dataesr/scientific-paragraphs-categorization huggingface dataset:
3 µL of nuclease-free water. PCR products of 1269, 881, and 1500 bp were obtained for EF-1α, RPB2, and TUB2, respectively. The identification of the Fusarium isolates was performed in NCBI BLAST algorithm (https://blast. ncbi. nlm. nih. gov/Blast. cgi/ (accessed on 30 November 2020)) by comparison of EF-1α sequences against NCBI database. Amplicons were purified using the Wizard SV PCR and Gel Clean-Up kit (Promega) and sent for sequencing (Macrogen INC) using forward and reverse primers. The obtained sequences were edited, aligned by ClustalW, and concatenated using MEGA version 7. 0. 26 [27] , the phylogenetic tree was generated through maximum likelihood method based on the best model Hasegawa-Kishino-Yano (HKY + G) using concatenated EF-1α, RPB2, and TUB2 data supported by 1000 bootstrap replicates.
Phylogenetic Analysis of Fusarium Isolates To determine the genetic diversity of the Fusarium isolates associated with coffee corkyroot disease and their relationships, we generated separated phylogenetic trees based on the single sequences of EF-1α, RPB2, and TUB2, which recovered similar topologies and clade support (data not shown) compared to the combined sequence dataset, which is presented in Figure  1. In this figure, concatenated phylogenetic tree comprising EF-1α, RPB2, and TUB2 sequences showed the formation of two main clades separating F. solani from F. oxysporum. Isolates CBF-303 and CBF-267, which showed high EF-1α identity against F. oxysporum f. sp. phaseoli, and CBF-309 were clustered together with reference sequences of F. oxysporum f. sp. phaseoli and F. oxysporum f. sp.
Phylogenetic Analysis of Fusarium Isolates To determine the genetic diversity of the Fusarium isolates associated with coffee corky-root disease and their relationships, we generated separated phylogenetic trees based on the single sequences of EF-1α, RPB2, and TUB2, which recovered similar topologies and clade support (data not shown) compared to the combined sequence dataset, which is presented in Figure  1. In this figure, concatenated phylogenetic tree comprising EF-1α, RPB2, and TUB2 sequences showed the formation of two main clades separating F. solani from F. oxysporum. Isolates CBF-303 and CBF-267, which showed high EF-1α identity against F. oxysporum f. sp. phaseoli, and CBF-309 were clustered together with reference sequences of F. oxysporum f. sp. phaseoli and F. oxysporum f. sp.
The group C shared with the group B the absence of Fum and SIX11 genes, it was characterized by low frequencies of presence of SIX8 and SIX14. No correlation was found between the SIX genes repertoire and the relationships of the Fusarium isolates observed across the phylogenetic tree (Figures  1  and 2 ). We performed a three-dimensional non-metric multidimensional scaling (3D-NMDS) analysis based on the binary data (presence/absence of the SIX genes) by using Jaccard dissimilarity index to visualize the grouping of the Fusarium isolates. The stress value obtained for the overall dataset was 0. 10, the 3D-NMDS plots were divided in three figures to avoid the overlapping of the multiple variables related to municipality and locality (Figure  S1a-c ). PERMANOVA analysis revealed that the SIX gene profiles of the Fusarium isolates were influenced by the species, i.
Analysis -"Filament severing": a set of randomly picked 10 µm-long actin segments was monitored from time t=0 when cofilin (or buffer) was first injected. We measured the time at which each segment either severs or is lost, due for example to severing somewhere else on the filament (e. g. at the anchor). We calculated the survival fraction from this set of severing or loss times, using a standard Kaplan-Meier method. Survival fraction, confidence intervals and p-values (log-rank test) were calculated using the Python package Lifelines. Survival fractions were fitted with a single exponential on Excel (Microsoft Office) to obtain reaction rates (shown as thin continuous grey lines on plots). -"Severing per cofilin domain": we tracked single cofilin domains, from the frame on which they appear (nucleation) which defines t=0.
Statistics -Individual filaments were selected for analysis with no knowledge of their behaviour. We only excluded filaments sticking to the surface, and, during analysis, photo-induced pauses occurring during depolymerization  (Niedermayer et al. , 2012). -All experiments were performed at least twice and at least one representative movie was analyzed. -P-values for cofilin and Tpm domain growth, and F-actin oxidation rates were calculated with a Welch's t-test. We used Python function "scipy. stats. ttest_ind" with unequal variance. -P-values for survival fractions were calculated with a log-rank test using Python package Lifelines.
ACKNOWLEDGEMENTS This work was supported by the  CNRS, Institut Pasteur , the  ANR  ( grant RedoxActin to A. E. and G. R. L. ) and the  ERC  (grant StG -679116  to A. J. ). The authors thank  Till Boecking  for the plasmid of neonGreen-Tpm1. 8,as well as  Tommi Kotila  and  Pekka Lappalainen  for cytoplasmic actin.
These preliminary data suggest that MSCA1 and CD36 might play a role during obesity-related AT accumulation by affecting processes related to AT function. However, most of the studies have been performed in patients with morbid obesity, and the relevance for processes involved in early obesity progression remains poorly understood. In this regard, studies in children are of special value since the functional relevance of adipocyte progenitor markers can be addressed during AT accumulation with normal development and with early development of obesity and related AT dysfunction  (21). Within this study, we assessed an association of MSCA1 and CD36 gene expression with physiological or obesityrelated AT accumulation in children. Furthermore, we investigated whether an elevated MSCA1 and CD36 expression is related to functional properties of adipocyte progenitor cells, such as proliferation, differentiation, and mitochondrial function.
Samples (Leipzig AT Childhood Cohort) Subcutaneous AT samples were retrieved from the Leipzig AT Childhood Cohort from 133 Caucasian children (0-18 years) undergoing elective surgery  (21). All children were free of diseases or medications that potentially affect AT biology. Children with diabetes, generalized inflammation, malignant diseases, genetic syndromes, or permanently immobilized children were excluded. Written informed consent was obtained from all parents. The study was approved by the local ethics committee (Reg. No. 265-08, 265-08-ff, University of Leipzig; NCT02208141). BMI data were standardized to age-and sex-specific German reference data and are given as BMI SD score (SDS)  (22). A cutoff of 1. 28 and 1. 88 SDS defined overweight and obesity in children, respectively. Fasting blood samples were obtained prior to surgery.
Statistical analyses Data that did not represent a Gaussian distribution were transformed logarithmically before being analyzed. Quantitative variables were analyzed by parametric tests (Student's t test, analysis of variance, Pearson correlation analysis), whereas for categorical variables a χ² test was performed. For multiple regression analyses, the stepwise forward model was used. Statistical analysis was performed using Statistica 13. 3 (StatSoft, Tulsa, OK, USA) and GraphPad Prism 6 (GraphPad Software, Inc, San Diego, CA, USA).
Acknowledgments We thank  Antje Berthold  for technical assistance. Financial Support: This work was supported by grants from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )-Projektnummer  209933838-SFB 1052  project  C05  to A. K. and the  Federal Ministry of Education and Research (BMBF) ,  Germany , FKZ:  01EO1501  ( IFB Adiposity Diseases) , and the  ESPE   Early Career Scientific Development Grant  to M. H.
FACS and microscopy Cells were incubated with Fc block (anti-CD16 and anti-CD32; BD Biosciences, Franklin Lakes, New Jersey, USA) and different fluorochrome-coupled antibodies as follows: anti-CD45 (clone 30-F11; FITC; BD Biosciences), anti-CD5 (clone 53-7. 3; PE and PE-Vio770; Miltenyi Biotec), anti-CD11b (clone REA592, Vioblue; Miltenyi Biotec), anti-CD22 (clone Cy34. 1; Pe-Vio770; Miltenyi Biotec), anti-CD23 (clone B3B4; APC; Miltenyi Biotec), anti-CD38 (clone 90. 4; APC; Miltenyi Biotec), anti-CD43 (clone REA840; PE; Miltenyi Biotec) and anti-CD138 (clone REA104; PE; Miltenyi Biotec), anti-CD19 antibody (clone RE749; APC-770; Miltenyi Biotec). The analysis was performed by FACS CANTO II (BD Biosciences) and FlowJo V10 software. Positive populations were identified using fluorescence minus one (FMO) controls.
Statistics Unless specified in the legend, the results were analyzed via ANOVA followed by a Dunnett test. The significance threshold, alpha, was fixed at 0. 05. *p < 0. 05, **p < 0. 01 and ***p < 0. 001 indicate significant differences compared to the control. B-Lymphocyte populations were analyzed simultaneously in all treated mice at the same time point. As statistical analyses (one-way ANOVA and Dunnett's test) revealed no significant change in control mice, their results were combined in the graphs, labelled as "CTRL". In vitro analysis was carried out up to three times with three replicates. Graphs and analyses were performed using GraphPad Prism Software. Bars represent means ± SD. A graphical abstract cartoon was generated using Biorender.
Consequently, we have identified a minimum of 136 new linear epitopes, including 25 new immunodominant linear B cell epitopes -i. e. , B cell epitopes targeted in at least 1% of all individuals and not already reported in the Immune Epitope Database: https://www. iedb. org (34) (Supplemental Table  3 ). Next we assessed the seroprevalence of HCoV-229E, -NL63, -HKU1, and -OC43 in the 3 cohorts separately. To do so, we computed species score values as described earlier  (30, 32, 35)  by counting the significantly enriched peptides for a given HCoV species that shared less than 7 amino acids linear sequence identity. We considered an individual seropositive for any of the endemic HCoVs if the number of nonhomologous peptides enriched in a given sample met our previously established species-specific cutoff value (Methods).
Each serum or plasma sample was tested in duplicate, and samples were analyzed in batches with up to 96 samples per batch. Only samples that satisfied a minimum read count of 1 × 10 6 as well as a Pearson correlation coefficient of at least 0. 7 in the 2 technical repeats were considered for downstream analysis. Data from 30 individuals of the ABD cohort and 2 individuals of the QBB cohort were excluded from the downstream analysis because of insufficient sequencing read depth, of low sequencing data quality, or 1 of 2 technical replicates had failed (data not shown). Peptide enrichment analysis. To filter for enriched peptides, we first computed -log 10 P values as described previously  (30, 32, 35)  by fitting a 0-inflated generalized Poisson model to the distribution of output counts and regressed the parameters for each peptide sequence based on the input read count.
Positive OR and lod values indicated more frequent peptide enrichment among pediatric study subjects, whereas negative OR and lod values indicated more frequent peptide enrichment among adult subjects. Clustering of peptides for shared linear B cell epitopes. To estimate the minimum number of linear B cell epitopes among the enriched peptides, we built a pairwise distance matrix that captured the maximum size of linear sequence identity of amino acids (d i,j ) between all enriched peptides. Groups of peptides that shared ≥ 7 amino acids linear sequence identity (d i,j ≥ 7) were assigned to a cluster. Peptides of a given cluster were considered to share a linear B cell epitope (Supplemental Figure  9 ). Software. For statistical analyses and principal component analysis, we used open-source Python modules detailed below.
Multiple sequence alignments were done using the MAFFT  (74, 75)  via EMBL-EBI's web services and Java Alignment Viewer (Jalview) for visualization  (76). Network analysis of peptide clusters (Figure  5  and Supplemental Figure  9 ) was performed using Python module NetworkX (version 2. 5). Linear B cell epitopes were predicted using BepiPred-2. 0  (77). Protein structures graphics were generated using PyMOL (Schrödinger). Data and code availability. All data are available in the manuscript or the supplemental materials. Raw PhIP-Seq reads and Python in-house scripts used in this study are readily available upon request. The pipeline for processing the PhIP-Seq data has been published previously  (29). Statistics. All statistical analyses were performed with Python (version 3. 6) using open-source modules, such as SciPy (version 1.
Acknowledgments We would like to thank the participating study subjects, the QBB management and staff -in particular  Nahla Afifi  and  Elizabeth Jose , for allowing us to access and analyze samples and data from the  QBB -and Stephen Elledge (Brigham and Women's Hospital and Harvard Medical School ) for providing the VirScan phage library used in this study. We would also like to thank  Patrick Tang ,  Mohammed Yousuf Karim ,  Evonne Chin-Smith , and  Damien Chaussabel  ( Sidra Medicine ) for critical reading of the manuscript. This work was supported in part by a grant from the  Qatar National Research Fund  ( PPM1-1220-150017 ) and funds from  Sidra Medicine. I Meyts  is a Senior Clinical Investigator at the  Research Foundation -Flanders  and is supported by the  CSL Behring Chair of Primary Immunodeficiencies ; by the  KU Leuven   C1  grant  C16/18/007 ; by a  VIB   GC PID grant ; by  FWO  grants  G0C8517N ,  G0B5120N , and  G0E8420N ; and by the  Jeffrey Modell Foundation.
Methods We prospectively included 310 patients admitted to the Universitair Ziekenhuis Brussel (University Hospital of Brussels), Belgium between March 2020 and April 2020. COVID-19 was confirmed by real-time reverse transcription polymerase chain reaction test. One hundred and eight patients with incomplete information on comorbidities, treatment history, lack of systematic evaluation of the clinical status or biomarkers, and/or short period of hospitalization (< 48 h in hospital stay) were excluded from the analysis. Clinical information was collected at admission and during hospitalization by the physicians in charge. Data included demographics, medical history, comorbidities, clinical evaluation, laboratory exams, in-hospital treatment, complications, and outcomes. Two hundred and two (65.
Limitations of the study This study has several limitations. This is a single-center study with a limited number of patients. It might be argued that cardiac troponin was assessed in more severe forms of COVID-19. Due to logistical restriction at the beginning of the pandemic, some data regarding echocardiography, cardiac magnetic resonance, or systematic measurements of cardiac biomarkers were lacking in some patients. Cardiovascular examinations were performed in selected cases based on the clinical and biological status of the patients, according to current guideline recommendations to avoid the risk of cross-infections  [27, 30]. Therefore, serial echocardiographic evaluation was not available in all COVID-19 patients. Nonetheless, patients who died were more likely to have missing data, including serial cardiac troponin testing.
For each test, approximately 24 similar fruits were collected at the ripe stage from 8-12 fruit spikelets removed from the upper-middle section of the bunch. The bases of the fruit containing the AZ were then collected, and phenotypes were determined, as described previously  [6, 16]. The heritability was estimated based on the data from individuals with repeated measurements (n = 12) as the ratio of the genotypic vs. phenotypic variances. The broad-sense heritability (H 2 ) was calculated based on the data from individuals with repeated measurements as follows: H 2 = V g /V p Genetic (V g , n = 12) and phenotypic (V p , n = 25) variances were calculated using var function in R software (https://www. R-project. org/, accessed on 20 June 2021).
AZ tissues were flash frozen in liquid nitrogen, stored at -80 • C, then transported on dry ice to Montpellier. Total RNAs were extracted as described previously  [17]. Construction of the twelve cDNA libraries and llumina (NovaSeq 6000) sequencing of the corresponding RNA samples was carried out by MGX platform (Montpellier Genomix, http://www. mgx. cnrs. fr/, accessed on 8 November 2019). The data discussed in this publication were deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE186050. The boxed green circles are the two individuals with contrasted abscission phenotypes selected for the transcriptome analysis. The green boxed circle with the negative AI is the non-shedding individual selected for the transcriptome analysis because it does not shed its fruit under field conditions, and seed germination can be observed (Figure  2 ).
Analysis of Differential Gene Expression Reads obtained by Illumina NovaSeq sequencing (single reads, 100 cycles) were demultiplexed and trimmed using Bcl2fastq (v2. 20. 0. 422). Total trimmed reads per sample are indicated in the Table  S1. All the trimmed reads (331,907,473 total reads) passed the FastQC quality checks and were then mapped using the BWA-MEM package with default parameters  [18]  to a reference transcriptome derived from the genomic sequence of the African oil palm (E. guineensis) for which a fully annotated genome sequence is currently available  [19]. The latest version of the oil palm transcriptome released by NCBI was used for mapping (http://genomsawit. mpob. gov. my; GCF_000442705. 1_EG5_RNA. fna, accessed on 23 July 2019). Read counts were obtained from the resulting bam files by means of the program Samtools-idxstats  [20].
For each sample, the total reads aligned (277,584,786 total reads, 84% of total trimmed reads) to reference genome is indicated in the Table  S1. Counts corresponding to different transcripts of the same locus were then merged. The R-based software DESeq package (default parameters, version 1. 28. 1  [21]  was used to identify genes displaying significantly different expression levels in the Sd and NSd palms, applying padj cutoff < 0. 01 and log2 fold change (LFC) cutoff (-1 > LFC > 1). After mapping to the reference transcriptome, the number of reads per kilobase and million reads (RPKM) in each sample were then calculated.
Acknowledgments: This work was supported by financial support from  PalmElit  and  IRD/CIRAD  to F. M. and T. J. T. We thank the staff at the  PDA/Murrin Companies  ( DANEC group ) in Quinindé, Ecuador for the logistical support for the study. MGX acknowledges financial support from  France Génomique National  infrastructure, funded as part of the " Investissement d'Avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
The p-values obtained were corrected for false positives by using independent hypothesis weighting (package IHW) multiple testing adjustment method. Genes were considered significantly differentially expressed if log2 fold change was over 1 or less than -1, and the false discovery rate was less than 0. 05. For the differentially expressed genes, overrepresentation and gene set enrichment analysis (GSEA) were done using clusterProfiler package, and the results were plotted using enrich Plot. Further GSEA was performed using GSEA software (http:// software. broadinstitute. org/gsea/). The gene sets used are described in Table  2.
Statistical Analysis Histogram and data are shown as mean ± SD of a minimum of three independent experiments. Statistical analyses were performed using GraphPad Prism, version, 6 for Windows (GraphPad Software, La Jolla, CA), (www. graphpad. com). The Wilcoxon matched test was used to compare the expression levels between OS and matched normal tissue. The Mann-Whitney test was used to compare the difference between the two groups. A p-value under or equal to 0. 05 was considered statistically significant.
Database Kaplan-Meier analysis of osteosarcoma patient tumor samples was performed using the R2 Genomics Analysis and Visualization Platform. The genome-wide gene expression analyses of highgrade osteosarcoma are from GSE42352 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE42352). The datasets supporting the current study have been deposited in the public repository GSE184003 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE184003).
mock-transfected samples), we observe that the expression of a YAP unable to interact with TEAD also stimulates the development of lung metastases (Figure  1B ; YAPS94Avs. mock-transfected samples), suggesting that two mechanisms, one TEAD dependent and one TEAD independent, are involved in the YAP-driven OS metastatic process. To validate the medical relevance of these observations, we next analyzed RNAseq data from the biopsies of patients. As shown in Figure  1C , the data demonstrate that YAP is overexpressed in OS patients with metastases compared with OS patients without metastases. Taken together, these findings demonstrate (a) that YAP stimulates OS lung metastasis development, (b) that two mechanisms, one TEAD dependent and one TEAD independent, may be involved in the YAP-driven OS metastatic process, and (c) a correlation between high YAP expression and the presence of metastases in OS patients.
Finally, to determine the clinical significance of the role played by Smad3 in OS tumor progression, we investigated Smad3 gene expression using data extracted from the GSE21257 database. While we previously showed increased nuclear localization of Smad3 in biopsies from OS patients with metastases at diagnosis  (24) , high Smad3 transcript is also significantly correlated with the presence of metastases (Figure  4H ). In addition, high Smad3 gene expression   21 ) from an OS patient cohort comprising 53 samples. Analysis was done using R2 (http://r2. amc. nl). *p < 0. 05. (I) Kaplan-Meier analysis of the survival outcomes of 88 patients dichotomized into high level and low level of Smad3 expression, following the analysis of the RNAseq dataset GSE42352  (21). The analysis was performed using R2 (http://r2.
A representative of Coomassie blue-stained gel of three independent experiments is shown (upper panel). Quantification of relative MMP-2 and MMP-9 activities (lower panels). Bars indicate the means ± SD of three independent experiments (*p < 0. 05). (G) PDGFb and VEGFa mRNA steady-state levels were quantified by RT-q-PCR analysis in mock-, YAPS94A-, and YAPS127A-transfected K-HOS cells in the presence or absence of TGF-b (5 ng/ml for 6, 12, 24, and 48 h). Bars indicate the means ± SD of three independent experiments, each performed in duplicate. (H) Relative Smad3 gene expression in OS patients with or without metastases at diagnosis following bioinformatics analysis of RNAseq data seq GSE42352 (21) from an OS patient cohort comprising 53 samples. Analysis was done using R2 (http://r2. amc.
MicroRNA host genes analysis For this analysis, we considered all the dysregulated miRNAs (P ≤ 0. 05) in DMD patients aged 4-12 years old, compared with their age-related healthy controls (Supporting Information, Table  S2 ). MicroRNA host genes were retrieved from the miRIAD database  20  and were manually validated on the Ensembl human genome browser GRCh38. p7. We considered all miRNAs embedded on the sense strand of introns and exons of protein-coding genes. We assigned to the hostgenes the fold change and P-values of their embedded miRNAs. When both À3p and À5p isoforms of the same pre-miRNA were dysregulated, their host-genes were considered only once, with the lower P-value miRNA isoform. Similarly, host genes for a number of different miRNAs in a miRNA cluster were considered only once, with the highest dysregulated miRNA (according to P-value).