dataesr/scientific-paragraphs-categorization huggingface dataset:
Predicted prophages were clustered using a network analysis performed with vCONTACT2 (v0. 9. 8)  [51] , using the RefSeq. 88 database, with all other sequenced bacteriophages included using the Millardlab database as of January 2021  [47]. The resulting network was visualised in Graphia (v2. 1)  [52]  with nodes of vOTUs identified in this work coloured in blue and those that were reference sequences coloured in grey, key viral families were manually annotated.
Prophage Annotation and Functional Analysis. Sequences were annotated using prokka (v1. 14. 5)  [53]  and a custom database made from all phage genes using the Millardlab database from September 2019. The -addgenes and -locus-tags options were also used. Resulting amino acid files were clustered at 90% identity using CD-Hit (v4. 8. 1)  [54]  and representative sequences from each cluster were analysed using EggNOG-mapper(v2. 0) [55]  and default parameters. Phage lifestyle prediction was calculated in the same way as Cook et al  [56]  using HMM profiles for proteins that indicate a temperate lifestyle, and hmmscan (v3. 3)  [57].
Distribution of prophages in individuals. A set of reference contigs was constructed using the vOTUs identified from the metagenomes. The genomes of any remaining temperate coliphages isolated from the samples that had not been identified as vOTUs using the method described above were also added. Additionally, a set of 249 reference crAss genomes were downloaded from the dataset constructed by Guerin et al  [62] , to try and capture the diversity of the crAss-like family of viruses that are abundant in the gut. This set of predicted phages, coliphages and crAss phages were then dereplicated at 95% ANI using dedupe2. sh to remove any remaining redundancy  [46]. Trimmed and QC'd reads from individual metagenomes were mapped against the set of reference contigs using bbsplit. sh with random mapping of ambiguous reads, and a minimum identity of 0.
(ii) Genetics and genomics of grapevine stress tolerance. The description of the grapevine genome is more and more precise. The genetic variations among genotypes are now revealed with new technologies with the sequencing of very long DNA molecules. High throughput technologies for DNA sequencing also allow now the genetic characterization at the same time of hundreds of genotypes for thousands of points in the genome, which provides unprecedented datasets for genotype-phenotype associations studies. We review the current knowledge on the genetic determinism of traits for the adaptation to climate change. We focus on quantitative trait loci and molecular markers available for developmental stages, tolerance to water stress/water use efficiency, sugar content, acidity, and secondary metabolism of the berries.
, 2007) , the genetic variations among genotypes are now revealed by new technologies with very long reads of single DNA molecules  (Chin et al. , 2016). High throughput technologies for DNA sequencing also now allow the genetic characterization at the same time of hundreds of genotypes for thousands of points in the genome, which provides unprecedented datasets for genotype-phenotype associations studies. At last, new methods for genome editing open the gate for efficient and stable genetic transformations of the grapevine.
Adaptation of grapevine to new environmental conditions will be all the more efficient as the physiological responses to drought, elevated temperatures, or combined stress on plant growth, development, and berry composition are precisely described. To achieve this goal, the first challenge is to characterize the levels of stress imposed in experiments in a way the results can be extrapolated in other environmental conditions and that they make sense in real vineyard conditions. The second challenge is to develop and to use methods able to integrate and interpret large datasets that include genomic sequences, transcriptomic, proteomic, and metabolomic data. This requires huge efforts toward integrated network analysis and "system biology. " The final goal is to build a corpus of knowledge that includes the responses to quantified environmental variables and genetic variability.
18 It is important to realize that the pathogenicity of some of the previously reported variants has been called into question upon reanalysis because they appeared to have a relatively high frequency in control exomes/genomes. 19, 20 We therefore recommend, when using older literature describing "pathogenic" variants, to carefully determine what is strength of the supporting evidence for pathogenicity and also consult updated clinical variant databases, such as ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/) (open access), LOVD( https://www. lovd. nl/) (open access) or HGMD professional (https://digitalinsights. qiagen. com/productsoverview/clinical-insights-portfolio/human-gene-mutation-database/) (licensed). In recent years, the efforts of organisations like ClinGen (https://clinicalgenome.
70, 72, 73 ] [76]  Genetic variants identified in aHUS, C3G, and related complement disorders are published in the complement genetic variant database (www. complement-db. org/home. php), with displays of the variant sequence, reported phenotype, and structural, functional and allele frequency data. This database represents a valuable tool in guiding genetic data interpretation. 77 S and even GS could be indicated in familial recessive forms with infantile onset to identify rare intronic pathogenetic DGKE variants  78  or other underlying genetic conditions. 79, 80 ] [83]  Both in aHUS and IC-MPGN/C3G, we recommend parallel screening for genetic and biochemical abnormalities as soon as the clinical diagnosis has been established. The analyses should be performed in experienced laboratories.
19  higher rate of mutations will be obtained when testing CAKUT affecting both renal parenchymas, with or without urinary tract defect, with or without familial history of CAKUT and with or without extra-renal defects. Mutations in genes other than HNF1B, PAX2 and EYA1 represent, for each gene, only a small percentage of cases. Interpretation of variants in CAKUT genes can be even more difficult because the pathogenicity of some variants previously reported as pathogenic mutations, is nowadays, with the available knowledge of large databases such as gnomAD, questionable. This is for instance the case for gene variants that would currently be classified as VUS, as they have been reported in only a single (or in few) report(s) without data regarding segregation (or sometimes with the variant inherited from a healthy parent), and with a minor allele frequency too high in population and/or inhouse databases.
The Arpin-TNKS Interaction Participates in the Regulation of Cell Migration To examine the role of the Arpin-TNKS interaction, we used the MCF10A ARPIN KO cell line, previously generated using CRISPR/Cas9  [32] , to isolate clones stably re-expressing exogenous WT Arpin or its derivatives. We first focused on two Arpin mutations, G218A and W224A, that impair TNKS and Arp2/3 binding, respectively (Figure  2G ). Exogenous Flag-tagged Arpins were moderately overexpressed compared to the endogenous Arpin in the parental MCF10A cell line (Figure  4A ). A major role of the Arp2/3 pathway in cell migration is to mediate migration persistence through positive feedback, and the Arp2/3 inhibitory protein Arpin antagonizes this role  [4, 6]. We recorded the random migration of single cells using these cell lines, extracted the cell trajectories, and analyzed them using DiPer software  [33].
Nano-LC-MS/MS experiments were conducted in a data-dependent acquisition method by selecting the 20 most intense precursors for CID fragmentation and analysis in the LTQ. Data were processed with Proteome Discoverer 1. 3 software, and protein identification was performed using the Swiss-Prot database and MASCOT search engine (Matrix Science, Merrillville, IN, USA). For PC immunoprecipitation of PC-Arpin WT or mutants (Figure  2 ), 293T cell lysates were lysed in 50 mM KCl, 10 mM Hepes, pH 7. 7, 1 mM MgCl 2 , 1 mM EGTA, and 1% Triton TX100 supplemented with a protease inhibitor cocktail (Roche). Twenty microliters of HPC4 beads were supplied with 1 mM Ca 2+ , protease inhibitor cocktail (Roche), and 2 µM ADP HDP PARG inhibitor (Merck Millipore, Burlington, MA, USA). Beads were incubated with extracts for 1 h at 4 • C, washed 5 times in the same buffer, and analyzed by Western blotting.
SEC-MALS For SEC-MALS, purified proteins were separated in a 15-mL KW-803 column (Shodex, Shanghai, China) run on a Shimadzu HPLC system. MALS, QELS, and RI measurements were achieved with a MiniDawn Treos, a WyattQELS, and an Optilab T-rEX (all from Wyatt Technology, Santa Barbara, CA, USA), respectively. Mass calculations were performed with ASTRA VI software (Wyatt Technology) using a dn/dc value of 0. 183 mL•g -1.
Live Confocal Imaging GFP-TNKS2 condensates were imaged using a confocal laser scanning microscope (TCS SP8, Leica, Wetzlar, Germany) equipped with an inverted frame (Leica), a high NA oil immersion objective (HC PL APO 63×/1. 40, Leica), and a white light laser (WLL; Leica). The acquisition was performed using LASX software. To capture condensate fusion events, GFP-TNKS2 expressing cells were imaged every 0. 79 s for 158 s. For the FRAP experiment, single GFP-TNKS2 condensates were bleached during 1. 5 s and imaged every 5 s for 105 s. The aggregate intensity was manually normalized to the background, and the recovery curve presents the mean intensity and standard deviation (SD) (n = 30 condensates from 16 cells). To measure the fraction of cells with detectable condensates (n > 1), MCF10A cells co-expressing GFP-TNKS2 and mScarlet, mScarlet-Arpin WT, or the G218D mutant were imaged (data from 3 independent experiments with at least 115 cells analyzed for each condition are represented).
Live Imaging and Analysis of Cell Migration and Statistics MCF10A cells were seeded onto glass-bottomed µ-Slide (Ibidi, Gräfelfing, Germany) coated with 20 µg/mL of fibronectin (Sigma). Imaging was performed on an Axio Observer microscope (Zeiss) equipped with a Plan-Apochromat 10×/0. 25 air objective; the Hamamatsu camera C10600 OrcaR2; and the Pecon Zeiss incubator XL multi S1 RED LS (Heating Unit XL S, Temp module, CO 2 module, Heating Insert PS, and CO 2 cover). Images were acquired every 10 min for 24 h. Single-cell trajectories were obtained by tracking cells with ImageJ and analyzed using DiPer software  [33]  to obtain the migration parameters: directional autocorrelation, mean square displacement, average cell speed, and single-cell trajectories plotted at the origin. Data from two (Flag-Arpin cell lines) or three (GFP-Arpin knock-in cell lines) independent experiments were pooled for the analysis and plotted.
The results are expressed as the means and standard errors of the mean (s. e. m). The average cell speed was analyzed using GraphPad software with the Kruskal-Wallis test. For migration persistence, a statistical analysis was performed using R. Persistence, measured as the movement autocorrelation over time was fit for each cell by an exponential decay with a plateau (as described in reference  [51] ): A = (1 -A min ) * e -t τ + A min where A is the autocorrelation, t the time interval, A min the plateau, and τ the time constant of decay. The plateau value A min is set to zero for the cell lines in vitro, as they do not display overall directional movement. The time constant τ of the exponential fits were then plotted and compared using the Kruskal-Wallis test. Four levels of statistical significance were defined: * p < 0.
Acknowledgments: We thank  Susan Smith  for the kind gift of the TNKS double-KO cell line;  Véronique Henriot ,  Anaïs Pitarch ,  Angelina Chemeris , and  Sai P. Visweshwaran  for technical support;  Marc Lavielle  and  Chuang Yu  for their help with the statistical tools; and  Svetlana Rubtsova  for the English corrections. This work was supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01. We thank  Manuela Argentini  and  Christophe Velours  from the  I2BC facilities in Gif-sur-Yvette  for the mass spectrometry analyses (SICaPS) and for the SEC-MALS, respectively. We thank the  Polytechnique Bioimaging Facility  for assistance with live imaging by using their equipment, which was partly supported by  Région Ile-de-France (interDIM)  and  Agence Nationale de la Recherche  ( ANR-11-EQPX-0029 Morphoscope2 ,  ANR-10-INBS-04 France BioImaging ).
Objective: To report the efficacy and safety of the approved ixekizumab (IXE) dose over 5 years from UNCOVER-3 (NCT01646177). Methods: Patients (N = 1346) were randomized 1:2:2:2 to receive subcutaneous injections of placebo, etanercept 50 mg twice weekly, or IXE 80 mg every 2 weeks or every 4 weeks after an initial dose of IXE 160 mg, respectively. At week 12, patients entered the long-term extension period with dosing of IXE every 4 weeks and could escalate to every 2 weeks after week 60. Efficacy was reported for the IXE every 2 weeks/every 4 weeks group of the intent-to-treat population. Safety was reported for patients who received at least 1 dose of IXE every 2 or every 4 weeks. Results: Using modified nonresponder imputation, 78. 8%/67. 1%/46. 2% of patients receiving the approved dose of IXE every 2 weeks/every 4 weeks (n = 385) achieved $75%, $90%, or 100% improvement from baseline in the Psoriasis Area and Severity Index, respectively, at week 264; static Physician's Global Assessment score of 0/1 and 0 responses were 69.
Study design and participants UNCOVER-3 was a randomized, double-blinded, multicenter, phase 3 study in patients with moderate to severe plaque psoriasis. Details of the study design and patient population were previously reported. 3, 6, 7 Briefly, 1346 patients were randomized 1:2:2:2 to receive subcutaneous injections of placebo, etanercept 50 mg twice weekly or IXE 80 mg every 2 weeks or every 4 weeks after an initial dose of IXE 160 mg. At week 12, all patients were switched to IXE every 4 weeks for the long-term extension (LTE). After week 60, patients could escalate dosing to every 2 weeks at investigator discretion. This report focuses on LTE data from patients who received the approved dosing regimen (initial 160 mg, IXE 80 mg every 2 weeks up to week 12, and IXE 80 mg every 4 weeks thereafter), including patients whose dosage was escalated to every 2 weeks.
Statistical analysis The intent-to-treat (ITT) population, defined as all randomized patients regardless of treatment compliance, was analyzed for efficacy. Data from visits in which patients were treated with the approved IXE dosing regimen were included for primary efficacy analysis. Efficacy data were summarized at each post-baseline visit through 264 weeks by using 3 methods: as observed, multiple imputation (MI), and modified nonresponder imputation (mNRI). For the as-observed method, only data from patients who completed the visit were analyzed, and no missing data imputation was performed. The MI method handled missing data by estimating what observations would have been if the patient had continued with the hypothetical treatment strategy. The mNRI method analyzed patient response status with a composite strategy: those with missing data due to study drug-related events (e.
CAPSULE SUMMARY d Ixekizumab is an efficacious treatment for moderate to severe plaque psoriasis with a well-characterized safety profile. d Patients experienced sustained response rates for skin clearance and a consistent safety profile through week 264. Overall, ixekizumab therapy is appropriate for long-term treatment of patients with plaque psoriasis. included data collected at visits with every-2-week dosing escalations. The statistical analyses of efficacy and safety measures were performed with SAS, version 9. 2 (SAS Institute).
RNA Seq Data Processing Raw sequencing reads from each library were aligned with the Capra hircus Ensembl genome (ARS1 GCF_001704415. 1) and the PPRV genome (GCF_000866445. 1_ViralProj15499) using Hisat2 (v. 2. 1. 0). Read count data were obtained using StringTie (v. 2. 0. 3) and were imported in RStudio software (v. 1. 1. 456) using the tximport package (v. 1. 20. 0). Differential expression analysis between PPRV-infected and not-infected PBMCs was performed using the DESeq2 package. A false discovery rate (FDR) < 0. 05 and a |log2 Fold Change| > 1 were set as the default thresholds for significant differential expression. Venn diagrams between upand downregulated genes were drawn with InteractiVenn (accessible at http://www. interactivenn. net/). Gene ontology categories based on biological processes, molecular functions and cellular components were retrieved for all up-and downregulated genes using gProfiler  (36).
Ingenuity Pathways Knowledge Base in QIAGEN's IPA software (QIAGEN, Redwood City, USA) was used with the lists of differentially expressed genes to identify the canonical pathways, upstream regulators and the most significant biological processes. Core and multiple analyses were performed for each dataset to identify the activated (Z score > 2) or inactivated (Z score < -2) canonical pathways and bio functions. DEG lists were also ranked based on the P-values adjusted (FDR) for a gene set enrichment analysis with goat blood transcriptional modules (BTM). BTM represent gene sets created based on a high level of interactions identified ex vivo in human PBMC  (37) , we had previously adapted for use in sheep  (38)  and pigs  (39). The annotation of these genes was based on either the gene descriptions given in the ENSMBL data based or on homology with other species, including cattle, pigs, human and/or mouse.
Homologies were obtained using the Biomart software accessible on the ENSEMBL web site. The goat BTMs were adapted to the goat by replacing human genes with their goat homologs. This step required extensive manual curation for genes that differed in name or have not yet been annotated in goats. The homologies, novel annotations, and the final lists of BTM genes are provided in Supplementary Materials. Module activity during the different infections was compared using a false discovery rate < 0. 01. This threshold was set to select specific BTMs and to reduce false positive BTMs.
Proteomic Data Acquisition and Processing PPRV-infected and not-infected PBMCs were prepared for proteome analysis using a homemade protocol. First PBMCs were washed twice with 1X PBS and collected in DIGE buffer at different times. DIGE buffer comprised 7 M urea, 2 M thiourea, 4 % 3 -[ ( 3 -c h o l am i d o p r o p y l ) -d i m e t h y l a m m o n i o ] -1propanesulfonate (CHAPS) and 30 mM TrisHCL. The pH was adjusted to 8 with HCl. This step allowed cell lysis and the conservation of protein structure. The quantity of proteins obtained was evaluated using a 2D gel and colorimetric dosage. Samples were transferred to the Functional Proteomics Platform (FPP, Montpellier, France) where tryptic digestion of the samples was carried out on S-Trap Mini Spin columns (Protifi). Samples were loaded onto a 50-cm reversed-phase column (75 mm internal diameter; Acclaim PepMap 100 C18; Thermo Fisher Scientific) and separated with an UltiMate 3000 RSLC system (Thermo Fisher Scientific) coupled to a QExactive HF system (Thermo Fisher Scientific).
uniprot. org) of C. hircus and Peste-des-petits-ruminants virus (proteome IDs UP000291000 and UP000118493, respectively) and 250 frequently observed contaminants, as well as reversed sequences of all entries. The maximum false peptide and protein discovery rate was set at 0. 01. The representative protein ID in each protein group was automatically selected using our inhouse Leading tool (v3. 4)  (42). The protein-protein interaction was predicted using the STRING database (STRING: functional protein association networks, accessible at string-db. org) with Capra hircus as the reference species  (43).
BTMs involved in the role of myeloid cells in inflammation were found to be repressed upon infection with VN751 (Figure  5C ). Proteins differentially expressed between infections were analysed using a proteomic approach. Principal component analysis of the proteomic data at 48, 72 and 96 hpi revealed distinct clusters that reflect the course of the infections. The IC89 and VN751 groups had similar protein distribution and expression, while the MOCK and MA08 groups showed distinctive patterns (Supplementary Figure  1B ). A set of proteins involved in the antiviral response was differentially expressed among the infection groups from 72 to 96 hpi (Figure  6 ). These proteins were significantly detected in all infections but at different expression levels. Fewer proteins were detected in the MA08 infection group.
Thus, this tool has confirmed its effectiveness and could be used in the future to detect the mechanism of action and factors related to the response of veterinary species to infection or vaccination  (38). Upstream regulators are genes or transcriptional regulators that govern multiple pathways and gene activation. These regulators may explain the observed changes in gene expression and pathway activation. Upstream regulator analysis is based on knowledge of the expected effects between transcriptional regulators and their target genes stored in the Ingenuity knowledge database. Since the activation zscore of these regulators is obtained by a calculation based on the presence and activation status of their known targets, it is not surprising to have inactivated upstream regulators although there are no downregulated genes in the data set  (58).
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The RNA sequencing raw data have been deposited in the Sequence Read Archive (SRA) under the following identifier PRJNA748573. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE (70) partner repository with the dataset identifier PXD024890 and 10. 6019/PXD024890.
Since 2013 it has offered evaluation labs in the fields of layperson and professional health information extraction (IE), management, and retrieval (IR) with the aims of bringing together researchers working on related information access topics and providing them with datasets to work with and validate the outcomes. These labs and their subsequent workshops target: 1. developing processing methods and resources (e. g. , dictionaries, abbreviation mappings, and data with model solutions for method development and evaluation) in a multilingual setting: (a) to enrich difficult-to-understand eHealth texts, (b) to provide personalized reliable access to medical information, and (c) to provide valuable documentation; 2. developing an evaluation setting and releasing evaluation results for these methods and resources; 3.
contributing to the participants and organizers' professional networks and interaction with all interdisciplinary actors of the ecosystem for producing, processing, and consuming eHealth information. The vision for the Lab is two-fold: (1) to develop tasks that potentially impact patient understanding of medical information and (2) to provide the community with an increasingly sophisticated dataset of clinical narratives, enriched with links to standard knowledge bases, evidence-based care guidelines, systematic reviews, and other further information, to advance the state-of-the-art in multilingual IE and IR in health care. The ninth annual CLEF eHealth evaluation lab, CLEF eHealth 2021, aiming to build upon the resource development and evaluation approaches proposed in the previous years of the lab  [40, 19, 10, 18, 11, 39, 20, 15] , offered the following two tasks: -Task 1.