold decrease. Values represent mean ± SE; the number within each bar indicates the number of animals.</p><p>*<italic>p</italic> &lt; 0.05, and **<italic>p</italic> &lt; 0.01 compared with control.</p></caption></fig>", "<fig id=\"f3-ehp-116-1243\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Effect of short-term oral administration of ESF on morning serum LH and FSH levels. Values represent the mean ± SE; and the number within each bar indicates the number of animals.</p></caption></fig>", "<fig id=\"f4-ehp-116-1243\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Effect of short-term oral administration of ESF on morning, afternoon, and NMA-stimulated LH release <italic>in vivo</italic>. Values for morning and afternoon represent the mean ± SE of three samples for each time point from each animal; post-NMA LH levels represent the peak value (number of animals: controls, <italic>n</italic> = 12; 0.5 mg/kg NMA, <italic>n</italic> = 14; 1.0 mg/kg NMA, <italic>n</italic> = 16).</p><p>*<italic>p</italic> &lt; 0.05 compared with control. **<italic>p</italic> &lt; 0.05 compared with afternoon values.</p></caption></fig>" ]

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[ "<graphic xlink:href=\"ehp-116-1243f1\"/>", "<graphic xlink:href=\"ehp-116-1243f2\"/>", "<graphic xlink:href=\"ehp-116-1243f3\"/>", "<graphic xlink:href=\"ehp-116-1243f4\"/>" ]

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[ "<title>Methods</title>", "<p>In 2002 Congress directed the CDC to establish a regional clinical consortium to provide medical and mental health monitoring of WTC rescue and recovery workers. In response, the WTC–MMTP was established, with the Department of Community and Preventive Medicine, Mount Sinai School of Medicine, as the coordinating entity and the Bellevue/New York University Occupational and Environmental Medicine Clinic, the SUNY (State University of New York)-Stony Brook/Long Island Occupational and Environmental Health Center, the Center for the Biology of Natural Systems at Queens College in New York, and the Clinical Center of the Environmental &amp; Occupational Health Sciences Institute at University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School in New Jersey as the other members of the consortium.</p>", "<p>Subjects were recruited for participation in a clinical monitoring program through outreach that included union meetings, mailings, media articles, and some 50,000 telephone calls in multiple languages. The first 10,132 participants who completed a self-administered mental health questionnaire and provided written informed consent to permit aggregation of their data into a research database are included in these analyses. Institutional review board approval was obtained at each of the participating institutions. Eligibility for the clinical examination required either having worked or volunteered as part of the rescue, recovery, restoration, or cleanup in Manhattan south of Canal Street, barge-loading piers in Manhattan, or the Staten Island landfill, for at least 24 hr during 11–30 September 2001, or for &gt; 80 hr between 11 September and 31 December 2001. Employees of the Office of the Chief Medical Examiner had no minimum hour requirements. New York City firefighters participated in a separate program.</p>", "<p>The clinical examination included a mental health screening questionnaire that used standard instruments to assess emotional status. These included the PTSD Symptom Checklist (PCL; ##REF##8870294##Blanchard et al. 1996##); the Patient Health Questionnaire (PHQ) for assessing depression, anxiety, and panic (##REF##10568646##Spitzer et al. 1999##); the CAGE questionnaire for alcohol abuse (##REF##6471323##Ewing 1984##); and the Sheehan Disability Scale to estimate the extent to which emotional problems disrupted work, social life, and family and home responsibilities (##UREF##7##Leon et al. 1997##). Single items to measure changes in alcohol consumption were also included. Threshold criteria were defined for each measure. Those who met criteria for any scale were referred, on the same day, for clinical evaluation by mental health professionals. These criteria are described in an earlier report on the mental health of the first 1,138 responders examined in the program (##UREF##2##CDC 2004b##). All workers also underwent physical examinations and had medical and exposure interviews. The questionnaire included a child symptom checklist from the disaster supplement of the Diagnostic Interview Schedule that asked the responder to state whether his or her children exhibited any of 12 symptoms for the period during the responders work on the WTC site and in the month before the examination (##UREF##15##Robins 1983##).</p>", "<p>We calculated three commonly used algorithms for defining probable PTSD. The first two are based on summing up the responses to the 17 Likert-like items in the PCL checklist shown in ##FIG##0##Figure 1##. We classified a responder as having probable PTSD if the score was ≥ 44 or ≥ 50, where each item was scored as 1–5 (corresponding respectively to not at all, a little bit, moderately, quite a bit, or extremely). We also calculated a score using the algorithm from the <italic>Diagnostic and Statistical Manual of Mental Disorders</italic> (##UREF##0##APA 2000##) as follows. For any item in the checklist, a response at one of the three highest levels (moderately, quite a bit, or extremely) was considered positive. Items were divided into three separate clusters that were scored as follows: cluster B (intrusion)—at least one positive item from items 1–5; cluster C (avoidance)—at least three items from items 6–12; cluster D (hyperarousal)—at least two items from items 13–17. The prevalence of probable PTSD was 20.1%, 11.1%, and 17.1%, respectively, based on these three formulations. We thus used the PCL checklist with a cutoff score of 50 for our definition of probable PTSD in the analyses that follow, as this represented the most conservative estimate for the group as a whole.</p>", "<p>Because we are interested in understanding the extent to which mental health symptoms may represent an ongoing problem in members of our population who do not meet diagnostic criteria for probable PTSD, we calculated the frequency distribution of responses to the individual items on the PCL in workers without probable PTSD (responses from moderately to extremely). For comparison purposes, we also tabulated the number of workers who met Schuster’s definition of substantial stress from the post-9/11 national telephone survey (at least one moderately to extremely response to PCL items 1, 4, 13, 14, or 15) (##REF##11794216##Schuster et al. 2001##).</p>", "<p>We used PHQ responses and the coding algorithm provided by ##REF##10568646##Spitzer et al. (1999)## to assess major depression and panic disorder. A positive response to one of the four CAGE items defined a probable alcohol problem. We defined the interval to first visit as time elapsed between the first day working at the WTC site and date of the first mental health screening examination.</p>", "<title>Statistical analysis</title>", "<p>Two-tailed chi-square tests were used to identify associations between probable PTSD and diagnoses for probable major depression or panic disorder with demographic and exposure characteristics of the population and for the reports of child symptomatology. Mantel-Haenszel common odds ratios (ORs) were calculated to provide risk estimates and confidence intervals for PTSD and comorbid mental health outcomes, probable alcohol problem, and reported deaths or injuries in family members or friends as a result of the attacks. We used linear or logistic regression analyses to determine the extent to which the independent variables in ##TAB##0##Table 1## and comorbid depression predicted the PCL checklist scores or dichotomous outcomes indicating presence or absence of probable PTSD, major depression, alcohol problems, or social disability as measured by the Sheehan scale.</p>" ]

[ "<title>Results</title>", "<p>##TAB##0##Table 1## presents demographic characteristics, distribution of hours and days spent in WTC-related activities, and presence at the site during the first 48 hr by our cohort. More than 62% arrived within the first 48 hr after the attack; 84% were present during the first week, and 91% arrived by 24 September 2001. In addition, the majority continued recovery work for ≥ 3 months and thus was present for the early postattack days and also for the arduous and stressful working conditions that followed. Average age of the responders was 42.1 ± 9.1 years (mean ± SD). Associations of probable PTSD, probable major depression, and panic disorder are also shown in ##TAB##0##Table 1## for each of the respondent characteristics. Significant relationships were observed for probable PTSD with all respondent characteristics except sex; 11.1% met criteria for probable PTSD, 8.8% met criteria for depression, and 5.0% met criteria for panic disorder. We observed widespread symptomatology in the group that was not assigned probable PTSD, panic disorder, or major depression (##FIG##0##Figure 1##); nearly half (45%) met Schuster’s definition of a substantial stress reaction.</p>", "<p>Fewer than 5% of the cohort reported losing members of their family to the attacks, but &gt; 36% reported losing friends. About one-third lost more than one person. We observed significantly elevated rates of probable PTSD [OR = 1.66; 95% confidence interval (CI), 1.21–2.28] and emotional disability (OR = 1.48; 95% CI, 1.16–1.87) associated with having lost a family member to the attack and where disability was measured by the Sheehan scale and signifies emotional disruption of the ability to work or engage in family or social activities. Reported loss of friends resulted in a significant but smaller elevated risk for probable PTSD only (OR =1.2; 95% CI, 1.02–1.43).</p>", "<p>More than 17% of the cohort was classified with a probable alcohol problem based on the CAGE. About 24% of alcohol users reported drinking more than usual following 9/11, and about 47% reported that they drank more during the time at which they were working at the WTC in rescue and recovery operations. About one-third reported still drinking more than usual within the month before their clinic visit.</p>", "<p>In ##TAB##1##Table 2##, we present data on psychological comorbidity (which we define as the presence of more than one psychiatric condition) and its relationship to emotional disability (Sheehan scale) and alcohol problems. Approximately half of those classified as having probable PTSD also were classified with either probable panic disorder, depression, or both. WTC-responders with probable PTSD had highly elevated ORs for probable depression (OR = 13.9; 95% CI, 11.9–16.2) and panic disorder (OR = 9.2; 95% CI, 7.6–11.1). This comorbidity is associated with being 40–86 times as likely to be rated as disabled, as measured by the Sheehan scale. For the group as a whole, regardless of comorbidity status (##TAB##1##Table 2##), probable PTSD was associated with more than double the risk for an alcohol problem (OR = 2.3; 95% CI, 2.0–2.5) and more than 17-fold risk for reported social disability (OR = 17.3; 95% CI, 15.1–19.8) compared to those with no psychological morbidity. The point-prevalence of probable PTSD declined from 13.4% at 10 months postattack to 9.3% at 60 months, a rate far higher than in the general population.</p>", "<p>##TAB##2##Table 3## presents the extent of perceived child symptomatology reported by the workers and by whether or not they have been classified with probable PTSD. The first set of ORs compares the perceptions of those with and without probable PTSD for their children’s symptoms during the time in which the responders were working at the WTC site. The second set of ORs is for the symptoms that these workers perceived their children to be exhibiting in the month before the clinic visit. In all cases the ORs are significantly elevated for workers with PTSD compared to those without the probable diagnosis.</p>" ]

[ "<title>Discussion</title>", "<p>This study documents that the rescue and recovery and cleanup efforts carried out by the workers at the World Trade Center are associated with substantial chronic psychological morbidity and extensive impairment of social functioning. Of 10,132 WTC workers whom we examined, 11.1% had probable PTSD within the month before their mental health examination.</p>", "<p>These findings on the high prevalence of PTSD in WTC workers are similar to those encountered in U.S. war veterans. ##REF##15229303##Hoge et al. (2004)##, using the same PTSD diagnostic checklist that we used and the same cutoff score of 50 on this PCL checklist, found a prevalence of 11.5% for probable PTSD among soldiers returning from Afghanistan. Although not directly comparable to our 1-month prevalence rates, the 12-month prevalence estimates for PTSD among the general adult population in the United States range between 3% and 4% (##REF##15939839##Kessler et al. 2005##).</p>", "<p>Consistent with previous studies, PTSD was not the only type of postdisaster psychopathology observed in this cohort. Almost 9% of the cohort met criteria for probable depression during the month in which they were examined, and 5.0% fulfilled diagnostic criteria for probable panic disorder. Another 17% had probable excess use of alcohol. Personal loss of family and friends appears to have increased these rates. Rates of psychological comorbidity were also high. Among the responders with probable PTSD, 12.7% also met criteria for panic disorder or depression, and 1.7% met criteria for all three disorders: probable PTSD, depression, and panic disorder. Of note, approximately half of the workers with probable PTSD also had a probable comorbid psychiatric condition, and these workers were at far higher risk for social dysfunction and alcohol problems. Further, the point prevalence of PTSD in the comorbid group did not decline over time, suggesting that PTSD in this group may be more chronic.</p>", "<p>Our results are consistent with other reports in which PTSD has been strongly associated with functional impairment, including interference with occupation, family, school, and leisure activities, among subjects in community samples (##UREF##5##Kessler 2000##; ##REF##11986143##North et al. 2002##), male and female veterans (##UREF##6##Kulka et al. 1988##; ##REF##9247398##Stein et al. 1997##), primary care patients (##REF##9396947##Zatzick et al. 1997##), and female survivors of interpersonal violence (##UREF##13##Rapaport et al. 2002##). Level of functional impairment associated with PTSD has been comparable to levels observed in severe chronic depression (##REF##12816406##Laffaye et al. 2003##). In our population, the odds ratio for social impairment was significantly elevated more than 17-fold among those with PTSD compared to those without probable PTSD.</p>", "<p>Many workers who did not meet study criteria for probable PTSD nevertheless reported suffering from PTSD-related symptoms of stress in the month before their evaluation (##FIG##0##Figure 1##). For example, approximately one-third of the responders without probable PTSD reported disturbing memories, thoughts, or images; having trouble falling asleep or staying asleep, and being “super-alert.” Nearly half (45%) of all responders without probable PTSD reported suffering from a substantial stress reaction as long as 5 years after the WTC disaster, a rate comparable to the nationally representative sample of U.S. adults surveyed only 3–5 days after the attacks when symptoms typically are at their highest level (##REF##12215130##Silver et al. 2002##).</p>", "<p>Most research studies and clinical interventions focus on patients who meet full criteria for PTSD. Our data show that such an approach would fail to meet the needs of many of the responders in the present population who were not classified as having probable PTSD but did have a high prevalence of distressing symptoms and functional impairment. In fact, in the current sample, the likelihood of experiencing marked functional impairment in workers with substantial stress symptoms in the absence of probable PTSD was nearly as elevated as this likelihood for workers with panic disorder alone (OR = 3.3; 95% CI, 2.7–4.0). Focusing attention only on probable psychiatric disorders markedly underestimates the full psychological burden and its social ramifications. It is likely that some of these individuals would benefit from appropriate treatment.</p>", "<p>Most WTC workers reported one or more psychological/behavioral symptoms in their children during the time that they worked at the disaster site (##UREF##4##Duarte et al. 2006##). ##UREF##16##Stuber et al. (2005)## and ##REF##12150669##Schlenger et al. (2002)## reported similar findings of substantial WTC-related stress among children in New York City at the time of the disaster and for months afterward. Also consistent with previous research, WTC workers with probable PTSD were far more likely to report psychological symptoms and behavioral problems in their children compared with WTC workers without probable PTSD. Similar results were observed in victims of the Chornobyl reactor disaster (##REF##12194897##Bromet et al. 2002##).</p>", "<p>Unlike most previous findings in civilian trauma survivors of mass disasters or individual traumatic events, the association between post-disaster PTSD and sex was not significant in the present study. Similar findings have been reported in samples of 655 urban police officers (21% female) and 207 exposed disaster workers (11.5% female) as well as in military populations (##REF##7760253##Sutker et al. 1995##). ##UREF##12##Pole et al. (2001)## suggested that selection and/or training factors may help to stress-inoculate women involved in police and military work.</p>", "<p>The present study has a number of strengths, including use of standardized assessment instruments, comprehensive psychosocial and medical evaluation, inclusion of both males and females, ethnic diversity of subjects, and large cohort size. In an empirical review of the scientific literature from 1981 to 2001 on disaster victims, ##REF##12405079##Norris et al. (2002)## noted problems with small sample sizes as well as demographic and ethnic homogeneity. During that 20-year period, the median sample size for studies related to psychosocial adjustment after disasters was only 159 subjects.</p>", "<p>Study limitations include the use of self-administered rather than clinician-administered questionnaires, variability in time to presentation, potential inaccuracy of recall with the passage of time, possible under-reporting of psychological symptoms due to stigma, and lack of assessment within the first few months after 9/11. Because the earliest assessments occurred at least 10 months after the attacks, it is not possible to differentiate delayed onset versus chronic PTSD, and it is not possible to accurately determine rates of acute PTSD. Our self-selected cohort is also a limitation in that we do not know whether workers with psychological symptomatology were more or less likely to enroll. Also, degree of psychological symptomatology may be related to the presence of physical symptoms, and it may be that those with physical illnesses were more likely to seek medical monitoring. Our ability to generalize from this self-selected cohort is enhanced to some extent by its large size, but generalizability is hampered without knowing the true number of WTC rescue and recovery workers nor their sex, race, or ethnicity. Our estimate of 40,000 at-risk workers is considerably lower than the nearly 92,000 estimated by the World Trade Center Registry (##REF##17285683##Murphy et al. 2007##), largely because of the registry’s less stringent criteria for eligibility into their recovery worker cohort. The registry requires a worker or volunteer to have spent one shift at a WTC site between 11 September 2001 and 30 June 2002. The true number of workers and volunteers undoubtedly falls between the two estimates.</p>", "<p>Keeping such limitations in mind, the prevalence of probable PTSD among workers in the present study is comparable to that seen in airline-crash recovery workers 13 months after the event (##REF##15285961##Fullerton et al. 2004##) and that observed by ##REF##11986143##North et al. (2002)## in firefighters interviewed 34 months after the Oklahoma City bombing.</p>", "<p>The present study has a number of implications for public health. Persistent post-disaster mental illness from 10 months to 5 years after the disaster in this cohort underscores the need for long-term mental health screening and treatment programs targeting this population. Chronic mental health disorders constitute a major public health concern. In this cohort, alcohol problems and impairment in occupational, social, and family life were strongly associated with diagnoses of probable PTSD, depression, or panic disorder alone and even with a large number of workers who did not meet criteria for a probable psychiatric disorder but who nevertheless experienced trauma-related psychological symptoms. It is particularly important to screen for comorbid conditions because in our population comorbid conditions were common, and those with these comorbid conditions were at far greater risk for alcohol problems and social dysfunction. The presence of comorbidity may also affect long-term outcome and response to treatment (##UREF##9##McFarlane 2002##).</p>", "<p>Psychiatric disability has effects far beyond the personal suffering of the individual and his or her immediate family. For example, PTSD has substantial economic costs from workdays lost and suboptimal performance. It is estimated that PTSD is associated with approximately 3.6 days of work impairment per month, on average (##REF##9672053##Breslau et al. 1998##). Further, the National Comorbidity Study found that PTSD was associated with marital instability and increased unemployment (##UREF##5##Kessler 2000##). Taken together, our findings indicate that a substantial public mental health burden exists in the responder population, which puts them at risk for a variety of adverse health and social consequences.</p>", "<p>Unfortunately, once psychopathology, such as PTSD, becomes chronic in nature, it can be difficult to treat. The military, recognizing the high frequency of posttraumatic stress symptoms in response to dangerous and life-threatening situations, as well as the costly effects of these symptoms on psychological well being and performance, has recently instituted periodic behavioral health evaluations on all troops returning from Iraq and Afghanistan. Stigma is reduced by requiring every returning soldier to participate in these evaluations. Similarly, rescue and recovery workers after future environmental disasters would likely benefit from routine behavioral health evaluations that are fully integrated into medical evaluations, as well as early treatment when appropriate. Although such efforts should help reduce chronicity of the mental health sequelae of disaster exposure, long-term provision of accessible mental health services for rescue and recovery workers likely should still constitute part of future disaster planning.</p>", "<p>Finally, it will be essential in future environmental disasters to understand that mental health problems will almost certainly accompany effects of toxic exposures on physical health. It is also essential that accurate records be kept of the rescue and recovery cohorts so that postdisaster outreach efforts can be improved and better estimates of injury, illness, and disability can be made. Additional rigorous research is needed to better understand and modify the impacts on health of the physical and psychological risk factors that associated with work after environmental disasters (##UREF##11##North 2004##).</p>" ]

[]

[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>The World Trade Center (WTC) attacks exposed thousands of workers to hazardous environmental conditions and psychological trauma. In 2002, to assess the health of these workers, Congress directed the National Institute for Occupational Safety and Health to establish the WTC Medical Monitoring and Treatment Program. This program has established a large cohort of WTC rescue, recovery, and cleanup workers. We previously documented extensive pulmonary dysfunction in this cohort related to toxic environmental exposures.</p>", "<title>Objectives</title>", "<p>Our objective in this study was to describe mental health outcomes, social function impairment, and psychiatric comorbidity in the WTC worker cohort, as well as perceived symptomatology in workers’ children.</p>", "<title>Methods</title>", "<p>Ten to 61 months after the WTC attack, 10,132 WTC workers completed a self-administered mental health questionnaire.</p>", "<title>Results</title>", "<p>Of the workers who completd the questionnaire, 11.1% met criteria for probable post-traumatic stress disorder (PTSD), 8.8% met criteria for probable depression, 5.0% met criteria for probable panic disorder, and 62% met criteria for substantial stress reaction. PTSD prevalence was comparable to that seen in returning Afghanistan war veterans and was much higher than in the U.S. general population. Point prevalence declined from 13.5% to 9.7% over the 5 years of observation. Comorbidity was extensive and included extremely high risks for impairment of social function. PTSD was significantly associated with loss of family members and friends, disruption of family, work, and social life, and higher rates of behavioral symptoms in children of workers.</p>", "<title>Conclusions</title>", "<p>Working in 9/11 recovery operations is associated with chronic impairment of mental health and social functioning. Psychological distress and psychopathology in WTC workers greatly exceed population norms. Surveillance and treatment programs continue to be needed.</p>" ]

[ "<p>It is estimated that between 40,000 and 92,000 men and women were involved in the rescue, recovery, and cleanup operations that followed the 11 September 2001 (9/11), terrorist attacks on the World Trade Center (WTC), depending on the criteria used for cohort eligibility [##UREF##1##Centers for Disease Control and Prevention (CDC) 2004a##, ##UREF##2##2004b##; ##REF##17285683##Murphy et al. 2007##]. Service in these operations exposed workers to toxic and unsafe working conditions, including smoke, fumes, and highly alkaline dust (pH 10–11). We previously reported on elevated rates of pulmonary symptomatology in a cohort of WTC workers (##REF##15121517##Landrigan et al. 2004##). In addition to hazardous chemical and physical environmental exposures, the working conditions at the WTC involved exposures to serious psychosocial stressors, including long hours and arduous work, treacherous and chaotic working conditions, fear for personal safety, and handling body parts and personal effects of victims, or working in close proximity to such operations (##REF##15121517##Landrigan et al. 2004##; ##UREF##10##Neria et al. 2006##; ##REF##11843202##Platner 2002##).</p>", "<p>Psychological stress and trauma, particularly when chronic, can interact with chemical and physical environmental exposures in ways that are still not well understood and can exacerbate or contribute to the development of a wide range of medical conditions, including cardiovascular, pulmonary, gastrointestinal, neurologic, and autoimmune disorders (##UREF##8##McEwen and Lasley 2002##). There is evidence that stressful conditions can increase susceptibility to toxic insult (##REF##17687441##Peters et al. 2007##), and environmental researchers are considering the various ways in which mental health fits into the conceptual framework of environmental health sciences (##REF##17687431##Schmidt 2007##).</p>", "<p>Psychological stress and trauma can also cause or exacerbate psychiatric disorders. The best known of the psychiatric responses to stress is posttraumatic stress disorder (PTSD). PTSD is characterized by repetitive reexperiencing of the traumatic event in the form of intrusive and unwanted memories of the trauma; nightmares and flashbacks about the trauma; difficulty modulating arousal as evidenced by insomnia, irritability, angry outbursts, hypervigilance, difficulty concentrating, and exaggerated startle response; avoidance of stimuli associated with the trauma; and a general numbing of emotions with a feeling of detachment from others. Elevated rates of PTSD have previously been reported in a number of studies of WTC rescue and recovery workers (##UREF##2##CDC 2004b##; ##UREF##3##Difede et al. 1997##; ##REF##16891606##Gross et al. 2006##; ##REF##17728424##Perrin et al. 2007##; ##REF##15898096##Tapp et al. 2005##).</p>", "<p>Other psychological disorders have also been associated with traumatic stress, including depression and panic disorder (##REF##15939839##Kessler et al. 2005##). Major depressive disorder is defined as depressed mood or loss of interest or pleasure in nearly all activities for a period of at least 2 weeks [##UREF##0##American Psychiatric Association (APA) 2000##]. Other symptoms include significant weight gain or weight loss when not dieting, insomnia or hypersomnia, slowed and retarded movement or hyperactive movement, fatigue or loss of energy, excess or inappropriate guilt or feelings of worthlessness, indecisiveness or difficulty thinking and concentrating, and recurrent thoughts of death. Panic disorder is characterized by recurrent, unexpected panic attacks marked by discrete periods of intense fear or discomfort accompanied by a least four of the following symptoms: palpitations; pounding heart or accelerated heart rate; sweating, trembling, or shaking; sensations of shortness of breath or smothering; feeling of choking; chest pain or discomfort; nausea or abdominal distress; feeling dizzy, unsteady, lightheaded, or faint; feelings of unreality or detachment; fear of losing control or going crazy; fear of dying; numbness or tingling; and chills or hot flashes (##UREF##0##APA 2000##).</p>", "<p>PTSD, depression, and panic disorder associated with traumatic stress often co-occur in the same individual, a situation referred to as comorbidity. Rates of psychiatric comorbidity have been found to be high in community, at-risk, and clinical populations of individuals who have been diagnosed with PTSD (##UREF##14##Resick 2001##). For example, in the National Comorbidity Study, a community sample assessing the rates of mental disorders in the general population, ##REF##15939839##Kessler et al. (2005)## reported that among subjects diagnosed with PTSD, 88% of men and 79% of women also met criteria for at least one other comorbid mental disorder.</p>", "<p>In the present analysis, as part of the WTC Medical Monitoring and Treatment Program (MMTP) supported by the National Institute for Occupational Safety and Health (NIOSH), we expanded our earlier report of 1,138 WTC workers in which we described prevalence rates of probable PTSD, major depression, and panic disorder. In a much larger sample of this cohort, we investigated prevalence of probable PTSD over a 5-year period and the prevalence of major depression and panic disorder (##UREF##2##CDC 2004b##). In addition, we examined psychiatric comorbidity as well as on the extensive symptomatology detected in workers who do not meet all of the diagnostic criteria for one of these psychiatric disorders. We also assessed the degree to which probable psychiatric disorders, comorbid psychiatric disorders, and substantial stress reactions are related to impairment in functioning, as measured by problems with alcohol and disruption of social functioning at work and with friends and family. Finally, we examined workers’ beliefs about behavioral symptoms in their children. Overall, these data provide a more complete understanding of the enduring psychiatric burden experienced by this cohort of WTC workers.</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1248\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Percentage of responders without probable PTSD diagnosis with positive scores on individual PCL symptom checklist items in the month before examination. Items are taken from the PCL (##REF##8870294##Blanchard et al. 1996##). A response at one of the three highest levels (moderately, quite a bit, or extremely) to a PCL item was considered positive. Numbers in parentheses indicate the item numbers in the checklist.</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1248\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Prevalence and associations between probable PTSD, major depression, and panic disorder by respondent characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Characteristic</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">PTSD (%)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Depression (%)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Panic (%)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">10,132</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sex (<italic>p</italic>-value)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">8,847</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,285</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Age (<italic>p</italic>-value)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≤35 years</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">2,474</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 36–45 years</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">4,085</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 45–55 years</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">2,726</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥55 years</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">847</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Marital status (<italic>p</italic>-value)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Single</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,689</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Married/partnered</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">6,709</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Separated/divorced</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,277</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Widowed</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">77</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Education (<italic>p</italic>-value)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; High school</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">885</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High school</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">2,565</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Attended college</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3,785</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Graduated college</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,405</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Graduate school</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">786</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Race/ethnicity (<italic>p</italic>-value)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.01</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.05</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> White</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">6,480</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Black</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,129</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Hispanic</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">2,230</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Asian</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">131</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Other</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">162</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Union member (<italic>p</italic>-value)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.01</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Member</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">8,663</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Not a member</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,421</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Days at site (<italic>p</italic>-value)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≤ 2 weeks</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,938</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Up to 1.5 months</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,976</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Up to 3 months</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">2,170</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Up to 5.5 months</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,948</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &gt; 5.5 months</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,991</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Present 9/11–9/12 (<italic>p</italic>-value)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">6,146</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">3,986</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.8</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1248\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Psychiatric comorbidity and risk for alcohol abuse or emotional disruption of function.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Alcohol problem\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Emotional disruption of function\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Psychiatric condition</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. (%)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">OR (CI)<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1248\">a</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">OR (CI)<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1248\">a</xref></th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">No PTSD, panic, or depression</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8,377 (82.7)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,264</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">406</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Any PTSD, panic, or depression</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,755 (17.3)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">498</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.3 (2.0–2.5)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">821</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.3 (15.1–19.8)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Probable PTSD only</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">563 (6.3)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.6 (2.2–3.2)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">154</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.4 (6.0–9.1)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PTSD plus either panic or depression</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">416 (4.7)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">126</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.5 (2.0–3.0)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">278</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.6 (31.5–50.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PTSD plus both panic and depression</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">145 (1.7)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.2 (2.3–4.6)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">118</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">85.8 (55.8–131.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Depression only</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">368 (4.2)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.6 (1.2–2.0)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">187</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20.3 (16.2–25.5)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Panic disorder only</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">199 (2.3)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.7 (1.3–2.4)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.9 (3.4–7.1)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Panic and depression only</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">64 (0.8)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.3 (0.7–2.4)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">43.2 (25.2–74.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10,132 (100)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,762</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" rowspan=\"1\" colspan=\"1\">1,227</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1248\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Responder reports of child symptoms during WTC work period and in month before examination in those with and without probable PTSD.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">While on site [no. (%)]\n<hr/></th><th colspan=\"3\" align=\"center\" rowspan=\"1\">Month before visit [no. (%)]\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Child symptom</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No PTSD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">PTSD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No PTSD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">PTSD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">More fearful</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,126 (52.1)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">329 (70.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.2 (1.8–2.7)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">730 (20.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">162 (39.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.6 (2.1–3.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">More clingy</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,509 (37.9)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">261 (60.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.5 (2.0–3.1)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">773 (21.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">178 (45.1)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.0 (2.4–3.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">More withdrawn</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">256 (6.5)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">110 (25.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.9 (3.8–6.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">266 (7.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">76 (19.10)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.9 (2.2–3.8)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">More aggressive</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">260 (6.5)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">121 (27.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.5 (4.3–7.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">333 (9.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">96 (24.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.1 (2.4–4.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Trouble with sleep</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">608 (15.2)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">158 (36.2)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.2 (2.6–3.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">360 (10.1)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">102 (26.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.1 (2.4–4.0)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Frequent nightmares</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">355 (9.0)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">140 (32.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.9 (3.9–6.2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">270 (7.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">80 (20.9)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.2 (2.4–4.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Physical complaints</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">135 (3.4)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">68 (15.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.3 (3.9–7.3)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">250 (7.0)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">60 (15.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.4 (1.8–3.2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Change in appetite</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">202 (5.1)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">97 (22.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.4 (4.1–7.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">276 (7.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">79 (20.1)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.0 (2.3–3.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Immature behaviors</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">197 (5.0)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">92 (21.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.2 (4.0–6.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">279 (7.9)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">79 (20.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.0 (2.3–3.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">School behavior problems</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">267 (6.7)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">95 (21.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.8 (3.0–5.0)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">309 (8.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">68 (17.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.2 (1.7–2.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Home behavior problems</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">313 (7.9)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">125 (28.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.6 (3.7–5.9)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">380 (10.7)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">115 (29.1)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.4 (2.7–4.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Poor grades in school</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">284 (7.2)</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">98 (22.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.8 (2.9–4.8)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">316 (8.9)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">68 (17.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.2 (1.6–2.9)</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>We thank S.D. Stellman for critical comments. We also thank Disaster Psychiatry Outreach, the Robin Hood Foundation Relief Fund, the American Red Cross Liberty Fund, The September 11th Recovery Program, The Bear Stearns Charitable Foundation, the September 11th Fund, staff and patients of the World Trade Center Medical Screening and Treatment Program, and labor, community, and volunteer organizations.</p></fn><fn><p>This study was supported by the Centers for Disease Control and the National Institute of Occupational Safety and Health, contract 200-2002-00384 and grants U1O 0H008232, U10 OH008225, U10 UOH008239, U10 OH008275, U10 OH008216, and U10 OH008223.</p></fn><fn><p>Views expressed in this article do not represent the views or policies of the Department of Health and Human Services or the National Institutes of Health.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1248\"><p>NS, not significant.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn2-ehp-116-1248\"><label>a</label><p>ORs are the odds for reporting an alcohol problem (CAGE scale) or emotional disruption (Sheehan scale) in those with the conditions listed compared to those with no PTSD, panic, or depression (reference group).</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn3-ehp-116-1248\"><p>ORs (95% CIs) represent the comparison of those with probable PTSD compared to those who responded and did not have a probable PTSD diagnosis.</p></fn></table-wrap-foot>" ]

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[ "<title>Methods</title>", "<title>Data sources</title>", "<title>Health data</title>", "<p>Eligible subjects were individuals 0–17 years of age for whom health data were collected during 2000–2001 as part of the CHIS, who resided in Los Angeles or San Diego County during the same period, and who reported a physician diagnosis of asthma at some point in their lives. The CHIS is a two-stage, geographically stratified, random-digit-dialed telephone survey of California households. One adult was interviewed from each selected household. In households with adolescents and/or children (12–17 and 0–11 years of age, respectively), one adolescent and/or child was randomly selected for an interview. Information on the child respondent was collected from an adult who was most knowledgeable about the child. Except for insurance information provided by the interviewed adult, adolescents were directly interviewed after a parent or guardian gave permission. Information on demographic characteristics, health conditions, health-related behaviors, access to health care, and insurance coverage was collected. Questions pertaining to asthma were modified from existing national health surveys (NHIS and BRFSS), with additional assessment of symptom frequency in children with asthma. Respondents were also asked to report the name of their residential street and the nearest cross-street. Detailed descriptions of CHIS 2001 sampling and survey methods are reported elsewhere (##UREF##6##Center for Health Policy Research 2002##). This research was approved by the University of California Los Angeles Office for the Protection of Research Subjects and informed consent was obtained from all CHIS 2001 participants.</p>", "<p>Within households in Los Angeles and San Diego Counties, interviews were completed for 1,391 adolescents and 3,405 children. We selected 612 respondents (12.8% of those interviewed) who reported ever having been diagnosed with asthma by a physician. Respondents who reported a lifetime diagnosis of asthma were asked to report the frequency of asthma symptoms such as coughing, wheezing, shortness of breath, chest tightness, or phlegm during the 12 months preceding the interview date. In addition, respondents were asked whether they had ever visited a hospital emergency department (ED) or had been hospitalized because of asthma during this period.</p>", "<title>Exposure data</title>", "<p>Exposure to outdoor air pollution was assessed using two sources of existing information: <italic>a</italic>) routine measurement data collected by the California Air Resources Board and South Coast Air Quality Management District at an existing network of air monitors during 1999–2001, and <italic>b</italic>) annual average daily traffic (AADT) data from the California Department of Transportation (Caltrans) for the year 2000. Detailed information on the Caltrans AADT data was previously reported (##REF##10464078##English et al. 1999##; ##REF##12743618##Gunier et al. 2003##; ##REF##14712141##Reynolds et al. 2004##; ##REF##12573907##Wilhelm and Ritz 2003##). Briefly, the AADT represents the annual average number of vehicles per day traveling in both directions along a given road segment based on rotating traffic counts conducted every 3 years. During noncount years, the AADT is estimated using traffic trends for that location. Counts are collected for all highways and most major roads in the state, but counts on smaller, residential roads with low traffic volume are not typically taken.</p>", "<title>Linkage</title>", "<p>We estimated traffic density (TD) for each subject based on their reported street of residence and nearest cross-street. Specifically, reported residential cross-streets were geocoded using geographic information system (GIS) software and data (ArcView StreetMap 2000 version 1.1; ESRI, Redlands, CA). We then identified each subject’s probable home street segment, which had the reported nearest intersection at the center bound on both ends by the adjacent cross-streets (##REF##17521030##Meng et al. 2007##). We drew a 500-foot buffer around the probable home street segment of each subject and identified all roadways within this buffer that had an AADT value based on the year 2000 Caltrans data. The 500-foot criterion is based on environmental measurement data showing that the impact of direct traffic emissions on ambient concentrations becomes insignificant at approximately this distance (##REF##17519039##Zhou and Levy 2007##). However, the buffers used here were &gt; 0.028 mi² in area (as would be the case with a 500-foot radius circle around a specific geocoded home location), because the buffers were defined not by a point but by a probable street segment and its length. On average, the buffers were 0.068 mi² in area (range, 0.031–0.199 mi²). There was not much difference in buffer size between urban areas (average, 0.067 mi²; range, 0.033–0.168 mi²) and suburban areas (average, 0.069 mi²; range, 0.031–0.199 mi²). Most subjects resided in urban areas (71%); 29% were suburban, and none lived in rural areas.</p>", "<p>Similar to methods of ##REF##12743618##Gunier et al. (2003)## and ##REF##14712141##Reynolds et al. (2004)##, we estimated the TD value for each subject by first calculating the vehicle miles traveled (VMT) for each attributed road segment within the buffered area; VMT was estimated by multiplying the AADT by the road segment length. We then calculated TD as the sum of the VMT for all road segments in the buffer divided by the area of the buffer, that is,</p>", "<p>where <italic>TD</italic> is traffic density (vehicles × miles/day/mi²), <italic>AADT</italic> is the annual average daily traffic count (vehicles/day), <italic>L</italic> is the length of roadway segment (miles), and <italic>A</italic>_B is the area of the 500-foot buffer around the probable street segment (square miles). Subjects with no Caltrans-counted streets within their buffers (<italic>n</italic> = 47) were included in the low-traffic referent category, because we assumed that these individuals had only residential streets with low AADT near their homes.</p>", "<p>In addition to TD values, we assigned to each subject the annual average concentrations of O₃, NO₂, PM_2.5, PM₁₀, and carbon monoxide measured at the nearest monitoring station within 5 miles of the reported residential cross-street intersection (82% of subjects). We used ArcView GIS (ESRI) software to estimate the distance between each respondent’s residential cross-street intersection or ZIP code population-weighted centroid (for the 18% of subjects whose reported cross-street could not be geocoded) and the nearest station that measured each pollutant. Subjects residing outside a 5-mile range were excluded from the pollutant analyses. Annual average concentrations were calculated for the 1-year period before the interview date. These averages were based on hourly measurements for the gaseous pollutants (CO, NO₂, and O₃) and 24-hr average measurements for PM₁₀ and PM_2.5 (with most stations recording measurements every 6 and 3 days for these pollutants, respectively). The 5-mile range was chosen to ensure a large enough radius for sufficient sample size balanced against a possible increase in exposure misclassification due to greater residential distances from monitoring stations.</p>", "<title>Statistical analyses of exposure–asthma outcome relationships</title>", "<p>We evaluated associations between air pollution and TD and asthma morbidity using logistic regression. Specifically, we examined differences in our exposure metrics for <italic>a</italic>) children with asthma reporting daily or weekly symptoms in the previous year versus those reporting less than weekly symptoms, and <italic>b</italic>) children with asthma reporting at least one asthma-related ED visit or hospitalization in the previous year versus children with asthma not reporting such visits. The analyses incorporated sampling weights that adjusted for unequal probabilities of selection into the CHIS sample. We initially grouped TD values into quintile categories according to their distributions in the total population. Because the effect estimates in the three middle quintiles were similar, we collapsed them and thus created three categories roughly based on these quintile distributions: <italic>a</italic>) low TD, defined as ≤ 20,000 daily VMT/mi² (approximately ≤ 20th percentile); <italic>b</italic>) medium TD, defined as 20,001–200,000 daily VMT/mi² (approximately 21–80th percentile); <italic>c</italic>) high TD defined as &gt; 200,000 daily VMT/mi² (approximately the 80th percentile).</p>", "<p>Measured air pollutants (O₃, PM₁₀, PM_2.5, CO, NO₂) were evaluated continuously, using both single and multipollutant models. We evaluated changes in point and 95% confidence interval (CI) estimates when including covariates such as age, sex, race/ethnicity, poverty level, insurance status, delays in receiving care for asthma, asthma medication use, and county in our models (##TAB##0##Table 1##). Based on a 10% change-in-estimate criterion (##UREF##12##Rothman and Greenland 1998##), race/ethnicity and poverty level were included in our final models. Some subjects were excluded because of missing data for these covariates. Final sample sizes for each model are reported in the tables.</p>" ]

[ "<title>Results</title>", "<title>Data sources</title>", "<title>Health data</title>", "<p>Of the 612 children in Los Angeles and San Diego Counties participating in CHIS 2001 and reporting a physician asthma diagnosis, 56 (9.3%) reported suffering from daily or weekly symptoms, and 68 (11.2%) reported an ED visit or hospitalization for asthma in the previous 12 months. The prevalence of daily or weekly symptoms tended to increase with age, with the highest prevalence observed in children 12–17 years of age with asthma, whereas ED visits and hospitalizations were more prevalent in children ≤ 5 years of age (##TAB##0##Table 1##). Daily/weekly symptoms were more common in boys than girls. African Americans and Asians/others were more likely to be reported as suffering from daily/weekly symptoms, whereas African Americans and Latinos had higher rates of ED visits/hospitalizations than other racial/ethnic groups (##TAB##0##Table 1##). Children from a family with an income below the poverty level, experiencing delays in receiving care for asthma, taking asthma medication, and residing in Los Angeles County reported poorly controlled asthma more often than their counterparts without these characteristics (##TAB##0##Table 1##).</p>", "<title>Exposure data</title>", "<p>In Los Angeles County, 14 stations measured CO, 15 stations NO₂ and O_3, 8 stations PM₁₀, and 10 stations PM_2.5 during 1999–2001. In San Diego County, four stations measured CO, eight stations NO_2, nine stations O₃, six stations PM₁₀, and five stations PM_2.5. On average 8,314, 8,148, and 8,248 hourly air pollution values were available to estimate annual averages for CO, NO₂, and O₃, respectively. For PM₁₀ and PM_2.5, 75 and 167 24-hr values were available to estimate annual averages, respectively. Annual average concentrations of NO₂, PM_2.5, and PM₁₀ were highly positively correlated with each other and moderately correlated with CO (##TAB##1##Table 2##). Levels of O₃ were strongly negatively correlated with these pollutants, whereas residential TD measures were not correlated with any of the station-based pollutant measures.</p>", "<title>Linkage</title>", "<p>We were able to successfully geocode reported residential cross-street intersections and calculate TD values for 500 (81.7% of 612) respondents. Only three subjects who were geocoded could not be assigned a TD value because of missing AADT data in the Caltrans file. The reasons reported locations were not successfully mapped included missing information for one or both streets (63%) and inability to find a reported cross-street in the reference street map (i.e., one or both streets were not found based on spelling provided or reported streets did not intersect, 37%). The mean TD value was 152,311 (median of 86,513), with a range of 0–1,557,027 daily VMT/mi² (##TAB##1##Table 2##). The percentage of subjects excluded from analyses based on annual average air pollution averages varied depending on pollutant (because not every station measured every pollutant, and we used a 5-mile exclusion criterion, as explained above). Approximately 26%, 47%, 36% of subjects were excluded from the O₃, PM₁₀, and PM_2.5 analyses, respectively. Mean annual average pollutant concentrations did not differ substantially for subjects included versus excluded from analyses based on this criterion; however, the percentages of children with asthma reporting daily or weekly symptoms or ED visits/hospitalizations were lower in the excluded population (by approximately 2–5% depending on pollutant and outcome).</p>", "<title>Statistical analyses of exposure–asthma outcome relationships</title>", "<p>Based on logistic regression models, we did not observe associations between our asthma symptom outcome measures and NO₂ or CO; thus, we limit the following discussion to residential TD, O₃, and particles (PM₁₀ and PM_2.5) (##TAB##2##Tables 3## and ##TAB##3##4##). We observed an approximately 2-fold increase in the odds of daily/weekly symptoms in children with asthma for each 1 part per hundred million (pphm) increase in annual average O₃ [odds ratio (OR) = 1.96; 95% CI, 1.23–3.13], and this estimate did not change appreciably when we controlled for race/ethnicity and poverty level or when we added particle measures to the model, although the 95% CIs widened because of the inclusion of these covariates and the reduction in sample size (##TAB##2##Table 3##). There were suggestive associations between particles (PM₁₀ and PM_2.5) and daily/weekly symptoms after adjustment for O₃, but estimates were imprecise. We also observed an approximately 2-fold increase in the odds of ED visits/hospitalizations for asthma per 1-pphm increase in O₃ after adjusting for particles (particle and O₃ exposure estimates were strongly negatively correlated). Furthermore, we also estimated 2- to 3-fold increases in the odds of ED visits/hospitalizations per 10-μg/m³ increases in PM₁₀ and PM_2.5 after adjusting for O₃ (##TAB##2##Table 3##).</p>", "<p>We observed associations between residential proximity to traffic and asthma-related ED/hospitalizations but not for daily/weekly symptoms (##TAB##3##Table 4##). We estimated an approximately 3-fold increase in the odds of ED visits/hospitalizations for children with asthma in the highest TD exposure category (TD &gt; 200,000 daily VMT/mi²) compared with those with TD values ≤ 20,000 daily VMT/mi² after adjusting for race/ethnicity and poverty level (OR = 3.27; 95% CI, 1.08–9.89). A model that included TD, O₃, and particle measures was consistent with these findings (results not shown). Approximately 48% of the subjects with TD &gt; 200,000 daily VMT/mi² had a freeway within their buffer. (Approximately 41% of children in Los Angeles County versus ~ 84% in San Diego County had a freeway in their buffer, yet of all children with TD values &gt; 200,000, ~82% resided in Los Angeles.)</p>" ]

[ "<title>Discussion</title>", "<p>This project demonstrated methods for linking and analyzing existing health and environmental databases as part of the national EPHT initiative currently under development in the United States (##REF##15471734##McGeehin et al. 2004##). Based on this linkage/analysis effort, we documented that children with asthma living in more highly polluted areas—as assessed by ambient air monitoring data and measures of traffic near homes—experienced worse asthma morbidity than those living in less polluted areas. Our findings are in general agreement with existing evidence that short-and long-term exposure to O₃ and particulate matter increases childhood asthma morbidity (##REF##16581557##Gilmour et al. 2006##; ##REF##15805986##Trasande and Thurston 2005##) and previous reports that children with asthma residing near heavy traffic are more likely to seek medical attention or be hospitalized for asthma than those residing near low-traffic roadways (##REF##7518223##Edwards et al. 1994##; ##REF##10464078##English et al. 1999##; ##REF##11908931##Lin et al. 2002##). More important, however, our goal was to evaluate the overall success of this project in terms of an EPHT framework. In the following sections, we discuss the advantages and disadvantages of the data sources, linkage, and analyses presented here for EPHT of air pollution and childhood asthma morbidity.</p>", "<title>Data sources</title>", "<title>Health data</title>", "<p>The CHIS is the largest statewide health survey in the country. CHIS 2001 included 18,393 children (ages 0–17 years) compared with 13,376 children included in the 2000 NHIS. Because of the sampling design, CHIS data can provide disease prevalence estimates at the local level (e.g., county level) and for specific racial/ethnic groups (the survey is conducted in six languages). These factors, along with the biennial data collection, are all strengths within the context of EPHT. Another major strength is collection of residential information at the cross-street (CHIS 2001 for Los Angeles and San Diego Counties) and residential address level (CHIS 2003 and 2005 for all counties), which allows improved spatial resolution for air pollution exposure assessment.</p>", "<p>Limitations of the CHIS asthma data include their cross-sectional nature and reliance on self-report of physician diagnoses. As with all cross-sectional data, there is potential temporal ambiguity between exposure and disease. In this study, we did not have lifetime residential histories, and we assigned monitoring stations and estimated TD based on current home location. Thus, our estimates could be biased depending on residential mobility patterns of children with asthma. If families with children with poorly controlled asthma tended to move away from highly polluted areas after learning that air pollution might worsen asthma, our pollution associations may be underestimated. Subsequent CHIS surveys collect information on timing of asthma diagnoses and length of residence in the same house and neighborhood. Thus, in future analyses, we will be able to examine the extent to which this bias may have affected our results. In general, collection of information on residential and school histories would help improve exposure assessment further and allow a better quantification of associations between air pollution and asthma morbidity in children as part of EPHT efforts (as discussed below). CHIS 2001 relied on parental or self-reports of physician-diagnosed asthma and related symptoms and thus may have missed a certain segment of the population with undiagnosed asthma. CHIS 2003 asked about symptoms among individuals who were not physician diagnosed. Finally, the CHIS is a telephone-based survey and generally achieves a response rate of 40%. Estimating the magnitude and direction of bias due to nonresponse on our study results would require additional data collection (i.e., a follow-up study of a sample of nonresponders to determine health and air pollution exposure status). A discussion of other potential health data sources for tracking childhood asthma and its relation to air pollution is provided in Supplemental Material (online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10945/suppl.pdf\">http://www.ehponline.org/members/2008/10945/suppl.pdf</ext-link>).</p>", "<title>Exposure data</title>", "<p>The advantages of using existing government air monitoring data to assess air pollution exposures for EPHT are that the data are readily available and, in general, measurements have been consistently recorded over many years and offer a fine temporal resolution (i.e., hourly, daily, or every third or sixth day measurements are available). The tradeoff for this fine temporal resolution is lower spatial coverage, that is, the number of stations available to characterize concentration gradients throughout an urban area is limited. Such data may be inadequate for accurate exposure assessment, especially for pollutants like CO, nitrogen oxides (NO_x), and ultrafine particles that exhibit small-scale spatial variability in concentrations depending on proximity to sources (##REF##17519039##Zhou and Levy 2007##). Another major issue with regard to exposure assessment is accounting for interindividual exposure variability due to personal mobility and time spent outdoors versus indoors.</p>", "<p>Epidemiologic studies are increasingly using residential and/or school proximity to traffic as surrogate measures of motor vehicle exhaust exposure (##REF##18043269##Salam et al. 2008##), as we did in this study. Caltrans traffic data are a readily available source of information for generating such measures (see the Traffic Density Mapping Tool of the California Environmental Health Tracking Program; <ext-link ext-link-type=\"uri\" xlink:href=\"www.ehib.com/tools\">www.ehib.com/tools</ext-link>), but there are a number of limitations involved (##REF##12573907##Wilhelm and Ritz 2003##). Briefly, these shortcomings include <italic>a</italic>) lack of information on vehicle engine types (gasoline vs. diesel) and vehicle ages in a given 24-hr traffic count, which are important determinants of emission levels, <italic>b</italic>) lack of information concerning the influence of meteorology (e.g., wind direction) on dispersion of these emissions, and <italic>c</italic>) differences between outdoor and indoor exhaust concentrations based on ventilation characteristics of the home. However, as noted previously, a growing number of studies are linking relatively simple measures of traffic exposure such as the one we used in this study to adverse respiratory outcomes in children (##REF##10464078##English et al. 1999##; ##REF##16222162##Gauderman et al. 2005##, ##REF##17307103##2007##; ##REF##16675435##McConnell et al. 2006##). For example, ##REF##16222162##Gauderman et al. (2005)## reported that a relatively simple measure of residential distance to freeway was as strongly and precisely associated with childhood asthma as a more complex exposure estimate based on air dispersion modeling of freeway emissions and even measured NO₂ concentrations. However, whether this would be the case in other settings is currently unknown; very few studies have used more complex models that account for time–activity patterns of children and spatial heterogeneity of air pollution concentrations within urban areas. Alternatives for air pollution exposure assessment include geostatistical methods such as kriging (e.g., ##REF##15292906##Jerrett et al. 2005##; ##REF##15687058##Kunzli et al. 2005##) and land use–based regression modeling (e.g., ##UREF##8##Jerrett et al. 2007##; ##REF##16047040##Ross et al. 2006##), as discussed below.</p>", "<title>Linkage</title>", "<p>An advantage of the CHIS for linkage of health and environmental data is knowledge of residential locations at the cross-street (CHIS 2001) and address (CHIS 2003 and 2005) level. Knowledge of residential locations is particularly important for assessing source-specific exposures, such as exposures to certain key traffic exhaust pollutants (e.g., ultrafine particles) whose concentrations change rapidly within approximately 300 m of the source (roadway) (##REF##17519039##Zhou and Levy 2007##). Mapping home locations to the census tract or even census block group level likely results in exposure misclassification for such pollutants. Here, only residential cross streets were available, which likely introduced error into the traffic exposure metric; future CHIS surveys collect information on residential addresses. Knowledge of residential locations at the cross-street and address level also allows determination of distance to existing monitoring stations; thus, subjects living far from stations can be excluded from analyses (based on the assumption that accuracy of exposure assessment decreases as one moves farther away from a station). The disadvantages of using such exclusion criteria are that they reduce sample size and may limit generalizability.</p>", "<p>Geostatistical methods such as kriging and land use–based regression modeling are alternatives for assessing air pollution exposure [see ##REF##15292906##Jerrett et al. (2005)## for an in-depth discussion of these methods and others such as inverse distance weighting and air dispersion modeling]. Kriging spatially interpolates pollution levels measured at existing monitoring stations across urban areas. A major advantage is that it allows the estimation of both predicted values and their standard errors (kriging variance) at unmeasured locations (##REF##15292906##Jerrett et al. 2005##). However, the predictive capability of this method is dependent on the density of monitoring stations in a given area. Reliance on existing monitors (which are generally limited in number) normally results in surfaces that oversmooth the true pattern of pollution and may introduce large errors in estimates over portions of a study area for which few observations are available (##REF##15292906##Jerrett et al. 2005##). This problem may be more severe for pollutants known to vary significantly over small scales, such as NO_x and other direct traffic pollutants. For these pollutants, land use–based regression (LUR) detects small-area variations in air pollution more effectively than kriging (##UREF##2##Briggs 1997##; ##UREF##9##Lebret et al. 2000##). In the LUR modeling approach, concentrations of vehicle exhaust markers such as NO_x are measured simultaneously at many locations throughout an urban area, using relatively inexpensive passive monitors (e.g., Ogawa monitors). Various GIS parameters (such as traffic and roadway density, population density, and land use) are used to predict the measured concentrations (##UREF##8##Jerrett et al. 2007##). The developed model can then be used to estimate concentrations at home and school locations of study subjects based on GIS parameter values at these locations. Although these methods allow generation of air pollution exposure estimates at a wide number of locations, a number of statistical assumptions are needed (##REF##15292906##Jerrett et al. 2005##). They also require additional expertise to implement, and in the case of LUR, additional data collection, which can be labor intensive. Furthermore, LUR models developed in one area may not be readily transferable to other urban areas.</p>", "<p>Another major issue in air pollution exposure assessment is accounting for time–activity patterns of the subjects (time spent at home, at school, in vehicles) and extent of infiltration of outdoor air pollution into indoor spaces (homes and schools). Exposure misclassification resulting from the lack of such information may be one explanation for the differences we observed in particle and TD results for the two outcomes (daily/weekly symptoms vs. ED visits/hospitalizations) in this study. Specifically, the particle associations for daily/weekly symptoms were weaker than for ED visits/hospitalizations. This may reflect more nondifferential exposure misclassification of our residence-based particle measures for older children more likely to spend time away from home during the day; although daily/weekly symptoms were more frequently reported for this age group, ED visits/hospitalizations were more common in younger children (##TAB##0##Table 1##). The same explanation could also hold for the null association we observed between residential TD and daily/weekly symptoms. Although levels of O₃ tend to be more spatially homogeneous within communities (##UREF##0##Avol et al. 1998##; ##REF##10706534##Geyh et al. 2000##; ##UREF##11##Monn 2001##), several O₃ exposure assessment studies in Southern California children have noted the important contribution of time–activity patterns (e.g., time spent outdoors) and housing characteristics (e.g., use of air conditioning, age and size of house, use of open windows and fans for ventilation) to personal O₃ levels (##UREF##0##Avol et al. 1998##; ##REF##10706534##Geyh et al. 2000##; ##REF##9074882##Liu et al. 1997##; ##REF##16295276##Xue et al. 2005##). In this study, we observed associations between ambient O₃ measures and both outcomes, but point estimates were generally lower for ED visits/hospitalizations (younger children). This may suggest that station-based measures of O₃ are more accurate estimates of exposure for older children, who may spend more time outdoors and who may engage in outdoor activities that increase their exposure, compared with younger children (infants and young toddlers), who may spend more time indoors and thus generally have lower O₃ exposures. Subsequent CHIS surveys collected information on school locations so that potential differences in exposure based on location of the children can be taken into account and the impact on estimates of association can be assessed. Increased sample sizes when adding CHIS 2003 and 2005 data will also allow us to determine the robustness of our findings, which were relatively imprecise when we relied on 1 year of data for Los Angeles and San Diego Counties only. Future EPHT tracking efforts focusing on O₃-related health effects may also be improved by collecting housing information, such as air conditioning use and house size, and age and time–activity information, such as time spent at school and outdoors. Collection of such data, in concert with more spatially resolved outdoor pollution gradients (e.g., through kriging or LUR), would allow the development of more complex exposure models for air pollution (see, for example, ##UREF##10##Marshall et al. 2006## and ##UREF##13##Wu et al. 2005##). This type of modeling may also be possible without the collection of time–activity and housing information directly from subjects as part of surveys, that is, by using data from the U.S. Census and existing travel demand surveys coupled with stochastic modeling. [See ##UREF##10##Marshall et al. (2006)## for an example using existing Southern California Association of Governments travel demand and mobility data] However, the usefulness of such stochastic modeling for EPHT needs further investigation.</p>", "<title>Statistical analyses of exposure–asthma outcome relationships</title>", "<p>The CHIS includes a large, statewide sample, thereby facilitating statistical analyses of associations between air pollution and asthma while taking into consideration potential confounding factors. For this particular demonstration project, residential cross-street information was available only for respondents in Los Angeles and San Diego Counties; thus, our resulting air pollution effect estimates were relatively imprecise. CHIS 2003 and 2005 each included approximately 3,000 children with a reported asthma diagnosis or asthma-like symptoms and residential address information, providing much larger sample sizes for future analyses. In addition to a relatively large sample size, CHIS data include information on a number of potential confounding factors including race/ethnicity, income, health insurance status, usual source of care, type of usual source of care, experience of delays in receiving asthma care, and asthma medication use. For this study, we lacked information on some potentially important confounders of the association between air pollution and asthma morbidity, including exposure to secondhand tobacco smoke and indoor allergens such as pets, cockroaches, and molds, parental history of asthma, day care attendance, and breast-feeding history. Such factors may also be important to investigate as effect measure modifiers of the relationship between air pollution exposure and asthma exacerbation. Subsequent CHIS surveys (CHIS 2003 and 2005) will allow us to examine potential residual confounding of our outdoor air pollution effect estimates by some of these factors. Systematic collection of all of these potential confounding variables may not be feasible within the framework of EPHT. Nevertheless, within an EPHT framework it may be necessary to first establish a minimally sufficient set of variables necessary to allow estimation with little residual confounding.</p>", "<p>The goals of EPHT are not only to quantify and examine links between exposure to environmental agents—such as air pollution—and disease, but also to determine whether increases or decreases in exposure result in subsequent changes in health indicators. From a public health perspective, such information supplies data to support policy decisions—for example, mandating cleaner-burning motor vehicles and fuels. Previous studies have capitalized on natural experiments (##REF##11180733##Friedman et al. 2001##; ##REF##8777371##Pope 1996##) or prospectively followed schoolchildren (##REF##11739136##Avol et al. 2001##; ##REF##12893648##McConnell et al. 2003##) to demonstrate a lowering of asthma morbidity resulting from changes (reductions) in air pollution. However, serial cross-sectional surveys such as the CHIS may be more amenable to EPHT for assessing the impact of air pollution regulations, as they provide asthma prevalence data on a more routine basis and cover a diverse and representative sample of children (in this case, for California). The specific mechanics for relating changes in regulatory reductions in air pollution to consequent changes in asthma health end points are beyond the scope of this paper. We currently have exposure metrics only for the 2001 CHIS participants. However, major factors that would need to be taken into account for such analyses include <italic>a</italic>) disentangling the effects of meteorology versus emissions reductions on air pollution levels, <italic>b</italic>) evaluating and accounting for potential confounding factors that may also change over time (e.g., changes in access to health care, use of asthma management plans) in statistical models, and <italic>c</italic>) attributing emissions reductions and subsequent health improvements to specific regulations. It may also be of interest to examine “what if” scenarios whereby the impacts of projected air pollution levels with and without the implementation of a specific emission reduction program on subsequent health end points are evaluated. This may be particularly important in areas where major changes in population or VMT are occurring.</p>" ]

[ "<title>Conclusions</title>", "<p>We demonstrated a method of linking and analyzing existing health and environmental data as part of the national EPHT initiatives currently under development in the United States. Our data linkage and analyses indicate that children with asthma living in high O₃ and PM₁₀ areas in Los Angeles and San Diego Counties experience symptoms more frequently than those living in less-polluted neighborhoods. Children with asthma living close to heavy traffic also report more ED visits and hospitalizations than those with less traffic near their home. Limited asthma surveillance information other than data from hospital discharge records is collected at the state or national level. The advantages of the CHIS data for EPHT are its relatively large sample size and representation of the diverse California population, biennial data collection including information on potentially important covariates, and residential locations at the address level, allowing for more spatially refined exposure assessment. Disadvantages include the cross-sectional nature of data collection, reliance on parental reports of asthma diagnoses and symptoms, lack of information on some potential confounders, and lack of residential and school histories. Some of these shortcomings were addressed in CHIS 2003 and 2005. Overall, the results from these first linkage efforts indicate the potential importance of collecting routine data on children’s home and school locations (especially for studies interested in traffic exhaust impacts). Additionally, timing of asthma diagnoses, residential histories, and assessment of respiratory symptoms in undiagnosed children may be required to rule out potential biases in EPHT linkage efforts. Collection of information on children’s time–activity patterns (time spent outdoors) and housing characteristics (e.g., use of air conditioning and level of ventilation) and advanced exposure modeling (LUR and kriging) may further improve exposure assessment.</p>" ]

[ "<p>Formerly with University of California, Los Angeles Center for Health Policy Research.</p>", "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Despite extensive evidence that air pollution affects childhood asthma, state-level and national-level tracking of asthma outcomes in relation to air pollution is limited.</p>", "<title>Objectives</title>", "<p>Our goals were to evaluate the feasibility of linking the 2001 California Health Interview Survey (CHIS), air monitoring, and traffic data; estimate associations between traffic density (TD) or outdoor air pollutant concentrations and childhood asthma morbidity; and evaluate the usefulness of such databases, linkages, and analyses to Environmental Public Health Tracking (EPHT).</p>", "<title>Methods</title>", "<p>We estimated TD within 500 feet of residential cross-streets of respondents and annual average pollutant concentrations based on monitoring station measurements. We used logistic regression to examine associations with reported asthma symptoms and emergency department (ED) visits/hospitalizations.</p>", "<title>Results</title>", "<p>Assignment of TD and air pollution exposures for cross-streets was successful for 82% of children with asthma in Los Angeles and San Diego, California, Counties. Children with asthma living in high ozone areas and areas with high concentrations of particulate matter &lt; 10 μm in aerodynamic diameter experienced symptoms more frequently, and those living close to heavy traffic reported more ED visits/hospitalizations. The advantages of the CHIS for asthma EPHT include a large and representative sample, biennial data collection, and ascertainment of important socio-demographic and residential address information. Disadvantages are its cross-sectional design, reliance on parental reports of diagnoses and symptoms, and lack of information on some potential confounders.</p>", "<title>Conclusions</title>", "<p>Despite limitations, the CHIS provides a useful framework for examining air pollution and childhood asthma morbidity in support of EPHT, especially because later surveys address some noted gaps. We plan to employ CHIS 2003 and 2005 data and novel exposure assessment methods to re-examine the questions raised here.</p>" ]

[ "<p>Asthma is one of the most prevalent chronic conditions affecting children in the United States today. According to National Health Interview Survey data for 2005, &gt; 9 million children &lt; 18 years of age (13%) had ever been diagnosed with asthma (##UREF##1##Bloom et al. 2006##). The California Health Interview Survey (CHIS) for 2005 indicated a lifetime prevalence of 16% for this age group. Asthma is a multifactorial disease in which genetic susceptibilities influence responses to environmental exposures (##REF##16581557##Gilmour et al. 2006##). Exposure to outdoor air pollution has been widely studied as a potential risk factor for asthma, and it is generally well established that short-term increases can exacerbate respiratory symptoms in children with asthma (##REF##16581557##Gilmour et al. 2006##; ##REF##14532321##Thurston and Bates 2003##; ##REF##15805986##Trasande and Thurston 2005##). Ozone, particulate matter &lt; 10 and &lt; 2.5 μm in aerodynamic diameter (PM₁₀ and PM_2.5), and nitrogen dioxide are the pollutants linked most consistently with exacerbation of asthma symptoms. Although long-term exposures to O₃, PM₁₀, and NO₂ have been associated with chronic respiratory impairments such as reduced lung function and growth, bronchitis, and chronic cough, evidence for the impact of air pollution on asthma incidence is less conclusive (##REF##16581557##Gilmour et al. 2006##; ##REF##11844508##McConnell et al. 2002##; ##REF##15805986##Trasande and Thurston 2005##). Recently, focus has turned to respiratory effects caused by exposure to specific motor vehicle exhaust components such as polycyclic aromatic hydrocarbons adsorbed to particles from diesel engines and ultrafine particles (&lt; 0.1 μm in aerodynamic diameter), which are able to penetrate cellular targets in the lung and enter systemic circulation (##REF##12948969##Künzli et al. 2003##; ##REF##12370390##Li et al. 2002##, ##REF##12676598##2003##; ##REF##11834468##Pandya et al. 2002##). Various measures of traffic exhaust exposure have been associated with adverse respiratory outcomes, including reduced lung function and growth; asthma hospitalizations; and prevalence of asthma, wheeze, bronchitis, and allergic rhinitis (##REF##10464078##English et al. 1999##; ##REF##16222162##Gauderman et al. 2005##, ##REF##17307103##2007##; ##REF##15184208##Kim et al. 2004##; ##REF##16675435##McConnell et al. 2006##).</p>", "<p>Despite the impact of asthma on children’s health, there is no comprehensive system of surveillance at the state or national level for this disease. The national Behavioral Risk Factor Surveillance System (BRFSS) [##UREF##3##Centers for Disease Control and Prevention (CDC) 2008a##] provides only limited data on asthma prevalence and has poor geographic resolution (i.e., the estimates of asthma prevalence are considered valid only at the state level and thus are not useful for assessing trends in relation to environmental exposures). The National Health Interview Survey (NHIS) (##UREF##5##CDC 2008c##) and the National Health and Nutrition Examination Survey (##UREF##4##CDC 2008b##) use a sampling design to represent the population of the entire United States and provide data that cannot be used even at the state level for asthma prevalence estimation; neither of these national surveys yield detailed data on asthma symptoms. Asthma hospitalization data are available from every state, but such data represent only a small fraction of the burden of exacerbations of the disease. Because of the growing body of evidence linking outdoor air pollution exposure to both exacerbation and possibly causation of asthma, tracking its occurrence and severity in relation to pollutant exposures is an important public health goal.</p>", "<p>A review by the Pew Environmental Health Commission found that existing efforts to gather information on chronic diseases, such as asthma, and their potential environmental links are highly fragmented and inadequate for truly understanding where, why, and how often these diseases occur; thus, they issued a call to close this environmental health gap (##UREF##7##Environmental Health Tracking Project Team 2000##; ##REF##15471734##McGeehin et al. 2004##). In response, a new initiative to establish a national environmental public health tracking (EPHT) network led by the CDC was launched in 2002. EPHT is the ongoing collection, integration, analysis, and dissemination of data from environmental hazard monitoring, human exposure tracking, and health effect surveillance (##REF##15471734##McGeehin et al. 2004##). As part of this initiative, the goal of the project described here was to develop a model tracking system that links asthma data from the CHIS with existing information on outdoor air pollution exposures, specifically ambient air monitoring station and traffic data. If successful, such linkage and analysis could provide a way to assess impacts of future air pollution control strategies on reducing asthma symptoms in children in California and provide an ongoing mechanism for EPHT of asthma. Here we present results and lessons learned from this first tracking effort based on CHIS 2001 data and ambient air monitoring and traffic data for Southern California.</p>" ]

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[ "<table-wrap id=\"t1-ehp-116-1254\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Weighted prevalence of frequent symptoms or ED visits/hospitalizations by demographic characteristics among CHIS children with reported asthma diagnoses, Los Angeles and San Diego Counties.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Daily or weekly symptoms (Total children with asthma = 597)\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">ED visit or hospitalization (Total children with asthma = 607)\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Characteristic</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Prevalence (%)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. total population</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Prevalence (%)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. total population</th></tr></thead><tbody><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Age (years)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 0–5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">117</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">119</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 6–11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">257</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">263</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 12–17</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">223</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">225</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Sex</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Male</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">341</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">346</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">256</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">261</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Race/ethnicity</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Latino</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">176</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">182</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Asian/other</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">81</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">83</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> African American</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">97</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">99</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">243</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">243</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Household federal poverty level (%)<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1254\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &lt; 100</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">107</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">109</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 100–299</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">221</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">225</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥ 300</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">269</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">273</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">County</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Los Angeles</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">481</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">489</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> San Diego</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">116</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">118</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Insurance status</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Currently uninsured</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">45</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Uninsured any time in past 12 months</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Insured entire past 12 months</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">527</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">537</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Delays in care<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1254\">b</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">37.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">571</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">580</td></tr><tr><td colspan=\"5\" align=\"left\" rowspan=\"1\">Taking asthma medication</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Yes</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">296</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">300</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> No</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">300</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">306</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1254\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Pearson correlation coefficients for annual average air pollutant concentrations for residents within 5 miles of a monitoring station.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Pollutant</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean (range)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">O₃</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">PM₁₀</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">PM_2.5</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">NO₂</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">CO</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">TD</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃ (pphm)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.1 (1.1–4.2)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀ (μg/m3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">37.7 (26.2–46.9)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.70</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM_2.5 (μg/m3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.3 (10.6–24.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.79</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.85</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">NO₂ (pphm)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.0 (1.4–4.4)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.82</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.89</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CO (ppm)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0 (0.34–1.8)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.67</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.41</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.60</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.57</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TD (daily VMT/mi²)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">152,311 (0–1,557,027)<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1254\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">−0.16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1254\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Association [OR (95% CI)] between annual average air pollution concentrations and asthma outcomes in CHIS children 0–17 years of age.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"4\" align=\"center\" rowspan=\"1\">Daily/weekly symptoms\n<hr/></th><th colspan=\"4\" align=\"center\" rowspan=\"1\">ED visit or hospitalization\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Single pollutant only (crude)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Single pollutant, race/ethnicity, poverty level</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Two pollutants (O₃ + PM₁₀), race/ethnicity, poverty level</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Two pollutants (O₃ + PM_2.5), race/ethnicity, poverty level</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Single pollutant only (crude)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Single pollutant, race/ethnicity, poverty level</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Two pollutants (O₃ + PM₁₀), race/ethnicity, poverty level</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Two pollutants (O₃ + PM_2.5), race/ethnicity, poverty level</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">O₃<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1254\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44, 391</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44, 391</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36, 269</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36, 327</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">53, 390</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">53, 390</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38, 272</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">47, 322</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> (per 1 pphm)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.96 (1.23–3.13)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.09 (1.28–3.41)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.29 (1.01–5.23)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.51 (1.45–8.46)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.16 (0.74–1.81)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.35 (0.85–2.14)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.89 (1.32–6.34)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.48 (1.14–5.38)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM₁₀<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1254\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36, 269</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36, 269</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36, 269</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38, 272</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38, 272</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38, 272</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  (per 10 μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.09 (0.63–1.87)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.09 (0.62–1.93)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.82 (0.86–3.87)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.46 (0.85–2.52)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.46 (0.84–2.55)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.76 (1.33–5.71)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">PM_2.5<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1254\">a</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36, 327</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36, 327</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36, 327</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">47, 322</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">47, 322</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">47, 322</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">  (per 10 μg/m³)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.70 (0.29–1.67)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.69 (0.27–1.72)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.89 (0.86–17.5)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.26 (0.57–2.80)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.09 (0.47–2.50)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.68 (0.98–13.8)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4-ehp-116-1254\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>Association (OR, 95% CI) between residential TD (in daily VMT/mi²) and asthma outcomes in CHIS children 0–17 years of age.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">Daily/weekly symptoms\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">ED visit or hospitalization\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">TD only (40, 453)<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1254\">a</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">TD, race/ethnicity, poverty level (40, 453)<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1254\">a</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">TD only (52, 448)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">TD, race/ethnicity, poverty level (52, 448)<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1254\">a</xref></th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TD ≤20,000</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">20,001 &lt; TD ≤200,000</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.12 (0.53–2.34)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.95 (0.44–2.06)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.05 (1.11–8.38)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.45 (0.87–6.88)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TD &gt; 200,000</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.77 (0.30–2.03)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.61 (0.22–1.69)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.01 (1.72–14.6)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.27 (1.08–9.89)</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>Supplemental Material is available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10945/suppl.pdf\">http://www.ehponline.org/members/2008/10945/suppl.pdf</ext-link></p></fn><fn><p>We thank H. Yu, Y. Xiong, and others for statistical and programming support as well as S. Nathan and M. Kuruvilla for research assistance.</p></fn><fn><p>This study was supported by the Agency for Toxic Substances and Disease Registry (ATSDR U61/ATU972304) and the Centers for Disease Control and Prevention (CDC U50/CCU922409). Its contents do not necessarily represent the official views of ATSDR and the CDC.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1254\"><label>a</label><p>Percentages were defined using 2001 federal poverty guidelines ($9,044 for one person, $11,559 for a family of two; incomes at ≥ 300% federal poverty level were three times these amounts).</p></fn><fn id=\"tfn2-ehp-116-1254\"><label>b</label><p>Individuals who reported delaying or foregoing any medical care they felt they needed (such as seeing a doctor, a specialist, or other health professional) for asthma were assigned a value of 1 for delays in care.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn3-ehp-116-1254\"><label>a</label><p>Average includes 47 subjects assigned TD = 0 because of no Caltrans-counted streets in buffer.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn4-ehp-116-1254\"><label>a</label><p>Values represent number of children with asthma with daily/weekly symptoms or ED visits/hospitalizations in previous year, total number of children with asthma.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn5-ehp-116-1254\"><label>a</label><p>Values represent number of children with asthma with daily/weekly symptoms or ED visits/hospitalizations in previous year, total number of children with asthma.</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1254e1.jpg\" position=\"float\" orientation=\"portrait\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>Study population</title>", "<p>Study participants were identified from among pregnant women receiving antenatal care between 1994 and 1995 at three hospitals in Mexico City that serve low- to middle-income populations. The women were approached before giving birth to participate in a randomized trial of calcium supplementation to lower blood lead levels over the course of lactation (##REF##12606887##Hernández-Avila et al. 2003##). Infant development was also measured as part of this study, which was conducted as part of an established interinstitutional collaborative effort between researchers in the United States (Harvard University) and Mexico [National Institute of Public Health and American British Cowdray (ABC) Hospital]. The research protocol was approved by the Human Subjects Committees of the Harvard School of Public Health and the National Institute of Public Health and the participating hospitals in Mexico and has complied with all federal guidelines governing the use of human participants.</p>", "<p>Data collection methods and exclusion criteria have been described in detail elsewhere (##REF##9346987##González-Cossío et al. 1997##). Interviewers explained the study to and obtained written informed consent from eligible pregnant women who were willing to participate. Anthropometric data from the mother and newborn as well as umbilical cord and maternal venous blood samples were gathered within 12 hr of delivery. Information on estimated gestational age and characteristics of the birth and newborn period was extracted from the medical records. Baseline maternal information on reproductive and health status, social and demographic characteristics, risk factors for environmental lead exposure, and dietary assessment of nutrient intake was collected from all eligible participating mothers.</p>", "<p>Infants of participating mothers identified before delivery had umbilical cord blood samples collected at birth (<italic>n</italic> = 520). Children were subsequently assessed for neurocognitive development, and blood lead levels were obtained at 24, 30, 36, 42, and 48 months of age. Data on the neurocognitive test performance are presented elsewhere (##REF##12093955##Gomaa et al. 2002##). The present analysis is limited to data from 422 infants who had archived umbilical cord blood available and were successfully genotyped. After genotyping, the full data set was anonymized to protect the confidentiality of study subjects and to conform with current institutional review board policies. A total of 341 subjects had at least one follow-up blood lead level available and data available on covariates of interest for inclusion in the longitudinal analyses.</p>", "<title>Blood measurements</title>", "<p>Blood lead measurements were performed using graphite furnace atomic absorption spectrophotometry (model 3000; Perkin-Elmer, Wellesley, MA, USA) at the ABC Hospital Trace Metal Laboratory in Mexico City according to a technique described by ##REF##3445938##Miller et al. (1987)##. The laboratory participates in the CDC blood lead proficiency testing program administered by the Wisconsin State Laboratory of Hygiene (Madison, WI, USA), which provided external quality control specimens varying from 2 to 88 μg/dL. Our laboratory maintained acceptable precision and accuracy over the study period [correlation = 0.98; mean difference = 0.71 μg/dL; SD = 0.68].</p>", "<p>Complete blood count (red blood cells, white blood cells, hematocrit, hemoglobin, mean corpuscular volume) in manually diluted samples of whole blood (Beckman/Coulter CBC-5 Hematology Analyzer; Block Scientific Inc, Holbrook, NY, USA) and serum ferritin (Ferritin RIA Kit, Kodak Clinical Diagnostics, Ltd. Amersham, Bucks, UK) were measured using standard clinical methods at the ABC Hospital.</p>", "<title>HFE and TF genotyping</title>", "<p>We extracted high-molecular-weight DNA from white blood cells of archived umbilical cord blood with commercially available PureGene Kits (Gentra Systems, Minneapolis, MN, USA). After DNA quantification, samples were adjusted to TE buffer (containing Tris, a common pH buffer, and EDTA, a chelating agent), partitioned into aliquots, and stored at −80°C. Multiplex polymerase chain reaction (PCR) assays were designed using Sequenom SpectroDESIGNER software (Sequenom, Inc., San Diego, CA) by inputting sequence containing the single nucleotide polymorphism (SNP) site and 100 base pairs of flanking sequence on either side of the SNP. The extension product was then spotted onto a 384-well spectroCHIP (Sequenom, Inc.) before being flown in the MALDI-TOF (matrix-assisted laser desorption ionization–time of flight) mass spectrometer (Sequenom, Inc.).</p>", "<p>For this study, we included three SNPs: hemochromatosis (<italic>HFE</italic>) <italic>C282Y</italic> (rs1800562) and <italic>H63D</italic> (rs1799945) [Reference Sequence NM_139011, GenBank (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=91718887\">http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=91718887</ext-link>)], and transferrin (<italic>TF</italic>) <italic>P570S</italic> (rs1049296) [Reference Sequence NM_001063, GenBank (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=21536430\">http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=21536430</ext-link>)]. Specifically, the following primers were used in the multiplex assay:</p>", "<p>For <italic>HFE H63D</italic> (rs1799945): forward PCR primer, 5′-ACGTTGGATGTCTACTG-GAAACCCATGGAG-3′; reverse PCR primer, 5′-ACGTTGGATGTTGAAGC-TTTGGGCTACGTG-3′; extension primer 5′-GCTGTTCGTGTTCTATGAT-3′</p>", "<p>For <italic>HFE C282Y</italic> (rs1800562): forward PCR primer, 5′-ACGTTGGATGTACCCCA-GATCACAATGAGG-3′; reverse PCR primer, 5′-ACGTTGGATGTGGATAAC-CTTGGCTGTACC-3′; extension primer 5′-GAAGAGCAGAGATATACGT-3′</p>", "<p>For <italic>TF P570S</italic> (rs1049296): forward PCR primer, 5′-ACGTTGGATGTGAGTTG-CTGTGCCTTGATG-3′; reverse PCR primer, 5′-ACGTTGGATGATCTTTC-CGTGTGACCACAG-3′; extension primer, 5′-CGCATACTCCTCCACAG-3′.</p>", "<title>Statistical analysis</title>", "<p>We examined distribution of <italic>HFE</italic> and <italic>TF</italic> alleles and genotypes and tested frequencies using a chi-square statistic to compare observed and expected counts according to principles of Hardy–Weinberg equilibrium. <italic>A priori</italic> the two <italic>HFE</italic> variants (<italic>H63D</italic> and <italic>C282Y</italic>) were combined into a single indicator term (i.e., presence of one or two copies of either gene’s variant allele), and subsequent analyses compared carriers of <italic>HFE</italic> or <italic>TF</italic> variants with wild-type subjects, thus assuming dominant effects. We calculated summary statistics for child characteristics, stratified by genotype. Bivariate associations between children’s blood lead, ferritin, and hemoglobin levels by genotype (wild type vs. carrier) at each time point were compared using Student’s <italic>t</italic>-test.</p>", "<p>Blood lead levels followed a log-normal distribution and were log transformed for the analyses; thus, beta coefficients from regression models represent percent change in blood lead. We used multivariate linear regression to model the effect of genotype on blood lead in cross-sectional analyses at each time point, adjusting for maternal blood lead level at delivery (as a measure of prenatal lead exposure) and child’s concurrent anemia status (as a marker of dietary iron intake). Anemia was defined as a hemoglobin concentration &lt; 12.1 g/dL, based on recommendations for children 2 to &lt; 5 years of age living at an altitude of 7,000–7,999 feet above sea level (##UREF##1##CDC 1998##). We used random-effects models with unstructured covariance to model the association between <italic>HFE</italic> and <italic>TF</italic> genotype and repeated measures of log-transformed blood lead in separate models. These models are flexible with respect to imbalance in the data and, in addition, take into account the correlation between repeated measures on subjects. To explore a possible gene–gene interaction between <italic>HFE</italic> and <italic>TF</italic> genotypes in predicting blood lead, we modeled combined <italic>HFE</italic> plus <italic>TF</italic> joint genotype as a dichotomous variable (any variant present vs. both wild type) and then, to assess allele “dose” effects, as an ordinal variable (both variants present, <italic>TF</italic> variant/<italic>HFE</italic> wild type, <italic>TF</italic> wild type/<italic>HFE</italic> variant, both wild type) with wild type for both variants as the reference group. Separate models were repeated controlling for concurrent hemoglobin and concurrent ferritin to account for potential differences in dietary iron intake. We also constructed two-way interaction terms between <italic>HFE</italic> and <italic>TF</italic> genotype, and between each gene and lead, hemoglobin, and ferritin levels at each time point to explore potential interactions. Finally, we used logistic regression (PROC NLMIXED) to examine the odds of having a blood lead level ≥ 10 μg/dL associated with presence of gene variants. All statistical analyses were performed using SAS software version 9.1.3 (SAS Institute Inc., Cary, NC, USA).</p>" ]

[ "<title>Results</title>", "<p>Four hundred twenty-two children were genotyped, and 17.7%, 3.3%, and 18.9% carried the <italic>HFE H63D</italic>, <italic>HFE C282Y</italic>, and <italic>TF P570S</italic> variants, respectively (##TAB##0##Table 1##). In addition, 1% of children carried both the <italic>HFE C282Y</italic> and the <italic>TF P570S</italic> variants, and 3% of children carried both the <italic>HFE H63D</italic> and the <italic>TF P570S</italic> variants (##TAB##1##Table 2##). There were no statistically significant differences in mean gestational age, sex, umbilical cord blood lead levels, or anthropometric measures at birth between carriers and wild-type subjects (##TAB##2##Table 3##).</p>", "<p>Unadjusted mean blood lead levels were consistently higher for carriers of either the <italic>HFE</italic> or <italic>TF</italic> variant compared with wild-type subjects at each age (24, 30, 36, 42, and 48 months), but these differences were non-significant (##TAB##3##Table 4##). There were no statistically significant differences in hemoglobin or ferritin levels at any age between carriers and wild-type subjects for either gene variant (results not shown). Unadjusted mean blood lead levels of subjects carrying any variant or both variants (combined <italic>HFE</italic> + <italic>TF</italic> genotype) were consistently higher (at any age) than subjects who were wild type, although these differences were also not statistically significant (##TAB##3##Table 4##).</p>", "<p>In cross-sectional analyses, adjusting for maternal blood lead level at delivery and child’s concurrent anemia status, the relationship between genotype (either <italic>HFE</italic> or <italic>TF</italic>) and blood lead level (at any age) was non-significant (data not shown). However, in the longitudinal analysis (using repeated measures of blood lead at 24, 30, 36, 42, and 48 months of age) adjusting for covariates (maternal blood lead level at delivery and child’s concurrent anemia status), the relationship between <italic>HFE</italic> genotype and blood lead was statistically significant (β = 0.11, <italic>p</italic> = 0.04), and the relationship between <italic>TF</italic> genotype and blood lead was borderline significant (β = 0.10, <italic>p</italic> = 0.08) (##TAB##4##Table 5##). Thus, on average, carriers of either the <italic>HFE</italic> or <italic>TF</italic> variant had blood lead levels that were 11% and 10% higher, respectively, than wild-type subjects.</p>", "<p>There was a statistically significant unadjusted gene-by-gene interaction (β_unadj = 0.37, <italic>p</italic> = 0.02) between the <italic>HFE</italic> and <italic>TF</italic> genotypes and, when adjusted for maternal blood lead level at delivery and child’s concurrent anemia status, this interaction term was marginally significant (β_adj = 0.31, <italic>p</italic> = 0.06) (data not shown). There were no significant interactions between either gene with concurrent hemoglobin or with concurrent ferritin concentration (data not shown).</p>", "<p>We then examined the data to explore whether there was an interactive effect of the presence of both <italic>HFE</italic> and <italic>TF</italic> variants on blood lead levels. We first examined the cross-sectional association between having any variant present (those with <italic>HFE</italic> and/or <italic>TF</italic> variant) versus both wild type. The dichotomous combined <italic>HFE</italic> + <italic>TF</italic> genotype was not significantly related with blood lead (at any age) in cross-sectional models (data not shown). Next, we defined a combined joint genotype by grouping subjects into four categories (both variants, either <italic>HFE</italic> and/or <italic>TF</italic> variant, both wild type) to examine the dose effect for presence of gene variant(s). This specification did not reveal any statistically significant associations with blood lead (at any age) in cross-sectional models (data not shown).</p>", "<p>In longitudinal models, the result of having any variant present (β = 0.08, <italic>p</italic> = 0.07) was similar to having either <italic>HFE</italic> (β = 0.11, <italic>p</italic> = 0.04) or <italic>TF</italic> (β = 0.10, <italic>p</italic> = 0.08) variant compared with subjects who were wild type for both variants (##TAB##4##Table 5##). When examining the variant dose effect, compared with subjects who were wild type for both variants, subjects who carried both <italic>HFE</italic> + <italic>TF</italic> variants had higher blood lead levels (β = 0.41, <italic>p</italic> = 0.006) than subjects who carried either <italic>TF</italic> variant/<italic>HFE</italic> wild type (β = 0.04, <italic>p</italic> = 0.5) or <italic>HFE</italic> variant/<italic>TF</italic> wild type (β = 0.06, <italic>p</italic> = 0.3) (##TAB##4##Table 5##). Therefore, subjects carrying both variants had blood lead 50% higher than subjects who were wild type for both <italic>HFE</italic> and <italic>TF</italic>. These results were unchanged when adding concurrent ferritin or concurrent hemoglobin (in place of anemia status) as covariates in our models (data not shown).</p>", "<p>##FIG##0##Figure 1## shows the risk of elevated blood lead levels (≥ 10 μg/dL) by presence of variant allele(s). Those subjects with either the <italic>HFE</italic> or <italic>TF</italic> (any) variant present had significantly higher odds of having a blood lead greater than or equal to 10 μg/dL [odds ratio (OR) = 2.3; 95% confidence interval (CI), 1.0–5.5] compared with those who were wild type. Those subjects with both variants present had significantly higher odds of having a blood lead ≥ 10 μg/dL (OR = 18.3; 95% CI, 1.9–177.1) compared with those wild type for both <italic>HFE</italic> and <italic>TF</italic>, though the wide CIs are attributed to the fact that &lt; 5% of our study population had both variants present.</p>" ]

[ "<title>Discussion</title>", "<p>In our current study of Mexican children, carriers of the <italic>HFE</italic> variant genotype had blood lead levels 11% higher than wild-type subjects. Furthermore, carriers of both <italic>HFE</italic> and <italic>TF</italic> variant alleles had 50% higher blood lead levels compared with wild-type subjects in models comparing the joint effect of combined <italic>HFE</italic> + <italic>TF</italic> genotype on blood lead levels. Those subjects with both gene variants present also had significantly higher odds of having a blood lead level ≥ 10 μg/dL. Our results suggest that genes affecting iron metabolism also affect lead metabolism, and this impact may be magnified by the presence of multiple genotypic variant alleles.</p>", "<p>This is the first study to examine the association between iron metabolism genes and lead exposure in children. There are at least three previous reports in adults or studies of mixed ages. ##REF##8051482##Barton et al. (1994)## found higher blood lead levels in subjects (children to young/middle-aged adults) with HH compared with normal controls, although they used family history and clinical data to determine case status rather than genotype and therefore some subjects in the control group were likely <italic>HFE</italic> variant carriers. In another study, ##REF##10753085##Åkesson et al. (2000)## found no difference in blood lead levels between adult subjects (average age, 55.5 years) with hemochromatosis and controls, but found increased blood lead levels in HH patients who had increasing number of years of treatment by phlebotomy, suggesting an up-regulation of absorption (due to lowered iron stores) with a subsequent increase in lead absorption.</p>", "<p>In a previous population-based cohort study (##REF##15121519##Wright et al. 2004##), we found lower blood/bone lead levels in hemochromatosis variant (<italic>H63D</italic> or <italic>C282Y</italic>) carriers in a group of elderly men. These results contrast particularly with those of ##REF##8051482##Barton et al. (1994)##. We hypothesized that the results of these three studies may have differed partly because of variations in body iron stores, which correlate with age and sex. Because of menstrual losses, women tend to have lower body iron stores than men, which would up-regulate iron/lead absorption (##REF##1957820##Hallberg and Rossander-Hulten 1991##). In addition, because no mechanisms for iron excretion exist, in the absence of chronic bleeding disorders, iron stores tend to increase with age. We postulated that the <italic>HFE</italic> gene effect on blood lead level may vary by age because of these differences in iron stores/needs that correlate with age/sex, and that in a younger population with higher body iron needs, the effect of <italic>HFE</italic> variants may be to increase lead absorption among variant carriers. Conversely, in an older population of elderly males, the <italic>HFE</italic> variant effect may be attributable to down-regulation of iron and lead absorption, because iron stores in men are higher than in women. Our current study supports this hypothesis; in a population of young children with high body-iron needs, we found higher blood lead levels among <italic>HFE</italic> variant carriers. This study is among the first to present evidence that gene environment interactions may vary by life stage.</p>", "<p>In addition, our current study explored the interactive effect of <italic>HFE</italic> + <italic>TF</italic> combined genotype on blood lead levels in children. Our results suggested that iron metabolism and body lead burden are affected more profoundly by the joint presence of genotypic variant alleles in both <italic>HFE</italic> and <italic>TF</italic>. Given the relatively small sample size to detect gene–gene or gene–environment interactions, we used longitudinal random–effects models, because these models are flexible with respect to imbalance in the data. We were able to include subjects with incomplete data to increase the power of the study to detect these effects.</p>", "<p>There are several limitations to our study. Our sample was restricted to a homogeneous sample of Mexican children with a lower prevalence of the <italic>C282Y</italic> variant (heterozygotes, 3.3%) than people from Europe (9.2%) and the Americas (9.0%), but a higher prevalence than people from Africa/Middle East (0.2%), the Indian subcontinent (0.5%), Asia (0%), and Australia (0%) (##REF##11479183##Hanson et al. 2001##). Therefore, because of the wide variability in prevalence rates of the <italic>C282Y</italic> variant (heterozygotes), it will be important to replicate this study in different populations. The potential for misclassification should also be considered, because there may be other polymorphisms in the <italic>HFE</italic> or <italic>TF</italic> genes or in a proximal gene that is in tight linkage disequilibrium with these genes that could account for our findings. Residual confounding is always a concern in observational studies. We chose covariates for this analysis based on biological plausibility as confounders (i.e., factors likely to be associated with both <italic>HFE</italic> genotype and blood lead levels). Among common predictors of blood lead, few are likely associated with <italic>HFE</italic> or <italic>TF</italic> genotype. For example, <italic>HFE</italic> and <italic>TF</italic> are not X-linked; therefore, sex should not be a confounder because it is not associated with <italic>HFE</italic> or <italic>TF</italic> genotype, and any sex-related differences due to menstruation are not yet an issue in a pre-pubescent population. Genotype should also be independent of environmental lead levels. Iron status is plausibly related to blood lead and <italic>HFE</italic> genotype (##REF##15121519##Wright et al. 2004##), but should theoretically be a modifier of the association of <italic>HFE</italic> with blood lead rather than a confounder. Our results did not demonstrate evidence of effect modification by serum ferritin or hemoglobin. Race and socioeconomic status were accounted for in our study design, because we enrolled Mexican children from hospitals that serve a relatively homogeneous group of low-to-moderate income families. Therefore, to account for potential confounding, we controlled for maternal blood lead level at delivery (to account for differences in prenatal lead exposure) as well as child’s concurrent anemia status (to account for differences in dietary iron intake).</p>", "<p>With respect to the gene-by-gene interaction findings, a limitation of this study is that carriers of both variants represent &lt; 5% of our study population, and even though the result is statistically significant, this could be a chance finding. However, there is biological plausibility to the relationship between <italic>HFE</italic> and <italic>TF</italic>, and ours is not the first study to find synergy between these two variant alleles. The <italic>HFE</italic> gene product regulates the binding of transferrin to transferrin receptors, which in turn regulates the transfer of iron (and potentially other transferrin-bound metals) across cell membranes (##REF##10085150##Roy et al. 1999##; ##REF##10318901##Salter-Cid et al. 1999##). Furthermore, lead has been shown to bind transferrin, down-regulating transferrin gene expression (##REF##8247401##Adrian et al. 1993##). Further evidence of this epistatic relationship is seen in clinical studies. ##REF##15060098##Robson et al. (2004)## found that carriers of the C2 variant of the <italic>TF</italic> gene and the <italic>C282Y</italic> allele of the <italic>HFE</italic> gene were at five times greater risk for Alzheimer disease compared with all others, whereas neither allele alone had any effect on risk for Alzheimer disease. Although outside the scope of this study, ##REF##10383894##Beckman et al. (1999)## examined the association between genetic variants of iron metabolism (<italic>HFE</italic>, <italic>TF</italic>, and the two combined) and risk for multiple myeloma, breast cancer, and colorectal cancer. In their study, the <italic>HFE</italic> and <italic>TF</italic> genotypes tested separately were not associated with any of these neoplastic disorders, but there was a significant difference between patients and controls with respect to the two genotypes combined.</p>", "<p><italic>HFE</italic> genotype is associated with higher blood lead levels in Mexican children over time. Our results also suggest an interaction between <italic>HFE</italic> and <italic>TF</italic> genotype in predicting higher blood lead in young children. These results differ from reports in elderly adults in which <italic>HFE</italic> variants predicted lower blood lead levels, demonstrating that genetic effects differ by life stage.</p>", "<p>In conclusion, iron deficiency has been associated with increases in absorption and deposition of lead (##REF##712193##Barton et al. 1978##); however, the relationship between iron and lead is complex and not completely understood. There is no evidence that iron supplements themselves change lead levels after lead exposure. In a randomized control trial controlling for initial blood lead level, Mexican children who were administered iron (or iron plus zinc) did not have lower blood lead concentrations than the placebo group. Iron supplementation of these lead-exposed children significantly improved iron status but did not reduce blood lead levels (##REF##16920858##Rosado et al. 2006##). Based on previous research, it has been recommended that iron supplementation should be prescribed only to iron-deficient children, regardless of blood lead levels, and not as a treatment for lead poisoning in children (##REF##10349106##Wright 1999##). Thus, children with genetic variants affecting iron metabolism may present a unique challenge: They may be more susceptible to low environmental levels of lead exposure because of increased lead absorption and may also be resistant to interventions such as iron supplementation. Another study evaluating the association between blood lead concentration and a vitamin D receptor (VDR) gene polymorphism found the VDR-<italic>Fok1</italic> variant to be an effect modifier of the relationship of floor dust lead exposure and blood lead concentration (##REF##14527848##Haynes et al. 2003##). In combination, these results highlight that some children may be more susceptible to lead exposure or absorption, which emphasizes the importance of further reducing lead exposure to protect the most at-risk children around the world. In addition, these children might represent a vulnerable population that may benefit from promising new interventions such as environmental enrichment, as defined by a combination of “complex inanimate objects and social stimulation” (##REF##12509847##Guilarte et al. 2003##; ##REF##11257907##van Praag et al. 2000##). Guilarte and colleagues exposed rats pre- and postnatally to lead, and then randomized them to an enriched environment (cage with a greater space allowance per rat, more varied toys and fixed objects, and increased human handling) versus a standard laboratory cage with minimal handling. The rats reared in the enriched environment showed improvement in spatial learning performance, compared with rats reared in the standard laboratory cage, suggesting the enriched environment mitigated some of the neurotoxic effects of lead exposure (##REF##12509847##Guilarte et al. 2003##). These results are particularly applicable to children, as risk factors for increased lead exposure in children include low socioeconomic conditions such as poor housing and decreased maternal education. Therefore, programs aimed at defining and providing environmental enrichment for children at increased risk of lead exposure might not only reduce risk but also mitigate the neurotoxic effects of exposure.</p>" ]

[]

[ "<p>The authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Given the association between iron deficiency and lead absorption, we hypothesized that variants in iron metabolism genes would predict higher blood lead levels in young children.</p>", "<title>Objective</title>", "<p>We examined the association between common missense variants in the hemochromatosis (<italic>HFE</italic>) and transferrin (<italic>TF</italic>) genes and blood lead levels in 422 Mexican children.</p>", "<title>Methods</title>", "<p>Archived umbilical cord blood samples were genotyped for <italic>HFE</italic> (<italic>H63D</italic> and <italic>C282Y</italic>) and <italic>TF</italic> (<italic>P570S</italic>) variants. Blood lead was measured at 24, 30, 36, 42, and 48 months of age. A total of 341 subjects had at least one follow-up blood lead level available and data available on covariates of interest for inclusion in the longitudinal analyses. We used random-effects models to examine the associations between genotype (<italic>HFE</italic>, <italic>TF</italic>, and combined <italic>HFE</italic> + <italic>TF</italic>) and repeated measures of blood lead, adjusting for maternal blood lead at delivery and child’s concurrent anemia status.</p>", "<title>Results</title>", "<p>Of 422 children genotyped, 17.7, 3.3, and 18.9% carried the <italic>HFE H63D</italic>, <italic>HFE C282Y</italic>, and <italic>TF P570S</italic> variants, respectively. One percent of children carried both the <italic>HFE C282Y</italic> and <italic>TF P570S</italic> variants, and 3% of children carried both the <italic>HFE H63D</italic> and <italic>TF P570S</italic> variants. On average, carriers of either the <italic>HFE</italic> (β = 0.11, <italic>p</italic> = 0.04) or <italic>TF</italic> (β = 0.10, <italic>p</italic> = 0.08) variant had blood lead levels that were 11% and 10% higher, respectively, than wild-type subjects. In models examining the dose effect, subjects carrying both variants (β = 0.41, <italic>p</italic> = 0.006) had blood lead 50% higher than wild-type subjects and a significantly higher odds of having a blood lead level &gt; 10 μg/dL (odds ratio = 18.3; 95% confidence interval, 1.9–177.1).</p>", "<title>Conclusions</title>", "<p>Iron metabolism gene variants modify lead metabolism such that <italic>HFE</italic> variants are associated with increased blood lead levels in young children. The joint presence of variant alleles in the <italic>HFE</italic> and <italic>TF</italic> genes showed the greatest effect, suggesting a gene-by-gene-by-environment interaction.</p>" ]

[ "<p>Despite efforts to reduce lead in the environment by removing lead in gasoline and banning lead-based paint, an estimated 310,000 U.S. children 1–5 years of age have elevated blood lead levels [##REF##15917736##Centers for Disease Control and Prevention (CDC) 2005##]. In developing countries, lead exposure is a concern because of continued use of lead-containing products and lack of regulations or enforcements policies (##REF##12971691##Meyer et al. 2003##). Furthermore, research suggests that lead exerts its neurotoxic effects in children at blood levels lower than the current CDC action level of 10 μg/dL (##REF##12700371##Canfield et al. 2003##; ##REF##18288325##Jusko et al. 2008##; ##REF##16002379##Lanphear et al. 2005##). There is growing interest in identifying host factors that increase the risk of elevated blood lead levels in children. Gene variants within metabolic pathways that influence lead absorption may be such a susceptibility factor and could place children at increased risk of lead poisoning even at low environmental levels (##REF##10698721##Onalaja and Claudio 2000##).</p>", "<p>Because of the well-described inverse relationship between iron stores and lead absorption (##REF##15325155##Kwong et al. 2004##), iron metabolism genes are potential candidates to modify lead absorption and stores. ##REF##11675273##Bradman et al. (2001)## and ##REF##8680621##Hammad et al. (1996)## showed an inverse relationship between dietary iron and blood lead levels, and ##REF##10394314##Wright et al. (1999##, ##REF##12520247##2003)## found an association between biomarkers of iron deficiency and elevated blood lead levels both cross-sectionally and longitudinally.</p>", "<p>Iron absorption is regulated by iron stores and erythropoiesis (##UREF##0##Bothwell et al. 1979##) and is influenced by dietary iron intake and hypoxia (##REF##12547229##Morgan and Oates 2002##). Hereditary hemochromatosis (HH) is an autosomal recessive disorder leading to excessive iron stores secondary to increased intestinal iron absorption (##REF##2019794##McLaren et al. 1991##). Two predominant variants of the hemochromatosis (<italic>HFE</italic>) gene account for most cases: the <italic>C282Y</italic> and <italic>H63D</italic> variants, which are common in the U.S. population, with a prevalence of 7–17% and 10–32%, respectively (##REF##9575458##Bradley et al. 1998##).</p>", "<p>Transferrin forms a stable complex with the HFE protein to facilitate iron transfer (##REF##9465039##Feder et al. 1998##; ##REF##10660486##Lee et al. 1999##). It has been suggested that the effects of <italic>HFE</italic> gene on iron absorption depend on the complex relationship between HFE and the transferrin receptor (TfR) (##REF##12547229##Morgan and Oates 2002##). ##REF##9465039##Feder et al. (1998)## demonstrated that the <italic>HFE H63D</italic> variant altered transferrin binding, leading to a loss of HFE-repressor function for transferrin uptake, and thereby increasing iron transport into cells. A common missense variant of the transferrin gene (<italic>TF</italic>) is <italic>P570S</italic>, with a prevalence rate of about 15% in the general population (##REF##10660486##Lee et al. 1999##).</p>", "<p>Previous reports suggest that subjects with clinical hemochromatosis have higher (##REF##8051482##Barton et al. 1994##) or equivalent (##REF##10753085##Åkesson et al. 2000##) blood lead levels compared with normal controls. These studies were not population based, but used case–control designs comparing adult subjects with clinical disease with those without. In a population-based cohort, our group reported lower bone lead levels among elderly men carrying at least one copy of <italic>H63D</italic> or <italic>C282Y</italic> (##REF##15121519##Wright et al. 2004##). The differences in the design, sex, and age of the different cohorts studied might account for the seemingly disparate findings, with older, male subjects with <italic>HFE</italic> variants being most likely to have high body iron stores and down-regulated lead absorption. In our 2004 report (##REF##15121519##Wright et al. 2004##), we hypothesized that variants in iron metabolism genes might predict higher blood lead levels in young children over time because of their greater dietary iron needs and lower body iron stores, which would up-regulate iron and lead absorption differentially among <italic>HFE</italic> variant carriers and children with wild-type genotypes. In this study, we sought to determine whether genetic variants of iron metabolism (<italic>HFE C282Y, HFE H63D, TF P570S</italic>) were associated with blood lead levels in children.</p>" ]

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[ "<fig id=\"f1-ehp-116-1261\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Risk of elevated blood lead levels (≥ 10 μg/dL) by presence of variant allele. OR (and 95% CI) from nonlinear mixed-effects models (adjusting for maternal blood lead level at delivery and child’s concurrent anemia status; reference group = wild type; <italic>n</italic> = 708 observations used; <italic>n</italic> = 340 subjects; maximum number of observations per subject = 3). Vertical lines delineate four separate models. var, variant.</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1261\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Genotype frequencies of children born in Mexico City, 1994–1995.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Genotype</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No. (%)</th></tr></thead><tbody><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\"><italic>HFE C282Y</italic><xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1261\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>C282Y</italic> homozygous wild type (CC)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">408 (96.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>C282Y</italic> heterozygous (CY)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14 (3.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>C282Y</italic> homozygous variant (YY)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0)</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\"><italic>HFE H63D</italic><xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1261\">b</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>H63D</italic> homozygous wild type (HH)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">345 (82.3)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>H63D</italic> heterozygous (HD)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">70 (16.7)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>H63D</italic> homozygous variant (DD)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4 (1.0)</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\"><italic>TF P570S</italic><xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1261\">c</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>P570S</italic> homozygous wild type (CC)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">339 (81.1)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>P570S</italic> heterozygous (CT)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">77 (18.4)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> <italic>P570S</italic> homozygous variant (TT)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2 (0.5)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1261\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Combined genotype frequencies<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1261\">a</xref> of children born in Mexico City, 1994–1995.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\"><italic>TF</italic> genotype\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\">Wild (CC) No. (%)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Variant (CT or TT) No. (%)</th></tr></thead><tbody><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\"><italic>HFE</italic> genotype</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Wild (CC, HH)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">269 (65)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">62 (15)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Variant (CY, YY, HD, DD)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">65 (16)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16 (4)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1261\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Baseline characteristics of study population by <italic>HFE</italic> genotype and <italic>TF</italic> genotype.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"4\" align=\"center\" rowspan=\"1\"><italic>HFE</italic> genotype\n<hr/></th><th colspan=\"4\" align=\"center\" rowspan=\"1\"><italic>TF</italic> genotype\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">All\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Wild type\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Variant\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Wild type\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Variant\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Characteristic</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1261\">a</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Gestational age (weeks)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">413</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.2 ± 1.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">329</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.2 ± 1.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.1 ± 1.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">337</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.2 ± 1.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.0 ± 1.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sex (% female)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">414</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">330</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">338</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">54</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cord blood lead (μg/dL)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">364</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.6 ± 3.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">289</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.6 ± 3.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.9 ± 4.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">296</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.7 ± 3.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">69</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.5 ± 3.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Birth weight (g)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">415</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3,149 ± 409</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">331</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3,164 ± 402</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3,087 ± 433</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">338</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3,147 ± 419</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3,139 ± 369</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Birth length (cm)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">411</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.5 ± 2.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">327</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.4 ± 2.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.5 ± 2.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">335</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.5 ± 2.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50.2 ± 2.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Head circumference (cm)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">399</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.9 ± 1.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">315</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.0 ± 1.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.7 ± 1.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">324</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.9 ± 1.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">77</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.9 ± 1.5</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4-ehp-116-1261\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>Children’s blood lead levels (micrograms per deciliter) by age and by <italic>HFE, TF,</italic> and combined genotype (at each age).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"4\" align=\"center\" rowspan=\"1\"><italic>HFE</italic> genotype\n<hr/></th><th colspan=\"4\" align=\"center\" rowspan=\"1\"><italic>TF</italic> genotype\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">All\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Wild type\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Variant\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Wild type\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">Variant\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Age (months)</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">283</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2 ± 4.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">231</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0 ±4.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.1 ± 4.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">227</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0 ±4.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">55</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.8 ± 4.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.4 ± 3.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.3 ±3.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.2 ± 3.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">134</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.4 ±3.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.3 ± 4.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">206</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.4 ± 5.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2 ±5.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.9 ± 4.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">161</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.3 ±5.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.8 ± 5.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">215</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.5 ± 6.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">171</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.4 ±6.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.7 ± 4.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">169</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.4 ±6.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.0 ± 4.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">227</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2 ± 3.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">183</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0 ±3.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.9 ± 4.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">179</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.1 ±3.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.6 ± 4.1</td></tr></tbody><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"10\" align=\"center\" rowspan=\"1\">Combined genotype\n<hr/></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"6\" align=\"center\" rowspan=\"1\">Variant dose effect\n<hr/></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td colspan=\"2\" align=\"center\" rowspan=\"1\">No variant Both wild type<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1261\">a</xref>\n<hr/></td><td colspan=\"2\" align=\"center\" rowspan=\"1\">Any variant <italic>HFE</italic> and/or <italic>TF</italic> variant\n<hr/></td><td colspan=\"2\" align=\"center\" rowspan=\"1\"><italic>HFE</italic> variant/<italic>TF</italic> wild\n<hr/></td><td colspan=\"2\" align=\"center\" rowspan=\"1\"><italic>TF</italic> variant/<italic>HFE</italic> wild\n<hr/></td><td colspan=\"2\" align=\"center\" rowspan=\"1\">Both variants present\n<hr/></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Age (months)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No.</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No.</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No.</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No.</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">No.</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td></tr><tr><td colspan=\"11\" align=\"left\" rowspan=\"1\">\n<hr/></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">185</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0 ± 4.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.5 ±4.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2 ± 4.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.9 ±3.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.0 ± 6.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">110</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2 ± 3.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.9 ±4.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.0 ± 3.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.7 ±3.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.6 ± 5.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">125</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.9 ± 3.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">77</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.6 ±4.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.4 ± 3.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2 ±5.7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.6 ± 6.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">132</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0 ± 4.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.5 ±3.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.1 ± 3.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.3 ±3.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.0 ± 6.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">141</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0 ± 3.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">85</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.5 ±3.9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.5 ± 3.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.0 ±3.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.0 ± 6.1</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t5-ehp-116-1261\" orientation=\"portrait\" position=\"float\"><label>Table 5</label><caption><p>Longitudinal associations<xref ref-type=\"table-fn\" rid=\"tfn8-ehp-116-1261\">a</xref> between genotype and log-transformed blood lead levels.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">No.</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">β</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">SE</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>HFE</italic> variant present<xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1261\">b</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">341</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.04</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>TF</italic> variant present<xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1261\">b</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">341</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.08</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Any variant present<xref ref-type=\"table-fn\" rid=\"tfn9-ehp-116-1261\">b</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">337</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.07</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Both variants<xref ref-type=\"table-fn\" rid=\"tfn10-ehp-116-1261\">c</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">337</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.41</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.006</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>TF</italic> variant/<italic>HFE</italic> wild type</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>HFE</italic> variant/<italic>TF</italic> wild type</td><td align=\"center\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>We acknowledge the American British Cowdray (ABC) Hospital in Mexico City for the use of their research facilities.</p></fn><fn><p>This study was supported by U.S. National Institute of Environmental Health Sciences (NIEHS) grants P42-ES05947, R01-ES07821, R01-ES014930, P30-ES 00002, K23-ES000381, and K01-ES014907, and by Consejo Nacional de Ciencia y Tecnología (CONACyT) grant 4150M9405, and CONSERVA, Department of Federal District, México.</p></fn><fn><p>The contents of this study are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS or the National Institutes of Health.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1261\"><label>a</label><p>Eleven children were missing genotype data; results in Hardy–Weinberg equilibrium: χ² = 0.12, <italic>p</italic> = 0.73.</p></fn><fn id=\"tfn2-ehp-116-1261\"><label>b</label><p>Fourteen children were missing genotype data; results in Hardy–Weinberg equilibrium: χ² = 0.05, <italic>p</italic> = 0.83.</p></fn><fn id=\"tfn3-ehp-116-1261\"><label>c</label><p>Fifteen children were missing genotype data; results in Hardy–Weinberg equilibrium: χ² = 1.16, <italic>p</italic> = 0.28.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn4-ehp-116-1261\"><label>a</label><p>Twenty-one children were missing data on combined <italic>HFE</italic> + <italic>TF</italic> genotype.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn5-ehp-116-1261\"><label>a</label><p>Because of missing data, numbers do not equal 422.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn6-ehp-116-1261\"><label>a</label><p>Both wild type is the reference group for any variant and variant dose effect classifications.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn7-ehp-116-1261\"><p>Shading indicates four separate models.</p></fn><fn id=\"tfn8-ehp-116-1261\"><label>a</label><p>Adjusted for maternal blood lead level at delivery and child’s concurrent anemia status.</p></fn><fn id=\"tfn9-ehp-116-1261\"><label>b</label><p>Reference group is wild-type genotype.</p></fn><fn id=\"tfn10-ehp-116-1261\"><label>c</label><p>Reference group is wild type for both genotypes.</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1261f1\"/>" ]

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{ "acronym": [], "definition": [] }

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[ "<title>Materials and Methods</title>", "<title>Study design</title>", "<p>The study used a record-based prevalence design based on the Western Australia Midwives’ Notification System and Birth Defects Registry for the years 2000–2004 inclusive.</p>", "<title>Water sampling and analysis</title>", "<p>We collected water samples at 47 separate locations within the greater Perth metropolitan area in Western Australia (##FIG##0##Figure 1##) from publicly accessible taps in highly populated areas, including drinking fountains, outdoor taps close to picnic areas in public parks, and cold-water taps located in public toilets, public parks, and popular restaurants. We chose sampling sites at locations where taps would be frequently used, thereby limiting the storage time of water in delivery pipe networks.</p>", "<p>We collected the water samples on six separate occasions from April 2005 to March 2006, inclusive. We flushed taps at each location for 2 min before collection. We collected water samples in 40-mL glass vials, each dosed with the quenching agent sodium thiosulfate, to prevent further THM formation once we took samples. We stored the collected samples on ice in an enclosed insulated carrier. During each collection, we filled vials to maximum level to prevent air bubbles upon sealing, to prevent loss of THMs due to volatilization. We collected samples from all 47 locations within an 8-hr period on each sample day and delivered them to the Chemistry Centre of Western Australia for analysis at the completion of each sampling run.</p>", "<p>We performed THM water analyses using the “purge and trap” technique for sample concentration and delivery, followed by gas chromatography with mass spectrometric detection (GC/MS) for the separation and quantification of each of the four analytes (##UREF##14##U.S. EPA 2003##). In this system, the sample is purged with an unreactive gas and the analytes trapped at a low temperature on an adsorbent trap. The trap is then heated rapidly and the desorbed compounds refocused at the head of a GC chromatographic column for GC/MS analysis. An internal standard is used to correct for slight differences in temperatures and trapping conditions throughout the run. The method is based on the U.S. EPA 5030C (purge-and-trap for aqueous samples) method (##UREF##14##U.S. EPA 2003##) and is accredited by the Australian National Association of Testing Authorities. Routine quality controls are performed with each batch of samples, such as replicate analyses, sample spiking, and blank analyses. We analyzed each sample for TTHM concentration (in micrograms per liter) and for the four individual THMs chloroform, bromoform, DBCM, and BDCM.</p>", "<title>Data on births and BDs</title>", "<p>We obtained number of total births and BDs from post codes corresponding to the water sample collection sites for the years 2000–2004 inclusive from the Western Australia Midwives’ Notification System and Birth Defects Registry, respectively (##UREF##3##Bower et al. 2007##). These two data sets form part of the comprehensive Western Australia Linked Database, which systematically links available administrative health data within the state using probabilistic matching of patient names and other identifiers (##REF##10575763##Holman et al. 1999##). We obtained deidentified records on birth date, post code of maternal residence at time of birth, maternal age, socioeconomic status code for the maternal residence at time of birth (from linked census data), and the presence of BDs. The Western Australia Birth Defects Registry uses multiple sources of case ascertainment to identify BDs in live births, stillbirths, and pregnancies terminated with fetal abnormality. We did not include minor malformations in our analyses unless they were disfiguring or required treatment. Cases with only minor malformations account for approximately 10% of all BDs registered. We did not include terminations in the present data, but these comprise a relatively small proportion of all cases and have been relatively constant for the past few years.</p>", "<p>The Midwives’ Notification System is statutory and is completed for all live births and stillbirths of ≥ 20 weeks’ gestation or birth weight ≥ 400 g. The Birth Defects Registry in Western Australia has a high level of case ascertainment (##REF##11115034##Bower et al. 2000##, ##REF##11169551##2001##).</p>", "<p>A BD is defined by the registry as a structural or functional abnormality that is present at conception or occurs before the end of pregnancy and is diagnosed by 6 years of age. We coded each recorded BD using the British Paediatric Association (BPA) International Classification of Diseases, version 9, system (##UREF##4##British Paediatric Association 1979##). We investigated associations of DBPs with any BD and also with the following major groups of BDs: nervous system defects (BPA codes 74000–74299), cardiovascular defects (BPA 74500–74799), respiratory system defects (BPA 74800–74899), gastrointestinal defects (BPA 74900–75199), urogenital defects (BPA 75200–75399), musculoskeletal defects (BPA 75400–75699), and congenital anomalies of integument (BPA 75700–75799). Because of low case frequencies, we did not examine the following groups separately: congenital anomalies of eye (BPA 74300–74399); congenital anomalies of ear, face, and neck (BPA 74400–74499); chromosome defects (BPA 75800–75899); and all other defects (BPA 24390–24399, 25520–25529, 27000–27099, 27010, 27100–27199, 27700, 28220, 28240–28249, 28600–28620, 35900–35999, 75992, 77100, 77800).</p>", "<title>Data analysis</title>", "<p>We used STATA version 9.0 (StataCorp, College Station, TX, USA) for data analysis. We classified collection sites and post codes of maternal residence in three exposure areas: low (TTHM &lt; 60 μg/L), medium (TTHM &gt; 60 to &lt; 130 μg/L), and high (TTHM ≥ 130 μg/L). We the calculated prevalence proportions for any BD and categories of major BDs for low, medium, and high exposure groups. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for any BD and for categories of BDs using binomial logistic regression models, and calculated estimated risks for the medium and high exposure areas using the low exposure area as the comparison group. We adjusted all estimates for maternal age at an individual level using the deidentified and highly complete case data from registry and midwives’ records and data on socioeconomic status [using Socio-Economic Indexes for Areas (SEIFA) coding (##UREF##1##Australian Bureau of Statistics 2001##)]. SEIFA values are supplied by post code and are calculated based on variables including the proportion of families with high incomes, people with a tertiary education, and employees in skilled occupations; low values indicate areas of disadvantage, and high values indicate areas of advantage.</p>" ]

[ "<title>Results</title>", "<title>TTHM levels</title>", "<p>We averaged measurements for each collection site over the six collection dates, and identified all post codes as low, medium, or high exposure as described in “Materials and Methods.” Average TTHM concentrations (±1 SD) for low, medium, and high exposure areas were 54 ± 16.6 μg/L, 109.3 ± 28.9 μg/L, and 137 ± 24 μg/L, respectively. ##TAB##1##Table 2## summarizes area THM levels, including individual THM concentrations. We also assessed the THM levels within each assigned exposure category using analysis of variance. The mean square errors and <italic>F</italic>-test value (<italic>F</italic> = 204292.41; <italic>p</italic> &lt; 0.00001) indicated highly significant between-group differences and relatively little within-group variability for the specified category cutoff points. Preliminary analysis of HAAs and HANs during the December testing month showed very low HAA and HAN concentrations by international standards (data not shown), so we did not continue monitoring these compounds.</p>", "<title>Prevalence of BDs</title>", "<p>A total of 1,097 individuals with BDs among 20,870 live births were recorded for the years 2000–2004 inclusive in the study region. In each of the years assessed, BD prevalence showed an increasing trend with higher categories of TTHM exposure, with the exception of 2001 (##TAB##2##Table 3##).</p>", "<title>Risk estimates</title>", "<p>Women living in high-TTHM areas at the time of birth of their child showed an increased risk of any BD (adjusted OR = 1.22; 95% CI, 1.01–1.48) compared with women living in low-TTHM areas. For individual BDs, we identified a significantly elevated OR for any cardiovascular BD (adjusted OR = 1.62; 95% CI, 1.04–2.51) for women living in high-TTHM areas compared with women living in low-TTHM areas. ##TAB##3##Table 4## summarizes all results. We evaluated maternal age as an independent risk factor for BDs and a potential confounder in this analysis. Using maternal age (in 5-year age strata) as a predictor, we calculated an OR of 1.03 (95% CI, 0.98–1.09) for BD overall. Because average maternal age varied slightly by region, we included it as an individual-level covariate in the final model. For the full model with TTHM exposures included, we undertook restricted analysis on mothers &lt; 35 years of age to explore possible impacts of advanced maternal age. ORs for the restricted population were comparable with the original analysis (data not shown). We also conducted mixed-effect multilevel analyses, but risk estimates did not substantially differ from those using the original regression models, with persistence of the significant findings for overall BD and cardiovascular BD (data not shown).</p>" ]

[ "<title>Discussion</title>", "<p>The results from this record-based prevalence study indicate that women living in high-TTHM areas in Perth at the time of birth of their baby have a 22% greater risk of having a baby with any BD. More specifically, classification in the high-exposure group is associated with an increased risk of 62% for having a baby with a cardiovascular defect. In this analysis, results for musculoskeletal and urogenital defects are suggestive but non-significant, with adjusted ORs of 1.48 (95% CI, 0.99–2.21) and 1.40 (95% CI, 0.98–1.99), respectively.</p>", "<p>These findings suggest that there may indeed be an area-related effect in Perth, attributable to the city’s heterogeneous water supply and quality. As discussed, the differential exposure patterns most likely relate to the quantity of precursors in the source waters, which display a rough latitudinal gradient. The analyses of BD rates by year also suggest that, except for 2001, the trend toward high defect rates for higher THM levels has been consistent. By international standards, a high proportion of the THMs in the areas tested were in the brominated form. Brominated THMs are thought to possess a greater heath risk compared with chloroform, and this largely relates to differences in their metabolism and toxicokinetics (##REF##8835225##Bull et al. 1995##). Reproductive and developmental toxicity has been observed in particular with BDCM (##REF##11158724##Bielmeier et al. 2001##, ##UREF##2##2003##; ##REF##14691210##Chen et al. 2004##; ##UREF##7##Hunter 2002##; ##REF##9398485##Narotsky et al. 1997##). We assessed low, medium, and high water consumption in a subpopulation of pregnant women living in Perth in 2004–2005 (data not shown). Based on the ingestion rates for the surveyed population, an exposure model estimated that residence in the highest TTHM exposure group ingesting the highest self-reported intake amount of 4.67 L/day will expose a pregnant women to an equivalent of 3.85 μg TTHM/day. Using this estimation technique, an estimated 3.47 μg TTHM/day (90.1%) are brominated THMs (Chisholm K, Cook A, Weinstein P, unpublished data).</p>", "<p>These findings are compatible with a critical review that found evidence for a weak association between the presence of any BD and TTHMs; the risk appeared highest for urinary tract BDs specifically (##REF##11603954##Graves et al. 2001##). A review published in 2006 formed similar conclusions (##REF##16624462##Tardiff et al. 2006##). Limited research has been conducted on musculoskeletal defects, with weak associatons reported (##REF##11603954##Graves et al. 2001##; ##REF##16624462##Tardiff et al. 2006##). The present study showed no significance for nervous system defects, which contrasts with results from ##REF##11404448##Dodds and King (2001)## that neural tube defects were significantly associated with exposure to &gt; 20 μg/L BDCM (OR = 2.5; 95% CI, 1.2–5.1), the same level of BDCM we found in Perth water supplies (##TAB##1##Table 2##).</p>", "<p>Recent literature suggests that lower socioeconomic indices are often predictive of higher BD rates (##REF##12839538##Carmichael et al. 2003##; ##REF##9807535##Wasserman et al. 1998##), but in this study our regression models did not suggest an obvious relationship between SEIFA, a composite measure of socioeconomic deprivation available at census district levels (which contain, on average, 225 households), and rates of BDs in the suburbs examined. In this study, we aggregated the SEIFA codes to calculate an average by post code, which was the spatial unit of BD data (##TAB##1##Table 2## summarizes the number of post codes per exposure area). In relation to the exposure, areas with higher socioeconomic (SEIFA) scores commonly fell within the highest THM category; that is, mothers resident in the more affluent suburbs were either just as or more likely to be highly exposed to THMs compared with those in socioeconomically deprived suburbs (##TAB##1##Table 2##). This suggests that socioeconomic factors themselves are not a plausible explanation for the observed findings, and—if anything—the spatial relationship between high socioeconomic status and low quality water in the study area may have tended to attenuate the risk estimate. The particular hospital or clinic used for delivery is also unlikely to be a source of error because the system for collecting BD data is consistent across clinics and routinely audited. In addition, differential maternity/obstetric hospital use by location of residence (and hence exposure) is unlikely given the small number of such hospitals in the catchment areas used for the study. We assessed potential confounding by maternal age, but it was not a significant predictor of BDs in most full exposure models (including those with positive findings for THM exposure). The average age of the mothers varied slightly by TTHM exposure region, but we adjusted for possible confounding effects at the individual level using the deidentified case data.</p>", "<p>We acknowledge several limitations in this analysis. Because we used an ecologic exposure measure in this study, the use of residential TTHMs to assign individual exposure raises the possibility of exposure misclassification. For example, women may not have lived in the area at the critical time window relevant to formation of BDs (at conception and first trimester for most BDs). A survey of 78 pregnant women in Perth that we conducted in 2004–2005 (data not included) found that 15.6% of participants reported moving residence during their pregnancy. This finding is similar to recent data reported from a cohort of 584 pregnant women recruited from within Sydney between 2004 and 2006. Of these women, 18% moved residences, although this estimate includes residential movement up to 6 months postpartum (##UREF##11##Raynes-Greenow et al. 2007##). If exposure misclassification has occurred as a result of migration, it is likely to be nondifferential, so risk estimates presented in this study may be overly conservative. Because we have not accounted for individual exposure assessment, we have not taken into account contact with other disinfected water sources (e.g., through swimming and work-related exposures). However, a small survey of 54 pregnant women in metropolitan Perth we conducted in 2004 (data not shown) indicated that 68.5% consumed tap water during their pregnancy, suggesting that bottled water is not a commonly used alternative among this population. Rainwater is not commonly used in Western Australia. A total of 96.3% of the women interviewed used publicly supplied water for showering and bathing, indicating that contact with the town supply remains relevant given that inhalation and dermal absorption are important routes of exposure to THMs (##REF##12162870##Meek et al. 2002##; ##REF##8146396##Weisel and Chen 1994##; ##REF##12392965##Xu et al. 2002##; ##REF##15138448##Xu and Weisel 2005##).</p>", "<p>The measurements used were not taken during the same time period as the health outcome, and limited public availability of complete TTHM levels in Perth precludes the reliable use of historical data. The limited DBP data published for the Perth region is consistent with our exposure estimates, but this historical data set consists of, on average, four samples (or fewer) per year and covers only half of the suburbs that we tested in our study (Water Corporation of Western Australia 2002–2003). Many factors are known to affect DBP formation, including pH, contact time, temperature, concentration and properties of NOM, concentration of both chlorine and residual chlorine, and concentration of bromide, and these may be subject to variation seasonally and from year to year (##UREF##9##Krasner et al. 1989##; ##UREF##13##Singer 1993##). We observed elevated THM levels during the summer months, and we consistently recorded the highest levels at sites within the high-TTHM regions. Because the increases and decreases in THM levels occurred concurrently across all regions by season, the differences between average levels observed within low, medium, and high regions remained consistent. For confidentiality, birth and BD data were not available by date of birth, so we could not subclassify based on season.</p>", "<p>Finally, we extracted BD data for this study in 2007, so for children born after 2001, defects not recognized until later in their lives have yet to be ascertained and included in the registry. However, there is no reason to suppose that this process would be differential by post code, and any incomplete ascertainment for more recent years of birth would be similar for all study locations. Western Australian also has only one centralized Birth Defects Registry, with consistent ascertainment and quality assurance protocols, making substantial variability by region unlikely, particularly within the metropolitan area.</p>", "<p>It is reasonable to argue that public health benefits from chlorination in terms of microbiologic safety prevail over the conflicting evidence of potential health risks associated with DBP exposure (##REF##16624462##Tardiff et al. 2006##). However, in Australia and other centers that permit elevated DBP levels, parts of the population are exposed to levels higher than those considered prudent by other industrialized countries. We suggest that national or regional authorities that permit higher TTHM levels may need to reexamine their water quality guidelines to reduce avoidable adverse birth outcomes. If a true elevation in risk is confirmed, a massive and costly engineering “fix” to reduce DBP exposure may not necessarily be warranted. Public health interventions in problem regions of low water quality and availability may be as simple as recommending and/or providing bottled water for pregnant women or ensuring adequate filtration of contaminants before consumption.</p>" ]

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[ "<title>Background</title>", "<p>By international standards, water supplies in Perth, Western Australia, contain high trihalomethane (THM) levels, particularly the brominated forms. Geographic variability in these levels provided an opportunity to examine cross-city spatial relationships between THM exposure and rates of birth defects (BDs).</p>", "<title>Objectives</title>", "<p>Our goal was to examine BD rates by exposure to THMs with a highly brominated fraction in metropolitan locations in Perth, Western Australia.</p>", "<title>Methods</title>", "<p>We collected water samples from 47 separate locations and analyzed them for total and individual THM concentrations (micrograms per liter), including separation into brominated forms. We classified collection areas by total THM (TTHM) concentration: low (&lt; 60 μg/L), medium (&gt; 60 to &lt; 130 μg/L), and high (≥ 130 μg/L). We also obtained deidentified registry-based data on total births and BDs (2000–2004 inclusive) from post codes corresponding to water sample collection sites and used binomial logistic regression to compare the frequency of BDs aggregately and separately for the TTHM exposure groups, adjusting for maternal age and socioeconomic status.</p>", "<title>Results</title>", "<p>Total THMs ranged from 36 to 190 μg/L. A high proportion of the THMs were brominated (on average, 92%). Women living in high-TTHM areas showed an increased risk of any BD [odds ratio (OR) = 1.22; 95% confidence interval (CI), 1.01–1.48] and for the major category of any cardiovascular BD (OR = 1.62; 95% CI, 1.04–2.51), compared with women living in low-TTHM areas.</p>", "<title>Conclusions</title>", "<p>Brominated forms constituted the significant fraction of THMs in all areas. Small but statistically significant increases in risks of BDs were associated with residence in areas with high THMs.</p>" ]

[ "<p>Disinfection by-products (DBPs) are a major group of water contaminants, and their role in causing adverse health outcomes has been subject to extensive epidemiologic and toxicologic research and review (##REF##11834464##Bove et al. 2002##; ##REF##15820735##Butterworth 2005##; ##REF##10051416##Butterworth and Bogdanffy 1999##; ##REF##14689225##Goebell et al. 2004##; ##REF##11603954##Graves et al. 2001##; ##REF##10711274##Nieuwenhuijsen et al. 2000##; ##REF##8930546##Reif et al. 1996##; ##REF##16624462##Tardiff et al. 2006##; ##REF##12594192##Villanueva et al. 2003##). Determination of safe levels in drinking water has been subject to extensive debate, with a wide range of acceptable levels set across the industrialized world [##REF##11404449##Keegan et al. 2001##; ##UREF##10##National Health and Medical Research Council (NHMRC) 2004##; ##REF##15556212##Rodriguez et al. 2004##; ##UREF##15##U.S. Environmental Protection Agency (EPA) 2004##].</p>", "<p>DBPs are formed by the reaction of chemical disinfectants with natural organic matter (NOM) in water (##REF##12955391##Piccolo and Spiteller 2003##; ##UREF##12##Rook 1974##; ##REF##22248415##Thurman and Malcolm 1981##). The structure and quantity of NOM in water sources—and hence the quantity and type of DBPs formed—vary with geographic location and season (##UREF##5##Chen and Weisel 1998##; ##REF##11317905##Rodriguez and Serodes 2001##). DBPs commonly found in drinking water include trihalomethanes (THMs) [often measured as total trihalomethanes (TTHMs)], halogenated acetic acids (HAAs), and halogenated acetonitriles (HANs), although TTHMs have most often been the focus of previous toxicologic and epidemiologic research. Research to date has focused on the four regulated THMs: chloroform, bromoform, bromodichloromethane (BDCM), and dibromochloromethane (DBCM) (##REF##10912587##Da Silva et al. 2000##). Toxicologic studies have shown THMs may result in adverse effects in laboratory animals, including hepatotoxicity, nephrotoxicity, mutagenesis, carcinogenicity, and adverse reproductive effects (##REF##12359347##DeAngelo et al. 2002##; ##REF##14969591##Geter et al. 2004##; ##UREF##8##International Life Science Institute 1995##; ##REF##9747604##Keegan et al. 1998##; ##REF##10473651##Landi et al. 1999##; ##UREF##0##Agency for Toxic Substances and Disease Registry 1993##).</p>", "<p>Maternal DBP exposure has been linked to an elevated risk for a range of reproductive outcomes, including pregnancy loss, prematurity, and low birth weight (##REF##10230830##Dodds et al. 1999##, ##REF##15127910##2004##; ##REF##10908833##Kallen and Robert 2000##; ##REF##11017894##King et al. 2000##, ##REF##15657195##2005##; ##REF##9504280##Waller et al. 1998##; ##REF##10964797##Yang et al. 2000##), as well as neural tube defects (##REF##10230830##Dodds et al. 1999##; ##REF##11404448##Dodds and King 2001##; ##REF##12899208##Hwang and Jaakkola 2003##; ##REF##10401872##Klotz and Pyrch 1999##), cardiac defects (##REF##12181108##Hwang et al. 2002##), urogenital defects (##REF##10468423##Magnus et al. 1999##), and oral cleft defects (##REF##10230830##Dodds et al. 1999##). ##TAB##0##Table 1## summarizes the most recent research on DBP exposure and birth defects (BDs). Importantly, these studies show significant effects for several adverse birth outcomes at levels of exposure to THMs that have been observed in Australian metropolitan areas, such as Perth (##REF##10230830##Dodds et al. 1999##; ##REF##11017894##King et al. 2000##).</p>", "<p>Overall, findings relating to adverse effects on reproductive health have been equivocal and are often limited by crude exposure measures or implausible retrospective estimates of exposure to DBPs. Because concentrations of NOM and other chemical precursors in source waters vary in both time and place, DBP levels also fluctuate, complicating exposure assessment in epidemiologic studies. Several authors have indicated a number of concerns with existing studies, particularly regarding their failure to account for all exposure pathways or the temporal and spatial variability in DBPs within regions of water supply (##REF##11603954##Graves et al. 2001##; ##REF##10711274##Nieuwenhuijsen et al. 2000##; ##REF##8930546##Reif et al. 1996##; ##REF##12606885##Shaw et al. 2003##). Also, in general, studies have not allowed for residential mobility, use of alternative water sources for drinking and bathing, or exposure to water sources outside the home. Dermal absorption of THMs is particularly poorly estimated despite evidence indicating that showering and other forms of immersion are significant sources of exposure (##REF##12410017##Nieuwenhuijsen et al. 2002##; ##REF##8834861##Weisel and Jo 1996##; ##REF##12392965##Xu et al. 2002##).</p>", "<p>Given the ongoing controversy surrounding DBPs and adverse pregnancy outcomes, especially regarding associated BDs, a precautionary approach to DBP exposure during pregnancy is somewhat justified. Most industrialized countries have regulated the permitted levels of TTHM in drinking water, and many of these limits are well below the 250 μg/L level currently accepted in Australia (##UREF##10##NHMRC 2004##): Canada, 80 μg/L (##REF##15556212##Rodriguez et al. 2004##); United States, 80 μg/L (##UREF##15##U.S. EPA 2004##); and the United Kingdom, 100 μg/L (##REF##11404449##Keegan et al. 2001##). By international standards, a number of Australian water supplies continue to contain elevated DBP levels. The water supplied to Perth, Western Australia, is distinctive in several respects (##UREF##6##Heitz et al. 2001##). Depending on the suburb, the residential supply may be derived from groundwater from aquifers underlying the city, surface water from catchments along the eastern edge of the city, or a mixture of both. Shallow groundwater resources in Perth contain both high levels of the dissolved fraction of NOM [dissolved organic carbon (DOC), 10–50 mg/L] and highly variable concentrations of bromide (0–2.2 mg/L), leading to a high fraction of brominated DBP formation (##UREF##6##Heitz et al. 2001##). As a percentage of NOM, the DOC in groundwater can range from 7% to 77% (##REF##12214640##Wong et al. 2002##). Historical records from the city’s water authority, the Water Corporation of Western Australia, indicate that THM levels range from &gt; 200 μg/L in some water distribution areas to &lt; 4 μg/L in others. The geographic variation in these levels provides an opportunity to examine the relationship between DBP exposure and adverse outcomes of pregnancy within a discrete geographic area. In this study we compared the prevalence of BDs in urban populations whose residential water supplies contained markedly different THM levels.</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1267\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Map of the northern metropolitan area of Perth outlining individual sampling sites. Shaded areas indicate water catchments.</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1267\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Review of principal associations and risk measures for DBP exposure and BDs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Study design, region, reference</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Exposure range</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Defect type</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Risk estimate (95% CI)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective cohort, Canada (##REF##10230830##Dodds et al. 1999##)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">High (&gt; 100 μg/L) versus low (&lt; 50 μg/L) THM levels</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Neural tube defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.96 (1.26–6.62)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Major cardiac defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.83 (0.97–3.92)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Oral cleft defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.17 (1.18–7.26)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Case–control, USA (##REF##10401872##Klotz and Pyrch 1999##)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">High (&gt; 40 μg/L) and low (&lt; 5 μg/L) THMs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Neural tube defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.10 (1.10–4.00)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">High (&gt; 3 μg/L) and low (&lt; 0.5 μg/L) HANs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Neural tube defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.30 (0.60–2.50)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">High (&gt; 35 μg/L) and low (&lt; 3 μg/L) HAAs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Neural tube defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.20 (0.50–2.60)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective cohort, Sweden (##REF##10908833##Kallen and Robert 2000##)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Chlorinated versus nonchlorinated water</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Major cardiac defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.10 (0.90–1.30)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Oral cleft defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.10 (0.80–1.60)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective cohort, Canada (##REF##11404448##Dodds and King 2001##)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">High (&gt; 20 μg/L) and low (&lt; 5 μg/L) BDCM</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Neural tube defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.50 (1.20–5.10)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Major cardiac defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.30 (0.20–0.70)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Oral cleft defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.60 (0.20–0.90)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cross-sectional, Norway (##REF##12181108##Hwang et al. 2002##)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Chlorination and color versus nonchlorinated</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">All sentinel anomalies</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.18 (1.02–1.36)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ventricular septal defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.81 (1.05–3.09)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Respiratory defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.89 (1.00–3.58)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Urinary tract defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.46 (1.00–2.13)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Case–control, Sweden (##REF##12123645##Cedergren et al. 2002##)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">High (&gt; 10 μg/L ) and low (&lt; 5 μg/L) THMs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cardiac defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.30 (1.08–1.56)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Case–control, USA (##REF##12606885##Shaw et al. 2003##)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50–74 μg/L THMs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Study 1: Neural tube defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.62 (0.26–1.50)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Study 2: Neural tube defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.80 (0.26–1.50)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Oral cleft defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00 (0.32–3.40)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Registry-based cohort, England and Wales (Niewenhuijsen et al. 2008)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">High (4 μg/L) versus low (&lt; 2 μg/L) bromoform</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Major cardiac defects</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.18 (1.00–1.39)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1267\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Demographics and TTHM concentrations for exposure areas.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" rowspan=\"1\">Area of TTHM exposure\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Characteristic</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Medium</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">High</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">No. of post codes included in exposure area</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Average maternal age range (years)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">25–29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">30–34</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">30–34</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">SEIFA<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1267\">a</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Range</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">945–1,026</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1,002–1,100</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">873–1,082</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Average</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">994.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1033.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">982.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Median</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">989.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1028.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">997.6</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Average THM concentration (μg/L)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> TTHM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">54.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">109.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">137.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Chloroform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Bromoform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> BDCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> DBCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">54.5</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Median THM concentration (μg/L)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> TTHM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">51.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">115.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">140.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Chloroform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Bromoform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> BDCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">27.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> DBCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.5</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">53.0</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Maximum individual site THM concentration (μg/L)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> TTHM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">140.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">160.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">190.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Chloroform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">52.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Bromoform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">54.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">66.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">78.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> BDCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">57.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> DBCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">28.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">72.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">81.0</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">Minimum individual site THM concentration (μg/L)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> TTHM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">36.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">39.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">61.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Chloroform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Bromoform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> BDCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.8</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> DBCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.0</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">25th percentile THM concentration (μg/L)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> TTHM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">93.25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">120.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Chloroform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.20</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Bromoform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">33.50</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">20.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> BDCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">14.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> DBCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.75</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">46.0</td></tr><tr><td colspan=\"4\" align=\"left\" rowspan=\"1\">75th percentile THM concentration (μg/L)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> TTHM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">62.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">130.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">150.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Chloroform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Bromoform</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">34.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">47.25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">53.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> BDCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> DBCM</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">53.25</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">62.0</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1267\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Number of births, BDs, and BD prevalence for TTHM exposure areas for births within the Perth metropolitan area, 2000–2004 inclusive.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"6\" align=\"center\" rowspan=\"1\">Year\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">TTHM exposure group</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Births and BDs</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">2000</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">2001</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">2002</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">2003</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">2004</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">2000–2004</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Low (&lt; 60 μg/L)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">TB (<italic>n</italic>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">518</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">556</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">572</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">639</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">659</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,944</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">BD (<italic>n</italic>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">134</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">BDP (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Medium (&gt; 60 to &lt; 130 μg/L)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">TB (<italic>n</italic>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">958</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">934</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">930</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">945</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">981</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4,748</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">BD (<italic>n</italic>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">58</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">235</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">BDP (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">High (≥ 130 μg/L)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">TB (<italic>n</italic>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,638</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,666</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,593</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,557</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,724</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13,178</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">BD (<italic>n</italic>)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">174</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">149</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">146</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">132</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">728</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" rowspan=\"1\" colspan=\"1\">BDP (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.5</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4-ehp-116-1267\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>ORs (95% CIs) for the association between TTHM exposure and any BD or individual BD, 2000–2004 inclusive.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Birth outcome<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1267\">a</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Case number<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1267\">b</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Adjusted OR (95% CI)<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1267\">c</xref></th></tr></thead><tbody><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Any BD</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">134</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Medium</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">235</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.98 (0.75–1.28)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">728</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.22 (1.01–1.48)<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1267\">*</xref></td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Cardiovascular (BPA 74500–74799)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Medium</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">55</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00 (0.55–1.81)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">181</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.62 (1.04–2.51)<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1267\">*</xref></td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Musculoskeletal (BPA 75400–75699)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Medium</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.05 (0.60–1.83)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">200</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.48 (0.99–1.21)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Gastrointestinal (BPA 74900–75199)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Medium</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.27 (0.55–2.96)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">66</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.20 (0.63–2.30)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Urogenital (BPA 75200–75399)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Medium</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.09 (0.68–1.77)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">235</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.40 (0.98–1.99)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Nervous system (BPA 74000–74299)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Medium</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.78 (0.55–5.80)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.08 (0.41–2.85)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Respiratory system (BPA 74800–74899)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Medium</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.06 (0.13–8.87)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.88 (0.18–4.18)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Integument congenital anomalies (BPA 75700–75799)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Low</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Medium</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.91 (0.36–2.33)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> High</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.95 (0.49–1.83)</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>We acknowledge P. Cosgrove from the Institute of Child Health Research and the Data Linkage Unit at the Western Australia Department of Health for their assistance with data collection. We also thank the Chemistry Centre (Western Australia), A. Jardine, and S. Joyce for their technical support. This work was performed at the School of Population Health, The University of Western Australia, Perth, Australia</p></fn><fn><p>The authors declare they have no competing financial interests.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1267\"><label>a</label><p>Low values indicate areas of disadvantage, and high values indicate areas of advantage.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn2-ehp-116-1267\"><p>Abbreviations: BDP, BD prevalence for exposure area; TB, total number of births for exposure area.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn3-ehp-116-1267\"><label>a</label><p>Low, &lt; 60 μg/L; medium, &gt; 60 to &lt; 130 μg/L; high, ≥ 130 μg/L.</p></fn><fn id=\"tfn4-ehp-116-1267\"><label>b</label><p>Total births for each exposure area: low, 2,944; medium, 4,748; high, 13,178.</p></fn><fn id=\"tfn5-ehp-116-1267\"><label>c</label><p>Adjusted for maternal age and socioeconomic status.</p></fn><fn id=\"tfn6-ehp-116-1267\"><label>*</label><p><italic>p</italic> &lt; 0.05.</p></fn></table-wrap-foot>" ]

[ "<graphic xlink:href=\"ehp-116-1267f1\"/>" ]

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[ "<title>Materials and Methods</title>", "<title>Study design and health assessment</title>", "<p>The EBCRHS study design has been described elsewhere (##REF##15184208##Kim et al. 2004##; ##UREF##4##Singer et al. 2004##). Briefly, in 2001 we recruited students in grades 3–5 from 10 neighborhood schools located at various distances from major roadways. No residences were near major stationary sources of pollution.</p>", "<p>We obtained respiratory health outcomes by parental questionnaire. Main outcomes examined were <italic>a</italic>) current asthma [physician-diagnosed asthma at some time in the past (ever asthma) plus “an episode of asthma” or “wheezing” in the preceding 12 months] and <italic>b</italic>) bronchitis symptoms in the preceding 12 months (being told by a doctor the child had bronchitis or persistent cough or phlegm production in the preceding 12 months). Additional questionnaire data included demographics, familial history of asthma, home and environmental factors, and the child’s activity patterns. Parents gave written informed consent before the study. The Committee for the Protection of Human Subjects of the California Health and Human Services Agency reviewed and approved the study protocol. We have complied with all applicable requirements of the California Health and Human Services Agency.</p>", "<p>Other sources of data for this study included <italic>a</italic>) California Department of Transportation (Sacramento, CA) annual average daily traffic (AADT) for 2001 and road classification data for all freeways, highways, and major (nonlocal) roads; <italic>b</italic>) meteorologic data for Oakland and Hayward airports (Western Region Climate Center, Reno, NV); and <italic>c</italic>) traffic pollutant measurements conducted for this project. For additional details on study design and methods, see Supplemental Material (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10735/suppl.pdf\">http://www.ehponline.org/members/2008/10735/suppl.pdf</ext-link>).</p>", "<title>Exposures to traffic pollution</title>", "<p>We geocoded residential addresses of study participants and determined residential proximity to traffic using metrics that previous studies found associated with adverse health outcomes (##REF##10464078##English et al. 1999##; ##REF##16222162##Gauderman et al. 2005##; ##REF##12743618##Gunier et al. 2003##). We conducted GIS analyses using ArcGIS 8.3 (Environmental Systems Research Institute, Redlands, CA). We calculated traffic metrics for our study participants, as described in ##TAB##0##Table 1## [see Supplemental Material (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10735/suppl.pdf\">http://www.ehponline.org/members/2008/10735/suppl.pdf</ext-link>)]. These measures used data on traffic counts on nearby roads, distances from home to road, and/or road length within a given radius of the home.</p>", "<p>To explore the influence of wind direction, we also calculated a three-level ordinal variable incorporating both residential proximity to a freeway/highway and location upwind or downwind of a freeway: <italic>a</italic>) ≤300 m of a freeway/highway and downwind; <italic>b</italic>) ≤300 m of a freeway/highway and upwind; and <italic>c</italic>) &gt; 300 m from a freeway/highway, regardless of wind direction (reference group). Freeways and highways in the study area generally run north/south, and prevailing winds are from the west. Therefore, we designated locations east of the freeways as downwind, and those west of the freeways as upwind. A few residences (<italic>n</italic> &lt; 10) located upwind of a major freeway and downwind of an intersecting smaller highway were designated as downwind.</p>", "<title>Measured traffic pollutants versus GIS-based traffic metrics</title>", "<p>NO_x and NO₂ are good indicators of nearby traffic (##UREF##2##Rodes and Holland 1981##; ##UREF##4##Singer et al. 2004##). In our earlier study (##REF##15184208##Kim et al. 2004##), we had measured NO_x, NO₂, particulate matter ≤10 μm in aerodynamic diameter (PM₁₀), PM_2.5, and black carbon (BC) at 10 school sites. In this expanded monitoring study, we measured only NO_x and NO₂ because of logistical and financial constraints. We measured outdoor concentrations of NO_x and NO₂ using Ogawa passive diffusion samplers (Ogawa &amp; Co., Inc., Pompano Beach, FL) deployed for a 1-week period at 52 locations in the study area (10 schools, 41 student residences or neighborhood locations, and 1 regional air monitor), as previously described (##UREF##4##Singer et al. 2004##). These sites were at varying distances upwind or downwind of a major freeway. We determined locations of the samplers using a global positioning system device. For each location, we determined GIS-based traffic metrics and upwind/downwind status as described above. Initial NO_x emissions in traffic exhaust are primarily in the form of nitric oxide, which subsequently reacts with ambient oxidants to form NO₂. Thus, we estimated the concentration of NO by the difference NO = NO_x − NO₂.</p>", "<p>We evaluated the relationships between NO_x, NO₂, and NO and GIS-based traffic metrics at the same locations using Spearman’s correlation coefficient. We used univariate analysis to assess the relationship between NO_x and distance to a freeway or the natural logarithm of distance to a freeway. To evaluate the influence of wind direction, we added an interaction term between downwind and natural log of distance. We tested whether median pollutant levels differed by the categories &gt; 300 m, ≤300 m downwind, and ≤300 m upwind using the Wilcoxon rank-sum test (α adjusted for Bonferroni inequality).</p>", "<title>Associations of traffic exposure with health outcomes</title>", "<p>We examined associations between each traffic measure and health outcomes using multivariate logistic regression. We identified potential confounders and effect modifiers via parental responses to questionnaires distributed through the children’s schools. For model development, we evaluated risk factors that previous studies showed to be predictors of respiratory disease, including demographic variables (e.g., race/ethnicity, parental education, household income), host factors (e.g., family history of asthma), and home environmental factors (e.g., home exposure to environmental tobacco smoke, household mold), as previously described (##REF##15184208##Kim et al. 2004##). We identified initial variables using univariate regressions, with variable retention if <italic>p</italic> ≤0.15. We used stepwise logistic regression to identify individual-level covariates that were best associated with health outcomes in multivariate models. Using stepwise backward elimination, we retained covariates that changed regression estimates of traffic metrics by &gt; 10% in the final model. We calculated adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each quintile of traffic and for the 90th percentiles based on the metric’s distribution for the study population. We explored possible dose–response relationships across quintiles by testing for trend using quintiles as categorical variables (##UREF##0##Jewell 2004##).</p>", "<p>We also calculated odds for a simpler traffic metric, distance of residence to major road, using either linear- or log-distance. For distance to major road, we evaluated risks of current asthma or bronchitis for the categories ≤75 m, &gt; 75 and ≤150 m, &gt; 150 and ≤300 m, and &gt; 300 m, based on results of previous studies demonstrating that elevated pollutant concentrations near freeways decreased to background levels by around 150–300 m downwind (##UREF##2##Rodes and Holland 1981##; ##UREF##5##Zhu et al. 2002a##, ##REF##12269664##2002b##). We looked for associations between respiratory symptoms and residential proximity to other principal arterial roads, as classified by federal standards [see Supplemental Material (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehpon-line.org/members/2008/10735/suppl.pdf\">http://www.ehpon-line.org/members/2008/10735/suppl.pdf</ext-link>)], after restricting our analysis to those who did not live within 150 m of a freeway/highway. We also evaluated traffic metrics incorporating wind direction. We conducted several sensitivity analyses related to exposure assessment, including <italic>a</italic>) evaluating distance-Gaussian-weighted traffic density (another measure of traffic density), as proposed by ##REF##10464078##English et al. (1999)## and ##REF##12573907##Wilhelm and Ritz (2003)##; <italic>b</italic>) increasing the buffer radius of traffic measures to 300 m; <italic>c</italic>) restricting the sample to those who had lived at their current residence for at least 1 year; and <italic>d</italic>) determining whether traffic pollution from both home and school were independently associated with respiratory morbidity, which we tested by including both exposure locations in the regression model, with school exposures measured using either the traffic-based metrics or the pollution measurements taken at the schools, as in ##REF##15184208##Kim et al. (2004)##. Additionally, we evaluated associations using a different definition of current asthma (told by a doctor that the child had asthma in the preceding 12 months). Finally, we conducted stratified analyses to explore whether associations between residential proximity to traffic and health outcomes differed by sex and family history of asthma.</p>", "<p>We conducted all statistical analyses using SAS, versions 8.2 and 9.1 (SAS Institute Inc., Cary, NC) or STATA version 8 (Stata Corp., College Station, TX).</p>" ]

[ "<title>Results</title>", "<title>Study population and demographics</title>", "<p>More than 70% of students who received questionnaires participated in the study (1,111 of 1,571). We were able to geocode 1,086 (98%) participant addresses. Among these participants, we excluded four because they resided in a neighboring county for which traffic data were not readily available, and two because they had cystic fibrosis. The final study population consisted of 1,080 participants.</p>", "<p>##TAB##1##Table 2## summarizes data on demographics, home environmental factors, health status, and traffic exposures. The study population was of lower economic status and more racially and ethnically diverse than the general population of California, reflecting the demographics of the study area. More than 30% of household incomes were at or below the federal poverty level. Sixteen percent of study participants lived within 100 m of a major road (principal artery, expressway, highway, or freeway); 5% lived within 100 m of a freeway/highway. This indicates that a considerable proportion of children in our study resided in close proximity to busy roads [for additional data on distribution of traffic exposures, see Supplemental Material (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10735/suppl.pdf\">http://www.ehponline.org/members/2008/10735/suppl.pdf</ext-link>)]. Our population was considerably mobile; only 30% had lived at the same address since before 2 years of age; 19% had lived at their current address for less than 1 year.</p>", "<title>Measured traffic pollutant versus GIS-based traffic metric</title>", "<p>Pollutant measurements took place in spring 2001 during one of two nonconsecutive weeks. We did not monitor all sites simultaneously because of resource constraints, but we monitored 11 sites during both weeks. These 11 sites showed no statistical difference between the pollutant concentrations. This allowed us to combine data from both weeks into a single data set.</p>", "<p>##TAB##2##Table 3## shows correlations between measured NO_x, NO₂, and NO and traffic metrics based on 52 samples. Most traffic metrics were better correlated with NO_x and NO compared with NO₂. Traffic density and maximum AADT were significantly correlated with pollutants and explained between 35% and 60% of the variability in NO_x and NO. Correlations between NO₂ levels and traffic metrics (other than distance to freeway/highway) were significant only for metrics using 300-m buffers. Plots of NO_x and NO₂ versus distance to the closest freeway/highway suggest that <italic>a</italic>) levels differ for a given distance depending on whether the location was upwind or downwind of the freeway, and <italic>b</italic>) the pollutant concentration decayed exponentially downwind (##FIG##0##Figure 1##). Consistent with the observed exponential decay, the log of distance from the freeway/ highway to a residence was a better predictor of NO_x than the linear distance in univariate regressions. An interaction term between log-distance and an indicator of wind direction was significant (<italic>p</italic> &lt; 0.001) in regression models of predictors of NO_x, NO₂, and NO.</p>", "<title>Health outcomes and their associations with residential proximity to traffic</title>", "<p>##TAB##3##Table 4## presents ORs for current asthma and bronchitis within the preceding 12 months with increasing residential traffic, within a 150-m radius, adjusted for important covariates. Overall, comparing the highest with the lowest quintiles, traffic density and maximum AADT were associated with increased ORs for current asthma. A test for trend with increasing quintiles of traffic was significant (<italic>p</italic> ≤0.05) for traffic density and current asthma. For bronchitis, we observed associations for the 90th percentile, with traffic density being statistically significant.</p>", "<p>Using distance to major roads as an exposure metric, we found associations between current asthma (or bronchitis) and log distance to highways and for those within 75 m of highways (##TAB##3##Table 4##). Associations were elevated but not significant using distance to freeway/ highway on a linear scale. Those living downwind and within 300 m of a freeway/highway were at increased risk of both outcomes; however, results were not statistically significant, possibly due to small numbers in the higher exposure categories. We could not examine wind effects at fine cut-points because of limited sample size. To explore whether other major roads were associated with respiratory problems, we restricted our analysis to those participants who did not live within 150 m of a freeway/highway (<italic>n</italic> = 867). We found no clear associations between current asthma (or bronchitis) and living within 75 m of a principal artery among this subgroup (##TAB##3##Table 4##).</p>", "<p>Our sensitivity analyses indicated that <italic>a</italic>) using a different measure of traffic density (distance-Gaussian-weighted traffic density) yielded results generally similar to those found using traffic density reported in ##TAB##3##Table 4## [see Supplemental Material, Table 3 (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10735/suppl.pdf\">http://www.ehponline.org/members/2008/10735/suppl.pdf</ext-link>)]; <italic>b</italic>) associations using traffic metrics with buffer size of 300 m generated lower point estimates and wider CIs compared with the buffer sizes of 150 m; <italic>c</italic>) after restriction of the sample to those who lived at their current residence for at least 1 year, overall point estimates remained similar but with wider CIs because of smaller sample size; and <italic>d</italic>) we were unable to discern independent effects of school traffic exposure. When we added school-based concentration of BC or NO to multivariate models containing residential-based traffic, the effect estimate for residential traffic was mildly attenuated and no longer statistically significant. School-based NO and BC had borderline significance in the models (<italic>p</italic> &lt; 0.12). Effect estimates for residential traffic were essentially unchanged with the addition of the school pollutant NO₂, PM₁₀, or PM_2.5.</p>", "<p>Our findings were robust to different questionnaire-based definitions of current asthma [see Supplemental Material, Table 4 (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10735/suppl.pdf\">http://www.ehponline.org/members/2008/10735/suppl.pdf</ext-link>)]. In our stratified analysis, we found no clear difference in associations between current asthma or bronchitis and residential proximity to traffic when stratified by sex. When stratified by history of maternal asthma, we found that associations between traffic (log distance to freeway) and current asthma were higher among children without history of maternal asthma compared with those with a maternal history of asthma. Paternal history of asthma was not a risk factor or effect modifier for current asthma.</p>" ]

[ "<title>Discussion</title>", "<p>We demonstrated associations between residential proximity to traffic-related air pollution and current asthma using several indicators of nearby traffic. Additionally, an association was suggested between bronchitis symptoms in the preceding 12 months and traffic proximity at the highest levels of exposure. The traffic metrics we used in this study correlated with measured traffic pollutants, supporting their use. The traffic metric most weakly correlated with actual pollutant measurements (closest AADT) was not associated with respiratory symptoms.</p>", "<p>This study adds to a growing body of evidence linking proximity to traffic and adverse respiratory effects. When we initiated this study, several studies, primarily in Europe, had identified associations between proximity to traffic and adverse respiratory outcomes [reviewed by ##REF##12194890##Delfino (2002)##]. However, extrapolations of the results of European studies to the United States is subject to a variety of sources of uncertainty, including differences in fleet composition (diesel vs. gasoline), emission controls, land use patterns, and population characteristics. Additionally, California has the most stringent emissions standards for motor vehicles in the United States. These differences could result in lower exposures to traffic pollutants among California residents relative to those in European cities.</p>", "<p>Our study location and design allowed us to evaluate the effects of traffic pollution in a region of California with relatively low levels of regional air pollution. This restricted study area allowed us to focus on variations in air quality related to localized traffic-related air pollution. Our air monitoring pilot study confirmed that this small area variation in air quality was attributable to local impacts of traffic. Therefore, our study implicates local traffic as an important risk factor for respiratory disease in an urban area that meets federal air quality standards for ozone and annual average PM_2.5 and has rare exceedances of the 24-hr PM_2.5 standard. Other American studies of traffic and respiratory health involving populations from Southern California, the northeastern United States, and Anchorage, Alaska, had moderate to high regional levels of ozone and/or PM_2.5 (##REF##10464078##English et al. 1999##; ##REF##14569190##Garshick et al. 2003##; ##REF##16222162##Gauderman et al. 2005##; ##REF##11908931##Lin et al. 2002##; ##REF##16675435##McConnell et al. 2006##) or volatile organics from gasoline exhaust (##REF##16007113##Gordian et al. 2006##). Thus, our study provides additional evidence that local traffic may have respiratory impacts even in an area with good regional air quality.</p>", "<p>In the present study, we sought to reduce uncertainties related to exposure assessment in several ways. In our previous work, we reported modest effects using exposures assigned at a group level (based on neighborhood school measurements of traffic pollutants). In contrast, in this analysis, we found stronger associations using residential proximity to traffic, which may be attributable to less exposure misclassification. Also, we were able to evaluate and confirm the correlation between GIS-based indicators of traffic exposure and measured levels of traffic pollutants. As noted above, few epidemiologic studies relating respiratory health risks to traffic-related pollution have used actual pollution measurements or have validated their surrogate measures of traffic (##REF##17251230##Brauer et al. 2007##; ##REF##16222162##Gauderman et al. 2005##; ##REF##12401246##Hoek et al. 2002##; ##REF##12948892##Janssen et al. 2003##; ##REF##12797488##Nicolai et al. 2003##). Finally, most traffic pollution models have not incorporated wind direction. Our study area has strong prevailing winds, and there was some suggestion that those living downwind of traffic might be at greater risk of respiratory symptoms, but results were not significant.</p>", "<p>In addition to traffic metrics that used traffic counts within a given buffer, we also evaluated two simpler metrics based on distance and log distance of residences to busy roads (e.g., major artery or freeway). Our findings that children living within 75 m of a freeway/highway were at markedly increased risk of current asthma are consistent with studies in Massachusetts and Southern California that found elevated respiratory risks primarily among those living within the first 50–75 m of a busy road (##REF##14569190##Garshick et al. 2003##; ##REF##16675435##McConnell et al. 2006##). In contrast, the same investigators in Southern California found, in a different cohort of children, that although the risk of asthma declined with increasing linear distance from a freeway, increased risks extending beyond several hundred meters (##REF##16222162##Gauderman et al. 2005##). It is unclear whether the more linear decline in risks in the latter study were attributable only to direct impacts of freeway traffic emissions or whether other covariates (e.g., other major roads, area sources, and land use differences near freeways in urban areas) played an etiologic role. Additionally, ##REF##16675435##McConnell et al. (2006)## found an increased risk of asthma among those living near other major roads, whereas our results were less clear. The traffic volumes on some freeways and major roads in Southern California can be as much as double those experienced in the San Francisco Bay Area, which may explain the null findings in the current study.</p>", "<p>Our study and several others have found that risks of either current or ever asthma are associated with proximity to traffic, and were elevated primarily among children with no reported family history of asthma (##REF##16007113##Gordian et al. 2006##; ##REF##16675435##McConnell et al. 2006##) or maternal history of asthma (present study). Paternal history of asthma was not a risk factor or effect modifier for asthma in our study but may have been underreported by the parent respondent (6.6% reported a paternal history of asthma vs. 12.3% maternal history). The implication that children with no family history of asthma may be at increased risk for development or persistence of asthma from traffic-related pollutants deserves further investigation.</p>", "<p>The cross-sectional nature of our study design is an important limitation of our study. Additional limitations include the relatively small sample size, the use of surrogates of exposure, and potential unmeasured confounders. In our previous study where we used school-based measurements, we found that modest effect estimates were slightly increased, and results became significant only after restricting analyses to those living at their current address as long-term residents, whereas in this analysis effect estimates remained similar. It is likely that the measurement error was greater when we used school-based measures, so restricting our analyses to long-term residents may have resulted in significant improvements in the estimates. In contrast, the residential metrics appear to have less measurement error, so less improvement in the estimates was gained by restricting analyses to long-term residents in this study.</p>", "<p>Race/ethnicity and other socioeconomic status (SES) covariates were only weakly associated with current asthma in our study (crowding was a covariate in our final models), which may be attributable partly to our study design (i.e., we selected the schools to have relatively similar measures of SES profiles). Nonetheless, our results are consistent with several recent longitudinal studies in Europe and Southern California that have found associations between residential traffic and asthma incidence (##REF##16222162##Gauderman et al. 2005##; ##REF##16675435##McConnell et al. 2006##).</p>", "<p>Regarding exposure, we used measures of residential proximity to traffic as proxies for exposures to traffic-related pollution. Recent studies have found good correlations between personal exposures to traffic pollutants and residential proximity to traffic (##UREF##1##Nethery et al. 2007##; ##REF##16650461##van Roosbroeck et al. 2006##). In our study area, traffic pollution is likely to readily penetrate indoors, because this region experiences mild climate conditions, and the generally older housing stock tends not to have air conditioning or the degree of thermal insulation found in colder climates. However, the traffic metrics used in this study are surrogates for a complex mixture of traffic pollutants composed of reactive gases and PM, not just NO_x. Many constituents of traffic exhaust may contribute to toxicity. For instance, human exposure studies have found that both PM_2.5 in diesel exhaust and NO₂ can enhance allergic responses (##REF##12418589##Barck et al. 2002##; ##REF##15696072##Riedl and Diaz-Sanchez 2005##). Most epidemiologic investigations of traffic emissions, including ours, have not been designed to disentangle the relative contributions of diesel versus gasoline combustion. However, to the extent that our findings were strongly influenced by proximity to freeways, this suggests that something specific to freeway traffic (e.g., higher percentages of diesel trucks as well as high traffic volume) may be important.</p>", "<p>It is interesting to note that in our study, NO₂, a secondary product of traffic emissions, had stronger correlations with 300-m metrics than with 150-m metrics. However, traffic metrics at 300 m (traffic density within 300 m and maximum AADT within 300 m) had weaker associations with current asthma compared with the corresponding metric at 150 m. This may be purely a dose-related phenomenon, reflecting the exponential decay of pollutant concentrations with distance from freeways, or may suggest that “fresh” primary traffic emissions, such as ultrafine PM_0.1, may be important determinants of the observed associations with current asthma. Although we did not design this study to look separately at the contribution of traffic at school versus home, nor was the sample size sufficient to do so, we saw some mild attenuation of residential traffic when we added study-averaged concentrations of BC or NO (but not NO₂) to multivariate models, again suggesting that “fresh” primary emissions may be important constituents.</p>", "<p>Our results contribute to a growing body of evidence linking residential proximity to traffic with the prevalence of respiratory symptoms and asthma in children. These findings are observed across diverse populations worldwide, despite differences in demographics, lifestyle, transportation patterns, and levels of regional air pollution. Although the identities of the constituents of traffic pollution most strongly linked with health impacts have yet to be elucidated, traffic emissions clearly have an adverse impact on both local and regional air quality and respiratory health. Reducing exposures to traffic pollution will provide a healthier environment for children where they live, play, and learn.</p>" ]

[]

[ "<p>Current address: ICON Clinical Research, San Francisco, CA, USA.</p>", "<p>A.H., a research assistant with Impact Assessment, Inc. at the time of this project, is currently employed by Chevron Energy Technology Corporation. The other authors declare they have no competing financial interests.</p>", "<title>Background</title>", "<p>Living near traffic has been associated with asthma and other respiratory symptoms. Most studies, however, have been conducted in areas with high background levels of ambient air pollution, making it challenging to isolate an independent effect of traffic. Additionally, most investigations have used surrogates of exposure, and few have measured traffic pollutants directly as part of the study.</p>", "<title>Objective</title>", "<p>We conducted a cross-sectional study of current asthma and other respiratory symptoms in children (<italic>n</italic> = 1,080) living at varying distances from high-traffic roads in the San Francisco Bay Area, California, a highly urbanized region characterized by good regional air quality due to coastal breezes.</p>", "<title>Methods</title>", "<p>We obtained health information and home environmental factors by parental questionnaire. We assessed exposure with several measures of residential proximity to traffic calculated using geographic information systems, including traffic within a given radius and distance to major roads. We also measured traffic-related pollutants (nitrogen oxides and nitrogen dioxide) for a subset of households to determine how well traffic metrics correlated with measured traffic pollutants.</p>", "<title>Results</title>", "<p>Using multivariate logistic regression analyses, we found associations between current asthma and residential proximity to traffic. For several traffic metrics, children whose residences were in the highest quintile of exposure had approximately twice the adjusted odds of current asthma (i.e., asthma episode in the preceeding 12 months) compared with children whose residences were within the lowest quintile. The highest risks were among those living within 75 m of a freeway/highway. Most traffic metrics correlated moderately well with actual pollutant measurements.</p>", "<title>Conclusion</title>", "<p>Our findings provide evidence that even in an area with good regional air quality, proximity to traffic is associated with adverse respiratory health effects in children.</p>" ]

[ "<p>Epidemiologic studies have linked proximity to busy roads with adverse health outcomes, including respiratory symptoms, asthma, adverse birth outcomes, and cardiopulmonary mortality (##REF##9115026##Brunekreef et al. 1997##; ##REF##12401246##Hoek et al. 2002##; ##REF##12573907##Wilhelm and Ritz 2003##). Methods for estimating exposures to traffic pollutants have included neighborhood- or school-based estimates of traffic (##REF##9115026##Brunekreef et al. 1997##; ##REF##15184208##Kim et al. 2004##), distance to freeways or busy roads (##REF##16222162##Gauderman et al. 2005##), presence of a busy road within a given buffer (##REF##11751183##Venn et al. 2001##), and traffic density within a given radius (##REF##10464078##English et al. 1999##; ##REF##12573907##Wilhelm and Ritz 2003##). More recent studies have used geographic information systems (GIS) to estimate traffic exposure metrics. However, few have evaluated these GIS-based traffic metrics against measured traffic-related pollutants (##REF##17251230##Brauer et al. 2007##; ##REF##16222162##Gauderman et al. 2005##; ##REF##12401246##Hoek et al. 2002##; ##REF##12797488##Nicolai et al. 2003##). Additionally, many of these studies were conducted in areas with moderate or high levels of regional air pollution.</p>", "<p>We conducted the East Bay Children’s Respiratory Health Study (EBCRHS) in the San Francisco Bay Area, California, a highly urbanized region of the United States where traffic is the major source of air pollution. This region ranks among the top four metropolitan areas with the worst traffic congestion in the United States (##UREF##3##Schrank and Lomax 2005##). However, the area experiences relatively good regional air quality due to onshore breezes. Thus, in contrast to most major metropolitan areas in the United States, there are only occasional exceedances of the federal ozone or fine particulate matter [particles ≤2.5 μm in diameter (PM_2.5)] 24-hr standard. This allowed us to examine the impacts of local variations in traffic in the absence of significant levels of background ambient pollution.</p>", "<p>In the first phase of this study, we found modest but statistically significant associations between measured traffic pollutants and recent episodes of asthma and bronchitis. In that analysis, we measured traffic-related pollutants at schools as indicators of neighborhood air pollution levels, which we used to estimate children’s exposure to traffic emissions (##REF##15184208##Kim et al. 2004##).</p>", "<p>In this analysis, we sought to refine exposure estimates using GIS-derived traffic measures at the children’s residences and to evaluate associations between residential proximity to traffic and respiratory health outcomes for the study population. We then evaluated whether traffic pollutants measured at the schools were independently associated with the health outcomes. We also evaluated the correlation of GIS-derived traffic proximity metrics and vehicular emissions for a subset of households using measurements of traffic-related pollutants (total nitrogen oxides and nitrogen dioxide).</p>" ]

[]

[ "<fig id=\"f1-ehp-116-1274\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Concentrations of NO_x (<italic>A</italic>) and NO₂ (<italic>B</italic>) as a function of distance to freeway/highway. Data are for week 1.</p></caption></fig>" ]

[ "<table-wrap id=\"t1-ehp-116-1274\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Traffic metrics used in exposure assessment.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Traffic metric<xref ref-type=\"table-fn\" rid=\"tfn1-ehp-116-1274\">a</xref></th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Description</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Reference</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Maximum AADT within 150 m</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Highest traffic count of any road within a 150-m radius</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##10464078##English et al. 1999##</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Closest AADT within 150 m</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Traffic count of the closest nonlocal road within a 150-m radius</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##10464078##English et al. 1999##</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Traffic density</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Vehicle miles traveled (VMT) within a 150-m radius of the residence: VMT = sum of [(bidirectional AADT) × (length of respective road segments)].</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##12743618##Gunier et al. 2003##</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Distance to major road</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Different definitions of “major road” evaluated based on federal highway designations (e.g., interstates, highways, major arteries); we used natural logarithm of distance in some analyses</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">##REF##16222162##Gauderman et al. 2005##</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2-ehp-116-1274\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Demographics, home characteristics, health status, and residential traffic exposures of study participants (<italic>n</italic> = 1,080).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Characteristic</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Value</th></tr></thead><tbody><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Sex (%)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Percent female</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">52.3</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Race/ethnicity (%)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> White</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Black</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Hispanic</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">43.3</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Asian</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">13.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Other/multiracial</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.2</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Indicators of SES</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Household at/below federal poverty level (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">31.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Parent’s education, high school or less (%)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">29.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Crowding [no. people/bedroom (median)]</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Family history (%)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Mother with asthma</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Maternal smoking during pregnancy</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">10.4</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Home indoor environment (%)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Smoker in the household, current</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">7.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> With furry pet in the house</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">37.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> With pests, preceding 12 months</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">63.1</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> With gas stove</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">63.2</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> With indicator of mold/mildew, preceding 12 months</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">44.8</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Health characteristics (%)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Ever asthma</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">19.7</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Current asthma</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.5</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Bronchitis in the preceding 12 months</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">12.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Hay fever or allergic rhinitis</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">11.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Chest illness before 2 years of age</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">23.5</td></tr><tr><td colspan=\"2\" align=\"left\" rowspan=\"1\">Residential proximity to traffic [median (range)]</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Maximum AADT within 150 m<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1274\">a</xref> (vehicles/day)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">9,500 (0–245,000)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Closest AADT within 150 m<xref ref-type=\"table-fn\" rid=\"tfn2-ehp-116-1274\">a</xref> (vehicles/day)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">8,190 (0–245,000)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Traffic density within 150 m (vehicle-km traveled)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2,884 (0–74,042)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Distance to freeway/highway (m)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">791 (22–3,671)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Distance to major road (principal artery, expressway, highway, or freeway) (m)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">246 (7–996)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Percent living within 100 m of major road (principal artery or higher)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">16.0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Percent living within 100 m of freeway/highway</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3-ehp-116-1274\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Spearman correlation (ρ) between GIS-based traffic metrics and traffic pollutants.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">NO₂\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">NO_x\n<hr/></th><th colspan=\"2\" align=\"center\" rowspan=\"1\">NO\n<hr/></th></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Traffic metric</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ρ</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ρ</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">ρ</th><th align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Within 150 m</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Maximum AADT</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.14</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.325</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.43</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Closest AADT</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.957</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.22</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.118</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.26</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.058</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Traffic density</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.14</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.333</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.36</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.008</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.41</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Distance to freeway/highway<xref ref-type=\"table-fn\" rid=\"tfn3-ehp-116-1274\">a</xref></td><td align=\"right\" rowspan=\"1\" colspan=\"1\">−0.30</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">−0.48</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">−0.69</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Within 300 m</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Maximum AADT</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.38</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.56</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.60</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Closest AADT</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.14</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.324</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.29</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.034</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.22</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.117</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> Traffic density</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.58</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">0.62</td><td align=\"right\" rowspan=\"1\" colspan=\"1\">&lt; 0.001</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4-ehp-116-1274\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>Associations between metrics of residential proximity to traffic and current asthma and bronchitis in the preceding 12 months.<xref ref-type=\"table-fn\" rid=\"tfn4-ehp-116-1274\">a</xref></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"center\" rowspan=\"1\">OR (95% CI)\n<hr/></th></tr><tr><th align=\"center\" rowspan=\"1\" colspan=\"1\">Traffic metric</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Current asthma (<italic>n</italic> = 88/724)</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Bronchitis (<italic>n</italic> = 87/745)</th></tr></thead><tbody><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Maximum AADT within 150 m (vehicles/day)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 1st quintile (local traffic only )</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 2nd quintile (up to 7,120)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.50 (0.67–3.36)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.93 (0.46–1.87)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 3rd quintile (7,121–18,900)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.33 (1.03–5.28)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.02 (0.49–2.12)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 4th quintile (18,901–28,657)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.60 (0.21–1.69)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.46 (0.19–1.12)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 5th quintile ( 28,658–245,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.50 (1.13–5.53)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.42 (0.71–2.81)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥90th percentile (67,000–245,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.40 (1.13–5.07)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.96 (0.97–3.95)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Closest AADT within 150 m (vehicles/day)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 1st quintile (local traffic only)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 2nd quintile (up to 5,700)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.39 (0.62–3.11)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.77 (0.38–1.57)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 3rd quintile (5,701–10,534)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.83 (1.23–6.54)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.40 (0.67–2.91)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 4th quintile (10,535–23,800)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.40 (0.60–3.29)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.90 (0.43–1.86)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 5th quintile (23,801–245,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.58 (0.69–3.65)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.90 (0.42–1.9)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥90th percentile (35,100–245,000)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.16 (0.53–2.54)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.11 (0.52–2.33)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Traffic density within 150 m<xref ref-type=\"table-fn\" rid=\"tfn5-ehp-116-1274\">b</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 1st quintile</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 2nd quintile</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.23 (0.53–2.83)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.58 (0.27–1.25)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 3rd quintile</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.96 (0.85–4.52)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.47 (0.73–2.95)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 4th quintile</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.40 (0.60–3.3)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">0.78 (0.36–1.67)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> 5th quintile</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.37 (1.05–5.36)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.16 (0.57–2.36)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≥90th percentile</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.14 (1.02–4.52)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.12 (1.09–4.10)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Log distance to freeway/highway<xref ref-type=\"table-fn\" rid=\"tfn6-ehp-116-1274\">c</xref></td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.43 (1.04–1.54)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.47 (1.11–1.96)</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Distance to freeway/highway</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≤75 m</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">3.80 (1.20–11.71)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.81 (0.94–8.39)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &gt; 75 to ≤ 150 m</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.87 (0.71–4.90)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.82 (0.75–4.40)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &gt; 150 to ≤ 300 m</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.25 (0.50–3.11)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">2.00 (0.93–4.29)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &gt; 300 m</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Distance to freeway/highway and wind orientation</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≤ 300 m, downwind</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.41 (0.81–2.46)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.42 (0.87–2.33)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≤ 300 m, upwind</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.05 (0.58–1.91)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.13 (0.66–1.95)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">&gt; 300 m</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td colspan=\"3\" align=\"left\" rowspan=\"1\">Distance to principal artery (excluding those near freeway/highway)<xref ref-type=\"table-fn\" rid=\"tfn7-ehp-116-1274\">d</xref></td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> ≤ 75 m</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.36 (0.51–3.62)</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.49 (0.61–3.67)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"> &gt; 300 m</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p>Supplemental Material is available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10735/suppl.pdf\">http://www.ehponline.org/members/2008/10735/suppl.pdf</ext-link></p></fn><fn><p>We thank B. Singer, A.T. Hodgson, and T. Hotchi, Lawrence Berkeley National Laboratories, for work on the air monitoring study; J. Hayes and D. Eisenhower, Survey Research Center, University of California, Berkeley, for coordinating the survey; C. Wolff, California Department of Public Health, for providing the traffic data layer; and staff of the Air Resources Board for reviewing earlier drafts. We also thank the school districts, principals, teachers, and all study participants and their families for their time and commitment to this project.</p></fn><fn><p>This study was supported in part by the California Air Resources Board (contract 03-327), the U.S. Environmental Protection Agency, Region IX (CH-97942501-2), and the Centers for Disease Control and Prevention (under cooperative agreement U50/CCU922449 with the California Department of Health Services).</p></fn><fn><p>The contents and opinions expressed in this article are solely those of the authors and do not represent the official policy or position of the Office of Environmental Health Hazard Assessment, the California Environmental Protection Agency, or the California Department of Public Health.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"tfn1-ehp-116-1274\"><label>a</label><p>We assigned local roads a value of zero. We also evaluated traffic metrics using a buffer radius of 300 m in the sensitivity analysis.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn2-ehp-116-1274\"><label>a</label><p>Local roads were assigned a value of zero.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn3-ehp-116-1274\"><label>a</label><p>Spearman correlations are same for natural-log distance to freeway.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tfn4-ehp-116-1274\"><label>a</label><p>ORs adjusted for crowding, pests, indicators of mold presence, and chest illness before 2 years of age. For asthma, we also adjusted models for maternal history of asthma.</p></fn><fn id=\"tfn5-ehp-116-1274\"><label>b</label><p>See Supplemental Material (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ehponline.org/members/2008/10735/suppl.pdf\">http://www.ehponline.org/members/2008/10735/suppl.pdf</ext-link>) for quintile ranges.</p></fn><fn id=\"tfn6-ehp-116-1274\"><label>c</label><p>For distance to freeway (and log distance), ORs are for the interquartile ranges, that is, the difference between the 25th and 75th percentiles of residential distance from the freeway: 75th percentile (1,352 m) – 25th percentile (413 m).</p></fn><fn id=\"tfn7-ehp-116-1274\"><label>d</label><p>Includes only those participants living &gt; 150 m of a freeway/highway (<italic>n</italic> = 980; median traffic counts on principal arteries were ~ 28,500 vehicles/day).</p></fn></table-wrap-foot>" ]

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[ "<p>Frank E. Speizer is the E.H. Kass Professor in Medicine at Harvard Medical School and professor of Environmental Science at the Harvard School of Public Health. He has conducted research in air pollution health effects for more than 40 years and was one of the principal investigators in the Harvard Six Cities Study.</p>", "<p>Aaron Cohen, a principal scientist, and Sumi Mehta, a senior scientist, both at the Health Effects Institute, manage an international program of epidemiologic research on the health effects of air pollution. They also are involved in scientific program development.</p>", "<p>The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Health Effects Institute (HEI) or its sponsors; however, F.E.S is chairman of the HEI International Scientific Oversight Committee that was responsible for guiding the conduct and providing input to the investigators as needed.</p>", "<p>The authors declare they have no competing financial interests.</p>" ]

[ "<p>Asia is currently experiencing rapid increases in industrialization, urbanization, and vehicularization. As a result, emission trends (e.g., energy, fuel, vehicle use), population trends (e.g., degree of urbanization, urban population growth, city size), health trends (e.g., age structure, background disease rates), and other important factors (e.g., broad changes in regulatory approaches, improvements in control technology) will influence the extent to which exposure to air pollution affects the health of the Asian population over the next several decades. Because the effects on air quality of recent, rapid development are clearly apparent in many of Asia’s cities and industrial areas, government decision makers, the private sector, and other local stakeholders are increasingly raising concerns about the health impacts of urban air pollution. Major Asian cities, such as Shanghai (China), Delhi (India), Ho Chi Minh City (Vietnam), and Manila (Philippines), now experience annual average levels of respirable particles [particulate matter ≤ 10 μm in aerodynamic diameter (PM₁₀)] in excess of the World Health Organization’s (WHO) newly revised world air quality guideline of 50 μg/m³ (##UREF##10##WHO 2006##).</p>", "<p>The health impacts in the region are already estimated to be substantial. The ##UREF##9##WHO (2002)## estimated that urban air pollution contributed to approximately 800,000 deaths and 6.4 million lost life-years worldwide in 2000, with two-thirds of these losses occurring in rapidly urbanizing countries of Asia. These estimates were made using the results of U.S. studies of long-term exposure to air pollution because such studies have not yet been conducted in the developing countries of Asia, where health, health care, exposure to pollution, and socioeconomic circumstances still differ markedly from the United States. This contributes considerable uncertainty to these and other recent estimates of health impacts of air pollution (##UREF##1##Cohen et al. 2004##).</p>", "<p>High-quality, credible science from locally relevant studies is essential to address the substantial air pollution challenges in Asia. Such studies will be critical in helping decision makers decide which policies are most likely to result in public health benefits. Although the number of published studies on the health effects of air pollution in Asia has grown nearly exponentially over the past quarter century, with &gt; 400 reports in the peer-reviewed literature [##UREF##5##Health Effects Institute (HEI) 2008##], few coordinated, multicity time-series studies have been conducted comparable to the robust and consistent results in the United States and Europe (##REF##11505171##Katsouyanni et al. 2001##; ##UREF##8##Samet et al. 2000##). The Public Health and Air Pollution in Asia (PAPA) studies in Hong Kong, Shanghai, and Wuhan, China, and Bangkok, Thailand, published in this issue of <italic>Environmental Health Perspectives</italic> (##REF##18795161##Kan et al. 2008##; ##REF##18795159##Qian et al. 2008##; ##REF##18795160##Vichit-Vadakan et al. 2008##; ##REF##18795162##Wong et al. 2008a##, ##REF##18795163##2008b##), comprise the first coordinated multicity analyses of air pollution and daily mortality in Asia. These studies, designed and conducted by local investigators in concert with local air pollution and public health officials and international experts, explored key aspects of the epidemiology of exposure to air pollution in each location, providing additional insight about how factors such as weather (particularly high temperatures) and social class might modify the air pollution relative risk. Although clearly relevant to contemporary Asian conditions, these results also have global relevance.</p>", "<p>The studies were conducted using the same types of mortality and air pollution data used in time-series studies throughout the world, and with methodologic rigor that matches or exceeds that of most published studies, including formal quality control in the form of detailed standard operating procedures for data collection and analysis, and external quality assurance audits of the data overseen by the funding organization. These studies also benefited from recent efforts to strengthen and refine methods for the analysis of time-series data; as a result they are on a par methodologically with the most recent U.S. and European analyses (##UREF##3##HEI 2003##).</p>", "<p>These five studies provide a relatively consistent, if limited, picture of the acute mortality impact of current ambient particulate air pollution in several large metropolitan areas in East and Southeast Asia. ##REF##18795163##Wong et al. (2008b)## report that a 10-μg/m³ increase in PM₁₀ level was associated with a 0.6% (95% confidence interval, 0.3–0.9) increase in daily rates of all natural-cause mortality, estimates comparable to or greater than those reported in U.S. and European multi-city studies. Interestingly, these proportional increases in mortality are seen at levels of exposure several times higher than those in most large Western cities (mean levels, 51.6–141.8 μg/m³), and in each city except Shanghai, the pattern of the exposure–response functions appear linear over a fairly large range of ambient concentrations up to and sometimes &gt; 100 μg/m³.</p>", "<p>Although only four cities were studied, these results may begin to allay concerns regarding the generalizability of the results of the substantial, but largely Western, literature on the effects of short-term exposure to air pollution. The results, which are broadly consistent with previous research (##UREF##4##HEI 2004##), suggest that neither genetic factors nor longer-term exposure to highly polluted air substantially modify the effect of short-term exposure on daily mortality rates in major cities in developing Asia. This provides support for the notion, implicit in the approach taken in the WHO’s world air quality guidelines (##UREF##7##Krzyzanowski and Cohen 2008##), that incremental improvements in air quality would be expected to improve health, even in areas with relatively high ambient concentrations.</p>", "<p>Health impacts in cities in developing countries of Asia result from exposures to a mixture of pollutants, particles, and gases, which are derived in large measure from combustion sources (Harrison 2006; ##REF##18795163##Wong et al. 2008b##). This is, of course, no different from in Europe and North America, but the specific sources and their proportional contributions are different, with open burning of biomass and solid waste materials, combustion of lower-quality fuels including coal, and two- and three-wheeled vehicles contributing a larger share in Asia. Time–activity patterns, building characteristics, and proximity of susceptible populations to pollution sources also differ in ways that may affect human exposure and health effects (Janssen and Mehta 2006). Our current knowledge of these issues is rudimentary, and additional research is clearly needed to inform effective and sustainable control strategies. From past experience in the West and current evidence in Asia, substantial increases in the combustion of fossil fuels for power generation and transportation in developing Asia will have important consequences for human health and environmental quality in Asia and beyond. Effective approaches to pollution control and reduction do exist, and investment in these approaches need not necessarily impede economic growth. Therefore, developing countries of Asia may be able to avoid increased environmental degradation and associated health impacts while reducing poverty and providing economic security for their populations (##UREF##0##Center for Science and the Environment 2006##).</p>", "<p>Thirty million people currently live in the four cities studied, so even the small proportional increases in daily mortality rates imply large numbers of excess deaths. That said, air pollution is but one of many factors that affect the health of people in developing Asia, and, unfortunately, not even the most important one (##REF##12423980##Ezzati et al. 2002##). Nonetheless, the substantial health impacts of exposure to air pollution should be of concern to public health policy makers faced with difficult decisions in transportation and energy policy. Given current predictions of even more accelerated urbanization in the regions, there will be an increasing need for more extensive monitoring of urban air quality designed to support health effects studies and impact assessments, and a corresponding need for more highly trained professionals in air quality monitoring, exposure assessment, and environmental epidemiology.</p>", "<p>Strategic planning for future research is also needed. Although our ability to draw firm conclusions from results in four cities is limited, the methods of ##REF##18795163##Wong et al. (2008b)## can be replicated in additional cities across the regions. In some cases, nonmortality outcomes, such as hospital admissions, may also be addressed, enabling policy makers to better quantify the health impacts of air pollution. However, while time-series studies such as the PAPA studies will continue to be important potential drivers of environmental and public policy, additional study designs, such as cohort studies—similar to the U.S. American Cancer Society (##REF##11879110##Pope et al. 2002##) and Six Cities (##REF##16424447##Laden et al. 2006##) studies—are needed in Asian populations to estimate effects of long-term exposure on annual average mortality and life expectancy, the metrics that may be the most meaningful and policy relevant to decision makers. These kinds of studies will require the building of multidisciplinary teams of investigators, with adequate long-term commitment of resources to work in collaboration with governmental officials, their industrial counterparts, and local stakeholders. The PAPA program is one model of how such resources can be brought together to support such an effort.</p>" ]

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[ "<fig id=\"f1-ehp-116-a370\" orientation=\"portrait\" position=\"float\"><caption><p>Frank E. Speizer</p></caption></fig>", "<fig id=\"f2-ehp-116-a370\" orientation=\"portrait\" position=\"float\"><caption><p>Aaron Cohen</p></caption></fig>", "<fig id=\"f3-ehp-116-a370\" orientation=\"portrait\" position=\"float\"><caption><p>Sumi Mehta</p></caption></fig>" ]

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[ "<graphic xlink:href=\"ehp-116-a370f1\"/>", "<graphic xlink:href=\"ehp-116-a370f2\"/>", "<graphic xlink:href=\"ehp-116-a370f3\"/>" ]

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[ "<p>On 12 May 2008, Paul Kotin, MD, pioneer environmental health physician-scientist and NIEHS director from 1966 to 1971, passed away in Laguna Beach, California.</p>", "<p>A pathologist by training, Kotin received his MD in 1939 from the University of Illinois, and later served in the U.S. Army Medical Corps during World War II, followed by private practice and appointments at Los Angeles County Hospital and the University of Southern California. He joined the National Cancer Institute (NCI) in 1962, where he became director of the Division of Cancer Etiology at the NCI. In November 1966, the NIEHS—originally named the National Institutes of Health (NIH) Division of Environmental Health Sciences—was officially established, with Kotin as head. Under his direction, the NIEHS achieved institute status in 1969 as the ninth institute in the NIH, with Kotin as the first director and a budget of $17.8 million.</p>", "<p>Sandy Lange, a staff assistant during Kotin’s tenure at NIEHS, as well as a former director of both the Office of Communications and Public Liaison and the National Toxicology Program Office of Liaison and Scientific Review, recalls how Kotin’s vision of the institute’s mission laid the groundwork for its growth into a definitive source of public health information: “Times for the NIEHS when Paul Kotin started as the first director . . . were challenging and exciting. He was totally committed and dedicated to educating and translating the mission and role of the new division to the scientific community at large, including the other NIH institutes and other government agencies, Congress, industry, labor, and the public interest groups. He laid the foundation for university-based centers of excellence and training programs when there were no environmental health programs within the universities. He and Hans Falk [his Scientific Director] laid the scientific foundation for the division. He built partnerships and did battle as was necessary to protect the role and location of this new program as a centerpiece within the federal government. During the years following his tenure at NIEHS, we spoke relatively often, and he followed the institute’s growth with interest. He had great respect for the institute’s accomplishments, its leadership, and its contributions—he often said to me that it takes one type of leadership to start a program and another to build it. He praised the work of the leaders and the scientists who followed. He was that kind of man; and it was truly an honor to work with him. NIEHS owes much to his early work.”</p>", "<p>In his later career, Kotin served as dean of the School of Medicine, vice president for Health Sciences, and provost at Temple University in Philadelphia (1971) and senior vice president for Health, Safety and Environment for the Johns-Manville Corporation in Denver (1974). After retiring in 1981, he remained active, serving on a National Academy of Sciences oversight committee for the Department of Energy’s management of the U.S. nuclear stockpile from 1988 to 1990.</p>", "<p>Kotin was widely regarded as an international expert on environmentally caused lung diseases, especially those caused by toxic substances such as asbestos and beryllium, and received a number of awards for his contributions to public health, including the U.S. Department of Health, Education and Welfare Superior Service Award and Distinguished Service Award and the Knudsen Award from the American Occupational Medicine Association.</p>", "<p>He is survived by his wife Pauline Kotin, two sons, four grandchildren, and two great-grandchildren.</p>" ]

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[ "<p>The author declares he has no competing financial interests.</p>" ]

[ "<p>##REF##18414605##Cohn et al. (2008)## suggested that birth cohort trends in breast cancer rates for women under 50 years of age are consistent with declining use of DDT (dichloro-diphenyltrichloroethane) after 1959. They cited ##REF##17301717##Weiss (2007)## in claiming that increased detection and treatment of <italic>in situ</italic> breast cancer must be considered when interpreting recent trends in breast cancer mortality rates in young women. The remarks of ##REF##17301717##Weiss (2007)## relate to women 40–49 years of age, and earlier detection and improved treatment of breast cancer has had a marked impact on breast cancer mortality rates in these women since 1990 (##REF##16251534##Berry et al. 2005##; ##REF##8901855##Chu et al. 1996##). The birth cohort trends relevant to examining the possible impact of childhood DDT exposure on U.S. breast cancer rates, however, were firmly established well before 1990 in women &lt; 40 years of age (##REF##17301717##Tarone 2007##).</p>", "<p>##REF##17938728##Cohn et al. (2007)## reported a large increase in breast cancer risk estimates for <italic>p,p′</italic>-DDT [1,1,1-trichloro-2,2-bis(<italic>p</italic>-chlorophenyl)ethane] exposure with successive birth cohorts after 1930. Their reported odds ratio estimates by period of birth for the highest tertile of <italic>p,p′</italic>-DDT exposure were 0.6 for women born in 1931 or earlier (i.e., ≥ 14 years of age in 1945), 3.9 for women born in 1932–1937 (i.e., 8–13 years of age in 1945), 9.6 for women born in 1938–1941 (i.e., 4–7 years of age in 1945), and 11.5 for women born in 1942 or later (i.e., &lt; 4 years of age in 1945) [Table 4, ##REF##17938728##Cohn et al. (2007)##]. In contrast, I have found no evidence of increasing breast cancer rates among young U.S. women born between 1930 and 1945 (##REF##17301717##Tarone 2007##). I quantified trends in breast cancer mortality rates for U.S. white women 20–39 years of age (by 5-year age group) born during 1930–1945 using linear regression analyses with the logarithm of the age-specific rate as the dependent variable and year of birth as the independent variable (with two-sided <italic>p</italic>-values) [##UREF##0##Surveillance, Epidemiology, and End Results (SEER) 2006##; ##REF##17301717##Tarone 2007##]. The slope estimates did not differ significantly from zero for women in the three youngest age groups (<italic>p</italic> &gt; 0.25), and there was a marginally significant decrease in rates for women 35–39 years of age (<italic>p</italic> = 0.04). Thus the trends in breast cancer mortality rates among women born in 1930–1945 are not consistent with the sharply increasing trend in odds ratios for childhood DDT exposure by birth period reported by ##REF##17938728##Cohn et al. (2007)##. The most recent mortality rate contributing to the reported regression analyses (corresponding to women in the 35- to 39-year age group born in 1945) was for 1983, well before improvements in detection and treatment would have had any impact on breast cancer mortality rates.</p>", "<p>Women born after 1945 were exposed to DDT for each of the first 13 years of life (and all years thereafter). In addition, DDT exposure increased from 1945 through 1959, when DDT use peaked (with dietary exposure peaking in 1965) (##REF##16172236##Wolff et al. 2005##). If DDT exposure early in life markedly increases breast cancer risk, then some evidence of the increasing DDT use after 1945 might be expected in breast cancer mortality rate trends for young women born from 1946 through 1959 (##REF##17301717##Tarone 2007##). Breast cancer mortality rates decreased significantly among women 20–24 years of age (<italic>p</italic> = 0.009) and 25–29 years of age (<italic>p</italic> = 0.0002) born between 1946 and 1959 (##UREF##0##SEER 2006##; ##REF##17301717##Tarone 2007##). The most recent rate contributing to these regression analyses was for 1987 (corresponding to women in the 25-to 29-year age group born in 1959). Breast cancer mortality rates decreased even more markedly (<italic>p</italic> &lt; 0.0001) for women in the 30- to 34-year and 35- to 39-year age groups born from 1946 through 1959; some of the recent rates in these latter age groups were almost certainly affected by improved breast cancer detection and treatment, although decreasing trends were apparent in both age groups for rates well before 1990 (##REF##17301717##Tarone 2007##). Thus, U.S. breast cancer mortality rates in women between the ages of 20 and 39 who were born between 1930 and 1959 show no evidence of an increase in breast cancer risk associated with their marked increase in DDT exposure during childhood.</p>", "<p>The observed birth cohort trends in breast cancer rates do not refute a possible association between childhood DDT exposure and breast cancer risk, and contrary to the implication of ##REF##18414605##Cohn et al. (2008)##, no such claim was made in my earlier letter (##REF##18414606##Tarone 2008##). The regression analyses reported above suffer the weaknesses of all ecologic analyses, and in fact, the decreasing birth cohort risk of breast cancer in baby boomers has been observed in spite of trends in established risk factors (e.g., parity, age at first birth, and oral contraceptive use) that would predict increasing breast cancer rates among U.S. women born after 1945. If, as suggested by ##REF##17938728##Cohn et al. (2007)##, the public health significance of DDT exposure early in life is large, then this would provide additional evidence that the factor or factors responsible for the paradoxical decrease in birth cohort risk of breast cancer observed among U.S. baby boomers must have a very powerful impact on breast cancer etiology, large enough to turn an expected increasing trend in breast cancer rates among baby boomers into a decreasing trend.</p>" ]

[ "<title>Editor’s note</title>", "<p>In accordance with journal policy, Cohn et al. were asked whether they wanted to respond to this letter, but they chose not to do so.</p>" ]

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[ "<p>In their article discussing the impacts of farm animal production on climate change, ##REF##18470284##Koneswaran and Nierenberg (2008)## called for “immediate and far-reaching changes in current animal agriculture practices” to mitigate greenhouse gas (GHG) emissions. One of their recommendations was to switch to organic livestock production, stating that</p>", "<p>These claims are terribly misleading. ##REF##18470284##Koneswaran and Nierenberg (2008)## compared organic beef produced in Sweden (22.3 kg of carbon dioxide-equivalent GHG emissions per kilogram of beef) with unusual and resource-intensive Kobe beef production in Japan (36.4 kg of CO₂-equivalent GHG emissions per kilogram) (##UREF##2##Cederberg and Stadig 2003##; ##UREF##4##Ogino et al. 2007##).</p>", "<p>To achieve the ultra-high fat levels in meat preferred by Japanese consumers, Japan’s wagyu cattle are raised and fattened for more than twice as long as typical U.S. beef cattle (##UREF##1##Cattle Marketing Information Service Inc. 2007##; ##UREF##4##Ogino et al. 2007##). Moreover, all of the feed and forage for the Japanese animals (from birth through slaughter) must be shipped especially long distances—&gt; 18,000 miles in the example cited. Hence, this beef has ultra-high GHG emissions and energy requirements.</p>", "<p>According to several analyses, typical nonorganic beef production in the United States results in only 22 kg of CO₂-equivalent GHG emissions per kilogram of beef, which is 0.3 kg less than the Swedish organic beef system (##UREF##3##Johnson et al. 2003##; ##UREF##5##Subak 1999##). These comprehensive life cycle analyses, which examined all aspects of beef production and all GHG emissions, seem to definitively rule out significant reductions in GHG emissions by switching to organic beef production.</p>", "<p>In fact, if nitrous oxide and other emissions from land conversion are included in the analysis, a large-scale shift to organic, grass-based extensive livestock production methods would increase overall GHG emissions by nearly 60% per pound of beef produced.</p>", "<p>According to ##REF##18258860##Searchinger et al. (2008)##, each acre of cleared land results in 10,400 lb/acre/year of CO₂-equivalent GHG (over a 30-year period, based on estimated emissions from a proportion of each land type converted to cultivation in the 1990s). Our own analysis (##UREF##0##Avery and Avery 2007##) using conservative beef production parameters from Iowa State University’s Leopold Center for Sustainable Agriculture shows that grain-finishing cattle is at least three times more land efficient per pound of finished beef compared to grass-finishing.</p>", "<p>Cattle industry statistics [##UREF##6##U.S. Department of Agriculture (USDA) 2008##] show that, in 2007, the United States used 2 billion bushels of corn to produce 22.16 billion lb finished grain-fed beef (17.3 million head steers and 10.2 million head heifers at average dressed weights of 830.2 and 764.8 lb, respectively). At 150 bushels/acre corn, this means we used 13.3 million acres to produce the feed grains. Converting all beef production to grass-based finishing would require at least an additional 26.6 million acres of pasture/grass to produce 2007 U.S. beef output.</p>", "<p>Using the 22 lb of CO₂-equivalent GHG per pound of grain-fed beef from ##UREF##3##Johnson et al. (2003)## and the 22.3 lb CO₂-equivalent GHG per pound of beef for organic grass of ##UREF##2##Cederberg and Stadig (2003)##, each system producing 22.16 billion lb of beef would directly and indirectly result in 487.5 and 494.2 billion lb of CO₂-equivalent GHG emissions, respectively.</p>", "<p>However, adding the “carbon debt” resulting from the additional cleared land required by the two-thirds less efficient grass finishing process (26.6 million acres × 10,400 lb/acre/year, or 276.6 billion lb/year) results in the organic system totaling 770 billion lb of CO₂-equivalent GHG emissions; or 58% higher than the conventional system’s total of 487.5 billion lb.</p>" ]

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[ "<disp-quote><p>Raising cattle for beef organically on grass, in contrast to fattening confined cattle on concentrated feed, may emit 40% less GHGs and consume 85% less energy than conventionally produced beef.</p></disp-quote>" ]

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[ "<fn-group><fn><p>In early 2007, the authors received funding from the GET IT (Growth Enhancement Technology Information Team) pharmaceutical companies that are members of the National Cattlemen’s Beef Association, to conduct an analysis of the environmental impacts and costs of various beef production systems.</p></fn></fn-group>" ]

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[ "<p>Both authors are staff members of the Humane Society of the United States. D.N. also serves as a senior fellow with the Worldwatch Institute.</p>" ]

[ "<p>Avery and Avery, who find comparing conventional Japanese and organic Swedish beef production misleading, propose relying on “comprehensive life cycle analyses” (LCAs) to quantify emissions from conventional U.S. beef production. However, neither study they cite (##UREF##5##Johnson et al. 2003##; ##UREF##10##Subak 1999##) appears to be a comprehensive LCA, and it is unclear whether these studies considered emissions created by facets of beef production such as feed transport or pesticide manufacturing, as did ##UREF##7##Ogino et al. (2007)##. Additionally, contrary to Avery and Avery’s conclusion, ##UREF##10##Subak (1999)## stated that</p>", "<p>For a more comprehensive analysis, additional production aspects must be considered. ##UREF##7##Ogino et al. (2007)##, for example, included the transportation of feed (&gt; 18,000 km, not miles, as stated by Avery and Avery in their letter), which accounted for 8.3% of emissions.</p>", "<p>A better comparison of conventional versus organic beef production may be an LCA of greenhouse gas (GHG) emissions from three Irish systems reported by ##REF##16397099##Casey and Holden (2006)##. Conventional production generated the most GHGs, followed by agri-environmental, with the organic system producing the least GHGs.</p>", "<p>In contrast to conventional production, organic farming can reduce nitrous oxide emissions by avoiding excessive amounts of manure, as stocking densities are limited to land available for manure application. Organic agriculture typically also uses less fossil-fuel energy, in part because thousands of feed transport miles may be reduced (##UREF##6##Kotschi and Müller-Sämann 2004##).</p>", "<p>Pasture-based systems require less operational fuel and feed than do conventional systems, and they adeptly sequester GHGs in the soil, tying up 14–21 million metric tons of carbon dioxide and 5.2–7.8 million metric tons of N₂O in pasture soil organic matter (##UREF##2##Boody et al. 2005##; ##UREF##8##Rayburn 1993##).</p>", "<p>##UREF##3##Dourmad et al. (2008)## concurred with our conclusion (##REF##18470284##Koneswaran and Nierenberg 2008##) that more research is needed and noted that existing LCAs often omit details such as land-use change information. Many LCAs—and other attempts to quantify GHGs from various systems (##UREF##0##Avery and Avery 2007##)—also lack data on pesticide use and animal transport from farms or feedlots to slaughter.</p>", "<p>In our article (##REF##18470284##Koneswaran and Nierenberg 2008##), we not only argued for refinement of agricultural practices but also for a concurrent reduction in animal product consumption in high-income nations, especially because the U.N. Food and Agriculture Organization has concluded that animal agriculture accounts for more GHGs than transport (##UREF##9##Steinfeld et al. 2006##). In addition to lowering GHG emissions, reducing animal product consumption could also decrease the incidence of cardiovascular disease, certain cancers, and obesity (##REF##17868818##McMichael et al. 2007##). Given the developing global food crisis, it is important to note, as did ##REF##17035955##Baroni et al. (2007)## in the <italic>European Journal of Clinical Nutrition</italic>, that plant-based diets “could play an important role in preserving environmental resources and in reducing hunger and malnutrition in poorer nations.”</p>", "<p>Although Avery remains skeptical over the role of anthropogenic GHG emissions in global warming (2008), the Intergovernmental Panel on Climate Change (##UREF##4##IPCC 2007##) concluded that</p>", "<p>The link between GHG mitigation and organic or extensive animal agriculture systems is well established, as are the other environmental and public health benefits of less-intensive production systems. Understanding the efficacy of less technology-dependent mitigation strategies is critical as the effects of global warming become more evident.</p>" ]

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[ "<disp-quote><p>These results indicate that the intensification of beef production systems may be counterproductive because net emissions of carbon dioxide as well as nitrogen and other pollutants would increase.</p></disp-quote>", "<disp-quote><p>Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic GHG concentrations.</p></disp-quote>" ]

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[ "<p>The authors declare they have no competing financial interests.</p>" ]

[ "<p>##REF##18629323##Chen et al. (2008)## examined the potential for social stressors to influence responsiveness to environmental pollution. Contrary to their initial hypothesis, and to results we reported previously (##REF##17687439##Clougherty et al. 2007##), their findings indicated that chronic stress was associated with asthma symptoms and heightened inflammatory profiles only in low nitrogen dioxide areas. We would like to note several key issues in the emerging research on social susceptibility to environmental pollutants that should be considered as research on this work moves forward.</p>", "<p>One key issue is that the relative timing of psychosocial stressors and physical exposures, which ##REF##18629323##Chen et al. (2008)## did not present, is critical for at least two reasons:</p>", "<p>Acute and chronic stress produce substantively different physiologic sequelae. Acute stress can induce bronchodilation with elevated cortisol (possibly masking short-term detrimental respiratory effects of pollution), whereas chronic stress can result in cumulative wear and tear (allostatic load) and suppressed immune function over time, increasing general susceptibility (##REF##10681886##McEwen and Seeman 1999##).</p>", "<p>Temporal relationships between stress and pollution exposures matter. Depending on when measures are obtained, exposure misclassification is possible, which may influence the directionality of observed interactions. ##REF##18629323##Chen et al. (2008)## stated that the measured 6-month stress and NO₂ periods do not overlap, but they did not specify whether the stress measure preceded the 1998–2003 NO₂ exposure window or the amount of time that passed between exposures. If the stress interval occurred first, some increased susceptibility to subsequent pollution is plausible, provided that chronic stress effects predominate over acute effects. If, however, the stress interval occurred after NO₂ exposures, the interaction is potentially problematic, because we must then assume that stress levels measured after the 6-year NO₂ period (1998–2003) are relevant for the earlier time, which may not be the case. If, for example, respondents compared current stress to prior experience, an individual reporting high stress for one interval may have experienced lower stress previously, during those “reference” periods corresponding to the NO₂ window—potentially producing a negative interaction, as ##REF##18629323##Chen et al. (2008)## observed. More broadly, careful attention to relative timing and durations of stress and pollution exposures is critical in maintaining directionality and interpretability as we progress with this research.</p>", "<p>Second, Chen et al.’s finding of significant effects of stress only in low-NO₂ areas (##REF##18629323##Chen et al. 2008##) points to the possibility of nonlinear interactions and saturation effects at high exposures. Similarly, our group (##REF##16801137##Clougherty et al. 2006##) reported that asthmatic children of families reporting higher fear of violence showed less symptom improvement in response to allergen-reducing indoor environmental interventions. Our results, counter to our initial hypotheses, suggested a saturation effect in our very high-exposure public housing cohort, where either high exposure alone may have been adequate to induce or maintain symptoms.</p>", "<p>Third, ##REF##18629323##Chen et al. (2008)## did not address the spatial covariance among stress, socioeconomic status, and pollution, which can confound geographic information system–based air pollution epidemiology. In particular, communities near highways, with higher traffic-related pollution and lower property values, may be disproportionately composed of families having lower socioeconomic status. Because of this potential for spatial autocorrelation and thus confounding, accurate fine-scale exposure measurement is critical. However, ##REF##18629323##Chen et al. (2008)## did not present pollution or stress maps, the NO₂ model was not formally validated to this cohort’s specific spatial characteristics, and spatial patterns in stress were not explored; thus we are left wondering whether, and how, spatial misclassification and confounding may be at play. Relatedly, social–physical correlations may vary by geographic scale (e.g., across vs. within neighborhoods); although a given neighborhood may have high mean pollution and stress, it is harder to argue that particular individuals (or residences) within these neighborhoods would be relatively more exposed to both (i.e., individuals living closer to highways are not necessarily more exposed to violence or family stress than are other community members).</p>", "<p>Fourth, ##REF##18629323##Chen et al. (2008)## reported results for 73 asthmatic children. However, in the absence of information on disease chronicity, severity, or adequacy of medical treatment, it may be difficult to truly assess the influence of either stress or traffic-related pollution. Relatedly, it is important to distinguish between processes related to illness onset from those related to progression or exacerbation, and whether the negative interaction observed in their study could be expected in healthy adolescents.</p>", "<p>Finally, the cohort studied by ##REF##18629323##Chen et al. (2008)## varied considerably in age (9–18 years), but the authors did not consider age-related asthma characteristics and responsiveness to family stressors and air pollution. Age stratification should have been used to compare the strength of individual and combined effects at multiple ages. It would also be interesting to know whether non–family-related stressors would produce similar interactions at all ages.</p>", "<p>The issues we have highlighted—temporal relationships between stressors and pollution, nonlinearity and saturation effects, spatial correlations, age-related susceptibility, and distinctions between illness etiology and exacerbation—will be critical in the further study of social–environmental interactions. These effects may distort observed associations (e.g., saturation effects may reverse interactions at high exposures), but with sustained attention to these issues, we can better understand joint effects of social and physical environments on health.</p>" ]

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[ "<p>The authors declare they have no competing financial interests.</p>" ]

[ "<p>We thank Clougherty and Kubzansky for their thoughtful review of our article (##REF##18629323##Chen et al. 2008##). We view our article, as well as their article on exposure to violence, air pollution, and asthma etiology (##REF##17687439##Clougherty et al. 2007##), as suggestive regarding how the social and physical environments operate in asthma. Although the nature of the interaction effects were different in these two studies, the broader point—that there are interactive effects between the social and physical environments in asthma —is consistent and is the key message that we wish to emphasize.</p>", "<p>We would like to address their specific comments. First, regarding temporal issues, Clougherty and Kubzansky raise the possibility that stress increases susceptibility to subsequent pollution. We agree that this is possible; we also recognize the possibility that chronic pollution exposure could heighten responses to subsequent stressors. As we stated in our “Discussion” (##REF##18629323##Chen et al. 2008##), the time frame of assessments that were available to us for these analyses was not ideal, and future studies should more specifically coordinate the timing of exposures to both stress and air pollution.</p>", "<p>Second, we agree it is possible that saturation effects may occur at high levels of pollution exposure. However, because pollution levels in Vancouver (British Columbia, Canada) are not extreme (the range in our sample was 10–30 ppb nitrogen dioxide), we think this is an unlikely explanation.</p>", "<p>Third, regarding spatial covariance, in our study (##REF##18629323##Chen et al. 2008##), family stress was measured at the individual level; thus, we do not have neighborhood-level stress maps or information on spatial patterns in stress. Although spatial covariance between socioeconomic status and air pollution has the potential to lead to confounding, the availability of individual measures of stress and air pollution exposure estimates at the resolution of individual addresses allowed us to evaluate interactions. Our longitudinal findings also diminish the likelihood of confounding. Further, previously published pollution maps (##REF##17438795##Henderson et al. 2007##) have shown that, in our study area, air pollution levels are not spatially correlated with neighborhood socioeconomic status [e.g., see ##UREF##0##UBC (University of British Columbia) Centre for Health and Environment Research 2008##].</p>", "<p>Fourth, we presented information about disease characteristics in Table 1 (##REF##18629323##Chen et al. 2008##). We also controlled for asthma severity and medication use in all analyses, as described in our article under “Potential confounders.”</p>", "<p>Finally, we agree that it would be interesting to know whether stress by air pollution effects vary by age. However, given the limited sample size in our study, we were unable to test this possibility.</p>" ]

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[ "<p>Compact fluorescent lamps (CFLs) are about 75% more energy efficient than incandescent light bulbs and last 10 times longer, and thus have quickly become a modern-day environmental icon. The U.S. Environmental Protection Agency (EPA) estimates that about 290 million CFLs were sold in 2007. But CFLs do have one dim spot on their otherwise bright green image: the mercury that makes the bulbs’ inner phosphor coating fluoresce and produce light. A new study from a group of researchers at Brown University characterizes for the first time how elemental mercury vapor escapes from broken CFLs and offers a real-world solution for capturing escaping mercury.</p>", "<p>According to a June 2008 fact sheet issued by the EPA Energy Star program, the use of CFLs results in a net reduction in mercury entering the environment because their lower energy draw means less mercury-emitting coal needs to be burned. The EPA estimates that using a 13-W CFL saves 376 kWh over its 8,000-hour lifespan, preventing 4.5 mg of mercury from being emitted by a coal-burning power plant. Each small, curly tube contains about 3–5 mg of mercury—significantly less than the 500 mg in older thermometers, but enough that environmental and human health concerns remain.</p>", "<p>The research group headed by Robert Hurt, director of the Institute for Molecular and Nanoscale Innovation, broke a series of new and used CFLs to measure the release of mercury vapor into the air. In the hour immediately after each breakage, the team recorded mercury gas concentrations near the bulb shards between 200–800 μg/m³. For comparison, the average 8-hour occupational exposure limit allowed by the Occupational Safety and Health Administration is 100 μg/m³. Within 4 days a new 13-W CFL released about 30% of its mercury, with the remainder appearing to remain trapped in the bulb debris; picking up the glass shards after breakage reduced mercury release by 67%. Used bulbs followed similar patterns but with lower rates. The study, which was funded by the NIEHS Superfund Basic Research Program, was reported in the 1 August 2008 issue of <italic>Environmental Science &amp; Technology</italic>.</p>", "<p>“The amount of mercury gas coming off [broken CFLs] is over a milligram over a few days. If you put that milligram into a poorly ventilated room, the concentration can be over the recommended limit for children [of 0.2 μg/m³],” says Hurt. “The overall risk is low, but it’s not zero risk, and there is definitely an opportunity to do better.”</p>", "<p>This kind of information could help regulators provide better information on how to handle broken CFLs. In 2007 the Maine Department of Environmental Protection performed one of the only other studies evaluating mercury exposure from broken CFLs. The EPA’s current recommendation to leave the room for at least 15 minutes immediately after breaking a CFL derives from that study. The EPA also recommends that broken CFL pieces be scooped up and placed in a plastic bag.</p>", "<p>However, Hurt’s research suggests that the peak for escaping mercury vapor lasts a few hours. The group also found that plastic bags leaked mercury vapor. “This new information may allow for modeling of airborne mercury concentrations following breakage, thus providing the capability to more fully assess the effectiveness of cleanup,” says Roxanne Smith, a press officer for the EPA.</p>", "<p>Hurt’s group also tested 28 sorbents for their ability to capture the released mercury gas. Because a sorbent’s surface area can affect how well it captures mercury, the team chose to test nanoscale formulations, which provide large surface area. One type of nanoselenium was found to be the most effective, removing 99% of the mercury vapor when impregnated in a cloth that was draped over a broken CFL or sprinkled over the breakage as a powder. When the mercury vapor reacted with nanoselenium, it formed mercury selenides, which are insoluble and metabolically inactive, according to a report in the November 2004 issue of the <italic>Seychelles Medical and Dental Journal</italic>. These compounds are also believed to be stable under landfill conditions (with the caveat that the environmental disposition and health effects of nanomaterials are still largely unknown).</p>", "<p>There are CFL recycling programs across the country at major retailers such as The Home Depot, but the Association of Lighting and Mercury Recyclers estimates that 98% of CFLs currently end up in landfills. Hurt’s group has therefore developed prototype packaging and disposal bags that can act as a barrier to prevent mercury from escaping as well as neutralize it. “Development of technology or material to more effectively clean up or capture mercury vapor may potentially minimize worker exposures during transport and disposal and, if readily available to consumers, may potentially minimize future inhalation exposures in residential settings,” says Smith.</p>" ]

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[ "<fig id=\"f1-ehp-116-a378\" orientation=\"portrait\" position=\"float\"><caption><p>Nanoselenium (inset) may be one answer to the question of how to safely clean up mercury from broken CFLs</p></caption></fig>" ]

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[ "<p>The vast majority of the nearly 2 million miles of paved roads in the United States are surfaced with asphalt pavement, which is made by combining a thick hydrocarbon mixture known as liquid asphalt binder with sand, gravel, or crushed stone (“aggregate”). Each year about 60 million tons of hot-mix asphalt (HMA) pavement are laid on U.S. roads, according to figures presented last spring at the 12th Annual Minnesota Pavement Conference. Asphalt pavement is tough, flexible, and easy to repair, but the commonly used HMA is energy-intensive to produce, releases greenhouse gases, and poses potential hazards for workers. So researchers are looking at lower-temperature asphalt pavements as a way around these problems.</p>", "<p>Warm-mix asphalt (WMA) can be used at temperatures of 212–284°F, about 50–100°F cooler than HMA, while cold-mix asphalt (CMA) is used at ambient temperatures. Both can be produced with minimal modifications to HMA plants, says University of Wisconsin–Madison civil and environmental engineering professor Hussain Bahia.</p>", "<p>In paving workers, inhalation of asphalt fumes can irritate the nose, throat, and lungs, as well as cause excessive fatigue and loss of appetite, according to the National Institute for Occupational Safety and Health. A study in the November 2004 issue of the <italic>Annals of Occupational Hygiene</italic> cited dermal and inhalational exposure of paving workers to polycyclic aromatic compounds, which have been labeled as reasonably anticipated to be human carcinogens by the National Toxicology Program. But whereas certain extracts of asphalt have caused a carcinogenic skin response in experimental animals, research to date has found no conclusive evidence of increased risk of skin or lung cancer in workers.</p>", "<p>HMA plants use petrofuels to heat the liquid asphalt binder to a workable temperature as well as dry and heat the aggregate to improve cohesion. “You inevitably have . . . the resultant emission of typical fuel combustion by-products like sulfur dioxide, carbon monoxide, carbon dioxide, volatile organics, and other substances, similar to a home heating furnace,” says Gary Fore, vice president for environment, health, and safety at the National Asphalt Pavement Association (NAPA), a trade association. “That’s why we’re continuing to explore lower-temperature alternatives.”</p>", "<p>In the September 2007 <italic>Europeanroads Review</italic>, Pierre Dorchies and colleagues wrote that CMA technology could afford a 30% energy saving over traditional HMA. NAPA president Mike Acott says, “The challenge with cold mix is to produce a surface as strong and reliable as hot mix, and there are some factors getting in the way. Cold mix is not generally used as a surfacing material and certainly not for roads subjected to medium to heavy traffic.” CMA is used in countries such as South Africa and India, where there is relatively little heavy traffic, and to a lesser degree in the United States.</p>", "<p>The issue, Acott says, “is to produce materials that perform as well as hot mix for the U.S. road infrastructure. That’s where we believe warm mix comes in. Our research indicates that warm mix can produce a surface laid down at a substantially lower temperature [that performs] as well as hot mix.”</p>", "<p>The 2007 NAPA report <italic>Warm-Mix Asphalt: Best Practices</italic> says a shift from HMA to WMA in Norway, Italy, the Netherlands, France, and Canada has yielded significant emissions reductions. Adoption of WMA in these nations is being driven largely by Europe’s participation in the Kyoto Protocol and implementation of the new Registration, Evaluation, Authorisation and Restriction of Chemical Substances (REACH) legislation, according to an article in the 14 April 2008 issue of <italic>Michigan Contractor and Builder</italic>. WMA shows signs of being as good as or better than HMA, but with a track record of only about 10 years, it hasn’t yet had time to prove itself in real-world settings. More data should be forthcoming: in 2008 the Asphalt Institute was awarded a $900 million 3-year grant by the Transportation Research Board of the National Academies to compare elements of WMA and HMA including performance and emissions.</p>", "<p>Peter Grass, president of the Asphalt Institute, another trade association, says several projects using WMA are under way in the United States, including plans this year to lay more than 1 million tons of WMA in Texas alone. The Massachusetts Port Authority Board has also just authorized $6.3 million to repave a runway at Boston Logan International Airport with WMA, making Logan the first U.S. airport to use the more environmentally friendly surface. In a 24 July 2008 press release, Port Authority Board CEO and executive director Thomas J. Kinton Jr. said, “Warm mix uses 20% less energy to make, produces 20% fewer greenhouse emissions when applied, and allows us to use a higher percentage of recycled asphalt pavement in the final product.”</p>", "<p>There is still a lot of research to be done before all the questions are answered and issues settled about CMA, a point with which Bahia agrees. Within his newly established Modified Asphalt Research Center, Bahia is exploring what it will take to make CMA a recognized replacement for HMA, including the possible addition of polymers or plastics to yield a quieter, safer, more durable pavement. Bahia says “Some 80% of the roads in this country are low-traffic-volume roads. Those are the applications where we believe cold mix would be appropriate. As energy prices continue to rise, taking asphalt prices along, we’re going to be forced to consider alternatives.”</p>" ]

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[ "<fig id=\"f1-ehp-116-a379a\" orientation=\"portrait\" position=\"float\"><caption><p>Cooler asphalt offers one route to more sustainable road building</p></caption></fig>" ]

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[ "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.paho.org/english/ad/dpc/dpc-page.htm\">http://www.paho.org/english/ad/dpc/dpc-page.htm</ext-link></p>", "<p>The Pan American Health Organization (PAHO), an international public health agency, supports a Communicable Disease Unit (CD) whose primary functions include building networks and mobilizing resources for the prevention and control of communicable diseases; providing national and local training in areas such as clinical case management and use of standard guidelines for prevention and control activities; promoting and coordinating research; and developing policies, plans, and guidelines. Information collected by the CD is available through the top menu bar of PAHO’s Health Surveillance and Disease Prevention and Control website.</p>", "<p>Besides information on diseases such as cholera, acute respiratory infections, and anthrax, the website provides information on mosquito-borne diseases such as dengue, malaria, West Nile virus, and yellow fever. Sections on each disease link to resources for disease surveillance, reports on the history of each disease, profiles of the disease in countries where it is occurring, and statistics on incidence and mortality. Each section also links to any available epidemiological surveillance systems and to laboratory networks established by PAHO for each disease. Visitors will also find resources for prevention, control, and educational efforts, including field guides, materials for families and communities, policy documents, overviews of global disease control strategies, travel advisories, journal articles, and bibliographic databases.</p>" ]

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[ "<p>The solar investment tax credit (ITC), created by the Energy Policy Act of 2005, allows those who invest in solar technology to deduct 30% of the purchase and installation costs incurred during the year from their taxable income. Although it’s been a boon for home and small business owners who can quickly install photovoltaic (PV) solar panels, water heaters, and other technologies, the ITC hasn’t provided comparable benefits to much larger solar power plants that could supply grid electricity to homes and businesses throughout the United States—a state of affairs that may be prolonged by political bickering.</p>", "<p>In a technology known as concentrating solar power (CSP), arrays of reflective mirrors spread over hundreds of acres collect and focus the sun’s heat to make steam that, in turn, creates electricity by running a turbine engine. A total of 10 CSP plants built in sun-drenched regions of California and Nevada during the 1980s—plus a 1-MW demonstration plant in Arizona—already power more than 300,000 homes at a cost of roughly 15¢ per kWh, comparable to standard rates in many U.S. locations. Electric utilities in those states, and also in Florida and Hawaii, have agreed to purchase 4,500 MW of additional solar-generated electricity from a total of 16 planned CSP facilities, which could power up to 3.5 million American homes.</p>", "<p>But before investors will fund those projects, they want to be sure ITC tax breaks will be available throughout plant construction. A typical CSP plant takes 4–6 years to build, explains Nate Blair, a senior energy analyst with the Department of Energy National Renewable Energy Laboratory. Congressional officials have proposed eight-year extensions to the ITC that would boost the entire solar industry while also making CSP costs more predictable in the long term. Those proposals are contained in two bills that are currently stalled in the Congress: HR 6049, which has been passed by the House of Representatives but not the Senate, and the Senate’s counterpart, S 3335, which has yet to be passed by either legislative chamber.</p>", "<p>According to Carol Werner, executive director of the Environmental and Energy Study Institute, a Washington, DC, think tank, investors need to be assured the ITC will be available for each year of construction so they can negotiate prices in advance for the power they sell. Until an extension long enough to cover construction time-lines emerges, she says, these projects will remain in limbo.</p>", "<p>“This is a big deal [because it] affects manufacturers’ and installers’ capacity decisions,” adds Justin Baca, a senior research analyst with the Solar Energy Industries Association (SEIA), a trade group. “An eight-year extension of the ITC . . . [is] much more effective than a series of short-term renewals.”</p>", "<p>Working with congressional supporters on both sides of the aisle, the solar industry has tried since 2006 to pass an eight-year ITC extension. Monique Hanis, director of communications for the SEIA, says Congress still can’t agree on how to cover the approximately $1.5 billion cost—which is far less than the roughly $40 billion in annual tax credits passed on to the fossil fuel industry (including coal, natural gas, and oil) every year. “Hopefully the Senate will pass one of the two bills later in the fall,” Hanis says. Meanwhile, the ITC is set to expire at the end of 2008.</p>" ]

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[ "<fig id=\"f1-ehp-116-a380a\" orientation=\"portrait\" position=\"float\"><caption><p>Nevada Solar One CSP plant Boulder City, Nevada</p></caption></fig>" ]

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[ "<title>Declaration on Port Pollution</title>", "<p>A 2008 UN Intergovernmental Panel on Climate Change report estimates the shipping industry produces nearly 4.5% of the CO₂ emitted worldwide and that these emissions will increase by 30% by 2020. Shipping emissions are not governed by the Kyoto Protocol, so in July 2008 a group of 55 port officials from 35 countries gathered at the World Ports Climate Conference where they signed the World Ports Climate Declaration, in which parties agree to cut their CO₂ outputs, possibly by using cleaner-burning fuel, reducing travel speeds, and developing better ship coatings to reduce drag. At the meeting, representatives of the International Maritime Organization announced progress in developing new emissions targets for the shipping industry, to be implemented by 2010.</p>", "<title>Keeping Apace with e-Waste</title>", "<p>The import of electronic junk into developing countries for recycling or disposal threatens both environmental and human health. Although recycling computers yields valuable metals, improper handling also releases lead, mercury, and other potential toxicants into the environment. At the June 2008 meeting of the Basel Convention on the Control of Transboundary Movements of Hazardous Waste and Their Disposal, parties launched a significant initiative to abate that threat: the Partnership for Action on Computing Equipment, or PACE. The partnership will establish international guidelines for environmentally sound methods of repairing and recycling computer goods, certify facilities that use such methods, and train workers in responsible practices. Parties to the Basel Convention must ensure that hazardous waste is managed in an environmentally sound manner.</p>", "<title>Walk to School Month 2008</title>", "<p>Each October community groups worldwide sponsor events that promote walking to school as a way of improving children’s health, reducing traffic emissions, and enriching community life. In 2007 millions of walkers in 42 countries participated in such events; in the United States, almost 3,000 schools from all 50 states took part. Interested parties can read more about the International Walk to School program at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.iwalktoschool.org/\">http://www.iwalktoschool.org/</ext-link>, which includes tips for sponsoring events as well as educational materials for teachers, parents, and children.</p>", "<title>Potable Water a Priority in Hurricane Preparedness</title>", "<p>In July 2008 the Harvard School of Public Health released survey results on hurricane preparedness of more than 5,000 participants from eight coastal states, plus a special sample of New Orleans residents. Three years after Hurricane Katrina, people affected by the storm named the need for fresh drinking water as a top priority in a storm’s aftermath, and 37% of participants reported keeping water purification supplies on hand. Some 34% of respondents affected by Katrina felt prepared if a major hurricane were to strike their communities within the next 6 months. The Atlantic Ocean hurricane season runs each year from June 1 to November 30.</p>", "<title>Skin Cancer Souvenir?</title>", "<p>A population-based study of young, white British women published online 10 July 2008 ahead of print in the <italic>Journal of Investigative Dermatology</italic> suggests that vacationing—but not necessarily living—in hotter or higher-altitude locations than one’s home is associated with a greater whole-body number of nevi (benign moles) in women aged 18–29 who normally live in temperate climates. The association was particularly strong for nevi on the trunk and lower limbs, which typically are only intermittently exposed to the sun. The researchers believe this finding supports the hypothesis that intermittent sun exposure is a primary environmental risk factor for developing nevi, and thus for melanoma. Having a large number of nevi is the strongest known risk factor for melanoma in whites.</p>", "<title>The Health Impact of Incense</title>", "<p>In Asian countries where Buddhism and Taoism are the major religions, incense is burned daily in homes and temples. A review published 25 April 2008 in <italic>Clinical and Molecular Allergy</italic> and a study published 9 May 2008 in <italic>Chemico-Biological Interactions</italic> focus on the potential respiratory and carcinogenic effects of incense smoke, which can contain benzene, toluene, xylene, 1,3-butadiene, polyaromatic hydrocarbons, and particulate matter. The first study found that exposure to incense smoke can cause airway dysfunction, elevated cord blood IgE levels, allergic contact dermatitis, and neoplasms, and advises people to reduce exposure when they worship and to ventilate homes during the burning of incense. The second found that temple workers in Thailand had significantly more DNA damage and reduced DNA repair capacity, and warns that exposure to incense smoke may increase the risk of cancer.</p>" ]

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[ "<fig id=\"f1-ehp-116-a380b\" orientation=\"portrait\" position=\"anchor\"></fig>", "<fig id=\"f2-ehp-116-a380b\" orientation=\"portrait\" position=\"anchor\"></fig>", "<fig id=\"f3-ehp-116-a380b\" orientation=\"portrait\" position=\"anchor\"><caption><p>Coast Guard workers distribute bottled water after Hurricane Katrina, 1 September 2005</p></caption></fig>", "<fig id=\"f4-ehp-116-a380b\" orientation=\"portrait\" position=\"anchor\"></fig>" ]

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[ "<graphic xlink:href=\"ehp-116-a380bf1\"/>", "<graphic xlink:href=\"ehp-116-a380bf2\"/>", "<graphic xlink:href=\"ehp-116-a380bf3\"/>", "<graphic xlink:href=\"ehp-116-a380bf4\"/>" ]

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[ "<p>In a significant step forward for alternative safety test methods designed to reduce, refine, or replace the use of live test animals, the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) recently announced the regulatory acceptance of two new <italic>in vitro</italic> ocular safety assays by the U.S. Food and Drug Administration, Environmental Protection Agency, and Consumer Product Safety Commission. The acceptance was based on recommendations made by ICCVAM after an extensive evaluation of the methods.</p>", "<p>The United States tallies an estimated 125,000 eye injuries in the home each year caused by accidental exposure to common household products such as bleach and oven cleaner, according to the American Academy of Ophthalmology. Proper identification and labeling of substances that can damage the eye is one way to combat such injuries. Several agencies require manufacturers to test new products for their potential to cause temporary or permanent blindness, irritation, or other eye injuries.</p>", "<p>Working with the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), ICCVAM evaluated and recommended the bovine corneal opacity and permeability (BCOP) and the isolated chicken eye (ICE) test methods—the first nonanimal ocular safety test methods to be accepted by the regulators. In both cases, the animal eyes used for the tests are slaughterhouse waste, so no animals are euthanized specifically to obtain these tissues. The assays have been in development since the early 1990s.</p>", "<p>Now that the BCOP and ICE assays have earned regulatory acceptance, they must be considered as the first option for ocular safety testing under the Animal Welfare Act, which requires the consideration of alternative methods before animals are used for procedures that may cause more than slight or momentary pain or distress. “If you get a positive result in either of these assays, you can use that as a positive for the purposes of classifying and labeling [a material] as a severe irritant,” says Marilyn Wind, chair of ICCVAM and a deputy associate executive director with the Consumer Product Safety Commission. “If it’s negative, then [manufacturers] have to go to the next step and test in animals. This eliminates the most corrosive and severe chemicals from having to be tested in animals, so there is a reduction in potential pain and distress.”</p>", "<p>Although precise numbers are not available for the use of live animals in ocular testing, William Stokes, director of NICEATM and executive director of ICCVAM, estimates that based on the relative distribution of adverse effects, use of the two assays could reduce the use of live animals for eye safety testing by 10% or more. “The overall goal is to come up with an integrated testing strategy using several nonanimal tests that will accurately predict whether chemical products have the potential to damage the eye or not,” he says. ICCVAM and NICEATM are in the process of evaluating other <italic>in vitro</italic> methods for ocular safety, hoping to eventually eliminate altogether the need for <italic>in vivo</italic> testing in this realm.</p>", "<p>In the near term, ICCVAM is working with its counterparts in Europe and Japan to expedite approval of the BCOP and ICE assays at the international level by the 30-member Organisation for Economic Co-operation and Development. This group includes the United States, Canada, Japan, and most of the European Union, where a ban on live animal testing of cosmetic ingredients takes effect in March 2009 and the newly implemented REACH (Registration, Evaluation, Authorisation and Restriction of Chemical Substances) legislation will require testing of thousands of chemicals by 2018.</p>" ]

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[ "<p>Dengue—a viral disease that can refer to both dengue fever and the more severe dengue hemorrhagic fever (DHF)—swept away records again this past spring as it raged across Brazil, infecting more than 160,000 people and killing more than 100. The reports were similar to those out of Southeast Asia in the summer of 2007, South America the previous spring, and India the fall before that. Although it may not be the most devastating of the mosquito-borne diseases—malaria strikes 10 times more people and yellow fever kills more of its victims—dengue has become a major public health concern for two reasons: the speed with which it is spreading and the escalating seriousness of its complications.</p>", "<p>In the nineteenth century, dengue fever was a mild illness found in the tropics. Deaths were rare, and years passed between major epidemics. But since the mid-twentieth century, the range of the dengue virus has steadily broadened. In the last 50 years, its worldwide incidence has increased 30-fold, and various estimates posit that anywhere from one-third to nearly one-half of the world’s population are now at risk of becoming infected.</p>", "<p>Moreover, today’s dengue infection is not what it once was. DHF, a complication of dengue infection that was not recognized until the 1950s (although cases probably occurred as early as 1870 in India), now appears in many dengue epidemics. In addition to the fever, rash, headache, and muscle and joint pain of classic dengue fever (which earned dengue its nickname of “breakbone fever”), DHF sometimes causes hemorrhaging that can lead to shock and even death. Epidemic DHF is now a leading cause of hospitalization and death among children in several Southeast Asian countries. Worldwide, of the 50 million dengue infections estimated by the World Health Organization (WHO) each year, there are 500,000 cases of DHF and 22,000 deaths, mainly among children.</p>", "<p>Once considered mainly an Asian disease, dengue fever and DHF now also permeate the tropical Americas. Between 1995 and 2001, the number of dengue cases in the Americas doubled, according to the WHO. By 2007, the annual incidence there reached nearly 900,000 cases, with more than 25,000 people suffering DHF.</p>", "<p>The dengue virus comes in four distinct serotypes. Individuals who become infected with one serotype obtain lifelong immunity against that serotype but not against the other three—and there is good evidence that a previous dengue infection increases the odds of developing DHF upon infection with a different serotype. “Somehow, having that prior infection enhances invasion of target cells by new dengue [serotypes],” explains Laura Harrington, a medical entomologist at Cornell University.</p>", "<p>Dengue experts agree on many of the causes of the disease’s spread, including demographic changes and interruptions in vector control efforts. But some controversy has surfaced over whether climate change—often cited as a factor in broadening disease vector habitats—has had or will have anything to do with the virus’s expansion. “It’s too early to predict what effects global warming will have, if any,” says David Morens, senior scientific advisor at the National Institute of Allergy and Infectious Diseases (NIAID) in Bethesda, Maryland. “But it’s certainly something to be concerned about.”</p>", "<title>Re-emergence of a Disease</title>", "<p>Several factors have assisted in the spread of dengue around the world. <italic>Aedes aegypti</italic>, the mosquito that is the chief carrier of the dengue virus, originated in Africa but migrated to other continents via the slave trade in the 1500s and 1600s, says Duane Gubler, director of the Asia-Pacific Institute of Tropical Medicine and Infectious Diseases at the University of Hawaii at Manoa in Honolulu. “As urban port cities developed, [the <italic>Ae. aegypti</italic>] mosquito became established and became highly adapted to humans,” he says. Accordingly, as the tropical developing world has become increasingly urbanized over the past few decades, <italic>Ae. aegypti</italic> have proliferated.</p>", "<p>Whereas <italic>Ae. aegypti</italic> originally bred in small natural water bodies such as tree holes or rock pools, it now breeds successfully in water that accumulates in discarded trash such as bottles, plastic and cellophane packaging, and tires, as well as in domestic water storage containers that are common in places where people do not have easy access to a regular supply of clean water. <italic>Ae. aegypti</italic> also prefers to live inside buildings rather than outside. Finally, this mosquito prefers to feed on humans, meaning viral transmission is not diluted by the mosquito feeding on other animals as well. <italic>Ae. aegypti</italic> therefore is “perfectly adapted to the urban environment,” says Gubler.</p>", "<p>During World War II, Japanese and Allied military movements spread viruses throughout Southeast Asia. In the aftermath of the war, “for the first time, several serotypes were coming together,” Harrington says, as people began to travel across the world more frequently. Subsequent economic boom and rapid urbanization in Southeast Asia led to conditions ideal for epidemics—cramped living quarters, low-quality housing, and poor management of water, sewage, and waste systems. Dengue’s progression from tropical nuisance to life-threatening epidemic reached a tipping point in the 1950s, when DHF was first reported in the Philippines and Thailand.</p>", "<p>Meanwhile, on the other side of the globe, dengue had been largely eliminated in the Americas, mainly thanks to attempts to control urban yellow fever in the 1950s and 1960s. The Pan American Health Organization (PAHO), an international public health agency, initiated a campaign to rid Central and South American, Caribbean, and southern U.S. regions of <italic>Ae. aegypti</italic>, which also transmits yellow fever virus. By going after <italic>Ae. aegypti</italic> aggressively with the insecticide DDT and systematically eliminating its breeding areas, the campaign largely eradicated the vector from Central and South America, although not the Caribbean and southern United States, says Gubler. In the course of eradicating yellow fever, the efforts also squashed dengue transmission in the region.</p>", "<p>DDT was banned in the United States in 1972. Coincidentally, says Gubler, <italic>Ae. aegypti</italic> eradication efforts were deemed successful and therefore largely abandoned, with resources redirected to other pressing issues of the day such as President Richard Nixon’s “War on Cancer.” Since then, <italic>Ae. aegypti</italic> has returned to nearly every region from which it was eliminated. “We have allowed <italic>Aedes aegypti</italic> to reinfest most if not all of the urban areas of tropical America,” says Gubler.</p>", "<p>In 1981 a serotype of dengue imported from southeast Asia caused an outbreak of DHF in Cuba—the first DHF epidemic in the Americas. Since then, all four serotypes have spread throughout the Americas, causing DHF outbreaks and becoming endemic in many countries.</p>", "<p>Increased global movement of people and cargo via air travel have undoubtedly assisted dengue’s growth, says Harrington. It is this movement that now allows multiple serotypes of dengue to encounter each other frequently, leading to the complications of DHF. And in the Americas, the reinvasion of <italic>Ae. aegypti</italic> after the lapse of eradication campaigns also contributed to dengue’s resurgence, Harrington says. “For those of us who work in dengue research, I think there’s a fairly strong consensus about what the major factors are [in dengue’s spread],” she says.</p>", "<title>The Climate Change Question</title>", "<p>One factor, however, remains debatable: the effect of climate change on the dissemination of dengue. Like many vector-borne diseases, dengue fever shows a clear weather-related pattern: rainfall and temperatures affect both the spread of mosquito vectors and the likelihood that they will transmit virus from one human to another. In a cool climate, the virus takes so long to replicate inside the mosquito that most likely the mosquito would die before it actually has a chance to transmit the virus to another person, says senior research fellow Simon Hales of the University of Otago, New Zealand. “There’s a consensus that climate is one of the necessary factors that has to be right for dengue to be able to be transmitted,” Hales says. “Whether or not climate change will affect the spread of dengue is probably more contentious.”</p>", "<p>Several studies have predicted that global climate change could increase the likelihood of dengue epidemics. In the 14 September 2002 issue of <italic>The Lancet</italic>, Hales and his colleagues published an empirical model of worldwide dengue distribution in which they reported that annual average vapor pressure (a measure of humidity) was the single climate factor that best predicted dengue fever distribution. They also used their model to predict likely effects of humidity on dengue distribution. If humidity were to remain at 1990 levels into the next century, a projected 3.5 billion people would be at risk of dengue infection in 2085, but assuming humidity increases as projected by the Intergovernmental Panel on Climate Change, the authors estimate that in fact 5.2 billion could be at risk.</p>", "<p>Other work has reported correlations between dengue and climate variables such as El Niño, temperature, rainfall, and cloud cover. In March 2008, the United Nations Intergovernmental Panel on Climate Change released its <italic>Fourth Assessment Report on Climate Change Impacts: Impacts, Adaptation and Vulnerability</italic>, concluding that climate change could increase the number of people at risk of dengue infection.</p>", "<p>But some dengue researchers feel that a case for a connection between dengue incidence and climate change has yet to be made. Global warming might influence dengue transmission “to the extent that it influences how water is managed and handled,” says Harrington, but temperature increases are probably not important for the virus’s expansion. “If you really sit down and look at the science, . . . there are no real hard data to show that [climate change is] having an effect,” she says.</p>", "<p>There’s no argument that global warming is occurring, says Gubler, but as for the suggestion that it has played any role in the expansion of dengue, “It’s all hype. A lot of public health officials and a lot of policy makers use global warming as a cop-out, an excuse for not controlling a disease that is very preventable.”</p>", "<p>In a plenary session at the May 2008 annual meeting of the American Institute of Biological Sciences, Gubler urged that policy makers not focus on climate change but resume addressing the chief known drivers of dengue’s spread—namely, population growth, urbanization, and modern transportation. Importantly, he said, “we need political will. With political will, we may get the economic support that we need to do the research to develop effective prevention and control strategies.”</p>", "<p>But even as there is no documentation that climate change is influencing the spread of dengue, Hales counters there also is no proof regarding many other factors claimed responsible for increased dengue—such as urbanization, population increase, and heightened travel—and that no published studies have attempted to assess the relative importance of these factors in comparison to temperature trends. It is not controversial, he adds, that dengue is highly temperature-sensitive, citing work published by Douglas M. Watts and colleagues in the January 1987 issue of <italic>The American Journal of Tropical Medicine and Hygiene</italic> showing that temperature-induced variations in how efficiently <italic>Ae. aegypti</italic> transmits the dengue virus may be “a significant determinant” in the annual cyclic pattern of DHF epidemics in Bangkok.</p>", "<p>As for whether dengue is very preventable, Hales points to the example of Singapore, where dengue persists despite the best efforts of this wealthy country with its well-developed public health infrastructure and vector control. Hales concedes that it’s too soon to say for sure whether climate change is promoting the spread of dengue, but that “other things being equal, we would expect [the disease] to spread with projected climate change.” If Earth warms as expected, “then a larger area of the planet’s surface will be climatically suitable for dengue,” he says.</p>", "<p>Global transport has helped another dengue vector spread to new territory. An article in the September 1987 issue of the <italic>Journal of the American Mosquito Control Association</italic> noted that the Asian tiger mosquito, <italic>Aedes albopictus</italic>, spread worldwide through the international trade in used tires. Over the past 25 years, the relatively cold-hardy <italic>Ae. albopictus</italic> has invaded many U.S. states, and rising average temperatures raise the possibility that the vector could move even further north.</p>", "<p><italic>Ae. albopictus</italic> is occasionally an important dengue vector in rural and suburban areas in Southeast Asia, says Philip McCall, a medical entomologist at the Liverpool School of Tropical Medicine, United Kingdom, and it was also behind Hawaii’s 2001 outbreak of 122 cases on the island of Maui. But Gubler says it is a mistake to assume that dengue epidemics will necessarily result from the spread of <italic>Ae. albopictus</italic>. Although this mosquito has been shown to be a highly efficient carrier in controlled experiments, it is far less so in real-world situations, he explains, mainly because it feeds on both humans and nonhuman animals, and it prefers rural environments to urban settings. If <italic>Ae. albopictus</italic> populations were to displace <italic>Ae. aegypti</italic>, then that could actually lead to reduced risk of dengue transmission, Gubler says.</p>", "<title>A Disease of Poverty</title>", "<p>Dengue is a disease of poverty, Hales says. “In the places where it’s really rife, typically urban shantytowns, people have got very poor services,” he explains. “Waste is piling up in the street. There’s no running water, so people have to collect water in vessels, which then breed mosquitoes. The people have got terrible housing, so they’re not able to protect themselves from getting bitten. And they’re living in very close proximity. It’s the perfect recipe for a huge epidemic.”</p>", "<p>Even if today’s temperate latitudes did become more suitable for dengue transmission, Gubler says, most of those regions are more developed and have good enough housing and water supply that dengue epidemics would remain unlikely. The standard of living in the United States will likely prevent any major dengue epidemics. “The United States is not going to have major epidemics of vector-borne diseases unless we allow our public health system to deteriorate completely,” Gubler says.</p>", "<p>But professor Peter Hotez of The Sabin Vaccine Institute and George Washington University worries about the effect of dengue and other diseases he calls “neglected infections of poverty” on the poorest people in the United States. “There’s always been this reservoir of people at risk, and my concern is that, because they’re poor and voiceless, we ignore them,” he says.</p>", "<p>Although dengue is endemic in Puerto Rico (where it has caused epidemics since the 1960s), it is absent from most of the continental United States, except in travelers returning from tropical locales. However, the disease appears occasionally along the U.S.–Mexico border, Hotez says. Along the border, reported incidence is much higher in the Mexican states than the U.S. ones, probably because of different living standards—window screens, air conditioning, and effective sanitation may help keep dengue at bay on the U.S. side, Hotez says.</p>", "<p>However, a study published in the October 2007 issue of <italic>Emerging Infectious Diseases</italic> found that dengue incidence was surprisingly high in the border town of Brownsville, Texas. The researchers found evidence of past dengue infection in 40% of Brownsville residents. People with low income, no air conditioning, and poor street drainage were most likely to have suffered infection.</p>", "<p>In a review published 25 June 2008 in <italic>PLoS Neglected Tropical Diseases</italic>, Hotez estimated as many as 200,000 U.S. cases of dengue fever occur each year, “but the estimates are pretty wide-ranging,” he says. There have so far been few reports of DHF in the United States. However, Hotez points out that DHF outbreaks have happened as close as Cuba; therefore, he says, “so there’s every reason to believe that it could happen in the United States.”</p>", "<p>Many of the people at risk of dengue infection in the United States are members of minority groups, Hotez says—something that also applies to other infections that many people think of as “tropical” disease. Besides dengue, low-income Hispanic communities near the Mexican border are also at risk of Chagas disease, cutaneous leishmaniasis, and cysticercosis, an infection caused by ingesting tapeworm eggs, which is now a leading cause of epilepsy and seizures in areas around the U.S.–Mexico border. Many of these infections have been around for a while, Hotez says; however, “We’ve just ignored and neglected them because we tend not to pay attention to the plight of the poor and underrepresented minorities.”</p>", "<p>Hotez suspects that people living in other areas prone to neglected infections—especially the post-Katrina Gulf Coast and elsewhere in the Mississippi River delta—are at some risk of dengue, but few data have been collected. “It’s not clear how many cases of dengue infection there are each year in the United States,” he says. “We’re not doing aggressive surveillance.”</p>", "<title>Curbing Dengue’s Expansion</title>", "<p>Researchers are coming at dengue from a variety of angles to try to curb the virus’s spread. There are no available vaccines or antivirals for dengue infection, leaving mosquito control as the only current method for prevention and control.</p>", "<p>“Ultimately, we need a vaccine for dengue,” says Harrington. “That’s probably the only way that we’re going to be able to have a significant impact.” Dengue vaccine development has proven challenging, largely because of the four different virus serotypes in circulation. Because DHF usually occurs when an individual already has immunity against one dengue serotype, researchers fear that vaccines that fail to provide equal immunity against all four serotypes may actually predispose people to hemorrhagic complications if they encounter a novel serotype after being vaccinated. “That has really slowed the development of the dengue vaccine,” Hotez says.</p>", "<p>Currently, researchers at the Korea-based Pediatric Dengue Vaccine Initiative, chaired by Gubler and funded by the Bill &amp; Melinda Gates Foundation, are facilitating the development of several different technologies to overcome those obstacles, for example, by helping some companies with clinical trials, establishing field sites, and working with developing countries to create the infrastructure to manage eventual vaccine distribution.</p>", "<p>As part of the Grand Challenges in Global Health program, also funded in part by the Gates Foundation, researchers are creating mosquitoes that are genetically incapable of transmitting the dengue virus. About a dozen scientists worldwide are tackling different facets of the project, says Harrington, whose laboratory is assessing whether the transgenic strains are likely to outcompete <italic>Ae. aegypti</italic> for resources and mates in the wild. “If you could dream about something that could really make an impact, this would be it,” she says.</p>", "<p>For now, dengue control still relies heavily on controlling the mosquito that transmits it. McCall and his colleagues have been running studies in Latin America and Southeast Asia to judge the effectiveness of household-based insecticide-treated materials (such as window curtains) and domestic water container covers as foils to the mosquito carriers and dengue transmission. Also, scientists from Vietnam and Australia reported in the January 2005 issue of <italic>The American Journal of Tropical Medicine and Hygiene</italic> that cultivating a natural predator of <italic>Aedes</italic> mosquitoes, the tiny crustacean <italic>Mesocyclops</italic>, in water storage containers virtually eliminated <italic>Ae. aegypti</italic> populations. “I was amazed,” McCall says. “I’m often skeptical about biological control, but in Vietnam, when used in combination with clean-up and education campaigns, this seems to have been spectacularly successful.”</p>", "<p>Research continues on all fronts, adding to the collective knowledge about dengue transmission and, in some cases, challenging long-held assumptions. A mathematical model published by Suwich Thammapalo and colleagues in the 12 February 2008 issue of <italic>Proceedings of the National Academy of Sciences</italic> showed that decreasing dengue transmission may sometimes cause an increase in cases of DHF. The model’s predictions were boosted by epidemiologic data from Thailand that were later published 16 July 2008 in <italic>PLoS Neglected Tropical Diseases</italic>. The authors of both papers speculate that the effect may arise from a brief, transient cross-protection that people experience when infected with one serotype of dengue. At very high levels of dengue transmission, people could then have immunity to all four serotypes of the virus. If transmission is reduced moderately, this cross-immunity would also be reduced. The results are controversial, McCall says, “but many in the field believe it to be the case.”</p>", "<p>Not everyone agrees, however, and Harrington summarizes some of the concerns about the paper. The authors based their conclusions on the relationship between dengue infection and transmission as a function of mosquito abundance as measured using the Breteau index, which reflects the number of water containers with mosquito larvae in 100 randomly selected houses in a community. But the Breteau index is a poor estimate of mosquito abundance, she says, and it rarely indicates what species is abundant. Moreover, it does not provide a large enough sample size to be powerful and meaningful.</p>", "<p>“This type of work is a prime example of scientists working in isolation,” she says. “It highlights the need for cross-collaborative work on models for dengue ecology and epidemiology where biologically meaningful models can be developed.”</p>", "<p>According to Hales, some of the most promising solutions may not directly involve mosquito eradication and may have little to do with technology. “What people [at risk] need is a decent environment in which to live,” he says. “If we had a dengue vaccine, most likely those people wouldn’t be able to afford it anyway. I’m not saying don’t look for a vaccine, but that’s probably not a short-term answer for the problem for these people.”</p>" ]

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[ "<fig id=\"f1-ehp-116-a382\" orientation=\"portrait\" position=\"float\"><caption><p>A child watches as a worker fumigates to prevent dengue fever and other mosquito-borne diseases, Old Havana, Cuba, January 2008.</p></caption></fig>", "<fig id=\"f2-ehp-116-a382\" orientation=\"portrait\" position=\"float\"><caption><p><italic>Aedes aegypti</italic>, the primary vector for dengue, has become perfectly adapted to the urban environment. In the wake of discontinued eradication efforts, <italic>Ae. aegypti</italic> has reinfested nearly every region from which it was eliminated.</p></caption></fig>", "<fig id=\"f3-ehp-116-a382\" orientation=\"portrait\" position=\"float\"><caption><p>In the wake of rapid urbanization and heightened global travel since World War II, the number of both dengue cases and countries reporting infection has climbed precipitously.</p><p>Source: WHO; <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.who.int/csr/disease/dengue/impact/en/index.html\">http://www.who.int/csr/disease/dengue/impact/en/index.html</ext-link></p></caption></fig>", "<fig id=\"f4-ehp-116-a382\" orientation=\"portrait\" position=\"float\"><caption><p>A systematic eradication program largely eliminated <italic>Ae. aegypti</italic> in the Americas by the 1970s. But once the program was discontinued, the vector came back stronger than ever.</p><p>Source: Arias JR. 2002. Dengue: how are we doing? Washington, DC: Pan American Health Organization.</p></caption></fig>", "<fig id=\"f5-ehp-116-a382\" orientation=\"portrait\" position=\"float\"><caption><p>Scientists recently modeled the estimated baseline population at risk for dengue infection in 1990 (A) and in 2085 (B) using climate data for 1961–1990 and projections for humidity change—a function of climate change—for 2080–2100. Ranges above indicate percentage of the population at risk: 0–10%, 10–20%, etc. However, many scientists do not agree that climate change will appreciably alter the risk of dengue.</p><p>Source: Hales S, et al. 2002. Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet 360:830–834.</p></caption></fig>", "<fig id=\"f6-ehp-116-a382\" orientation=\"portrait\" position=\"float\"><caption><p>Dengue is transmitted by mosquitoes that have become perfectly adapted to the urban environment. Areas where there is poor sanitation and overcrowding (such as Rio de Janeiro, Brazil, above and below) are ripe for epidemics. According to the Brazilian Ministry of Health, Rio was the site of about half the dengue cases in an epidemic that swept this country in spring 2008.</p></caption></fig>", "<fig id=\"f7-ehp-116-a382\" orientation=\"portrait\" position=\"float\"><caption><p>A worker fumigates a house in Old Havana, Cuba, January 2008. Control of mosquitoes with pesticides is one of the few methods currently available to rein in dengue. Systematic habitat destruction also has proved effective in the past.</p></caption></fig>" ]

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[ "<p>It’s happened again. For the third time in 15 years the Mississippi River massively burst its banks this spring, inundating tiny Missouri towns such as Winfield (population 720) and Foley (population 178), causing potentially billions of dollars’ worth of destruction—although the damages are still being assessed—and hiking corn prices to $8.00 a bushel in the wake of the lost crop. On 22 April 2008 scientists with the U.S. Geological Survey (USGS) measured the largest water volume on the lower Mississippi River since 1973, with a flow of 1.8 million ft³/sec, enough to fill more than 20 Olympic-size swimming pools in 1 second.</p>", "<p>The environmental fallout from the 2008 floods is still being assessed. The danger of floodwater is not simply that its level is too high, says Robert Criss, a geologist at Washington University. “Floodwaters are heavily polluted with sewage and filth, commonly bearing counts of coliforms, fecal <italic>Streptococcus</italic>, and other bacteria of many thousands per deciliter,” he explains. “The waters are laden with contaminants including agrochemicals, oils, detergents, and toxic metals. They are highly turbid, typically bearing particulate loads a hundredfold or more higher than normal river waters.” Those pollutants, contaminants, and sediments can be carried into homes, where the lingering dampness promotes mold growth. Moreover, floodwater pools trapped behind failed levees serve as breeding grounds for mosquitoes, flies, and other disease vectors.</p>", "<p>Meanwhile, observers are asking how such devastating floods could have occurred again so soon. The massive flooding is attributed largely to torrential spring rains in the Upper Mississippi Valley, which Paul Rydlund, a supervisory hydrologist with the USGS Missouri Water Science Center, says were even greater than those preceding the record-breaking 1993 Midwest flood. But as heavy as those rains were, the question in the minds of some is whether they were made worse by structures such as levees and other man-made interventions wrought upon the Mississippi River over time.</p>", "<title>The Hand of Man</title>", "<p>Changes to the river are hardly a latter-day phenomenon. John Anfinson, a historian with the National Park Service’s Mississippi National River and Recreation Area, notes that the early French explorers of the sixteenth and seventeenth centuries recognized that to capture the economic potential of the Mississippi “you have to harness the river; you have to make it behave.” That harnessing began with the settlement of New Orleans in the early 1700s, where the first levees were thrown up to create a city that actually lies below sea level. Thereafter, Anfinson says, “The levee system started growing upriver and the navigation system [i.e., dredging and channeling endeavors] started growing downriver.”</p>", "<p>In the centuries that followed, shipping and agricultural interests pressed Congress to allow alteration of the river. Deepening channels made shipping easier, and construction of levees permitted farming and the establishment of towns and cities along the river’s banks.</p>", "<p>Many experts point to levees as a major culprit in flood devastation. Levees narrow the flow of the water, preventing it from spreading out into the floodplain and forcing it to move faster, explains geologist Jeffrey Mount, who directs the Center for Watershed Sciences at the University of California, Davis. As a bolus of floodwater moves down a river, levees can get overwhelmed in their work. “The dark secret that no one wants to share is that there are two kinds of levees: those that have failed and those that will fail,” Mount says. And when that levee fails with a massive wall of water pressing on it, the water rushes with great force onto the land behind the levee. “The power of water is a function of the difference in elevation between the top of the water and the adjacent land,” Mount explains. “So the greater that difference, the more powerful the flow that comes out onto that land in terms of its velocity and its power to erode.”</p>", "<p>Wing dams on the river are another factor in exacerbating floods, says Criss. These rock jetties, situated roughly perpendicular to the riverbank, aid shipping by preventing the accumulation of sediment so the river channel stays deeper. Deeper water moves faster, meaning the impact of floods can be greater.</p>", "<p>Criss points to another problem: “There are general changes to the land that decrease the permeability of soils. As we convert our forests and prairies and fields to subdivisions, we increase the rate of runoff [into the river]—that’s the ubiquitous footprint of man.” According to geologist Nicholas Pinter of Southern Illinois University Carbondale, one particular land use change is quite serious—tile drainage. Because many agricultural soils are too poorly drained to naturally serve as good farmland, farmers install subsurface drains (“tiles”). “When it rains,” Pinter says, “the water comes out of farm fields faster than it would otherwise.”</p>", "<title>Ripe for Disaster?</title>", "<p>Land development in floodplains is a perilous exercise, according to Gerald Galloway, a professor of engineering at the University of Maryland and former commander of the U.S. Army Corps of Engineers (USACE). For one thing, he says, developers rely heavily on floodplain maps issued by the Federal Emergency Management Agency (FEMA) that demarcate where we can expect so-called 100-year floods—the sort of flood that can be expected once every 100 years. People erroneously believe if they are outside that demarcation they are safe. However, Galloway cautions, such maps are estimates only. Moreover, he says, “The records of the past don’t necessarily match the pattern we’re in right now in terms of the weather.”</p>", "<p>Criss agrees, saying that the rivers of today are not the rivers of the past, and that there is simply not enough historical information on which to base the notion of a 100-or 500-year flood. Besides, at many places, he argues that what have been called 100-year floods are now 10-year floods due to a combination of changing conditions over time.</p>", "<p>Larry Buss, who chairs the USACE National Nonstructural/Flood Proofing Committee, says that when a levee is built in compliance with the requirements of the National Flood Insurance Program, there is no longer any floodplain management in the area behind the levee. All kinds of new development can then occur in the area landward of the levee as if the floodplain no longer exists—when in fact it does.</p>", "<p>“Politically, it’s more acceptable if politicians come to people and say ‘I’m going to remove the flood threat from you by supporting building a levee so you can live in that floodplain as if the floodplain no longer existed even though flood risk remains,’” Buss says. “That’s more politically acceptable than telling them ‘I’m supporting a buyout’ or ‘I’m supporting a relocation plan where we’ll move you to high ground and have you safe from floods forever.’” However, when the levee does have a problem, the damages are much greater than they might have been because of the increased development in vulnerable areas.</p>", "<p>Buss says the U.S. approach to levees has been too focused on what he calls short-term economic/political gain that is ultimately transformed into long-term economic/political loss when major flooding results in levee failure or overtopping and then catastrophic flood damages and perhaps loss of life. In these cases, he says, there is a major disconnect between those who make land use decisions (i.e., local communities) and those who pay for the ill-starred consequences of those decisions (i.e., state and federal taxpayers).</p>", "<title>The Role of the Corps</title>", "<p>Noting the impact of man-made modifications of the river, critics question the judgment of the USACE in carrying out such modifications. “The Corps comes in with a community-by-community project with measures like levees, and it looks at that levee; it doesn’t look at the whole Mississippi as a system. And that’s a problem,” says Larry Larson, executive director of the Association of State Floodplain Managers.</p>", "<p>Pinter charges that the USACE has failed to examine the possibility of elevated flood risk in many navigational engineering projects, including a $5.3 million project in 2007 to construct three arc-shaped chevrons and other structures in the St. Louis Harbor area. The chevrons work much like wing dams, but they are located wholly in the river rather than extending from the shore. Composed of rock, they are designed to lessen the need for continual dredging of St. Louis Harbor, deepen the river bottom, and straighten the flow of the river. “The benefit of those structures was calculated versus their cost,” says Pinter.</p>", "<p>“The benefit was reduced dredging to help maintain the navigation channel. The cost was the construction cost with no calculation whatsoever of the additional financial cost in terms of elevated flood risk.”</p>", "<p>But Robert Davinroy, chief of river engineering at the Corps’ St. Louis office, says the USACE did examine the question of elevated flood risk. “We know how these structures work,” he says. “They’re submerged by about twelve feet when the river gets to flood stage—they have no effect on flood heights.” He adds there are no data to show the chevrons have any effect on the height of floods.</p>", "<p>As far as levees go, Galloway argues that the USACE is quite interested in understanding the impact of these structures on floods. “In the greater St. Louis area the Corps has been asking [Congress] for money for a study to see what is the cumulative effect of a lot of little levees. So far they haven’t gotten the money. People are not as much interested in learning that sort of information as they are in building levees,” says Galloway, referring to those legislators and constituents who view levees as flood protection. For example, a 20 September 2007 article in <italic>Time</italic> magazine that sharply criticizes a USACE flood control project in Missouri notes that Senator Kit Bond (R–MO) and Representative Jo Ann Emerson (R–MO) were responsible for pushing the project through.</p>", "<p>Politics and pressure do play significant roles in the selection of which projects to fund, notes Representative Eddie Bernice Johnson (D–TX), who chairs the Water Resources and Environment Subcommittee of the House Transportation and Infrastructure Committee. For instance, she says the USACE might initially say that a given project is not necessary. “The next time around they’ve been under so much pressure, they’ll go along with it. I’ve seen that happen,” she asserts. She says the pressure comes from local citizens through their representatives for projects that “shouldn’t be funded,” and the prospects for changing the system are dim.</p>", "<p>Moreover, Galloway observes that when Congress authorizes a USACE project, the money for it is quickly appropriated. But when it comes to appropriating money to remedy any environmental problems resulting from the project, Congress is very slow to allocate funds, an observation Johnson says is “probably true.”</p>", "<p>A 2005 Government Accountability Office report titled <italic>Wetlands Protection: Corps of Engineers Does Not Have an Effective Oversight Approach to Ensure That Compensatory Mitigation Is Occurring</italic> took the Corps’ mitigation efforts to task when it comes to restoring wetlands as part of mitigation efforts, labeling USACE guidelines as “vague and internally inconsistent.” However, by the time the same office issued the 2008 report <italic>Compensatory Mitigation for Losses of Aquatic Resources</italic>, mitigation efforts had reportedly improved and the USACE was meeting mitigation requirements except for the stipulation that any concern voiced about a project receive a 60-day review.</p>", "<title>A New Approach to Managing Floods</title>", "<p>From the perspective of the USACE, attitudes toward floods have been evolving into what Buss describes as a more “holistic” approach focused on reducing flood risk with the realization that floods will occur. This approach considers all flood risk reduction tools including not just levees but also buyouts and relocations. Buss says many professional flood risk experts believe the nation should consider levees only as a last resort after first considering measures such as buyouts, relocations, elevation, and zoning.</p>", "<p>Using floodplains for any development other than farms is simply asking for trouble, asserts Criss. “Floodplain development should be recognized as geologically stupid, economically unwise, environmentally harmful, and pernicious to mankind,” he says.</p>", "<p>Agriculture, however, can make valuable use of the floodplain. Farms in the bottom-lands of rivers tend to be quite productive, says Galloway, whereas “when you start farming in hill country, you’re back to the erosion problems we had in the thirties.” However, some farms are located at especially precarious points along the river—for example, in the former channel of the river. He proposes that such farms be bought out and the levees taken down. But that of course depends on whether the owner of the farmland is willing to sell.</p>", "<p>Galloway also proposes changing levees that protect farms so they are open to the river at certain times, as a way to reduce flood damage. It’s an idea that Peter Rabbon, program director of the Corps’ National Flood Risk Management Program, says has merit. One way to implement this idea would be to build an overflow system (or “flowage easement”) into a levee, which Rabbon says would allow water to flow onto farmland in a controlled way.</p>", "<p>“This idea has been talked about for ages,” says Mount. “The wisest and best use of these floodplains is farms, rather than cities, because during the hydrologic emergencies [floods] you can store water on those farms, creating a modest amount of dislocation rather than catastrophe. A wise society compensates the farmer for saving it billions of dollars in damages,” he says. Flowage easements are gradually being introduced, he says.</p>", "<p>One of the most celebrated buyouts recently was that of Valmeyer, Illinois. The community of 900 souls was devastated by the 1993 Mississippi flood, the latest in a series of inundations endured by that community since 1910. The community moved to higher ground nearby, an effort involving 22 government agencies and a cost in the range of $28 million. In the process of rebuilding, the people of Valmeyer incorporated several sustainable design elements into their new town, including energy-efficient construction and passive solar technology. But such efforts, says Buss, demand strong community leadership with long-term vision—something that can be hard to find.</p>", "<p>Whether relocations such as Valmeyer’s will be seen as a result of this year’s flood is still uncertain. Money and leadership are both needed. But recent and future legislation may force at least partial change.</p>", "<p>Representative James Oberstar (D–MN), who chairs the House Transportation and Infrastructure Committee, notes that the 2007 $ 23 billion Water Resources Development Act “requires that national water resources planning avoid the unwise use of floodplains and flood-prone areas and requires the President to report by 2010 on national vulnerability to flood damages.” The 2007 legislation also addresses the funding of mitigation efforts by stipulating that if mitigation is required for a particular construction project, then it must be carried out before or concurrently with that project.</p>", "<p>This year’s proposed water resource legislation also includes a number of other provisions to reduce flood damage, such as creating incentives to limit development in flood-plains, investing in natural buffers such as wetlands, and pursuing technology for improved understanding of flooding threats. “As we move forward with the next [Water Resources Development Act] bill, we will continue to look for ways to better ensure that mitigation is carried out where and when it is required,” Oberstar says.</p>" ]

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[ "<fig id=\"f1-ehp-116-a390\" orientation=\"portrait\" position=\"float\"><caption><p>The Mississippi River breaks through a levee between the Illinois towns of Quincy and Meyer, 18 June 2008.</p></caption></fig>", "<fig id=\"f2-ehp-116-a390\" orientation=\"portrait\" position=\"float\"><caption><p>The Mississippi River snakes through downtown LaGrange, Missouri, 21 June 2008. Many engineers and floodplain managers believe the risks that come with siting towns in floodplains are unacceptably high.</p></caption></fig>" ]

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[ "<p>Omega-3 polyunsaturated fatty acids, believed to lessen the risk of many chronic ailments including arthritis, cancer, heart disease, and memory loss, may also help protect the heart against certain damaging effects of air pollution. In a new study by an international team of researchers, supplementation with omega-3s was associated with significantly reduced cardiac stress caused by particulate matter less than 2.5 μm in diameter (PM_2.5) in a group of elderly individuals in Mexico City <bold>[<italic>EHP</italic> 116:1237–1242; Romieu et al.]</bold>. The study is the first to examine the effects of omega-3s on biomarkers of cellular response to the oxidative stress of air pollution.</p>", "<p>Exposure to high levels of particulates from vehicle exhaust and industrial emissions raises the risk of hypertension, heart arrhythmia, heart attack, and stroke, with the elderly being particularly susceptible. Some of the authors had previously shown both that PM_2.5 promotes heart disease by diminishing heart-rate variability and that omega-3 supplementation could increase heart-rate variability. The current study was intended to find out how omega-3s achieve their effects.</p>", "<p>The study population of 52 elderly nursing home residents was chronically exposed to high PM_2.5 levels; particulate levels inside the nursing home, where residents spent nearly all their time, correlated with the smoggy surroundings outside. For four months starting in 2001, half the participants in the double-blind study received fish oil supplements at doses typical for over-the-counter supplement users; the other half received soy oil supplements.</p>", "<p>The research team compared blood samples taken from subjects before and during supplementation and found that omega-3 use was associated with diminished oxidative damage in blood cells. The observed antioxidant effect of omega-3s was much greater in fish oil users than in soy oil users, a difference the investigators attribute to the different amounts and types of omega-3s in the two supplement types (docosahexaenoic acid and eicosapentaenoic acid in fish oil versus α-linolenic acid in soy oil).</p>", "<p>The authors note limitations of their study, such as the small sample size and limited exposure assessment. However, the finding that omega-3s appear effective against oxidative stress related to PM_2.5 exposure, with fish oil supplements offering more protection than soy oil supplements, merits further study in larger populations.</p>" ]

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[ "<p>Outdoor air pollution has been linked with increased risk of death from cardiorespiratory disease in epidemiologic studies in North America and Europe. Some studies have found that sex, age, or other modifying factors can cause increased susceptibility to air pollution in some individuals. However, few of these studies have been conducted in Asia. Now a new study of Shanghai residents reveals that the elderly, women, and individuals with lower educational backgrounds are especially vulnerable to outdoor air pollution during cooler weather <bold>[<italic>EHP</italic> 116:1183–1188; Kan et al.]</bold>.</p>", "<p>The researchers examined death certificates recorded between 1 January 2001 and 31 December 2004 in the central area of Shanghai and found an average of 119 nonaccidental deaths reported daily, with 49.1% due to cardiorespiratory disease. They collected daily air pollution data for particulate matter less than 10 μm in diameter (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) from the Chinese government agency that tracks air pollutants and assessed how mortality and pollutant levels varied by sex, age, educational status, and season of the year.</p>", "<p>They found that most air pollutant levels peaked in the cool season (October through March, when the temperature averages 58°F), correlating with a peak in the death rate; the exception was O₃, which had higher concentrations in the warm season (April through September, when the temperature averages 75°F). They observed a 2- to 3-times greater risk of death from cardiorespiratory disease in the cool season compared with the warm season, with SO₂, NO₂, and O₃ particularly showing seasonal differences in association with cause of death. The same air pollutants were also associated with a 3- to 4-fold greater risk of cardiovascular death in the cool season than in the warm season, possibly because exposure to air pollutants is reduced by staying inside air-conditioned buildings.</p>", "<p>Additionally, people older than 65 were up to 5 times more likely than younger people to die of cardiorespiratory disease. Compared with men, deaths in women were twice as likely to be linked to elevated O₃ and PM₁₀ levels. This may be due to men’s greater rate of smoking, the effects of which may override pollution-related effects in male smokers. Overall, people with less education were twice as likely as more educated residents to die during periods of elevated pollution. Educational level, a reflection of socioeconomic status, has been reported previously as a modifying factor for air pollution–related deaths in North America and Europe, but this is the first such report from mainland China, where the concentrations of PM₁₀, SO₂, and NO₂ are much higher.</p>" ]

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[ "<fig id=\"f1-ehp-116-a394b\" orientation=\"portrait\" position=\"float\"><caption><p>A study of Shanghai residents showed that cardiorespiratory deaths increased during the cool season, which runs from October through March.</p></caption></fig>" ]

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[ "<graphic xlink:href=\"ehp-116-a394bf1\"/>" ]

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[ "<p>The attacks on the World Trade Center (WTC) on 11 September 2001 exposed thousands of emergency responders and other recovery workers to a unique mix not only of airborne toxic pollutants but also psychological stressors. The physical consequences such as persistent respiratory ailments have been documented previously [e.g., <italic>EHP</italic> 114:1853–1858 (2006)]. The latest report from a 5-year study of health effects among WTC rescue and recovery workers describes a higher level of lingering mental health problems among these workers than in the general population <bold>[<italic>EHP</italic> 116:1248–1253; Stellman et al.]</bold>.</p>", "<p>More than 10,000 WTC workers completed several standard mental health questionnaires 10–61 months after the attacks. About 90% of the respondents worked at the WTC site during the first 2 weeks after 9/11, and the majority remained onsite for 3 months or longer. On the basis of an analysis of their responses, and in the absence of a clinical evaluation, the researchers classified 11.1% of workers with probable post-traumatic stress disorder (PTSD), 8.8% with probable depression, 5.0% with probable panic disorder, and 62% with substantial stress reactions (such as nightmares, flashbacks, and insomnia). Overall, mental health problems declined gradually from 13.5% to 9.7% among WTC workers during the course of the study.</p>", "<p>The incidence of PTSD in WTC workers, which parallels that reported in soldiers returning from combat duty in Afghanistan, was about 4 times higher than that for the general population in the United States. Probable PTSD was associated with having lost family members or friends in the attacks; those with probable PTSD had a 17-fold greater likelihood of reporting disruption of family, work, and social life. About half those with probable PTSD also experienced probable panic disorder, depression, or both. Workers with probable PTSD also perceived their children as having more psychological symptoms (such as clinginess or trouble sleeping) and behavioral problems than workers without PTSD.</p>", "<p>Alcohol-related problems also were abundant in the study group. More than 17% reported symptoms of probable alcohol abuse. Nearly half reported drinking more heavily than usual during the period they worked at rescue and recovery efforts, and months later a third were still drinking more than usual.</p>", "<p>The authors conclude that the variety of persistent mental health problems in responders “underscores the need for long-term mental health screening and treatment programs targeting this population.” Following future environmental disasters, they write, mental health problems are virtually certain to accompany physical effects of toxic exposures. Rescue and recovery workers therefore should receive behavioral health evaluations as well as medical evaluations to reduce adverse health and social consequences.</p>" ]

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[ "<fig id=\"f1-ehp-116-a395a\" orientation=\"portrait\" position=\"float\"><caption><p>A worker surveys the WTC site, 25 September 2001</p></caption></fig>" ]

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[ "<p>An estimated 310,000 U.S. children between ages 1 and 5 have elevated blood lead levels despite efforts to reduce lead in the environment. Research in the past decade has begun to focus on factors that could make some children more susceptible to lead poisoning even at low levels of exposure. A new study explores one such possible factor—gene variants that influence lead absorption—linking variants in two iron metabolism genes to higher blood lead levels in children <bold>[<italic>EHP</italic> 116:1261–1266; Hopkins et al.]</bold>.</p>", "<p>When researchers analyzed umbilical cord blood from 422 children in Mexico, they found that the presence of two variants of the hemochromatosis (<italic>HFE</italic> ) gene—<italic>HFE C282Y</italic> and <italic>HFE H63D</italic>—predicted blood lead levels 11% higher than those in children not carrying the variants. Moreover, the presence of either <italic>HFE</italic> variant combined with a variant form of the transferrin (<italic>TF</italic> ) receptor gene—<italic>TF-P570S</italic>—predicted blood lead levels 50% higher than in children with none of the variants.</p>", "<p>Although the <italic>HFE</italic> and <italic>TF</italic> genes normally regulate iron metabolism, they may also influence blood lead levels because lead—like iron—is a divalent metal. Thus, the two metals can be “mistaken” for each other during metabolic processes. The <italic>HFE</italic> gene regulates iron-binding proteins, including TF, and variant forms of this gene sometimes induce hemochromatosis, a disease characterized by increased intestinal absorption of iron that contributes to abnormally high iron stores in adulthood.</p>", "<p>The authors hypothesized that the <italic>HFE</italic> variants might similarly increase absorption of lead, a hypothesis supported by the results of this study. <italic>TF</italic> interacts with <italic>HFE</italic> to form a complex that down-regulates iron absorption. However, <italic>TF-P570S</italic> may interact with the <italic>HFE</italic> variants in ways that heighten metal absorption rates. Study results showed the <italic>TF</italic> and <italic>HFE</italic> variants produced higher lead levels than those predicted by either <italic>HFE</italic> variant alone.</p>", "<p>Previously published research by these investigators has shown that having the <italic>HFE</italic> variants predicted lower blood lead levels in elderly men compared with men without the variants. The contrasting findings, the authors speculate, may reflect age-specific differences in body iron stores and in the variants’ effect on lead metabolism. Among children with low iron body stores and high iron needs, the variants predicted higher blood lead levels. But as iron stores accumulate with age, the variants down-regulated iron and lead absorption, leading to progressive declines in blood lead levels. The study’s key implications are twofold: first, that children with variant iron-metabolizing genes may be especially susceptible to the effects of lead at low exposure levels, and second, that genetic variants may increase risk at one life stage and decrease it at others.</p>" ]

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[ "<p>In July 2001, employees of the U.S. Bureau of Reclamation closed the big steel gates across the intake of a major irrigation canal just outside the town of Klamath Falls, Oregon. Water stopped flowing to about 85% of the farms in the Klamath Basin, a high-elevation bowl ringed by forested mountains on the Oregon–California border. The ostensible purpose of the shutdown was to protect three threatened or endangered fish species living in the lakes and rivers tapped by the irrigation system.</p>", "<p>That water cutoff, coming in the middle of the irrigation season in a region almost wholly dependent on irrigated agriculture, ignited a legal and political firestorm. Hundreds of outraged farmers and their supporters surrounded the main irrigation canal’s control structure and turned the water back on, retreating only when federal law enforcement agents arrived. Lawsuits were filed. Protesters arrived from as far away as Nevada, Montana, and Malibu. Among their props was a giant metal bucket, a symbolic water delivery to parched Klamath farms. Three years later, although the out-of-town horde drawn by the water shutoff had long since departed, the bucket—with an American flag on top and the words “Klamath Bucket Brigade” painted on the side—still greeted visitors in front of the County Government Center in Klamath Falls.</p>", "<p>The dramatic standoff and its bitter aftermath drew nationwide media attention. Nearly every story printed or broadcast during that long, drought-parched summer focused on the spectacle of “farms versus fish,” an emotionally resonant but not particularly accurate or useful description of the conflict.</p>", "<p><italic>Water War in the Klamath Basin: Macho Law, Combat Biology, and Dirty Politics,</italic> a new book by a pair of veteran environmental-law experts, delves deep into the tangled legal underpinnings of the conflict, revealing it to have been about much more than farms and fish. Holly Doremus, a law professor at the University of California at Davis, and A. Dan Tarlock, distinguished professor of law at the Chicago-Kent College of Law, have crafted a dense but concise text that describes the assumptions underlying federal and state natural-resource policies and explains how legal contradictions, changing cultural values, and evolving scientific understanding combined to make the clash in the Klamath Basin both inevitable and intractable.</p>", "<p>The Klamath Basin contains about 1,400 farms encompassing more than 200,000 acres of cropland, most of it served by a large and complex federal irrigation system known as the Klamath Project. It is one of the oldest federal water projects in the nation, and like nearly all the great public works of the Reclamation Era, it was constructed with no concern for the effect its dramatic reworking of natural hydrology would have on the fish and other wildlife occupying the region’s lakes, streams, and wetlands.</p>", "<p>The 2001 water cutoff, at least superficially, represented a belated effort to mitigate the effect of agricultural irrigation diversions on three fish species: the Lost River and shortnose suckers, inhabitants of the basin’s largest lake and its tributaries; and coho salmon, found in the Klamath River downstream from the project. But as Doremus and Tarlock correctly point out, the Klamath conflict also pitted the interests of farmers and ranchers against those of commercial fishermen on the coast more than 200 miles away. It was about the conflicting cultural values of whites and Native Americans. And finally, it was about two contradictory ways of thinking about the environment: one deriving from the past and viewing natural resources from a purely utilitarian perspective, and one that arose only recently in American history, regarding ecosystems and their living components as having rights and value beyond their economic utility to human beings.</p>", "<p><italic>Water War in the Klamath Basin</italic> is sometimes a tedious read—the authors have an annoying habit, useful in introductory college textbooks but nowhere else, of reminding the reader about what they have already explained and previewing matters they will explain later—and it shortchanges the rich human component of the Klamath saga. But it is a useful and thorough primer on western water law and federal environmental policy. And it serves as a cautionary guide to the conflicts that inevitably will arise in other overallocated watersheds as population growth, a warming climate, and failing ecosystems conspire to reveal profound flaws in the hydraulic foundation of the West.</p>" ]

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[ "<fig id=\"f1-ehp-116-a402a\" orientation=\"portrait\" position=\"float\"></fig>" ]

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