dataesr/scientific-paragraphs-categorization huggingface dataset:
It may be that when there are ample opportunities to be physically active, individuals may become more motivated (or may need less motivation) to perform physical activity  (26, 27). Finally, objective data on the presence of destinations was collected using a virtual audit tool in Google Street View. Images depicted often did not reflect the built environment in 2014 (the year in which the survey was conducted). Despite this limitation, the use of virtual audits using remote imaging (such as Google Street View) has proven to be as valid and reliable as street audits  (39, 66). Two other important limitations are the low response rate and the use of self-report questionnaires. The use of selfreport data on obesity-related behaviours may have led to misclassification and same-source bias, especially because we were unable to dichotomize these variables based on current guidelines.
Functional benchmarks The third series of benchmarks evaluated orthology in terms of functional similarity. Although orthology is an evolutionary and not a functional relationship, we chose to include functional benchmarks for two reasons. First, for similar levels of sequence divergence, orthologs have been shown to be moderately (but significantly) more conserved than paralogs in terms of Gene Ontology (GO) annotation similarity  11. For a given evolutionary distance, more accurate orthology inference is thus likely to be correlated with more functionally similar gene pairs. Second, many users are interested in using orthologs to identify functionally conserved genes; for this purpose, functional benchmarks are directly relevant. We assessed functional similarity based on experimentally backed annotations from the UniProt-Gene Ontology Annotation (GOA) database  35  and Enzyme Commission (EC) numbers from the ENZYME database  36.
Note: Any Supplementary Information and Source Data files are available in the online version of the paper. online methods Quest for orthologs reference proteomes and species tree. The QfO consortium has defined a consensus data set of proteomes and common file formats  6, 7  to be used by diverse orthology inference methods, allowing for standardized benchmarks and aiding integration of multiple ortholog sources. The QfO Reference Proteomes data sets were created as a collection of data providing a representative protein for each gene in the genome of selected species. Such data sets have been generated annually from the UniProt Knowledgebase (UniProtKB) database  42  for the past five years. To this end, a gene-centric pipeline has been developed and enhanced over these years by UniProt.
The QfO Reference Proteomes are a manually compiled subset of the UniProt reference proteomes, comprising well-annotated model organisms and organisms of interest for biomedical research and phylogeny, with the intention to provide broad coverage of the tree of life. These complete, nonredundant reference proteomes are publicly available at ftp://ftp. ebi. ac. uk/pub/databases/reference_proteomes/ QfO. The data sets are provided either in SeqXML 20 format or as a collection of FASTA files. The benchmarking effort reported here uses the reference proteomes data set released in 2011, which comprises 754,149 nonredundant protein sequences from 66 species (40 eukaryotes and 26 bacteria-archaea). The reference species tree used in this study was produced by the QfO species tree working group, which surveyed the literature to establish a well-supported tree topology for the 66 species  43  (Supplementary Fig.
1 ). The internal nodes of this reference species tree have assigned confidence levels based on the agreement among the resources surveyed (L90: congruent, significant branch support; L70: congruent; L50: one alternative species tree topology; L30: default level; L10: two or more alternative species tree topologies have been reported; for more detail, see Boeckmann et al. 43  ). The latest version of the tree can be retrieved from http://swisstree. vital-it. ch/species_tree. To minimize the chance of including cases of incomplete lineage sorting in the species tree discordance benchmark, we estimated the evolutionary times of all internal branches using the timetree resource  44  and collapsed branches that were shorter than 10 myr. Orthology databases and methods. EggNOG 26 (http://eggnogdb.
embl. de) is a database of Orthologous Groups (OGs) and functional annotation covering prokaryotic and eukaryotic species. Since version 4. 1, the EggNOG method is also capable of producing fine-grained (for example, pairwise) orthology predictions based on the automated analysis of phylogenetic trees. For this study, the complete set of 66 reference proteomes was independently analyzed using the EggNOG pipeline, which involved 1) joining proteins into inparalogous groups from closely related species and 2) de novo reconstruction of 38,513 OGs by clustering the obtained inparalogous groups based on triangles of their reciprocal best hits  45. Phylogenetic analysis and automated tree interpretation for each OG was subsequently performed using the workflow described in PhylomeDB22 as implemented in the ETE Toolkit v2.
3 (ref. 46). The phylogenetic approach used included testing three aligners (MAFFT 47 v6. 861b, Muscle 48 v3. 8. 31 and Clustal Omega 49 v1. 2. 1) and five evolutionary models (LG, WAG, JTT, VT and MtREV); applying alignment consensus and soft trimming techniques (M-Coffee 50 v10, trimAl 51 v1. 3); and using maximum likelihood tree inference (PhyML 52 v3). This workflow is labeled as eggnog41 when using the ETE-build command and was applied in a per-OG basis. Pairwise orthology predictions were derived from each tree using the species overlap algorithm  53  after rooting trees to midpoint. The predictions were submitted to the benchmark service in July 2015. Ensembl Compara 21 uses a gene-species tree reconciliation pipeline. The predictions were run using the code released in version 81 of the Ensembl (July 2015).
However, Treebest (the software used to build phylogenetic trees) had to be adapted to accept alignments of protein sequences. Treebest makes a consensus out of trees built with various phylogenetic methods and some of them required nucleotide sequences, which were not provided in the QfO data set. The list of maximum-likelihood models and distance methods (used for neighbor joining) was thus updated to: WAG, JTT and Dayhoff instead of WAG and HKY (for maximum likelihood), and JTT, Kimura and mixed amino acid models instead of dN, dS and mixed nucleotide models (for neighbor joining). The predictions were submitted to the benchmark service in June 2015. An older submission based on version 66 of the Ensembl code (June 2011) is also present on the benchmark service. Hieranoid  27  performs pairwise orthology analysis using InParanoid at each node in a guide (species) tree as it progresses from its leaves to the root.
0 (labeled Inparanoid (core)). The predictions were submitted to the benchmark service in June 2011. MetaPhOrs  31  (Meta Phylogeny-based Orthologs) is a repository of orthologs and paralogs that were computed using phylogenetic trees available in several databases or computed from graph-based orthologous groups. For each orthology-paralogy prediction, MetaPhOrs (http://orthology. phylomedb. org/) provides two reliability scores: Evidence Level (informing about number of repositories from which prediction is retrieved) and Consistency Score (defining overall agreement of source databases about given prediction). MetaPhOrs does not include predictions for the three reference genomes Streptomyces coelicolor, Thermotoga maritima and Pyrococcus kodakaraensis (strain KOD1). The predictions were submitted to the benchmark service in February 2013.
(iii) Lastly, we constructed hierarchical orthologous groups (OMA HOGs). These are nested groups that contain genes that descend from a single common ancestral gene within a given taxonomic range using the GETHOGs algorithm  54. The predictions were submitted to the benchmark service in June 2011 (OMA pairs and groups) and in March 2013 (OMA HOGs). OrthoInspector  30  is a database of precomputed orthology and inparalogy relationships and a stand-alone package allowing largescale predictions of orthology between thousands of proteomes (http://lbgi. fr/orthoinspector/). The resource has recently undergone a major new release, with improved speed and visualisation tools, but the inference algorithm is unchanged from the initial graph-based method described in Linard et al. 55. The predictions were submitted to the benchmark service in June 2011.
PANTHER 8. 0  22  is based on version 8. 0 of the PANTHER database (http://pantherdb. org), released in 2012 (the current version is 10. 0, released in 2015). Family membership of each sequence is based on HMM scoring to the PANTHER 'library' of HMMs (at both the family and subfamily levels). Sequences were aligned with MAFFT  56  and the resulting alignment was used to construct phylogenetic trees with the GIGA program  57. GIGA (version 1. 1 was used for PANTHER version 8. 0) uses a species tree to guide tree construction, and all nodes in the tree are labeled as speciation or gene duplication events; these labeled nodes are used to infer orthologs (pairs of genes with a speciation event as their common ancestor). PANTHER predicts two types of orthologs: least-diverged orthologs (LDO) and other orthologs (O).
5 across the whole data set were called orthologs. The predictions were submitted to the benchmark service in June 2013. RBH  24  (Reciprocal best hit) is a classic method consisting of identifying the pairs of genes with mutually highest alignment score between every pair of species. Here, we use reciprocal blastp hits as orthologs, with minimum E-value of 1e-2, and we keep all hits that are ≥99% of the highest score. The predictions were submitted to the benchmark service in January 2016. RSD  25  infers orthology relationships by finding pairs of genes whose nearest gene, computed using PAML, is the other gene in the pair. Candidates genes are also filtered using BLAST E-value and multiple-sequence alignment divergence thresholds. This method is implemented in the database RoundUp  58  , a largescale orthology database developed by the Wall Lab.
The database is no longer maintained, but the source code is still available at https://github. com/todddeluca/reciprocal_smallest_distance/. To identify orthologs, we ran the algorithm with divergence and E-value cutoffs of 0. 8 and 1e-5, respectively. The predictions were submitted to the benchmark service in February 2012. Benchmarks. Generalized species tree discordance. The idea behind the species tree discordance test is simple. Two genes are orthologous if they started diverging through a speciation event. Therefore, if we sample putative orthologous genes such that all resulting genes are related through speciation events, the resulting tree should be congruent with the species tree. Previously, we presented a sampling strategy for fully imbalanced tree topologies  15. Here, we extend this idea to arbitrary reference trees, including those with soft polytomies (unresolved nodes).
Finally, we combine all the families by building averages of the rates. As an example, for the positive predictive value this results in, Functional tests. We downloaded the Gene Ontology annotations  62  for all the genes in the reference genomes from the November 2014 release of UniProt-GOA  35  and excluded any annotation with a 'NOT' qualifier from this set. For the analysis shown here, we only use annotations with experimental evidence codes (EXP, IPI, IDA, IMP, IGI and IEP). Likewise, we collected the hierarchical EC number assignments of the ENZYME database  36  maintained by Swiss-Prot. The computation of the functional similarities between gene pairs is done in the same way for both types of data, using the approach of Schlicker et al. 37  : the semantic similarity between annotations sim(i,j) is measured using Lin's metric  63  ; between any two genes, the most similar pairs of annotations are identified and averaged, i.
ACknowledGments This work was supported by  Swiss National Science Foundation  grant  PP00P3_150654  (to C. D. ),  UK Biotechnology and Biological Sciences Research Council  grant  BB/L018241/1  (to C. D. ),  Spanish Ministry of Economy and Competitiveness  grant  BIO2012-37161  (to T. G. ),  Qatar National Research Fund   NPRP 5-298-3-086  (to T. G. ),  European Research Council  grant  ERC-2012-StG-310325  (to T. G. ),  National Institutes of Health (NIH)  grant  R24 OD011883  (to S. E. L. ),  U41 HG002273  (to S. E. L. and P. D. T. ),  U41 HG007822  (to M. J. M. and I. X. ),  Swiss State Secretariat for Education, Research and Innovation (SERI)  funding (to I. X. and C. D. ),  US National Science Foundation EAGER Award # 1355632  (to K. S. ) and  ANR  project  BIP-BIP   ANR-10-BINF-03-02  (to O.
In this study, the considered sectors are three: industry, commerce and service areas. The sectoral GVA for the starting period is taken from the Eurostat online database (year 2006), whereas the sectoral GVA for subsequent year is derived from an economic projection derived by the GEM-E3 model (E3M-LAB 2010). The GEM-E3 provides annual GVA growth rates with national and sector detail. The main assumption of this approach is that the intensity of ICS land, measured in economic terms for a given year in the past, remains unchanged in time. Therefore, the predicted demand for ICS land is driven directly by the predicted changes in the economic output of the respective sectors.
Measuring the impact of the 2020 land-take milestone All the proposed indicators are computed for year 2020, taking into account the milestone for 2020 (i. e. being on track towards a 'no net land take' by 2050). Table  1  contains the specific indicators that were used to assess the land-take milestone, the land-use intensity and the impact on other land uses. In this assessment we used, for the baseline year, the refined CLC and the population data at NUTS 2 retrieved from Eurostat (2006); for the 2020 period the projected urban fabric areas for both Reference and Target 0 scenario and the projected population for 2020 from EUROPOP2010 database. The land take, land-use intensity and the impact on other land uses were computed at regional level (NUTS 2) using the projected land use map for 2020 simulated by LUISA.
Individual Data Individual data (particularly preterm birth) came from birth records of the district office of maternal and childhood protection, via the mandatory health certificates at birth completed by the maternity prior to an infant's discharge. Validation of the individual data drawn from birth records was carried out according to a methodology approved by the Research, Study, Evaluation and Statistics Directorate-DREES (French Ministry of Social Affairs and Health)  [26]. The available individual data were socio-economic factors (parental occupations), social factors (single-parent family), adverse behaviors (smoking during pregnancy), medical factors (number of previous pregnancies, quality of follow-up, caesarian section, medical history of preterm birth in siblings), fetal factors (sex, birth weight) and gestational age (number of weeks of amenorrhea, reported by obstetrical staff at birth), age of mother and place of residence at birth.
Area-Level Data Area-level socioeconomic status was characterized by the European Deprivation Index (EDI). The geographical units used were IRIS (regrouped statistical information blocks) as defined by the French National Institute for Statistics and Economic Studies (INSEE), an IRIS representing the smallest geographical census unit available in France. The regional capital and other major towns are divided into several IRIS units, and small towns form a single IRIS. Each IRIS includes approximately 2000 individuals with relatively homogeneous social characteristics. In 2007, Deux-Sèvres counted 305 municipalities and 362 IRIS. The EDI reflects objective and subjective, material and social deprivation  [28]. Score of the EDI in France for each IRIS is a sum of weighted variables (percentage of overcrowding houses, exclusive use of bath or shower, foreign nationality, households without a car, no business leaders-company managers/intermediate occupations, lone parent household, low level of education, unemployed, not be owner) for which weights came from multiple logistic regression  [28].
EDI was divided into tertiles. The higher the index, the more the IRIS is considered to be deprived  [28]. The data needed for its construction were drawn from the population census carried out by INSEE in 2007. Mothers' addresses were geocoded at the IRIS level through a correlation map effective in 1999. The map was provided by the Maurice Halbwachs Center (Paris, France), which collects surveys and databases following agreements with the INSEE those mothers lived in the district at the time, several ministerial statistical services and other public institutions  [29].
During a given trimester of pregnancy, each mother-neonate pair had several samples taken (from 1 to 34): 75% of pairs had one sample, 23% two and 2% three. We calculated the mean atrazine metabolite concentration for each trimester. When the mean was above the LQ, atrazine metabolite exposure was defined as positive for the trimester  [30]. As atrazine metabolite concentrations are left censored data with repeated measures, the maximum likelihood estimation method has been employed to impute these data  [31] , according to the LQ (0. 05 µg/L between 2004-2006 and 0. 02 µg/L between 2007-2010). Exposure of atrazine metabolite was treated as continuous variable and was categorized in three groups. Mixture exposure was defined by a combination of the two variables: positive exposure to atrazine metabolite when atrazine metabolite concentration by trimester was above the LQ (negative exposure when it was under LQ) and tertiles of nitrates concentration  [30].
Exclusions to the Dataset Included Multiple births, early deaths (before birth record completion) and newborns with congenital malformations were excluded. Mothers residing in municipalities supplied by several CWSs, mothers who were exposed to other pesticides in drinking-water, or who had undergone no measurement of nitrate and/or atrazine metabolites during the second trimester of pregnancy, were excluded. Mother/neonate pairs that could not be located in an IRIS unit because they lived in a municipality of which the configuration had changed since 1999 or on account of an incomplete address were also excluded.
Statistical Analysis We firstly have imputed left censored data of exposure to atrazine metabolite (57% of data) with a maximum likelihood estimation method for repeated measures (PROC NLMIXED)  [31]. We examined association between each individual risk factor, neighborhood deprivation, exposure and preterm birth with a logistic regression analysis. We compared exposure to atrazine metabolite according to neighborhood deprivation, rural area and season through chi2 tests (Pearson and Cochran-Armitage test). We included seasonal and rural variables in the analyses because pesticide usage is predominant in summer and autumn and in rural areas. An interaction between EDI and exposure was examined but was not significant in multivariable analysis (p = 0. 93). Multivariable logistic regression was used to model the relationship between preterm birth prevalence, exposure to atrazine metabolites and neighborhood deprivation taking the available and relevant individual risk factors into account.
We carried out sensitivity analysis separately mother/neonate pairs from 2004 to 2006 and from 2007 to 2010, as LQ changed in 2007, and as usually the global level of atrazine in water distribution systems decrease over time. The analysis have been done for both degradates (hydroxyatrazine and desethylatrazine) in a separate analysis to see if any effect it might have on the incidence of PTB, but result presentation is restricted to 2-hydroxyatrazine as results were not different. We carried out the same analysis replacing exposure to atrazine metabolite by exposure to nitrate/atrazine metabolite mixture. Analyses were performed using SAS version 9. 3 (SAS Institute Inc. , Cary, NC, USA), and the significance level was set at 5% for all analyses.
Results Out of 24,316 pairs we excluded 5091 through exclusion criteria and 69% of the births were successfully geocoded. We also excluded 1669 pairs because of missing IRIS and 3092 because of missing atrazine exposure. Sample included 13,654 mother/neonate pairs (Figure  1 ), belonging to 279 IRIS and supplied by 51 CWSs (49 ˘49 pairs per IRIS, median 32, from 1 to 307 pairs). Of the 13,654 pregnant women included in the analyses, 586 (4. 3%) had a preterm birth. The rate of preterm birth was 5. 2% in non-geocoded pairs and 3. 7% in excluded pairs due to missing data on exposure to atrazine metabolites. Mean birth weight of neonate was 3290 ˘495 g (660-5510 g) and mean gestational age at birth was 39 ˘2 weeks (25-42 weeks). Population characteristics and risk of preterm birth according to population characteristics are presented in Table  1.
Were the same type of study to be performed on a geographically larger, regional or inter-regional sample, it would undoubtedly improve exploration of the effect of residential context on risk of preterm birth. A limitation of the study arises from the database and study population. The database was quite exhaustive because certificate issuance is mandatory in France. Selection bias related to participation of more materially and psychologically invested mothers described in prospective cohorts has been avoided  [42]. However, the database was incomplete as 517 (2%) of the births declared in Deux-Sèvres were not recorded. If we consider all these unrecorded births as preterm births, the rate of preterm birth in this study becomes closer to the known rate of preterm births in the department (from 4% to 6%)  [43].
We are thus likely to have a selection bias that underestimated the rate of preterm birth in this study as well as the relationship between exposure to atrazine metabolites and preterm birth when preterm neonates are more exposed. However, we found the expected relationship between some individual data, such as history of preterm birth, primiparity, quality of follow up and preterm birth. In the database, we could not geocode 9% of the pairs included, as did Zeitlin et al. [13] , and these mothers were less exposed than geocoded mothers. This factor could have introduced an overestimation of exposure and an underestimation of the relationship between exposure to atrazine metabolites and preterm birth. Data on mother/neonate pairs were limited to the information available on the health certificates at birth.
We lacked data on socio-economic factors such as education level that were not recorded in the database. However, we used maternal occupation, which is a good indicator of socio-economic status  [44]. As social factors, we used the single-parent status. We lacked complete data on adverse behaviors but the only type of information that could not be used at all was alcohol consumption, whereas other studies contain no information on smoking during pregnancy  [13]. The rate of smoking during pregnancy was comparable with the French national survey rate (20% versus 17%)  [45]. We also lacked data on nutritional status. However, these nutritional factors contribute less to preterm birth than do material deprivation factors  [10]. The remaining data were reliable; they had undergone a quality control through which the procedures employed were validated.