dataesr/scientific-paragraphs-categorization huggingface dataset:
e. observation of turtle tracks, individual crawling on a beach and aborted attempt at digging), mating events and observations of gravid females. Each time, the month, year and location of the event were indicated. In the case of nesting events, available information on the number of eggs discovered, incubation duration (time between the day the eggs were laid and the day the first hatchling emerged) and number of hatched eggs were extracted and relevant published references cited. Although loggers were deployed to measure nest temperature at the different sites, temperature data will be the focus of a separate study, and, thus, are not presented here. Finally, in addition to the date and location, the size and mass of gravid turtles, along with the mass of follicles measured during necropsy, were recorded.
Evaluation of trends in sea surface temperature. Daily sea surface temperature (SST) data in the north-western Mediterranean (Fig. 6 ) between 1982 and 2020 were extracted from the National Oceanic and Atmospheric Administration (NOAA)'s National Centers for Environmental Information. NOAA's daily optimum interpolation sea surface temperature is a robust long-term climate record starting late 1981 and incorporating data from different platforms (i. e. ships, satellite, Argos floats)  56. Monthly mean SSTs were then calculated for the entire study area, and used to compute annual and seasonal (summer: May to September; winter: October to April) SST time series. To detect trends in the time series, non-parametric Mann-Kendall tests were performed with the "Kendall" R package (version 2. 2). Seasonal Mann-Kendall tests were used in the case of the summer and winter time series.
Acknowledgements We would like to thank all the RTMMF observers who have volunteered their time to collect the data used in this study. We are particularly grateful to the staff and volunteers of the CESTMed and CRFS rescue centres, as well as to fishermen and citizen science programme volunteers who contributed to data collection. Finally, we thank  M. Messié  for her assistance with the extraction of sea surface temperature data. This study was funded through a Convention between the  French Ministry of Environment and MNHN [MTE-MNHN  Conventions n°2102636187 (2019) and n°2102994526 (2020)]. Data collection in the field was made possible by funding from the  DREAL Occitanie  ( DREAL Occitanie-CESTMed Conventions ),  OFB  ( Office Français de la Biodiversité ; Convention  OFB-SHF 4889-OFB 20 0973 ) and  Marineland Association.
, 2019) not only illustrates that efficient hepatocellular priming can occur under specific conditions but also provides the opportunity to identify which cellular and molecular determinants drive immunogenic responses within the tolerogenic liver microenvironment. Several formulations of IL-2 have been variably used in the past >25 years as a therapy to augment T cell responses against viral or tumor Ags  (Blattman et al. , 2003; Pol et al. , 2020; West et al. , 2013) , and at the moment of writing, more than 40 clinical trials are evaluating the immune stimulatory potential of this cytokine in different oncological indications (https://www. clinicaltrials. gov). As the IL-2 functional pleiotropy has often driven undesired toxicity in the clinical setting  (Pol et al. , 2020) , deconvoluting the biology responsible for its efficacy may help improve the therapeutic potential of IL-2-based strategies.
Taken together, these results indicate that optimal reinvigoration of intrahepatically primed CD8 + T cells by IL-2 requires the capacity of KCs to cross-present HBV Ags, possibly derived from circulating virions and/or damaged hepatocytes. Single-cell RNA-seq identifies two distinct populations of KCs among liver-resident macrophages Next, we asked whether the IL-2-responsive KCs represent a distinct subpopulation. To this end, we used high-dimensional single-cell RNA-seq (scRNA-seq) to characterize KC heterogeneity at steady state. We flow cytometry-sorted live CD45 + Lineage À CD64 + F4/80 + liver macrophages from C57BL/6 mice (Figure  3A ), isolated RNA, and generated transcriptional profiles for each cell (n = 169) using the Smart-seq2 pipeline  (Picelli et al. , 2014). This dataset was analyzed using Seurat  (Stuart et al.
All samples were subjected to an indexed paired-end sequencing run of 2x151 cycles on an Illumina HiSeq 4000 system (Illumina, San Diego, CA, USA) (298 samples/lane). The RSEM tool  (Li and Dewey, 2011)  was used to perform Transcript Per Million (TPM) normalization starting from FASTQ files. Single cell data analysis was performed using Seurat (v3. 2. 2)  (Stuart et al. , 2019). 169 cells were obtained after applying a filter to the TMP matrix of at least 200 genes expressed per cell and only genes expressed in at least 3 cells were retained. TPM expression was further normalized and scaled using the SCTransform function, and Umap reduction was then applied on first 12 Principal Components after running PCA. Unbiased clustering was made using the FindClusters function in Seurat with default parameters and a resolution value of 1.
RNA-seq bioinformatics analysis Bulk RNA-seq experiment on CD206 -, CD107b -and CD206 + , CD107b + cells (shown in Figure  S4 ): raw reads were obtained and mapped to the mouse genome build GRCm38. Gene counts were generated using featureCounts (part of the R subread package)  (Liao et al. , 2019)  with GENCODE version M9 annotations. Differential Expression Analysis genes (DEGs) and MA plots were performed using the R package edgeR  (Robinson et al. , 2010). Bulk RNA-seq experiment on total KCs (shown in Figure  2 ) and on sorted KC1 and KC2 (shown in Figure  4 ): raw reads were aligned to mouse genome build GRCm38 using STAR aligner  (Dobin et al. , 2013). Read counts per gene were then calculated using featur-eCounts based on GENCODE gene annotation version M16. Read counts were subject to log2 TPM (transcript per million) normalization to account for transcript length and library size.
Only genes with a TPM value higher than 1 in at least 4 (for the total KC experiment in Figure  2 ) or 3 (for the KC2 versus KC1) samples were considered for following analysis. Differentially Expressed Genes (DEGs) between groups treated with IL-2c and PBS were identified by generating a linear model using LIMMA R package  (Ritchie et al. , 2015). Only DEGs with an adjusted P value < 0. 05 (using Benjamini Hochberg correction method) were selected for further analysis. For the final KC2 versus KC1 comparison an additional | logFC| > 1 filter was applied.
Functional enrichment analysis Bulk RNA-seq analysis of the experiment described in Figure  2 : of the 4073 significant (FDR < 0. 05) identified DEGs between control (PBS) and treated (IL-2c) samples, 1515 were upregulated and 2558 were downregulated. Those were subject to a functional enrichment analysis using the EnrichR R package  (Kuleshov et al. , 2016). Both the up-and the downregulated DEGs were checked for any biological signature enrichment in both the Gene Ontology Biological Process Database and the Kyoto Encyclopedia of Genes and Genomes for Mouse. After merging the results for the two databases, 858 significant (FDR < 0. 05) Terms were identified, of which 428 were derived from the upregulated DEGs and 430 from the downregulated ones. In order to select the top enriched terms, only those with a high Combined Score (-log(p value) * Odds Ratio) were considered.
Clustering of upregulated terms For visualization and analysis, both upregulated and downregulated terms were subject to a clustering algorithm, in order to identify the most prominent biological signatures. Briefly, a Jaccard Index Similarity score was calculated for each pair set of terms, based on the DEGs annotated for each term, using an in-house developed script. Next, terms were clustered using a hierarchical clustering method, using as distance measure the Pearson correlation between the calculated Jaccard Index Similarity scores. An arbitrary number of clusters was selected and manually annotated based on the terms present. To visualize the result, the pheatmap R package was used.
Network plot visualization Network plot (Figure  S3F ) was built using Cytoscape software (V 3. 8. 0 for MacOS). Briefly, starting from EnrichR tables (Table  S1 ), a matrix defining every pair of term-gene was generated, and used as a node list input for Cytoscape. Gene set enrichment analysis Gene Set Enrichment Analysis (GSEA) from bulk RNA-seq of KC1 and KC2 (Figure  4 ) was performed using the GseaPreranked Java tool  (Subramanian et al. , 2005)  using pre-ranked Log2 fold changes between KC2 and KC1 populations in expressed genes. HALL-MARK_IL2_STAT5_SIGNALING Gene Set contained in MsigDB (Broad Institute)  (Liberzon et al. , 2015) , Version 6. Since the gene set is based on human genes, mouse orthologs in humans were identified using the homologene R package (https://cran. r-project.
Immunoblot analysis Immunoblot on plated KCs was performed as described  (Zordan et al. , 2018). Primary Abs include anti-STAT5 and anti-pSTAT5 (Tyr694) (rabbit; Cell Signaling Technology #8215) and b-actin (polyclonal; Abcam ab228001). As secondary Ab horseradish peroxidase-conjugated goat anti-rabbit IgG (Jackson ImmunoResearch, Cat# 111-035-003) was used. Reactive proteins were visualized using a Clarity Western ECL substrate kit (Bio-Rad), and exposure was performed using UVItec (Cambridge MINI HD, Eppendorf). Images were acquired by NineAlliance software.
Software and algorithms FlowJo V10 FlowJo https://www. flowjo. com/ RSEM tool (Li and Dewey, 2011) https://deweylab. github. io/RSEM/ Seurat (v3. 2. 2) (Stuart et al. , 2019) https://satijalab. org/seurat/ Enrichr (Kuleshov et al. , 2016) https://maayanlab. cloud/Enrichr/ featureCounts (Liao et al. , 2019) https://www. rdocumentation. org/packages/ Rsubread/versions/1. 22. 2/topics/featureCounts edgeR (Robinson et al. , 2010) http://bioconductor. org/packages/release/bioc/ html/edgeR. html STAR aligner (Dobin et al. , 2013) https://github. com/alexdobin/STAR LIMMA R package (Ritchie et al. , 2015) https://bioconductor. org/packages/release/bioc/ html/limma. html pheatmap R Raivo Kolde https://www. rdocumentation. org/packages/ pheatmap/versions/1. 0. 12/topics/pheatmap fmsb R Minato Nakazawa https://cran.
r-project. org/web/packages/fmsb/ index. html Cytoscape Cytoscape https://cytoscape. org GseaPreranked (Subramanian et al. , 2005) https://gsea-msigdb. github. io/gseaprerankedgpmodule/v6/index. html homologene R package Ogan Mancarci, Leon French https://cran. r-project. org/web/packages/ homologene/index. html Prism 9 GraphPad software https://www. graphpad. com/scientific-software/prism BD FACSDiva V8 BD Biosciences https://www. bdbiosciences. com/us/instruments/ research/software/flow-cytometry-acquisition/bd-.
, 2019) N/A Chemicals, peptides, and recombinant proteins Recombinant mouse IL-2 protein R and D Systems R and D Systems Cat# 402-ML-500/CF Clodronate liposomes Liposoma Liposoma Cat# C-025 Diphtheria toxin Millipore Millipore Cat# 322326 Tamoxifen Sigma Sigma Cat# T5648-5G Corn oil Sigma Sigma Cat# C8267-500ML Viobility 405/520 fixable dye Miltenyi Biotec Miltenyi Cat# 130-109-816 LIVE/DEAD fixable far-red dye Thermo Fisher Scientific Thermo Fisher Scientific, Cat# L34973 DAPI Thermo Fisher Scientific Thermo Fisher Scientific Cat# D1306; RRID:AB_2629482 Phosflow perm buffer III BD Biosciences BD Biosciences Cat# 558050; RRID: AB_2869118 O. C. T. Bio Optica Bio Optica Cat# 05-9801 FluorSave reagent Millipore Millipore Cat# 345789 Critical commercial assays Foxp3 / transcription factor staining buffer set Thermo Fisher Scientific Thermo Fisher Scientific, Cat# 00-5523-00 CellTrace violet cell proliferation kit Thermo Fisher Scientific Thermo Fisher Scientific, Cat# C34571 Anti-F4/80 MicroBeads UltraPure, mouse Miltenyi Biotec Miltenyi Biotec Cat# 130-110-443, RRID: AB_2858241 EasySep mouse naive CD8+ T cell isolation kit Stem Cell technologies Stem Cell technologiesCat# 19858 Nextera XT DNA library preparation kit Illumina Illumina Cat# FC-131-1024 DNA high sensitivity reagent kit Perkin Elmer Labchip Perkin Elmer Labchip Cat# CLS760672 Arcturus picoPure RNA isolation kit Thermo Fisher Scientific Thermo Fisher Scientific Cat# KIT0204 ERCC RNA spike-In mix Thermo Fisher Scientific B Immunoblot analysis Thermo Fisher Scientific Cat# 4456740 ReliaPrep RNA miniprep system Promega B Confocal immunofluorescence histology and histo-Promega Cat# PRZ6012 TURBO DNA-free kit Thermo Fisher Scientific chemistry Thermo Fisher Scientific Cat# AM1907 Qubit RNA HS assay kit Thermo Fisher Scientific B Biochemical analyses Thermo Fisher Scientific Cat# Q32852 NovaSeq reagent kits Illumina d QUANTIFICATION AND STATISTICAL ANALYSIS Illumina Cat# 20028312 Clarity Western ECL substrate kit Bio-Rad Bio-Rad Cat# 1705060S Deposited data SUPPLEMENTAL INFORMATION Bulk RNaseq data This manuscript NCBI Gene Expression Omnibus (GEO) accession GEO: GSE152211 Single-cell RNaseq data This manuscript NCBI Gene Expression Omnibus (GEO) accession GEO: GSE168989 (Continued on next page) d RESOURCE AVAILABILITY B Lead contact B Materials availability B Data and code availability d EXPERIMENTAL MODEL AND SUBJECT DETAILS B Mice B Viruses and viral vectors d METHOD DETAILS B Naive T cell isolation, adoptive transfer and in vivo treatments B Cell isolation and flow cytometry B Cell purification B Single-cell RNA-seq B RNA purification and RNA-seq library preparation B RNA-seq bioinformatics analysis B Functional enrichment analysis B Clustering of upregulated terms B Radar plots visualization B Network plot visualization B Gene set enrichment analysis.
MATERIALS AND METHODS Data gathering. TssK, TssG, and TssB EAEC sequences were obtained from the genome of EAEC strain 55989 (Table  S1C ). Importantly, a single species might have more than one T6SS gene cluster. Using KEGG  (76)  together with SecReT6 (77) annotations, we selected TssK, TssG, and TssB sequences from the 3 different T6SS gene clusters on the EAEC genome  (53, 78, 79). The first gene cluster (EAEC1) belongs to subtype i1, the second (EAEC2) to subtype i4b, and the third (EAEC3) to subtype i2. In the present study, we used EAEC3 as our T6SS model thanks to the available structural data of the TssK-TssG interface (wedge complex cryo-electron microscopy [EM] atomic structures; PDB IDs 6GIY and 6N38). TssK, TssG, and TssB sequences for other pathogenic T6SSs examined were also classified into their corresponding T6SS subtypes following the information retrieved from KEGG and SecreT6 databases (Table  S1B  and C ).
Uniref90 (  80 ) was downloaded and converted into a BLAST database using the makeblastdb application  (81). We also downloaded all bacterial genomes contained in the SGB collection metagenomic database  (82). Using ORFM  (83) , we identified all possible genes and translated their open reading frames from which putative protein sequences were converted into a BLAST database, using makeblastdb. Homologous search. The search of homologous sequences for TssK, TssG, and TssB was conducted using BLASTp (81) on the Uniref90 (80) and SGB databases  (82). We queried the TssK, TssG, and TssB sequences from T6SS EAEC1, EAEC2, and EAEC3 gene clusters, as well as all the gene clusters from a selected list of pathogenic T6SSs (Table  S1C ). All these sequences, belonging to different gene clusters and pathogens, are referred as to the query sequences here.
Homologous sequences whose length was less than 50% or more than 150% of the query sequence length were discarded. All the sequences resulting from the multiple BLASTp searches were merged and clustered by 0. 9 sequence identity using cd-hit  (84). Multiple sequence alignment. TssK, TssG, and TssB multiple-sequence alignments (MSAs) were generated by multiple rounds of (i) homologous sequence alignment performed by MUSCLE (  85 ) and (ii) removal of outliers by EvalMSA  (86). After the last iteration, we manually curated the MSAs using JalView  (87). Clusters of variants. Each sequence from the MSA of a given protein was assigned to the most similar query sequence, using blosum62 as a similarity score  (88). The assignment allowed us to cluster all the sequences, where each cluster is a set of closely homologous variants of the corresponding query.
The conservation level of a cluster of ,5 variants is set to 0. Data analysis, visualization, and storage. Residue conservation was computed with JalView (87) and mapped onto the wedge complex structure (PDB ID 6N38) using Chimera (89) (Fig. 2A  and B ). Residue frequencies (Fig. 2C  and D and Fig. S6E ) were mapped on the alignment using the WebLogo (90) server. MSA figures (Fig. S8 ) were generated with JalView. Hierarchical clustering figures (Fig. 6  and Fig. S7 ) were generated with seaborn (open-source Python package for statistical data visualization)  (91). All molecular visualizations were rendered by UCSF Chimera (89) All Python and shell scripts specifically developed for this work are available together with the initial genomic data (TssK, TssB, and TssG sequences) and the resulting MSA (TssK, TssK NTD, TssB, TssG, TssG foot 1, and TssG foot 2) at https:// gitlab.
The simulations, which ran for 200 ps for each candidate, generated a total of 100 snapshots per sequence. The snapshots were subsequently minimized, and the median of the 100 final total energies was used to rank the peptides (Fig. S8 ). Coevolution analysis. Residue coevolution identified between TssG and the TssK NTD was detected by combining RaptorX (93) complex contact prediction and Gremlin (94) monomer contact prediction. First, we extracted the concatenated MSA generated by RaptorX when TssK and TssG EAEC sequences were input. Second, we trimmed the TssK NTD and TssG foot 1 from the Raptor MSA and used the resulting concatenated MSA as input for a Gremlin monomer contact prediction search. Finally, we selected the TssK-TssG pairs with a probability above a given threshold and a distance between their side chains below 8 Å on the atomic structure of the wedge structure (PDB ID 6N38).
4 ml followed by 18 injections of 2 ml. Two assays were performed. In the first, 25 mM WT TssK ligand was in the cell and 1 mM WT BCP analyte was in the syringe. In the second, 25 mM TssK L14A ligand was in the cell and 1 mM WT BCP analyte was in the syringe. The reaction was performed with a constant stirring speed of 750 rpm; each injection lasted for 4 s with a 150-s space between injections. A constant heat control (offset) was removed from the raw data to account for heat dilution before integration. The data were fitted using the One Set of Sites model in the PEAQ-ITC analysis software. TssK focus in vivo inhibition. Fluorescence microscopy experiments were performed as previously described  (97, 98). An overnight culture in LB of EAEC strain with a chromosomal GFP fusion to TssK was diluted 1/200 in SIM medium and grown at 37°C until the culture reached an A 600 of ;0.
45), an Orca-Fusion digital camera (Hamamatsu), and a perfect focus system (PFS) to automatically maintain focus so that the point of interest within a specimen is kept in sharp focus at all times despite mechanical or thermal perturbations. All fluorescence images were acquired in Hilo mode using an Ilas2 TIRF module (Gataca Systems). Exposure times were typically 100 ms for phase contrast and 100 ms using the GFP channel. For image treatment, noise and background were reduced by filtering large structures down to 40 pixels in the FFT Bandpass Filter function of ImageJ  (102). The GFP foci were automatically detected using the MicrobeJ plugin  (103). NMR experiments. All NMR experiments were recorded at 300 K on a Bruker Avance-II 600 MHz spectrometer equipped with a cryoprobe at the IMM (Institut de Microbiologie de la Mediterranée) NMR platform.
ACKNOWLEDGMENTS We thank the  Cascales lab  for giving access to the EAEC parental strain and  Thierry Doan  for his advice on the analysis of fluorescence microscopy data. We thank  Elisabetta Mileo  and  Annalisa Pierro  for helpful discussion with the osmoporation protocol. We thank the  NMR platform of the Institut de Microbiologie de la Méditerranée  and  Olivier Bornet  for providing NMR facilities. We thank  Hugo Le Guenno  from the  « Plateforme de Microscopie de l'Institut de Microbiologie de la Méditerranée » (IMM ,  FR 3479 ) for providing access to the fluorescence microscope and for technical assistance. We thank  Prof. Mario F. Feldman  for proving the genomic DNA of Acinetobacter baumannii 17978 and  Prof. Sophie Bleves  for proving the genomic DNA of Pseudomonas aeruginosa PAO1.
This work was funded by the  Centre National de la Recherche Scientifique , the  Aix-Marseille Université , and grants from the  Agence Nationale de la Recherche  ( ANR-18-CE11-0023-01  and  ANR-17-CE11-0039 ) and  European Society of Clinical Microbiology and Infectious Diseases (ESCMID)  to E. D. E. D. is supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM). Y. C. is funded by a doctoral school Ph. D. fellowship from the  FRM  ( ECO20160736014 & FDT201904008052 ). V. S. is funded by a postdoctoral fellowship from the association Espoir contre la Mucoviscidose. E. D. , R. P. , and Y. C. conceived the project. E. D. , R. P. , Y. C. , V. S. , D. B. , I. F. -M. , S. B. , X. M. , and M. N. designed the experiments. Y. C. , V. Schmidt,  V. Sgoluppi , and R. C. performed the in vitro and in vivo studies.
Introduction: Ceftolozane-tazobactam (CT) and ceftazidime-avibactam (CZA) are new beta-lactam/beta-lactamase inhibitors (BL/IBL) and antibiotics. There are few data regarding their impact on Clostridioides difficile infections (CDI). The objective of our study was, therefore, to determine and compare the number of CDI occurring after treatment with CT or CZA and carbapenem (CBP). Methods: All patients who received at least one dose of CT or CZA in our hospital between 1 January 2018 and 31 December 2019 were included. We compared, during the same period, the number of CDI after CT or CZA treatment and CBPs by using a chi-square test of Fischer's exact test when required. p value < 0. 05 was considered as significant. Results: Among the 53 patients receiving CZA and 42 patients receiving CT, two and one, respectively, developed a CDI within 90 days.
In particular, the place of CT in the treatment of multidrug-resistant p. aeruginosa infections is now well recognized  [5]. Extensive use of antibiotics has been linked to increasing Clostridioides difficile infections (CDI), especially through the modification of the gut microbiota. There are few data concerning the impact of BL/BLI on gut microbiota and CDI, although it has been shown that CDI impacts the length of stay, morbidity and mortality of infected patients  [6, 7]. CDI incidence has increased in France with 3. 6 cases per 10,000 patient-days in French acute healthcare facilities in 2016. According to the French national discharge hospital database, the estimated number of hospital stays with a CDI diagnosis increased from 9270 in 2010 to 19,480 in 2016 (+14%/y). Death occurred in 12% of stays and colectomies were performed in 1% of stays.
Data Collection Prescription of Antibiotics All prescriptions of CZA, CT and CBP are under close supervision in our hospital using a nominative prescription questionnaire and daily delivery with mandatory review for drug continuation at Day 1 by the antimicrobial stewardship team. All patients who received at least one dose of CT or CZA in our hospital between 1 January 2018 and 31 December 2019 were included. Duration and cumulative doses were collected for CT and CZA. CBP was used as a comparator to CZA and CT because both latter drugs may be used in place of CBP in order to decrease CBP use in the context of increasing CBP resistance, as recommended in France  [11]. CDI A CDI episode was defined as a compatible clinical presentation with the presence of C. difficile using the two step algorithm recommended by the ESCMID (antigen + presence of free toxin or positive result for toxin encoding genes by PCR)  [12].