dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). Criterion 1 was considered satisfied if it was passed by ≥5% of all possible pairs of hits taken from the two communities  24. If the sets of TE copies composing the two hit communities did not show any DNA sequence homology (no blast hit) and insufficient (<100 bp) protein sequence identity (as determined by blast searches of TE copies against a database of TE proteins  24  ), we considered that these sets of copies could represent non-overlapping parts of a TE that underwent fragmentation or differential degradation within genomes after a unique transfer. In such case, we considered criterion 1 as passed. Criterion 2 is detailed in the Supplementary Methods and Supplementary Fig. 7. Briefly, its evaluation involved comparing the average Ks of hits composing the two communities, which represents the time since the transfer(s), to the 0.
Acknowledgements This work was supported by the  National Natural Science Foundation of China  (No. 31560308 ,  31700318  and  31960035  to H. H. Z. ), the  Funds for Distinguished Young Scientists of Jiangxi Province  (No. 20192BCBL23028  to H. H. Z. ),  Agence Nationale de la Recherche  Grants  ANR-15-CE32-0011-01 TransVir  and  ANR-18-CE02-0021-01  TranspHorizon (to C. G. ); the 2015-2020 State-Region Planning Contract and  European Regional Development Fund  (to J. P. ); and intramural funds from the  Centre National de la Recherche Scientifique  (to J. P. and C. G. ) and the  University of Poitiers  (to J. P. ). We thank the genotoul bioinformatics platform  Toulouse Midi-Pyreńeés  (bioinfo. genotoul. fr/) and genouest (https://bipaa. genouest. org/)for providing computing and storage resources, and  Laurent Duret  for his comments on the manuscript.
Research Methods The samples were first cropped to leave only the volume with internal cells and cell walls. Then, after the processes of binarization, watershed, border kill, etc. , the cell size, anisotropy, sphericity, orientation, and spatial coordinates of each cell in different conditions could be obtained by the software Avizo. Thicknesses of solid part, including cell membranes and plateau borders, were obtained by the plugin "local thickness" in the software Fiji. Porosity distribution of tomography slices was also calculated in software Fiji. The coordination number was calculated by the spatial coordinates using the software Matlab. The correlation between the deformation degree and initial cell parameters was fitted from one sample and then verified by the result from the other sample.
A quick data analysis of the gene expression profiles (GEP) confirmed the high expression of the expected cell-specific markers, including CD19 and CD40 for B cells, CD3, CD4, CD40L for Tfh, and CD90/THY1, CD105/ENDOG, CD106/VCAM1 for MSC, without detectable cross-contamination. We selected a multistep process for our dataset analysis (Figure  1A ). In the first step, we reduced our dataset to the 25,882 expressed probesets (PS), corresponding to 13,918 annotated genes. This large-scale approach allowed us to draw the global landscape of the GEP in normal and FL contexts. PCA analysis confirmed tight clustering of each cell type, with the first two axes sustaining nearly 60% of the statistical variance, separating B cells, Tfh, and MSC regardless of their origin (Figure  1B ). The third axis (8.
Based on these results, we established a gene list corresponding to the non-redundant overexpressed genes in the FL context whatever the considered cell population. These 3405 up-regulated genes were postulated to be representative and specific of the FL supportive niche (Table  S1 ). Metabolic and cell signaling pathways implicated in this FL niche were analyzed using gene-set enrichment analysis (GSEA). We selected the top 25 pathways according to the EnrichR combined scores and NCI Nature or Panther databases (Figure  2A ,B, respectively). Interestingly, we could identify pathways related to the inflammation and the lymphocyte (and especially T cell) activation (Figure  2 ). T cell activation could also be evoked by highlighting the IL-6, IL-12, and IFN-γ pathways, and FoxO family signaling.
Based on these results, we established a gene list corresponding to the non-redundant overexpressed genes in the FL context whatever the considered cell population. These 3405 upregulated genes were postulated to be representative and specific of the FL supportive niche (Table  S1 ). Metabolic and cell signaling pathways implicated in this FL niche were analyzed using gene-set enrichment analysis (GSEA). We selected the top 25 pathways according to the EnrichR combined scores and NCI Nature or Panther databases (Figure  2A ,B, respectively). Interestingly, we could identify pathways related to the inflammation and the lymphocyte (and especially T cell) activation (Figure  2 ). T cell activation could also be evoked by highlighting the IL-6, IL-12, and IFN-γ pathways, and FoxO family signaling.
Furthermore, besides lymphocyte-mediated signals, several strongly represented networks could be linked to stromal supportive niches and extracellular matrix, including integrin signals, Notch pathway, n-cadherin, or VEGF/VEGFR-mediated events, as well as TGF receptor, FGF, and EGF receptor signaling pathways. We next questioned whether we could identify receptor-ligand pairs that could reflect the crosstalk between cell populations in the FL synapse (Figure  1A ). To this end, we compiled information from Refseq, NCBI, and Genecard datasets, and we established a dataset of 258 genes encoding cytokines, chemokines, TNF family members, immune checkpoint molecules, growth factors, and their respective receptors, as well as a large list of adhesion molecules. We analyzed the three cell populations, and comparisons were drawn between nonmalignant and FL-derived populations (Figure  3  and Figure  S2  for detailed expression of FL and HD B cell populations).
Gene Expression Profiling Total RNAs were extracted using AllPrep™ DNA/RNA Mini kit (Qiagen, Valencia, CA, USA) or with Qiazol (Qiagen) reagent, including DNAse treatment, as recommended by the manufacturer. RNA quality was evaluated by capillary electrophoresis using the Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA). The gene expression profiles (GEP) were determined on Affymetrix Human Genome U133 Plus 2. 0 microarrays containing 54,675 probe sets (PS). Hybridization and raw data of expression signal intensities extraction were processed by the CIT platform (www. cit. ligue-cancer. net). Data are available via the NCBI Gene Expression Omnibus (GSE85233 for B lymphocytes, GSE85229 for BM-MSC fractions and GSE66384 for Tfh populations). Raw data of all samples were first normalized together by the Robust Multichip Averaging algorithm using GC content (GC-RMA) and Log2 transformation with Partek ® Genomics Suite TM software (Partek, St.
Tools for Affymetrix Dataset Analysis Gene-set enrichment analysis (GSEA)  [58]  was performed through EnrichR method  [59]  and NCI Nature  [60]  or Panther  [61]  metabolic and cell signaling pathway databases. This approach combines gene function, ontology, pathways, and statistical analysis tools to rank enriched terms. Genes were explored through functional pathways ordered by statistical overrepresentation using combined scores (cScore) corresponding to the multiplication of the p-value (Fisher exact test, p < 0. 05) and the z-score of the deviation from the expected rank.
Supplementary Method for GEP Analysis Lists of known receptor-ligand pairs were elaborated using the NCBI database and Genecard datasets. Each selected molecule was associated with its ascension number, HGNC gene symbol and synonyms, and then linked to the Affymetrix database (Netaffx) in order to identify all corresponding PS. The PS intensities were selected in our normalized microarray dataset, and only one was retained according to successive filters: (1) expression by at least one sample in the dataset, (2) highest intensity in the dataset, and (3) highest standard deviation within compartments. The selected data were then adjusted by mean centering and intensity plots were visualized separately for FL and normal subsets using d3Heatmap R package.
Acknowledgments: This work is part of the " Carte d'Identité des Tumeurs (CIT)" program  developed by the  "Ligue contre le Cancer ", we thank the personnel of its platforms for RNA qualification ( Saint-Louis Hospital, Paris ) and  Affymetrix expression array (Institut de Génétique et de Biologie Moléculaire et Cellulaire, Strasbourg). We thank all technicians for their valued work and especially  Pauline Migaud  and  Céline Nonn. The authors are indebted to the pathologists, biologists and clinicians, especially from the  BREHAT group , who participated to the collection of sample and clinical data annotations. The  "Centre de Ressources Biologiques (CRB)-Santé " ( BB-0033-00056 ) of Rennes hospital is gratefully acknowledged for its support in the processing of biological samples. Cell sorting was performed at the  Biosit Flow Cytometry and Cell Sorting Facility (University of Rennes 1, France ).
Methods: To avoid mosquito euthanasia, the present study assessed whether aedine mosquitoes could be identified by MALDI-TOF MS biotyping, using their respective exuviae. In this way, exuviae from the fourth-instar and pupal stages of Aedes albopictus and Aedes aegypti were submitted to MALDI-TOF MS analysis. Results: Reproducible and specific MS spectra according to aedine species and stage of exuviae were observed which were objectified by cluster analyses, composite correlation index (CCI) tool and principal components analysis (PCA). The query of our reference MS spectra database (DB) upgraded with MS spectra of exuviae from fourth-instar larvae and pupae of both Aedes species revealed that 100% of the samples were correctly classified at the species and stage levels. Among them, 93. 8% (135/144) of the MS profiles reached the threshold log score value (LSV > 1.
MS spectra analysis MS spectra profiles were firstly controlled visually with flex Analysis v3. 3 software (Bruker Daltonics). MS spectra were then exported to ClinProTools v2. 2 and MALDI-Biotyper v3. 0. (Bruker Daltonics) for data processing (smoothing, baseline subtraction, peak picking). MS spectra reproducibility was assessed by the comparison of the average spectral profiles (MSP, main spectrum profile) obtained from the two spots of each exuviae from fourth-instar larvae and pupae according to species with MALDI-Biotyper v3. 0 software (Bruker Daltonics). MS spectra reproducibility and specificity were objectified using cluster analyses and composite correlation index (CCI) tool. Cluster analyses (MSP dendrogram) were performed based on a comparison of the MSP given by MALDI-Biotyper v3.
0. software with clustering according to protein mass profile (i. e. their mass signals and intensities). The CCI tool from MALDI-Biotyper v3. 0. software was also used, to assess the spectral variations within and between sample groups, as previously described  [20, 26]. CCI matrix was calculated using MALDI-Biotyper v3. 0. software with default settings (mass range 3. 0-12. 0 kDa; resolution 4; 8 intervals; auto-correction off ). Higher correlation values (expressed by mean ± standard deviation, SD) reflecting higher reproducibility for the MS spectra, were used to estimate MS spectra distance between species for each exuviae stages (larval vs pupal). To visualize MS spectra distribution from Aedes species according to exuviae stage, principal components analysis (PCA) from ClinProTools v2.
2 software was used with the default settings. The top 5 of the most intense MS peaks per Aedes species using exuviae from immature stages were analyzed with ClinProTools software to estimate their performance to discriminate these species for each stage. The parameter settings in ClinProTools software for spectrum preparation were as follows: a resolution of 300; a noise threshold of 2. 00; a maximum peak shift of 800 ppm and a match to calibrating agent peaks of 10%. Peak calculation and selection were performed on individual spectra with a signal-to-noise threshold of 2. 00 and aggregation of 800 ppm. Based on the peak list obtained for each exuviae stage per species, the top 5 of the most intense m/z peaks were selected to include them into the genetic algorithm (GA) model. The selected peaks by the operator gave a recognition capability (RC) value together with the highest crossvalidation (CV) value.
Database creation and blind tests The reference MS spectra were created using spectra from fourth-instar and pupal exuviae of four specimens per species using MALDI-Biotyper software v3. 0. (Bruker Daltonics)  [27]. Reference MS spectra were created with an unbiased algorithm using the information on the peak position, intensity and frequency. MS spectra from the fourth-instar and pupal exuviae were tested against the in-house MS reference spectra database (DB), including mosquito legs and thoraxes reference MS spectra, but none from exuviae  [20]. For all MS profiles queried against the MS DB, a matching is proposed. The reliability of species identification was estimated using the log score values (LSVs) obtained from the MALDI Biotyper software v. 3. 0, which ranged from 0 to 3. To distinct untrustworthy from reliable identification, a threshold value was established according to previous studies  [27, 28].
LSVs greater than 1. 8 were considered reliable for species identification. Data were analyzed by using GraphPad Prism software version 5. 01 (GraphPad, San Diego, CA, USA). The raw data of exuviae MS spectra from Ae. albopictus and Ae. aegypti at fourth-instar and pupal stages which were included in the MS reference database are freely downloadable (Additional file 1).
MS reference spectra database creation and validation step The sixteen exuviae MS spectra, from fourth-instar larvae and pupae of both Aedes species used for MSP dendrogram analysis, were added to our MS spectra DB. This DB already included reference MS spectra of legs and thoraxes from 8 distinct mosquito species  [20]. The 144 remaining MS spectra of exuviae from fourthinstar larvae and pupae of both Aedes species were queried against this upgraded reference MS DB. The LSVs ranged between 1. 65-2. 72 for fourth-instar exuviae and 1. 23-2. 70 for pupal exuviae (Fig. 4 ). 93. 8% (135/144) of exuviae LSVs reached the threshold value (LSVs > 1. 8) for reliable identification  [24, 27] , among which 100% of the samples were correctly classified at the species and stage levels. Among the nine MS spectra which did Fig.
Introduction The combination of artesunate with amodiaquine is widely used to treat malaria in sub-Saharan Africa. However, in 2012, a researcher noticed something concerning: 43 reports in adults and 6 reports in children of acute extrapyramidal reactions associated with the use of this combination  [1]. These reports, collected by the World Health Organization (WHO)'s global Individual Case Safety Report VigiBase database, hosted and managed by Sweden's Uppsala Monitoring Centre, along with data gathered by the manufacturer, resulted in a new labeling and package insert for the medicine combination that warned of the potential for acute extrapyramidal reactions  [2]. (Drug-induced extrapyramidal side effects are movement disorders with acute and long-term symptoms, including dystonia, akathisia, parkinsonism, bradykinesia, tremor, and tardive dyskinesia.
Hierarchical clustering performed on averaged single cell expression data within clusters showed that A and B were closer to each other than any of the other subsets (Figures  1B-1D ). Cluster D fell between the group of clusters A and B and cluster C (Figure  1B ). Classical cDC2 markers, such as CLEC10A, FCER1A, and major histocompatibility complex (MHC) class II genes (HLA-DQA, HLA-DPA, HLA-DRA, and the MHC class II-associated invariant chain CD74) were expressed prominently in clusters A,  B, and D (Figures 1D and 1E; Figure S1C; Heidkamp et al. , 2016; Lavin et al. , 2017; Segura et al. , 2013). In contrast, monocytic markers such as CD14, S100A8, S100A9, S100A12, and VCAN were more (A) Gating strategy used to define mononuclear phagocytes expressing CD14 and/or CD1c. Cells expressing CD14 and/or CD1c were sorted by flow cytometry from 3 healthy donors and pooled before scRNA-seq analysis.
To improve the resolution of CD1c + subsets, the cellular input was enriched in CD1 high cells (Figure  S1A ). Single cells were isolated using a droplet-based approach and sequenced. Dimensionality reduction of scRNA-seq data was performed using dimensionality reduction (t-distributed stochastic neighbor embedding  [tSNE] ). Clusters A, B, C, and D were identified using the shared nearest neighbor (SNN) clustering algorithm. Each dot represents an individual cell (n = 1,622). (B) Hierarchal clustering of groups A, B, C, and D based on average gene expression (14,933 genes). (C) Absolute number of differentially expressed genes (DEGs) for pairwise comparisons between groups A, B, and D. (D) Heatmaps displaying relative expression of up to 20 DEGs defining each cluster. (E) Violin plots illustrating expression probability distributions across clusters of representative DEGs (226 total DEGs).
Flow cytometry analysis Cells were stained in flow cytometry buffer (PBS 5mM EDTA 1% BSA) according to antibody panels (Key Resources Table ) for 30 min. Dead cells were stained using DAPI or Live/Dead Blue staining (Thermo Fischer). Lineage (Lin) included CD3, CD19, CD20, NKp46, CD56, CD203c and CD66b, all conjugated with biotin. Multiparameter analysis was performed on LSRFortessa and Symphony (BD Biosciences) flow cytometers and analyzed using FlowJo software (Tree Star). The flow cytometry-sort was performed on BD FACS Aria II or BD FACS Aria Fusion at the Biomedical Research Centre (BRC) Flow Core Facility (Guy's and St Thomas' NHS Foundation Trust and King's College London). Unsupervised clustering of flow cytometry data (Figure  1F ) was performed using Self-Organizing Map clustering algorithm Flow-SOM (Cytofkit R package).
In brief, amplified cDNA was fragmented through sonication on Covaris E220 (Covaris Inc), repaired and polished followed by ligation of indexed adapters. Adaptor ligated cDNA were pooled before final amplification to add flow cell primers. Libraries were sequenced on HiSeq2500 (Illumina Cambridge) for 100 cycles PE in Rapid mode. Bulk RNA sequencing data processing The raw sequencing data was initially processed using open source, web-based platform Galaxy (version 18. 05. rc1) (https:// usegalaxy. org). Reads were filtered for quality with more than 80% of the sequence having quality score > 33 using FastQC tool. Mapping against reference genome was performed with Hisat2 to the hg38 human genome. Adaptor sequences were detected automatically with TrimGalore!. Reads under 20bp were discarded.
All processed sequencing files were imported in PartekÒ Genomics Suite softwareÒ, version 7. 0ª; 2017 (PGS), where they were processed further. Primary data analysis and visualization mRNA was quantified using PGS built in RNA-seq workflow. Normalization method used was Reads Per Kilobase per Million mapped reads (RPKM) and mRNA was quantified against RefSeq Transcripts 2018-11-20 database. Hierarchal clustering on average expression within the group was performed on all identified protein coding genes (19791 genes). Based on the CD14 -CD1c + CD206 +/À two blue cluster similarity, they were both considered as cDC2-like cells and their datasets were merged for further analysis. The same was done for the two macrophage-like cells (gray and brown). Differentially expressed genes (Fold-Change % À2 or R 2 and p value < 0.
05) were determined using one-way ANOVA in all pairwise comparisons with three donors grouped and visualized as Volcano plots in PGS. Individual samples were visualized via principal component analysis (PCA) using 500 most variable genes, which were determined based on median absolute deviation (MAD). The expression patterns of selected gene lists were displayed in the form of heatmaps, where rows and/or columns were ordered based on hierarchical clustering using Euclidean distance and average linkage in Morpheus (Broad Institute; https://software. broadinstitute. org/morpheus/). A few gene names are depicted next to the heatmap.
Single-cell RNaseq data processing and analysis Cell Ranger (version 2. 1. 1) (from 10x genomics) was used to process Chromium single cell 3 0 v2 RNA-seq output files. First, we generated fastq files for the Read1 for cell barcode and UMI and Read2 for transcript applying cellranger mkfastq (with default parameters). Second, we aligned the Read2 to the human reference genome GRCh38 using STAR (version 2. 5. 1) with cell ranger count (with default parameters) (https://support. 10xgenomics. com/single-cell-gene-expression/software/pipelines/latest/using/mkfastq). Further analysis was performed using Seurat package (version 2. 3. 4) in R (version 3. 4. 0) (Butler et al. , 2018). Before performed analysis, we applied the following filtering step: only genes expressed in 3 or more cells have been preserved and cells with less 200 unique genes and more than 4000 unique expressed genes were discarded (as they are potentially cells doublets).
After filtering step, we used an expression matrix resulting in 14933 genes across 1622 cells (among 1625 cells) for the rest of the analysis. The matrix was normalized using genes expression values for each cell were divided by the total number of transcripts and multiplied by median of UMI counts. Then, these values were natural log-transformed before downstream analysis. For dimensionality reduction analysis, we first identified 3288 genes as highly variable genes across the single cells (cutoff value for dispersion = 0. 5; cutoff value for average expression = 0). PCA performed using the variable genes as input and determined 10 PCs as significant PCs. These principal components were used as input for t-Distributed Stochastic Neighbor Embedding (tSNE)  (van der Maaten, 2008). We used the shared nearest neighbor (SNN) modularity optimization-based clustering algorithm from the Seurat package (FindClusters function with default parameters) to identify the clusters of cells, following by Clustree analysis (clustree R package, version 0.
2. 2) by changing the resolution parameters from 0 to 2. Finally, we kept a resolution parameter at 0. 8 and defined distinct 9 clusters. After controlling expression of some quality control genes, we excluded clusters E, F, G, H and I as contamination. Finally, we identified 4 relevant clusters. We identified cell specific marker by comparing cells in a specific cluster with cells in all other clusters using Fin-dAllMarkers from Seurat package (MAST; logFC threshold = 0. 5; only positive markers). Heatmap, feature plots and violin plots were performed using Seurat package.
Signature expression analysis Single-cell RNA sequencing matrix for clusters A, B, C and D was created with gene signatures from  Villani et al. (2017). Signatures were defined as mean expression of discriminative markers for cDC2s and DC3s among lin -CD14 -cells (cDC2 enriched and DC3 enriched, respectively) and of discriminative markers for CD14 + monocytes within lin -CD14 +/À and/or CD16 +/À monocytes (CD14 + mono enriched). The average expression of signature genes for each cell was calculated and plotted as a violin plot using R package ggplot2 (version 3. 1. 0).