dataesr/scientific-paragraphs-categorization huggingface dataset:
Seven-year risk was selected because it approximates the median follow-up time for HHF. We conducted 32 sensitivity analyses to investigate (1) whether the S100A12 prognostic value was modified according to prior HF history after exclusion of patients with high probability of pre-existing HF based on baseline NT-proBNP >1000 pg/mL (subgroups analysis), (2) whether NT-proBNP concentration (as covariate) modified our findings, and (3) if we could evidence a potential S100A12 cut-off for the HHF prediction by the Youden index. P values <0. 05 were considered statistically significant. Statistical analyses were carried out using the SAS version 9. 4 software package (SAS, Cary, NC, USA) and GraphPad Prism software version 9. 1 (San Diego, CA, USA).
Acknowledgements All patients included and followed in the cohort study are warmly thanked for their kind participation in this research. Their GPs are acknowledged for their help in collecting clinical information. Florence Beaudry  ( Montreal Heart Institute, Montreal, Québec, Canada ) is acknowledged for her excellent technical assistance in S100A12 concentrations measurements. E. Migault  ( INSERM CIC1402 ,  Poitiers, France)  and the staff of the Diabetes Department at Poitiers hospital are acknowledged for their help with data collection and monitoring. We thank  Sonia Brishoual  ( Biological Resources Center ,  BRC BB-0033-00068 , Poitiers, France) for biological determinations and samples handling. We acknowledge  A. Pavy ,  M. -C. Pasquier  ( Information Technology Department, CHU de Poitiers, Poitiers, France ), and  A.
Funding The SURDIAGENE cohort was supported by grants from the  French Ministry of Health  (PHRC-Poitiers 2004;  PHRC-IR 2008 ), the  Association Française des Diabétiques  ( Research Grant   2003 ) and the  Groupement pour l'Etude des Maladies Métaboliques et Systémiques  (GEMMS Poitiers, France). S100A12 measurements were supported by a research grant from  ELSAN, France  ( 2020 ).
RNA-seq data trimming and mapping To facilitate comparisons, raw sequences of the twelve libraries followed the same steps from trimming to count. A preprocessing of quality control was applied, including trimming library adapters and removing PE reads with bad quality (Phred Quality Score < 20, read length < 30 bases). Ribosomal RNA sequences were discarded with the sortMeRNA tool  (Kopylova et al. , 2012). By using the software STAR (version 2. 7. 3) with the mode parameters 'keep the best results' and 'adapt intron length from min 5 bp to max 60,000 bp'  (Dobin et al. , 2013) , the retained PE reads were mapped simultaneously to the genomic scaffolds of the pepper genome of CM334 with annotation Annuum. v. 1. 6 [35,884 genes, http://peppergenome. snu. ac. kr/, Kim et al. , 2017]  and to the Phyca11 version of P.
Identification of differentially expressed genes and co-expressed genes The full RNA-seq analysis from quality controls to co -expression analysis and differential analysis based on contrasts inside generalized linear models was performed with DiCoExpress, a script-based tool implemented with R language  (Lambert et al. , 2020). To keep genes with enough mapped PE reads, genes with at least one read in at least two replicates of each host-isolate interaction were filtered. For each selected gene, we applied the Trimmed Mean of M-values (TMM) normalization  (Robinson et al. , 2010)  in order to normalize their number of PE reads counted in the twelve samples. We considered the TMM normalization of counts for each sample suitable for further analysis since the boxplots of normalized counts are similar across the twelve samples (Supplementary Figures  S1A, B ).
capsici. To identify among the selected P. capsici genes those exhibiting a differential expression (DEG) according to modalities of the biological factors, the TMM-normalized CPM values of each selected gene were compared using the GLM-Poisson (generalized linear models) implemented in the R-package edgeR  (McCarthy et al. , 2012) , by constructing a model including the host (H), the isolate (I), their pairwise interaction (H*I), and the replicate factor (R) which represents the intra-condition heterogeneity: Log 2 (TMM-CPM value) = H + I + H*I + R. The interaction term H*I in the model may reveal meaningful specific interactions between the host and the isolate. DiCoExpress automatically generates a list of contrasts based on the model and provides the DEGs of P. capsici when comparing the two host plants (R and S) and the two isolates (A and N) (Figure  1A ).
A gene was considered differentially expressed (DEG) when its Benjamini-Hochberg-corrected (BH-corrected) p-value (False Discovery Rate or FDR) was less than 0. 01. To identify groups of co-expressed genes (CEGs), DiCoExpress uses a Gaussian mixture model based on the normalized expression profiles after an arcsine transformation implemented in the coseq R-package and clusters genes according to their expression profile in all samples.
Data accessibility All steps of the experiment, from growth conditions to bioinformatic analyses, were managed in CATdb database  (Gagnot et al. , 2008) , with the project identifier NGS2013_07_ Pcapsici. This project was submitted from CATdb into the international NCBI repository GEO  (Edgar et al. , 2002)  and SRA under the project identifier GSE206447, which is publicly accessible at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE206447.
The dataset enables the comparison of transcriptional expression of 7,240 genes of Phytophthora capsici After stringent quality assessment and trimming of the twelve libraries corresponding to three biological replicates for each of the four host-isolate interactions, we mapped ~29 to ~41 million paired-end (PE) reads (per library) to the P. capsici and C. annuum reference genomes, and ~23 to ~32 million PE reads to the gene annotations of both genomes. To focus on gene expression of the pathogen, we considered, for each of the twelve libraries, the 2. 6 10 4 to 1. 8 10 6 PE reads mapped to the 20,052 predicted genes of P. capsici, corresponding to 0. 11 to 6. 97% of the total reads (Supplementary Table  S1 ). As expected, libraries from the S_A interaction showed the highest number and percentage of P.
This process consequently causes the abscission of the leaf (removing the infected tissues). We also observed leaf abscission in CM334 whereas there was no abscission but spread of a large necrosis in Yolo Wonder when infiltrated with a solution of P. capsici zoospores (data not shown). In Arabidopsis, a protein belonging to the family of transcription cofactors NPR1 (nonexpressor of pathogenesis-related genes 1) was shown to be responsible for the cell differentiation in the abscission zone and to be a positive regulator of systemic acquired resistance in plants as a receptor of salicylic acid and inducing defense genes  (Olsson and Butenko, 2018). In the pathosystem Arabidopsis-Pseudomonas syringae, the leaf abscission was described as a defense mechanism triggered by effectors  (Patharkar et al.
, 2004). This is why we preferred to use PVX-mediated agro-infection that limits the number of bacteria in contact with plant providing more reproducible results (data not shown). Agro-infection consists of delivering the PVX genome including targeted RxLR effectors via Agrobacterium into plants. Then, Agrobacteria translocate the T-DNA into plant cells around the wound, and the PVX further spreads into adjacent cells and expresses the RxLR gene. We demonstrated that the tested pepper lines were all susceptible to PVX, even if we observed variability in response to PVX infection that may modify the efficiency of agro-infection. To assess host response to an RxLR construct, we also favored agro-infections with only one construct per leaf to avoid the risk of cross-reactions that can occur when different constructs are agro-infected on the same leaf.
SUPPLEMENTARY TABLE 3 Primers of genes from Phytophthora capsici and Capsicum annuum used for RT-qPCR experiments and for Gateway cloning. Note: Gene accession numbers for P. capsici can be retrieved on https://mycocosm. jgi. doe. gov/ Phyca11/Phyca11. home. html. CUST_2407, CUST_14997 and CUST_16519 are differentially expressed in RNA-seq. CUST_5402, CUST_15481 and CUST_17572 are not. Amplification efficiency of primer pairs was calculated based on the slope of the standard curve, using the equation: E (%) = (-1+10^(-1/slope))x100.
Acknowledgments We are grateful to  Drs. Odile Berge ,  Marie-Laure Martin-Magniette  and  Etienne Delannoy  for their valuable advice and helpful discussions of this work. We acknowledge  Dorine Achard  and  Marion Szadkowski  for technical assistance for the cloning of effector genes in PVX binary vector and for advices for assessing susceptibility level of pepper lines to PVX, respectively, the  INRAE GAFL's genebank CRB-Leǵ  as supplier of seeds for pepper accessions used in this study, members of the  INRAE GAFL 's plant culture team that took care of plants, and  Dr. Sean Chapman , who kindly supplied the Gateway PVX expression vector pGR106 used in transient expression experiments. KL received a fellowship from  Agropolis Fondation  to spend a sabbatical year at INRAE GAFL. GM received a doctoral scholarship ( SOLEFFECT  project) from  INRAE Plant biology and breeding Division  ( 50% ) and  French Provence-Alpes-Cote d'Azur Region  ( 50% ), with the support of breeding companies  Gautier Semences  and  Rijk Zwaan France.
Funding This work was supported by  INRAE Plant biology and breeding Division  ( EFFECAPS  project),  Agropolis Fondation  (Prote ́ines phytopathogènes  1300-002  project) and  French National Research Agency  ( EFFECTOORES ANR-13-ADAP-0003  project). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Factors independently associated with the prevalence of S. aureus CC398 nasal carriage were determined using logistic regression models. Multivariable models were built using a backward elimination process with independent variables with a value of p <0. 10 in univariable analysis. All analyses were performed using the software package Stata, version 14. 1 (Stata Corp. , College Station, TX, United States). A value of p <0. 05 was considered statistically significant.
Prevalence of Staphylococcus aureus and Staphylococcus aureus CC398 Among nasal swab samples from HBDs, 175 (29. 6%) were positive for S. aureus. Only 2 HBDs were positive for MRSA (1. 1%). CC30 was the most common CC (n = 48, 27%), followed by CC398 (n = 40, 24%), CC45 (n = 18, 10%), and CC8 (n = 12, 7%) (Figure  1 ). Among HPs, 138 (21. 3%) nasal swabs were positive for S. aureus upon admission, of which seven were MRSA (5%). Among 295 HPs with a second swab, 58 showed nasal colonization with S. aureus. Among 238/295 HPs with an initial negative sample, 18 showed S. aureus colonization on day 7. Whole genome sequencing data were not available for 8 HPs due to sequencing problems (seven patients at inclusion and one patient on day 7). Overall, the genomes of 148 S. aureus isolates from HPs were analyzed.
aureus CC398 nasal colonization was similar to that of blood-stream infection in our hospital. Second, we did not use broth enhancement or PCR to detect S. aureus. Indeed, broth enhancement before plated on specific media could increase sensitivity of S. aureus detection. However, most of studies were performed for MRSA carriage, with few data on MSSA. It could be therefore possible that the prevalence of S. aureus nasal carriage had been under estimated  (Safdar et al. , 2003; Palavecino, 2020). Third, the calculated number of patients to include in our study was 1,000 in each group. However, due to the COVID-19 pandemic, inclusion of patients was interrupted for 3 months. Nonetheless, the higher prevalence of S. aureus CC398 than expected did not modify the precision of the prevalence estimate.
Acknowledgments We thank  Elisabeth Botelho-Nevers  ( Department of Infectious Diseases, University Hospital of Saint-Etienne, France ),  Nathalie van-der Mee-Marquet  ( Department of Hygiene, CPias Centre Val de Loire, University Hospital of Tours, France ),  Pierre Tattevin  ( Department of Infectious Diseases, University Hospital of Rennes, France ), and  Florent Valour  ( Department of Infectious Diseases, HCL Croix Rousse, University Hospital of Lyon, France ) for their help in improving the manuscript. We thank  Pascal Morel  and  Etablissement Français du Sang, Bourgogne Franche-Comté, France  for their help to include blood donors. We thank  Adrien Biguenet  ( Service d'hygiene hospitalière, CHU Besancon ) for his help for genotyping analysis.
The refractive index of the prism and glass substrate are obtained using equations described in supporting information. The transition temperature of VO2 is theoretically set at 68 °C  [18, 21] ; thus, the two chosen temperatures represent both the insulating and conducting states of the phase-change material. Simulations were performed using MATLAB as the programming algorithm and the transfer matrix The dispersion relation of gold was obtained from the Lorentz-Drude model and that of VO 2 from experimental data. The refractive index of the prism and glass substrate are obtained using equations described in supporting information. The transition temperature of VO 2 is theoretically set at 68 • C  [18, 21] ; thus, the two chosen temperatures represent both the insulating and conducting states of the phase-change material.
77% and 72. 4% of the a chain sequences in v-CTRL and in v-A12 mice, respectively, represented less than 1% of the clones and occurred in only one sample (Figure  2A ). There was overlap between v-CTRL and v-A12 mice for a single a chain sequence (VVGDRGSALGRLH), which is considered NKT cell receptor sequence according to the immune epitope database (IEDB) and expansion of two clones in the v-A12 group that occurred with a frequency of 4. 6% (ALTANNYAQQGLT) and 1% (VVADRGSALGRLH; Figure  2A ). In livers from v-CTRL mice, there was no clonality in the b chain sequences and no overlap with v-A12-vaccinated mice, which exhibited a single clone (ASSDRDR-GEQY; Figure  2A ). Of note, in fibrotic livers, we observed increased TCR clonality (defined as a TCR sequence with a frequency > 1% and detectable in more than one sample of each treatment group) of liver-infiltrating T cells from v-A12-and control-vaccinated mice with minimal overlap to v-CTRL mice in two a chain sequences (VVVDRGSALGRLH and VVGDRGSALGRLH) (Figure  2B ).
Moreover, when we analyzed fibrosis after 9 weeks instead of 6 weeks of fibrosis treatment in the therapeutic model (vaccination is performed after 3 and 4 weeks of fibrosis treatment as in the scheme of Figure  3A ) as additional time point to assess the durability of fibroblast depletion, the reduction in liver collagen staining after ADAM12 vaccination was still measurable (Fig-  ure S7H ). This suggests a fairly sustained antifibrotic T cell response upon ADAM12 vaccination. ADAM12 protein expression in humans under homeostatic conditions is restricted to the male testis and the female placenta during pregnancy and not detectable in the gut and skin (https://www. proteinatlas. org/ENSG00000148848-ADAM12/tissue). In line with this, ADAM12 gene expression is very high in the placenta and low in other tissues including the skin and the gut according to BioGPS database (http://biogps.
In silico epitope prediction for murine Adam12 and Gli1 Prediction of antigen processing and presentation through the MHC class I pathway was performed with the prediction tool from Immune Epitope Database Analysis Resource. Briefly, to identify potential MHC-I ligands, i. e. peptides that are naturally processed from their source proteins and presented by MHC class I molecules, the T Cell Epitopes -Processing Prediction (Proteasomal cleavage/TAP transport/MHC class I combined predictor) tool was used (http://tools. iedb. org/processing/) and fed with the respective protein sequences. Adam12 protein reference sequence: Q61824, Gli1 protein reference sequence: P47806. For predicted Adam12 derived epitopes see Figure  S1A  and for predicted GLI1 derived epitopes see Figure  S6A.
ELISpot assays The frequency of IFN-g-secreting T cells in response to a specific stimulus was determined with the mouse IFN-gamma ELISpot assay (Diaclone, 862. 031. 010S). Splenocytes were isolated, then, CD8 + T cells (CD8a (Ly-2) MicroBeads, mouse, Miltenyi, 130-117-044) and CD4 + T cells (CD4 (L3T4) MicroBeads, mouse, Miltenyi, 130-117-043) were purified. Next, 1 3 10 5 CD4 + T cells or 1 3 10 5 CD8 + T cells were cocultured with previously transfected 1 3 10 5 MutuDC1940 cells (see ADAM12 and GLI1 expression by antigen presenting cells section). The detection part of the experiment was conducted according to manufacturer's instructions. The number of spots was evaluated in blinded fashion by AID EliSpot/FluoroSpot Reader System operated by AID EliSpot Software Version 7. 0. Parameters and were adjusted following the International Harmonization Guidelines for ELISPOT plate evaluation.
In vitro cytotoxicity assays In vitro cytotoxicity assays were performed with purified and expended CD8a + T cells and the NIH 3T3 Adam12 expressing cells, HSC and YAC-1 target cell lines. Target cells were washed and labelled with CFSE (ThermoFisher, C34554). Following the washing steps, CD8a + T cells were co-cultured with target cells at E:T ratios of 1:1 for 6 h at 37 C and 5% CO 2 in RPMI 1640 medium (ThermoFisher, 42401018) supplemented with 10% FBS, 50 U/mL penicillin-streptomycin (Lonza, DE17-602E). Culture of target cells alone was used as a negative control. Each experimental condition was performed in three replicates. Then cells were washed with PBS containing 0. 5% BSA  (Miltenyi Biotec,  and labelled with a Live/Dead Fixable Zombie Violet (BioLegend, 423114), according to the manufacturer's protocol before data acquisition on a BD FACSymphony A5 Flow Cytometer operated by BD FACSDiva 9.
0 Software. Data were analyzed using FlowJo v10 (Treestar). The target cells were identified as CFSE + , and effector cells were identified as CFSE À. The dead target cells were identified as CFSE + Live/Dead +. Spontaneous death was defined as the proportion of dead target cells cultured alone (negative control), and this value was subtracted from the proportion of dead target cells cocultured with effector cells. Post-assay supernatants were used in ELISpot assay in order to determine the IFNg secretion.
The VECTASTAINÒ Elite ABC-HRP Kit (Vectorlabs, VEC-PK-6101) was used according to the manufacturer's protocol (VectorLabs), and then DAB Peroxidase Substrate Kit (Vector Laboratories, SK-4100) was applied for visualization. Samples were counterstained with hematoxylin for 30 sec, followed by washing with running cold water for 15 min. Samples were dehydrated and then mounted with DPX mounting media. Immunofluorescence-stained and IHC/DAB-stained sections were imaged using either a DMI 6000B microscope (Leica) or an Axio Scan. Z1 slide scanner (Zeiss). Image analysis and quantifications were performed using ImageJ 1. 49 (National Institutes of Health, USA) and ZEN (Zeiss) imaging software.
Gene expression by quantitative PCR The cells were homogenized in RLT buffer (Qiagen). Total RNA was isolated with Qiagen RNA extraction kits following the manufacturer's instructions. For real-time PCR analysis, the isolated RNA was reversely transcribed (High-Capacity cDNA Reverse Transcription Kit; Applied BiosystemsÔ; 4368814). For PCR reactions, SYBR Green I Master MIX (LightCyclerÒ 480 SYBR Green I Master; Roche; 04887352001) was used. 10 ng cDNA was used as template to determine the relative amount of mRNA by real-time PCR (Roche Detection System). PCR conditions were as follows: 95 C for 10 min followed by 45 cycles of 95 C for 15 seconds and 60 C for 1 min. Data were normalized to 16S mRNA levels. The following primers were used: 16s forward primer: 5 0 -AGATGATC GAGCCGCGC-3 0 ; Sftpa1 forward primer: 5'-ACCTGGATGAGGAGCTTCAGAC-3'; ADAM12 primer: QIAGEN, QT0016697.
ACKNOWLEDGMENTS We appreciate the support of the  Swiss National Fund  ( 310030_179235 ), the  Swiss Cancer League  ( KFS-4398-02-2018  and  KFS-5402-08-2021 ), and the  Swiss National Centre for Competence in Research ''Kidney. CH''  ( N-403-06-26   HCP  project grant) to C. S. M. S. was supported by the  internal postdoc funding program  of the  University of Zurich  ( Forschungskredit UZH Postdoc 2019 ). N. T. received grant  JP21gm6210028  from the  Japan Agency for Medical Research and Development (AMED). We thank  Louis Babour Stockmann  for proofreading the figures. We thank the  Laboratory Animal Services Center (LASC)  for animal care and the  UZH Irchel Flow Cytometry core facility. We acknowledge the support from the  SKINTEGRITY. CH collaborative research program.
The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP. However, the attention heads' ability to judge the grammatical acceptability of a sentence has been underexplored. This paper approaches the paradigm of acceptability judgments with topological data analysis (TDA), showing that the topological properties of the attention graph can be efficiently exploited for two standard practices in linguistics: binary judgments and linguistic minimal pairs. Topological features enhance the BERTbased acceptability classifier scores by up to 0. 24 Matthew's correlation coefficient score on COLA in three languages (English, Italian, and Swedish). By revealing the topological discrepancy between attention graphs of minimal pairs, we achieve the human-level performance on the BLIMP benchmark, outperforming nine statistical and Transformer LM baselines.
Data We use three LA classification benchmarks in English (COLA;  Warstadt et al. , 2019) ,  Italian (ITACOLA; Trotta et al. , 2021)  and Swedish (DALAJ;  Volodina et al. , 2021). COLA and ITA-COLA contain sentences from linguistic textbooks and cover morphological, syntactic, and semantic phenomena. The target labels are the original authors' acceptability judgments. DALAJ includes L2-written sentences with morphological violations or incorrect word choices. The benchmark statistics are described in Table  1  (see Appendix C). We provide examples of acceptable and unacceptable sentences in English (3), Italian (4), and Swedish (5) from the original papers. "This woman have impressed me. " (5) a. Jag kände mig jättekonstig. "I felt very strange. " b. *Alla blir busiga med sociala medier. "Everyone is busy with social media.
Empirical estimate. Computing the H 0 M /RTD features with 144 BERT heads in the worst case of a 512-token text takes 2. 41 and 94. 5 sec (NVIDIA Tesla K80 12GB RAM). However, the actual computation time on the considered tasks is empirically more optimal. We provide empirical estimates on the entire BLiMP and LA datasets: 2. 4/15. 7 hours on BLiMP (H 0 M /RTD) and up to 2 hours on COLA/ITACOLA/DALAJ (estimates by the feature groups: topological features=24%; features derived from barcodes=70%; and features based on distance to patterns=6% of the total time).
Application Limitations We also outline several application limitations of our approach. (i) The LA classifiers require preliminary fine-tuning of Transformer LMs to extract more representative attention graph features and, therefore, achieve better performance. (ii) RTD operates upon a one-to-one vertex correspondence, which may be hindered by tokens segmented into an unequal amount of sub-tokens. As a result, identifying the topological discrepancy between pairs of attention graphs can be restricted in practice, where the graphs are of an arbitrary number of nodes. Regardless of the potential information loss due to sentence truncation in such cases, the RTD heads still receive the best overall score on BLIMP. (iii) The head selection procedure relies on auxiliary data to identify the best-performing head configurations.