dataesr/scientific-paragraphs-categorization huggingface dataset:
The largest nuclear plane for each cell type (esg+ ISC/EB, esg-diploid EE and esg-polyploid Ecs) was determined manually and the average fluorescent intensities of Kismet, Utx and DAPI were calculated with Fiji for these planes. In Figures  5I  and 5J , EGFR staining intensity in esg+ cells was quantified per square region within each posterior midgut. The largest plane for each esg+ cell was determined manually in order to measure the cell area and mean fluorescent EGFR intensity with Fiji software and to calculate total EGFR intensity. Statistical analysis were performed using the Graphpad Prism softaware and significance calculated by either two-tailed Mann-Whitney or chi 2 statistical tests with ns for non-significant, * for p<0. 05, ** for p<0. 01, *** for p<0. 001 and ****p<0. 0001.
ACKNOWLEDGMENTS We would like to thank the members of the Bardin team and  P. -A. Defossez  for comments on the manuscript. We thank  O. Ayrault  and  A. Forget  for help with biochemical experiments;  C. Carre  ´for help with polytene experiments; and  S. Maffioletti ,  A. Osnato , and  T. Gonc  ¸alves for their technical help on some experiments. We thank  A. Shilatifard ,  B. A. Edgar ,  D. R. Marenda ,  S. Bray ,  M. Haenlin ,  J. Skeath ,  J. Veenstra , and the  Bloomington and Vienna stock centers for (G and H)  Average number of cells and ISC per clone from (A)-(F). (I and  J) Clones  of wild-type (I) and  Cbl RNAi  (J) 10 days AHS. (K and L) Average number of cells and ISC per clone from (I) and (J). (M and N) ISC-specific expression of GFP (M) and  Cbl RNAi  (N) driven by esg ts -NRE-GAL80 for 10 days at 29 C.
c Zoom-in of panel b to the locus on human chromosome 16 with the SNP regulating the hECM network (rs9936589, in red), and the linkage disequilibrium (LD) blocks in the genotyped samples (n = 187). d, e Density plot showing the distribution of the Spearman's pairwise correlations (ρ) between WWP2 expression levels and each of the genes in the hECM network in DCM (d, red) or rTOF hearts (e in red) and control hearts (in gray). P for significance was calculated by Mann-Whitney U test. f Network graph showing the core set of hECM-network genes with strongest significant correlation (FDR < 0. 01) to WWP2 in both DCM and rTOF patients. Full results in Supplementary Data 3e. Nodes represent genes and edges denote protein-protein interaction or co-expression in STRING database (https://string-db.
Methods Data generation and processing in the rat. This cardiac expression data set consists of RNA-sequencing in the heart (LV) from the 30 rat RI strains, which were previously published  75. Data were processed with TopHat 1. 2. 0 (https://ccb. jhu. edu/software/tophat/), which was used to map the reads to the Brown Norway (BN) reference genome RGSC 3. 4 (known splice junctions in the Ensembl reference database were supplied, de novo splice junction detection was also enabled). After mapping the reads to the reference genome, Cufflinks 1. 0. 2 (http://cole-trapnelllab. github. io/cufflinks/releases/v1. 0. 2/) was used to assemble the aligned RNA-seq reads into transcripts by using the Ensembl annotation of the rat transcriptome. Cufflinks is able to reconstruct the set of transcripts that "explains" the reads observed in our RNA-seq experiment, in our case, by using a transcriptome reference annotation.
Six steps followed for gene level FPKM computation and filtering of the data, as follows. (1) Replace the values of all the isoforms with isoform quantification status not OK by missing values. The isoform quantification status is part of the output from Cufflinks and it measures the successful deconvolution of each isoform. (2) Remove all isoforms with more than 10 missing values across all samples. (3) Impute all the missing values by using R function pca from the pcaMethods R package 1. 56. 0 (https://bioconductor. riken. jp/packages/3. 0/ bioc/html/pcaMethods. html). The initial number of isoforms was 39,492. After removing those with more than 10 missing values across all samples, the number of isoforms got reduced to 39,181. (  4 ) Sum up all the FPKM values of all the isoforms of each gene to get the FPKM value at the gene level (number of genes: 29,469).
These data were published in Heinig et al. 26  and are available at the European Genome-phenome Archive under the accession number EGAS00001002454. Reads were mapped to the human genome with TopHat 1. 4. 1. TopHat was run using annotation from Gencode release 19 (GRCh37. p13) and allowing only two mismatches per 100 bp. In addition, TopHat was run with option -r 0, which specifies as zero the expected mate inner distance (as fragment size was 200 bp and read length was 100 bp). Option -M was also set, which removes multimapping reads before aligning to the transcriptome. Default options were chosen for the rest of parameters. TopHat mapping resulted in a mean of 173M reads per sample being uniquely mappable (a mean of 92% of the total number of reads with all the samples having a mapping percentage higher than 60%) in the samples from DCM patients.
Controls samples resulted in a mean of 185M reads per sample being uniquely mappable (a mean of 90% of the total number of reads with all the samples had a mapping percentage higher than 60%). RNA-seq read gene counts were computed with HTSeq software 0. 5. 3p3 (https://htseq. readthedocs. io/). In HTSeq the mode "intersection-nonempty" was selected (mode suitable to quantify overlapping transcripts on different strands). To compute gene counts with HTSeq, we used the Gencode human annotation version 19 with a custom TTN annotation  26. Steps followed for processing of the data: (1) Gene selection: From HTSeq output, only "protein coding" genes with "known" status in Ensembl genes (GRCh37. p13 data set) were consider in the analyses (n = 19,456). (2) Filtering of low expressed genes: Transcript lengths were downloaded from Biomart Ensembl genes (GRCh37.
The SNP data set chosen to run SNAP was the 1000 Genomes pilot 1 in the CEU reference population with an R 2 threshold of 0. 8 and a distance limit of 500 bp. From SNAP output, SNPs returning a warning message as not present in the SNAP database were removed and not considered further analyses (the number of missing SNPs was 101,020 SNPs). SNAP web tool outputs the input SNPs clustered in LD blocks (a total number of 126,922 LD blocks were returned by SNAP). To obtain a genomic map filtered by LD, we selected one single SNP for each of the LD blocks (R 2 = 0. 8). SNPs that were not included in any of the LD blocks (not clustered by SNAP) were added at the end. For each LD block we followed these steps: (1) Get all the SNPs in the LD block and compute their pairwise Spearman's ranked correlation.
For the rTOF patients, the clinical data from non-invasive investigations (performed as part of the surgical work-up) were available. This clinical information included electrocardiography, chest radiograph, echocardiography, and CMR. RV myocardial tissue samples from the 27 patients were snap-frozen in liquid nitrogen at the time of tissue sampling intra-operatively. RV tissue from 11 structurally normal hearts donated for cardiac transplantation was also collected (age donors: 34. 0 ± 13. 0, sex, male/female: 6/5). RV myocardial biopsies were made available via the Cardiovascular Biomedical Research Unit Biobank of the Royal Brompton & Harefield NHS Foundation Trust. Donor RV control tissue was collected and stored at the time of surgery following similar procedures as with the TOF tissue samples.
RINs ranged from 6. 3 to 9. 1 (mean 8. 2 ± 0. 6). One microgram of total RNA was used to prepare the RNA-Seq libraries. RNA-Seq libraries were prepared with Illumina TruSeq RNA sample preparation kits by using the protocol for poly-A enriched mRNA. To avoid batch effects, samples were pooled (4-5 samples/pool, 2 lanes per pool). Finally, paired-end 2 × 100 bp sequencing was performed on the Illumina Hi-Seq platform (mean sequencing depth of 196M). TopHat 2. 0. 12 (https://ccb. jhu. edu/software/tophat/) with Bowtie2 2. 2. 3 (http://bowtie-bio. sourceforge. net/bowtie2/) and Samtools 0. 1. 18 (https://sourceforge. net/projects/ samtools/files/samtools/0. 1. 18/) was run by using human genome version GRch38 (hg38. 78) reference genome. RNA-seq read counts were computed with HTSeq 0. 6. 1 (https://htseq.
78  ). Therefore, the gene expression counts of the control samples were adjusted only for sex by taking the residuals of a linear model in which sex was added as a predictor. Differentially expressed genes between rTOF patients (n = 27) and controls samples (n = 11) were computed using the R package DESeq2 1. 6. 3 and adding sex as covariate in the model (age was not added as there were several RV control samples with missing age values). CooksCutoff DeSeq2 parameter was set to False. The rest of parameters were left to default. Gene co-expression network analysis. Co-expression networks were inferred by using weighted gene correlation network analysis (WGCNA) 1. 42 (https://horvath. genetics. ucla. edu/html/CoexpressionNetwork/Rpackages/WGCNA/). Two independent WGCNA runs were carried out in the processed RNA-seq LV data (as described in previous sections): one in rat (RI panel, n = 30) and one in the DCM cohort (n = 126).
The full list of rat and human co-expression networks can be found in Supplementary Data 1a, b and 2a, b. Gene-annotation enrichment analysis of the rat (n = 41) and human (n = 48) networks was performed by carrying out functional gene list enrichment analysis using the R DAVID Web Service 1. 4. 0 (https://bioconductor. riken. jp/ packages/3. 0/bioc/html/RDAVIDWebService. html). Rat networks were queried independently from human networks for Gene Ontology (Biological Process, BP, Molecular Function, MF, and Cellular Component, CC) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation categories. The gene reference background was set to the group of genes from which the gene co-expression networks were inferred. In the rat data this background was constituted by the genes robustly expressed in the rat LV data (i.
Here we used the measurements of interstitial and perivascular fibrosis in the rat after normal transformation (qqnorm R function used) and adjustment for mean BP. Student P values were obtained for each correlation estimate by using the function corAndPvalue from the R package WGCNA 1. 42. All the rat co-expression networks were tested for enrichment of genes varying with fibrosis in the rat heart by carrying out GSEA  25. In this analysis, each of the rat co-expression networks was considered as a gene set. The corresponding Student P values of the Spearman's ranked correlation between each of the robustly expressed rat genes and each of the fibrosis measurements were used for ranking all the rat genes (n = 12,061) and run GSEA. GSEA 2. 1. 0 was run in classic, pre-ranked mode with 10,000 iterations.
test. See the density plots with the obtained P values in Fig. 2d, e. The correlation between WWP2 and all the genes in the hECM-network in LV DCM and RV rTOF patients is included in Supplementary Data 3e. Nominal WWP2gene correlation P values were corrected for multiple testing by using the R function p. adjust (method "fdr"). In this multiple testing correction, we corrected for the number of genes in the hECM-network, i. e. , 683. A core set of genes correlated to WWP2 was extracted by taking all the genes with correlation FDR < 0. 01 in both DCM and rTOF patients (326 genes). STRING protein-protein interaction database 10. 0 (https://string-db. org) was queried with this set of genes on the 05/09/18. The resulting network was retrieved and imported into Cytoscape 2. 8. 1 (https://cytoscape.
org/) to generate a network visualization graph (see Fig. 2f , only the largest connected component is displayed, 40 genes). To obtain this network graph from STRING database, we only considered experimental connections and co-expression interactions with a minimum interaction score of 0. 4. In the network graph, each node corresponds to a gene and color was mapped to the correlation between WWP2 and each gene (the average correlation in DCM and rTOF patients). Genes annotated in STRING with Gene Ontology Cellular Component (CC) term "extracellular matrix" were also retrieved and highlighted with thicker node border. Expression QTL (eQTL) mapping of WWP2 isoforms in DCM. WWP2 isoform expression levels were quantified in the DCM patients from the TopHat output with Sailfish 0. 6. 3 (http://www.
cs. cmu. edu/~ckingsf/software/sailfish/). Sailfish was run with default parameterizations. The expression levels in DCM patients of all interred WWP2 isoforms (in Reads Per Kilobase Million, RPKM) are included in Supplementary Data 3. These data were adjusted for relevant technical ("RIN score" and "library preparation day") and clinical ("sex" and "age at tissue collection") covariates by using a multivariate linear model and then mapped to the regulatory locus identified in human chromosome 16 (1 Mb centered around WWP2). In this case we mapped the 27 SNPs tagging this region in our genetic map. From the output MPPIs, BFs were computed as described above. Results of this test for the three WWP2 isoforms with highest expression in the DCM heart are included in Fig. 2i  along with a boxplot showing the expression level by genotype at the regulatory SNP rs9936589 (the expression level is displayed after controlling for clinical covariates effects as described above).
Mice that died undergoing surgery before the sample collection were excluded from statistical analysis. Data from the animal studies were collected in a blinded manner. Cell culture experiments. Murine cardiac fibroblasts were taken from the LV of mice. Minced LV pieces (1-3 mm 3 ) were placed in 6 cm dishes with DMEM supplemented with 20% fetal bovine serum for less than 10 days to generate mice cardiac fibroblasts (P0) and passaged to P1 and P2 DMEM supplemented with 10% fetal bovine serum for experiments. In each experiment, all the cells from WWP2 Mut/Mut and WWP2 wt/wt (WT) heart were cultured at the same time with same generation. Human cardiac fibroblasts were isolated from the right atrium (RA) appendage obtained from patients on cardiopulmonary-by-pass during cardiac surgery operations by digesting the tissue with Collagenase II.
ism. ac. jp/ packages/3. 7/bioc/html/scran. html). Scran size factors were computed from cell pools by doing a pre-clustering of the data with the quickCluster function (the output object of this function was provided to the computeSumFactors function and then run the normalize function was run, all of them with default parameterizations). t-SNE was computed by providing the Log 2 scran-normalized data to the function plotTSNE from scater 1. 8. 4R package (http://packages. renjin. org/package/org. renjin. bioconductor/scater). Two tSNE components were computed setting a random seed of 123456 using automatic perplexity (after removing an imposed minimum of 50, i. e. , floor (number cells/5)). In the t-SNE graph, each cell was colored by Wwp2 expression level (Log 2 scran-normalized gene counts).
2b (https://github. com/alexdobin/STAR) and quantified with RSEM 1. 2. 31 (https://github. com/deweylab/RSEM/). The average mapping rate (unique and multimapping) was 94. 5%. Gene annotation was retrieved from Ensembl version 89 (m38) using the R library biomaRt 2. 30. 0 (https://bioconductor. riken. jp/packages/3. 4/bioc/html/biomaRt. html). Ribosomal genes (Ensembl gene biotype "rRNA") and mitochondrial genes were removed (391 genes). Gene counts were rounded using the R function round and differential expression analysis was performed with DESeq2 1. 14. 1 with a pre-filtering step in which we considered only genes with more than 1 count when summing up across all samples. DESeq2 was run pairwise comparing WWP2 Mut/Mut with Angiotensin II against WT mice with Angiotensin II using the Wald test, with the outlier correction parameter cooksCutoff set to false (default parameterizations for the rest of parameters).
In the DESeq2 model, we added RNA concentration and sequencing lane as covariates. Functional enrichment analysis of the differential expression results was performed with GSEA 25 software v 2-2. 2. 2. From all genes included in DESeq2 output, we selected those with one-to-one mouse-human ortholog relationships (as downloaded from Biomart, 14,820 genes) and then we mapped them to human gene symbols. Then ranked all the genes by the corresponding DESeq2 output Wald statistic (i. e. , the estimate of the log2 fold change divided by its standard error). GSEA was run to assess the overrepresentation of the following gene sets and pathways derived from the Molecular Signatures Database gene sets 5. 1 (http://software. broadinstitute. org/gsea/msigdb/collections. jsp) (gene sets were queried using gene symbols): Hallmark gene sets (i.
e. , coherently expressed gene signatures derived from the aggregation of many MSigDB gene sets to represent well-defined biological states or processes), Gene Ontology, and Reactome databases. GSEA was run in classic pre-rank mode with 10,000 permutations to assess false discovery rate (FDR). In the GSEA runs, maximum gene set size was set to 5,000 and minimum gene set size was set to 10. In this test, upregulated processes and pathways in the WWP2 Mut/Mut will be positively enriched, whereas downregulated processes will be negatively enriched. Gene sets were deemed as enriched if FDR < 0. 05. All Hallmark enriched gene sets are displayed in Fig. 3j. The rest of the results can be found in Supplementary Data 4a. In addition, GSEA was run a second time using the same parameterization but this time testing for overrepresentation of all the human co-expression networks.
Anti-Lamin A/C (Abcam, #ab8984, 1:5000) and anti-PARP (Abcam, #ab6079,1:5000) were used as nuclear controls. Blots were visualized by labeling with anti-Rabbit HRP (Bethyl Laboratories, #A120-101P, 1:5000 or Thermo Fisher # 101023, 1:1000) and anti-Mouse HRP (Bethyl laboratories, #A90-116P, 1:5000) and developed on a Kodak automated developer with the ECL and Femto Detection Systems (Pierce) and quantified using densitometry with ImageJ (version 2. 0. 0-rc-43). The unprocessed scans of the western blots are shown in Supplementary Fig. 18. Statistical analyses. Data are expressed as mean ± standard deviation (SD). The applied statistical tests were dependent on the number of groups being compared and the study design, and are detailed in each figure legend. Unless otherwise indicated, a two-tailed Mann-Whitney U test was used to compare two groups, with * denoting P < 0.
Acknowledgements The research was primarily supported by  National Medical Research Council (NMRC)   Singapore grant   NMRC/CBRG/0106/2016  (to E. P. ) and the  British Heart Foundation (BHF)   Ph. D. Studentship grant   FS/11/25/28740  (to E. P). We acknowledge additional funding support from  European Union   FP7 CardioNeT-ITN-289600  (to E. L. -P. , S. A. C. , and P. J. R. B. ),  Heart Research UK  (to P. J. R. B. ),  NIHR CV BRU of Royal Brompton and Harefield, NHS Foundation Trust  (to S. A. C. and P. J. R. B. ),  BHF  (to S. A. C. ),  Leducq Foundation  (to S. A. C. ),  MRC UK  (to S. A. C. ),  BHF Program  Grant no. RG/15/5/31446  (to C. E. and E. P. ). M. P. was supported by  Praemium Academiae award of the Czech Academy of Sciences  and grant  14-36804G  from the  Czech Science Foundation.
Data availability All the data generated in this study supporting the main findings have been deposited to NCBI's Gene Expression Omnibus (GEO) and accessible through GEO Series accession number GSE133017, including GSE130468 (bulk RNA-seq data from mouse heart) and GSE133015 (single-cell RNA-seq data from mouse heart). The rest of the data are available from the authors on reasonable request, please refer to author contributions for specific data sets.
Determinants of interpersonal variability The first step of WG1 was to select compound classes of interest and to set up the criteria for a literature review to collect the correct information to allow for the dissection of the determinants of inter-individual variability in ADME of selected bioactive compounds. Following a questionnaire in which research interests and expertise were probed, participants organized themselves into different compound subgroups focusing on anthocyanins, carotenoids, lignans and phenolic acids, ellagitannins, flavanones, flavonols, catechins and phytosterols (Fig. 2 ). A data entry template was established to collate literature information and indicate what articles cover determinants such as age, sex, genetics and microbiome (Fig. 2a ). Over 3000 papers were reviewed, from which 511 were eventually selected as the basis to write several reviews.
As these are key limitations, the WG1 of this COST Action evaluated and improved the analytical coverage of untargeted methods for plant food bioactives and contributed to the development of databases and tools to facilitate the identification of plant food metabolites in metabolomics profiles. Plant food bioactives and their derived human metabolites represent a broad variety of chemical structures, with masses from 100 to > 1300 Da and a large spectrum of polarity, from very polar small microbial metabolites to hydrophobic compounds such as carotenoids or phytosterols. Analyzing simultaneously such an array of chemicals, even with an untargeted approach, represents more than a challenge and analytical choices have to be made. Up to now, metabolomics platforms developed their own profiling methods independently, and no standardization effort has been undertaken as yet.
Use of online databases in the identification of plant-based compounds and their metabolites Untargeted MS profiling methods are widely used but typically, only 5-20% of the detected signals are identified, leaving a vast pool of potential information unresolved. Databases are essential tools in the identification process. They are queried with experimental spectral data and return the chemical structures matching these data, as hypotheses of identification to be further confirmed. Many compoundcentered or spectra-centered databases with various contents are available online and must be used in a complementary manner for a wider chemical coverage. Among the most commonly used are HMDB (www. hmdb. ca), Metlin (https ://metli n. scrip ps. edu/), PubChem (https ://pubch em. ncbi. nlm. nih. gov/), ChemSpider (www.
chems pider. com/), MassBank (http://massb ank. eu/), MoNA (http://mona. fiehn lab. ucdav is. edu/), NIST, Golm Metabolome database, LipidMaps (https ://www. lipid maps. org/), mzCloud (https ://www. mzclo ud. org/), GNPS (https ://gnps. ucsd. edu/), ReSpect (http:// spect ra. psc. riken. jp/) and FooDB (http://foodb. ca/). The detected signals are not easily identified when the corresponding compounds are not yet present in one of these databases, which is the case for many metabolites of less studied food phytochemicals. PhytoHub (http://phyto hub. eu/) is an online database conceived to facilitate the identification of food phytochemicals and their metabolites in metabolomics profiles  [42]. It contains > 1700 compounds and is continuously updated by invited experts. The literature survey conducted by WG1 of the COST Action POSITIVe led to a major upgrade of PhytoHub.