dataesr/scientific-paragraphs-categorization huggingface dataset:
2E4 or a maximum injection time of 40 ms and activated using HCD fragmentation (34% NCE). The fragments were analyzed in the Ion Trap Analyzer at normal scan rate. MaxQuant search: LC-MS/MS runs of all 24 samples were searched together using MaxQuant 103 (version 1. 6. 11. 0) with mainly default search settings, including a false discovery rate (FDR) set at 1% on peptide-to-spectrum matches, peptide and protein level. Spectra were searched against the human protein sequences in the Swiss-Prot database (database release version of 2019_06), containing 20,960 sequences (www. uniprot. org). The mass tolerance for precursor and fragment ions was set to 4. 5 and 20 ppm, respectively, during the main search. Enzyme specificity was set as C-terminal to arginine and lysine, also allowing cleavage at proline bonds with a maximum of two missed cleavages.
Variable modifications were set to oxidation of methionine residues and acetylation of protein N-termini, while carbamidomethylation of cysteine residues was set as fixed modification. Matching between runs was enabled with a matching time window of 0. 7 min and an alignment time window of 20 min. Only proteins with at least one unique or razor peptide were retained leading to the identification of 3515 proteins. Proteins were quantified by the MaxLFQ algorithm integrated in the MaxQuant software. A minimum ratio count of two unique or razor peptides was required for quantification. Further data analysis of the shotgun results was performed with an in-house R script using the proteinGroups output table from MaxQuant. Reverse database hits were removed, LFQ intensities were log2 transformed, and replicate samples were grouped.
Functional enrichment analysis and network: functional enrichment analysis was performed using DAVID 106 on a list of proteins with significantly upregulated expression under increased stiffness conditions, and a background list of all the proteins quantified in the dataset was used in the analysis. A 0. 1 EASE score cut-off allowed us to identify a group of enriched UP_KEYWORDS and KEGG pathways. The results from non-CNF1 toxin-treated cells (NT) and CNF1-treated cells (CNF1) were analyzed separately. A network of associations between the HACE1 protein interactome (retrieved from the IntAct database  107  and the five sets of proteins belonging to common enriched UP_KEYWORDS in the NT and the CNF1 analysis was created with Cytoscape (version 3. 9. 1)  106.
Cell biology techniques Immunofluorescence was performed on paraformaldehyde-fixed cells permeabilized in 0. 5% Triton X-100. FITC-phalloidin at 1 μg/ml (Sigma, P5282) was used to stain actin, and DAPI (Life Technologies, D1306) was used to stain nuclei. The adhesive structures were stained with antibodies against paxillin (349, BD-Transduction Laboratories, 1/ 200), zyxin (ZZ001, Invitrogen, 1/100), and integrin alpha 5 (AB1928, Chemicon, 1/400). Images were acquired on a Nikon A1R confocal laser microscope using a ×60 oil objective and NIS Elements software (version 4. 50. 00). QMPIA of immunofluorescence images was performed in two sequential rounds of calculations by the automated image analysis software Motion Tracking (version 8. 80. 23 ×64), described previously  108. Briefly, in the first round, nuclei were found by maximum entropy-based local thresholding and cells by a regiongrowing algorithm based on the watershed transform.
1A4 (Santa Cruz, 1/1000), anti-GAPDH clone 0411 (Santa Cruz, 1/5000), anti-p-MLC Y18/S19 (Cell Signaling Technologies, 1/500), Beta1 integrin clone 18/CD29 (BD Transduction Laboratories, 1/ 1000), ICAP-1 (ITGBP1) (Atlas Antibodies, 1/500), Vinculin clone hVIN1 (Sigma, 1/500), Talin clone TA205 (Millipore, 1/500), HACE-1 (Custommade, clone 9D3, 1/500), GFP clones 7. 1 and 13. 1 (Roche, 1/1000). The secondary antibodies used were HRP-conjugated anti-mouse or anti-rabbit (Dako, 1/5000). Signals were imaged using the Fujifilm LAS-3000 system and quantified with ImageJ software (version 2. 3. 1). For immunoaffinity precipitations: EZ view Red Anti-FLAG M2 Affinity Gel (Sigma, F2426) and EZ view Red Anti-c-Myc Affinity Gel (Sigma, E6654). For immunofluorescence: paxillin clone 349 (BD-Transduction Laboratories, 1/200), zyxin clone ZZ001 (lnvitrogen 1/100), and integrin alpha 5 (Millipore, 1/400).
Statistical analysis Statistical tests were performed using GraphPad Software Prism 9. 0. All experiments were repeated to ensure reproducibility. p values were calculated using Wilcoxon rank sum test, or one-way or two-way ANOVA with corrections for multiple comparisons as indicated in the figure legends. Significance levels are indicated as follows: ns, not significant p > 0. 05, *p ≤ 0. 05, **p ≤ 0. 01, ***p ≤ 0. 001, ****p ≤ 0. 0001. Exact p values, when possible, are provided in the figure legends.
Acknowledgements This study was supported by institutional  INSERM ,  CNRS  and  Institut Pasteur fundings, "Investments for the Future" LabEx SIGNALIFE   ANR-11-LABX-0028-01  and grants from the  Ligue Nationale contre le Cancer (LNCC labellisation  and  RS20/75-63 ),  Fondation ARC pour la Recherche contre le Cancer  ( ARC PJA20191209650 ), the  French National Research Agency  ( ANR-21-CE15-0006 ),  EPIC-XS , project number  823839 , funded by the  Horizon 2020 program  of the  European Union  ( PRC-5074 ) and FRM-Piraud prize to E. L. ,  Bio-SPC "Contrat Doctoral " PhD fellowship to S. P. , LNCC PhD fellowship to  D. H. Fundings  from the  Vancouver Prostate Centre  to M. D. , and  Team Finn  and  The Ride  to  Concur Cancer  to P. H. S. , fundings from  ANR  to  A. A. ("ImmunoMeca "  ANR-12-BSV5-0007-01 , " Initiatives d'excellence" Idex   ANR-11-IDEX-0005-02 , and " Labex Who Am I? "  ANR-11-LABX-0071 ) and to F.
L. ( ANR-10-EQPX-04-01  and  FEDER 12001407 ). B. L. acknowledges financial supports from the  Mechanobiology Institute , the  European Research Council  under the European Union's  7th Framework Program  ( FP7/2007-2013)/ERC no. 617233  and  NUS-USPC collaborative program. The authors would like to thank  Yannis Kalaidzidis  from the laboratory of  Marino Zerial  ( MPI-CBG, Dresden, Germany ) for the free access to Motion tracking software;  P. Tafelmeyer  ( Hybrigenics, France ),  O. Visvikis ,  P. Munro  ( INSERM U1065 ) and  A. Marabelle  ( INSERM U1015 ) for helpful advices;  P. A. Roldan-Quiros  ( ITCR, Costa Rica ),  A. Loubat  at the iBV-Cell sorting Facility and  D. Van Haver  from the  VIBS Proteomics Core (UGent Dept of Biomolecular Medicine, Gent, Belgium)  for their technical support.
Data availability All data generated or analyzed during this study are included in this published article and its Supplementary Information files. The mass spectrometry proteomics data have been deposited to the Proteo-meXchange Consortium via the PRIDE partner repository with the dataset identifier PXD035396. Source data used to generate the graphs are provided in the Supplementary Information/Source Data file. GMO can be obtained from the corresponding authors. Source data are provided with this paper.
Background: The COVID-19 pandemic inevitably had consequences on routine surgical procedures. The objective was to quantify changes to five surgical procedures during the COVID-19 pandemic namely cataract surgery, hip and knee arthoplasties, coronary revascularization by angioplasty and definitive cardiac stimulation. Materials and method: All hospitalizations with at least one act of each surgery between January 1, 2019, and June 30, 2021, were included from the database of all French residents' health-related expenses. Percentage changes between observed and expected numbers of hospital stays were calculated for each surgery in 2020 and the first half of 2021 with 95% Confidence Intervals. Expected numbers were calculated from the number in 2019 by applying an average annual change between 2015 and 2019.
Data sources We used data from the French National Health Data System (SNDS). This database covers the entire French population (67 million inhabitants) and includes all their health-related expenses. In this database, an anonymous unique personal identifier links information from the national hospital discharge database (PMSI) and the national health insurance claims database (DCIR): -The PMSI database contains details of all private and public hospi- The SNDS has already been used to conduct large-scale pharmacoepidemiologic studies that have been published in international scientific journals, including studies on the COVID-19 pandemic  [1, 2, [12] [13] [14] [15] [16] [17]. The research group has permanent regulatory access to the data from the French National Health Data System (French decree No.
Study population All hospitalizations for at least one of cataract surgery, hip arthroplasty, knee arthroplasty, coronary angioplasty revascularization and definitive cardiac stimulation between January 1, 2019, and June 30, 2021 were included in separate cohorts. Each surgery was defined by specific procedures codes. The details of all procedure codes used can be found in the Supplementary Material. We excluded certain hospitalizations due to personal identifier problems in the SNDS data, such as some foreign residents, twins and people with data missing for age and sex; they represent less than 1% of the initial cohorts.
Detailed average annual changes are presented in the Supplementary Material. Analyses were performed using SAS, version 9. 4 (SAS Institute Inc). The work has been reported in line with the STROCSS criteria  [18]. The study is registered with an unique identifying number "resear-chregistry7541" on the website Research Registry under the link htt ps://www. researchregistry. com/browse-the-registry#home/registrat iondetails/61e04e165acf3a001f9d7c2d/.
Discussion In France, five routine surgical procedures were strongly impacted by the COVID-19 epidemic compared to expected levels: from -11% for coronary angioplasty to -25% for knee arthroplasty in 2020, and from -5% for cataract surgery to -22% for knee arthroplasty in the first half of 2021. This decrease reflects the potential cancellation or postponement of more than 350,000 operations. Despite the gradual national rollout of the vaccine, activity did not return to normal in the first half of 2021. The observed decrease was greater during the first spring lockdown in 2020 than during the other lockdowns, probably because the restrictions were tighter and because no catch-up phenomenon was observed after this lockdown period. Three studies (one from US health insurance representatives and the others from UK national health databases) show a similar decrease during the first lockdown period: up to -43% for coronary angioplasty, -91% for cataract surgery and -44% for cardiac stimulation compared to the same period in previous years  [19] [20] [21].
Some studies provide evidence of a reduction in emergency hospital admissions for injuries/falls or cardiac accidents during the first pandemic wave (between -20% and -44% compared to the same period in 2019)  [26] [27] [28] [29] [30]. Unfortunately, underdiagnosis and late or missed treatments could be deleterious to ACS patients or those with femoral neck fractures, potentially life-threatening conditions whose outcome is closely linked to prompt recognition and treatment. Mesnier et al. , using the Fire Department database, revealed that the incidence of  ACS: Acute Coronary Syndrome. a The expected number in the first half of 2021 was calculated from the number in the first half of 2019 by applying twice the annual average plus the squared average. Detailed average annual changes are presented in the Supplemental Material.
Strengths and limitations The key strength of our study is its use of the PMSI database, which is mandatory for all hospitals and clinics; its information on hospital stays is accurate and precise because such data are used to allocate budgets to the hospital sector. In addition, for each patient identified by a personal card, a specific code for each reimbursed heathcare act is entered, thus avoiding recall or recording bias. Furthermore, this is the first study to cover a recent period for an entire population: it provides a pragmatic and comprehensive picture of surgical activity during the COVID-19 pandemic. We were able to quantify whether there has been a change for each surgery over an 18month period covering three epidemic waves including the start of the national COVID-19 vaccination campaign until the first half of 2021.
Ethical approval Not applicable. The National Health Data System (SNDS) is a set of strictly anonymous databases, comprising all mandatory national health insurance reimbursement data, particularly data derived from processing of health care claims (electronic or paper claims) and data from health care facilities (PMSI). No informed consent was required because data are anonymized. EPI-PHARE has a regulatory permanent access to the data from the SNDS. This permanent access is given according the French Decree No. 2016-1871 of December 26, 2016 relating to the processing of personal data called "National Health Data System"  [1]  and French law articles Art. R. 1461-13  [2]  and 14  [3]. This study was declared prior to initiation on the EPI-PHARE registry of studies requiring the use of the SNDS.
These results are compared with the temperatures acquired by the Mars Environmental Dynamics Analyzer (MEDA) at a frequency of up to 2 Hz but with a longer response time  (Rodríguez-Manfredi et al. , 2021) , with the large-scale atmospheric predictions from the Mars Climate Database (MCD) and with the predicted small-scale rapidly varying PBL turbulence from Large Eddy Simulations (LES) (see Texts S1-S3 in Supporting Information S1 for data sets and models). Our analyses cover the first half of Martian Year 36 (Northern spring and summer), from L s = 5. 1°, up to L s = 190°, where L s is the areocentric solar longitude. This corresponds to the first 378 Sols of the mission (a Sol is a Martian solar day), during the exploration of the crater floor unit of Jezero  (Sun et al. , 2022). Munguira et al.
Across all observations, the measured sound speed ranges from 244 m/s (Sol 365 at 08:49 Local True Solar Time [LTST]) to 270 m/s (Sol 357 at 12:47 LTST). Each sound speed measurement is then converted into air temperature, hereafter called "sonic temperature," considering the ideal gas law, correcting the specific heat at constant volume for the unique sound speed dispersion observed on Mars  (Maurice et al. , 2022)  due to CO 2 at low pressure (see Text S5 in Supporting Information S1). Furthermore, as the acoustic wave propagates in a moving medium, the measured sound speed is influenced by the background wind velocity (e. g. , rapid gusts and vertical drafts), projected along the propagation path. MEDA wind data (velocity and direction), when collected simultaneously, are used to correct the measured sound speed.
supporting the mission that has enabled the scientific research presented in this manuscript. This work was funded in the US by the NASA Mars Exploration Program support to the Mars 2020 mission, and in France was conducted under the authority of CNES and CNRS. B. C. is supported by the Director's Postdoctoral Fellowship from the Los Alamos National Laboratory, grant 20210960PRD3. A. M. , R. H. and A. S. L. are supported by the Spanish Ministry of Science and Innovation project PID2019-109467GB-100 funded by MCIN/AEI/10. 13039/501100011033/ and Grupos Gobierno Vasco IT1366 19. G. M. wants to acknowledge the Jet Propulsion Laboratory funding from USRA Contract number 163878. C. L. and N. L. are funded by LANL LDRD grant 20210424ER. R. C. W. is funded by the Jet Propulsion Laboratory contract 46010000 to Purdue University.
Data Availability Statement All Perseverance data used in this study are publicly available via the Planetary Data System: • SuperCam: Wiens and Maurice (2021), https://doi. org/10. 17189/1522646. • MEDA: Rodriguez-Manfredi and de la Torre Juarez (2021), https://doi. org/10. 17189/1522849. • Climate predictions using the Mars Climate Database v5. 3 (MCD, e. g. ,  Forget et al. , 1999; Millour et al. , 2018)  are accessible at http://www-mars. lmd. jussieu. fr/mcd_python/.
IMGT ® , the international ImMunoGeneTics information system ® , http://www. imgt. o rg, (accessed on 11 November 2021)  [23] , is the global reference in immunogenetics and immunoinformatics  [1]. IMGT ® is a high-quality integrated knowledge resource specialized in IG, TR, major histocompatibility (MH) of human and other vertebrate species, and in the immunoglobulin superfamily (IgSF), MH superfamily (MhSF), and related proteins of the immune system (RPI) of vertebrates and invertebrates. Thanks to the currently "representative genome" of the rhesus monkey published on NCBI database: Mmul_10  [24] , IMGT ® performed the complete annotation of the three IGH, IGK, and IGL loci of this genome, and the corresponding IG germline data were integrated within IMGT ® databases, tools, and web resources, improving the amount of rhesus monkey IG germline data which until then were based on Mmul_051212  [25]  and individual gene sequences.
Materials and Methods The biocuration was performed manually assisted by internally developed tools IMGT/LIGMotif  [32] , NtiToVald  [33]  and IMGT/Automat  [33]  based on the IMGT-ONTOL-OGY axioms and concepts: "IDENTIFICATION", "DESCRIPTION", "CLASSIFICATION", "NUMEROTATION", "LOCALIZATION", "ORIENTATION", and "OBTENTION"  [34]. IMGT-ONTOLOGY includes the controlled vocabulary and annotation rules which are indispensable to ensure accuracy and consistency. The IMGT ® biocuration pipeline for locus annotation has been described previously  [35]. Each locus sequence was localized on the corresponding chromosome and subse-quently extracted from NCBI assembly  [36]  Mmul_10 in GenBank format. The delimitation of the locus was performed through research of the "IMGT bornes", which are coding genes (other than IG or TR) conserved among species, located upstream of the first or downstream of the last gene of an IG or TR locus (http://www.
Therefore, the REV locus sequences were placed in the 5 to 3 locus orientation. Each locus sequence thus obtained was assigned an IMGT ® accession number (IGL: IMGT000062, IGK: IMGT000063, IGH: IMGT000064). According to the "CLASSIFICATION" axiom of IMGT-ONTOLOGY, the nomenclature of all V genes of each IG locus, was characterized based on the human V genes by using IMGT/V-QUEST  [37]  and NGPhylogeny. fr [38]  to define the subgroups. All the V genes are designated by a number for the subgroup, followed by a hyphen and a number for their localization from 3 to 5 in the locus  [2]. Two genes are assigned to the same subgroup if their V-REGION show a percentage of identity greater than 75% at the nucleotide level. The V genes which were pseudogenes and did not match in any subgroup were assigned to a clan also characterized according to the human clans (http://www.
imgt. org/IMGTScientificChart/SequenceDescription/IMGTfu nctionality. html (accessed on 11 November 2021). An allele is considered as functional (F) if its coding region has an open reading frame without stop codons, no defect in the splicing sites, recombination signals and/or regulatory elements. An allele is considered as open reading frame (ORF) if its coding region has an open reading frame without stop codons but shows alterations in the splicing sites, recombination signals, regulatory elements, and/or changes of conserved amino acids. A gene allele is considered as pseudogene (P) if the coding region has stop codon(s) and/or frameshift mutation(s). The main concepts of the 'DESCRIPTION' axiom of IMGT-ONTOLOGY correspond to IMGT ® standardized labels in the tools and databases used to describe the organization of the IG genes in the IGH, IGL, and IGK loci  [34].
Then, a progressive multiple sequence alignment (MSA) was performed for the V genes 5 UTR of each locus and each subgroup separately. The MSA analysis was performed using MATLAB bioinformatics toolbox  [54] , as previously described in several studies  [55, 56] , along with Clustal Omega tool/EMBL-EBI  [57] , MAFFT version 7  [58]  and NGPhylogeny. fr [59]. The results of the MSA analysis were visualized through the Jalview platform  [60]. Thus, guided by the IMGT ® reference of the Homo sapiens promoter sequences and distances between elements, the motif identification and extraction, along with the calculation of the distance between elements were carried out in Macaca mulatta loci. For further investigation and element validation, the study was assisted by bioinformatics tools and biological databases concerning eukaryotic promoter elements, including PROMO  [61] , gene-regulation  [62] , TRANSFAC database  [63] , Sequence Manipulation Suite  [64] , Transcription factor Affinity Prediction (TRAP) Web Tools  [65] , GPMiner  [66] , and SoftBerry/Nsite  [67].
Characterization of the IGH Genes Briefly, 288 genes and 447 alleles of the IGH locus have been annotated and integrated in the IMGT ® databases. Among the 288 genes, we have identified for the IGHV: 79 F, two ORF, 140 P, and seven genes that have alleles with different functionalities (F or ORF 'FO': IGHV3-186, IGHV3-103; F or P 'FP': IGHV3-46, IGHV3-119, IGHV4-57, IGHV4-92 and IGHV4-106) (Table  2 ); for the IGHD: 44 F and one ORF; for the IGHJ: seven F, and for the IGHC eight F. Six duplicated V genes were found within the IGH locus: IGHV(II)-33D (P), IGHV3-153D (F), IGHV(II)-154D (P), IGHV(III)-155D (P), IGHV1-156D (F), and IGHV3-157D (P). IGHV3 is the most represented subgroup  [68]  with 78 genes and 124 alleles. It also presents the greatest number of F with 39 genes and 62 alleles, followed by the IGHV1 and IGHV4 subgroups with 11 F and 19 F genes respectively.
For the IGHC genes, four genes belong to the IGHG (IGHG1, IGHG2, IGHG3 and IGHG4) while the other four genes encode the isotypes IGHA, IGHD, IGHE and IGHM, respectively. The IGHC genes are F except for the IGHM and IGHG3 gene which have alleles with two different functionalities (F or P). In fact, both have one allele P, while the other two IGHM and six IGHG3 alleles are F (Table  6 ). Finally, the IMGT ® databases count, for the IGHV, 228 genes and 336 alleles; for the IGHD, 45 genes and 49 alleles; for the IGHJ, seven genes and 10 alleles and for the IGHC eight genes and 52 alleles. Further, 95 additional (26 F, 69 P) IGHV genes have been annotated from the IMGT ® reference sequence IMGT000064 (Mmul_10) compared to the IGH locus sequences in assembly Mmul_051212. However, two IGHD genes (IGHD1-1-1 and IGHD4-41) were only found in the scaffolds of the assembly Mmul_051212 with accession numbers NW_001121239 and NW_001121238 respectively.
Characterization of the IGL Genes Briefly, 165 genes and 247 alleles have been annotated and integrated on the IMGT ® databases. Among the 165 genes, we have identified: for the IGLV, 72 F, two ORF, 74 P, and one gene (IGLV3-16), which has two alleles with different functionalities (F or P) (Table  2 ); for the IGLJ: nine F, one ORF and one gene (IGLJ7) which also has two alleles with different functionalities (F or ORF); and for the IGLC: six F and two P. The most represented subgroup in the IGL locus is IGLV3 with 25 F genes and 35 F alleles, one gene and three alleles have been identified as ORF, 16 genes and 23 alleles have been identified as P, and one gene has a double functionality (FP) (Table  8 ). The IGLV1, IGLV2, and IGLV5 are also well represented with 14 F genes, 11 F genes, and eight F genes, respectively.
Finally, the IMGT ® databases count 149 IGLV genes and 225 alleles among which 72 genes and 108 alleles are F, two genes and five alleles are ORF, 74 genes and 110 alleles are P and one gene and two alleles have two functionalities (FP); eight genes and nine alleles for the IGLJ and eight genes and 13 alleles for the IGLC. Further, 27 additional genes were annotated from the IMGT reference sequence IMGT000062 (Mmul_10) compared to the IGL locus sequence NW_001095158 (Mmul_051212 assembly) which has 245 gaps. Among the 27 genes, 17 IGLV genes were P and eight IGLV, one IGLJ, and one IGLC were F.