dataesr/scientific-paragraphs-categorization huggingface dataset:
2009; El Bouhssini et al. 2009 , 2011; Bari et al. 2012 Bari et al. , 2014)) , and to net blotch of barley  (Endresen et al. 2011). This study included the first attempt to compare FIGS with another subset, the Reference set of the Generation Challenge Program (GCP) selected from the global barley core collection based on diversity using SSR markers. FIGS sub-setting using filtering approach has allowed to identify higher percentages of accessions when combining R and MR reactions compared to GCP subset in case of field tests (except SAT2018). The reduced sample size as well as the non-balance between the two classes (Resistant and Susceptible) could explain the low predictability of the machine learning models. Modeling outcomes using machine learning approach were dependent on the isolates or predominant field pathogen populations and the environments.
Acknowledgements The authors would like to thank  Dr. Kenneth Street  for his help in developing FIGS subset and  Mr. Amer El-Omrani , the technician at  Sidi Allal Tazi  experimental station for his help with field experiments. Funding This study is supported financially through  GIZattributed  funding to ICARDA genebank and the  CAIGE-GRDC -ICA00010  project.
Test methods Ultrasound elastography was performed using an ACUSON S2000 device (Siemens Medical Solutions, Mountain View, CA, USA); the device was fitted with a superficial linear array probe (model 9L4). We evaluated three lower limb muscles: the rectus femoris (RF), the tibialis anterior (TA), and the soleus (SOL) muscles. All three play major roles in rising from a chair and locomotion in general and defects participate to disability and frailty. Acoustic radiation force impulse ultrasound elastography quantification was performed using Virtual Touch Tissue Quantification software (Siemens Medical Solutions). All quantitative ARFI measurements were realized in consensus by a single experienced radiologist and a PhD engineer within relaxed muscles. Muscle areas free of large blood vessels were chosen and lay ≥1 cm below the aponeurosis (Figure  1 ).
Statistics All statistical analyses were performed with SPSS ver. 11. 5. 1 software (SPSS, Chicago, IL, USA). For each muscle, intra-class correlation coefficients (ICCs, model 2. 1) were calculated to determine the intra-observer reliability. The intra-observer reliability was further explored by calculating interquartile ranges (IQRs) and the IQR:median ratios of the ARFI measurements. If both reflect variability, the IQR:median ratio is also currently used to assess the reliability of transient elastography. According to the manufacturer, data validity requires that the IQR of all successful measurements must be <30% of the median. 27 Between-group differences in ARFI measurements and SPPB scores were assessed using global linear models adjusted for gender. The confidence level of all tests was set to 0.
This single-centre prospective study was approved by the local ethics committee (Committee for Human Protection in Biomedical Research, CPP SOOM3, Bordeaux, France) and the French Regulatory Authority (ANSM) and was in agreement with Good Clinical Practices (ICH version 4 of 1 May 1996 and the decision of 24 November 2006) and the Declaration of Helsinki. All participants provided written informed consent (NCT001484821).
Acknowledgements This work was supported by the  National Hospital Program of Clinical Research  ( Programme Hospitalier de Recherche Clinique 2010 ,  PHRC10_04-03 ). No Program professional played any role in the research or the preparation of the manuscript. The work is a component of the  CAPARFI study ['Assessment of in vivo skeletal muscle viscoelasticity (ARFI imaging ) with aging and cancer', ClinicalTrials. govidentifier:  NCT01484821 ].
This single-centre prospective study was approved by the local ethics committee (Committee for Human Protection in Biomedical Research, CPP SOOM3, Bordeaux, France) and the French Regulatory Authority (ANSM) and was in agreement with Good Clinical Practices (ICH version 4 of 1 May 1996 and the decision of 24 November 2006) and the Declaration of Helsinki. All participants provided written informed consent (NCT001484821). had any functional dependency, or were under legal protection. Old patients (OP group) were volunteers from the CAPADOGE study [the multicentre National Hospital Program of Clinical Research (sponsor CHU Bordeaux; codes PHRC10_04-03 and NCT01432067)]. The inclusion criteria were age ≥70 years, receiving curative cancer care, and European Co-operative Oncology Group performance status <4.
After incubation, the gel was stained for 1 h with 0. 5% Coomassie Blue R-250 (Bio-Rad, Marnes-La-Coquette, France) solution (w/v) and then destained in 40% methanol and 10% glacial acetic acid solution (Sigma-Aldrich, Saint Quentin Fallavier, France). MMP-1 activity was detected as a clear band against a Coomassie Blue-stained gel background. Quantification was performed on performed ChemiDoc XRS+ Molecular Imager (Bio-Rad, Marnes-La-Coquette, France) with Image Lab version 6. 01. 1 software (Bio-Rad).
Patient data We extracted patient data from the Human Urinary Proteome database of capillary electrophoresis mass spectrometry (CE-MS) data on urinary peptides  3, 4  that at the time of the extraction contained datasets from 50 651 individuals. Data were included in the study if: (i) data on sex, age, estimated glomerular filtration rate (eGFR), systolic (SBP) and diastolic blood pressure (DBP), and diabetic status were available; (ii) age was above 20 years; (iii) data passed quality control criteria for CE-MS measurements, post-acquisition data processing and calibration, as previously described. 4 Exclusion criteria were diagnosis of cancer, solid organ transplantation, or intensive care unit stay at the time of inclusion. Urine samples from 5608 participants (individuals with and without HF) were available.
Overall, 1180 patients had HF and 4428 patients did not have a diagnosis of HF. Urine samples were obtained from the following studies: (i) STANIS-LAS cohort study 5 ; (ii) Hull LifeLab  6  ; (iii) study on diagnostic and prognostic utility of the proteomic classifier HF1 7 ; (iv) FLEMENGHO and EPOGH 8 ; (v) Generation Scotland cohort  9  ; (vi) an echocardiographic and cardiac biomarker study  10  ; and (vii) cross-sectional multicentre cohort study. 11 Further details such as patients' characteristics and information on EF measurements, if applicable, are reported in the relevant manuscripts. For 82% of HF patients, urine samples were collected within 24 h of the assessment of the EF. For remaining HF patients, timing was not recorded. The investigation conforms with the principles outlines in the Declaration of Helsinki 12 ; all data were anonymized and the study was approved by the ethics committee of the University Aachen (EK163/19).
Data curation Heart failure was defined using the European Society of Cardiology (ESC) guidelines recommended at the time of patients' recruitment. In all cases, the diagnosis was based on the presence of symptoms or signs of HF, caused by a structural and/or functional heart abnormality. Patients with HF with missing measurements of EF or natriuretic peptides [N-terminal prohormone of B-type natriuretic peptide (NT-proBNP) or B-type natriuretic peptide (BNP)] were excluded (n = 154). Only patients with NT-proBNP or BNP >125 pg/mL or >35 pg/mL were included. To account for an update in the ESC guidelines, the diagnosis of HF subtypes was assigned based on the EF thresholds in the ESC recommendations  13   ). The latter group included, among others, healthy individuals, patients with chronic kidney disease (CKD), hypertension, diabetes or coronary artery disease (or a combination of these pathologies).
CKD was defined based on the clinical diagnosis and/or on the kidney function (eGFR < 60 mL/min/1. 73 m 2 ). eGFR was estimated based on the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Hypertension was diagnosed based on the use of antihypertensive drugs, SBP of 140 mmHg or higher, and/or DBP of 90 mmHg or higher. 14 Patient selection and data curation are graphically depicted in Figure  1A.
Case-control matching Matching of cases to non-HF controls was performed in R ('MatchIt' 15 ). A 'nearest neighbour' matching-method was applied, allowing for discarding of datasets from both groups that were outside the distance-measure and re-estimation of distance-measure after discarding. Logistic regression was used to estimate the distance-measure (default setting). Case-control matching at a 1:1 ratio was performed based on sex, age, eGFR, SBP, DBP, and the presence of diabetes and hypertension. HF patients were matched to non-HF controls. Those patients were divided based on EF, followed by matching of the controls selected from the group of controls already matched to the complete HF cohort. Patients presenting with specific HF subtype were subsequently matched to each other. The following groups were matched based on these variables: (i) HF vs.
Capillary electrophoresis mass spectrometry Preparation and measurements of urine samples using CE-MS were conducted as described previously. 4 CE-MS analysis was performed with a P/ACE MDQ CE (Beckman Coulter, Fullerton, CA, USA) coupled to a micro-TOF-MS (Bruker Daltonic, Bremen, Germany). Raw MS data were evaluated using MosaFinder 3 applying a probabilistic clustering algorithm and using both isotopic distributions and conjugated masses for charge state determination. Normalization of the CE-MS data was based on 29 collagen fragments that are generally not affected by disease and serve as internal standards. 3, 4 Missing values were interpreted as zero. Identified HF biomarkers were assigned in silico to sequenced peptides from the Human Urinary Proteome database as described elsewhere.
Unsupervised classification Unsupervised clustering was performed using the R package 'ConsensusClusterPlus'. 17  Abundances for sequenced peptides (independent of the significance level) were transformed to ranks, and pairwise correlations between samples were calculated with Spearman's rank correlation coefficient. The correlation matrix was submitted for agglomerative hierarchical clustering and using clustering centroids and average linkage  17  for a pre-defined number of solutions (tested 2-8 clusters). Class assignment for each solution was determined after 1000 permutations by resampling 95% of the sample set, and the output was evaluated based on the consensus heat maps, and cluster size. 17 Principal component analysis (PCA) was conducted on the same input data, using the base R function 'prcomp()' and the argument scale = FALSE.
Cohort characteristics Among the 4970 participants included in the study after data curation (Figure  1A ), case-control matching resulted in the selection of 773 patients with HF and 773 controls. Cases were stratified into three subtypes: HFrEF (n = 442), HFmrEF (n = 144) and HFpEF (n = 187). For each of the subtypes, the same number of controls were matched (Figure  1B ). The key matching characteristics were similar between groups (Mann-Whitney test, P > 0. 05), as shown in Table  1. Patients with HFrEF, HFmrEF and HFpEF were matched to each other, resulting in the selection of 117 individuals in each group. The characteristics of the matched HF patients are presented in online supplementary Table  S1.
Discussion This is the first investigation of the urinary proteomic profile of more than 1500 patients with HF encompassing the full spectrum of EF and non-HF controls (Graphical Abstract). While our data demonstrate highly significant differences between patients with. HF and non-HF controls for multiple urinary peptides, there were very few differences among the different HF subtypes defined by EF. Our study was also designed to identify HF specific peptides. To limit the potential impact of co-existing diseases (CKD) and various other factors (e. g. age) on urinary peptides, analysis was performed in matched cohorts. Identified peptides significantly associated with HF were clearly distinct from those peptides associated with CKD, supporting their specificity for HF. Our data demonstrate highly significant differences in multiple urinary peptides between patients with HF and matched controls.
Study strengths and limitations The strength of our study was a large sample size covering patients with HF across the full range of EF. As a result, confidence in the validity of the findings is very high. Moreover, all urinary peptide datasets were acquired using the same analytical platform enriched based on the predicted proteases and proteins representing the urinary peptides. Networks are coloured based on cluster ID, the thickness of the edge represents the similarity score. The most significant term from each cluster was selected as label and for those terms, P-value corrected using Banjamini-Hochberg (BH) procedure is given. Among the most prominent findings, significant enrichment for pathways related to collagen turnover (highlighted in blue) and immune response (highlighted in orange) was observed.
*In silico predicted proteases. (CE-MS), with analysis and data processing performed according to ISO13485 standards. This allows for generation of highly comparable data, enabling comparisons in the wide spectrum of conditions. Limitations included that individual patient data were derived from the different cohorts of HF patients and controls enrolled at multiple clinical centres. However, case-control matching for clinical/demographical variables known to have an impact on urinary peptides allowed to harmonise the data in the respective comparisons. Although efforts were made to collect urine samples on the same day as other study assessments, this may not have been the case for every participant. There was no standard operating procedure applied for the collection of all urine samples, specifically there were no requirements made for an early morning or mid-stream sample.
Funding AC, CD, JD, AG, AM, JR, NG, PR, FZ, HM, ZZ, JS, AL are supported by the  EU Commission  via the  HOMAGE  ( HEALTH-FP7-305507 HOMAGE ) project. CD, FZ, HM, JD and JS were previously supported by the  EU-MASCARA  ( HEALTH-FP7-278249 ) project, and CD and JM receive support from the  British Heart Foundation  ( RE/18/6/34217 ). TH is supported by the  EU Commission  via the CaReSyAn Project (MSCA-ITN-2017-Project ID:  764474 ). VJ is supported by the  Deutsche Forschungsgemeinschaft' (DFG, German Research Foundation ) by the  Transregional Collaborative Research Centre  ( TRR 219 ;  Project-ID 322900939 ) (subproject  S-03 ). PR, NG, FZ are supported by the  RHU Fight-HF , a public grant overseen by the  French National Research Agency (ANR)  as part of the  second 'Investissements d'Avenir' program  (reference:  ANR-15-RHUS-0004 ), and by the  French PIA  project ' Lorraine Université d'Excellence '(reference:  ANR-15-IDEX-04-LUE ).
No sequence specificity elements are visible in our structure suggesting that any sequence upstream or downstream from the m6A would bind similarly. When comparing our model to the recently published Arabidopsis thaliana CPSF4-YTH domain bound to a 10 mer RNA (pdb id: 5ZUU)  (Hou et al. , 2021) , which forces a dimerization of the YTH/ RNA complexes through the 5'-C 6 U 7 A 8 G 9 -3' palindromic sequence, the backbone conformation of the downstream nucleotides after the m6A remain remarkably conserved (Figure  6 -figure supplement 1A). This can also be observed in YTHDC1 complexes with single strand DNA (pdbid:6WEA;  Woodcock et al. , 2020)  which has visible 5' nucleotides before the m6A and is coherent with the overall structural conservation of these domains (Figure  6 -figure  supplement 1B ) which all display a similar positively charged binding groove dedicated to 3' binding after the m6A (Figure  6 -figure  supplement 1C ).
This same tool was now used to conduct a detailed time-course measurements of mRNA levels using Illumina RNA-sequencing (RNAseq) (GSE168155), which allowed the detection of a sizeable fraction of mRNAs that accumulated following the IAA-dependent depletion of CPSF4, and in a gradual manner as shown by hierarchical clustering analyses (Figure  7C , Figure  7 -figure supplement 1A-B and Supplementary file 3). Although some genes had their expression induced as early as after 7 hr after the IAA-dependent depletion of CPSF4 (e. g. TGME49_208730 in Figure  7D ), the main transcriptional phenotype pointed to possible alterations of the transcription termination occurring in the context of the KD of CPSF4. At many loci, the depletion of CPSF4 was accompanied by an apparent transcriptional readthrough that went beyond the annotated 3'end sequence of a certain gene to breach into the adjacent one, and that generated very long transcripts (top of Figure  7E  and Figure  7 -figure supplement 2).
For this purpose, we proceeded by assessing the outcomes of diminishing this mark at the level of its deposition by employing the previously described METTL3 KD cell line (Figure  3A-B ) to generate nanopore-sequenced RNA data, at 24 hr post induction of the knock-down, a time that is short enough to be able to discriminate primary from secondary transcriptomic effects (SRA data PRJNA705300). Nanopore DRS analysis revealed recurrent events of transcription termination defects in METTL3depleted cells when compared to untreated cells, at loci that exhibited similar patterns in the context of CPSF4 being depleted (Figure  7E , Figure  8  and Figure  7 -figure supplement 3). We detected and assessed the average distribution of readthrough transcripts at a genome-wide level, by using ChimerID scripts  (Parker et al.
(C) Volcano plot illustrating changes in RNA levels before and after the induced Knock-down of CPSF4. The orange dots indicate transcripts that were significantly up and down regulated, using adjusted p < 0. 1 (Bonferroni-corrected) and ± 3-fold change as the cut-off corresponding to each comparison. X-axis showing log2 fold change, Y-axis showing -log10(p-value). Vertical dashed lines indicate threefold up-and down-regulation. (D) Density profile from illumina RNA-seq data for a representative gene targeted by the knock-down of CPSF4. RNAs were extracted from untreated cells as well as after 7, 24, and 48 hr of KD-inducing IAA treatment. RPKM values are shown on the y axis, and chromosomal positions are indicated on the x axis. (E) Density profile for a representative gene targeted by the KD of CPSF4, with extracted RNAs being sequenced both through illumina-RNA-seq data (on top) and aligned DRS reads (600 read stack for each condition, no strand coloring and no splicing characteristics displayed).
To support this claim, and to identify the sites of m6A across the genome, we employed DRS to indirectly detect modified nucleotides. In fact, the presence of an m6A mark is known to induce a higher rate of base calling errors on, or within, the close proximity of m6A sites, as would any other nucleotide modification. The recently developed Epinano pipeline  (Liu et al. , 2019)  uses error variations between two sets of aligned DRS reads (WT vs KD) to map significantly modified error sites. Using such an approach to compare UT vs IAA-dependent METTL3-KD DRS datasets, allowed us to presume that most of these detected differential error sites are m6A sites (Figure  10A ). We then analyzed the motifs around which the most significant peaks of error were mapped, which revealed a high and significant enrichment of a motif consisting of ARACW (R = A/G, W = A/T/G) (Figure  10B ).
The color scale indicates log2-transformed fold changes. (B) A representative analysis of the read-through from the ROP35 transcript into the TGME49_304730 locus. On top are displayed the illumina-RNA-seq data before and after the IAA-dependent KD of CPSF4, at different times. The y-axis represents the RPKM values. Below is the nanopore-based RNA sequencing of RNAs extracted before and after the IAA-dependent knock-down of METTL3 (in red), of CPSF4 (in blue), and of MORC (in yellow). The y-axis represents the read-depth counts. (C) A more detailed close-up look of the respective nanopore data at the same locus allowing a clear assessment of the read-through phenotype that is seen following the KD of both CPSF4 and METTL3 but not of MORC which only resulted in a conventional upregulation of the initially repressed TGME49_304730 gene.
9 mm, Dr. Maisch) using gradients of 35 min. MS and MS/ MS data were acquired using Xcalibur (Thermo Fisher Scientific). Peptides and proteins were identified using Mascot (v. 2. 7. 0) through concomitant searches against the Toxoplasma gondii database (ME49 taxonomy, v. 30, downloaded from ToxoDB), the UniProt database (H. sapiens taxonomy, January 2021 version), a homemade database containing the sequences of 250 classical contaminants found in proteomic analyses (human keratins, trypsin, bovine albumin, etc. ), and the corresponding reversed databases. Trypsin was chosen as the enzyme and two missed cleavages were allowed. Precursor and fragment mass error tolerances were respectively set at 10 ppm and 0. 6 Da for LTQ-Orbitrap Velos Pro data and 10 ppm and 20 ppm for Q-Exactive Plus data.
Peptide modifications allowed during the search were: carbamidomethyl (C, fixed), acetyl (protein N-term, variable) and oxidation (M, variable). The Proline software was then used to perform a compilation and grouping of the protein groups identified in the different bands. Proline was also used to filter the results: conservation of rank one peptide spectrum matches (PSMs), peptide length ! six amino acids, PSM score ! 25, false discovery rate of peptide-spectrum-match identifications < 1% as calculated on peptide-spectrum-match scores by employing the reverse database strategy, and a minimum of 1 specific peptide per identified protein group. Proteins from the contaminant database were discarded from the final list of identified proteins. MS1-based label-free quantification of the protein groups was performed using Proline to infer intensity-based absolute quantification values (iBAQ) that were used to rank identified Toxoplasma proteins in the interactomes.
Isothermal titration calorimetry The affinity of at-YTH or tg-YTH for RNA or RNAm6A were determined using a MicroCal iTC200 system (Malvern Panalytical, Malvern, UK). Experiments were performed at 25˚C in 20 mM Tris pH 8, 50 mM NaCl. The YTH proteins solutions (at concentration ranging from 25 mM to 60 mM) were loaded in the calorimetric cell. RNA (at concentrations ranging between 0. 6 mM to 0. 9 mM) were titrated in the protein sample typically by performing 16 injections of 2. 5 mL aliquots. Control titrations were performed by titrating RNA into buffer. The dissociation constants (Kd), enthalpy of binding (DH), and stoichiometries (N) were obtained after fitting the integrated and normalized data to a singlesite binding model. Data were processed using Origin 7. 0 (Malvern Panalytical, Malvern, UK).
Crystal were subsequently fished out in cryo-loops (Hampton) and directly flash-frozen in their crystallization mother liquor. All PEG and associated chemical formulations were obtained from Sigma and purchased as chemical compounds. X-ray diffraction data for m6A / CPSF4-YTH crystals were collected by the autonomous European Synchrotron Radiation Facility (ESRF) beamline MASSIF-1  (Bowler et al. , 2015; Svensson et al. , 2015)  using automatic protocols for the location and optimal centering of crystals  (Svensson et al. , 2018). Strategy calculations accounted for flux and crystal volume in the parameter prediction for complete datasets. Apo CPSF4-YTH and RNA co-crystals were remotely mounted at the diamond light source beamline IO4. In all cases, diffraction was performed at 100K. Data reduction was performed using XDS  (Kabsch, 2010)  while amplitude scaling/merging was performed by multiple software packages (Aimless or Truncate) by Autoproc or Staraniso (Global Phasing Ltd).