dataesr/scientific-paragraphs-categorization huggingface dataset:
Images for fat fraction and wT 2 measurement were processed with custom written Python scripts. wT 2 values were calculated based on a tri-exponential fitting procedure to account for fat replacment  [30]. The sodium and wT 1 data sets were reconstructed offline with custom written MAT-LAB scripts (MathWorks, Natick, MA, USA). The signal evolution from the MR fingerprinting were matched to a two-component model for water-fat separation and to obtain wT 1 values  [9]. Sodium signals were calibrated using the standard deviation of the background noise and the mean signal of two agarose phantoms (20 and 40 mM NaCl in 5% agarose). ROIs were delineated on the middle slice of the multi-spin-echo images and on the middle slice of the 1 H anatomical reference image for the 23 Na part. All ROIs were outlined conservatively within the muscle.
Statistical analysis Statistical analysis was performed using MATLAB (Math-Works, Natick, MA, USA) and descriptive statistics including boxplots of the average MRI metrics for every ROI were used to evaluate each outcome. Thresholds for aberrant values of any outcome measure were defined as the mean of the control cohort plus two standard deviations. Nonparametric statistics were used as not all the measures from the patient group were normally distributed as assessed by the Lilliefors test. Wilcoxon rank-sum tests compared all MRI parameters for each muscle between dystrophic and control groups. Relationships of MRI parameters with clinical measures of strength were determined using Spearman correlation coefficients for ordinal or non-normally distributed data. False discovery rate (FDR) correction was applied for multiple comparisons  [32].
The dystrophic muscles were characterized by fatty replacement as well as increased water T 2 , water T 1 and sodium signals compared to the controls. The decrease in the IR/TSC ratio was significant in the tibialis anterior (TA). In the patient group, the gastrocnemius medialis (GM), soleus (SOL), and TA muscles were more affected than the tibialis posterior (TP) muscle. The middle line in the box depicts the median value and the top and bottom edges of the box the 25th and 75th percentiles of the data, respectively. The whiskers extend to the most extreme data points not considering outliers. TSC tissue sodium concentration, IR inversion-recovery signal, TQF triple-quantum filter signal, *p < 0. 05 difference between control and FSHD 1 3 84% of the variability of the FF (adjusted R 2 = 0.
While Fig. 3  Analysis of muscles grouped according of their FF and STIR score. 1 H and 23 Na MRI indices in the leg muscle of FSHD patients (orange-brown) and controls (CTR, blue). The middle line in the box depicts the median value and the top and bottom edges of the box the 25th and 75th percentiles of the data, respectively. The whiskers extend to the most extreme data points not considering outliers. In the top row, the muscles of FSHD patients were gathered in three groups according to their intramuscular fat fraction (low FF: FF < 10%, med. FF: 10% < FF < 50%, and high FF: FF > 50%). Muscles with FF < 10% resembled muscles of healthy controls for all disease activ-ity indices except the TSC and IR. In the bottom row, the muscles of FSHD patients were grouped according to the STIR-T 2 w signal intensity (STIR 0: normal appearance, STIR 1: very mild hyperintensities, STIR 2: mild diffuse/some moderate elevation, and STIR 3 and 4: moderate/severe involvement).
Though other muscles are also involved, we only examined the lower leg due to the geometry of our 23 Na coil. Time constraints prevented us from acquiring TQF and STIR-T 2 w images from all subjects. Thus, the number of examined muscles differ between MRI measures to due missing data points (TQF and wT 1 not acquired for all patients, sodium acquisition failed once due to problems with coil) or because the fitting procedure failed due to high fat infiltration (wT 2 ). Furthermore, results regarding disease duration should be interpreted with caution since the retrospective determination of the onset presented a challenge despite medical records and self-reports.
Four distinct GluCl subunits are conserved between C. elegans, B. malayi and P. univalens Searches for homologs of GluCl subunits encoding genes from C. elegans (i. e. glc-1, glc-2, glc-3, glc-4, avr-14 and avr-15)  (26, 36, 38, 43, 45, 46)  in B. malayi and P. univalens genomic/transcriptomic databases, allowed the identification of 4 independent sequences corresponding to putative homologs of avr-14, glc-2, glc-3 and glc-4 from both species. In contrast, no homolog could be found in B. malayi and P. univalens for glc-1 nor avr-15. The corresponding full-length cDNA of avr-14, glc-2, glc-3 and glc-4 from the three nematode species were cloned into a transcription vector for subsequent functional analysis. All GluCls identified including AVR-14B, GLC-2, GLC-3 and GLC-4 from C. elegans, B. malayi and P.
After identification of the 5' and 3' ends, two pairs of new primers per subunit were designed to amplify the full-length coding sequence of all GluCl subunits by nested PCR with the proofreading Phusion High-Fidelity DNA Polymerase (Thermo Scientific). Then, PCR products were cloned into the transcription vector pTB-207 (69) using the In-Fusion HD Cloning kit (Clontech) as described previously  (70). Recombinant constructs were purified using EZNA Plasmid DNA Mini kit (Omega Bio-Tek) and sequence-checked (Eurofins Genomics). The novel complete coding sequences of Bma-AVR-14B, Bma-GLC-2, Bma-GLC-3, Bma-GLC-4, Pun-AVR-14B, Pun-GLC-2, Pun-GLC-3 and Pun-GLC-4 were deposited to Genbank. Constructs were linearized with MlsI, PaeI or PacI restriction enzymes (Thermofisher) depending on the construct.
E. Sequence analysis and phylogeny Prediction of signal peptides and transmembrane domains were performed with SignalP4. 0  (71)  and Simple Modular Architecture Research Tool  (72). Deduced amino-acid sequences of GluCls from B. malayi, C. elegans, H. contortus and P. univalens were aligned using the MUSCLE algorithm and further processed with GENEDOC (IUBio). Percentage of identity between deduced amino acid of mature protein without peptide signal were obtained with EBI Global Alignment EMBOSS Needle  (73). The distance trees were constructed using the SeaView software  (74)  with BioNJ (Poisson) parameters. Significance of internal tree branches was estimated using bootstrap resampling of the dataset 1000 times. The tree was edited using the FigTree software (http://tree. bio. ed. ac. uk/software/figtree/).
The sequences used in this study are available on GenBank under the followed accession numbers : Brugia malayi (Bma) : AVR-14B (MW196269), GLC-2 (MW196266), GLC-3 (MW196267), GLC-4 (MW196268); Caenorhabditis elegans (Cel) : AVR-14A (AAC25481),  Dose responses relationships for glutamate were carried out by challenging oocytes with 5-10 s applications of increasing concentration of glutamate (between 1 µM to 30 mM depending on the receptor) with 2 min washing steps with Ringer solution between each application. The peak current values were normalized to the maximum response obtained with a saturated concentration of glutamate. The concentration of agonist required to mediate 50% of the maximum response (EC 50 ) and the Hill coefficient (nH) were determined and compared using non-linear regression on normalized data with R Studio using the drc package v3.
Acknowledgments We thank  Pr. Adrian Wolstenholme  from the  Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, 30602, USA  for providing the B. malayi worms and the careful and critical reading of the manuscript. We thank the group of  UEPAO (Experimental Unit of Orfrasière Animal Physiology, INRAE Centre Val de Loire, Nouzilly , 37380 , France ) for providing P. univalens eggs and adults.
4 ( 4 D. Identification and cloning of full-length GluCls coding sequences from nematodes PCR amplification were performed according to the manufacturer's recommendations with the Phusion High Fidelity Polymerase (New England BioLabs, Ipswich, MA, USA) using cDNA as template. Full-length coding sequences were cloned into the transcription vector pTB-207 and RACE-PCR product were cloned into pGEM-T (Promega, Madison, WI, USA). Eurofins Genomics (Luxembourg, Luxembourg) sequenced all constructs. Sequences of Cel-AVR-14B (CAA04170), Cel-GLC-2 (AAA50786), Cel-GLC-3 (CAB51708) and Cel-GLC-NP_495489. 2) from C. elegans were available on Genbank as well as Pun-AVR-14B (ABK20343) subunit coding sequence from P. univalens. Using the GluCls sequences of C. elegans, Haemonchus contortus and Pun-AVR-14B as queries, tBLASTn searches in NCBI (http://blast.
Data set We used DNA methylation data measured using Illumina's Infinium Human Methylation450 Beadchip (Illumina Inc. , San Diego, CA) on placentas obtained at delivery from 301 pregnant women who participated in the NICHD Fetal Growth Studies-Singletons  [28, 29]. The self-reported race/ethnic profiles of the 301 women were 102 Hispanics, 77 Whites, 72 Blacks, and 50 Asians. None of the pregnant women smoked cigarettes in the past 6 months prior to conception. The mean (± standard deviation (SD)) birthweight and gestational age at delivery were 3165 (± 451) g and 39. 5 (± 1. 1) weeks, respectively. Detailed characteristics of participants of the NICHD Fetal Growth Studies and the 301 women and neonates included in the present study have been described previously  [28, 29]. The major characteristics of the 301 women were similar to those in the overall NICHD Fetal Growth Studies cohort, except prepregnancy body mass index (Additional Table  1 ).
Functional annotation and regulatory enrichment We examined the birthweight-associated CpGs for enrichment in regulatory regions of the genome based on the ENCODE and Roadmap Projects  [32]. The birthweight-associated CpGs were enriched (p < 0. 05) in CpG island shore regions (N-Shore) (p = 0. 009), chromatin states flanking active TSS (p = 0. 009), and chromatin states flanking transcription at 5′ and 3′ gene loci (p = 0. 04) (Additional Table  3 ). To further understand the functional relevance of the birthweight-related CpGs, we performed pathway enrichment analysis of genes annotated to the top 100 CpGs using the ingenuity pathway analysis (IPA) tool (QIAGEN Inc. , https://www. qiagenbioinformatics. com). The top enriched IPA canonical pathways included cytotoxic T lymphocytes, protein kinase A signaling, mTOR signaling, and virus entry via endocytic pathways.
Phenotypic annotation of birthweight-associated CpGs and genes Using the EWAS Atlas database  [39]  and the NHGRI-EBI catalog of published GWASs  [37] , we assessed whether our birthweight-associated CpGs and annotated genes have been reported to be associated with diseases or traits. We found that cg00768487 (UBXN11) and cg03110787 (MLLT1) have been associated with papillary thyroid carcinoma  [40] , cg10806146 (SLC20A2) and cg15936066 (SLC20A2) with prostate cancer  [41] , cg15936066 (SLC20A2) with systemic lupus erythematosus  [42] , and cg07538190 (C8orf58) with psoriasis  [43]  (Fig. 4A , Additional Table  6 ). The CNIH2 gene is a GWAS locus for gout  [44, 45]. Out of the 15 top genes in our study, four genes (MLLT1, PDE9A, ASAP2, and SLC20A2) have been mapped to CpGs associated with birthweight in a recent cord blood EWAS meta-analysis  [18].
Data set and study population The present study included 312 women who provided placenta samples at delivery as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies-Singletons. Out of the 312 women, 301 were available for analysis after quality control procedures described below. The NICHD Fetal Growth Studies-Singletons is a prospective longitudinal cohort of 2802 pregnant woman without major pre-existing medical conditions from four self-identified race/ethnic groups (i. e. , non-Hispanic White, non-Hispanic Black, Hispanic, and Asian or Pacific Islander) recruited from 12 clinic sites in the USA and followed through delivery  [28, 66]. Details about the study design and data collection methods have been previously reported  [28, 66].
, San Diego, CA). Standard Illumina protocols were followed for background correction, normalization to internal control probes, and quantile normalization. Normalization was performed using the modified Beta MIxture Quantile dilation (BMIQ) method to correct the probe design bias in the Illumina Infinium Human Methylation450 Beadchip and achieve between-sample normalization  [68, 69]. The resulting intensity files were processed with Illumina's GenomeStudio, which generated average betascores (i. e. , the fraction of methylated sites per sample by taking the ratio of methylated and unmethylated fluorescent signals at each queried CpG) and detection p values. Details of quality control filters applied on the genotype and DNA methylation data have been previously reported  [29]. Beta scores with an associated detection p values ≥ 0.
Placental RNA extraction and quantification RNA was extracted from 80 placentas using TRIZOL reagent (Invitrogen, MA), and sequenced using the Illumina HiSeq2000 system. The RNA sequence preprocessing procedure has been previously described  [35]. Using the RNA-seq reads, the expression of the transcripts were quantified using Salmon  [71]. A total of 75 samples with both methylation and RNA-seq data were included in the tests for associations between DNA methylation and gene expression, as well as covariate-adjusted associations between gene expression and birthweight.
Epigenome-wide association analysis Epigenome-wide analyses were performed by fitting simple linear regression models on the methylation Mvalues of each CpG as the dependent variable and birthweight as independent variable as implemented in the R/ Bioconductor package limma, version 3. 24. 15. So far, there is no reference for placental cell-type composition; therefore, our model included SVA using the Combat function that has been validated to account for heterogeneity in cell-type composition  [30]. Placental genomewide SNP data were used to estimate 10 genotype-based PCs representing population structure. The R package "prcomp" was used to calculate methylation-based PCs  [72]. All analyses adjusted for gestational age at birth (continuous), neonatal sex (male, female), maternal race/ ethnicity (Hispanic, Black, White, Asian), five methylation plates representing batch of the 96-well plate on which samples were assigned, top ten genotype-based PCs based on the eigenvectors which represent most of the variance in the genotype data, top three methylation-based PCs, and 20 SVA components that represent putative cell-type composition in our samples.
None of the women studied reported smoking in the 6 months prior to pregnancy. Quantile-quantile (QQ) plots of p values and the corresponding genomic inflation factor (λ = 1. 23) are presented in Additional Figure  1. Statistical significance was considered based on FDR of 5%. The differentially methylated CpG sites were mapped to the nearby gene using R/Bioconductor package with a background reference comprising the whole set of genes present in the Illumina 450 k platform. To simplify interpretation, we report effect estimates in the result on the original methylation values, such that effect estimates represent change in DNA methylation beta values for 1 g increase in birthweight.
Genetic correlates of DNA methylation in placenta and blood To examine whether genetic variants influence DNA methylation levels of the birthweight-associated CpGs, we explored the 15 CpGs in cis-methylation quantitative locus (cis-mQTL) resources. We queried the 15 CpGs using a list of previously published placental mQTLs  [34, 35]. For the same query in blood, we used the mQTL database (http://www. mqtldb. org/) of mQTLs at serial time points across the life-course (pregnancy, birth, childhood, adolescence, and middle age)  [36]. Furthermore, we assessed whether there are genetic variants within the proximity (± 500 kb) of the birthweightassociated CpGs by exploring the relative distances between our CpGs and birthweight GWAS loci in Warrington et al. [38]. The NHGRI-EBI GWAS catalog  [37]  was used to assess the relevance of the identified cis-mQTLs in birthweight and cardiometabolic diseases.
Evaluation of previously reported CpG sites associated with cardiometabolic traits We used the EWAS Atlas database  [39]  to assemble CpGs in offspring tissues previously reported to be associated with maternal perinatal cardiometabolic traits (pre-pregnancy BMI, hypertensive disorders of pregnancy, and preeclampsia), and CpGs in adult/child blood previously reported to be associated with adult/child cardiometabolic traits (myocardial infarction, lipid traits, blood pressurerelated traits, type 2 diabetes, and body mass index; see Additional Table  7 ). We queried our birthweight EWAS output to assess whether these previously reported CpGs were associated with birthweight at FDR < 0. 05.
Acknowledgements The authors wish to thank research teams at all participating clinical centers (which include Christina Care Health Systems;  Columbia University ;  Fountain Valley Hospital, California ;  Long Beach Memorial Medical Center; New York Hospital, Queens ;  Northwestern University; University of Alabama at Birmingham; University of California, Irvine; Medical University of South Carolina; Saint Peters University Hospital; Tufts University ; and  Women and Infants Hospital of Rhode Island). The authors also acknowledge the  Wadsworth Center ,  C-TASC , and The EMMES Corporations in providing data and imaging support. This work utilized the computational resources of the  NIH HPC  Biowulf cluster (http://hpc. nih. gov).
MetHods Data for this retrospective, observational, chart review study were collected between July 2014 and January 2016 using online patient record forms (PRFs; online supplementary appendix A) in France, Germany, Italy, Spain, the UK, the Netherlands, Denmark, Norway and Sweden. Due to small sample size, the Netherlands, Denmark, Norway and Sweden were grouped into one region (North). Physicians completed PRFs electronically, with respondents recruited from a range of regions and settings ensuring a balanced representation of each country (eg, university and general hospitals, cancer and reference centres, office-based specialists). Respondents were asked to confirm patient eligibility and to report each patient only once. Programming rules were implemented in the PRF system to avoid extreme or incoherent values at data collection.
study population This study was conducted and data were collected by Genactis SAS on behalf of the study scientific committee and sponsor. It included patients aged ≥18 years with a current diagnosis of mPAC who had completed a firstline anticancer treatment during the review period. Physicians were encouraged to enter patients who underwent second-line metastatic treatment to obtain data on first-line and second-line treatment patterns. Physicians were included if they were certified medical oncologists or gastroenterologists with an oncology specialty (France and Germany) currently treating patients with mPAC and were involved in treatment choices. Verified physicians from the panel were randomly invited and underwent a screening process (telephone or online) of seven questions (online supplementary appendix B) to confirm their practice and experience.
Acknowledgements: We thank the team at the IPBES Secretariat, particularly  Anne Larigauderie  and team, including  Maximilien Guèze , for the support and opportunity to contribute to the  Global Assessment , and the  Co-Chairs Sandra Díaz ,  Eduardo S. Brondízio  and  Josef Settele  for their guidance during the process. We are grateful to  Kari Sivertsen  of the  Norwegian Institute for Nature Research (NINA)  for designing Figure 1 and assisting with the others. Pamela McElwee  acknowledges the support of  Dean Robert Goodman  and the biodiversity fund of the  School of Environmental and Biological Sciences at Rutgers  and a  National Science Foundation  grant # 1853759  " Understanding the Use of Ecosystem Services Concepts in Environmental Policy ";  Mireille Chiroleu-Assouline  acknowledges support of the  Agence Nationale de la Recherche  ( ANR-17-EURE-0001 );  Cindy Isenhour  acknowledges support from the  National Science Foundation Convergence Program ;  Eszter Kelemen  has received support from the  János Bolyai Research Grant  of the  Hungarian Academy of Sciences ;  Daniel Miller  acknowledges support from the  John D.
Data and Code Availability The data on existing and proposed COVID recovery plans generated during this study for Figure  2  were generated from the websites https://www. imf. org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19, https://www. carbonbrief. org/coronavirus-tracking-how-the-worlds-green-recovery-plans-aim-to-cut-emissions, https://www. energypolicytracker. org/region/g20/ and https://www. climateinteractive. org/ci-topics/great-recoverypolicies/. Links to the sources for specific monetary values can be found via a dataset available at Mendeley: http://dx. doi. org/10. 17632/whf4hkfwdx. 1 and in Table  S2.
Each score (i. e. , number of times) must be multiplied by its corresponding metabolic equivalents (MET) value according to the intensity of the physical activity practiced: mild (x 3), moderate (x 5), and vigorous (x 9 ). An overall score of 24 METs or more identifies physically active subjects, total scores between 14 and 23 METs identify moderately active subjects and scores of less than 11 METs identify sedentary subjects. This study restricted the inquiry to the 3 days between baseline and follow-up visit. The variable of interest, as a declarative proxy for the amount of physical activity engaged in, was the number of METs spent in 'moderate to vigorous physical activities' in 72 hours. The standardized effort test for max HR assessment was practiced by each participant on a bicycle connected to a computer by a TACX hometrainer ® software.
chr17q21. 31) colocalized variants associated with multiple eGenes with distinct tissue specificities (Fig. 5 , Supplementary Data 21). WMH risk variants at the chr2q33 locus for instance showed evidence for colocalization with eQTLs for NBEAL1 in nerve and arterial tissues, for ICA1L and KRT8P15 in brain tissues, and for CARF1 in right atrial appendage. Among the 39 WMH eGenes with high colocalization probability (Fig. 5 , Supplementary Data 21), 4 (MAPT, CRHR1, CALCRL, NOTCH4) are registered as targets of approved drugs in the DrugBank database and the Therapeutic Target Database (Supplementary Data 22).
Blood pressure measurements that are closest to the MRI scan were used to define HTN status. Participants with a SBP ≥ 140 mmHg, a DBP ≥ 90 mmHg, or taking antihypertensive medication were classified as having HTN. Genotyping and imputation. Genome-wide genotyping platforms are described in Supplementary Data 2. Prior to imputation, sample-specific quality control (QC) on heterozygosity, missingness, gender mismatch, cryptic relatedness, and analysis of principal components (PC) for population stratification, as well as SNP-level QC on genotyping call rate and Hardy-Weinberg equilibrium were applied (Supplementary Data 2). Samples and SNPs passing the cohort-specific QC measures were then imputed to the 1000 genomes cosmopolitan panel phase 1 version 3 (1000 G p1v3) for CHARGE cohorts, while for the UK Biobank the dataset version 3 was imputed to the combined UK10K and Haplotype Reference Consortium (HRC) reference panels.