dataesr/scientific-paragraphs-categorization huggingface dataset:
| Study population Our study sample was selected from the EPIPAGE-2 populationbased cohort. EPIPAGE-2 is a prospective national study of preterm infants born between 22 and 34 completed weeks in France in 2011, including terminations of pregnancy, stillbirths and live births. 10, 11 milies received information and agreed to participate in the study before any data collection began. Maternal, obstetric and neonatal data were collected prospectively at birth and during the neonatal hospitalisation, from medical records. All infants live born between 24 and 26 weeks of gestation enrolled in the EPIPAGE-2 without severe congenital malformations and subsequently admitted to NICU were included in this secondary analysis. Infants born at 22 (n = 2) and 23 (n = 7) weeks of gestation were not included in the analysis WWLST, CNS injury was the main cause of death (47%), whereas respiratory disease (56%) and infection (20%) were the main causes in case of death without WWLST.
| Other variables We collected maternal and obstetrical characteristics: maternal age, maternal place of birth, parity and type of pregnancy (single or multiple). The cause of preterm birth was classified as spontaneous (preterm labour or preterm premature rupture of membranes [PPROM]) or induced (maternal hypertensive disorders with or without foetal growth restriction [FGR], placental abruption or FGR without hypertensive disorders) according to a previously defined classification. 14 We also collected data on antenatal corticosteroid administration (at least one injection before birth), mode of delivery (vaginal or caesarean) and inborn/outborn status. Neonatal characteristics collected were sex, birth weight (<600, 600-749, ≥750 g) and gestational age at birth in completed weeks (defined as the best obstetric estimate combining the last menstrual period and ultrasonogram assessment).
| Statistical analysis First, maternal and neonatal characteristics were compared across the three groups: alive at discharge, death with WWLST and death without WWLST. Second, we described the distribution of causes, circumstances and postnatal age at death according to gestational age. Finally, we also described proportionate mortality for causes of death according to postnatal age in all deaths and separately in each of the two sub-groups of death with and without WWLST to specifically address the causes of death according to the circumstances. Categorical variables were compared using χ2 or Fisher's exact tests. All tests were two-sided with p < 0. 05 considered statistically significant. Statistical analyses were performed using SAS (version 9. 3, SAS Institute, Inc, Cary, NC).
Funding information Agence Nationale de la Recherche , Grant/ Award Number:  ANR-11-EQPX-0038  and  ANR-19-COHO-001 ;  Fondation de France , Grant/Award Number:  11779 ;  Fondation pour la Recherche Médicale , Grant/Award Number:  SPF20160936356 ;  Fondation PremUP ;  French Institute of Public Health.
FU N D I N G I N FO R M ATI O N The  EPIPAGE-2 study has been  funded with support from: (1) The  French Institute of Public Health Research/Institute of Public Health  and its partners: the  French Health Ministry , the  National Institute of Health and Medical Research (INSERM) , the  National Institute of Cancer , and the  National Solidarity Fund for Autonomy (CNSA). (2) The  National Research Agency  through the  French EQUIPEX program  of investments in the future (reference  ANR-11-EQPX-0038  and  ANR-19-COHO-001 ). (3) The  PREMUP Foundation. (4) Fondation de France  (reference 11779). (5)  Fondation pour la Recherche Médicale  ( SPF20160936356 ). (6)  Programme Hospitalier de Recherche Clinique Epinutri  ( DGOS13-040 ).
21  for more details). The examiners were all asked to strictly apply the definitions to avoid any influence from personal experience and were provided with reference images to minimize variability in assessing CPPD  (22). Investigators were blinded with regard to clinical and histologic data of the patients. Reliability. Each reader performed 2 evaluations of the DICOM files, the first evaluation to assess the interreader agreement, and the second evaluation, performed 3 weeks after the first evaluation, to calculate the intrareader agreement. The order of the DICOM files was different between the 2 rounds of evaluations, and the knee to be scored was clearly indicated in the scoring sheet. The interreader agreement was assessed among all 4 readers, between the 2 radiologists, and between the 2 rheumatologists.
01-0. 20 were considered poor to slight agreement, 0. 21-0. 40 as fair agreement, 0. 41-0. 60 as moderate agreement, 0. 61-0. 80 as substantial agreement, and 0. 81-1. 00 as almost perfect agreement. Readers were asked to score images as absence or presence of CPP crystals, and no indeterminate data were expected. If patients had missing histology data, they were excluded from the accuracy analysis. To reach an expected kappa value of 0. 85, with an expected confidence interval lower bound of 0. 75, 4 readers and 67 patients were sufficient. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated; these diagnostic indexes were calculated for all the knee structures analyzed at histology (medial and lateral meniscus and tibiofemoral hyaline cartilage).
Methods Data from the Avon Longitudinal Study of Parents and Children (ALSPAC) were used for the discovery analysis. ALSPAC is a prospective population-based cohort (see www. alspac. bris. ac. uk) that recruited pregnant women living in the former Avon area of the UK who were due to give birth between April 1991 and December 1992. [19] [20] [21]  In total, 14,541 pregnancies were enrolled, which resulted in 14,062 live births. Children, mothers and their partners have been followed up repeatedly ever since. [19] [20] [21]  The study website contains details of all the data that is available through a fully searchable data dictionary and variable search.
Confounders Analyses were adjusted for a set of confounders defined as known or plausible determinants of urban environment and blood pressure (i. e. , maternal education, age at delivery, ethnicity, and area deprivation). Sex, although not a confounder, was also included in the adjustment to reduce residual variance. All confounders, except area deprivation, were obtained through questionnaires assessed during pregnancy. Area deprivation was based on data from the UK Ministry of Housing, Communities & Local Government and linked to the geocoded addresses at birth. More details on the confounders are presented in supplementary material.
Statistical analysis We included participants who had information on at least one prenatal urban environmental exposure and at least one blood pressure measurement between mean ages 3 and 24 years, and who had complete data on the confounders. This resulted in the inclusion of 5,095 (for noise exposures) to 7,454 (for average greenness exposures) participants with between 28,132 and 41,216 blood pressure observations (Supplementary Figure  1 ). An exposome-wide association study (ExWAS) approach  25  was used to assess the association of each exposure individually with SBP and DBP trajectories from childhood to early adulthood. Associations were examined using linear mixed-effects models with linear splines for age (as a fixed effect) to allow for nonlinear change in blood pressure with age.
e. 0. 05/42 = p-value threshold 0. 0012). For associations that reached our multiple-testing p-value threshold, average predicted means for SBP and DBP from childhood to early adulthood in the 25 th and 75 th percentile of the exposure were calculated from the linear mixed-effects models. Within each exposure-blood pressure trajectory, complete case analyses were undertaken, which gives unbiased results when the chance of being a complete is independent of the outcome after taking the covariates into consideration. 27  To explore potential impact of missing data, we compared characteristics of those included and not included in the analysis due to missing data on one or more confounders. The analyses were performed in the software Stata 17. 0 (Statacorp, College Station, TX, USA) and the software MLwiN 3.
Results In ALSPAC, up to 7,454 individuals with 41,216 blood pressure observations were included in the analysis. Those included were more likely to have mothers with higher education and older age at delivery, to have White ethnicity and to live in a less deprived area at birth than those not included in the analysis due to missing data on one or more of the confounders (Supplementary Table  2 ). Up to 11 measures of blood pressure were used (mean 6. 7, standard deviation [SD] 2. 1), the mean age of the participants ranged from 3. 1 (SD 0. 02) at baseline to 24. 4 (SD 0. 79) years at the last follow-up, and the number of individuals with blood pressure measure in each assessment varied from 800 (at age 5 years) to 6,056 (at age 7. 5 years) (Supplementary Table  3 ). Out of the 46 environmental exposures, four were excluded due to very high correlation (Supplementary Figure  3 ).
Strengths and limitations We explored the longitudinal association between urban environment in pregnancy and offspring blood pressure using repeated data spanning from childhood to early adulthood. We used an ExWAS approach to assess the association of a range of urban environmental exposures with blood pressure trajectories, taking account of multiple testing and attempting to replicate findings in independent cohorts were possible, in order to minimise chance findings. Our analyses included all participants with at least one blood pressure measure, under the missing at random assumption (i. e. the probability that an outcome value is missing depends on observed values of the outcome, conditional on the covariates in the model), which may have reduced selection bias due to missing outcome data.
Acknowledgements We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The Generation R Study is conducted by the  Erasmus MC, University Medical Center Rotterdam  in close collaboration with the  School of Law and Faculty of Social Sciences of the Erasmus University Rotterdam , the  Municipal Health Service Rotterdam area , Rotterdam, the  Rotterdam Homecare Foundation , Rotterdam and the  Stichting Trombosedienst & Artsenlaboratorium Rijnmond  (STAR- MDC) , Rotterdam. We gratefully acknowledge the contribution of children and parents, general practitioners, hospitals, midwives and pharmacies in Rotterdam.
Sampling campaigns Two complementary field cruises were conducted almost simultaneously in June-July 2013 in the Black Sea with the vessel R/V Pelagia to obtain (i) Hg chemistry data, hgcA quantitative PCR (qPCR), and 16S rRNA gene amplicon sequencing data (MEDBlack cruise) and (ii) planktonic metagenomes (Phoxy cruise). Following the definitions in Stewart et al. (32)  and the vertical discretization model of Rosati et al. (25) , the water column was decomposed into three redox layers: the oxic layer (OL, from 0 to 75 m depth, characterized by O 2 concentrations between 30 and 300 µM and undetectable HS -concentrations), the suboxic layer (SOL, from 75 to 120 m depth, characterized by dissolved O 2 concentrations <20 µM and HS -concentrations <5 µM), and the anoxic layer (AOL, below 120 m depth, characterized by undetectable O 2 concentrations and HS -concentrations between 15 and 400 µM).
The MEDBlack cruise was conducted from 13 to 25 July 2013 and occupied 12 stations along a west-to-east transect. In the present work, we include five stations (1, 2, 5, 6, and 9, according to previous nomenclature  [25] ; Fig. 1 ). For microbiology, water samples were collected from three water depths: OL (30-50 m), SOL (90-110 m, except coastal station 6 sampled at 180 m depth), and AOL (140-150 m, except coastal station 6 sampled at 250 m depth) (exact sampling depths are provided in Data Sheet S1A). Samples were filtered with in situ Stand Alone Pump System (Challenger Oceanic, from NOC, Southampton) equipped with two 293 mm diameter filters: a Petex nylon mesh pre-filter (51 µm; 150 µm for station 1) and a polycarbonate filter (1 µm) further stored at -20°C. The Phoxy cruise 64PE371 was conducted on 9 and 10 June 2013 in the western gyre of the Black Sea.
Physicochemical measurements For the Phoxy cruise, chemical parameters included the dissolved concentrations of O , respectively  (33). A summary of available and new data for both campaigns is provided in Table  1. For the MEDBlack cruise, available measured physicochemical parameters (Data Sheet S1A) include pressure, temperature, conductivity, fluorescence, salinity, density, and the dissolved concentrations of O 2 , HS -, NH 4 + , NO 2 -, NO 3 -, PO 4 3-, Si, Fe, DOC, total Hg, and MeHg (sum of mono-and di-MeHg) according to previous protocols  (25). Specific analysis of Hg and MeHg is detailed in Supplementaal Information 1. 1. The detection limit was 0. 025 and 0. 001 pM for HgD and MeHgD, respectively. Nutrients, HS -, and dissolved O 2 concentrations were measured similarly to the Phoxy cruise with the same detection limits.
Amplicon sequencing and qPCR estimates of 16S rRNA genes For the MEDBlack cruise DNA samples, the hypervariable V4-V5 region of the 16S rRNA gene from Bacteria and Archaea was amplified with high-fidelity Phusion Hot Start II DNA polymerase (Thermo Scientific, Waltham, MA, USA) using universal primers 515F and 928R  (34)  and a two-step PCR protocol  (35) , as detailed in Supplemental Information 1. 2. and Table  S1. The correct amplicon size and the absence of non-specific bands were checked by agarose gel electrophoresis. Amplicons were sequenced using a 2 × 250 bp paired-end MiSeq system (Illumina, USA) at the Genotoul platform (Toulouse, France). The raw sequences have been deposited at NCBI GenBank, SRA database, under the BioProject accession number PRJNA895066. Raw sequences were analyzed on the Galaxy bioinformatics platform through the FROGS pipeline, version 3.
2. 3, as detailed in the Supplemental Information. Especially, operational taxonomic units (OTUs) were defined by sequence clustering, using the high-resolution SWARM algorithm v3. 2. 3  (36). After filtering at 0. 005% of abundance, OTUs were taxonomically annotated with the SILVA 16S database (version 138. 1). Bacterial and archaeal abundances were quantified in the MEDBlack cruise DNA samples by quantitative PCR with Takyon No Rox SYBR 2X Master Mix (Eurogentec, Seraing, Belgium). Protocol details are provided in Table  S1.
4. Obtained DNA sequences were translated into amino acid sequences and compared with hgcA sequences from the Hg-MATE database  (38)  and from the 15 metagenomes obtained in the Phoxy cruise (see following section). The sequence analysis (Supplemental Information 1. 4 and Fig. S2 ) showed that hgcA sequences obtained with this primer set clustered with Euryarchaeota and Chloroflexota sequences. Thus, from thereon, hgcA qPCR estimates from Archaea will be referred to hgcA qPCR estimates from Archaea-Chloroflexota.
Metagenomic estimates of hgcA genes The 15 DNA extracts from the Phoxy cruise were used to prepare TruSeq nano libraries, sequenced with Illumina MiSeq (five samples multiplexed per lane) at Utrecht Sequenc ing Facility, generating 4. 5 × 10 7 paired-end reads (2 × 250 bp), which were further processed as previously reported  (31)  and summarized in Supplemental Information 1. 5. hgc homologs were identified and annotated in the amino acid FASTA file (generated from protein-coding genes detected in the metagenomes coassembly), using the HMM profiles of hgcA and hgcB genes derived from the Hg-MATE database v1. 01142021  (38)  and the function hmmsearch from HMMER 3. 2. 1  (39). We considered genes with E-values <10 -3 as significant hits. To further confirm putative hgcA and hgcB genes (or hgc-like genes) within the HMM search hits, we used the high stringency cutoff defined by Capo et al.
(  8 ) by looking for unique conserved motifs from hgcA gene (NVWCA(A/G/ S)GK) and performed a manual inspection of the presence of hgcA genes. Coverage values of hgcA genes were calculated as the number of reads mapped to the gene divided by its length (base pairs, bp) and were further normalized by dividing them by the coverage values of the marker gene rpoB. The rpoB genes were detected using the function hmmsearch from HMMER 3. 2. 1  (39)  and HMM profile TIGR02013. hmm for bacterial rpoB genes and applying the trusted cutoff provided in HMM files. The obtained hgcA homologs, translated in amino acid sequences, were taxonomi cally affiliated through a phylogenetic analysis, by placing them onto the HgcA reference tree using a pplacer approach  (40). The construction of the reference tree was based on the reference package "hgcA" from Hg-MATE database v1, according to the protocol in the README.
txt of the Hg-MATE database  (38)  and in Capo et al. (8) , as detailed in Supplemental Information 1. 5. As a complement to this community-level analysis, metagenome-assembled genomes generated from the same previously published metagenomes  (30, 31)  were screened for hgc genes. The hgc genes found in MAGs were taxonomically identified using the MAG phylogeny and taxonomy (Data Sheet S1D).
Data analysis The qPCR estimates were statistically analyzed by two-way analysis of variance (ANOVA) with the aov function (R software). The selected environmental parameters for corre lation analysis were the concentrations of dissolved O 2 , HS -, and MeHg. Spearman correlation plots were obtained using the functions rcorr and corrplot from the R packages Hmisc and corrplot, respectively. Shannon diversity indices were calculated with the estimate_richness function in the vegan R package. Principal coordinates analysis (PCoA) was performed applying the function wcmdscale to Bray-Curtis dissimilarity matrixes built with the function vegdist from the 16S rRNA gene-based OTU abundance table and the hgcA gene abundance (normalized coverage values) in the R package vegan. The discriminant hgcA genes explaining the most the clustering of hgc + commun ity according to depth zones were identified by linear discriminant analysis (run_lefse function, microbiomeMarker package) with cumulative sum scaling normalization and 100 bootstraps.
hgc genes in the water column are affiliated to diverse taxa with specific niches A total of 91 hgcA genes were found in the Black Sea metagenome co-assembly, including 14 genes found next to hgcB genes on the same contig (Data Sheet S1D). Among them, 15 hgcA genes were found in MAGs affiliated to various orders of Desulfobacterota (Desulfobacterales, Desulfobulbales, unclassified), Chloroflexota (Anaerolineales), Bacteroidota (Bacteroidales), Planctomycetota (Phycisphaerales), Verrumicrobiota (Kiritimatiellales), and KSB1 candidate division (unclassified) (Fig. 5A ). Among Desulfobacterota, the hgc + MAGs were identified as uncultured species of U Desulfacyla, U Desufatibia, U Desulfobia, and Desulfobacula. From the alignment to the Hg-MATE database, the remaining 76 unbinned hgcA genes were affiliated predomi nantly to Desulfobacterota  (32) , Planctomycetota (14, among which 4 Phycisphaerae), Verrucomicrobiota (9 Kiritimatiellales), Chloroflexota (8 Anaerolineales), and various microbial lineages (Data Sheet S1D).
To avoid the biases due to the large polyphyletic diversity of the hgcA gene  (9) , and the overrepresentation of hgcA sequences from Desulfobacterota in databases of known Hg methylators used for primer design  (8, 43) , clade-specific qPCR primers have been developed, targeting hgcA genes from each of the three dominant Hg-methylating clades, i. e. , Desulfobacterota, Arch aea, and Firmicutes (37). This approach yielded results similar to metagenome-derived estimates for the identification of Hg methylation biomarkers  (42) , and is preferable to detect taxa-dependent correlations with environmental and/or functional outcomes. It is important to highlight that the outcomes of different methods should be interpre ted cautiously since some discrepancies have been previously reported between, e.
ACKNOWLEDGMENTS This study was financially supported by the  French National Research Institute for Sustainable Development (IRD)  through the M. I. O. internal grant " ACTION SUD-Commet " We thank the chief scientists of the  MEDBlack cruise ,  M. J. A. Rijkenberg ,  L. J. A. Gerringa , and the shipboard party for their support. A special thanks goes to  Matthew Patey  for handling the in situ pumps. We thank  Sophie Guasco  at the  M. I. O. for technical support with cloning sequencing of hgcA genes. The computations were performed on resources provided by the  Swedish Research Council  (grants to S. B. ) and  SNIC  through  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  using the compute project  SNIC 2021/5-53. The authors have no competing interests to declare.
The reference phylogenetic tree was provided by the Hg-MATE database using hgcA sequences from (i) pure culture/environmental microbial isolates (204 sequences), (ii) single-cell genome sequences (29 sequences), and (iii) metagenome-assembled genomes (787 sequences), as detailed in Materials and Methods and Supplemental Information 1. 5. (B) Vertical distribution of MG-estimated coverage values of hgcA genes normalized by rpoB coverage values along the water column depth in the Black Sea. The corresponding compartmentation into oxic (OL), suboxic (SOL), and anoxic (AOL) layers is shown on the left axis. Color code corresponds to taxonomic affiliation.
( L. Cabrol), the Severo Ochoa Excellence Program postdoctoral fellowship awar ded in 2021 to Eric Capo (CEX2019-000928-S), and the Ramón y Cajal program (RYC2019-028400-I, AEI Spain) awarded to Andrea G. Bravo. We acknowledge the Dutch funding agency (project number: 822. 01. 015) of the national science foundation NWO for funding this work as part of GEOTRACES. L. -E. H. -B. 's work was supported by research grant ERC-2010-StG_20091028 from the European Research Council.
Remarks on the implementation and hardware computing resources All strategies have been implemented in the AMPFETI module of the finite element suite Z-set (http://www. zset-software. com/). The MUMPS solver 42 (version 5. 3. 5, http://mumps. enseeiht. fr/) is used in association with the BLAS library provided by Intel 17. 0. 4 MKL, for partial factorization and associated solves (reduction and expansion phases). Eigen library (version 3. 3. 8, http://eigen. tuxfamily. org/) is used for dense linear algebra, for the singular value decomposition of the Schur complement and for all iterative solvers involved in the computation of centrality measures. The eigenvalues computation needed for the evaluation of condition numbers make use of the ARPACK library through the Python module scipy. sparse.
In addition, the resulting solids have been chemically and microstructurally characterized by Scanning Electron Microscopy with Energy Dispersive X-ray spectroscopy (SEM-EDX), using a Quanta 650 FEG electronic microscope coupled to an EDX-Oxford X-Max device with AZtec 3. 1 software. For SEM-EDX characterization, some measurements have been performed directly over powder samples. Other measurements have been executed over the cross section of aggregate pieces of the final solids embedded in resin and polished.
Acknowledgements We thank  Dr V. M. Dixit  for Ripk3 knockout mice. We also thank  J. Ternacle ,  B. Drayton Libotte ,  L. Guillaud  and  X. Decrouy  for technical assistance and  C. Laisne  and  D. Gelperowic  for taking good care of animals, and the  VOUSH group  for fruitful discussions. This work was supported by the  Association Française contre les Myopathies (AFM-Téléthon)  through the Translamuscle I (# 19507 ) and Translamuscle II (# 22946 ) programs.