dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This project is funded by the  Total Foundation ,  Institut Pasteur ,  Odyssey Re-Insurance company, Pasteur Foundation  Switzerland as well as the  Nutricia Research Foundation. PV was supported by an  Early. Postdoc. Mobility and Advanced. Postdoc. Mobility Fellowship from the  Swiss National Science Foundation , a  Roux-Cantarini Fellowship  and a  L'Oréal-UNESCO for Women in Science France Fellowship. PJS is a  HHMI Senior Foreign Scholar and CIFAR scholar  in the human microbiome consortium. Work in BBF's group is supported by the  Canadian Institute for Health Research. The funders had no role in study design or collection, analysis, and interpretation of data and in writing this manuscript.
Insufficient study duration and sampling frequency are primarily due to the cost of water microbial analyses. In the 1990s, the development of healthcare databases provided new resources for public health surveillance. Concurrently, continuous monitoring of water turbidity and chlorine became more widespread, making large datasets available which could be used to build proxies for exposure. The availability of both health and water records encouraged the use of time series studies (TSS)  [10]. Since data were continuously available and free, TSS could last long enough to meet the study requirements in terms of adequate statistical power and representativeness for the most common combinations of epidemiological and hydrological contexts, and yield a robust estimate of the risk. Turbidity measures the light diffraction capacity of suspended particles in water to estimate the particle load.
Materials and Methods My review process complied with the "Preferred reporting items for Systematic Reviews and Meta-Analysis" (PRISMA) recommendations  [19]. I first collected all published TSS addressing the association between water turbidity or precipitation and acute gastroenteritis (AGE). My search of the Medline database (the last search was in November 2017) was based on the occurrence of ("Time series*" AND ("Drinking water" OR Waterborne) AND (Gastrointestinal* OR gastroenteritis OR Diarrhea OR Infection) AND (Turbidity OR Precipitation) NOT Review) in the tittle, abstract or keyword list. All terms were MeSH terms, apart from "Time series", "Turbidity", and "Precipitation". No constraint on the publication date was imposed. I completed the search by examining the reference lists in the primary set of articles.
• Minimal significance (p < 0. 1) of the turbidity-risk function (TRF); • Sufficient plausibility of the shape of the TRF: an increasing TRF was required for the shape to be considered plausible; and • Good fit between (1) the turbidity-AGE latency observed in the model and (2) the delay for both water transit through the distribution network and infection incubation in sick people. I also evaluated the robustness of the TRF through documenting (i) the consistency of the TRF over several consecutive lags; (ii) its reproducibility in different age classes, in different health indicators, and in mono-and multi-exposure models (i. e. , where several exposure proxies were included together in the risk model); and (iii) when extreme values of exposure were excluded from the dataset. When possible, I homogenized the exposure scenarios used by the authors to express risks, in order to enable comparisons of the risk levels between the studies.
• The first study carried out in Le Havre  [26]  that used a moving average model yielding conditional risk estimates. Among selected sites, several DWS shew complex production-distribution patterns with DZ fed by several DWTP (Le Havre, Paris, New York) and including sectors with time-varying water origin; but no exposure misclassification remained at the population level, since adequate operation data (e. g. , daily records of turbidity and produced flow) were available for all DWTP involved, thus allowing to calculate unbiased exposure estimates (e. g. , mean turbidity weighed by produced flows). Eighteen studies, therefore, met selection criteria (Table  1 ), and concerned 17 different sites corresponding to nine articles and seven reports. Seven sites were located in North America, nine in France, and one in Sweden.
Int. J. Environ. Res. Public Health 2018, 15, x FOR PEER REVIEW 8 of 25 With respect to the statistical power of the selected TSS and the robustness of the risk estimates (Figure  2 ), four studies included fewer than 10,000 AGE cases: the study performed in Le Havre, the two in Philadelphia, and the study in Québec. The second Philadelphia study lasted only two years and its study periods was embedded within the period of the previous study, while the Québec study lasted three years. In contrast, I counted 10 studies each with a study period of at least six years and each with approximately 100,000 AGE cases: Atlanta (with the longest study period of 12 years); New York; Nantes; Paris-Est; and the three sites in the Paris area (PA), defined as follows: PA-Nord, PA-Est (N. B. : not to be confused with Paris-Est), PA-Sud, Nancy, Vancouver (with the largest dataset of 1.
Health Outcomes AGE is used in epidemiology as a generic indicator for infections arising from fecal pathogens, as the syndrome is very common  [40, 41]. This provides potential sensitivity, and as AGE has a short incubation period, it also provides good reactivity to environmental triggers. AGE indicators used in TSS depend on the availability of syndromic data, i. e. , on the local health care system. Indicators should, however, meet sensitivity and specificity if referring to a symptomatic definition of a case to achieve an accurate risk assessment, and remain stable over time. In the US, the availability constraint led epidemiologists to examine visits and admissions to hospital emergency departments from different data providers. Both diagnosis by medical staff and the use the International Classification of Diseases (AGE related codes in ICD9: 001 to 009.
In France, epidemiologists used prescriptions of General Practitioners (GP) from a health insurance database  [42]. The indicator results from the application of an algorithm based on the test of the nature and quantity of prescribed drugs, which enables true cases of AGE to be discriminated from others. The evaluation of this algorithm  [42]  concluded that sensitivity was acceptable (approximately ten percent of true medicalized cases were wrongly rejected) and specificity (approximately ten percent of included cases were not cases of AGE). The indicator was also calibrated with the consensus symptom-based definition of AGE cases  [43] : MG consultations accounted for 33% of total cases, according to Majovicz's definition  [41]. Thus, the French health insurance database provided enough sensitivity to perform separate analyses in children and adults in cities with over 50,000 people.
In the Vancouver study, authors systematically tested both hospital admission and visits to GPs and found that hospital admission data yielded a risk attributable to tap water turbidity which was approximately half that from visits to GPs (0. 6% vs. 1. 6%). The shapes of the risk function were not always linear or quasi-linear. On the contrary, the use of spline functions showed that non-linearity was a common feature. In four sites (Nantes, Boston, Paris-Centre, and Paris-Sud), non-linear TRF leveled off and became inaccurate at high turbidity values. This suggests that an additional variable was needed in the model to correctly characterize the risk at high turbidity.
In Search for Additional Exposure Proxies from Water Operation Data In all of the French studies, water operation and weather data were systematically explored to identify alternative or complementary proxies of exposure to waterborne pathogens. Fourteen variables were tested in total, or 22 when distinguishing the different measurement points inside the treatment chain (e. g. , Tu_RW and Tu_FW) and derived variables (e. g. , the number of consecutive dry days derived from the precipitation time series) (Table  2 ). Some variables were tested only once and others repeatedly, according to the availability of data. This exploration stage highlighted four variables as major contributors to the risk model: qualitative change in treatment (2/2), river flow for river DWS (5/6; Figure  4 ), supply water temperature (6/6; Figure  5 ) and produced flow (8/9; Figure  6 ).
The levels of risk associated with these alternative exposure proxies were higher and more significant than the risk associated with turbidity. Since no strong collinearity was observed within the exposure set or between exposure levels and control covariates, risk estimates did not change with the removal of other exposure variables from the model. In addition, variations in the dataset used to adjust the model, e. g. , adult instead of children health data, did not affect the set of significant exposures or the shape of the risk functions. The shapes of the risk function were not always linear or quasi-linear. On the contrary, the use of spline functions showed that non-linearity was a common feature. In four sites (Nantes, Boston, Paris-Centre, and Paris-Sud), non-linear TRF leveled off and became inaccurate at high turbidity values.
The levels of risk associated with these alternative exposure proxies were higher and more significant than the risk associated with turbidity. Since no strong collinearity was observed within the exposure set or between exposure levels and control covariates, risk estimates did not change with the removal of other exposure variables from the model. In addition, variations in the dataset used to adjust the model, e. g. , adult instead of children health data, did not affect the set of significant exposures or the shape of the risk functions. The Le Havre study showed that decantation-triggered when Tu_RW exceeded 3 NTU (syst. 1)-strongly modified the TRF associated with Tu_FW, with a shift from a high risk (IQ-ERR = 18% (14; 22)) in the absence of decantation, to a low and non-significant risk (IQ-ERR = 3% (0; 7)) with decantation.
TSS Do Not Support the Quantitative Health Impact Assessment In a former review  [60] , authors stated that the set of published TSS is still inadequate to draw a meta-risk estimate representative of the risk in cities of developed countries. The insufficiency of the dataset not only results from the small number of available studies and from the recruitment provisions leading to an overrepresentation of "good performers", but mainly from the intrinsic heterogeneity of turbidity that makes pooled analyses irrelevant. The quantitative health impact assessment  [61]  aims to predict the possible effect of prevention actions on risk. It is based on the existence of a robust causal risk function. TSS do not offer an interesting perspective for the building of a generic meta-TRF. Even when considering a single site, the specific TRF (if any) does not enable operators to forecast the effect on risk of a reduction in turbidity.
ACKNOWLEDGMENTS This work has been partially funded by the  European Commission  as part of a  H2020   MSCA  project named  HUGS  under the grant agreement #  675325. YW would like to thank the  China Scholarship Council (CSC)  for the financial support. This work was performed, in partnership with the  COST Action FP 1306 Valorization  of ligno-cellulosic biomass streams for sustainable production of chemicals, materials and fuels using low environmental impact technologies.
Methods: We implemented a three-step analysis strategy in three datasets: The Epidemiological study on the Genetics and Environment of Asthma (EGEA) dataset: 1,660 subjects; The Saguenay-Lac-Saint-Jean (SLSJ) dataset: 1,138 subjects; and The Medical Research Council (MRC) dataset: 446 subjects). This strategy included a single-SNP genome-wide association study (GWAS), the selection of related gene pairs based on statistical filtering of GWAS results and text-mining filtering using GRAIL and SNP-SNP interaction analysis of selected gene pairs.
Study datasets and definition of atopy The EGEA study combines a case-control and a family-based study of asthma. The whole study population includes 388 families ascertained through at least one asthmatic proband recruited in chest clinics (1,705 probands and first-degree relatives) plus 415 population-based controls (total of 2,120 subjects). All subjects were born in France and were of European ancestry. 9] [20]  Atopy was assessed by skin prick tests (SPT) performed in 1,978 subjects. A positive SPT response was defined as a wheal diameter ≥ 3mm to at least one of 11 aeroallergens belonging to three groups (indoor allergens, outdoor allergens, molds). After quality control (QC) of genotypic data, 925 atopic and 735 non-atopic subjects were included in the analysis. The Saguenay-Lac-Saint-Jean and Quebec City Familial Asthma Collection (SLSJ) consists of a French-Canadian founder population panel of 253 multigenerational families from Saguenay-Lac-Saint-Jean region, ascertained through two asthmatic probands.
21 This study has been described elsewhere. 21 Skin tests were done in 1,195 SLSJ subjects and atopy was defined similarly as in EGEA. After QC of genotypic data, the analysis dataset included 641 atopic and 497 non-atopic subjects. The Medical Research Council (MRC) UK study includes 207 nuclear families, recruited through at least one proband with childhood-onset asthma (MRCA sample). The study protocol has been described elsewhere. 22 Atopy was defined similarly as in EGEA. To increase the number of unaffected subjects (controls), we included subjects from another MRC-UK dataset that were recruited through probands with eczema (MRCE sample). Only subjects without asthma, without eczema and with low IgE levels were used as controls in this study. We checked that the age and gender distributions were similar in MRCA and MRCE samples.
Genotyping Both EGEA and SLSJ datasets were genotyped using the Illumina 610-Quad array (Illumina, San Diego, CA), as part as of the Gabriel asthma consortium GWAS. 23 Stringent quality criteria were applied to select both individuals and SNPs and have been previously detailed. 23, 24 After QC, there was a final set of 501,167 autosomal SNPs for analysis. The offspring in MRCA families and MRCE controls were genotyped using the Illumina Sentrix HumanHap300 BeadChip (307,981 autosomal SNPs), as part of the first asthma GWAS. 22, 23 QC for MRC samples has been detailed elsewhere. 22, 25 In order to get a number of SNPs in MRC sample as large as in EGEA and SLSJ samples, SNP imputation was performed using MACH v1. 00 software  26  and HapMap2 release 21 CEU haplotypes as reference panel. Imputed SNPs were kept for analysis if their imputation quality score (rsq)  27  was ≥ 0.
Genome-wide single-SNP analysis We performed a two-stage GWAS. In the first stage, association analysis between individual SNPs and atopy was carried out in the EGEA dataset. This analysis was based on a logistic regression model assuming an additive model for SNP effect, using Stata® V14. 1. This model was adjusted for significant effects of age and sex and two principal components (PCs) to account for population structure. We took into account familial dependencies using the cluster and robust options of the logit function in Stata®. Test of SNP effect was based on a Wald-test. In a second stage, the SNPs reaching P≤10 -4 in EGEA were followed-up in SLSJ and MRC. The association analysis in SLSJ and in MRC used the same model as in EGEA. The results of stage 2 datasets and, then, of the three datasets were combined using a fixed-effects meta-analysis.
Selection of gene pairs using both statistical and text-mining filters The statistical filtering consisted of selecting two sets of genes using the GWAS results: genes showing significant association with atopy (set-1) and genes showing suggestive association with atopy (set-2). The set-1 included all genes lying at a distance of 50 kb or less from SNPs reaching the genome-wide significance level in the GWAS meta-analysis. The set-2 included all genes that were at most 50 kb apart from SNPs having P≤10 -4 in the stage 1 EGEA dataset and were not part of set-1. To assign SNPs to genes, we used NCBI dbSNP Build 137 and human Genome Build 37. 3. We further filtered gene pairs (formed by crossing set-1 genes with set-2 genes) through GRAIL  17  text-mining of PubMed abstracts (available in October 2014).
SNP-SNP interaction analysis for selected gene pairs As for the single-SNP association analysis, we performed a two-stage SNP-SNP interaction analysis. At stage 1, we analyzed all SNP-SNP interactions for the GRAIL-selected gene pairs in the EGEA dataset. For each gene, we considered all SNPs lying within gene boundaries. Pairwise SNP-SNP interactions were evaluated by logistic regression assuming an additive model for SNP main effects and interaction and adjusting for the same covariates (age, sex, PCs) as in the GWAS, using Stata® V14. 1. We used the same coding scheme as usually proposed for SNP-SNP interaction modelling. 29 We modeled the additive effect of a SNP by coding the genotypes of homozygotes for the minor allele, heterozygotes and homozygotes for the major allele as 1, 0, and -1; the interaction term between two SNPs was obtained by multiplication of these genotypic values for the two SNPs.
Test of interaction was based on a likelihood-ratio test which follows a Chisquare distribution with one degree of freedom. We discarded all SNP pairs for which one or more of the nine genotypic combinations appeared in fewer than five subjects (cases or controls). In a second stage, all SNP pairs showing suggestive evidence for interaction in EGEA (Pint≤5x10 -3 ) were followed-up in SLSJ and MRC. The results of the stage 2 datasets and, then, of the three datasets were meta-analyzed using a fixed-effects model. To correct for multiple testing, we computed, for each gene pair investigated, the effective number of independent interaction tests from the eigenvalues of the correlation matrix of products of SNP variables, using an extension of Li and Ji's method. 30 The corrected threshold to declare an interaction statistically significant was equal to the 5% type I error divided by the sum of effective number of independent interaction tests over all gene pairs tested.
Stratified analyses according to asthma status Because family samples were ascertained through asthmatic probands, we investigated whether SNP associations and SNP-SNP interactions detected with atopy might be related to the presence of asthma. Single-SNP and SNP-SNP interaction analyses were repeated in the two groups of asthmatic and non-asthmatic subjects separately. These analyses were performed for the SNPs that showed significant results in the meta-analyses of the three datasets. Homogeneity of the oddsratios (ORs) between the two groups was tested using the Cochran's Q statistic. 31.
Descriptive statistics A total of 1,660 EGEA, 1,138 SLSJ and 446 MRC subjects were included in this study. The proportion of atopic subjects was similar in EGEA and SLSJ (55. 7% and 56. 3% respectively) but was lower in MRC (23. 8%; P≤10 -3 ). In each study, there was a higher proportion of males in atopic than in non-atopic subjects and atopic subjects were younger than non-atopic subjects (see Table  E1  in the Online Repository). As expected, the proportion of asthmatic subjects was higher in atopic than in non-atopic subjects in all datasets (Table  E1 ). In EGEA (respectively in SLSJ and MRC), 78. 0% (75. 0% and 78. 3%) of atopic subjects had positive SPT to indoor allergens, 55. 5% (77. 5% and 52. 8%) to outdoor allergens, and 34. 8% (14. 8% and 12. 3%) to molds.
Genome-wide single-SNP analysis In the stage 1 EGEA dataset, no SNP reached the genome-wide significance level of 1. 5x10 -7 (see quantile-quantile (QQ) plot and Manhattan plot in Figures  E1  and E2 ). However, 73 SNPs lying in 47 loci showed associations with atopy exceeding the screening threshold of P≤10 -4. These SNPs were followed-up in the stage 2 SLSJ and MRC datasets and meta-analyzed (Table  E2 ). The SNP rs4916831 within ADGRV1 gene at 5q14 reached the genome-wide significance level (Pmeta=6. 8x10 -  ) in the overall meta-analysis of the three datasets (Table  I ). Four other SNPs at that locus, in moderate linkage disequilibrium (LD) with rs4916831 (r 2 ranging between 0. 51 and 0. 79), showed suggestive association (4. 3x10 -7 ≤Pmeta≤3. 8x10 -  ; Table  I ).
DISCUSSION By combining genome-wide single-SNP analysis and epistasis analysis driven by statistical and knowledge-based evidence in three asthma-ascertained family datasets, we identified significant association of atopy at a novel 5q14 locus harboring ADGRV1 gene and significant interaction between ADGRV1 and DNAH5 genetic variants. The interaction between ADGRV1 and DNAH5 variants has biological relevance as these two genes are both involved in ciliopathies and ciliary function. Ciliopathies comprise a group of disorders associated with genetic mutations encoding defective proteins, which result in either abnormal formation or function of cilia. 34 Mutations in the adhesion G protein-coupled receptor V1 (ADGRV1) gene cause Usher syndrome type IIC, a ciliopathy characterized by hearing loss and visual impairment,  35, 36  while mutations of dynein axonemal heavy chain 5 (DNAH5) gene cause primary ciliary dyskinesia type 3, a ciliopathy which combines upper and lower tract respiratory manifestations, male infertility, and situs inversus.
** Pint is the P-value of the likelihood-ratio test for interaction between SNPs (which follows a chi-square distribution with one degree of freedom assuming an additive model). † † Pstage2-int is the P-values associated with the Wald test of meta-analyzed interaction effect in the stage 2 datasets (SLSJ and MRC). ‡ ‡ Pmeta-int is the P-values associated with the Wald test of meta-analyzed interaction effect in the three datasets (EGEA, SLSJ, MRC); Pmeta-int is shown in bold when it reached the multiple-testing corrected threshold of 7. 3x10 -5. § § PCochran is the P-value associated with Cochran's Q test of homogeneity across the three datasets.
Sources of support: This work was supported by the  French National Agency for Research  ( ANR-11-BSV1-027-GWIS-AM ;  ANR-USPC-2013-EDAGWAS ) and  Université   Pierre et Marie Curie doctoral fellowship , The Canada Research Chair held by  C Laprise  and the funding supports from  Canadian Institutes of Health Research (CIHR)  enabled the maintenance and continuation of the SLSJ asthma study. C. Laprise is the director of the  Asthma Strategic Group of the Respiratory Health Network  of the  Fonds de la recherche en santé du Québec (FRSQ)  and member of Allergen network. Genotyping was supported by grants from the  European Commission  (No. LSHB-CT-2006-018996-GABRIEL ). Medical Research Council funded collection of nuclear families with Asthma MRCE:  Medical Research Council  funded collection of nuclear families with Eczema.
37 -0. 39 (0. 08) 9. 7x10 -7 -0. 14 (0. 11) 0. 18 -0. 29 (0. 21) 0. 16 -0. 17 (0. 09) 6. 5x10 -2 -0. 30 (0. 06) 7. 8x10 -7 0. 18 rs949787 90,251 G/T 0. 28 -0. 33 (0. 08) 5. 5x10 -5 -0. 23 (0. 10) 2. 0x10 -2 -0. 19 (0. 20) 0. 36 -0. 22 (0. 09) 1. 2x10 -2 -0. 28 (0. 06) 3. 2x10 -6 0. 65 * Position in kilobases (kb) according to NCBI dbSNP Build 137 † Major allele/Minor allele ‡ Minor allele frequency § beta is the regression coefficient for a one-unit increase of the effect allele in logistic regression assuming an additive model; se is the standard error associated with the regression coefficient. ║ P is the P-value associated with the Wald test of SNP effect. ** Pstage2 is the P-values associated with the Wald test of meta-analyzed SNP effect in the stage 2 datasets (SLSJ and MRC). † † Pmeta is the P-value associated with the Wald test of meta-analyzed SNP effect in the three datasets (EGEA, SLSJ, MRC); the P-value is shown in bold when it reached the multiple-testing corrected threshold of 1.