dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA library preparation, sequencing and bioinformatics analysis were conducted at GENEWIZ, Inc. (USA). Briefly, NEBNext® Ultra™ RNA Library Prep Kit (New England Biolabs, USA) was used for RNA-seq library preparation. Agilent TapeStation (Agilent Technologies, USA) was employed to validate the sequency library and cDNA were quantified using Qubit® 2. 0 Fluorometer (Invitrogen, USA) as well as by quantitative PCR (KAPA Biosystems, USA). Sequencing libraries were loaded on the Illumina® HiSeq 4000 (Illumina Inc. , USA) in high output mode and samples sequenced using a 2 × 150 paired end configuration. The HiSeq Control Software conducted image analysis and base calling. Raw sequence data (. bcl files) generated from Illumina® HiSeq was converted into fastq files and de-multiplexed using Illumina's bcl2fastq 2.
17 software. One mis-match was allowed for index sequence identification. Sequence reads were trimmed to exclude adapter sequences and poor quality nucleotides using Trimmomatic v. 0. 36. Reads mapping was performed using STAR aligner v. 2. 5. 2b on the human reference genome available on ENSEMBL. Unique gene hit counts were calculated using feature Counts from the Subread package v. 1. 5. 2 and counted only when falling within exon regions. Following gene hit count extraction, data were used to perform differential expression gene (DEG) analysis. Comparison of gene expression between the groups of samples was performed within the package DESeq2.
Gene ontology enrichment Database for Annotation, Visualization, and Integrated Discovery (DAVID, http://david. ncifcrf. gov) was used to perform Gene ontology (GO) enrichment analysis on DEG data from RNA-seq. We obtained the data plots by combining expression data with functional analysis in the R package GOplot (http://cran. r-project. org/web/packages/GOplot). Biological processes identified functional classes of genes and biological processes GO terms were queried. Significant terms were determined as those with P < 0. 05. The top ten biological processes for each time point were chosen after removing processes with greater than 80% redundancy.
In addition, diagnostic fragmentation ions to determine N-and O-glycans was performed  60. To identify α-linked Gal, terminal hexose-hexose units were considered. Following MIRAGE guidelines  81  for glycomic analysis, N-and O-glycan annotated structures were submitted and are currently available at the Unicarb-DB database link https://unicarb-dr. glycosmos. org/references/352. To compare relative N-and O-glycan abundance across different samples, each structure was quantified relative to the total content by integration of the extracted ion chromatogram peak area. Specifically, the Area Under the Curve (AUC) of the N-/ O-glycan structure was normalised to the total AUC and indicated as a percentage. Analysis of the peak area was processed using Progenesis QI (Waters Corporation, USA). Clustering analysis was conducted using Clustering Explorer v.
Before each sample was run, a specific lock mass (1221. 9906 m/z) and a calibration segment (10 mM sodium formate cluster solution) were applied to improve mass accuracy  83. DataAnalysis™ v. 4. 1 Sp4 (Bruker Daltonics, Germany) was used to elaborate data and protein identities and relative abundancies were determined using Peaks Studio 8. 5 (Bioinformatics Solutions Inc. , USA)  84. Each sample was run and analysed as two independent replicates. For protein identification, Uniprot's reference database of Ovis aries was accessed on Feb 2018, 556,825 sequences; 199,652,254 residues. In particular, the following parameters were set: enzymatic digestion performed by trypsin, allowing one missed cleavage; precursor mass tolerance was 20 ppm; fragment mass tolerance of 0. 05 Da; carbamidomethylation as fixed modification.
False-positive identification rate (FDR) was set ≤1%, and a peptide score of -Log(p-value) ≥ 20 was considered adequate for confident protein identification. De Novo ALC score was set ≥50%. Relative peptide signal intensity was calculated only for confidently identified peptide features. Then, the AUC of each extracted ion chromatogram was calculated and used for the relative quantification after normalisation Total Ion Current (TIC). Cumulative peak areas of the proteins were measured by considering only unique peptides assigned to specific proteins. Only proteins with more than two unique peptides were considered for the analysis. Ingenuity Pathway Analysis (IPA®) analysis of transcriptomic and proteomic data DEG data were processed as. xls files. The gene list was sorted to set all the data relative to the different groups to a summed-up ID gene list.
The created. xls file had columns reporting each condition's log 2 (fold-change) DEG data compared to the healthy animals. As per the manufacturer's instructions, this file was loaded in IPA® (Qiagen, Germany) software. Here, log 2 (fold-change) > 1. 5 and < -1. 5 and adjusted-P < 0. 05 were applied as cut-offs for Diseases&Functions, Canonical Pathway and Causal Network Analyses  85. Comparative analysis was conducted on the files of core ischaemic samples on days 7 and 28 post-NSTEMI. Canonical pathway and upstream regulators data were compared by their activation z-score and associated P. Identified protein data from nLC-ESI-MS/MS label-free quantification with a unique peptide number above 2 were selected and listed in a. xls file. Protein entries from the infarcted samples were reported in terms of fold-change ratio to healthy conditions.
Electrocardiograms data from NSTEMI patients The electrocardiograms (ECGs) which validated functional changes consistent with induced NSTEMI in sheep (Supplementary Fig. 3b ) were standard 12-lead ECGs in human subjects. These patients presented with signs and symptoms suggestive of an MI which were subsequently confirmed on the ECG and serum troponin measurements at University Hospital Galway (Ireland). The protocol for obtaining consistent ECGs is based on hospital protocols derived from American Heart Association (AHA) recommendations  86. Neither Institutional Review Board approval nor informed consent was required for the use of electrocardiogram data from NSTEMI patients as it falls under the category of Non-Human Subject Research, as the research team did not have access to identifiers or keys to link coded data (even temporarily).
Statistics and reproducibility The in vivo study was designed and powered for statistical significance (power = 0. 8) to determine the sample size. Animals were randomly selected for each experimental group and investigators were blinded to allocation during experiments. All animals were named and given an identification number in the chronological order of surgery-induction using a systematic labelling for identification. Animals from at least two different batches of surgeries were analysed for each group in all analyses to avoid a single batchdependent bias. No data were excluded from the analyses. Statistical analyses were performed using Prism® v9. 0. 0 (GraphPad Software Inc. , USA) and differences with P < 0. 05 were considered significant. Normality and equality of variance were tested before a statistical test.
Acknowledgements This study was funded by the  European Commission  funding under the  AngioMatTrain 7th Framework Programme  (Grant Agreement Number  317304 ) and by the research grant from  Science Foundation Ireland (SFI)  co-funded under the  European Regional Development Fund  under Grant Number  13/RC/2073 and13/RC/2073_P2  to A. P. The authors acknowledge the use of the  Centre for Microscopy and Imaging  facilities at the  University of Galway. Mass spectroscopy analysis of glycans was performed by the Swedish infrastructure for biological mass spectrometry (BioMS) supported by the  Swedish Research Council. Prof. P. Dockery ,  P. Lalor  and  E. McDermott  from the  Anatomy Department at the University of Galway  assisted in processing for TEM analysis. R. Grigalevičiūtė  and  A. Kučinskas  from the  LSMU  assisted with veterinary procedures and in ensuring animal well-being.
Data availability The authors declare that all data supporting the findings of this study are available from the authors on request. RNA-seq raw data used in this study are available in GEO database under accession code GSE164245 at the link https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE164245. Proteomic data were deposited in MassIVE repository (accession code MSV000089576) and are available at https://massive. ucsd. edu/ProteoSAFe/dataset. jsp? task=2bf4eed7d3484de9b99759cff4011ccf. Glycomics-derived annotated structures were deposited in the Unicarb-DB database and are available at the link https://unicarb-dr. glycosmos. org/ references/352. Source data are provided with this paper.
M dwarfs observed with HARPS We first built a list of known M dwarfs by crossing two main catalogues,  Gaidos et al. (2014)  and  Winters et al. (2014). The latter was updated with  Winters et al. (2021) , which did not add any target, and was then completed with the release of the Gliese and Jahreiss catalogues  (Stauffer et al. 2010 ) and the 100 closest systems gathered by the Research Consortium On Nearby Stars (RECONS 1  ;  Henry et al. 2018). This list of stars will also be used in a companion paper  (Mignon et al. 2023 ) in which we study the RVs of M dwarfs in the solar neighbourhood. We then crossed this list of M dwarfs of the solar neighbourhood with the database of public spectra taken with the high-accuracy RV planet searcher (HARPS) installed on the ESO 3. 6 m telescope at La Silla Observatory in Chile  (Mayor et al.
2003). Each spectrum covers the 380-690 nm spectral range, spread over 71 orders, with a median resolving power of 115 000, and is wavelength calibrated with a high accuracy. All observations are downloaded on the ESO archive (spectral data products query form 2 ) and come from several observation programs of M dwarfs (listed in the acknowledgements): this represents about 15 000 spectra from January 2003 to June 2019 for 490 stars.
(2018) , the parallax of L43-72 is from  Winters et al. (2017) , and the parallax of LP 993-116 is from  Riaz et al. (2006) ; the distance of GJ 3332 is from  Riedel et al. (2014). Masses are from the mass-luminosity relation of  Delfosse et al. (2000) , with a weighted average of the masses derived from H, K, and J from the CDS: these band magnitudes were taken from the 2MASS database  (Cutri et al. 2003)  with the exception of H and J for GJ 205 from  Ducati (2002) , and H of GJ 1 and K of GJ 803 from  Koen et al. (2010). T eff is from the CDS. The flag indicates whether the star is in the season sub-sample. Only the beginning of the table is shown for information. The full table is available online at the CDS. Notes. Typical timescales τ cyc from our analysis of chromospheric time series and photometric time series.
Acknowledgements. This work has been supported by a grant from  LabEx OSUG@2020  ( Investissements d'avenir   -ANR10LABX56 ). This work was supported by the  "Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU  co-funded by  CEA  and  CNES. This work was supported by the  Programme National de Planétologie (PNP) of CNRS/INSU , co-funded by  CNES. We thank the  Solar Influences Data analysis Center  (SILSO data/image,  Royal Observatory of Belgium, Brussels). The HARPS data have been retrieved from the ESO archive at.
Acknowledgements We thank  J. -L. Barrat ,  M. Ediger ,  V. F. Hagh ,  H. Ikeda ,  F. C. Mocanu ,  M. A. Ramos ,  F. Sciortino  and  P. G. Wolynes  for discussions, and  K. Yoshihara  for his contribution to the preliminary patchy particle experiment. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement n. 723955 -GlassUniversality). W. K. is member of the  Insititut universitaire de France.
The predicted topology of human PLLP is a tetraspan protein with both N-terminal and C-terminal domains located intracellularly  [2]. Correspondingly, the extracellular areas of PLLP are limited to two loops, encompassing amino acid residues 125-144 (AVDLTSLRGTR-PYNQRAAAS), referred to as loop 1 (the largest one) and 57-64 (DTPYHLYP), referred to as loop 2 (the smallest one). Alpha-Fold has not satisfactorily resolved the structure of these loops  [14, 15] , consistent with secondary structure predictions that consider these regions as disordered domains  [16]. Indeed, apart from the transmembrane (TM) domains, the AlphaFold predictions for human PLLP that are available through the UniProt database (entry Q9Y342, https://www. uniprot. org/uniprotkb/Q9Y342/entry) have either low (70 > pLDDT > 50) or very low (pLDDT <50) confidences indexes.
15 mol/L using the tools "Add solvation box" and "Add ions" in VMD software  [36]. The molecular dynamics simulations were performed using NAMD 2. 14 (Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign, Urbana-Champaign, IL USA) for Windows 10 and the force field CHARMM36m  [37]. Before production runs, each system was minimized for 5000 steps and equilibrated at constant temperature (310 K) and constant pressure (1 atm). The cutoff for calculating noncovalent intermolecular interactions was set at 12 Å, and the PME algorithm was used to calculate long-range interactions.
20. 38. 0. 54864 (LECO, Saint Joseph, MI, USA). Data were deconvoluted using the LECO NTD software (LECO, USA), and the annotation was confirmed with the injection of pure standards. The amount of compounds was calculated as the area under the GC-MS peak representing each compound analysed, normalized by the weight of the corresponding sample (mg). Standard solutions of sucrose, trehalose, raffinose, stachyose and galactinol were injected. Two extracted ion -in experiment  (2). Stachyose, sucrose and galactinol were analysed by GC-MS in aphid batches and expressed as peak area (no units) corrected by the batch weight (mg). The number of observations is between 3 and 9. Letter codes indicate significant differences between fed aphids at level alpha=0. 05 by Kruskal-Wallis analysis. Fig. S3.
Acknowledgements We thank  Pascale Mistral ,  Virginie Chareyron ,  Karine Leyre  and  Véronique Chovelon  for their excellent technical assistance. We also thank  Olivier Chevallier  for his significant help in the development of liquid chromatography analysis methods. This work was supported by the  University of Avignon  through the provision of metabolomic equipment. This manuscript was edited by  Rebecca Stevens.
2, 3  We report a new meta-analysis of 7 case-control HCM GWAS datasets, including 3 not previously published, comprising a total of 5,900 HCM cases, 68,359 controls and 9,492,702 variants with a minor allele frequency (MAF)>1% (Supplementary Table  1 ; Study flowchart in Figure  1 ). Using the conventional genome-wide significance threshold (P< 5x10  -8  ), 34 loci were significantly associated with HCM, of which 15 were novel (Table  1 ). We then performed 2 stratified analyses in HCMSARC+ (1,776 cases) and HCMSARC- (3,860 cases), and identified an additional 1 locus and 4 loci, respectively (Table  1 ; Supplementary Table  2 ; Supplementary Figure  1 ). Using conditional analysis  4  , we identified additional suggestive and independent associations with HCM, HCMSARC+, and HCMSARC-with a false discovery rate (FDR) <1% (Supplementary Table  3 ).
11  SVIL is highly expressed in cardiac, skeletal, and smooth muscle myocytes in the Genotype Tissue Expression (GTEx) v9 single-nuclei RNA sequencing (snRNA-seq) dataset  12  , and SVIL morpholino knockdown in zebrafish produces cardiac abnormalities. 13  In humans, loss of function (LoF) SVIL variants have been associated with smaller descending aortic diameter  14  and homozygous LoF SVIL variants have been shown to cause a skeletal myopathy with mild cardiac features (left ventricular hypertrophy). 15  To provide further evidence linking SVIL to human HCM and to explore the association of SVIL LoF variants with HCM, we performed rare variant burden analysis including 1,845 clinically-diagnosed unrelated HCM cases and 37,481 controls. We demonstrate a 10. 5-fold (95% CI: 4. 1-26. 8; P:2.
Results from the primary MR inverse variance weighted (IVW) analysis are shown in Figure  5A  and sensitivity analyses results appear in Supplementary Table  16  and Supplementary Figures  14 15. Although significant heterogeneity in the exposure-outcome effects are limitations, MR findings support a causal association between increased LV contractility and increased risk for both nHCM and oHCM, with a substantial risk increase of 12-fold and 29-fold per standard deviation increase in strain circ , respectively (Figure  5A ). Altogether, these data suggest that increased contractility is involved in both oHCM and nHCM development, and thus myosin inhibitors currently approved for symptom control in oHCM may also be of clinical benefit in nHCM. Last, we also performed MR analyses exploring whether increased systolic (SBP) and diastolic (DBP) blood pressure, and pulse pressure (PP=SBP-DBP) are causally associated with nHCM and oHCM.
Only variants with a MAF<10 -4 in the non-Finish European ancestral group of gnomAD v2. 1. 1 45 were selected. Only LoF variants present in the Matched Annotation from NCBI and EMBL-EBI (MANE) / canonical transcript (NM_021738. 3; ENST00000355867. 9) were retained for the analysis. The proportion of cases and controls with SVIL LoF variants were compared using the Fisher Exact test for each of the 3 case-control datasets, followed by a fixed-effect model meta-analysis. We also performed a secondary analysis where association of SVIL LoF variants with HCM was restricted to variants that cause LoF in the primary LV transcript (ENST00000375400), and excluding variants expected to escape nonsense-mediated decay. Locus definition and annotation. Genomic loci associated with all LV traits were annotated jointly.
Specifically, summary statistics were combined and a P value corresponding to the minimal P value (minP) across all 10 summary statistics. The minP summary statics was then used to define loci using FUMA v1. 4. 2 22 using a maximum lead SNP P-value of 5x10 -8 , maximum GWAS P-value of 0. 05 and r 2 threshold for independent significant SNPs of 0. 05 (using the European 1000 Genomes Project dataset), and merging LD blocks within 250kb. Loci were then mapped to genes using positional mapping (<10kb), eQTL mapping using GTEx v8 restricted to atrial appendage, left ventricle and skeletal muscle tissues, and chromatin interaction mapping using left and right ventricles. See FUMA tutorial for detailed methods. Genes mapped using FUMA were further prioritized by querying the Clinical Genomes Resource (ClinGen) 48 for genes linked to Mendelian heart disease with moderate, strong or definitive evidence, and using a recent review of overlapping GWAS and Mendelian cardiomyopathy genes.
Genetic correlations between HCM and LV traits Pairwise genetic correlations for HCM and the 10 LV traits were assessed using LD score regression (LDSC, v. 1. 0. 1). 18  The analysis was restricted to well-imputed non-ambiguous HapMap3 SNPs, excluding SNPs with MAF<0. 01 and those with low sample size, using default parameters. We then assessed genetic correlations for each of the 55 pairs (HCM and 10 LV traits) using precomputed LD scores from the European 1000 Genomes Project dataset. We did not constrain the single-trait and cross-trait LD score regression intercepts. The results of the genetic correlation analyses are shown in Figure  3  and Supplementary Table  9.
Specifically, we queried the Clinical Genome Resource (ClinGen 48,57 ) for genes associated with any cardiomyopathy phenotype with a level of evidence of moderate, strong or definitive and included genes with robust recent data supporting an association with Mendelian cardiomyopathy. 37 We also prioritized genes based on RNA expression data from bulk tissue RNAseq data in the GTEx 58 v8 dataset accessible at the GTEx portal and snRNA-seq data from Chaffin et al 23 accessible through the Broad Institute single cell portal (singlecell. broadinstitute. org). Using the GTEx v8 data, we assessed specificity of LV expression by computing the ratio of median LV transcripts per million (TPM) to the median TPM in other tissues excluding atrial appendage and skeletal muscle and averaging tissue within types (e.
Two-sample Mendelian randomization (MR) We assessed whether increased contractility and blood pressure are causally linked to increased risk of HCM globally and its obstructive (oHCM) and non-obstructive (nHCM) forms using two-sample MR. LV contractility and blood pressure parameters were used as exposure variables, and HCM, oHCM and nHCM as outcomes. Analyses were performed using the TwoSampleMR (MRbase) package  61 (v. 0. 5. 6)  in R (v. 4. 2. 0). Four exposure variables corresponding to measures of LV contractility were used separately: LVEF as a volumetric marker of contractility, and global strain (strain circ , strain rad and strain long ) as contractility markers based on myocardial tissue deformation. Instrument SNPs for contractility were selected based on the LV trait GWAS presented here using a P value threshold of <5x10 -8.
Only independent SNPs (using r 2 <0. 01 in the European 1000 Genomes population) were included. Instrument SNPs for the blood pressure analysis were selected with a similar approach using a published blood pressure GWAS. 36  The outcome summary statistics were those of the single-trait HCM case-control meta-analysis (5,927 cases and 68,359 controls). We also performed a GWAS metaanalysis including data from HCMR and the Canadian HCM cohort (Supplementary.
Acknowledgements This work was supported by funding from the  British Heart Foundation  ( BHF ,  RG/18/9/33887 ,  RE/18/4/34215 ,  FS/15/81/31817 ); the  National Heart, Lung, and Blood Institute  ( NIH  grant  U01HL117006-01A1 ); the  Wellcome Trust  ( 201543/B/16/Z ,  107469/Z/15/Z ,  200990/A/16/Z ); the  Wellcome Trust  core awards ( 090532/Z/09/Z ,  203141/Z/16/Z ); the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre ; the  NIHR Imperial College Biomedical Research Centre ;  NIHR Royal Brompton Cardiovascular Biomedical Research Unit ;  Sir Jules Thorn Charitable Trust  [ 21JTA ]; the  Medical Research Council (MRC, UK) ; the  Dutch Heart Foundation  ( CVON 2018-30 PREDICT2 ); the  Horstingstuit Foundation ; the  Montreal Heart Institute Foundation ; the  Philippa and Marvin Carsley Cardiology Chair ; the  Fonds de la Recherche du Québec-Santé  ( 254616 ,  265449 ); the  Canadian Institutes for Health Research (CIHR ,  428321 ).
this submission. The views expressed in this work are those of the authors and not necessarily those of the funders. R. T. holds the Canada Research Chair in translational cardiovascular genetics. S. L. Z. received support from  BHF Centre of   Research Excellence Clinical Research Fellowship  ( RE/18/4/34215 ). C. F. received funding from a  BHF   Clinical Research Training Fellowship  ( FS/15/81/31817 ). A. R. H. received support from the  MRC Doctoral Training Partnership. X. X. is currently a post-doc scientist funded by  MRC-London Institute of Medical Sciences. R. W. received support from an  Amsterdam Cardiovascular Sciences  fellowship. M. T. receives support from  Monat Foundation. S. J. J. was supported by a  Junior Clinical Scientist Fellowship  ( 03-007-2022-0035 ) from the  Dutch Heart Foundation , and by an  Amsterdam UMC Doctoral Fellowship.