dataesr/scientific-paragraphs-categorization huggingface dataset:
Coloc Whether gene expression level in tissue with lowest P-value from the multitissue TWAS analysis colocalised with at least 1 HF phenotype under study (posterior probability of shared causal variants >0. 8). The colocalization analysis was performed with the R coloc package allowing for multiple causal variants  15  using gene expression data from GTEx v8  79. In addition, we derived an Overall predictor score based on weighted average of PoPS, highest V2G, and -log10 P MulTiXcan values with 2:2:1 weight ratio, scaled to 0-100 value using a quantile transformation with uniform output distribution as implemented in python scikit-learn 80 library. Finally, for each locus, we ranked genes based on total classifier score (sum of True values) and the overall predictor score. Genes that are top-ranked or have a total classifier score ≥2 were prioritised as most likely effector genes for HF.
Phenome-wide association studies in UK Biobank To characterise pleiotropic effects of HF genetic loci across human diseases, we performed a phenome-wide association study (PheWAS) by testing the associations of the lead variants at 66 HF loci with 294 disease phenotypes in 408,480 UK Biobank participants of European ancestries. Phenotypes were derived from clinical events recorded in data from linked hospital admission, death certificate, primary care visit, and self-reported cancer diagnosis, non-cancer diagnosis, and procedure history; using expert-curated phenotype definition and category described elsewhere  43. For a given phenotype, participants were classified as cases if they ever had a record of at least one matching code in any data source; otherwise as controls. For each phenotype -lead variant pair, we ran a case-control genetic association analysis implemented in PLINK2, requiring at least 100 cases and 100 controls per phenotype.
Pleiotropy network analysis Using results from the identified genotype-phenotype associations from the locus PheWAS, we performed a network analysis to evaluate the connections between HF genetic loci based on their pleiotropic effects across human diseases. First, we constructed a network dataset with nodes (vertices) representing loci or phenotypes; and edges representing unidirectional associations of the locus lead variant with a given phenotype. The resulting network was then visualised using the Davidson-Harel layout algorithm 97. To help with interpretation, edges on the graph were coloured by the effect direction of HF risk-increasing allele, with thickness of the line representing absolute Z statistics for the association. Phenotype nodes were labelled by disease name and coloured by disease category; whereas locus nodes were labelled by most likely effector genes.
Eigenvector centrality measure was calculated to estimate node influence within the network (Supplementary Figure  20 , Supplementary Table  22 ), and then mapped to the node size so that a more influential node appears larger. Further, we performed a data-driven community detection analysis on the graph using a walktrap algorithm  98  weighted by absolute Z score of the edges to identify clusters of densely connected nodes. Overall modularity 99 of the resulting clusters was calculated to quantify the strength of the division, where a higher value reflects a connection between nodes that is denser for nodes within the same cluster but sparser for nodes belonging to different clusters. The network analysis and visualisation was performed using the igraph, tidygraph and ggraph package in R.
Cross-trait colocalisation To test whether locus-specific genetic associations with HF and a given trait derived from common causal variants, we performed pairwise cross-trait colocalisation analysis implemented in R coloc package  15  , allowing multiple causal variants per locus by integrating Sum of Single Effects (SuSiE) regression framework  76. This approach first runs SuSiE to detect credible set(s) L1 and L2 corresponding to trait 1 and trait 2 respectively; followed by colocalisation to estimate posterior probabilities of common causal variants (hypothesis 4, H4) for each pairwise combination of elements of L1 x L2  15. To run coloc, we used the default marginal prior probabilities for association with trait 1 (p1) and trait 2 (p2) of 10 -4 , and a prior probability of joint causal association (p12) of 10 -5.
Study design The present meta-analysis included 1,946,349 individuals from 42 studies participating in the HERMES Consortium (40 studies) or publicly released GWAS data of heart failure (Biobank Japan and FINNGEN r3). Sample genotyping was performed locally per study per phenotype per ancestry (one dataset) using high-density genotyping arrays and imputed against Haplotype Reference Consortium 59 (75 datasets), 1000 Genomes Project 60 (6 datasets), TOPMED 61 (15 datasets), or population-specific reference panels (10 datasets). Study-level GWAS was performed locally per dataset using logistic regression (for prevalent cases) or cox proportional hazard (for incident cases) assuming additive genetic effect with adjustment for sex, age at DNA draw, genetic principal components, and study-specific covariates.
The sample was harvested immediately after the positive result was observed, at the next dialysis session, after 2 days (Table  2 ). After obtaining informed consent from each subject, a blood sample (~2 mL) was taken on EDTA and subsequently processed using the simple Lyse-Wash protocol: CD14, CD16, CD300e, HLA-DR, CD64, and CD45 (complete with fluorochromes and providers), and was analyzed with Gallios software (Navios ™ ) (Beckman-Coulter; Fullerton, CA). Collection of peripheral blood samples was performed by intravenous puncture and then placed in tubes with EDTA. Samples were kept at room temperature until transported to the laboratory. Approximately 100 mL of blood was taken from each sample and placed in a Falcon tube together with 5-20 mL of labelled monoclonal antibodies. The samples were vortexed, incubated for 15 min in the dark at room temperature, the red blood cells were lysed, and then the tubes were inserted into the cytometer for data acquisition.
We used the Gallios flow-cytometer, produced by Becton Dickinson, which was equipped with three lasers and 10 colors. For the study of monocytes, antibodies were used to identify the markers: CD14, CD16, CD300e, HLA-DR, CD64, and CD45. Monoclonal antibodies from the following manufacturers were used: Beckman-Coulter, Immunotech (BD Biosciences ® San Jose, CA), BD Pharmingen, Dako, Cytognos, CD14, APCH7, MFP9, BD; CD16, FITC, CLB-FcGran1, BC; CD300e, APC, IREM-2, Immunotech; HLA-DR, V450, L243, BD; CD64, PE, 10,1, BD; CD45, OC515, HI30, Immunostep. The Shapiro-Wilk test was used to determine if the variables had a normal distribution. If there was no exception, a paired t-test was used. If the data deviated from normality, then the Wilcoxon test was applied using Jasp and NCSS software. Statistically significant differences were considered when p<0.
The CPB extraction ratio (ER) was calculated using the following equation: CPB ER ð%Þ ¼ 100 X ðpre CPB cefazolin concentration À post CPB cefazolin concentrationÞ= pre -CPB cefazolin concentration: The fraction of cefazolin cleared into urine (UrFE) over dosing interval (0-4 hours) was calculated as a function of the AUC in urine and plasma: UrFE = (AUC(0-4h) urine / AUC(0-4h) plasma) x 100. Population pharmacokinetic modelling. To describe total and unbound cefazolin concentrations, two and three-compartment models were developed with the Nonparametric Adaptive Grid (NPAG) algorithm within the freely available Pmetrics software package for R (Los Angeles, CA)  [10, 11]. Elimination of the total drug from the central compartment, inter-compartmental distribution into the peripheral compartment (three compartment model) and similarly inter-compartmental distribution into the unbound drug compartment (two and three compartment model) were modelled as first-order processes.
5 g 2 hours later, were simulated. Three different levels of renal function were tested that reflected the broad distribution of values observed in this patient population (SeCr of 50, 75 and 100 μmol/L). Three different serum albumin concentrations  (30, 35  and 40 g/L) were also simulated for a patient with a SeCr of 75 μmol/L. Fractional target attainment calculation. MIC data of S. aureus, S. epidermidis and Escherichia coli from the EUCAST database were used to determine fractional target attainment (FTA)  [14]. The fractional target attainment identifies the likely success of treatment by comparing the pharmacodynamic exposure (PTA) relative to the MIC distribution. The fractional target attainment was calculated using 100% fT >MIC. The PTA for achieving 100% fT >MIC was calculated using Monte Carlo simulations (n = 1000) for various doses of 1 g 4-hourly, 2 g 4-hourly, 3 g 4-hourly, 1 g 2-hourly and 2 g 2-hourly during the first 4 hours of surgery for patients with SeCr of 50, 75, 100 μmol/L at MIC values from 0.
05). After including these parameters the -2LL values decreased (-17 Δ-2LL, p = 0. 02) and the goodness-of-fit improved. As such, serum creatinine concentration and albumin were retained in the final model. A multiplicative error equation was supported for use in this model with the equation: Error ¼ ðSD * gÞ 2 ; where SD represents the standard deviation of observations and γ represents process noise. In addition, assay error for plasma data was included as: Error ¼ C0 þ C1 * obs; where obs represents observations; and coefficients C0 (limit of quantification) and C1 (% coefficient of variation of assay) were included as 1. 0 mg/L and 0. 1 respectively. For unbound data, C0 and C1 were 0. 1 and 0. 1 respectively. The final clearance model was described as follows: Cefazolin CL = TVCL*(90/SeCr) where TVCL is the typical value of cefazolin clearance and SeCr is the serum creatinine concentration in μmol/L.
Flow cytometry analysis Spleen leukocyte suspensions were prepared using DNAse I and collagenase D  (Baranek et al, 2012). For FACS sorting and analysis, we used previously reported protocols  (Mossadegh-Keller et al, 2013; Kandalla et al, 2016) , published HSPC definitions  (Bryder et al, 2006) , indicated antibodies (see Table  EV1 ), FACSCanto, LSRI, LSRII and FACSAriaIII equipment and DIVA software (BD), analyzing only populations with at least 200 events. For human samples, an antibody panel was used to distinguish progenitors of human HSPCs (Lin À CD34 + ) such as common lymphoid progenitors (CLPs, Lin À CD34 + CD38 À/low CD45RA + ), common myeloid progenitors (CMPs, Lin À CD34 + CD38 + CD45RA À ) or granulocytemacrophage progenitors (GMPs, Lin À CD34 + CD38 + CD45RA + ) with mature GMPs additionally expressing HLA-DR from mature myeloid cells (either CD11b + CD66b À or Lin + AE CD14/CD16) whose IL15Ra expression was quantified.
Microfluidic real-time RT-PCR gene expression analysis Total mRNA extraction from 50,000 FACS-sorted cells and cDNA synthesis were performed with lMACS one step T7 template kit (Miltenyi) and specific gene expression (primers in Table  EV3 ) was detected according to Fluidigm protocols as previously described  (Soucie et al, 2016)  or by SybrGreen method  (Mossadegh-Keller et al, 2013). Ct values were calculated by BioMark Real-time PCR Analysis software (Fluidigm) using the DDCt method and HPRT for normalization.
Impact Overall, our preclinical results support the efficacy and feasibility of M-CSF administration as a prophylactic host-directed therapy that confers rapid protection from lethal CMV infection. Moreover, our data suggests a paradigm by which M-CSF boosts innate antimicrobial immunocompetence, which may constitute a well-tolerated general host-directed therapeutic in states of severe immunosuppression. A Median of absolute numbers of (CD45. 2 + ) recipient (left) and (CD45. 1 + ) donor (right) NK cells (CD19 À CD3 À Ly6G À NK1. 1 + ). **P < 0. 01 by Mann-Whitney U-test. B Gene expression profiling of transcription factors measured by nanofluidic Fluidigm array RT-qPCR of host-derived NK cells, which were isolated from the spleens of control or M-CSF-treated recipient mice 1. 5 days after MCMV of infection or time-matched, mock-infected, HSPC-transplanted mice.
The "Monocytes" (CD14 + ) gate permitted identification of CD14 + CD16 + monocyte-derived macrophages. At baseline (d0), this gating strategy was used to identify classical monocytes (CMs, CD14 + CD16 À ), intermediate monocytes (IMs, CD14 + CD16 + ) or non-classical monocytes (NCMs, CD14 À CD16 + ) accordingly. D IL15Ra expression measured on myeloid, monocytic cells, or monocytes identified in (C). E The gating strategy of OMIP-027 with minor modifications by gating on the Lin À cells from the dot plot shown in (C). Source data are available online for this figure. Prashanth  K Kandalla et.
20110421320 ) to MHS, ' Agence Nationale de la Recherche ' ( ANR-11-BSV3-026-01 ,  ANR-17-CE15-0007-01  and  ANR-18-CE12-0019-03 ) to MHS,  French National Cancer Institute  ( 13-10/405/AB-LC-HS ) to MHS,  Fondation ARC pour la Recherche sur le Cancer  ( PGA1 RF20170205515 ) to MHS,  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement number  695093 MacAge ) to MHS. The funders of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript. MHS is an Alexander von Humboldt Professor at the  TU Dresden. PKK was partially funded by  SATT Sud. JS is funded by the  Deutsche Forschungsgemeinschaft  (Clinician Scientist position,  SU 1360/1-1 ). The authors declare no competing financial interests.
Protein extraction and western blot Thirty micrograms of total cell protein from snap-frozen microdissected tissues and cultured cells were loaded onto 4-12% polyacrylamide gel (Invitrogen) and transferred to a 0. 2 µM polyvinylidene fluoride membrane (Bio-Rad) as further detailed in online supplemental materials. Membranes were blocked for unspecific antibody binding and incubated with suitable primary and secondary antibodies (see online supplemental materials) before development by chemiluminescence-based detection with Amersham ECL Prime (GE HealthCare) following manufacturer's instructions. Images were acquired using the ImageQuant LAS 4000 softwareassisted imager (GE HealthCare).
Immunofluorescence cytofluorometric analyses Freshly harvested tumors were dissociated to single cell suspensions and stained with fluorochrome-conjugated immune phenotyping antibodies for lymphocyte and myeloid populations for flow cytometry purposes as previously described  17  and detailed in online supplemental materials. Fully stained samples were run through a BD LSRFortessa X-20 Cell Analyzer using BD FACSDiva software (BD Biosciences). Post-acquisition analyses were performed using Omiq. ai (https://app. omiq. ai/) following the recommended pipeline (see also online supplemental materials).
snRNAseq analyses All snRNAseq analyses were conducted with Seurat V. 4. 3 R package on R V. 4. 2. 2 (https://www. r-project. org). 25 ndividual sample data were filtrated and nuclei with more than 15% of mitochondrial genes were excluded. Nuclei with more than 1. 000 RNA counts and 500 expressed genes in B6BC original tumor (OT) and more than 500 RNA counts and 300 expressed genes in B6BC transplanted tumors (TT) were retained for subsequent analyses. Filtered data were normalized and the 5. 000 more variable features were identified using 'vst' method to select features and anchors for data integration of individual data sets. The merge data set was then used to perform classical Seurat analysis pipeline with data scaling and scanning for actual most variable features on principal component analysis (PCA) (explained by 20 first principal components) before dimensionality reduction by uniform manifold approximation and projection (UMAP).
27 GO terms for graphical representation were selected taking into account the number of genes of interest contained in that GO term normalized by the number of total genes constituting the GO term (called % term size) with different thresholds for each cluster adapted to the number of genes of interest. Volcano plot representation of DEG was done with EnhancedVolcano V. 1. 14 (https://github. com/ kevinblighe/EnhancedVolcano) while gene density plots on UMAPs were generated with plot_density function from the Nebulosa R package V. 1. 6. 28 The heatmap was done using ComplexHeatmap package V. 2. 12. 1  29  with gene expression converted by the score: score = log 2 ( Gene i expression + 50 Gene i total expression + 50 ).
Open access Prism software V. 6 and V. 9. 5 and the tests indicated in the figure legends. For MTT cell proliferation assays, p values were extracted from the coefficients that are specific to a given treatment at a given time compared with time-matched vehicle-treated controls according to the function 'lm(CellProliferation~time/treatment+experiment)' of R (see online supplemental materials). Heatmap of online supplemental figure 1C was done using https://software. broadinstitute. org/morpheus/. In vivo data representation and mouse numbers are indicated in the figure legends. Statistical analyses for tumor growth (TG) and survival curves were performed on the raw data (tumor area over time for each mouse) of individual or pooled experiments considering all measured days until the following day the first mouse in either group compared was sacrificed (for TG) or until all mice achieved tumor endpoint (for survival) and were calculated using TumGrowth web tool (https://kroemerlab.
B6BC TT are poorly infiltrated by lymphocytes and are not under T-cell immunosurveillance Tumor infiltrating immune cells were present in all studied tumors figure  1I ) and were the second most abundant population recovered by snRNAseq in B6BC OT and TT (figure  3A-C ). To better characterize these cells, we examined different panels of phenotypic leukocyte markers on freshly dissociated bulk B6BC, TS/A and E0771 TT by immunofluorescence-based multiparameter flow cytometry. We first analyzed these data by manually supervised analysis (figure  3 ), normalizing cell counts by the number of total living cells to minimize the impact of structural tumor differences (online supplemental figure 1I; see online supplemental figure 4A for cell per tumor weight counts). Tumor-infiltrating leukocytes (CD45 + ) represented more than 60% of living dissociated cells in TS/A and E0771 TT, but less than 3% in B6BC TT.
Acknowledgements We thank  Dr Laurence Zitvogel  ( Institut Gustave Roussy, Paris ),  Dr Clothilde Thery  ( Institut Curie, Paris ) and the cell culture facility of  CEINGE-Biotecnologie Avanzate s. c. a. r. l. ( Naples, Italy ) for kindly providing murine and human breast cancer cell lines. We are grateful to  Kevin Garbin  and  Oumar Maiga  from the  CHIC platform at Centre de Recherche de Cordeliers  and to  Olivia Bawa  from the PETRA platform at  Institut Gustave Roussy  for their help with histological sample processing and stainings. We also thank  Dr Aurelien de Reynes  for providing help with snRNAseq analyses. Contributors MP-L performed most in vivo and in vitro experiments and analyzed most of the data. VC performed informatics analyses of snRNAseq. PC carried out histological analyses.
Funding GK is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR) -Projets blancs; AMMICa  US23/CNRS UMS3655 ; Association pour la recherche sur le cancer (ARC);  Cancéropôle Ile-de-France ;  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  Equipex Onco-Pheno-Screen ;  European Joint Programme on Rare Diseases (EJPRD) ;  European Research Council   Advanced Investigator Award  ( ERC-2021-ADG ,  ICD-Cancer , Grant No. 101052444 ),  European Union   Horizon 2020 Projects Oncobiome and Crimson ;  Fondation Carrefour ;  Institut National du Cancer (INCa) ;  Institut Universitaire de France ;  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); a  Cancer Research ASPIRE Award  from the  Mark Foundation ; the  RHU Immunolife; Seerave Foundation ;  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and  SIRIC Cancer Research and Personalized Medicine (CARPEM).
An impact hammer with a vinyl tip is used to excite 48 points distributed over the entire structure, in order to achieve a relatively fine representation of the mode shapes. Each point is excited in the three directions when possible. The extraction of modal parameters from the measured transfer functions is performed up to 2000 Hz using the PolyMAX algorithm  [9] , implemented in the commercial software Simcenter Testlab (Siemens). An example of measured transfer-mobility is shown in Figure  3  and reveals the existence of 9 modes between 20 Hz and 500 Hz.
Numerical study of the resonator The numerical modal analysis of the resonator is carried out with a finite element model (FEM), using the commercial software COMSOL Multiphysics. The geometry is constructed from the actual dimensions of the resonator previously studied. The bolts are included in the geometry. The threaded rods are modeled by cylindrical rods with uniform cross-section but diameter slightly smaller than the actual outer diameter of the thread. This effective diameter is one of the quantities used for the recalibration of the model. Clamped boundary conditions are considered at the base of the resonator. The mesh is made of tetrahedral elements, whose size is between 3 mm and 40 mm. The material properties considered in the model are shown in Table  1. For each material, the density was directly estimated from measurements.
Every time it is possible, the experiments themselves must be accompanied with control/blank experiments aiming at evaluating the cleanliness and status of the chambers as well as the chemical background reactivity of the reactive mixture (bath gas, purity of water, of precursors…). Preparing a chamber for conducting robust atmospheric simulation experiments, is not only about applying carefully standard cleaning protocols and reactive mixture recipes, but also about implementing the full traceability of the experimental conditions, that implies the curation of blank experiments datasets, together with the precise preparation protocol applied. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material.
Procedures (1) Choice of Reference Compounds-The reference compound should have a similar rate coefficient to that predicted (e. g. via structure-activity relationships),  (Atkinson 1987)  otherwise the ratio measurements will be imprecise if one reagent has hardly reacted while the other has almost disappeared. The reference rate coefficient should be well defined and ideally have been reviewed in an evaluation (e. g. IUPAC Kinetics Evaluation). Databases such as EUROCHAMP and NIST are other sources of reference information if evaluated data are not available. Avoid using rate coefficients that are themselves derived from relative rate measurements. A major source of error in RR measurements is if a reagent is removed by another species, so in a study of OH reacting with a saturated species, where OH is generated from CH 3 ONO photolysis  (Atkinson et al.
1981; Jenkin et al. 1988 ) there is a potential for O 3 formation, so using an alkene as the reference compound (hence removal by OH and O 3 ) would not be a good choice. Use a photolysis database (e. g. Mainz Photolysis Database) to ensure that neither the reference nor substrate is predicted to be lost by photolysis (this should always be checked too). It is good practice to use more than one reference compound. (2) Experimental method • Introduce RH and SH into the chamber to test for wall-loss rates (Chap. 4). Leave for a reasonable period of time, (certainly much longer than the mixing time) sufficient to ensure an accurate estimate of the wall loss. Checks on wall-loss rates should be done on a regular basis as the conditions of the walls may change and wall-loss rates can vary with temperature and pressure.