dataesr/scientific-paragraphs-categorization huggingface dataset:
We used data from the Isere register which is not necessarily representative of other French departments. In addition, even if this work allows several hypotheses, it is a descriptive epidemiological study, which makes it difficult to assess the evolution of risk factors and their consequences on our survival and incidence data. We only had tumor subtypes from 2011, which limited the number of cases for a more powerful analysis. Moreover, we did not have tumor grade and Ki67. Thus, we could not divide the luminal category into groups A or B for more accuracy. The strengths of our work are multiple: The registry information system, based on general population data, allows for the completeness of invasive BC diagnoses. Moreover, the incidence data were calculated over the last three decades, which allows us to have reliable hindsight in our analysis, and a large number of patients included, allowing for significant power.
The HC group was made up of two males and two females, between 5 and 11 years of age, with no clinical history of gastrointestinal disease or any other pathology, and they tested negative for Helicobacter spp. in fecal samples by rt-PCR. The DC group was made up of four males and three females, between 7 and 11 years of age, with a clinical history of GI disorders characterized by recurrent episodes of diarrhea, rare episodes of vomiting and weight loss. In the DC group, all cheetahs tested positive for Helicobacter spp. in fecal samples by rt-PCR analysis. All of the animals included in the study did not undergo dietary changes or medical treatments (in the two months prior to the study); parasitic diseases were also excluded by the fecal examination performed in the zoos of origin. The cheetahs' blood sampling related to the data reported herein was performed for routine programmed screening/monitoring evaluations, which was in the interest of the patient.
1% Coomassie Brilliant Blue R250 and then de-stained until the protein spots became evident and the gel background became transparent. The PDquest software (Version 7. 1. 1; Bio-Rad Laboratories) was used to analyze the gel images (scanned at 600 dpi resolution) and the isoelectric point (pI); the molecular mass and the normalized quantity of each protein spot were calculated according to the protocol provided by the software manufacturer. In particular, the pIs of the single spots were determined using a linear 3-10 distribution, and molecular mass determinations were based on the markers (Prestained Protein Sharpmass VII; range 6. 5-270 kDa; Euroclone, Pero, MI, Italian). For each group (HC and DC), 2DE analyses were performed in triplicate. The quantitative data of the protein level in both the HC and DC groups are presented as means ± SE.
Discussion The present study represents the first fecal proteome investigation in cheetahs with GI disorders compared with healthy cheetahs. In the diseased cheetahs, the spots DC17 and DC20 were identified as albumin isoforms, the spots DC18 and DC19 were not identified by mass spectrometry but could likely also correspond to albumin isoforms since they showed, after the PDQuest software analysis, a molecular weight and a pI corresponding to albumin and very close to the values found for the spots DC17 and DC20. Albumin (and relative isoforms) was previously found in both healthy dogs and cats  [7]  and was interpreted as the result of the physiological mucosal cellular turnover. In the case of DC, considering that albumin was found only in this group and not in the healthy one, it is possible to hypothesize that its presence may be due to the damage associated with GI disturbances, as is also assumed for the dogs suffering from intestinal lymphangiectasia  [9].
12, 2392 4 of 9 300 • C. The obtained MS spectra were extracted and analyzed by the MASCOT software (http://www. matrixscience. com/, accessed on 10 March 2022) with the following search parameters: database, SwissProt; Taxonomy, Mammalia; Enzyme, trypsin; peptide tolerance, 1. 2 Da; MS/MS tolerance, 0. 6 Da; and the allowance of one missed cleavage.
Acknowledgments: The data reported herein are part of the PhD Thesis of one of the authors (S. M. ). The authors want to thank the keepers of the  Parco Zoo Falconara (Italy) ,  Parco Faunistico Le Cornelle (Italy)  and  Zoo La Palmyre (France). The authors also want to express their appreciation for the  European Association of Zoos and Aquaria (EAZA)  and the  EAZA Biobank  for their activities and their support for research projects on endangered species.
Animals 2022 , 2022 12, 2392 4 of 9 300 • C. The obtained MS spectra were extracted and analyzed by the MASCOT software (http://www. matrixscience. com/, accessed on 10 March 2022) with the following search parameters: database, SwissProt; Taxonomy, Mammalia; Enzyme, trypsin; peptide tolerance, 1. 2 Da; MS/MS tolerance, 0. 6 Da; and the allowance of one missed cleavage.
The second phase involves surveying farmers to examine the factors behind their intended willingness to participate or support conservation. The data were analyzed using ENVI 5. 3, GIS 10. 3, SPSS 20, and AMOS 20. The results of phase one reveal a signi cant increase of bare lands around the wetlands. The survey shows that attitude and subjective norms had positive in uences on farmers' behavioral intentions toward wetland conservation to a statistically signi cant extent.
Materials/Procedures This quantitative study uses a non-experimental research design. It involved a cross-sectional survey of farmers in 2020. Data were collected using a researcher-designed questionnaire. The validity and reliability of the survey instrument were tested and con rmed by a panel of experts (either faculty members or eld practitioners having extensive experience in socio-ecological interventions for sustainable wetlands management) using Cronbach's alpha coe cients which exceeded the acceptable rates for all components of the questionnaire (Table  3 ). Three other indicators (composite reliability, convergent validity (CV) (or average variance extracted (AVE)), and divergent validity) were also used to con rm the validity of the indices. The divergent validity of the questionnaire was evaluated by the average shared squared variance (ASV) and the maximum shared squared variance (MSV).
Following the Theory of Planned Behavior  (Ajzen (2002) , four perceptual variables were created: attitudes, perceived behavior control, subjective norms, and behavioral intention. The data were analyzed using SPSS (version 20) and AMOS (Analysis of Moment Structures, version 20). A Likert seven-point scale (from fully disagree (1) to fully agree (  7 )) was used for all questions. The summary of the answers was computed as a total score (alpha coe cient) for each variable (Table  3 ). The values for skewness and kurtosis did not identify any serious violations of normality as all the coe cients were below ± 2. The study area images were classi ed into six classes (Fig. 2 ). Visual analysis shows that there were signi cant changes to the proportions of the study area covered by each category.
Non-canonical ITGB2 signaling activates EGFR in SCLC. The previously reported enrichment of ITGA2, ITGB2 and ITGB6 in the membrane of lung progenitor cells  35  suggests a potential interaction between these integrin receptor subunits. To test this hypothesis, we performed in silico analyses of proteomic data repositories derived from protein-protein interaction databases (Figure  S1A ). Using the STRING database, we identified 50 interaction partners of human ITGA2 with high confidence (combined score ≥ 0. 9; 2 nodes, Table  S1 ), including ITGB2 (combined score=0. 96) and ITGB6 (combined score=0. 97). To confirm the interaction of ITGA2 with ITGB2 and ITGB6, we performed coimmunoprecipitation (Co-IP) assays using total protein extracts from mouse lung epithelial cells (MLE-12) transiently transfected with HIS-ITGA2 and YFP-ITGB2 or GFP-ITGB6 (Figure  1A ).
ITGB2 induces a novel SCLC gene expression signature. We retrieved and analyzed RNA-seq data of a previously published cohort of SCLC patients from the European Genome-Phenome Archive (Figure  S7 )  39. Correlation analysis of these RNA-seq based transcriptomes allowed us to group the SCLC patients into two clusters (C1 and C2; Figure  2A ). Over-representation analysis (ORA) based on the Reactome database  40  for genes with increased expression in C2 (Figure  2B ; 5,149 transcripts with fold change (FC) ≥ 3; Source Data S1) revealed a significant enrichment of genes related to the term "integrin cell surface interactions" (P=9. 1E-3) as one of the top items of the ranked list. In addition, gene set enrichment analysis (GSEA) of the up-regulated transcripts in C2 (Figure  2C ) showed a high enrichment score (ES) of 0.
SCLC proteins. ORA of the SCLC-ITGB2-sig based on the Reactome database revealed significant enrichment of genes related to glycosphingolipid metabolism (Figure  5A ). In addition, we found elevated expression of the proto-oncogenes MYCN and MYCL in SCLC cell lines (Figure  5B ). Furthermore, GO enrichment analysis based on Biological Processes of transcripts with high levels in SCLC cell lines revealed significant enrichment of genes related to the GO term "Vesicle transport" (Figure  S9A-B ). Since all these events are related to the secretion of EVs  45  , we isolated EVs from the cell culture medium, in which A549, NCI-H82 and NCI-H196 cells were grown, and characterized them by various downstream analyses (Figure  5C  and S9C). EVs produced by these three cell lines showed similar size with a radius of approximately 45 nm (Figure  S9C ).
Cell culture, transfection, treatment and siRNA-mediated knockdown Mouse lung epithelial cells MLE-12 (ATCC CRL-2110) were cultured in complete DMEM/F12 (5% FCS, 1% Penn-strep) at 37 °C in 5% CO2. Human SCLC cells NCI-H82 (ATCC HTB-175) and NCI-H196 (ATCC CRL-5823), and NSCLC cells A549 (ATCC CCL-185) were cultured in complete RPMI (10% FCS, 1% Penn-strep) at 37 0 C in 5% CO2. During subculturing, cells were 1x PBS washed, trypsinized with 0. 25% (w/v) Trypsin and subcultivated at the ratio of 1:5 to 1:10. The cell lines used in this paper were mycoplasma free. They were regularly tested for mycoplasma contamination. In addition, they are not listed in the database of commonly misidentified cell lines maintained by ICLAC. Cells were transfected with plasmid DNA or siRNA using Lipofectamine 2000 (Invitrogen) following the manufacturer's instructions, and harvested 48 h later for further analysis.
Protein-interaction prediction Prediction of protein-protein interaction was observed using the STRING online database  69  (Retrieval of Interacting Genes-Proteins-http://string. embl. de/) with a cut-off criterion of a combined score 0. 9 (highest confidence) and including a maximum of 50 interactors on the 1 st shell and 20 interactors on the 2 nd shell. Network nodes represent proteins, while edges are the protein-protein associations. Small nodes represent protein of unknown 3D structure and large nodes proteins with some known or predicted 3D structures. Colored nodes represent query proteins and first shell of interactors and white nodes second shell of interactors. Interactions are depicted by color as follows: known interactions were obtained from curated databases (turquoise), or experimentally determined (purple); predicted interactions were defined by neighborhood (green), gene fusions (red) and gene co-occurrence (blue), textmining (light green), co-expression (black) and protein homology (violet).
RNA sequencing and data analysis RNA sequencing generated for this paper was sequenced as previously described  67, 70. Briefly, total RNA from A549 (Ctrl, ITGB2 or mutITGB2) and NCI-H196 cells was isolated using the Trizol method. RNA was treated with DNase (DNase-Free DNase Set, Qiagen) and repurified using the RNeasy micro plus Kit (Qiagen). Total RNA and library integrity were verified on LabChip Gx Touch 24 (Perkin Elmer). Sequencing was performed on the NextSeq500 instrument (Illumina) using v2 chemistry with 1x75bp single end setup. Raw reads were visualized by FastQC to determine the quality of the sequencing. Trimming was performed using trimmomatic with the following parameters LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 HEADCROP:4, MINLEN:4. High quality reads were mapped using with STAR with reads corresponding to the transcript with default parameters.
RNA-seq reads were mapped to human genome hg19. After mapping, Tag libraries were obtained with MakeTaglibrary from HOMER (default setting). Samples were quantified by using analyzeRepeats. pl with the parameters (hg19 -count genes -rpkm; reads per kilobase per millions mapped). Gene expression was quantified in reads per kilo base million (RPKM). Expression values of zero were set to the overall minimum value and all data were log2 transformed. Genes expressed (log2 transformed expression >0. 2) were included in the analysis. The correlations of genes were measured using Pearson's correlation. Overlapping genes in the cell lines RNAseq dataset were processed using the functional enrichment analyses KEGG, Gene Ontology and Reactome.
RNA sequencing meta-analysis RNAseq data of human lung adenocarcinoma (LUAD) (n=16) were downloaded from the TCGA data portal (https://tcgadata. nci. nih. gov/docs/publications/luad_2017/tcga. luad. rnaseq. 20121025. csv. zip). The RNAseq data of SCLC and NSCLC cell lines was obtained from GEO under the accession number GSE30611  41. 34 out of 675 samples were selected as NSCLC and 17 as SCLC for analysis based on the following sample annotations: "Organism part" is lung and "Diseases" is lung carcinoma, lung adenocarcinoma, lung anaplastic carcinoma, non-small cell lung carcinoma, squamous cell lung carcinoma, large cell lung carcinoma, lung mucoepidermoid carcinoma, lung papillary adenocarcinoma, lung adenosquamous carcinoma, bronchioloalveolar adenocarcinoma, or squamous cell carcinoma.
For details on the original processing of the data, refer to the original paper  37. The transcriptome profile in NCI-H82 and NCI-196 was measured by the mean normalized expression of the genes in the A549 cell line. For analysis of RNA-seq data retrieved from the European Genome Archive  71  , RNAseq was performed on cDNA libraries prepared from PolyA+ RNA extracted from SCLC tissues and cells. A library with an insert size of 250bp allowed to sequence 95bp paired-end reads without overlap. Raw reads were visualized by FastQC to determine the quality of the sequencing. Trimming was performed using trimmomatic with the following parameters LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 HEADCROP:4, MINLEN:4. High quality reads were mapped using with STAR with reads corresponding to the transcript with default parameters.
Mass spectrometry: sample preparation, methods and data analysis Extracellular vesicle samples were subjected to in gel digest as described  72. The resulting peptides were analyzed by liquid chromatography/tandem mass spectrometry (LC-MS2) utilizing in-house packed reverse phase column emitters (70 μm ID, 15 cm; ReproSil-Pur 120 C18-AQ, 1. 9 μm, Dr. Maisch GmbH) and a buffer system comprising solvent A (5% acetonitrile, 0. 1% formic acid) and solvent B (80% acetonitrile, 0. 1% formic acid). 4] [75]  was used for peptide/spectrum matching, protein group assembly as well as label free quantitation in the context of human Uniprot database (canonical and isoforms; downloaded on 2020/02/05; 210,349 entries). Relevant instrumentation parameters were extracted using MARMoSET  76  and are included in the supplementary material together with MaxQuant parametrization.
Statistical Analysis The source data for all the plots presented in the article, including the values for statistical samples were analyzed at least in triplicates and experiments were performed three times. Statistical analysis was performed using Excel Solver and Prism9. Data in bar plots are represented as mean ± standard error (mean ± s. e. m. ). Two-tailed t-tests were used to determine the levels of difference between the groups and P-values for significance. P-values after twotailed t-test, *P ≤ 0. 05; **P < 0. 01, and ***P < 0. 001. Omnibus  78  and is accessible through SRA Sequence Read Archives NCBI with accession number PRJNA835424. The mass spectrometry-based interactome data have been deposited into the PRIDE archive and assigned to the project accession px-submission #576520.
EGAS00001000610 Total RNA-seq in SCLC patients and cell lines: European Genome Archive: EGAS00001002115, EGAS00001000299 The source data are provided with this paper. P ≤ 0. 05 after two-tailed t-test) highlights a group of 93 transcripts that are common in all four groups, the SCLC-ITGB2 gene expression signature (SCLC-ITGB2-sig). See also Table  S4. Figure Legends (F) External validation of the SCLC-ITGB2-sig. GSEA using independent RNA-seq data from SCLC cell lines  41             The images represent the change of morphology of cell aggregates after aggregate formation for 72h and 120h with initial 1000 or 5000 cells. KRAS Ctrl ITGB6 Ctrl H196 Input IgG ITGB2 -.
Acknowledgments We thank  Roswitha Bender  for technical support;  Julio Cordero ,  Stephanie Dobersch ,  Indrabahadur Singh ,  Ylia Salazar  and  Yanhan Xia  for helpful discussions;  Ernesto Guzmán-Díaz  for FFPE lung tissue;  Pourya Sarvari  for human PCLS;  Kerstin Richter  and  Alessandro Ianni  for antibodies;  Olga N Ilinskaya ,  Vera Ulyanova  and  Airat Kayumov  for binase;  Sylvie Fournel-Gigleux ,  Mohamed Ouzzine ,  Jean-Baptiste Vincourt ,  Sandrine Gulberti ,  Catherine Bui ,  Lydia Barré ,  Nick Ramalanjaona ,  Werner Seeger ,  Till Acker ,  Rajkumar Savai  and  Dulce Papy-Garcia  for comments.
Funding:  Guillermo Barreto  was funded by the  "Centre National de la Recherche Scientifique" (CNRS, France) , " Délégation Centre-Est " ( CNRS-DR6 ) and the  "Lorraine Université" (LU, France) through the initiative "Lorraine Université d'Excellence" (LUE)  and the dispositive " Future Leader ", the  Max-Planck-Society  ( MPG, Munich, Germany ) and the  "Deutsche Forschungsgemeinschaft" (DFG, Bonn, Germany)  ( BA 4036/4-1 ). Karla Rubio  was funded by the  "Consejo de Ciencia y Tecnología del Estado de Puebla" (CONCYTEP, Puebla, Mexico)  through the  initiative International Laboratory EPIGEN. Malgorzata Wygrecka  acknowledges fundings from the  DFG  ( WY119/1-3 ) and the  German Center for Lung Research. Work in the lab of Thomas Braun  is supported by the  Deutsche Forschungsgemeinschaft, Excellence Cluster Cardio-Pulmonary Institute (CPI) ,  Transregional Collaborative Research Center TRR81 ,  TP A02 ,  SFB1213 TP B02 ,  TRR 267 TP A05  and the  German Center for Cardiovascular Research.
Results and discussion Quantification of EBV-specific, MHC-I-restricted TCRβ sequences in HLA-A*02-positive MS patients and healthy controls In light of the finding that EBV infection precedes the development of MS and that some MS patients showed cross-reactive antibody binding to EBV, as well as CNS structures, we evaluated the hypothesis, if the TCR repertoire against EBV might also be altered in MS patients. For this, we collected multimerconfirmed TCRβ sequences from public, curated databases of peer-reviewed studies, resulting in 528 EBV-specific, 840 CMVspecific, 381 influenza A virus-specific and 644 SARS-CoV-2specific TCRβ sequences together with their MHC restriction elements (detailed in Table  S1 ). These sequences were then queried and quantified in deep TCRβ repertoires (ImmunoSEQ Assay) from peripheral blood of HLA-A*02-positive MS patients and controls, only assessing database sequences matched to the HLA combination of the respective individual (Table  1 ).
To test the feasibility of the analysis approach, we first queried 62 healthy controls and a published dataset of 278 COVID-19 patients  (Snyder et al. , 2020)  for SARS-CoV-2-specific TCRβ sequences, and found that COVID-19 patients presented with significantly more matches in their repertoires (Fig. 1  A; q = 4 × 10 -5 ). Additionally, a previously published cohort of individuals with positive CMV serostatus  (Emerson et al. , 2017)  presented with more CMV-specific sequences than individuals with negative CMV serostatus (Fig. S1 A ). All obtained EBV-specific TCRβ sequences were MHC-I restricted and consisted of 404 sequences against lytic and 124 against latent epitopes (Table  S1 ). Of note, <5% of the extracted database EBV sequences were specific for latent EBNA-1-derived epitopes. To cross-validate the specificity of these sequences for an individual's HLA type, the sequences were compared with previously published, highly specific HLA-classifying patterns  (DeWitt et al.
, 2018) , which showed that 57 of the 528 EBV-specific sequences could be found in HLA classifiers, 56 of them (98%) in the HLA for which the original multimer staining was specific (Table  S2 ). This suggests that (a) the database clones can be detected with high specificity in individuals expressing the respective HLA type and (b) HLA classifier patterns contain sequences specific for EBV, which could be expected given that EBV is such a highly prevalent pathogen. Assessment of the HLA-A*02-positive discovery cohort of 430 MS patients and 62 healthy controls revealed that MS patients' TCRβ repertoires contained a higher number of TCR sequences matching with EBV-specific database entries (Fig. 1  B; q = 1. 1 × 10 -2 ; Table  S1 ). To assess the specificity of this finding for EBV, we also queried database-derived TCR sequences specific for SARS-CoV-2, CMV, and influenza A virus.
This assessment validated our previous results, i. e. , detection of SARS-CoV-2 matches after vaccination as a positive control (Fig. 1  C; q = 2 × 10 -3 ) and the broader EBV-specific TCR repertoire in MS patients (Fig. 1  D; q = 3 × 10 -2 ). Of note, the adjusted effect size was +2. 1 EBV-specific matches in the MS patients compared to controls and, therefore, highly comparable to the discovery cohort. To evaluate, whether some of the database TCRβ sequences might not only be specific for EBV, but also to a certain degree associated with MS, the 528 sequences were subjected to lasso regression models within the HLA-A*02 discovery cohort and the MS condition as target variable, adjusting for the individuals' HLA type. The sequences were then ranked according to their P value with regard to prevalence in MS.
Assessment of EBV epitope-specific differences in MS patients and controls To assess individual EBV epitopes in a complementary approach, subanalyses were conducted using the whole dataset (HLA-A*02-negative and -positive individuals; Table  1 ), grouping only donors and database TCRβ sequences fitting to the restriction element of an individual epitope in each subanalysis (Table  S3 ). S3 ). The biggest effect size of +1. 1 was detected for the RAKFKQLL epitope of the BZLF-1 protein in HLA-B*08.
Immune phenotype of peripheral and CSF EBV-specific CD8 T cells in MS patients and controls To characterize the cellular phenotype of the altered immune response to EBV in MS, the transcriptome of T cells with EBVmatching TCR sequences was assessed and compared between HD and MS patients. To this end, we first analyzed four HD single-cell RNA sequencing (scRNAseq) samples from the public domain, where 128,300 CD8 + T cells were sorted from peripheral blood with dextramers specific for 44 different epitopes  (Boutet et al. , 2019) , 12 of them EBV-derived with an overlap of seven epitopes from the database. Most of the CD8 + T cells with EBVmatching TCR sequences were located in two subsets of the effector-memory T cell (TEM) compartment, characterized by gene signatures of PDCD1, CD28, and KLRK1/NKG2D for TEM_1 and TIGIT, NCAM1, and CD244 for TEM_2  (Hao et al.