dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement. While the author has been the main developer of IMITATOR since 2008, several colleagues brought very valuable enhancements, notably  Camille Coti  and  Sami Evangelista  [14] (on distributed algorithms),  Nguy Hoàng Gia  [27] (on non-Zeno algorithms),  Vincent Bloemen  [12] (on minimal-time synthesis),  Laure Petrucci  and  Jaco van de Pol  [11] (on NDFS-based cycle synthesis), and  Jaime Arias  for multiple practical enhancements. Many thanks to  Dylan Marinho  for a careful rereading of this paper, and to  Stephan Merz  for useful suggestions.
A ranking of relevance to HAEC was created based on: (i) putative effect on the protein and comparison between the allele frequency in cases and controls detected by Next Generation Sequencing (NGS) and those reported in the genome aggregation database (gnomAD, https://gnomad. broadinstitute. org/ accessed on 14 July 2020) (WES score), (ii) biological role of the gene, as surmised from literature data (biological score). Of note, although the biological relevance to HAEC has been assigned in cieco with respect to the WES relevance, 36. 8% of variants in genes likely relevant to HAEC had a high WES score, in comparison to 15. 4% of variants in genes only possibly or unlikely to be related to HAEC (Table  S2 ). We selected the 5 most promising variants, located in the JAK3, OSMR, PRMT2, PIKFYVE, and NLRP14 genes, to undergo validation and replication by Sanger sequencing.
To prioritize the identified variants, we assigned a scale of relevance (WES score) based on the variants annotations (impact, deleteriousness prediction, frequency in the public databases, different distribution between HAEC and HSCR-only) and a scale of biological relevance (biological score) based on the genes' function and involvement in immunity and intestine reported in public databases and PubMed. Variants located in genes, often reported as mutated during exome analysis and thus likely to be false reports, were excluded  [42]. The WES and the biological scores were multiplied to get a final ranking (Supplementary Methods and Table  S1 ). Selected variants validation and follow-up genotyping have been performed by Sanger sequencing. Additional details on sequencing and variants filtering and prioritization are provided in the Supplementary Methods and Tables  S1  and S4.
In Silico Analyses of OSMR Protter (http://wlab. ethz. ch/protter/start/ accessed on 28 January 2020)  [43]  and Swiss Model, Swiss Institute of Bioinformatics, Lausanne, Switzerland (https://swissmodel. expasy. org/ accessed on 14 July 2020)  [20]  software were used to predict the OSMR protein secondary and tertiary structure based on the Protein Data Bank (PBD) 3l5h. 1A model template and to design the Ramachandran plot, which was further investigated by the RAMPAGE software (http://mordred. bioc. cam. ac. uk/~rapper/rampage. php accessed on 14 July 2020).
Immunofluorescence Immunoassays were performed by incubating with mouse anti-MAP kinase activated (Diphosphorylated ERK -1&2, cat. M9692, no. L089M4838V, Sigma-Aldrich), rabbit antivimentin (cat. HPA001762. no. LB114381, Sigma-Aldrich), or rabbit anti RAB11FIP5 primary antibodies (cat. HPA036407, no. LA104076, Sigma-Aldrich). Cells were then washed with PBS and incubated with Goat anti-Mouse IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor -488 (cat. A11001, no. L12599373, Invitrogen), and Goat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 555 (cat. A21429, Invitrogen). Finally, cells were examined with Olympus IX70 microscope by using a 40× oil objective lens; images were acquired by the ColorViewII digital camera and analyzed with the Olympus Soft Imaging Systems GmbH software, Münster, Germany.
Sample Preparation, NanoLC, and Mass Spectrometer Setup MS run and analysis were performed by the Core Facility unit of the Giannina Gaslini Institute on the HAEC GG (LY4579) (var) and the HAEC TT (LY3828) (wt) patients, four replicates for both the "untreated" (wt-and var-) and the "OSM treated" (wt+ and var+) conditions (16 runs in total). Sample preparation  [46] , NanoLC, and MS setup are described in Supplementary Methods. The intensity values were extracted and statistically evaluated using the Prote-inGroup Table and Perseus software, Max Planck Institute of Biochemistry, Martinsried, Germany, version 1. 6. 10. 50  [47] : the flowchart analysis is represented in Figure  S4 , and detailed in Supplementary Methods. Outliers were checked by Principal Component Analysis (PCA) (Figure  S6 ) and excluded by the following analyses.
We compared only proteins detected in at least three replicates in one or more of the four groups: wt-, wt+, var-, and var+. To identify proteins differently expressed after OSM activation, proteins significantly differently expressed in wt+ vs. mut+ (q < 0. 05) were filtered, excluding those already significantly different before treatment with OSM (wt-vs. mut-n. s. ). In an attempt to specifically select those proteins for which OSM activation leads to a loss of effect in the presence of the OSMR SNP variant, we further filtered proteins not significantly different between var-and var+ (Figure  S4 ). Hierarchical cluster analysis was performed to identify clusters of proteins overrepresented in wt+ with respect to var+ and vice versa. We run the hybrid hierarchical k-means clustering algorithm provided by Perseus on the z-scores normalized data applying the software default settings, which are the Euclidean distance method and average linkage agglomeration, pre-processing with k-means, number of clusters: 300; maximal number of iterations: 10.
The clusters were visualized by a color scaling heatmap. Pathway analysis on the distinct clusters have been followed using HumanBase, Flatiron Institute, Simons Foundation, New York, NY, USA (https://hb. flatironinstitute. org/ accessed on 14 July 2020)  [48] , which reports the one-sided Fisher's exact tests with Benjamini-Hochberg corrections for multiple tests' q-value, searching the lymphocyte network. The Venn diagram was drawn by an online software (https://www. meta-chart. com/venn#/display accessed on 3 August 2020). Variants detected by WES were compared between HAEC (cases) and HSCR-only (controls) patients by the Fisher exact test for the allelic association and by the Cochran-Armitage trend test implemented in SnipSift (one tail test, no multiple test correction); the lowest p-value was considered.
Besides binding to the DLE1 to DLE3 sequences, HOXA13 and HOXD13 also bind to various locations within the Nell2/ Dbx2/Ano6 genomic region. Many such sequences are only partially conserved across the eutherian lineage, in contrast with the high conservation of the DLE regions. While the functional significance of this large HOX13 coverage has not been addressed, it is clearly reminiscent F I G U R E 1 0 TAD structure and expression of the chicken Nell2/Dbx2/Ano6 genes. A, Top, Hi-C map of the Dbx2 genomic region in chicken HH20 wing buds and graphs showing the TAD-separation score (bottom) using standard window size parameters (gray lines; average in blue). Protein-coding gene loci are represented by blue (Dbx2) or gray boxes for all other genes. Data from Reference 62. TADs called by hicFindTADs are depicted with black boxes.
| Hi-C/ChIP seq/scRNA-seq data analysis, 4C-seq interaction profiling All scripts used to analyze data and generate figures are available at https://github. com/lldelisle/scriptsForBeccari EtAl2021. The calculations were performed using the facilities of the Scientific IT and Application Support Center of EPFL. For the chicken Hi-C analysis, the raw forelimb and hindlimb data were extracted from GEO (see Table  2 ) and processed independently using HiCUP v0. 8. 0 80 on galGal6. Valid pairs were obtained using a custom python script. Both valid pair files were merged before analysis. Valid pairs from Hi-C carried out on mouse material were downloaded from GEO (see Table  2 ). Valid pairs of each study were loaded in a cool file using cooler version 0. 8. 10 81 using a resolution of 5, 20, or 40 kb.
3] [84]  with-minBoundaryDistance 100 000 and either default parameters for the choice of window size or a fixed window size of 120 kb. Plots were obtained using pyGenomeTracks version 3. 6. 82, 85 hIP-seq paired-end (PE) fastq of HOXA13 and HOXD13 data, as well as single-read (SR) fastq from H3K27me3 and H3K27ac and corresponding inputs were downloaded from GEO (see Table  2 ). Adapter sequences and bad quality bases were removed with Cutadapt 86 version 1. 16 with options -a GATCGGAAGAGCACAC GTCTGAACTCCAGTCAC -A GATCGGAAGAGCGTCG TGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTA-TCATT -q 30 -m 15 (-A being used only in PE data sets). Reads were mapped with bowtie 2. 3. 5  87  with default parameters on mm10. Alignments with a mapping quality below 30 were discarded with samtools view version 1. 9. 88, 89 For HOXA13 and HOXD13, coverage and peak calling were computed by macs2 version 2.
1. 1. 20160309 with options -call-summits -f BAMPE -B. Coverage was then normalized by the number of million fragments used in macs2 coverage. For histone marks, coverage and peak calling were computed by macs2 with options -f BAM-nomodelextsize 200-broad using the BAM of input in -c. The coverage was then normalized by the number of million tags used in macs2 coverage. Plots were obtained using pyGenomeTracks version 3. 6. 82, 85 For DFL_E12_H3K27ac, the two replicates were averaged. For the scRNA-seq, matrices with counts were downloaded from GEO (see Table  2 ). UMAP and expression plots were obtained using Seurat package version 3. 2. 2  90 n each data set individually. We performed our 4C-seq experiments according to. 91 Briefly, 12 pairs of wild-type DFLs or PFLs were dissected, dissociated with collagenase (Sigma Aldrich/ Fluka) and filtered through a 35 μm mesh to isolate single cells.
Illumina RNA Sequencing, Data Assembly, Statistical Analysis We constructed RNA-Seq libraries with the Truseq stranded mRNA sample preparation kit from Illumina and sequenced them in two lanes on an Illumina HiSeq2500 (single-end, 50 bp, six libraries per lane). Image analyses and base calling were performed using the HiSeq Control Software (v2. 2. 70, Illumina, San Diego, CA, USA) and Real-Time Analysis component (v1. 18. 66. 4, Illumina, San Diego, CA, USA). Demultiplexing was performed using Illumina's conversion software (bcl2fastq 2. 20). The quality of the raw data were assessed using FastQC from the Babraham Institute and the Illumina software SAV (Sequencing Analysis Viewer, Illumina, San Diego, CA, USA). A splice junction mapper, TopHat 2. 1. 1  [44]  (using Bowtie 2. 3. 5. 1  [45] , Johns Hopkins University, MD, USA), was used to align the RNA-Seq reads to the Ursus arctos genome (GCA_003584765.
1 ASM358476v1 assembly downloaded from NCBI) with a set of gene model annotations (GCF_003584765. 1_ASM358476v1_genomic. gff downloaded on 17 June 2019, from NCBI). Final read alignments having more than three mismatches were discarded. Samtools (v1. 9) (http://samtools. sourceforge. net) was used to sort the alignment files. Then, the gene quantification was performed with Featurecounts 1. 6. 2 (http://subread. sourceforge. net/) [46]. As the data were from a strand-specific assay, the read had to be mapped to the opposite strand of the gene (-s 2 option). Before statistical analysis, genes with less than 30 reads (cumulating over all the analyzed samples) were filtered out. Differentially expressed genes were identified using the Bioconductor(https://bioconductor. org/)  [47]  package DESeq2 1.
Functional and Pathway Enrichment Analysis Hierarchical clustering of bear transcriptomic data (log-transformed) was performed using Cluster v3. 0 software (University of Tokyo, Tokyo, Japan) from the 13531 reads  [49]. Parameters were set as follows: median centering and normalization of genes for adjusting data and centroid linkage clustering for both genes and arrays. Dendrograms were generated and viewed using the Java Treeview v1. 3. 3 program (Alok Saldanha, Stanford University, Stanford, CA, USA)  [50]. To identify the differentially expressed genes (DEGs), we selected a fold change (FC) Winter/Summer >|1. 3| or <|0. 77| and an adjusted p-value < |0. 01| as cut-off standards, for the up-and down-regulated genes, respectively. Visualization of functional enrichment was performed using Metascape  [51] , a web-based portal for visualizing the inference of enriched biological pathways among the DEGs.
Transcriptomic Data Assembly and Statistical Analysis of Mouse Muscle We used transcriptomic data from an already published study  [42]. Briefly, in this study, C57BL6/J adult male mice were either kept unsuspended (Control, n = 4) or subjected to hindlimb unloading through tail suspension (Unloaded, n = 4) for 10 days. The fastq files of eight soleus muscles were downloaded from GEO (GSE102284). A splice junction mapper, TopHat 2. 1. 1(Johns Hopkins University, MD, USA)  [44] , was used to align the RNA-Seq reads to the mouse genome (UCSC mm10) with a set of gene model annotations (genes. gtf downloaded from UCSC on 29 October 2019; GeneIDs come from the NCBI gene2refseq file). Final read alignments having more than three mismatches were discarded. Samtools (v1. 9, http://www. htslib. org/) was used to sort the alignment files.
Then, the gene quantification was performed with Featurecounts 2. 0. 0 (http://subread. sourceforge. net/) [46]. As the data were from a strand-specific assay, the read had to be mapped to the opposite strand of the gene (-s 2 option). Before statistical analysis, genes with less than 20 reads (cumulating all the analyzed samples) were filtered out. Differentially expressed genes were identified using the Bioconductor  [47]  package DESeq2 1. 26. 0  [48]  as previously described (cf. 2. 2. 2).
Signals were detected after incubation with Luminata Crescendo Western HRP substrate (Millipore, Burlington, MA, USA) and visualized using G: BOX ChemiXT4 (XL1) imaging system (Syngene, Frederick, MD, USA). Signals were then quantified using the GeneTools software (Syngene, Cambridge, UK) and normalized against the total amount of proteins determined by TGX signals to correct for uneven loading. Protein data were presented as individual values. The bilateral ratio paired Student's t-test was used to compare the muscles of bears during summer and winter (S and W, respectively). For muscles of control and unloaded mice (C and U, respectively), statistical significance was determined using the bilateral unpaired Student's t-test. Statistical analysis was performed using Prism 8 (GraphPad Prism 9, San Diego, CA, USA).
Discussion Although basic knowledge regarding the underlying mechanisms of muscle atrophy is continuously growing, essentially from rodent models and clinical studies in humans, there are still no efficient therapeutic strategies for its prevention and treatment. To explore new avenues, we compared a model of muscle atrophy resistance, the hibernating brown bear  [23] , and a mouse model of disuse-induced muscle atrophy  [42]. Therefore, we analyzed the bear muscle transcriptome and identified sweeping changes in gene expression between the summer-active period and the winter-hibernating period, and we compared them with transcriptomic data from muscles of the unloaded mouse. Whereas the loss of muscle mass during inactivity is often associated with a decrease in muscle protein synthesis  [42, [61] [62] [63] , we reported here that genes implicated in protein metabolism were mainly up-regulated in muscles of the hibernating bear.
e. , Duchenne muscular dystrophy or the amyotrophic lateral sclerosis)  [85]. CCN2 is one of the main pro-fibrotic cytokines acting downstream of TGF-β signaling and can amplify its effects through enhancement of TGF-β ligand-receptor binding  [85] [86] [87] [88] [89] [90]. Inhibition of CCN2 gene expression reduced fibrosis and improved muscle and locomotor performance in a rodent model of amyotrophic lateral sclerosis  [86]. During unloading, mRNA and protein levels of CCN2 were unchanged in the muscles of the unloaded mouse but were strongly lower in the muscles of the hibernating brown bear. Taken together, our data strongly suggest that TGF-β signaling is overall inhibited only in muscles resistant to atrophy during bear hibernation. The BMP pathway is a potent inducer of bone and cartilage formation  [91].
Many targeted therapies to counteract muscle atrophy already focus on TGF-β inhibition  [99]. Our data open the way for further studies and clinical trials to test the effects of strategies to switch on (or sustain) the BMP pathway in combination with TGF-β inhibition to prevent disuse-induced muscle atrophy. (GSE144856). Other data that support the findings of this study are available in the supplementary material of this article.
Acknowledgments: The authors thank the field capture team ( D. Ahlqvist ,  A. Friebe ,  H. Nordin ,  H. Blomgren , and  S. Persson ) and the  IEN (INRAE Clermont-Ferrand-Theix, France)  for excellent assistance with mouse care ( A. Cissoire ,  Y. Delorme , and  M. Djelloul-Mazouz ), and  L. Parry  for help during animal slaughtering. The authors are also grateful to  T. Brioche  ( Unité Mixte de Recherche INRAe, Dynamique Musculaire et Métabolisme, Université de Montpellier, France ) for his help in implementing the unloading model in mouse and  F. Rocher  for helpful discussions. This is paper N • 317 of the  Scandinavian Brown Bear Research Project.
Funding: This work was supported by the  Center National d'Etudes Spatiales  (CNES, # 865 , # 974 , # 1006 , # 1905 ), the  iSITE Challenge 3 Mobility program  ( Université Clermont Auvergne ), and the  CNRS and Strasbourg University  ( H2E  project;  MyoBears  project of the  PEPS ExoMod program ). MGX acknowledges financial support from  France Génomique National  infrastructure, funded as part of " Investissement d'Avenir" program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). L. C. (Laura Cussonneau) was supported by a grant from the  Institut National de la Recherche Agronomique et Environnement and Clermont Métropole. C. B. (Christian Boyerand)  and  C. B. (Charlotte Brun ) were supported by grants from the  Ministère Français de l'Enseignement Supérieur, de la Recherche et de l'Innovation.
Data Availability Statement: The analyzed transcriptomic mouse data that support the findings of this study are openly available in the GEO repository database at https://doi. org/10. 1093/gerona/ gly051, accessed on 16 June 2021, reference number (GSE102284). The generated transcriptomic bear data that support the findings of this study are openly available in the GEO repository database at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi, accessed on 16 June 2021, reference number.
3-D Analysis and Electrostatic Potential Surface Generation The 3-D structure of ACE2 was retrieved according to the published data [PDB: 6M1D;  (11) ]. Three amino acid segments  (30-41, 82-93, and 353-358)  from R. sinicus, M. musculus, and X. tropicalis ACE2 proteins were inserted into a human ACE2 backbone sequence to determine whether or not these substitutions may change the 3-D structure of ACE2. Protein modeling for these chimeric sequences was performed using the Phyre2 server  (40). The PyMOL 1. 8. 0 software (https://sourceforge. net/projects/pymol/files/pymol/1. 8/) and the Adaptative Poisson-Boltzmann Solver (APBS) tools plugin (https://pymolwiki. org/index. php/APBS) were used to generate electrostatic potential surfaces of the human ACE2 and its modified chimeric versions. The red color indicates an excess of negative charges near the surface and the blue color arises from a positively charged surface.
ferrumequinum. Unfortunately, the ACE2 sequence of R. affinis hosting the closest relative to SAR-CoV-2, BatCoV-RaTG13, was not available from the database. The ACE2 sequences from other bat species living in various ecosystem and with various geographic distribution, show increased amino acids substitutions at positions considered to be required for viral S1 spike binding (e. g. , D. rotundus with K31N, Y41, N82T, N90D, and K353N). It is worth noting that the three Rousettus and two Pteropus ACE2 proteins analyzed in this study were characterized by K31, Y41, and N82T (Rousettus) or N82A (Pteropus), N90D, and K353. We also found that the three ACE2 proteins from Myotis bats were characterized by K31N, Y41H, N82T, N90, and K353, suggesting that these species are unlikely to replicate SARS-CoV-2-like ancestor-related viruses.
The Central Role Played by ACE2 for Interspecies Virus Spread Unraveling which cellular receptors are used by SARS-CoV-2 for entry should provide insights into viral transmission among species. Before SARS-CoV-2, SARS-like CoV was previously found to circulate in Chinese horseshoe bats and to spread through wild Himalayan palm-civet sold as food in Chinese wildlife markets from Guangdong  (41) (42) (43). SARS-CoV was also identified in weasels and raccoons in Chinese wet markets  (37, 41, 43). Regarding SARS-CoV-2, the question remains how it got to humans. The hypothesis of pangolin (M. javanica) as an intermediate host for SARS-CoV-2 quickly became mainstream ) was used to compare the ACE2 protein sequences of these mammals considered at the origin of human coronaviruses. (A) Phylogenetic tree of bat ACE2 sequences built using the Clustal Omega multiple sequence alignment program and the UPGMA algorithm.
SARS-CoV-2 infection was recently reported in mink farms and there is evidence that employees were infected with SARS-CoV-2 after minks became infected, suggesting that mink farms might become a reservoir for future spillover of SARS-CoV-2 to humans  (73). We have aligned the mink ACE2 partial sequence available from GenBank (GenBank CCP86723. 1) with the human ACE2 and observed that the mink ACE2 carries the K353 amino acid, but it was not possible to compare the other amino acids (K31, Y41, and N90) important for SARS-CoV-2 binding because the N-terminal part (1-318) of the protein is missing (data not shown). Facing the SARS-CoV-2 epidemic in minks, in the last few days, the Danish Government announced the culling of 17 million minks in rearing after researchers in Denmark have identified some 170 mutations (including a Y453F mutation in the viral spike) in samples from 40 mink farms and the report of mink-specific mutations of SARS-CoV-2 found in humans  (89).
ACE2 protein sequences from the NCBI reference sequence database: Rousettus leschenaultii (GenBank: ADJ19219. 1), Rousettus leschenaultii (GenBank: BAF50705. 1), Rousettus aegyptiacus (NCBI Reference Sequence: XP_015974412. 1), Pteropus alecto (NCBI Reference Sequence: XP_006911709. 1), Phyllostomus discolor (NCBI Reference Sequence: XP_028378317. 1), Desmodus rotundus (NCBI Reference Sequence: XP_024425698. 1), Miniopterus natalensis (NCBI Reference Sequence: XP_016058453. 1), Pipistrellus abramus (GenBank: ACT66266. 1), Eptesicus fuscus (NCBI Reference Sequence: XP_008153150. 1), Myotis davidii (NCBI Reference Sequence: XP_015426918. 1), Myotis lucifugus (NCBI Reference Sequence: XP_023609438. 1), Myotis brandtii (NCBI Reference Sequence: XP_014399780. 1), Hipposideros armiger (NCBI Reference Sequence: XP_019522936.
1), Rhinolophus ferrumequinum (GenBank: ADN93470. 1), Rhinolophus pearsonii (GenBank: ABU54053. 1), Rhinolophus sinicus (GenBank: AGZ48803. 1), Rhinolophus pusillus (GenBank: ADN93477. 1), Rhinolophus macrotis (GenBank: ADN93471. 1), Homo sapiens (GenBank: BAB40370. 1), Macaca mulatta (NCBI Reference Sequence: NP_001129168. 1), Paguma larvata (GenBank: AAX63775. 1), Felis catus (GenBank: AAX59005. 1), Mustela putorius furo (NCBI Reference Sequence: NP_001297119. 1), Sus scrofa domestic (GenBank: ASK12083. 1), Sus scrofa (NCBI Reference Sequence: NP_001116542. 1), Rhinolophus sinicus (GenBank: AGZ48803. 1), Manis javanica (NCBI Reference Sequence: XP_017505752. 1), Mus musculus (NCBI Reference Sequence: NP_081562. 2), Rattus rattus (NCBI Reference Sequence: XP_032746145. 1), Gallus gallus (GenBank: QEQ50331.