dataesr/scientific-paragraphs-categorization huggingface dataset:
Mouse Gal-3 and cytokine assays Mouse plasma Gal-3 was quantified using the ELISA kit DY1197 from BioTechne. Ten soluble cytokines (namely IFN-γ, IL-10, IL-1β, IL-2, IL-4, IL-5, IL-6, KC/GRO, IL-12p70, and TNF-α) were also assayed in mice plasma using the V-plex® Plus mouse proinflammatory panel 1 K15048G from Meso Scale (Discovery, Rockville, USA). Statistical analyses were performed using SAS software v9. 4 on log-transformed data using a mixed ANCOVA model (baseline as covariate and animal as random) for comparison between the HOCl-treated and control groups (model induction) and using a mixed ANCOVA model for comparison between all tested items and their HOCl control group, using Dunnett's adjustment.
Sequencing parameters were adapted for 2 × 150 bp configuration with at least 20 M pair-end reads per sample. Raw reads were processed using the in-house RNAExp Expression pipeline 2. 1 and the quality was assessed using FastQC tools v. 11. 18. Then, reads were trimmed for adaptors using Cutadapt and trimmed reads were aligned to the mouse mm39 reference genome (https://www. ncbi. nlm. nih. gov/assembly/GCF_ 000001635. 27/) using the STAR aligner. Aligned data were evaluated for quality using several quality metrics (e. g. , mapping rate, %rRNA, % mRNA) using Samtools and Picard tools. Then, aligned reads per gene were quantified using FeatureCount. Read counts were normalized by the variance stabilizing transformation vst function from the DESeq2 R package v1. 30. 0. 55,416 genes were detected in the data.
Genes not coding for a protein were filtered (29,947 genes removed). Those with 0 count over all the samples or having an expression level below 1 in more than 95% were filtered. The final RNAseq dataset comprised 10,508 genes. Partial Least-Squares-discriminant analysis (PLS-DA) performs a dimension reduction by preserving the covariance between the expression matrix and the outcome variable. It was applied to represent the samples while maximizing their separation with regard to the outcome. In order to identify genes differentially expressed between groups, a linear model (lmFit function from limma v3. 46. 0R package) based on the vst transformation gene expression dataset was applied. The resulting p-values were adjusted for multiple hypothesis testing and filtered to retain differentially expressed genes with a FDR adjusted p-value ≤ 0.
Acknowledgements The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under the Grant Agreement Number  115565  ( PRECISESADS  project), resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) and  EFPIA  companies' inkind contribution. The authors thank all colleagues who participated at any given time in this global project. Iuliana Botez ,  Nicolas Faucher ,  Benjamin Chanrion ,  Adeline Giganti , and  Jennifer Serriere-Di Bartolo  contributed to paving the way for strategic thinking at the earliest stages of the project. The authors also thank  Philippe Pastoureau ,  Laurence Laigle ,  Philippe Moingeon ,  Fabien Schmidlin ,  Jeanne Allinne , and  Ross Jeggo  for supporting the project.
Data availability Human raw data are the property of the PRECISESADS consortium and are protected under the European General Data Protection Regulation (GDPR). PRECISESADS data are hosted by ELIXIR Luxembourg and available under controlled access at the permanent link: https://doi. org/10. 17881/th9v-xt85. The access procedure is described on the ELIXIR data landing page. The PRECISESADS Consortium is committed to secure patient data access through the ELIXIR platform. This commitment was formerly given by written agreement to all patients at the end of the project and to the involved Ethical Committees. Access can be requested from the data stewardship team of ELIXIR Luxembourg via lcsb-datastewards@uni. lu. The future use of the Project database was framed according to the scope of the patient information and consent forms, where the use of patient data is open to scientific research in autoimmune diseases, not-for-profit use only.
ELIXIR reviews the applicant's requests and prepares the Data Access Committee's decisions on access to Data, communicates such decisions to the Data Providers, who have 10 days to exercise their right to veto; otherwise, access is granted to the User. Sequencing data in the mouse HOCl model are available on the GEO repository under the accession number GSE226063. The source data underlying all figures and supplementary figures are provided as a Source Data file. Source data are provided in this paper.
Results in a-c are depicted as boxes and whiskers plots defined by minimal, maximal, and median values in each dataset. The upper whisker extends from the hinge to the largest value no further than 1. 5 * IQR from the hinge (where IQR is the inter-quartile range or distance between the first and third quartiles). The lower whisker extends from the hinge to the smallest value at most 1. 5 * IQR of the hinge. Data beyond the end of the whiskers are called outlying points and are plotted individually. Statistical analyses were performed using the Kruskal-Wallis test (a, b) or the two-sided Wilcoxon test (c). d Correlation between Gal-3 up and Gal-3 down scores and blood immune cell populations. Data were collected from n = 211 patients. e Correlation between Gal-3 up and Gal-3 down scores and neutrophil to lymphocyte ratio calculated from d.
We tested the interactions between all variables using separate models (Table  S6 ). Each possible two-way interaction was added at a time to the base model (with main effects only) and the resulting AICc values were used for model selection. If the ΔAICc was less than two, the simplest model was chosen. To further explore differences in species richness among habitats, we performed a post-hoc test using the emmeans package  (Lenth, 2020). We verified that our statistical models exhibited no evidence of spatial autocorrelation by computing spline correlograms of the models' residuals (with bootstrap confidence intervals based on 1000 resamples), using the centroid of transects as estimates of the geographical coordinates of sampling sites (Figure  S2 ). To assess β-diversity among habitat types, we first performed a nonmetric multidimensional scaling (NMDS) ordination analysis using the vegan package  (Oksanen et al.
Subsequently, we ran a series of pairwise PERMANOVA analyses using the same explanatory variables as above to determine whether the community composition of each species group statistically differed between pairs of habitat types. We used the Holm method to correct for the mass-significance that can arise when performing multiple comparisons. This method rejects hypotheses sequentially until no further rejections can be done and adjusts the p values according to the number of tested hypotheses  (Holm, 1979). For this analysis, we also tested the interactions between the explanatory variables and chose the simplest model when the ΔAICc was less than two. To further explore the variation in community composition we calculated total β-diversity, as well as the nestedness and turnover components, for each organism group in each habitat, using the r package betapart  (Baselga & Orme, 2012)  Finally, we used the package indicspecies  (Cáceres & Legendre, 2009)  to identify species ('indicator species') that have strong associations to certain habitat types (i.
3d ). We also observed a similar distribution for the investigated full-length hCOMPASS-like subunits. However, the sharp peak of KMT2A was shifted and located exactly at the TSS (Fig. 3e ). The nearly symmetrical peaks extended into the promoter region, but also downstream into the transcribed region. We obtained good-quality sequencing data for NLS-EYFP-2A13 (~ 12 000 peaks), NLS-EYFP-2D13 (~ 20 500 peaks), NLS-EYFP-2D46 (~ 18 500 peaks) and moderatequality sequencing data for NLS-EYFP-2C14 (~ 2 800 peaks) and NLS-EYFP-2C57 (~ 1 100 peaks). Importantly, the negative control of NLS-EYFP showed only around 70 peaks. In agreement with the observation from inspection in a genome browser, many binding sites of the PHDs were shared (~ 5 500 peaks for NLS-EYFP-2A13, NLS-EYFP-2D13, and NLS-EYFP-2D46) for the actual, but not randomly shuffled data (Fig.
Clustered PHDs bind H3K4me3 and H3 acetylation-rich loci, especially containing H3K9ac and H3K27ac, but not H4K16ac To better characterize loci bound by PHDs, we used a comprehensive set of HMM-imputed chromatin marks  [34]. First, we validated this set with experimental ChIP-Seq data from the ENCODE database  [35] , and our CUT&RUN data for H3K4me1 or H3K4me3 in HeLa S3 cells (Suppl. Figure  4a, b ). Next, we quantified the overlap between chromatin regions bound by PHD domains and HMM imputed histone marks, using the Jaccard overlap measure (Suppl. Figure  4c, d ). The analysis showed that clustered PHDs were specifically recruited to regions enriched with the active promoter and enhancer marks (H3K4me1-3/ac, H3K9ac, and H3K27ac), but not to poised or silent chromatin marks, like H3K27me3 or H3K9me3 (Suppl.
g. , "transcriptional misregulation in cancer" and "pathways in cancer"), following the known role of KMT2A-D and particularly of KMT2C as a tumor suppressor  [37]. We observed the most significant associations with alcoholism and systemic lupus erythematosus, as previously observed for the HOXB1 transcription factor  [38]. Results of the GO analysis of biological processes showed that most targets of clustered PHDs, but not H3K4me3, were associated with regulation of the mitotic cell cycle (Suppl. Figure  7b ). In the case of 2D13, developmental processes were also prominent. As KMT2A-D proteins are frequently mutated in cancer  [10] , we used the COSMIC database  [39]  to gain more insight into the role of PHD domains. According to the database, among the KMT2A-D proteins, KMT2C is the most frequently mutated (~ 4 050 cases of cancer patients), followed by KMT2D (~ 2 600 cases), KMT2A (~ 1 580 cases), and finally KMT2B (~ 1 250 cases).
NGS sequencing and data analysis The concentration of the libraries was measured by qPCR using a Kapa Library Quantification kit (Kapa Biosciences, KK4824), according to the manufacturer's protocol. Paired-end (2 × 100 nt) sequencing (10 MR histone modifications, 30 MR full-length hCOMPASS-like subunits and gC&R) was performed on an Illumina NovaSeq 6000 instrument, using a NovaSeq 6000 S1 Reagent Kit (200 cycles, Illumina) with the addition of 0. 5% control library Phix (Illumina). The analysis of sequencing data was performed using the Galaxy server  [70]. Original fastq files were trimmed using both the TrimGalore! and FastQTrimmer algorithms. The quality of the reads was confirmed by FastQC. Next, the paired-end reads were aligned to the human genome hg38 using Bowtie2  [71] , filtered for minimum MAPQ20 and against a blacklist, and deduplicated.
Conservation analysis (Shannon analysis) For sequence conservation analysis, UniProt  [82]  was queried for proteins annotated as KMT2A, KMT2B, KMT2C, or KMT2D. The program cd-hit  [83]  was used to cluster sequences at 80% sequence similarity. Fusion proteins or truncated proteins (any proteins with a length less than 90% of the length of the reference human protein) were excluded from the analysis. To avoid bias from multiple annotated splice variants, only one protein per paralogue and species was retained. Proteins were aligned using Clustal Omega  [84]. As in sequence logos, sequence conservation was defined as the difference between the maximally possible and actual Shannon entropy in bits (log2(20) + Σp i log2p i , where p i is the frequency of amino acid "i" in a given position). As scores were needed concerning reference to the human protein and not to a gapped alignment, BALCONY was not used directly, and the calculation of Shannon scores was re-implemented in Python.
COSMIC data analysis The data for the analysis of the distribution of mutations throughout the length of the KMT2A-D proteins and in the patient population were obtained from the Catalogue of Somatic Mutations in Cancer (COSMIC) release v. 92  [39]. The COSMIC Mutation Data (Genome Screens) dataset containing data on mutations found in whole genome sequencing (WGS) analysis was downloaded from the database as a filtered file to comprise the changes found in the KMT2A-D genes in all the malignancies described in COSMIC. The data from targeted screens (non-WGS) were not used in the distribution analysis of mutations as too many data points (~ 92%) are missing concerning the type and location of mutations for the non-WGS dataset to be informative. For the analysis of the distribution of mutations among patients, the COSMIC Sample Features non-filtered dataset was downloaded to calculate the number of patients diagnosed with a given malignancy and to assign the mutations described in the Mutation Data file to individuals.
Acknowledgements We thank  Marc Timmers  ( Freiburg University, Germany ) for the kind gift of MNase-anti-GFP nanobody, Diagenode for antibodies against modified histones used in CUT&RUN experiments,  Agnieszka Rawluszko-Wieczorek  ( Poznan University of Medical Sciences, Poland ) for her help in cloning some plasmids,  Dorota Adamska  ( CeNT, Warsaw University, Poland ) for Illumina sequencing,  Derek Janssens  ( Fred Hutchinson Cancer Research Center, USA ) for pieces of advice regarding CUT&RUN protocols,  Bartlomiej Czerwinski  who helped with informatics and  Karim Abu Nahia  and  Kamil Jastrzębski  ( IIMCB core facility ) for their help. We are also grateful to  Albert Jeltsch  ( Stuttgart University, Germany ),  Agnieszka Rawluszko-Wieczorek  ( Poznan University of Medical Sciences, Poland ) and  Aleksandra Pekowska  ( Nencki Institute of Experimental Biology, Warsaw, Poland ) for the fruitful discussions,  Anton Slyvka ,  Honorata Czapińska  and  Terry Karimi  ( International Institute of Molecular and Cell Biology ) for comments on the manuscript and to  Katarzyna Szafran  for comments on figures.
Survival differences between the two groups were represented and compared using the Kaplan-Meier method and log-rank tests. Landmark analysis was performed to evaluate outcomes at different follow-up time points. The univariable and multivariable Cox proportional hazard regression models were used to investigate the effect of postoperative variables on all-cause mortality. A p-value ≤ 0. 05 was considered statistically significant. The analysis was performed using SPSS (Version 22, IBM, Armonk, NY, USA).
Study Endpoints and Definitions Patient and prosthesis outcomes were defined according to EACTS/ESC/EAPCI guidelines for reporting mortality and morbidity after cardiac surgery  [19]  and according to the Valve Academic Research Consortium 3 criteria  [20]. Frailty was defined as the presence of two or more of the following criteria: 5 m walktest time of more than 6 s, serum albumin level of less than 3. 5 g/dL, and Katz Activities of Daily Living total score of 4 or less. Patients were followed by clinical evaluation, including at least one physical examination. Follow-ups were 100% complete, and data were collected from the electronic medical record system of each institution. The primary endpoints of the study were: (1) 30-day mortality; (2) overall survival (OS). The secondary endpoint was survival freedom from major adverse cardiovascular.
Study Endpoints and Definitions Patient and prosthesis outcomes were defined according to EACTS/ESC/EAPCI guidelines for reporting mortality and morbidity after cardiac surgery  [19]  and according to the Valve Academic Research Consortium 3 criteria  [20]. Frailty was defined as the presence of two or more of the following criteria: 5 m walktest time of more than 6 s, serum albumin level of less than 3. 5 g/dL, and Katz Activities of Daily Living total score of 4 or less. Patients were followed by clinical evaluation, including at least one physical examination. Follow-ups were 100% complete, and data were collected from the electronic medical record system of each institution. The primary endpoints of the study were: (1) 30-day mortality; (2) overall survival (OS). The secondary endpoint was survival freedom from major adverse cardiovascular and cerebrovascular events (MACCEs) defined as death from all causes, stroke/TIA, endocarditis, reoperation, permanent pacemaker implantation (PPI) and paravalvular leak (PVL) of grade ≥ 2.
Large randomized trials (namely NOTION, PARTNER 3 and The Evolut Low Risk) compared sAVR versus TAVI in patients at low-risk, giving an important insight into this topic. However, concerns arise from the results of these studies. The NOTION Trial reported a 30-day mortality in the TAVI group (2. 1%) consistent with ours and previous studies [7  -9,22,23]. Surprisingly, the mortality in the surgical arm of NOTION was higher than expected (EuroSCORE II predicted mortality: 2. 0% vs. Observed mortality: 3. 7%). The mortality rate of the surgical population of the NOTION trial was higher even if compared to isolated sAVR outcomes reported in the 2021 STS Database (mortality: 1. 9%)  [24] , raising concerns about patient selection. In the PARTNER 3 Trial, designed to randomize the same subset of low-risk patients, early mortality of sAVR versus TAVI was similar (sAVR: 1.
Transcriptomic signatures of cells in control and Nr5a1-deficient gonads. We then performed single-cell RNA sequencing (scRNA-seq) experiments using dissociated cell suspensions obtained from 12 control and 16 Nr5a1 SC-/-whole testes at E14. 5. After data processing and quality control, we assembled an atlas page 8 composed of 3,988 and 5,010 control and mutant testicular cells, respectively. On average, we detected ~13,860 unique molecular indices (UMIs) and ~3,556 genes in each individual cell. The 8,998 testicular cells were partitioned into 23 cell clusters (termed C1-C23) and projected into a two-dimensional space (Fig. 5A ; Table. S1). Known marker genes of distinct cell-types were used to identify each cluster (Suppl. Fig. 3 ). Only a few changes of gene expression were observed between control and Nr5a1 SC-/-testes for endothelial, immune and perivascular cells.
BrdU incorporation and TUNEL assays BrdU (Sigma-Aldrich, Saint-Quentin-Fallavier, France) dissolved at 5 mg/ml in PBS was injected intraperitoneally to pregnant females at 50 mg/kg of body weight. Two hours later, fetuses were collected (E13. 5-E15. 5), fixed and embedded as described above. BrdU incorporation was detected on 5 µm-thick sections by using an anti-BrdU mouse monoclonal antibody (diluted 1:100) and indirect IHC as described above. At least three samples per stage and per genotype were analyzed. Data were expressed as percentages of BrdU-positive cells related to the number of DDX4-positive cells. Statistical analysis was done by a two-tail Student t-test, assuming equal variances after arcsine transformation of the percentages. page 21 TUNEL-positive cells were detected on sections from PFA-fixed samples using the In Situ Cell Death Detection Kit, Fluorescein, according to the manufacturer's instructions (Roche, Mannheim, Germany).
Purification of YFP-positive Sertoli cells To dissociate cells, the testes of controls and mutants additionally bearing the Illumina P5 and P7 primers (Illumina, Evry, France), as well as sample index via end repair, A tailing, adaptor ligation and PCR with 10 cycles. Library quantifications and quality controls were determined using Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Libraries were then sequenced on Illumina HiSeq 4000 TM as 100 bases paired-end reads, using standard protocols. Sequencing data were processed using 10X Genomics software (https://support. 10xgenomics. com/single-cell-gene-expression/software/overview/welcome). Fastq files were processed with CellRanger (v6. 0) on a chimeric genome composed Cells with less than 200 detected genes and genes detected in less than 10 cells were removed.
Doublets were filtered out independently in each individual matrix by using the DoubletFinder R package (v. 2. 0. 2)  [73]. Subsequently, the Single-Cell Analysis Toolkit for Gene Expression Data in R (scater v1. 10. 1) was used to remove outlier cells by using several cell features including the proportions of reads mapping mitochondrial and ribosomal genes, the number of genes and UMIs per cell  [74]. Data were normalized using the NormalizeData and the SCT function implemented into Seurat by regressing out the unwanted variation due to cell cycle and to the proportion of mitochondrial genes. The top-3000 most varying genes were used to perform a principal component analysis with the RunPCA function implemented in Seurat. Cells were then clustered by using Seurat graph-based clustering (FindNeighbors and FindClusters functions) on the top-30 principal components, with default parameters.
Finally, we used the Uniform Manifold Approximation and Projection (UMAP) method (RunUMAP function) to project cells in a 2D space. Cell clusters were annotated using a set of known marker genes. The FindAllMarkers function implemented in Seurat was used to identify significantly differentially expressed genes (DEGs) between cell clusters. Gene ontology (GO) and pathway enrichment analysis was conducted for each gene expression cluster using the AMEN suite of tools  [75]  with an BH-adjusted p value of ≤ 0. 05. The current dataset was mapped on top of a reference atlas of gonadal development  [25]  using FindTransferAnchors and MapQuery functions with default settings. To address this issue, the reference atlas was first processed by using the same pipeline. Table  1. Primary antibodies used for immunohistochemistry experiments.
ACKNOWLEDGEMENTS The Nr5a1 mouse mutant line was established at the  Institut Clinique de la Souris  ( PHENOMIN iCS ) in the Genetic Engineering and Model Validation Department with funds from the  ANR  (see below). We thank  Pr Anne-Marie LEFRANCOIS-MARTINEZ  for discussions and advices. We also thank  Dr Christelle Thibault-CARPENTIER  from the Genomeast platform for her input (http://genomeast. igbmc. fr/). This research was funded by  Agence Nationale pour la Recherche Grants  (see below). A CC-BY-4. 0 public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions.
FUNDING This work was supported by grants from  Fondation pour la Recherche Médicale  ( FRM FDT201904008001 ),  CNRS ,  INSERM, UNISTRA and Agence Nationale pour la Recherche  project  ARESSERC  ( ANR-16-CE14-0017 ). It was also supported in part by the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  ANR  under the frame  Programme Investissements d'Avenir labelled   ANR-10-IDEX-0002-02.
Land use and other catchment characteristics Land cover characteristics of the studied catchments were determined using the GIS databases of the Czech Republic (topological-vector database ZABAGED, https://www. cuzk. cz/; public land registry LPIS, https://eagri. cz/public/app/lpisext/lpis/verejny2/plpis/), and LANDSAT 7 ETM+ satellite images (on the territory of Germany). This land cover data reflects the situation in 2009, though no significant changes have occurred since then. According to census of population, houses, and apartments, provided by the Czech Statistical Office (CZSO), the total population of the basin, which amounted to 140. 2 thousand people, remained relatively stable from 2001 to 2020, with a minor decrease of 0. 3%.