dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and code availability The datasets generated during this study are available at Gene Expression Omnibus (GEO) with accession codes GEO: GSE169749 and GEO: GSE190595, for spatial transcriptomics data at d0 vs. d14 and B cell depletion condition, respectively, and GEO: GSE163637 and GEO: GSE163638 for scRNAseq data. Bulk RNA-seq for DSS colitis kinetics used for VDJ reconstruction is publicly available under GEO: GSE131032. Bulk RNA-seq data from B cell depletion experiment in CD19-iDTR was deposited under accession number GEO: GSE207178. The 16S sequencing data were submitted to European Nucleotide Archive (ENA) under the accession number ENA: PRJEB54765. Code used for analysis of single-cell and spatial transcriptomics datasets as well as complementary details are available at https://github.
Flow cytometry Single cell suspensions were incubated with Fc-blocking (CD16/32) antibody and fixable viability dies (eBioscience) for 10 min at 4 C prior to staining with fluorochrome-conjugated antibodies for 10 min at 4 C. For staining of IL-10 cells, cells were incubated with Fixation and Permeabilization buffer (Foxp3 staining kit, eBioscience) for 60min at 4 C after cell surface staining, followed by staining for 30 min at 4 C with a fluorochrome conjugated antibody against IL-10. For staining of IL-10 cells were stimulated with PMA/Iono and GolgiStop for 6h at 37 C prior to staining. All the experiments were acquired using FACS Canto II or FACS LSR Fortessa flow cytometers (BD Biosciences) and analyzed with FlowJo software (TreeStar).
Analysis of VDJ repertoire during DSS colitis Identification and quantification of VDJ sequences under the course of DSS colitis was done by applying TRUST4 tool on raw sequencing reads from public bulk RNA-seq dataset of mouse colitis (GSE131032)  (Czarnewski et al. , 2019). Estimated VDJ counts were then used for comparison across time points. scRNA sequencing of colonic B cells B cells from the lamina propria were isolated as described above, followed by a Percoll gradient for enrichment of lymphocytes. Cells were stained with live/dead dye and CD45, CD3, CD19 and Ly6C surface antibodies and then sorted on a BDInflux Cell Sorter into a 15mL tube filled with complete RPMI medium with 10% FCS. Stromal and epithelial cells were isolated from the lamina propria as described above and stained with live/dead dye, CD45, CD90, CD31 surface antibodies or CD45, Ter119, EpCam, CD31, respectively.
Data analysis of single-cell sequencing For analysis, we employed 10x Genomics Cell Ranger 3. 1. 0, Seurat 3. 1. 3 (Stuart et al. , 2019) , Progeny 1. 10. 0 (Holland et al. , 2020; Schubert et al. , 2018)  SingleCellSignalR 1. 0. 0  (Cabello-Aguilar et al. , 2020)  packages implemented in R. For pre-processing and quality check of murine datasets the data alignment was carried out and quantification of the reads in each cell using cell ranger pipeline using mouse mm10 as reference genome. In total 8,156 (B cell dataset, 3,376 for B0 and 4,780 for B14) and 29,963 (stromal/epithelial dataset, 16,396 for the control and 13,567 for B cell depleted tissue) cells were retrieved. The median number of genes expressed in the B cell dataset was 896 (B0) and 861(B14) and in the stromal/epithelial dataset 1,264 (control) and 919 (B cell depleted).
QC and preprocessing were carried out using the Seurat pipeline. Cells expressing below 200 genes were filtered out as well as cells with mitochondrial content higher than 5% or 20% (epithelial/stromal dataset). For the epithelial/stromal dataset non-coding genes as well as genes expressed by less than 3 cells and a ribosomal content greater than 5% were additionally removed. After this quality check, removal of low-quality cells and contaminations and doublet filtering, 7,982 cells and 10,375 cells were left for downstream processing for the B cell dataset or the epithelial/stromal dataset, respectively. Data were normalized to identify 2,000 highly variable genes that exhibited high cell-to-cell variation in each dataset. The two samples from each dataset were integrated using anchorbased approach inbuilt in Seurat v3.
In the B cells integrated dataset, 7,982 cells were obtained, and 16,416 genes detected, out of which 2,890 were unique variable genes. In the case of the stromal/epithelial single cell dataset, the single cell data from both control and B cell depleted condition was integrated using an anchor-based approach and obtained an integrated object with 10,375 cells and 16,227 expressed genes. Dimensionality of datasets was reduced by the RunPCA function on previously scaled data (ScaleData function). The parameters for reduction were identified by Jack-Straw plot function. Cells were clustered using FindNeighbors and FindClusters functions and UMAP embedding was performed with a resolution set to 0. 2  (Stuart et al. , 2019). Topic Modeling Analysis: As an alternative approach to marker identification for each cluster in single cell datasets, we followed the Topic modeling protocol published in  Xu et al.
(2019). In this approach, we obtained multiple topics which are basically representations of the grade of membership models (based on different transcriptional rewiring of a cell), in which individual cells are assumed to have partial membership to different sub-populations of cells at the same time. This is different from hard grouping in cluster analysis, where cells only belong to the defined clusters. To obtain the topics in our single cell data, the expression matrix for the B cell single cluster within the dataset. Utilizing inbuilt ligand-receptor database in SingleCellSignalR, we were able to generate cellular interaction lists using the cell_signaling function inbuilt in the package. Each list comprises ligand -receptor interactions between two different clusters in a given single cell data set.
Spatial deconvolution of single-cell clusters Deconvolution of Visium spots were performed using weighted non-negative least squares regression (WNNLS) method implemented in SCDC  (Dong et al. , 2021) , which has been shown to outperform other methods in a recent benchmark  (Avila Cobos et al. , 2020). The deconvolution is highly affected by the representation of the reference datasets, and for this reason we included 2 bone marrow datasets (GSM3040900 and GSM3040901) from the Tabula Muris  (Tabula Muris Consortium et al. , 2018)  and ran the same single cell analysis pipeline above for clustering and identification of the major cell types. In the end, dendritic cells, monocytes, macrophages, erythrocytes, NK cells, CD4 and CD8 T cells, naive B cells, neutrophils, mast cells and eosinophils were identified (Figure  S7 ).
Next, we merged the bone marrow, epithelial, stromal and B cell datasets, performed differential gene expression across all clusters using Wilcoxon test and selected the top 10 genes with highest log fold change among the 30 most significant p value per cluster for cell-wise factor matrix estimation and subsequent spot estimation of cell type proportions with SCDC function  (Dong et al. , 2021). Quantification of sample-wise spatial cell-cell co-detection Formally, let P ˛½0; 1 p3N be the matrix containing p phenotypic readouts (such as cell type proportions or range-normalized gene expression) for all NVisium spots. The square matrix D ˛Rp3p representing the co-detection rates across all features (i. e. , cell populations or genes) was defined as the square root of the dot product of the estimated cell proportions divided by the total number of spots in each sample: D p 3 p = P p 3 N , P T N 3 p N 1=2 We next quantified the change in cell-cell co-detection rate (DD A;B ) between two conditions as the log 2 ratio of the co-detection scores for each condition (i.
e. , D A and D B ) with added constant value of to avoid division by zero, such that: DD A;B = log 2 D A + 1 D B + 1 Bulk deconvolution of human colitis Deconvolution of biopsies pediatric ulcerative colitis was done using SCDC method  (Dong et al. , 2021)  as described above, but using the human colitis dataset  (Smillie et al. , 2019)  using the datasets own cell type annotation. Briefly, we subsetted 50 cells from each cell type to balance out cell cluster composition and performed differential gene expression for identification of relevant genes. The top 10 most significant genes for each cluster were selected and used for deconvolution. Cell abundances were then plotted against the PUCAI score present in the dataset for each sample.
(D) Uniform manifold approximation and projection (UMAP) embedding of IEC and stromal cell clusters and overlay of Epcam and Vim, which were used to dissect the dataset into two main populations. (E) Heatmap of all differentially expressed genes among epithelial and stromal clusters used for identification of cell subtypes. (F) Pie charts comparing the IEC and stromal cell cluster compositions between control and B cell-depleted mouse groups.
ACKNOWLEDGMENTS We thank all members of the  Villablanca lab  for helpful comments during this project and  Maria Lim  for editorial assistance. A. F. was supported by a grant from the  German  research association (DFG,  808021 ). The computations and data handling were enabled by resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at  KTH , partially funded by the  Swedish Research Council  through grant agreement no. 2018-05973. We thank  Kevin Maloy  and the  3I Flow Core Facility  of the  University of Glasgow.
Software and algorithms FlowJo v10. 2 BD https://www. flowjo. com; RRID:SCR_008520 FACS Diva (v8. 0. 2) BD Biosciences http://www. bdbiosciences. com/ en-us/instruments/research-instruments/ research-software/flow-cytometryacquisition/ facsdiva-software Prism (v7. 0b) GraphPad Software Inc https://www. graphpad. com/; RRID:SCR_002798 SH800S Cell Sorter Software Sony https://www. sonybiotechnology. com/ us/instruments/sh800s-cell-sorter/ software/ Cell Ranger (v3. 1. 0) 10x Genomics https://github. com/10XGenomics/ cellranger; RRID:SCR_017344 R (v3. 6. 1) R Foundation for Statistical Computing https://www. r-project. org;.
001 uncorrected). Then it compares its predictions to the actual reward and compute again a new value for each expert. b. Model-free generative analysis. We generated one hundred sets of data using a free parameter from a normal distribution with mean and standard deviation calculated from the models fitted to the population, against the same artificial agent that participants played. We regressed the behavioral decision to stay after selection of a specific target on the previous trial depending on the interaction of the previous outcome and the action of the artificial agent ("Cooperativity signature"), the success or.
Stimulus creation From this pool of acoustic recordings of natural puppy vocalisations, we used 10 whine sequences at each age category (i. e. , 1-, 3-, 6-, 9-week-old, 40 sequences in total) from 26 different individuals as templates to create their associated fully synthetic copy (hereafter synthetic prototype). We choose whine sequences composed of three repeated whines without NLP to facilitate their synthesis with the R package soundgen  [31]. For each synthetic prototype, we prepared sets of vocalisations with systematic manipulations of NLP and f o (Table  1 ) for playback experiments. Specifically, we created synthetic whine stimuli at low, medium, and high levels of f o , with or without different types of NLP added at each f o level, based on those that naturally occur in puppy whines, including frequency jumps or varying proportions of subharmonics, sidebands, and chaos (Fig.
Experiment 2 included 840 stimuli: 40 prototypes * 7 conditions of NLP duration (none + 50% or 80% of the whine duration affected by subharmonics, sidebands, chaos = 1+ 2*3 = 7) * 3 f o levels. All audio stimuli and R code for their synthesis are available for download at https://osf. io/rp7m8/.
Funding M. M. and D. R. were supported by the  University of Lyon IDEXLYON  project as part of the ' Programme Investissements d'Avenir'  ( ANR-16-IDEX-0005 ) to D. R. A. A. was supported by grant  2020-06352  from the  Swedish Research Council. D. R & N. M. were supported by the  Institut Universitaire de France. Funding was provided by  LabEx CeLyA ,  CNRS, INSERM, University of St-Etienne.
But specific pathogens, including viruses, naturally infectious to BSFs have yet to be characterized  [13]. As reports of BSFs mortality are increasing, they demonstrate the need to investigate the virome of BSFs. As a first effort to characterize the BSF virome, we searched for virus-derived sequences in publicly available genomic and transcriptomic datasets. Such an approach has proven useful when characterizing new, free circulating, exogenous viruses (EXVs), which may be co-sequenced with that of the host  [22] [23] [24] [25]. Screening host genomes may also lead to identifying endogenous viral elements (EVEs), i. e. , complete or fragmented viral genomes that became integrated into the genome in the germline of their host and were vertically transmitted  [23, 26]. The characterization of EVEs within a robust paleovirological framework can yield unique insights into historical host-virus interactions  [26] [27] [28] [29] [30].
Datasets and Samples Publicly available black soldier fly transcriptomes were downloaded from the NCBI SRA database (https://trace. ncbi. nlm. nih. gov/Traces/sra/, accessed between 30 December 2020 and 1 October 2021). Samples were from the following bioprojects: PRJEB19091  [33] , PRJEB39181  [34] , PRJNA431833, PRJNA432297, PRJNA506627  [35] , PRJNA573413  [36]  and PRJNA575900  [37]. SRA toolkit (v2. 10. 9,  [38] ) was used to convert the SRA files and separate their sequence reads into forward and reverse read fastq files. Publicly available BSF genome assembly fasta files GCA_001014895. 1 (BGA1)  [39] , GCA_009835165. 1 (BGA2)  [36]  and GCF_905115235. 1 (BGA3)  [40]  were also retrieved from NCBI assemblies and RefSeq repositories  [41]. Of note, BGA3 is assembled at a chromosomal level and includes seven chromosomes and the mitochondrial genome, leading to a total of 1.
01 Gb in size and is predicted to be 98. 6% complete. The length of the chromosomes themselves ranges from 15. 4 to 222. 1 Mb, with an N50 value of 180. 46 Mb for scaffolds and 16. 01 Mb for contigs  [40]. Altogether the genome assemblies and transcriptomes represent BSF colonies from widespread origins. They were generated from BSFs reared in China  [35] [36] [37] , Germany  [33] , Italy  [34] , the United Kingdom  [40] , and the United States of America  [39]. In addition, BGA2 was also generated alongside transcriptomes produced in bioproject PRJNA573413  [36].
Screening BSF Genome for EVEs Virus-like sequences were screened for in BSF genome assemblies BGA1, BGA2, and BGA3, except for Retroviridae and Hepadnaviridae using a DIAMOND-python-and R-based pipeline (archived on Zenodo https://doi. org/10. 5281/zenodo. 6554302, accessed on 16th May 2022). Sequence regions with viral hits according to the NCBI Identical Protein Groups (IPG) database (8 January 2021) were extracted. Endogenous viral element hits from the same family and closer than 50 bp to each other were considered to correspond to a single EVE, and then screened against the full NCBI nr database (22 January 2021)  [41]. An R-script (R v4. 0. 3,  [42] ) was used to summarize the results and obtain the taxonomical information of each EVE candidate using the packages 'taxonomizr' (v0. 5.
3, [43] ) and 'data. table ' (v1. 13. 6, [44] ). EVE candidates were checked against Drosophila melanogaster proteins in the UniProtKB/Swiss-Prot database (30 March 2021) using BLASTx on NCBI for false-positive assessment. Only sequences that did not receive a D. melanogaster protein hit were retained as EVE candidates. Afterwards, to determine EVE locations on the BSF chromosomes, they were mapped onto BGA3 using the in-house mapping software of Geneious Prime (v2021. 1-2022. 02, https://www. geneious. com, accessed on 29 April 2022)). To assess the level of identity between related EVE sequences, if one EVE sequence mapped to a chromosome after the first round of mapping, 20 kb regions which contained the EVE site were extracted from the genome sequence and then the EVEs were remapped to each of the extracted regions.
Exogenous Virus Discovery Using Transcriptomic Data Quality checking of forward and reverse reads was performed using FASTQC (v0. 11. 9,  [45] ). Trimmomatic (v0. 39,  [46] ) was then used to filter reads and remove Illumina adapters. Contigs were then assembled using rnaSPAdes (v3. 15. 2, [47,48] ). Virsorter2 (v2. 1,  [49] ) was used to identify and extract viral-like sequences from the SPAdes assembled contigs, followed by using CheckV (v0. 7. 0,  [50] ) to assess the estimated completeness and accuracy of the viral-like sequences. CheckV results were searched for sequences that firstly had no warning messages, and for sequences with predicted virus genes. BLASTx (RRID:SCR_001653) was then used on sequences that were considered to be viral by CheckV to screen for false positives and to identify the closest hits against the NCBI 'Non-redundant protein sequences' database  [41].
The default settings were used: 100 Max target sequences, parameters automatically adjusted for short input sequences, an expected threshold of 0. 05, word size of 6, BLOSUM62 matrix, gap costs of existence: 11 and extension: 1 and with a conditional compositional score matrix adjustment and a filter for low complexity regions. Open reading frames (ORFs) were annotated using Geneious Prime. These ORFs were translated to proteins, and conserved regions were searched using BLASTp (RRID:SCR_001010) with the same parameters as BLASTx, but without filtering for low complexity regions. To identify conserved regions, the E-value threshold was set to 0. 01 with a maximum number of hits set to 500 against the CDSEARCH/CDD database.
Phylogeny of Totiviridae On Geneious Prime, the GAG and POL ORFs of the selected totiviruses were reannotated as per the majority of annotated genomes. Afterwards, the translated amino acid (AA) residues of the POL and GAG ORFs were aligned using the MAFFT aligner (v7. 45,  [51] ) using the G-INS-i algorithm, a BLOSUM62 scoring matrix, and the default values of 0. 123 for offset value and 3 for the gap open penalty. The alignments were trimmed at both ends, and alignment columns were retained if at least 10% of the sequences had an amino acid at that position. The alignments were concatenated. A maximum likelihood phylogenetic tree was reconstructed using the IQ-TREE 2 software (v2. 1. 3, [52] ), which allows for the selection of the best-fit evolutionary model for the data using the automated ModelFinder  [53]  and robustness assessment by ultrafast (UF) bootstrap (1000 iterations)  [54]  and Shimodaira-Hasegawa-like approximate likelihood ratio test (SH-aLRT)  [55].
Molecular Validation of EVE Genetic material from three BSF larvae originating from three rearing facilities was extracted using the ZymoBIOMICS DNA/RNA Miniprep Kit (cat. R2002, ZYMO Research, Freiburg im Breisgau, Germany), and DNA was used for the PCR screening of TotiEVE and to target the region which appears in some transcriptomes. To determine whether the endogenized Totiviridae sequence was present in available BSF colonies, the TotiEVE sequences, and hermetia illucens toti-like virus 1 (HiTV1) contigs were mapped to the BSF reference genome (GCF_905115235. 1). Primers were designed using Primer 3 (v2. 3. 7, [56] ) on Geneious Prime with settings for Tm between 50 and 58 • C and GC content between 40 to 60 %, and GC clamp to 1 (Table  S1 ). The max size of the target regions searched was 1000 bp, and 650 bp for the region where the short transcripts align (TotiEVE-STs).
Totiviridae genomes are double-stranded RNA and normally between 4. 6 to 7 kbp in length  [57] , although genomes of Totiviridae such as LbTV (NC_025218. 2) and papaya meleira virus (NC_028378. 1), can be as long as 8021 bp and 8768 bp, respectively. Like most Totiviridae  [57] , HiTV1 has a simple genome consisting of a GAG and a POL ORF, which encode for a capsid protein and an RdRP protein (Figure  2 ). The POL ORF contains an RdRP 4-like domain (PF02123), typically associated with viral families such as Totiviridae  [58, 59]. Read mapping of transcriptome SRR14339788 onto the HiTV1 contig 1 showed an average coverage of 53. 79x across the length of the viral genome (Figure  2 ), which gives high confidence in the HiTV1 assembly. Out of the 65 BSF RNAseq datasets screened, HiTV1 was found as nearly complete genome contigs in five transcriptomes from three distinct bioprojects, and as shorter contigs in another 48 transcriptomes (Table  S3f ).
1), can be as long as 8021 bp and 8768 bp, respectively. Like most Totiviridae  [57] , HiTV1 has a simple genome consisting of a GAG and a POL ORF, which encode for a capsid protein and an RdRP protein (Figure  2 ). The POL ORF contains an RdRP 4-like domain (PF02123), typically associated with viral families such as Totiviridae  [58, 59]. Read mapping of transcriptome SRR14339788 onto the HiTV1 contig 1 showed an average coverage of 53. 79x across the length of the viral genome (Figure  2 ), which gives high confidence in the HiTV1 assembly. (b) Using Bowtie2  [60]  and SAMtools  [61] , raw sequence reads from the transcriptome SRR14339788 were mapped onto the HiTV1 contig 1, which resulted from a SPAdes assembly of SRR14339788 reads. The average coverage of the reads across the contig without gaps was 53.
Acknowledgments: We acknowledge the people involved in generating the transcriptomic and genome assembly data for their publications and for making the data publicly available. We also would like to acknowledge members of the  ITN Insect Doctors consortium  and the current members of IRBI who provided some feedback throughout the study. We would also like to acknowledge the  BSF  farms for allowing us to use the genetic material from BSFs provided, and we thank  Jirka Manuel Petersen  for his technical help in preparing Figure S1.
Data Availability Statement: Data in this study was mainly generated from publicly available NCBI bioprojects (https://www. ncbi. nlm. nih. gov/bioproject/, accessed on 16 April 2022). The sequence for HiTV1 contig 1 found in Table  S3a , is also available in the Third Party Annotation Section of the DDBJ/ENA/GenBank databases under the accession number TPA: BK061373.
Data sets We evaluated our method on two plant species with contrasting leaf shapes and architecture, Chenopodium album (also known as wild spinach, called Chenopodium in the sequel) and Solanum lycopersicum (tomato plant). For the tomato plant, we reused the data set provided by the Pheno4D project  (Schunck et al. , 2021). For Chenopodium, we created 2 new data sets, one obtained from real plant architecture phenotyping, and the other from simulated computational models of Chenopodium growth. The main characteristics of the different data sets are reported in Table  1.
Construction of ground truth data To create a ground truth reference, we selected 5 point clouds in this data set to manually annotate clusters corresponding to the different plant organs. We used the CloudCompare software (CloudCompare, 2022) to separate each organ from the whole point cloud. We could then label it with labels taking into account the organ class and the instance (for example: from 300 to 399 for the apices, from 400 to 499 for the leaf blades). Finally, we merged all the labelled organs together to obtain an instance and semantic ground truth segmentation. We obtain a semantic ground truth segmentation with 5 semantic classes: main stem, branches, petioles, leaf blades, apices.