dataesr/scientific-paragraphs-categorization huggingface dataset:
Hospital deaths For HS, CoD data were collected from the two main hospitals: the national referral hospital at Port Vila on Efate (the main Island), and the secondary district hospital at Luganville, the main town on Espirito Santo (n = 954), excluding 27 deaths (no age recorded, and still births) (Table  1 ). These are the larger two of only three hospitals in Vanuatu routinely staffed by doctors. Hospital separation data were extracted from the Health Information System (HIS). This database is maintained at the national level, but local versions of the database were used as they are considered to be more complete. CoD is recorded according to principal diagnosis, with up to two additional fields for other causes as well as fields for accidents, external causes, and medical procedures. Hospital separation data would normally have the principal diagnosis recorded first, but this field was recorded as the mode of death (cardio-respiratory arrest, heart stopped, stopped breathing, etc.
) in many of the cases reviewed, indicating that tabulation based on the first field of the database would have been misleading. The fields appear to have been completed as a causal sequence, as found on a medical certificate, so underlying cause was selected and coded according to ICDv10 rules for medical certificates  [11]. Causes were coded by medical record staff at the hospital, with both text and code entered in the database. Medical certificates for hospital deaths are completed semi-routinely (and particularly upon request by family members). During the period of this study, medical certificates were not routinely coded or collated for Luganville, and although collated for Port Vila hospital, were not routinely coded, analyzed, or reported. Data were extracted from the original medical certificates at Luganville hospital and from a spreadsheet maintained by medical records staff at Port Vila hospital (n = 1,790).
Community health facility deaths Deaths in the community are captured through health system reporting by health centers, dispensaries, and aid posts. The locally trained nurses or nurse aides at each facility are required to submit a monthly report of activities including any deaths in their area. A notice of death form is also collected for deaths in the community. This includes name, age, date of death, place of death, and CoD as a single free-text field. Deaths are collated and coded according to ICDv10 and entered into the national HIS database. Data were sorted according to health facility code, and deaths that occurred in Vila or Luganville hospitals were removed. For deaths where specific age was not recorded, deaths recorded as "adults" were attributed to age group 15-59 years, and "old" or "elderly" to age group ≥60 years.
Analysis The approximate proportion of total deaths accounted for by each data set was calculated based on an average of 1,394 deaths per year as estimated by the Vanuatu National Statistics Office  [12] , assuming little variation over the period under investigation. Fetal deaths were identified and removed from all data sets: 13 from hospital medical certificates, 100 from hospital separation data, and 102 from community health facility data. Cases where there was uncertainty whether the death was fetal or early neonatal were counted as neonatal, as separate reporting mechanisms exist for stillbirths; in practice they should not be recorded in these data. Deaths without ages were also removed: 14 deaths from medical certificates, 78 from hospital deaths, and 46 deaths from facility data (Table  1 ).
Deaths were tabulated by broad age group: 0-4 years and 5-14 years for both sexes (because of small numbers), 15-59 years by sex to evaluate differences in CoD distribution by age, and ≥ 60 years (not coded for CoD). Causes of death were aggregated to ICDv10 General Mortality List One (104 causes), with childhood deaths (0-4 years) aggregated to ICDv10 Condensed List Three for U5M. Tabulations of reported deaths by cause and proportional mortality, excluding unknown and ill-defined deaths, were then calculated for each data source. Exclusion of unknown/ill-defined deaths from analysis of proportional mortality produces the same result as redistribution of deaths by cause. In order to produce national estimates of proportional mortality by cause, estimated total deaths by broad age group (<5, 5-14, 15-59, ≥60 years) and sex were derived from census analyses by application of reported annual age-and sex-specific mortality rates to populations (from the 1999 census)  [4]  and estimated annual deaths by age/sex reported in the 2009 census analytic report  [1].
These data were averaged to obtain estimated annual Vanuatu deaths by age/sex for the study period. Average annual community deaths were estimated from total deaths by deduction of annualized MC deaths as the best representation of hospital deaths. This produced source-weights for <5 years of 0. 33 for MC and 0. 67 for CHF deaths, and for 5-14 years of 0. 15 for MC and 0. 85 for CHF deaths (both sexes); and for 15-59 years 0. 32 for MC and 0. 68 for CHF male deaths, and 0. 37 for MC and 0. 63 for CHF female deaths. National proportional mortality by cause was estimated by a weighted average of MC and CHF proportional mortality. The national estimate of proportional mortality from maternal mortality was used to estimate maternal deaths from the adult female population, and this was divided by births derived from the 1999 and 2009 census analyses  [1, 4]  to produce a maternal mortality ratio for the period.
Acknowledgements This study is an output of the  Euroceans/IOC IndiSeas Program  (www. indiseas. org). The authors would like to thank all collaborators and colleagues who kindly provided data or insights on one or more of the ten ecosystems and four modelling approaches examined. We thank  Penny Johnson ,  Julia Blanchard ,  Jeroen Steenbeck ,  Ricardo Oliveros  for their work on Atlantis, Multispecies size spectrum, Ecosim and Osmose, respectively. The authors are grateful to three anonymous reviewers whose comments have helped improve our paper.
Funding This study was funded by  Euromarine  and the  EMIBIOS  project ( FRB Fondation pour la Recherche sur la Biodiversite  ´, Contract no. APP-SCEN-2010-II). JR was funded by the  French-South African ICEMASA program  and  IRD. LS was supported through the  South African Research Chair Initiative , funded through the  South African Department of Science and Technology (DST)  and administered by the  South African National Research Foundation (NRF) , as well as through additional funding granted by  IRD. AG was supported by  NOAA 's  Integrated Ecosystem Assessment (IEA) programme  (http://www. noaa. gov/iea/). GH was funded by  IRD-DPF   PhD fellowships program  of the  Institut de Recherche pour le Developpement (IRD). JEH was supported by a  Beaufort Marine Research Award  carried out under the  Sea Change Strategy  and the  Strategy for Science Technology and Innovation  ( 2006-2013 ), with the support of the  Marine Institute , funded under the  Marine Research Sub-Programme  of the Irish National Development Plan 2007-2013.
Qualitative gelatin zymography Serum expression profiles of MMP-2/MMP-9 (pro-and active forms) and proMMP-9/NGAL were analysed using 7. 5% (w/v) SDS-polyacrylamide gels containing 0. 1% gelatin (w/v), as described in  [38]. The MMPs' gelatinolytic activities were visualized as transparent bands on the background of Eza-blue-stained gelatin (Sigma-Aldrich, Saint Louis, Mo, USA). IMAGEJ software (version 1. 47) (National Institute of Health, USA) was used to analyse the bands after acquisition with an Appligen densitometer (Oncor, Illkirch, France). As previously described, culture supernatant (treated with aminophenylmercuric acid) from phorbol myristate acetate-activated U937 monocyte-derived cells was used as a positive control for pro-and active MMP-2/MMP-9 proteins, and primary leukaemic B cells were used as a positive control for proMMP-9 and the proMMP-9/NGAL complex  [18].
RNA isolation, cDNA synthesis and PCR RNA was extracted from PBMCs, and cDNA was synthesized as described in  [38]. The cDNAs for human b2-microglobulin (165 bp), MMP-2 (225 bp), MMP-9 (296 bp), MMP-12 (1069 bp), NGAL (479 bp) and IL-6 (562 bp) were amplified in PCRs using the published primer sequences (Eurogentec, Angers, France), as previously described in detail  [38] [39] [40] [41] [42] [43] [44]. The annealing temperature for primers was 57°C. The PCR products were visualized by electrophoresis in a 1. 8% agarose gel containing 0. 2 lg/ml ethidium bromide. The band intensity of the PCR products of interest (measured in an Appligen densitometer (Oncor) and analysed using IMAGEJ 1. 47 software) was normalized against that of b2-microglobulin (internal control).
Statistical analysis Data were expressed as the mean AE S. E. M. or the median (interquartile range, IQR). Groups were compared using Student's t-test, the Mann-Whitney U-test or an analysis of variance. Spearman's correlation coefficient was calculated as a guide to the relationships between MMP, NGAL, proMMP-9/NGAL, NE and IL-6 levels and respiratory parameters. An analysis of covariance was used to assess MMPs, proMMP-9/NGAL, NGAL, NE and IL-6 levels in healthy smokers (hereafter referred to as the healthy-S group) and COPD smokers (the COPD-S group) as a function of age and pack-years. All statistical analyses were performed using SPSS â software (version 17. 0 for Microsoft Windows, SPSS Inc. , Chicago, IL, USA) and GraphPad Prism software (version 6. 04; GRAPHPAD Software, La Jolla, CA, USA).
High MMP-3 and low MMP-2 serum levels in COPD smokers We examined the serum levels of (pro and active) MMP-2/MMP-3/MMP-7 and MMP-12 proteins in the healthy-S and COPD-S groups. The serum level of MMP-2 was lower in the COPD-S group than in the healthy-S group (105. 21 versus 110. 75 ng/ml, respectively; P = 0. 02; Table  3 ). In contrast, serum levels of MMP-3 and MMP-12 (but not MMP-7) were significantly higher in COPD smokers than in healthy smokers (MMP-3: 2. 45 versus 1. 44 ng/ml, respectively, P = 0. 01; MMP-12: 341. 55 versus 171. 65 ng/ml, respectively, P = 0. 03; Table  3 ). After adjustment for age and pack-years, MMP-2 and MMP-3 levels were still significantly higher in the COPD-S group than in the healthy-S group (Table  S1 ). There were no significant differences in MMP levels between the healthy-NS, healthy-S and COPD-NS subgroups (data not shown).
We identified one putatively pathogenic missense mutation c. 541C>T among the Finnish cancer patients and subsequently genotyped the mutation in additional breast cancer cases (n = 5259) and population controls (n = 3586) from Finland and Belarus. No significant association with breast cancer risk was seen in the meta-analysis of the Finnish datasets or in the large BCAC dataset. The association with previously identified risk variants rs999737, rs2588809, and rs1314913 was replicated among all breast cancer cases and also among familial cases in the BCAC dataset. The most significant association was observed for the haplotype carrying the risk-alleles of all the three SNPs both among all cases (odds ratio (OR): 1. 15, 95% confidence interval (CI): 1. 11-1. 19, P = 8. 88 x 10 -16 ) and among familial cases (OR: 1.
One identified missense mutation was in silico predicted to be pathogenic and was subsequently screened in a larger set of breast cancer cases and population controls from four datasets. In addition, the known RAD51B risk SNPs rs999737, rs2588809, and rs1314913 and RAD51B haplotypes were studied in the previously published 40 studies participating in the Breast Cancer Association Consortium (BCAC) including 44,791 breast cancer cases and 43,583 controls.
Genotyping of c. 541C>T The identified c. 541C>T, p. (Arg181Trp) missense change was genotyped in additional familial and unselected breast cancer patients and population controls from the Helsinki (cases = 2203, controls = 1278), Tampere (cases = 704, controls = 800), and Oulu (cases = 452, controls = 273) regions of Finland and in 1900 cases and 1235 controls from Belarus (S1 Text). The Helsinki and Tampere datasets were genotyped by sequencing the exon 6 and the Oulu and Belarus datasets by high-resolution-melting (HRM) analysis (S1 Table ). Written informed consent was obtained from all the participants and the study was approved by the Ethics Committees of Helsinki University Hospital, Tampere University Hospital, Oulu University Hospital, and by the institutional Ethics Commissions at the Minsk Mother and Child Hospital and at Hannover Medical School.
Bioinformatics and statistical methods The pathogenicity of the variants identified in the sequencing of RAD51B was predicted using the MutationTaster software as it considers both exonic and intronic variants  [25]. In addition, PON-P that utilizes results from SIFT, PhD-SNP, PolyPhen-2, SNAP, and I-Mutant 3 was used to predict the pathogenicity of the missense variants  [26]. Secondary structure prediction was done with RaptorX  [27]  and protein-protein interaction with PredictProtein  [28]. Statistical analyses were performed in R version 3. 0. 2 (http://www. r-project. org/). To study the association of the c. 541C>T mutation with breast cancer risk, two-sided P-values were calculated using Pearson's chi-squared test or, if the expected number of cell count was less than five, Fisher's exact test.
For meta-analysis, the estimates were combined using a fixed-effects metaanalysis, using the inverse variance-weighted method. Student's t-test was used to compare the age at diagnosis between the mutation carriers and non-carriers. Per-allele odds ratios (OR) and confidence intervals (CI) for the common RAD51B polymorphisms were estimated with logistic regression. Multivariate regression models including any two of the three SNPs at a time were used to study the independence of the association signals. Haplotype-specific ORs and CIs were estimated with the HaploStats package in R. A haplotype carrying the majoralleles of rs2588809 and rs1314913 and the minor-allele of the rs999737 was used as a reference. Study and principal components were used as covariates in all analyses with the BCAC dataset to correct for potential population stratification.
Results We sequenced the coding region and the exon-intron boundaries of the RAD51B gene in 168 breast (female and male) and 4 familial ovarian cancer patients from Southern Finland. Nine intronic and six missense variants were identified (Table  1 ). The c. 541C>T, p. (Arg181Trp), missense change was the only variant that was predicted to be pathogenic by both MutationTaster and PON-P software and was selected for further genotyping. Based on Finnish subjects in the ExAC dataset, the minor-allele frequency (MAF) for the c. 541C>T variant is estimated to be 1. 2% whereas in the non-Finnish Europeans the MAF is 0. 05% (Exome Aggregation Consortium (ExAC), Cambridge, MA (URL: http://exac. broadinstitute. org) [May, 2015] ). According to RaptorX secondary structure prediction software the arginine in position 181 is located in beta-sheet with the likelihood of 83.
4% but, as determined by PredictProtein, the amino acid is not predicted to directly participate in protein-protein interactions. The c. 541C>T missense variant was genotyped in additional 3359 female and male breast cancer patients and families, and in 2351 population controls from Southern (Helsinki and Tampere datasets) and Northern Finland (Oulu dataset). Altogether, when combining the two stages of mutation testing, c. 541C>T was screened in 2331 patients from Helsinki, 748 patients from Tampere, and 452 patients from Oulu. There was no evidence of association in any of the population-based case-control studies (Table  2 ). There was a suggestive association among breast cancer families with at least three first or second-degree relatives affected with breast or ovarian cancer, compared to population controls, in the Helsinki dataset (OR: 2.
31, 95% CI: 1. 20-4. 48, P = 0. 010) (Table  2 ). The mean age at breast cancer diagnosis was similar between carriers and Competing Interests: The authors have read the journal's policy and have the following competing interests: A. Mannermaa is a member of the PLOS ONE Editorial Board. This does not alter the authors' adherence to PLOS ONE Editorial policies and criteria. non-carriers among the familial patients (54. 2 for carriers and 53. 1 for non-carriers, P = 0. 725) and among all cases (55. 9 for carriers and 56. 4 for non-carriers, P = 0. 744). However, no association was observed for familial breast cancer in the Oulu dataset, and there was no evidence of association when all datasets were combined (OR: 1. 35, 95% CI: 0. 66-2. 73, P = 0. 410). The variant was also genotyped in 1900 breast cancer cases and 1235 population controls from Belarus but only three carriers were identified among cases and none among controls (Table  2 ).
We further studied the missense variant c. 541C>T and the previously reported common risk SNPs rs2588809, rs1314913, and rs999737 in a large breast cancer dataset from BCAC comprising 40 studies with 44,791 invasive breast cancer cases and 43,583 controls of predominantly European ancestry. The subjects were genotyped on an Illumina Infinium custom chip (iCOGS) for over 200,000 SNPs  [14] , including the rs2588809, rs1314913, and rs999737 SNPs. Genotypes for over 11 million SNPs, including the c. 541C>T missense variant, were imputed by using the 1000Genomes project as a reference panel  [24]. For the missense variant, we first performed an association test stratified by study including all the 40 BCAC studies. The overall information score for the c. 541C>T was 0. 673. To increase the imputation accuracy of this rare variant, studies with information score less than 0.
5 in the stratified analysis were excluded from the final analysis (S3 Table ). Altogether 26,969 cases and 27,092 controls from 23 studies were included and the information score for the variant in the final analysis was 0. 755. The MAF of the c. 541C>T missense variant was 0. 2% among all cases, familial cases, and controls and it did not associate with breast cancer among all cases (OR: 0. 97, 95% CI: 0. 76-1. 25, P = 0. 538) or familial cases (OR: 1. 04, 95% CI: 0. 65-1. 66, P = 0. 794). The highest frequency was observed in the Finnish studies HEBCS, KBCP, and OBCS with the overall MAF ranging from 0. 6% to 0. 7%. The HEBCS and OBCS studies are overlapping with the Finnish Helsinki and Oulu datasets that were genotyped for the variant. Breast cancer associations for two of the genotyped SNPs, rs2588809 and rs999737, in the BCAC dataset have been published before  [14, 24, 30]  and the results were replicated here.
Discussion To study the role of RAD51B in breast cancer predisposition, we screened the coding sequence in 172 Finnish breast or ovarian cancer patients. We also studied the common susceptibility variants in the genomic region of the gene in a large dataset from BCAC including 44,791  breast cancer cases and 43,583 controls. Among the Finnish patients, we identified one putatively pathogenic missense variant, c. 541C>T, p. (Arg181Trp), that was further screened in a larger set of unselected and familial breast cancer patients and population controls. In the Helsinki dataset, the c. 541C>T mutation had a suggestive association with familial breast cancer but this association was not confirmed in the other Finnish sample sets. In Belarus, the mutation was rare and was identified only in three cases but not among controls.
For the large BCAC dataset that had been genotyped on the iCOGS chip, c. 541C>T genotypes were imputed. The missense was very rare with a 0. 2% MAF and no association with breast cancer was seen among all breast cancer cases or in the subset of familial patients. Out of the common variants at the RAD51B region, rs999737 and rs2588809 have been associated with female breast cancer and rs1314913 with male breast cancer  [12] [13] [14] 30]. For rs999737 and rs2588809 the results from the previous BCAC studies for all cases and by ER status were replicated here  [14, 30]. The rs1314913 SNP has been previously associated with male breast cancer with an OR of 1. 57 (P = 3. 02 × 10 -13 )  [13]. It has not been previously reported in female breast cancer, and showed here similar associations as rs2588809 with OR of 1.
However, we cannot rule out rare risk-variants in the Finnish or other populations. The minor-alleles of the common polymorphisms rs2588809 and rs1314913 and the major-allele of rs999737 were associated with breast cancer risk in the large BCAC dataset. The strongest association was observed for a risk haplotype carrying the riskalleles of all the three SNPs with an OR of 1. 15 among all cases and 1. 24 among familial cases compared to the haplotype with the respective protective alleles.
grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n°223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/ A10118, C1287/A 10710, C12292/A11174, C1281/ A12014, C5047/A8384, C5047/A15007, C5047/ A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 -the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The Australian Breast Cancer Family Study (ABCFS) was supported by grant UM1 CA164920 from the National Cancer Institute (USA).
The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]. The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. The BBCS is funded by Cancer Research UK and Breakthrough Breast Cancer and acknowledges NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). ES is supported by NIHR Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust in partnership with King's College London, United Kingdom. IT is supported by the Oxford Biomedical Research Centre. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). The CECILE study was funded by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Ligue contre le Cancer Grand Ouest, Agence Nationale de Sécurité Sanitaire (ANSES), Agence Nationale de la Recherche (ANR).
The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837, coordinator: Rita K. Schmutzler). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0 and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany.