dataesr/scientific-paragraphs-categorization huggingface dataset:
RNAseq Clustering and Signature Analysis For the 32 canine MM samples, polyadenylated RNAs were extracted, sequenced, and analyzed, as previously described  [33]. All RNAseq fastq files are available in the SRA under the BioProject PRJNA749900. Briefly, the pipeline used the "canFam3. 1-plus" annotation as the reference annotation  [35] , and the canFam3. 1 assembly version as the reference genome  [36]. Based on the protocol described by Djebali et al. [37] , FASTQ reads were aligned to the transcriptome and genome using the STAR program (v2. 5. 0a)  [38]. Finally, gene expression levels were estimated as raw counts (unnormalized) using the RSEM program (v1. 2. 25)  [39]  for each sample individually, and subsequently merged to obtain a matrix expression file (with genes in rows and samples in columns).
This matrix of expression was then normalized and transformed across all samples in order to stabilize the variance using the DESEq2 (v1. 22. 2)  [40]  function vstcounts with the option "blind = TRUE". To select the genes that would provide the most information for clustering analysis, we used 6000 of the most variable genes, that is, those with the highest median absolute deviation. Then, the nonnegative matrix factorization (NMF) algorithm from the NMF R package (v0. 22. 0) [41]  was applied for different values of k clusters/ranks (k = 2-6). To determine the best k cluster, the nmfEstimateRank function was used (Appendix A Figure  A1 ), and the consensus matrix method (Silhouette) identified k = 2 as the optimal number of clusters. Finally, we employed the NMF function extractFeatures to obtain clusterspecific gene signatures for the two clusters (Table  S2 ).
These gene lists were then used as the input for the gprofiler2 (v0. 1. 9) R program  [42]  with the "gost" function, with the "organism" set to "cfamiliaris" to perform a gene set enrichment analysis over multiple databases (Gene Ontology, KEGG, CORUM). Finally, heatmaps were plotted using the complexHeatmap software (v2. 3. 1) [43]  to integrate and visualize multiple sources of information (gene signatures, sample clustering, SV content, and oncogene mutational status) (https://github. com/tderrien/Prouteau_et_al, accessed on 18 November 2021).
Mapping After sequencing, the raw reads were filtered (adapter sequences, contamination, and low-quality reads were removed) according to the manufacturer's guidelines. Sequence data were then aligned to the dog reference genome (assembly version canFam3. 1) using BWA-MEM (version 0. 7. 17)  [45] , and PCR duplicate reads were removed using Picard tools (version 2. 18. 23) (http://broadinstitute. github. io/picard/, accessed on 1 July 2021). The read data were processed according to the GATK best practices; specifically, the base quality score recalibration was assessed using GATK4 (version 4. 0. 12).
Somatic Variant Calling The Mutect2 tool from the GATK4 software was then used to call somatic SNVs and short INDELS against a panel of normal (PON), comprising matched normal samples, and against germline variants from 722 dog genomes (https://data. broadinstitute. org/ vgb/Ostrander_VCFs/722g. 990. SNP. INDEL. chrAll. vcf. gz, accessed on 1 July 2021). Variant annotation was performed using the VEP program  [46]  with the EnsEMBL "Canis familiaris" annotation (v. 95).
SV Calling Based on the aligned bam files from the BWA-MEM software, four structural variant types were identified: breakends (BND), deletions (DEL), duplications (DUP), and inversions (INV) using Delly software  [48]  (version v0. 8. 3). Only somatic SVs with the flags "PASS" and "PRECISE" were retained. Finally, circular representations of the SVs and the gain/loss from the DNA copies along the canine chromosomes were measured using the circlize R package  [49]  (version 0. 4. 10) (https://github. com/tderrien/Prouteau_et_al, accessed on 18 November 2021).
Each sample was measured thrice, and the relative amounts of the sequences were determined using the ∆∆Ct method. RNA was extracted from the 47 tumor samples and reverse transcription was performed on 1 µg of RNA from tumorous or healthy tissue using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Waltham, MA, USA), according to the manufacturer's instructions. RT-qPCR was performed using the same target genes. SAP130 was used as the control housekeeping gene, and a pool of data from non-affected oral tissues of four dogs was used as the external control. Each sample was measured thrice, and the relative amounts of the sequences were determined using the ∆∆Ct method.
Transfection with Specific and Control siRNAs and Cell Proliferation Assays The siRNAs (80 nM) for CDK4, GABPB1, TRPM7, USP8, SPPL2A, and control siRNA (IDT) were transfected into the Bear cell line using Lipofectamine 2000 (Invitrogen, Waltham, MA, USA), according to the manufacturer's recommendations (Tables  S1  and S3 ). CDK4 siRNA was used as the positive control. The siRNAs (10 nM) for GABPB1, TRPM7, and SPPL2A, as well as the control siRNA (IDT), were transfected into two human cell lines (HMV-2/CVCL_1282 -Merck-, WM3211/CVCL_6797 -Rockland-) using Lipofectamine 2000 (Invitrogen, Waltham, MA, USA) following the manufacturer's recommendations (Tables  S1  and S3 ). Cells were seeded in 6-well plates in 1 mL of RPMI 1640 medium (Gibco, Amarillo, TX, USA), supplemented with 10% fetal bovine serum and 0.
"High SV" MM Are Characterized by Focal Amplifications and Numerous Chromosomal Translocations To refine the annotation of SVs, we performed WGS on four canine MM cell lines (Bear, CML10, Dog-OralMel-18333, and Dog-OralMel-18395 cell lines) at an average depth of 30X. Then, the Delly program  [48]  was used to catalog a total of 5906 SVs (DEL, DUP, INV, INS, and BND) on the four cell lines (Table  S5 ) (see Section 2). Three canine cell lines showed strong focal amplifications of CFA 30 and/or CFA 10, which are recurrently observed in canine MM  [13, 24, 29, 63 ,64] (Figure  2 ). The Bear cell line showed focal amplifications of the CFA 30, targeting the 16-17 Mb region (up to 67 copies) and of the CFA 10 targeting MDM2 and CDK4 region (up to 46 copies). The Dog-OralMel-18333 cell line was characterized by focal amplifications targeting the same region of CFA 30 (up to 27 copies) and CFA 7 (up to 13 copies), whereas the Dog-OralMel-18395 cell line presented strong focal amplifications on CFA 10 (up to 41 copies) targeting MDM2 and CDK4.
This amplification of 29 copies of these genes was associated with high gene overexpression (16. 8-, 22. 7-, and 30. 2-fold for TRPM7, USP8, and SPPL2A, respectively). In another cohort of 34 patients with acral melanomas, we found one tumor with an intrachromosomal rearrangement on chromosome 15 targeting the genes TRPM7 and MYO5A  [78]. Furthermore, two recent studies involving MM cases that performed WGS on 65 and 67 tumor samples showed that chromosome 15 frequently had a deletion in the proximal part, and more rarely, amplifications targeting the candidate orthologous region identified in the present study  [14, 15]. Moreover, in a cohort of 65 patients with only oral melanoma, TRPM7 was one of the 48 significantly mutated genes  [15]. To confirm the importance of these genes in non-UV-induced melanomas in humans, we evaluated the proliferation capability of two human melanoma cell lines, one mucosal melanoma cell line (HMV-2/CVCL_1282), and one acral melanoma cell line (WM3211/CVCL_6797).
Thus, the likely presence of an effective T-cell infiltrate in tumors in this group, in addition to the overexpression of immune checkpoint proteins, such as CTLA-4, TIM3, and LAG3, favors a response to immunotherapy, especially when using checkpoint inhibitors  [52] [53] [54] 82]. Interestingly, cell-type-specific enrichment analysis from our MM bulk RNAseq data with the xCell program  [83]  confirmed that group 1 samples contained an enrichment of immune cells, such as dendritic cells, macrophages M1 and lymphocytes T (Figure  A9 ). While several studies have linked high tumor muta-tional burden to a better response of immunotherapy  [62, 84, 85] , two recent publications have shown that CNA/SV levels are also predictive of a response to immunotherapy in different cancer types. Among these cancers, cutaneous melanomas harboring a high level of CNAs have the poorest response rate to immunotherapy  [52, 53].
(p = 1. 0 × 10 -11 )  [53]  (Table  S9 ). However, several genes associated with the acquisition of an invasive, dedifferentiated, EMT-like phenotype were also overexpressed in this group. While a favorable tumor immune microenvironment is critical for effective immunotherapy, tumor cells could exploit the dedifferentiation program to resist immunotherapy. Further studies combining therapeutic trials with single-cell RNAseq would be very relevant to better assess the benefit of immunotherapies in this group. In a recent study, Newell et al. also identified two distinct subgroups of human MM based on the degree of localized complex rearrangements  [14]. While, here, we classified MM using a transcriptomic-based strategy, we confirmed the existence of two MM subgroups with different SV profiles targeting similar driver genes (MDM2, CDK4).
The whole blots and the densitometry readings of the band of interest are included. Informed Consent Statement: Not applicable. affected dogs, together with their clinical data, and who provided anatomopathological diagnoses, especially Olivier Albaric, Laetitia Dorso and Florian Chocteau (Laboniris, Oniris, Ecole Nationale Vétérinaire de Nantes, France), Marie-Odile SEMIN (LAPV, Amboise, France), Caroline Laprie (Vet-Histo, Marseille, France), Marie Lagadic (Idexx Alfort, France), and Frédérique Degorce-Rubiale (LAPVSO, Toulouse, France). We also thank the dog owners for their general health data and dog samples. Melanoma data were gathered through the Cani-DNA BRC (http://dog-genetics. genouest. org, accessed on 1 July 2021), which is part of the CRB-Anim PIA1 infra-structure, ANR-11-INBS-0003.
The authors thank A. Fautrel (Platform H2P2 Biosit Rennes, France) for support with FFPE samples. The authors especially thank Patrick Devauchelle and Pauline de Fornel, (MICEN-Vet, Cretail, France) as well as Didier Lanore (Bordeaux) for their advice on veterinary oncology, and Marie-Dominique Galibert (IGDR, Rennes, France) for her interest and follow-up. We also warmly thank Clotilde de Brito, Laetitia Lagoutte, Annabelle Garand, Anne Sophie Guillory and Frederique Allais (IGDR, Rennes, France) for their input on canine oral melanomas, and Stéphane Dréano (IGDR, Rennes, France) for Sanger sequencing. The authors also thank Jaime Modiano (Colorado State University) who provided the 2 oral melanoma cell lines (Bear -CVCL_OD14 and CML10 CVCL_IZ11). We are most grateful to Biogenouest Genomics, the Human & Environmental Genomics core facility of Rennes (Biosit/OSUR) for its technical support, the GenOuest Bioinformatic core facility (https://www.
genouest. org, accessed on 1 July 2021) for storing sequencing data, hosting the Cani-DNA website, and for providing the computing infrastructure. [83]  with 64 immune and stroma cell types and MM are then clustered using clustergrammer  [100]. Enrichment for immune cells such as dendritic cells, macrophages M1 and lymphocytes T is observed in the group 1 with low SV content (SV_clust = clustering of MM samples based on SV content with group1 in light green and group2 in orange, NMF_clust = clustering of MM samples based on NMF with RNASeq data with group1 in purple and group2 in orange). • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • •• • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • B ear_tu m o.
A. Prouteau (salary and direct costs) was funded through the FHU CAMIn (CHU-Univ Rennes-Brittany region & Ligue Nationale contre le cancer). The sample collection performed through the French Cani-DNA BRC was funded by the CRB-Anim infrastructure ANR-11-INBS-0003 in the frame of the Investing for the Future Program (PIA1). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of COMITÉ NATIONAL DE RÉFLEXION ETHIQUE SUR L'EXPÉRIMENTATION ANIMALE (protocol code CE07-2020-11-CA and date of approval: 23 November 2020).
Estimates of breeding values for all the animals can then be obtained by a linear combination of the corresponding design matrices and the vector of solutions. As in Fernando et al. [31] , estimated breeding values for nongenotyped animals can be obtained directly to improve computational efficiency. Adding the polygenic effect does not change the MME for APY-ssGBLUP but adds an extra term ( ε ) to the MME for APY-ssSNP-BLUP. In that case, the number of unknowns is equal to that in the model presented by  [22]. Therefore, APY-ssSNP-BLUP is more complex and involves more convoluted matrix multiplications. Whenever the MME are augmented, there is a question on the feasibility and convergence of the model with real datasets. Vandenplas et al. [5]  proposed a second-level preconditioner to ease convergence problems in ssSNP-BLUP, and Vandenplas et al.
Data analysis To determine the seropositivity and perform the statistical analysis, normalized and dilution corrected antibody data were log 10 -transformed. Detailed information on the processing of data can be found in Santano et al. 2021  [28]. The receiver operating characteristic (ROC) curves and their corresponding areas under the curve (AUC) for each antibody response were built with the R CRAN package pROC  [37]  using the log 10 -transformed MFIs as predictor and the microscopic, molecular or combination of both diagnoses as response variable. Since antibodies are acquired over time  [28] , we also performed the analysis stratified by age group (children and adults). In addition, to eliminate the possible interference of helminths and P. falciparum co-infections with the performance of the assessed markers, we estimated additional ROC curves and AUC analyses where the log 10 MFIs from samples with only one infection (cases) or no infection (controls) diagnosed by the corresponding method were used as predictors, and the microscopic, molecular or combination of both diagnoses as response variables.
In the case of P. falciparum antigens, we followed the same strategy but we included only the top performers to calculate the combinations, whether for the whole population (10 top performers, 1013 models) or stratified by age (adults: 10 top performers, 1013 models; children: 3 top performers, 4 models). For the analysis of antigen-specific IgG, MFI data from the 715 samples from endemic individuals and the 50 samples from Spanish donors were used. In the case of total IgE only MFI data from endemic individuals were used, since there are other IgE-inducing pathologies, such us allergy, which are prevalent in Spain and render these samples not useful as negative controls. Five seropositivity cutoffs were compared to evidence the options that can be used in scenarios requiring prioritization of specificity, sensitivity or a more balanced performance, and to contrast the performance of cutoffs calculated with data from non-endemic or endemic populations.
The Expectation-Maximization (EM) cutoff was estimated using Gaussian Mixture Models for each antigen-specific IgG and total IgE in the study population. Two models, estimated by the EM algorithm, were fitted assuming either equal or unequal variances, and each of those two models were built with two clusters for classification, which were meant to represent the distribution of seropositive and seronegative samples in the endemic population. For each antigen, classification of samples was done using the model showing the optimal Bayesian Information Criterion (BIC). The models were fitted using the R CRAN package mclust  [38]. The NC cutoff was calculated as the mean plus 3 standard deviations of the log 10transformed MFIs from the Spanish donors for each antigen-specific IgG. Finally, the ROC curves were used to generate three different cutoffs.
stercoralis diagnosis). Among these individuals, we selected those who had only one single infection (to avoid interferences from co-infections) or no infection diagnosed. Finally, we compared the antibody levels for each species to the levels of those with no infection diagnosed. Pairwise comparisons were done by Wilcoxon rank-sum test and Holm adjustment for multiple testing. P values < 0. 05 were considered statistically significant. All data processing and statistical analyses were performed using the statistical software R version 4. 0. 3. R CRAN packages used to manage data, generate tables and plots were tidyverse  [39] , ggpubr  [40] , ggbeeswarm  [41]  and UpSetR  [42].
However, the number of S. stercoralis infections detected by microscopy was very low (N = 8), probably giving unreliable results. MEA and Sm25 from Schistosoma spp. had AUC = 0. 746 and AUC = 0. 741, respectively, using S. mansoni microscopy data as reference (Figs  2A  and 3 ). Sm25 also showed moderate AUC when using Schistosoma spp. microscopy (AUC = 0. 721), Schistosoma spp. qPCR (AUC = 0. 712) or S. haematobium microscopy (AUC = 0. 711) as references (Figs  2A  and 3 ). The remaining Rows represent the number of infections by parasite (sets) and columns represent the frequency of single infections and coinfections (intersections) when helminth infection was detected by microscopy (A), qPCR (B) or the combination of both (C). In all cases, infection of P. falciparum was detected by qPCR.
Acknowledgments We are grateful to the volunteers and their families and to all field workers and lab technicians that participated in this study. We also thank  CISM Demography  and  Social Sciences department  for their indispensable assistance during field work. Special thanks to the team members from  ISGlobal , specifically  Laura Puyol  for the organization and coordination of shipment of samples,  Pau Cistero  ´for the assistance in setting up the P. falciparum qPCR,  Diana Barrios  and  Alfons Jime  ´nez for the technical support in the lab,  Miquel Vazquez-Santiago  and  Llorenc Quinto  ´for the statistical advice, and  Chenjerai Jairoce  for the aid in assay optimization. For antigen procurement, we also thank  Thomas Nutman  ( NIH/NIAID, USA) ,  Sukwan Handali.
Genetic selection focused purely on production traits has proven very successful in improving the productive performance of livestock. However, heightened environmental and infectious disease challenges have raised the need to also improve the resilience of animals to such external stressors, as well as their efficiency in utilising available resources. A better understanding of the relationship between efficiency and production and health traits is needed to properly account for it in breeding programmes and to produce animals that can maintain high production performance in a range of environmental conditions with minimal environmental footprint. The aim of this study was to perform a meta-analysis of genetic parameters for production, efficiency and health traits in sheep and goats. The dataset comprised 963 estimates of heritability and 572 genetic correlations collated from 162 published studies.
Dataset The dataset comprised genetic parameters for efficiency and resilience traits collated from 13 partners of the EU H2020 SMAR-TER project (n°772787) coming from seven countries (France, Ireland, Italy, Spain, Switzerland, United Kingdom, and Uruguay). Additionally, it was supplemented with papers published by other research groups to get a more complete overview of parameters reported for the analysed traits. For the purpose of this project, efficiency comprised of production traits and feed efficiency, whereas resilience comprised of disease resistance and survival. As parameters for key production traits for meat sheep have previously been summarised  (Fogarty, 1995; Safari et al. , 2005) , the search for papers was restricted to studies published after 1995. In case of dairy goats and dairy sheep, where the available data were more limited, no restriction on the publication date was imposed.
Altogether 232 published papers together with some unpublished results were considered. This original dataset contained 2 151 estimates of heritability and genetic correlation for 81 sheep (including composite breeds) and 14 goat breeds. Subsequently, the dataset was filtered to select traits with a suitable number of heritability and genetic correlation estimates together with accompanying information about each study such as breed, age of animals and number of phenotypes (for details, see Supplementary Tables  S1-S3 ). The age of animals was based on lactation number for dairy traits and age at measurement for meat traits. Each estimate of genetic parameters was required to have an accompanying standard error. Only traits with a minimum of three estimates (heritability or correlation) from two different studies were included in meta-analysis.
The three proxies for resistance to flystrike are scaled scores with the higher value indicating less resistance (maximal faecal soiling, highly fluid faeces or high number of strikes). Footrot was also assessed through scaled scores with greater values indicating severe footrot. Lamb survival was considered from birth to 365 days, with different intervals (for example from birth to 30 days, from birth to weaning, from weaning to 365 days). After initial data exploration, the following six datasets were created: (1) 81 estimates of heritability from 16 studies on dairy goats (Supplementary Table  S1 ); (2) 119 estimates of heritability from 26 studies on dairy sheep (Supplementary Table  S2 ); (3) 763 estimates of heritability from 118 studies on meat sheep (Supplementary Table  S3 ); (4) 13 estimates of genetic correlations (between resilience and efficiency traits) from four studies on dairy goats (Supplementary Table  S4 ); (5) 99 estimates of genetic correlations (44 between efficiency and resilience traits; 55 between efficiency traits) from 12 studies on dairy sheep (Supplementary Table  S5 ); (6) 465 estimates of genetic correlations from 50 studies on meat sheep (Supplementary Table  S6 ).
Acknowledgements The authors acknowledge the SMARTER participants that provided unpublished data:  Isabelle Palhiere (INRAE) ,  Beatriz Gutierrez-Gil  and  Juan Jose Arranz  (UELEON),  Sara Casu  and  Antonello Carta  (Agris),  Stefano Biffani  ( CNR -IBBA ), and  Antonio Pacheco  (SRUC).