dataesr/scientific-paragraphs-categorization huggingface dataset:
Demographic parameters (age and gender), the different co-morbidities, medications used, and laboratory data were collected from the systems for each patient at time of initial encounter. Stata for Windows (version 13) was used for statistical analyses. Results: There were 966 patients diagnosed with SLE during the period from 2006 to 2014. Mean (SD) age at presentations was 35. 5 (11. 5) years. The majority of patients were female which constitute 88. 7 % of the total SLE patients) with mean age 27. 6 (1. 4) years. Majority of the patients 786 (81. 4%) were in the age group 18-45 years old (Figure  1.
0 mL/min. The injection volume of samples was 5 mL. LONs were detected at a wavelength of 260 nm. All compounds were characterized by Mass Spectrometry. Matrix Assisted Laser Desorption Ionization (MALDI) mass spectrometry analyses were performed using a MALDI-ToF-ToF Ultraflex III (Bruker Daltonics, Bremen, Germany) equipped with a smartbeam TM laser technology. Average molecular masses of the products were measured in the linear positive mode and a mixture of standard compounds was used for external calibration in the suitable mass range (3-6kDa). For each sample, a MALDI mass spectrum was acquired by accumulating the ion signal from 1000 laser shots and the data analysis was performed by FlexAnalysis. LONs were dissolved in deionized water to a concentration of 20 µmol/L. The matrix 2,4,6trihydroxyacetophenone (THAP, Sigma-Aldrich) was prepared at a concentration of 20 mg/mL in a 4:1 mixture of ethanol and 100 mM aqueous ammonium citrate.
A drop (1 µL) of analyte/matrix (1:1) mixture was deposited on the MALDI target plate and allowed to dry at room temperature before analysis. For all compounds, theoretical and experimental average molecular masses and MALDI Mass Spectrum are presented in Supplementary Data (Table  S1 , Figures  S1, S2,  and S3 ). The LON concentration in samples was determined with a NanoDrop One UV-Vis spectrophotometer (ThermoScientific) based on absorbance measurement at 260 nm with 2 µL of sample. The concentration was calculated according to the molecular absorption coefficient of each molecule.
Apparatus and electrophoretic procedures CE experiments were performed on a Beckman Coulter P/ACE MDQ system hyphenated with a UV detector (Sciex, Framingham, MA). Data were collected using 32Karat software (Beckman Coulter, Sciex, Framingham, MA). The CE system was equipped with a capillary cartridge filled with a liquid coolant allowing efficient temperature control. Uncoated fused-silica capillaries (50 µm i. d. x 375 µm o. d. ) (Photon Lines SAS, Saint-Germain-en-Laye, France) with effective and total lengths of 30 cm and 40. 2 cm, respectively, were used. A new capillary was conditioned by successive rinsing with 1 M NaOH (20 psi, 3 min), 0. 1 M NaOH (20 psi, 3 min), ultra-pure water (20 psi, 3 min), and finally background electrolyte (BGE) (20 psi, 30 min). Between each run the capillary was rinsed with the BGE (20 psi, 5 min).
Acknowledgments We thank our laboratories for critical discussions. RX. C is supported by a  Postdoctoral fellowship  from the  Revive Labex  ( Investissement d'Avenir ;  ANR-10-LABX-73 ). N. O acknowledges  Research England's Expanding Excellence in England (E3)  award for funding. Work in P. N laboratory is supported by recurrent funding from the  Institut Pasteur , the  CNRS  and Revive, as well as by funding obtained from the  Agence Nationale de la Recherche  ( ANR 16 CE120004 01  MITMAT), the  Ligue contre le Cancer  ( LNCC EL2018  NAVARRO), and the  European Research Council  ( ERC-CoG-2017   BIND ).
Methylation measurements and quality control DNA methylation from newborns and older children was measured using the Illumina450K platform. Each cohort conducted their own quality control and normalization of DNA methylation data, as detailed in Fig. 1  An overview of the study design Additional file 1: Table  S2. Cohorts corrected for batch effects in their data using surrogate variables, ComBat  [32] , or by including a batch covariate in their models. To reduce the impact of severe outliers in the DNA methylation data on the meta-analysis, cohorts trimmed the methylation beta values by removing, for each CpG, observations more than three times the interquartile range below the 25th percentile or above the 75th percentile  [33]. Cohorts retained all CpGs that passed quality control and removed CpGs that were mapped to the X (n = 11,232) or Y (n = 416) chromosomes and control probes (n = 65), leaving a maximum total of 473,864 CpGs included in the meta-analysis.
Cohort-specific statistical analyses Each cohort performed independent EWAS according to a common, pre-specified analysis plan. Robust linear regression (rlm in the MASS R package  [34] ) was used to model gestational age as the exposure and DNA methylation beta values as the outcome. In the primary analysis, gestational age was used as a continuous variable excluding cohorts that had term-only infants. In secondary models, we modeled term-only children defined as a gestational age ≥ 37 weeks (≥ 259 days), but less or equal with 42 weeks. All models were adjusted for sex, maternal age (years), maternal social class (variable defined by each individual cohort; Additional file 1: Table  S2 ), maternal smoking status (the preferred categorization was into three groups: no smoking in pregnancy, stopped smoking in early pregnancy, smoking throughout pregnancy, but a binary categorization of any versus no smoking was also acceptable), parity (the preferred categorization was into two groups: no previous children, one or more previous children), birth weight in grams, age of the child (years) included for older children, batch or surrogate variables.
Look-up analyses in older ages Differentially methylated CpGs (three or more adjacent CpGs below the Bonferroni correction P < 1. 06 × 10 -7 ; n = 1276) from the newborn meta-analyses were analysed with a look-up approach using data from four early childhood, five school age, and five adolescence cohorts. Cohorts included the same covariates in these analyses as in the cord blood analyses and child age. We performed fixed effects inverse variance weighted metaanalyses using METAL  [38]  for these three age groups. For this hypothesis-driven analysis, CpG methylation association with gestational age was considered statistically significant at nominal P < 0. 05, followed by a comparison of the direction of effect with that in the cord blood meta-analysis.
Longitudinal analysis Longitudinal DNA methylation data from birth to early childhood and from birth to adolescence were analysed for the three or more adjacent Bonferroni significant 1276 CpGs found to be associated with gestational age. DNA methylation from two time points (birth and 4 years) in INMA and three time points (birth, 7 and 17 years) in ALSPAC were analysed separately. To estimate changes in DNA methylation, we applied linear mixed models with repeated measurement taking into account the within-person time effect. The models were adjusted for covariates and estimated cell count similar to crosssectional analysis. Interaction terms between age and gestational age were included in the model to capture differences in methylation change between birth and 4 years, birth and 7 years and 7 and 17 years per day increase in gestational age at delivery, respectively.
DNA methylation in relation to gene expression Correlations between DNA methylation and gene expression levels were tested using paired DNA methylation and gene expression data in publicly available datasets. We tested transcript levels of genes within a 500-kb region of the 1276 three adjacent CpGs (250 kb upstream and 250 kb downstream). The mRNA gene expression (Affymetrix Human Transcriptome Array 2. 0) and methylation (Illumina Infinium® HumanMethyla-tion450 BeadChip assay) were measured in cord-blood samples from 38 newborns  [51] [52] [53]. First, we created residuals for mRNA expression and residuals for DNA methylation and used linear regression models to evaluate correlations between expression residuals and DNA methylation residuals. These residual models were adjusted for covariates, estimated white blood cell proportions, and technical variation.
Study characteristics We meta-analysed Illumina's HumanMethylation450array results from 17 independent cohorts with data on newborn DNA methylation status, and 10 cohorts with data on DNA methylation in older children (age 4 to 18 years), including 4 cohorts with DNA methylation data both at birth and at an older age (Fig. 1 ). Table  1  summarizes the characteristics of participating cohorts. A summary of methods used by each cohort is provided in Additional file 1: Tables  S1  and S2. In our main "no complications" model, we excluded participants exposed to maternal pregnancy complications (maternal diabetes, hypertension or pre-eclampsia) and whose labour was induced or who were delivered by caesarean section. With continuous gestational age in the number of days as the exposure (gestational age range 186-294 days corresponding to 27-42 weeks), we analysed results from 3648 newborns and from 2481 older children.
This model was selected as the main model because associations of DNA methylation with gestational age related to pregnancy complications or potentially influenced by obstetric interventions may be less reflective of normal developmental processes than newborns with spontaneous uncomplicated delivery. However, we also analysed a larger dataset of 6885 newborns from 20 independent cohorts, including pregnancies with pregnancy complications and obstetric interventions, referred to as the "all births model" (see below).
Next, we performed a meta-analysis of the larger dataset of 6885 participants from 20 studies without excluding maternal complications and caesarean section delivery or induced delivery. In this "all births model", 17,095 CpGs located in or near 7931 genes were associated with gestational age after Bonferroni correction (P < 1. 06 × 10 -7 ). Not surprisingly given the higher levels of statistical significance in this much larger data set, we found somewhat more between-study heterogeneity than in the no complications model, but high levels (I 2 > 50%) were observed for only 1784 out of these 17, 095 CpGs (Additional file 1: Table  S4 ). We also observed a considerable overlap of CpGs between the two models with 93% of the 8899 CpGs in the no complication model also.
Analysis restricted to term-births To evaluate whether observed DNA methylation differences in relation to continuous gestational age were driven by preterm birth, we repeated the no complication model including only infants born at term (gestational age 37 to 42 weeks). In this analysis, we meta-analysed results from 18 cohorts (one additional cohort with term-birth data only was  included; GEN3G) (n = 3593). We identified 5930 sites significantly associated with gestational age at Bonferroni correction (P < 1. 06 × 10 -7 , median difference in mean methylation per additional gestational week = 0. 43%, IQR = [0. 32%-0. 58%]). The vast majority (5399; 91%) of these differentially methylated CpGs overlapped with those found in the main analyses (no complications model) without exclusion of those born preterm (Fig.
Assessment of CpG methylation in earlier embryonic stages We examined whether the CpGs detected in cord blood (that originate from embryonic germ layer mesoderm) were differentially methylated in relation to gestational age in other fetal tissues, lung and brain that originate from the two other embryonic germ layers, ectoderm and endoderm, respectively, collected prenatally  [47, 48]. To this end, we performed look-up analyses in DNA methylation data for 74 fetal lung samples representing gestational age 59 to 122 days (~8 to 17 completed gestational weeks)  [47]. Out of the 1276 CpGs, selected based on three or more adjacent CpGs from our no complications model, 1030 CpGs were available in the fetal lung dataset. We observed associations at Bonferroni look-up level correction significance (0.
Correlation of DNA methylation and gene expression For the 1276 CpGs differentially methylated in relation to gestational age with at least 3 adjacent CpGs, we assessed correlations between DNA methylation and gene expression (cis-eQTMs). From a publicly available dataset of expression and DNA methylation measured in 38 cord blood samples  [51] [52] [53] , 1174 out of the 1276 CpGs were located within a 500-kb (+/-250 kb) window of a transcript cluster. Of these 1174, 246 unique CpGs (367 total CpG-transcript associations) correlated significantly with gene expression (Bonferroni P < 0. 05, Additional file 1: Table  S15 ). Forty-six percent of these DNA methylation-expression correlations were negative, with the lowest P = 3. 55 × 10 -6 coeff = -6. 03 for cg01332054 and SEMA7A expression and the largest negative effect estimate (-12.
Discussion In this large consortium-based meta-analysis, we identified 8899 sites across the genome where gestational age at birth was associated with cord blood DNA methylation. We also identified numerous unique differentially methylated regions (DMRs) associated with gestational age by applying two independent methods. The results were consistent when restricted to births at term, demonstrating that the majority of our results were not driven by preterm births. We confirmed many of the findings from previously published EWAS of gestational age  [23, 26, 27, 29, 30, 67]  and found a very high correlation between the significant CpG point estimates in previously published datasets compared to our study (e. g. corr = 0. 92 between Hannon et al. CpGs and our data; Additional file 1: Table  S16 ), but importantly, we also found 3343 CpGs corresponding to 2577 genes that had not been described previously.
Low gestational age is a wellestablished risk factor for later cardiometabolic disease  [101]. Our expression findings likely reflect relevant for health outcomes associated with low gestational age. There are potential study limitations in our study including heterogeneity in normalization and quality control (QC) protocols since individual cohorts performed their own QC and normalization. However, one of our previous EWAS meta-analysis reported robust results comparing the non-normalized methylation and different data processing methods used across the cohorts for normalization  [20]. Furthermore, between-study heterogeneity at our pre-specified threshold was observed for only a minority of differentially methylated CpGs. Cohorts collected gestational age data from medical records, birth certificates or questionnaires in two ways, either ultrasound estimates and/or according to last menstrual period (or combined estimates), which may introduce bias.
However, gestational age determined by ultrasound correlates well with last menstrual period data  [102]. Despite a large sample size, we had few extreme premature births included in our dataset. Interpretation of effects of DNA methylation on gene expression was done for cis-effects only, not trans-effects. Since our analyses were primarily cross-sectional, we cannot infer the temporality in the associations and we cannot assume associations are causal  [103]. We recognize the possibility that the observed methylation patterns represent fetal maturity, accompanying a "normal" developmental process or determining time in utero; it was however not possible to include foetuses who did not survive pregnancy most of whom will have been delivered very early. The majority of study participants were of European ancestry, and very few cohorts were Hispanic.
We were unable to explore ethnic differences in detail since that would require large sample sizes for each ethnic group. However, when analyses were restricted to European-ancestry cohorts, the results were essentially identical with correlation coefficient 0. 97 (Additional file 3: Figure  S4 ) to those with all cohorts included. Finally, we acknowledge a potential limitation by applying a filter (regions with at least three or more adjacent CpGs with a Bonferroni-corrected P value < 0. 05) in order to capture a set of genes robustly affected by gestational age, which may have led to potentially important single CpGs not being included in the functional analyses. In addition, genes with few CpGs represented on the 450K array are likely underrepresented in the downstream analyses. The strengths of our study are large sample size, the comprehensive analyses using robust statistical methods, as well as the availability of samples at multiple ages and our ability to compare our findings with those in fetal tissue datasets.
Acknowledgements For all studies, detailed information can be found in Additional file 2: Supplementary information. All authors (SKM, AN, GCS, LKK, ATK, RR, LG, IAM, PJ, MP, MK, CA, FOV, NK, LAS, FIR, HZ, SS, DC, SLR-S, PEM, DAL, GP, CVB, KH, NB, LG, TSN, EC, PP, LD, EAN, MB, SLE, WK, SZ, CMP, ZH, M-RJ, JL, AAB, DA, PK, CLR, AB, BE, MHS, PV, HS, LB, VWJ, TIAS, MV, SHA, JWH, SEH, PM, TD, EBB, DLD, JMV, JN, KGT, IK, JLW, BH, JS, WN, MCM-K, KR, EO,  R-CH ,  STW , JMA, JB, AK, CS, CA, AC, OG,  C-JX ,  SER , JK, PB, OS, MW,NH, AG, M-FH, JFF, GHK, SJL, EM) read and critically revised subsequent drafts, and approved the final version. Correspondence and material requests should be addressed to EM (erik. melen@ki. se).
Availability of data and materials Genome-wide DNA methylation meta-analysis summary statistics corresponding to the main analysis presented in this manuscript are available at figshare (https://doi. org/10. 6084/m9. figshare. 11688762. v1) [44]. Individual cohort level data may be available by application to the relevant institutions after obtaining required approvals. All datasets used are previously published as described in Felix et al. [31]. Additional details and references to the study cohorts are available in Additional file 2.
Instruments, Inc. ). 126 Then, the 16S rRNA gene of bacterial communities was amplified and sequenced using the 127 variable V3V4 loops (341F: 5'-CCTACGGGNGGCWGCAG-3'; 805R: 5'-128 GACTACHVGGGTATCTAATCC-3')  (Klindworth et al. , 2013). Paired-end sequencing 129 (250 bp read length) was performed at the McGill University (Génome Québec Innovation 130 Centre, Montréal, Canada) on the MiSeq system (Illumina) using the v2 chemistry according 131 to the manufacturer's protocol. Raw sequence data are available in the SRA database 132 (BioProject ID PRJNA419907). 133.
For total bacteria 154 and total Vibrio 16S rDNA, we used the relative quantification calculated by the 2 -ΔΔCq 155 method  (Pfaffl, 2001)  with the mean of the measured threshold cycle values of two reference 156 genes (Cg-BPI, GenBank: AY165040, Cg-BPI F-ACGGTACAGAACGGATCTACG, Cg-157 BPI R-AATCGTGGCTGACATCGTAGC and Cg-actin, GenBank: AF026063, Cg-actin F-158 TCATTGCTCCACCTGAGAGG, Cg-actin R-AGCATTTCCTGTGGACAATGG). 159.
We filtered the dataset 169 for singletons and we annotated OTUs using Blast+ against the Silva database (release 123, 170 September 2015) to produce an OTU and affiliation table in standard BIOM format. 171 Rarefaction curves of species richness were produced using the {phyloseq} R package, and 172 the ggrare function (McMurdie and Holmes, 2013). In order to compare samples for alpha and 173 beta diversity, we used the rarefy_even_depth function to subsample dataset to 5148 reads per 174 sample excluding chloroplasts. The alpha diversity metrics (Chao1 and Shannon) were 175 estimated at the OTU level with the estimate_richness function. Moreover, Pielou's measure 176 of species evenness was computed using the diversity function in {vegan}. We also used 177 phyloseq to obtain abundances at differents taxonomic ranks (for genus and family) 178 (tax_glom function).
221 First, we searched for known opportunistic genera within microbiota of diseased oysters (de 222  Lorgeril et al. , 2018). Nine out of ten previously identified genera were present in our dataset 223 (Figure  3 ). Most were abundant for the oyster highly infected by OsHV-1 and displaying 224 bacteraemia (SF15. S. R14) (Figures  2  and 3A ). Both Vibrio and Psychromonas genera were 225 found within the three diseased oysters (Figure  3A ). However, Vibrio occurred in the majority 226 of healthy individuals as well (Figure  3B ). 227.
For 3,552 loci with evidence of a single association signal based on conditional analyses in EA (Supplementary Methods), we generated fine-mapping results for each trans-ethnic or ancestryspecific dataset using an approximate Bayesian approach (Methods)  (Wellcome Trust Case Control et al. , 2012). The 95% credible sets were smaller in the trans-ethnic meta-analyses than in the EA or EAS meta-analyses (Figure  2A ), indicating improved resolution owing to both increased sample size and different LD patterns. When comparing loci discovered in both the trans and EA analyses, we found that the 95% credible sets were 30% smaller among the trans results (median (interquartile range) number of variants per 95% credible set was 4 (2-13) in trans vs. 5 (2-16) in EA, Wilcoxon's P=3x10 -4 ). For instance, a locus on chromosome 9 associated with PLT counts included seven variants in the EA 95% credible set but only one in the trans set, an increase in fine-mapping resolution likely driven by limited LD at the locus in EAS (Figure  2B ).
Study design and participants All participants provided written informed consent and the project was approved by each institution's ethical committee. Supplementary Table 1 lists all participating cohorts. The SNPs we identified are available from the NCBI dbSNP database of short genetic variations (https://www. ncbi. nlm. nih. gov/projects/SNP/). No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment.
, 2018; Kanai et al. , 2018). For all other studies, unless specified in Supplementary Table  3 , we applied the following steps for genotype imputation of autosomal variants. We aligned all alleles on the forward strand of build 37/hg19 of the human reference genome (http://www. well. ox. ac. uk/~wrayner/strand) and converted files into the VCF format. We then applied checkVCF (http://genome. sph. umich. edu/wiki/CheckVCF. py) to confirm strand and allele orientation. We carried out genotype imputation using the University of Michigan (https://imputationserver. sph. umich. edu) or the Sanger Institute (https://imputation. sanger. ac. uk/) imputation servers. We phased genotype data using SHAPEIT  (Delaneau et al. , 2013) , EAGLE  (Loh et al. , 2016) , or HAPI-UR  (Williams et al. , 2012).
Million Veteran Program (MVP) blood-cell trait analyses for replication Phenotyping. Phenotyping methods published by the EMERGE Consortium and available on PheKB (https://phekb. org/) were used for retrieving lab data and exclusion criteria for all blood cell indices. This information was pulled from the VA electronic medical records for all MVP participants. Lab data was subject to the Boston Lab Adjudication Protocol. This entails five steps: (i) compile an initial spreadsheet of possible relevant lab tests, (ii) Subject Matter Expert (SME) does an initial review of possible tests, (iii) analyst adds relevant LOINC codes for SME review, (iv) second Subject Matter Expert (SME) review, (v) creation of a Lab Phenotype Table/Data Set. After restricting to only outpatient labs and applying the EMERGE exclusion criteria, for each trait and each person, the minimum, maximum, mean, median, SD, and number of labs was recorded.