dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients treated with ustekinumab We asked the centers who were involved in the randomized controlled trial  9  for screening their database to identify all CD patients who have been treated with ustekinumab to prevent endoscopic POR. Patients with prior exposure to ustekinumab before surgery were also included. One additional center was solicited to reach a sufficient number of patients (see sample size calculation). All these patients received intravenous induction. The doses of initial intravenous infusion of ustekinumab were adjusted on body weight (260 mg if < 55 kg, 390 mg if between 56 and 80 kg and 520 mg if > 80 kg). All the subsequent subcutaneous injections were performed using a dose of 90 mg. We excluded all the patients who started ustekinumab more than 90 days after intestinal resection or following stoma closure, and patients with persistent macroscopic lesions after surgery (Figure  1 ).
Data managing and statistical analyses Study data were collected and managed using REDCap electronic data capture tools hosted at Clermont-Ferrand University Hospital. 23 e potential differences in baseline characteristics between the treated group and the control group were tested with the Student ttest or the Mann-Whitney test for continuous variables (assumption of normality assessed using the Shapiro-Wilk test and homoscedasticity evaluated using Fisher-Snedecor's test), and the χ 2 or the Fisher's exact tests for categorical variables. The choice of treatment is usually influenced by the patients' characteristics in retrospective studies increasing the risk of indication bias. 24 We performed a propensity score analysis to adjust for these differences and to be as close as possible to the situation of a randomized trial.
Wound closure and dissection Timing: $1 h (per mouse) The mice are sacrificed on the third or seventh day after injury and wound treatments. The wounds are then dissected. 17. At day 3 after step 16, gently remove the transparent non-adherent dressing as well as silicone donut-shaped splints and sutures. CRITICAL: It is recommended that non-adherent dressings be removed on the third day after injury to minimize wound exudation. Alternatives: It is possible to sacrifice mice by other methods including cervical dislocation. If this latter method is preferred, take care to not damage the healed wounds. Optional: If you want to measure wound closure, place a millimeter ruler near the wounds and take pictures. Wound closure can be quantified using ImageJ software in a blinded manner through assessment of wound re-epithelialization on days 3 and 7 post-graft and can be calculated as follows: [1-(wound area on day X/wound area on day 0) 3 100].
). Note: It is possible to use RNA from mouse wounds to perform global or single cell RNA sequencing. Note: Manufacturer's instruction for the SuperScriptä III Reverse Transcriptase kit can be found here. 24. Dilute the cDNAs (complementary DeoxyriboNucleic Acids), previously obtained in step 23, at a 1:10 ratio with distilled DNase/RNase free water in a final volume of 200 mL. Mix them by pipetting and store them at 4 C for short storage and À20 C for long storage. Pause point: cDNAs can be stored at À20 C for several months. 25. Design primer sequences for reference and interest genes using Primer3 and BLAST software. Note: In our study, we used genes encoding for human or mouse TATA-Binding Proteins (TBP) as reference genes for the quantification of human or murine transcripts of interest, respectively.
Note: The species specificity of primers is assessed by using BLAST alignment. Alternatives: It is possible to use another SYBR Green mix, like Master Mix PCR Power SYBRä Green. Note: Do not forget, when you prepare the working solution that you need to work at least in duplicate and preferably in triplicate. c. Mix thoroughly each primer-pair mix by pipetting. d. Add 7. 5 mL of each primer-pair mix to wells of a 96-well plate and then add 5 mL of each cDNA sample dilutions to wells of a 96-well plate as in shown in Figure  4. e. Seal properly the plate with a seal compatible with qPCR, like MicroAmpä Optical Adhesive Film. f. Spin the plate briefly to prevent drops from remaining on the well sides. g. Run the qPCR reaction in detection system StepOnePlusä associated with the acquisition and analysis software StepOneä v2.
28. After exporting the data (in excel file, for example), calculate the Cycle Threshold (Ct) mean of all sample duplicate for each primer pair. 29. Then, calculate the DCt as follows: Ct interest gene -Ct reference gene. 30. Once all the DCt are obtained, calculate the DDCt as follows: DCt sample Y -DCt sample control. 31. Finally, calculate the Fold Change as follows: 2 -DDCt. See Table  S2  that illustrates calculations from step 28 to 31. Note: Prior the experiments, species specificity of primers must be tested by RT-qPCR in samples containing only mouse or human mRNA.
QUANTIFICATION AND STATISTICAL ANALYSIS Use GraphPad Prism software to analyze qPCR results. Express the data as mean G SD or mean G SEM. If you have n R 5 independent experiments, check the normality and equality of variances of the data using the Shapiro-Wilk and Browne-Forsythe tests, respectively. If data have normal distribution and equal variance, parametric tests should be used as for example, Ordinary one-way ANOVA followed by Tukey's multiple comparisons. Otherwise, use non-parametric tests such as one-way ANOVA on rank followed by Dunn's multiple comparisons. If you have n < 5 independent experiments, non-parametric tests should be applied as for example, one-way ANOVA on rank followed by Dunn's multiple comparisons. Results should be considered significant when p < 0,05. Mice should be randomly assigned to the different wound treatment groups and the investigator should not be informed of the wound treatment assignment during the experiments and analysis.
ACKNOWLEDGMENTS This work was supported by  Fondation des Gueules Cassees, Association Neurofibromatoses et Recklinghausen and Fondation de l'Avenir  ( AP-RM-19-001 ),  Association Francaise contre les Myopathies  (A. F. M. ) via  TRANSLAMUSCLE  ( PROJECT 19507 ), and the  7th Framework Program  of the  European Commission  (project  REBORNE  no. 241879 ).
Data Analysis From the CT images, the geometry of the heart (i. e. , epicardial and endocardial ventricular surfaces) and the container were reconstructed with GeomPeacs software (Peacs, The Netherlands) (Figure  1B ). Needle positions were determined in three dimensions by tracking the needles in the images and using the known interelectrode distances. Electrodes with >5 mm distance of the reconstructed myocardial surface were excluded, reducing the data set to endocardial and epicardial electrode sites. Furthermore, electrodes with ST-elevation or flat T-waves were excluded. From the measured remaining unipolar electrograms, ATs were determined as the maximum downward slope of the QRS complex, and RTs were determined as the maximum upward slope of the T-wave  (Haws and Lux, 1990; Coronel et al.
, 2006)  with respect to the onset of QRS on the pseudo-ECG in atrial beats or stimulus artifact in ventricular paced beats. The electrophysiological analyses were performed using custom-made software based on MATLAB  (Potse et al. , 2002). To determine the region that was separately perfused by the LAD, the following three aspects were investigated: (1) CT with contrast in LAD, (2) changing QT time and T-wave polarity on needle electrodes, and (3) photos from the heart where LAD was shortly blocked, giving a slightly blue color to its perfusion region. An estimate for the LAD perfusion area was delineated in the heart geometry by manually combining this information. The delineation of LAD was performed before the inversely reconstructed RT maps were available.
Statistical analysis For localization index analysis, one dividing cell and non-dividing cell were taken for each root (17 roots). For each two cells, the signal intensity was measure for the Apical-basal membranes, the two laterals membranes and in two elliptical regions of interest (ROIs) of the cytosol. Ratios were obtained by taking the average of the two measured intensities. Localization index comparison were performed using a Kruskal-Wallis rank sum test analyses in R (v. 3. 6. 1, (R Core Team, 2019), using R studio interface and the packages ggplot2  (Wickham, 2016). Graphs were obtained with R and R-studio software and customized with Inkscape (https://inkscape. org).
ACKNOWLEDGMENTS We are grateful to the  SiCE group  ( RDP, Lyon, France ) in particular  Y. Jaillais ,  V. Bayle  and  C. Miège  for comments and discussions. We thank  Y. Boutté  ( LBM, Bordeaux, France ), for sharing the KNOLLE-YFP marker,  Sabine Mueller  ( University of Tübingen, Germany ) for sharing the PHGAP1-YFP reporter and  Dolf Weijers  ( Wageningen University, Netherlands ) for sharing the LifeAct-tdTom. We thank  P. Bolland , and  A. Lacroix  from our plant facility. We acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL)  facilities:  C. Lionet ,  E. Chatre  and  J. Brocard  at the  LBI-PLATIM-MICROSCOPY  for assistance with imaging. This work was supported by the  French National Research Agency  ( INTERPLAY ;  ANR-16-CE13-0021 ;  MCC, AF)  and  PLANTSCAPE  ( ANR-20-CE13-0026 , MCC, AB); and two SEED FUND ENS LYON-2016&2021 (MCC).
The species names were updated, referring to several online sources such as the African Plants Database, West African Plants, The Plant List, African Plants, and the Burkina Faso vascular plants catalog  (Thiombiano et al. 2012). Several parameters such as species richness, specific contribution, the diversity index  (Shannon and Weaver 1963) , and the regularity index  (Pielou 1966)  were evaluated and defined. The specific contribution of species i (C Si ) is calculated as  (Barmo et al. , 2020)  : ∑ where Fsi specific frequency the «i» species. 55 represents the total number of species The diversity index used to assess the heterogeneity and diversity of a biotope is expressed in bits and generally varies from 0 to 5. It was calculated following the equation: ∑. Finally, the regularity or equitability index (varying between 0 and 1), which expresses the distribution of species in a unit, was calculated using the formula: with representing the maximum Shannon specific diversity, and S was the total number of species.
FUNDING This research was funded by  UMI3189 « Environnement, Santé, Sociétés »  ( UCAD, CNRS, CNRST, USTTB, UGB ) and the  Labex DRIIHM , programme Investissements d'avenir » with the reference  ANR-11-LABX-0010. UMI:  Unité Mixt Internationale, UCAD: Université Cheikh Anta Diop, CNRS :  Centre National pour la Recherche Scientifique (France) ,  CNRST :  Centre National de Recherche Scientifique et Technique (Burkina Faso ), USTTB:  Université des Sciences, des Techniques et des Technologies de Bamako (Mali) , UGB:  Université Gaston Berger  (Sénégal).
Data analysis All data collected from the three sampling periods (August, September, November) were entered and processed using the Excel spreadsheet and the free software R studio  (Xie et al. 2018)  for Discriminating Factorial Analysis (DFA), Principal Component Analysis (PCA), and floristic composition. The PCA was carried out on the eight measured parameters (ground cover, average height of species, number of species, slope, texture, humidity, VISITED WEB PAGES African Plants-A Photo Guide (2020). Retrieved November 12, 2020, from http://www. africanplants. senckenberg. de/root/index. php?page_id=78&id=226 The Plant List. Retrieved November 12, 2020, from http://www. theplantlist. org/tpl1. 1/search?q=Achyranthes+aspera West African Plants-A Photo Guide | ECHOcommunity. org. Retrieved December 2, 2020, from https://www.
, 2008; Demars et al. , 2013) , B4GALNT2 (n=1;  Drouilhet et al. , 2013) , MTNR1A (n=1;  Calvo et al. , 2018) , PRNP (n=7;  Hunter et al. , 1997; Acín et al. , 2004; Moum et al. , 2005) , TMEM154 (n=2;  Heaton et al. , 2012; Sider et al. , 2013) and HSP90AA1 (n=1; Salces-Ortiz et al. , 2015). The functional SNP coordinates and affected traits are indicated in the Table  1. Results were visualized with SNP Viewer 2 software version 4. 0  (LGC, 2013). Genotype data for each animal were exported for the statistical analysis.
46225766G>T rs400844237 PRNP Classical scrapie susceptibility [2,3]   17 17:g. 4857244 G>A rs408593969 TMEM154 Lentivirus susceptibility [5,6]   17 17:g. 4857350 T>A rs427737740 TMEM154 Lentivirus susceptibility [5,6]   19 19:g. 65645462C>G rs397514116 HSP90AA1 Sperm DNA fragmentation [7]   26 26:g. 15118464 G>A rs403212791 MTNR1A Reproductive seasonality [8]   X X:g. 50971644-50971660indel rs421419167 BMP15 Prolificacy/FecXR [9]   X X: g. 50971170C>T NA BMP15 Prolificacy/FecXGR [10]   X 50971158C>T NA BMP15 Prolificacy/FecXL [11]   Table  1. Functional SNPs jointly genotyped with the subset of 158 SNPs from the French panel for the validation by Kompetitive allele specific PCR (KASP). Information about the location, dbSNP name in Ensembl variation database, gene, and associated phenotype.
1-40 (Aβ1-40), Aβ1-42, pTau181, and total tau were quantified using a fullyautomated platform (Lumipulse; Fujirebio), following the published protocol. 21 The CSF NfL levels were measured with enzyme-linked immunosorbent assay (NF-Light Assay; UmanDiagnostics) according to the manufacturer recommendations. A subset of participants underwent 3-T magnetic resonance imaging (MRI; n = 175), fluorine 18-labeled ( 18 F) fluorodeoxyglucose (FDG) positron emission tomography (PET; n = 132), and/or amyloid PET (n = 75). The Computational Anatomy Toolbox (CAT12; Christian Gaser and Robert Dahnke) for the SPM12 software (Wellcome Centre for Human Neuroimaging, University College London, Queen Square Institute of Neurology) was used to preprocess the structural, 3-dimensional, T1-weighted MRI and extract the hippocampal and total intracranial volumes.
The 18 F-FDG PET images were intensity scaled by the pons-vermis region and spatially normalized using the SPM12 software. The standardized uptake value ratios were extracted from the region of interest in Landau et al. 23  Both the 18 F-FDG PET and the segmented and modulated gray matter maps were smoothed using an 8-mm full-width at half-maximum Gaussian kernel for voxelwise analyses. The amyloid PET data were collected using  18  F-florbetapir in Barcelona and carbon 11-labeled Pittsburg compound B in Cambridge. Images were spatially normalized using the MRI transformations computed with Advanced Normalization Tools  24  and scaled using the whole cerebellum as the reference region. 2, 25, 26 The mean cortical standardized uptake value ratio  22  was then transformed into centiloid units according to standard procedures.
Clinical and Neuropsychological Findings The overall proportion of individuals with DS with a diagnosis of symptomatic AD was similar between APOE ε4 allele carriers and noncarriers (36. 8% vs 29. 9%; χ  2  1,460 = 1. 4; P =. 24). However, an age-stratified analysis by 5-year intervals revealed 2 key differences (Figure  1A ). First, in the 40 to 45 years of age range, the prevalence of symptomatic AD was 40% in carriers and 12% in noncarriers (χ  2  1,75 = 4. 9; P =. 03). Second, in contrast to the noncarrier group in which 18 individuals were older than 60 years, we found no individuals carrying the ε4 allele who were older than 60 years, even when examining data from follow-up visits. Next, we used baseline and follow-up data to examine the age at which individuals with DS had been diagnosed with symptomatic AD.
The survival curve showed a significant difference in the distributions of the 2 groups (log-rank test, P =. 01), with the carrier group showing a greater probability of being diagnosed with symptomatic AD at an earlier age (Figure  1B ). The between-group comparison further con-firmed this earlier age at diagnosis in carriers compared with noncarriers (mean [SD] age, 50. 7 [4. 4] years vs 52. 7 [5. 8] years; P =. 02) (Figure  1C ). We also evaluated the performance on the CAMCOG-DS and mCRT tests (immediate and delayed recall) expressed as z scores and as a function of age (eFigures 1 and 2 in the Supplement have raw data and analyses in participants with mild and moderate levels of intellectual disability). All participants with DS showed decreased CAMCOG-DS z scores with age regardless of APOE genotype.
The two panels of genes, F-SARC-CoV and Control-CoV, were then analyzed by the enrichment technique using the ENRICH. R software to identify the molecular pathways that could discriminate between the two groups. Our data suggested an imbalance of familial sarcoidosis genes in favor of autophagy-and mitophagy-related pathways, thus suggesting a putative abnormal response to viral infections in sarcoidosis patients.
Selection of Sarcoidosis Patients and Families Sarcoidosis patients and families were selected from a national clinical network, the French Group on Sarcoidosis (GSF), which involves 28 French university departments of internal medicine and/or pulmonology. Clinical diagnostic criteria were assessed according to the American Thoracic Society/European Respiratory Society, the World Association of Sarcoidosis and other Granulomatous Disorders (WASOG)  [13]. Sarcoidosis diagnosis was confirmed in all cases by histological analysis of either lymph nodes and/or bronchial biopsies. The familial forms of sarcoidosis were collected through the GSF-SARCFAM observational project, whose primary objective was to establish a clinical and genetic database on sporadic and familial sarcoidosis  [14]. We selected 13 families (F1 to F13) with at least two or three first-degree relatives affected by sarcoidosis and including one or two healthy controls among siblings, five of whom (F1-F5) were previously published  [4].
As a control analysis, we used exome data from a series of 100 healthy individuals belonging to the Department of Genetics in our hospital (University Hospital-Lyon, France). Genetic screening by whole-exome sequencing was approved by the ethical committee approval (CPP SUD EST-2-IRB 00009118-21 September 2016). The ClinicalTrials. gov-Protocol Registration and Results System (PRRS) Receipt under the reference NCT02829853 register of clinical and genetic studies.
Genetic Analysis by Whole Exome Sequencing Library preparation, exome capture, sequencing, and data analysis were performed with IntegraGen SA (Evry, France). Genomic DNA was captured using the Twist Human Core Exome Enrichment System (Twist Bioscience HQ, 681 Gateway Blvd South San Francisco, CA 94080, USA) and IntegraGen Custom. For a detailed explanation of the process, see the publication of Gnirke in Nature Methods  [15]. Sequence capture, enrichment, and elution were performed according to the manufacturer's instruction and protocols (Twist Bioscience) without modification, except for library preparation, which was performed with a NEBNext ® Ultra II kit New England Biolabs France, Genopole Campus 1, 91030 EVRY Cedex, France). For library preparation, 150 ng of each genomic DNA were fragmented by sonication and purified to yield fragments of 150-200 bp.
Bioinformatics Sequence reads were mapped to the human genome build (hg38) using the Burrows-Wheeler Aligner (BWA) tool. The duplicate reads (i. e. , paired-end reads in which the insert DNA molecules have identical start and end locations in the human genome) were removed. Variant calling for the identification of SNVs (Single Nucleotide Variations) and small insertions/deletions (up to 20 bp) was performed via the Broad Institute's GATK Haplotype Caller GVCF tool (GATK 3. 8. 1) for constitutional DNA. Mutations with a QSS score below 20 and a VAF of tumor <0. 02 were removed. Variants were processed through Ensembl's Variant Effect Predictor (VEP, Version 95. 1) for further annotation. This tool annotates variants and determines their effect on relevant transcripts and proteins, and predicts the functional consequences of variants.
This tool is based on the data from the gnomAD database, in addition to the 1000 Genomes Project and KAVIAR databases. Moreover, an in-house database enabled sequencing artifacts to be filtered out. Five bioinformatics algorithms for pathogenicity networks were available to predict the functional, molecular, and phenotypic consequences of coding and non-coding SNPs. These included DANN, ALAMUT©, Mutation Taster, SIFT, and Polyphenv2  [16] [17] [18] [19]. Other information, including quality score, homo-or heterozygote status, count of variant allele reads, and presence of the variant in the COSMIC database, were reported. Genotyping concordance between our patients and controls DNA and exome resulted in the identification of 96 SNPs. The ADVANTA™ (FLUIDIGM Corporate Headquarters, San Francisco, CA 94080, USA) Sample ID Genotyping Panel is a 96 SNP assay enabling laboratories to generate a sample-specific genetic fingerprint in order to confirm the identity between the results delivered and the received DNA sample.
Selection of Pathogenic Gene Variants and Gene Set Enrichment Analysis Our study included 13 sarcoidosis families (F-SARC) that include 30 affected individuals. As previously reported, all sarcoidosis families collected through the SARCFAM project included two or more first-degree related patients and presented Mendelian features of autosomal dominant transmission  [4, 14]. For this reason, we selected only heterozygous gene variants present in all patients of a single family and absent from intrafamilial healthy individuals (one to two depending on the family). For the crossing of the genotypic data with the genes listed in the SARS-CoV2 (GHI) hostcell interactome, we analyzed in parallel a control series of 100 healthy individuals collected in our hospital (Lyon, France). As shown in Figure  1 , the selection of gene variants used the following criteria: a Minor Allele Frequency (MAF) less than 0.
05 (5%) and indication of in silico pathogenicity suggested by various software, such as SIFT and/or POLYPHENv2 and/or Mutation Taster and/or ALAMUT VISUAL TM  [16] [17] [18] [19]. We only retained heterozygous variants (missense, nonsense, frameshift, in-frame deletions, and insertions) present in the coding regions (exons) of genes at or around the splicing donor (GT) and acceptor (AG) consensus sites. The genes sharing the selected variants were analyzed by ENRICH-R, a comprehensive updated resource and gene set enrichment system for a curated database, and a search engine that helps to identify clustered pathways and biological interactions of the proteins encoded by selected genes available at ENRICH. R website (Available online: https://maayanlab. cloud/Enrichr/accessed on 24 May 2021)  [20] [21] [22].
05 may occur in more than one family. A MAF slightly greater than 0. 05 (5%) was observed for 12 genes (TEP1, ADGRV1, SORBS3, TTC21A, DTHD1, ZAN, ATG9B, SACS, NLGN2, CCDC38, GLT8D2, and APPL2) in two families or more. Table  S1  describes the whole F-SARC dataset. In a second step, we applied a MAF threshold at 0. 05 in both familial sarcoidosis and control series. All the variants were then sorted using in silico pathogenicity criteria with SIFT, POLYPHENv2, Mutation TASTER, and ALAMUT VISUAL© (SOPHiA Genetics Inc. , Interactive Biosoftware TM , Boston, MA 02116, USA) software  [16] [17] [18] [19]. As described in Table  S1  for F-SARC, we identified 37 and 3 genes sharing mutations in two and three independent families, respectively. Interestingly, seven genes share the same variant in two independent families (PDE4DIP, rs111880480, F6/F8, MF = 0.