dataesr/scientific-paragraphs-categorization huggingface dataset:
60  , developed for single-cell RNA-seq, to produce expression libraries of two replicates per population. Libraries were sequenced at an average depth of 5 million reads per library on an Ilumina NextSeq and aligned to the human reference genome (hg19). Reads were mapped using hisat (version 0. 1. 6); duplicate reads were filtered if they had identical unique molecular identifiers. Expression levels were calculated and normalized to the total number of reads using HOMER software. RNA-seq datasets have been deposited in the Gene Expression Omnibus (GEO) public repository (accession number GSE183669). Transcriptional profiling by BioMark. ILC2s were sorted as small bulks (25 cells) directly in 96-well qPCR plates with RT mix 1 (5× VILO Reaction Mix, 200 U µl -1 SUPERase-In, 10% NP-40 and nuclease free water).
f, ATP and ADP intensities quantified by label-free mass spectrometry in freshly sorted ILC2 and NKDim cells (n = 3). a-b, Data representative of 10 independent experiments with 2 to 4 donors each. c, Data from 12 healthy donors from at least 5 independent experiments. d-e, Data representative of 2 independent experiments with at least 3 donors. Dots are single cells in the analyzed field. Scale bar 5μm. f, Each dot represents a donor. A minimum of 2 technical replicates were analyzed per run. b, e, f, Floating bars indicate the mean, minimum and maximum values within the dataset. Statistics were assessed by two tailed t-test (b,e,f), not significant (ns), p > 0. 05; **p < 0. 01; ****p < 0. 0001. Letters NATuRE IMMuNOLOGy Extended Data Fig. 2 | ILC2 have enhanced mitochondrial activity compared to naïve T cells.
a, Gating strategy for CD4 + T cells subsets in the blood of healthy donors. FACS measurement of TMRM and MitoTracker gMFI in the indicated subsets (n = 4). b-f, Paired blood and tonsil samples were collected from pediatric donors. The percentage of ILC2 (b), GATA-3 gMFI (c), CRTH2 gMFI (d), representative plots of CD127, CD161, ST2 (e), TMRM and Mitotracker gMFI (f) were monitored by FACS (n = 3). a, Data representative of 2 experiments with at least 3 donors each. b-f, Data are representative of 2 independent experiments with 3 donors each. a, Floating bars indicate the mean, minimum and maximum values within the dataset. b-f, Bars are ± s. e. m. Statistics were assessed by one-way ANOVA with Dunnett correction (a) and two tailed t-test (b-f), not significant (ns), p > 0. 05; *p < 0. 05; **p < 0.
a,b Plot representative of 3 different donors. c-e, Data are representative of two experiments with 3 to 4 donors each. f, Data are representative of 3 experiments with at least 3 donors each. g-h, Data are representative of 5 independent experiments with 3 to 5 donors each. c-h, Floating bars indicate the mean, minimum and maximum values within the dataset. Statistics were assessed by one-way ANOVA with Tukey correction (d,e) and two tailed t-test (f-h), not significant (ns), p > 0. 05; *p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001. NATurE IMMuNOLOGy | www. nature. com/natureimmunology Letters NATuRE IMMuNOLOGy Extended Data Fig. Oxygen consumption is crucial upon ILC2 activation. a-c, Cells were cultured at 20% or 3% oxygen in IL-2-7 or IL-2-7-33 for 5 days. The gMFI of the indicated makers in the heatmap (a) and HIF1a gMFI (b) were analyzed by FACS (n = 3).
The level of expression of PKM, ST2 and GATA-3 transcripts was measured by Biomark (c) (n = 15). d-e, 1uM RO, or 1uM AA or with DMSO were added for 18h to the culture. Annexin V and L/D (d) (n = 3), Mitotracker and TMRM MFI (e) (n = 6) were analyzed by FACS. a, Data pooled from 3 independent experiments with 3 donors each. b, Data representative of 3 independent experiments. c, Data pooled from 2 independent experiments. d-e, Data are representative of a total of 4 independent experiments with 3 donors each. b-c, Bars are ± s. e. m. ; d-e, Floating bars indicate the mean, minimum and maximum values within the dataset. Statistics were assessed by one-way ANOVA with Tukey correction (b-e), not significant (ns), p > 0. 05; *p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001.
Acknowledgements We thank all the members of the  Innate Immunity Unit  for helpful discussions, the  Centre de Recherche Translationnelle and the logistic department of Institut Pasteur. We thank the  CB-UTechS platform  for cytometry support and the  Imagopole-CiTech  (part of  France-BioImaging  supported by  ANR  grant no. ANR-10-INSB-04-01 ,  Conseil de la Region Ile-de-France, FRM ) for technical support. The  Innate Immunity Unit  is supported by grants from the  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Institut Pasteur , the  Agence National pour le Recherche (ANR)  and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( 695467 , ILC_REACTIVITY). A. T. is supported by  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no.
765104. C. B. 's group was supported by the  Fondation pour la Recherche Médicale (FRM)  grant no. EQU201903007847  and the grant no. ANR-10-LABX-62-IBEID. L. S. was supported by an  SNSF-Early PostDoc. Mobility fellowship  and a  Marie Curie grant  ( H2020-MSCA-IF-2017 ).
VWF detection assays For western blot analysis, HuH7 and HUVEC protein lysates were prepared using 10 mM PBS (pH7. 4) containing 1% of Triton-X100 and protease inhibitors (Roche Diagnosis). Protein concentration was determined using the BCA Protein Assay (Thermo Fisher Scientific). SDS-page electrophoresis was performed in a 4-15% gradient polyacrylamide gel. After transfer, the membrane was blocked with Odyssey buffer (LI-COR Biosciences) and incubated with an anti-VWF antibody (rabbit polyclonal sc-14014, Santa Cruz Biotechnology). The membrane was washed and incubated with the appropriate secondary antibody (LI-COR Biosciences) and visualized by Odyssey Imaging System (LI-COR Biosciences). For western blot quantification, we used the Image Studio Lite Ver 4. 0 software. VWF antigen levels were measured using an immunosorbent-assay using polyclonal anti-VWF antibodies as described previously  [34].
Statistical analysis All the data shown in the present manuscript are reported as mean ± standard deviation (SD). The number of sampled units, n, upon which we reported statistic, is the single mouse for the in vivo experiments (one mouse is n = 1). Statistical analyses were conducted with GraphPad Prism 7 software (GraphPad Software). For all the data sets, data were analyzed by Student's t test for two-group comparisons or parametric tests (one-and two-way ANOVA with Tukey's or Dunnett's post hoc correction for comparisons with more than two groups). p values < 0. 05 were considered significant. The statistical analysis performed for each data set is depicted in figure legends.
3d ). A slightly higher VGCN in liver was observed for AAV9mut-C (Fig. 3d ). A similar trend was observed in the lung, where VGCN was lower in animals injected with AAV9mut-A and higher in animals injected with AAV9mut-C (Fig. 3d ). On the other hand, no significant differences between the vectors were observed in terms of VGCN in the heart, brain, and muscle (Fig. 3d ). Overall, integrated data from luciferase expression and tissue biodistribution (Fig. 3b-d ) showed two variants (AAV9mut-A and -D) displaying lower liver transduction, resulting in less luciferase activity over time, and two variants comparable to AAV9wt (AAV9mut-B and -C), with AAV9mut-C displaying a better liver and lung transduction. At the time of sacrifice, we also collected aortic samples from the mice, in order to analyze VGCN and transgene expression in the vasculature.
Expected benefits of EARS-Vet In order to support prudent veterinary use of antimicrobial therapy, and to implement a strong One Health strategy for AMR surveillance and antimicrobial consumption (AMC) control in Europe, building a European Antimicrobial Resistance Surveillance network in Veterinary medicine (EARS-Vet) should be considered. Such a network could be integrated with EARS-Net, FWD-Net, the EFSA monitoring for AMR in zoonotic and indicator bacteria and the European Surveillance of Veterinary Antimicrobial Consumption (ESVAC). This proposal, which we elaborate in the following sections, results from a collective agreement within the EU Joint Action on Antimicrobial Resistance and Healthcare-Associated Infections (EU-JAMRAI). It is in line with Regulation (EU) 2016/429 on transmissible animal diseases ('Animal Health Law'), which states that "effective collection and management of surveillance data should be established at Union level  […]  when relevant, for emerging diseases or antimicrobial-resistant pathogens"  [19] , and Regulation (EU) 2019/6 on veterinary medicinal products, which points out the need for AMR data  [20].
Surveillance objectives EARS-Vet would aim to describe the AMR situation, follow AMR trends and detect emerging AMR in bacterial pathogens of animals in Europe. The data generated could be used for (i) advising policy, (ii) monitoring interventions, (iii) evaluating marketing authorisations of antimicrobials, (iv) supporting antimicrobial stewardship initiatives, (v) generating epidemiological cut-off values, (vi) supporting risk assessment and (vii) estimating antimicrobial resistance burden. Data generated through EARS-Vet could serve policymakers in devising more targeted and efficient interventions based on a better understanding of the AMR situation and its evolution, as well as of the links between AMC and AMR in animals and humans. At present, these links are investigated in the Joint Inter-Agency Antimicrobial Consumption and Resistance Analysis (JIACRA) reports  [21] , using AMR data from healthy animals only.
Thus, links could be better explored using EARS-Vet data obtained closer to the animals' point of care, i. e. where antimicrobials are used in veterinary medicine. From 2024 onwards, it will become mandatory for EU countries to provide antimicrobial use data by animal species (in a stepwise approach, starting with the main food-producing animal species) under the framework of EU Regulation 2019/6  [20]. This should make EARS-Vet even more relevant, as resistance patterns in bacterial pathogens may then be linked to antimicrobial use at the animal species level. EARS-Vet data would also allow for monitoring of the impact of European efforts to tackle AMR in the animal sector, such as the EU One Health Action Plan against AMR and the Animal Health Law  [19]. They could also support the evaluation or revision of marketing authorisations of antimicrobials, in the framework of Regulation (EU) 2019/6  [20].
A pragmatic strategy to design EARS-Vet One of the first steps in designing EARS-Vet would be the definition of a surveillance framework, including its: (i) surveillance scope (i. e. the animal species, production types (e. g. dairy or beef cattle, laying hens or broilers), age categories, bacterial species, specimens and antimicrobials it covers), (ii) AST standards, (iii) metadata to be collected (including minimum and desirable variables), (iv) data governance, (v) frequency of data reporting, (vi) general data management system and (vii) required procedures (including data cleaning and validation), as well as (viii) how data would be analysed and communicated. This framework should ensure that EARS-Vet can meet the aforementioned surveillance objectives and facilitate broad country participation.
Such biased estimates can have important consequences, including wrongly recommending the use of critically important antimicrobials as first-line treatments when other antimicrobials will actually be effective in the majority of cases. In order to ensure comparability of AMR data between countries, the representativeness of AMR data would need to be assessed before results are interpreted. As in EARS-Net, a series of indicators of national geographic coverage and representativeness could be defined and regularly calculated to understand the validity of the surveillance data. In addition, pragmatic solutions should be explored collectively to address sampling biases. Of note, some countries have decided to subsidise ASTs (e. g. Czech Republic and Spain) to collect more representative AMR data comprising a broader range of cases, i.
Acknowledgements We are very grateful to all the professionals who were consulted as part of this work. These include participants of  EARS-Vet meetings in Finland, Sweden, Norway, Denmark, Germany , the  Netherlands , the  Czech Republic, Ireland, Spain, Belgium, Italy and Greece , as well as experts from the  ECDC, EMA, EFSA, Federation of Veterinarians in Europe, EUCAST, Food and Agriculture Organization of the United Nations (FAO) ,  World Health Organization (WHO)  and the  Netherlands' National Institute for Public Health and the Environment (RIVM) , as a  WHO Collaborating Centre for Antimicrobial Resistance Epidemiology and Surveillance. The views expressed in this publication are those of the authors and do not necessarily reflect the opinions of consulted experts and organisations.
Expression Analysis of TRAIL Receptors in Cancers To evaluate the expression pattern of TRAIL-Rs across cancers, we used the Gene Expression Profiling Interactive Analysis (GEPIA) web-based tool (http://gepia. cancerpku. cn/index. html (accessed on 12 June 2021), which uses RNA sequencing expression data from The Cancer Genome Atlas (TCGA) for tumor and adjacent tumor samples, and data from the Genotype-Tissue Expression (GTEx) project for healthy normal tissues. The differential expression genes (DEGs) data for 28 cancers with at least 10 normal samples were downloaded for 28 or matched GTEx samples. These cancers included adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiform (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC) and uterine Carcinosarcoma (UCS).
The numbers and information related to the samples of this study have been summarized in the Supplementary Materials, Table  S1. To generate a heatmap and the expression matrix, the complex heatmap R package version 2. 7. 1. 1009 (https://jokergoo. github. io/ComplexHeatmap-reference/ accessed on 6 December 2020) based on the hierarchical clustering of genes and cancers was used. The q-value < 0. 01 and log 2 FC > 1 (upregulated) or log 2 FC < 1 (downregulated) were considered to be significant values.
The images were then analyzed for the quantification of fluorescence variations with software (Metafluor, Molecular Devices, San Jose, CA, USA). Between 20 and 30 cells were studied per each field (at least three fields at three different Cells 2022, 11, 57 5 of 18 days were measured). An increase in the fluorescence emitted by this cell permeable probe (F/F0) reflects an increase in cytosolic Ca 2+.
of TRAIL-R1, along with the upregulation of TRAIL decoy receptors, are associated with resistance to TRAIL-mediated apoptosis in leukemia cells  [28]. Figure  1. TRAIL receptors expression pattern across 28 TCGA cancer types. The RNA sequencing data for 28 TCGA (The Cancer Genome Atlas) cancers as well as normal adjacent tumors and normal GTEx (Genotype-Tissue Expression) samples were extracted from Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Then, the differential mRNA expression levels (log2 fold change (FC)) of four well-known TRAIL-R1 (DR4), TRAIL-R2 (DR5), TRAIL-R3 (DcR1) and TRAIL-R4 (DcR2) were analyzed in each cancer compared to their corresponding normal samples. The heatmap construction and clustering were performed as mentioned in Materials and Methods. Green and red colors represent significant (p-value < 0.
The RNA sequencing data for 28 TCGA (The Cancer Genome Atlas) cancers as well as normal adjacent tumors and normal GTEx (Genotype-Tissue Expression) samples were extracted from Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Then, the differential mRNA expression levels (log 2 fold change (FC)) of four well-known TRAIL-R1 (DR4), TRAIL-R2 (DR5), TRAIL-R3 (DcR1) and TRAIL-R4 (DcR2) were analyzed in each cancer compared to their corresponding normal samples. The heatmap construction and clustering were performed as mentioned in Materials and Methods. Green and red colors represent significant (p-value < 0. 01) upregulation (log 2 FC > 1) or downregulation (log 2 FC < 1) of TRAIL-Rs in different cancers, respectively. TCGA cancer groups include BLCA (bladder urothelial carcinoma), BRCA (breast invasive carcinoma), CESC (cervical squamous cell carcinoma and endocervical adenocarcinoma), CHOL (cholangiocarcinoma), ESCA (esophageal carcinoma), GBM (glioblastoma multiforme), HNSC (head and neck squamous cell carcinoma), KIRC (kidney renal clear cell carcinoma), KICH (kidney chromophobe), KIRP (kidney renal papillary cell carcinoma), LAML (acute myeloid leukemia), LIHC (liver hepatocellular carcinoma), LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma), PAAD (pancreatic adenocarcinoma), PCPG (pheochromocytoma and paraganglioma), PRAD (prostate adenocarcinoma), SARC (sarcoma), SKCM (skin cutaneous melanoma), STAD (stomach adenocarcinoma), THCA (thyroid carcinoma), THYM (thymoma), UCEC (uterine corpus endometrial carcinoma).
Acknowledgments: We are grateful to  Patrick Legembre  for providing the Jurkat shRNA ORAI1 and PLC γ-deficient Jurkat cells (PLC γ1-/-) previously described in [38]. We thank  Morgane Lajoie  and  Corentin Barrière  for help in immunoprecipitation experiments, and  Zahra Nayyeri  for her assistance in bioinformatic analysis.
The PBS cell suspensions were supplemented with 250 ng/mL lysostaphin and incubated under static conditions at 30 °C for 60 min. Lysostaphin-mediated lysis was measured as the decline of OD600 versus time and is expressed as the percent of the initial optical density (n = 5 biological replicates). Data are presented as box and whisker plots showing the interquartile range (25-75%; box), median (horizontal line), and whiskers (bars; min/max). * p < 0. 05; ** p < 0. 01 (Ordinary two-way ANOVA followed by Holm-Sidak's multiple comparison test).
The PBS cell suspensions were supplemented with 250 ng/mL lysostaphin and incubated under static conditions at 30 • C for 60 min. Lysostaphin-mediated lysis was measured as the decline of OD 600 versus time and is expressed as the percent of the initial optical density (n = 5 biological replicates). Data are presented as box and whisker plots showing the interquartile range (25-75%; box), median (horizontal line), and whiskers (bars; min/max). * p < 0. 05; ** p < 0. 01 (Ordinary two-way ANOVA followed by Holm-Sidak's multiple comparison test).
Clinical Samples We retrospectively identified patients with suspected meningitis whose CSF samples were tested with the Eazyplex ® CSF direct M panel (AmplexDiagnostics GmbH, Gars-Bahnhof, Germany), at the physician's and/or microbiologist's discretion, from December 2016 to December 2019, at Nantes University Hospital. Redundant CSF specimens (i. e. , sampled from the same patient during the same clinical event) were excluded. Samples, results, and data were recorded during normal medical care of patients by the professionals who were monitoring them.
Eazyplex ® CSF Direct M Testing An amount of 125 µl from each sample was subjected to Eazyplex ® CSF direct M panel (V1. 0) testing according to the manufacturer's instructions. This panel test consists of a multiplex real-time LAMP assay for the identification of the 6 most common agents of community-acquired and neonatal bacterial meningitis (Neisseria meningitidis, Streptococcus pneumoniae, Escherichia coli, Streptococcus agalactiae, Haemophilus influenzae, and Listeria monocytogenes). The test contains a LAMP inhibition internal control. After a 30 min run, the Eazyplex ® software (eazyReportTM, Gars-Bahnhof, Germany) performs automated results analysis according to real-time fluorescence detection of amplification products. If internal control fails, the software automatically provides the result "invalid" for all the panel analyses.
Data Analysis and Discrepancy Investigation Patients' demographic data, CSF chemistry results (protein and glucose), additional microbiological analysis (specific N. meningitidis, S. pneumoniae or L. monocytogenes PCR, S. pneumoniae soluble antigen detection, and 16S rRNA gene PCR and sequencing), PCR testing for virus (enterovirus, herpes simplex virus 1 and 2 (HSV-1/2), and varicella-zoster virus (VZV)), concomitant positive blood culture, antimicrobial therapies administered before lumbar puncture and final clinical diagnosis were retrospectively recorded. When necessary, medical files were n necessary, medical files were retrospectively assessed by infectious diseases specialists blinded to the EP results, to confirm the final diagnosis. All data collected from patients' medical folders were filled in on a board under an anonymized code.
Molecular test results were analyzed according to blood and CSF cultures, CSF chemistry parameters as well as previous antibiotics administration and final clinical diagnosis. After resolution of discordant results, the EP results were considered true positive (TP), true negative (TN), false positive (FP), or false negative (FN). If the available data did not support nor exclude a diagnosis of meningitis due to one of the six pathogens targeted, the result was considered inconclusive.
Secondly, invalid results were excluded from the performance assessment, which could lead to an overestimation of these performances. Thirdly, this work was performed with the first version (V1. 0) of the Eazyplex ® CSF direct M panel. The manufacturer has recently marketed a new version of this test (V1. 1), notably including an additional reagent to reduce the inhibitory influence of CSF components. Moreover, the new manufacturer's instructions recommend confirming positive reactions with another technology when the time to positivity is greater than 25 min. This recommendation would have reduced the FP results generated by the automated software. When compared with the FA-ME panel (designed to identify 14 targets, including seven viruses and Cryptococcus neoformans/gattii), the EP detects fewer targets but offers the benefits of a focus on bacterial meningitis diagnosis and a lower price  [5].
Introduction Antimicrobial peptides (AMPs) provide a rst line of defense against a multitude of pathogenic microorganisms and can be released rapidly when infections occur. 1, 2 They are part of the innate immunity of a wide variety of species from the plant and animal kingdoms, including humans. 3 The corresponding databases list thousands of sequences and many are continuously added. 4, 5 Understanding PAPER their mechanism of action allows one to design molecules with favorable properties that mirror the essential characteristics of the template compounds. They have also been shown to modulate the immune response of the host organisms. 6 ] [9]  In this paper we focus on linear cationic antimicrobial peptides such as magainins which were rst discovered in frogs. 2  These antimicrobial peptides are membrane active and they act by interfering with the barrier function of bacterial lipid bilayers.