dataesr/scientific-paragraphs-categorization huggingface dataset:
5, clone SK3), anti-CD8 (BV570, clone RPA-T8), anti-CD137 (phycoerythrin-Cy7, clone 4B4-4), and anti-IFN-g (BV605, clone 4S. B3). All antibodies were purchased from BioLegend, unless indicated otherwise. Cells were analyzed on a LSR-II Fortessa flow cytometer (BD Biosciences) and FlowJo version 10 software (Tree Star). For ex vivo analysis, at least 2 3 10 6 events were recorded. Lymphocytes were gated based on the forward scatter (FSC) vs side scatter (SSC) profile and subsequently gated on FSC (height) vs FSC to exclude doublets. Unstimulated PBMCs were used as controls and respective background responses were subtracted from antigen-reactive cytokine production. Negative values were set to 0. Measurements were performed in the immunologic study laboratory of the BCRT.
Statistical analyses Data were analyzed using GraphPad Prims (GraphPad Software Inc) and represented as mean 6 standard error of mean (SEM) with *P ,. 05, **P ,. 01, ***P ,. 001, and ****P ,. 0001. Differences between groups were assessed using 2-way analysis of variance (ANOVA), nonparametric Bonferroni posttest. Survival was analyzed by Mantel Cox, log-rank test. Experimental procedures were specifically approved by the local ethic committee (nos. APAFIS# 3143 for rat and 2525 for NSG mice). Experimental procedures were specifically approved by the local ethic committee (no. APAFIS#11511-2017092610086943). This study was approved by the internal review board of H ôtel-Dieu Assistance Publique-H ôpitaux de Paris.
Acknowledgments The authors thank  Leila Amini  and  Nina Salabert-Leguen  for flow cytometry measurements. The authors also thank the  Biobanking Department staff of Henri-Mondor Hospital, Cr éteil, France (Plateforme de Ressources Biologiques des H ôpitaux Universitaires Henri Mondor , ref. BB-0033-0002 ). This work was supported by  LabEx IGO  (project  Investissements d'Avenir ;  ANR-11-LABX-0016-01 );  Institut Hospitalo-Universitaire-Cesti  (project funded by the  Investissements d'Avenir ;  ANR-10-IBHU-005  as well as by  Nantes Metropole  and  Region Pays de la Loire) ; the  Agence National de la Recherche (ANR)   COLT-45RC  ( 18-CE18-0024-02 ); the  Agence de la Biomedecine ; the  Fondation Centaure ; and the  SATT Ouest Valorisation.
For measurement of endosomal and Golgi STX6 intensity, a z-projection of the stack image was performed using the sum projection method. On the z-projection image, two individual ROIs were delimitated for each cell analyzed, one for the Golgi stacks and one for the cellular area outside Golgi. Measurements performed with ImageJ were limited to threshold and included ROI area, ROI mean fluorescence intensity, and integrated density. The results were reported as mean of fluorescence or integrated density/area. Statistical analysis was performed with GraphPad Prism software using unpaired t-tests.
A second PCR product was obtained using the megaprimer and the sense primer 5 -ACC GGT GCC ACC ATG CTA CTA GTA AAT CAG TCA C (AgeI site underlined). This PCR product was used to replace the fragment between the AgeI and EcoRI sites in pMAX-IRAP, creating pMAX-IRAP E465A, followed by sequencing to confirm the mutation. To obtain plasmids encoding full-length IRAP, the first 650 bp of IRAP were amplified using the primers 5 -GCT AGC GCC ACC ATG GAG TCC TTT ACC AAT GA-3 (NheI underlined) and 5 -TTG AAA TAT TAT GTC CTG TGC TAT-3 , using as template the cDNA clone MGC_144171 (plasmid IMAGE_40098425, obtained from Open Biosystems) corresponding to Genbank accession number BC120926. 1. The N-terminal IRAP fragment was cloned in pCR Blunt (Invitrogen) and sequenced. The C-terminal fragments of wt and inactive IRAP were removed from the pMAX plasmids using EcoRV and XhoI and cloned together with the N-terminal fragment, excised using NheI and EcoRV, between the NheI and XhoI sites in pCDH-EF1alpha vector (System Biosciences).
Statistical Analysis Statistical analyses including regression analysis, unpaired t-tests, and two-way ANOVA with p-values adjusted by Dunnett correction test (cross-presentation) were performed using GraphPad Prism TM software (version 7. 0). For crosspresentation experiments, the mean values of four independent experiments were pooled and statistical significance was determined for every dose of antigen (parameter 1) and for every cell treatment (parameter 2) by comparing both the vector-IRAP +/+ and the IRAP E465A -IRAP -/-conditions to the vector-IRAP -/-condition. For visual simplicity, the graph shows the p value applying to the three higher antigen concentrations.
Fish mortality and cortisol measurements Mortality was recorded at 0,  19, 24, 43, 48, 67, 72, 91, 96  and 115 hpi. To estimate log-rank values, we used the non-parametric Kaplan-Meier approach implemented in the survival R package  [30]. Differences in survival probability were considered significant when P < 0. 05. We assessed stress levels in fish by measuring scale cortisol content  [31]. The scales were collected from both sides of each individual, washed and then vortexed three times (2. 5 min; 96% isopropanol) to remove external cortisol origination from the mucus. Residual solvent traces were evaporated under nitrogen flux and samples frozen at -80 °C. To ensure the scales were dry, they were lyophilised for 12 h and then ground to a powder using a ball mill (MM400, Retsch GmbH, Germany).
Microbiota analyses Microbiome dynamics with the MiSeq short-reads dataset Quality of the raw reads and presence of adaptors was determined using fastQC v0. 11. 9 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). The remaining adaptor sequences were removed using BBDuk software (last modified January 25, 2018), implemented in BBtools package (https://jgi. doe. gov/data-and-tools/bbtools/), forcing left trimming of 20 bp 'forcetrimleft = 20'. We followed DADA2  [34]  standard procedure for detecting presence of remaining adaptors, reads filtering, error-correction and for building the exact sequence variant (ESV) table. Briefly, reads were filtered using default parameters (maxN = 0, maxEE = c (2,2), truncQ = 2) and trimmed at 190 bp. Errors modelling and correction was conducted using the pooled option on the merged reads.
Putative chimeras were detected de novo and removed. Finally, taxonomy was assigned using the formatted NCBI RefSeq 16S rRNA database supplemented by RDP (https://benjjneb. github. io/ dada2/training. html) which was complemented with the species-assignments training dataset to improve species levels assignment. Dataset was imported to QIIME2 platform v2019. 10 to build the phylogenetic trees. We explored alpha-diversity (Shannon, Fisher indexes) and beta-diversity (Bray-Curtis, unweighted and weighted Unifrac distances) using the phyloseq R package  [35]. Dissimilarity between samples was assessed by principal coordinates analysis (PCoA). Differences in alpha-diversity were tested using pairwise Wilcoxon rank tests and were considered significant when Holm adj. P < 0. 01. Differences in beta-diversity were tested using Anosim (1000 permutations) as implemented in the 'anosim' function of the vegan R package  [36].
Differences were considered significant when P < 0. 01. We explored pairwise differences using the 'pairwise. adonis' function of the pairwise. adonis R package (https://github. com/bwemheu/pairwise. adonis). Differences were considered significant when BH adj. P < 0. 01. We also searched for a 'core' microbiome of fish skin, considering those ESVs present in all the individuals across all treatments (infected, control and resistant) as belonging to this group. We finally searched for significant differences in specific ESV abundance across groups using Wald tests implemented in the DESeq2 R package  [37]. We used the apeglm method for log2FC shrinkage to account for dispersion and variation of effect size across individuals and treatments, respectively  [38]. Differences were considered significant when FDR < 0.
Microbiota consensus representation using Nanopore dataset Sequences were called during the MinION run with MinKnow software (v. 1. 7. 14). The demultiplexing and adaptor trimming were done with the porechop tool (https://github. com/rrwick/Porechop) using the option discard_middle. For each barcode, all Nanopore reads were mapped on the GreenGenes database (v. 13. 5, http://greengenes. lbl. gov) with minimap2 (v2. 0-r191) with the 'map-ont' pre-set options  [39]. All reference sequences of the GreenGenes database covered by more than 0. 01% of all reads were kept for the next step. A second round of mapping (using the same parameters) was done on the selected references in order to aggregate reads potentially mis-assigned during the first round of mapping. SAMtools and BCFtools were used to reconstruct consensus sequences for each reference sequence covered by more than 10 Nanopore reads with the following programs and options: mpileup -B -a -Q 0 -u; bcftools call -c --ploidy 1; vcfutils.
Compartment-specific differential expression analyses Read pre-processing For each individual, raw reads were filtered using Trimmomatic v0. 36  [40] , with minimum length 60 bp, trailing 20 and leading 20. Filtered PE reads were mapped against a combined reference including the host's transcriptome (See Supplementary Material Tables  S1  and S3  for details of the transcriptome assembly) and the genomes of Alteromonas mediterranea strain: AltDE1 (Genbank accession: GCA_ 000310085. 1), and Pseudoalteromonas phenolica strain: KCTC 12086 (Genbank accession: GCA_001444405. 1), Tenacibaculum maritimum strain: NCIMB 2154 T (Genbank accession: GCA_900119795. 1), Sphingobium yanoikuyae strain ATCC 51230 (Genbank accession: GCA_ 000315525. 1), Vibrio alginolyticus strain: ATCC 17749 (Genbank accession: GCA_000354175.
2,) and Vibrio harveyi strain: ATCC 43516 (Genbank accession: GCA_ 001558435. 2). To prevent multi-mapping biases, we used GSNAP v2017-03-17  [41]  with minimum coverage set at 0. 9, a maximum of five mismatches allowed, and removal of improperly paired and non-uniquely mapped reads (option 'concordant_uniq'). Reads with low mapping quality (MAPQ) were removed using SAMtools v1. 4. 1  [42]  with the minimum MAPQ threshold fixed at five. A matrix of raw counts was built using HTSeq-count v0. 9. 1  [43]. Transcripts from the host and bacterial species origin were then separated into different contingency tables using homemade scripts.
We tested the significance of the models and individual factors using 999 permutations. Effects were considered significant when P < 0. 01. Differential expression was assessed using the DESeq2 R package  [37] , using pairwise comparisons with Wald tests. Logarithmic fold changes (logFC) were shrunk using the 'apeglm' method, implemented in the DESeq2 R package  [37] , to account for dispersion and effect size across individuals and treatments  [38]. Differences were considered significant when FDR < 0. 01 and FC > 2. Group comparisons included infected 24h vs control 24h and resistant 96h vs control 96h. Gene ontology (GO) enrichment was tested using GOAtools v0. 6. 5  [48]  and the go-basic. obo database (release 2017-04-14) using Fisher's test. Our background list included the ensemble of genes in the host transcriptome.
Tenacibaculum maritimum gene expression in vitro or during infection A validation step for searching for transcript overrepresentation was assessed using 'majSequences. R' implemented in the SARTools suite  [44] , similarly to the fish transcriptome. Most represented sequences were attributed to ssrA-coding genes, but represented less than 8% of the total library. We applied a similar shrinkage method and pairwise comparisons (infected vs in vitro), as for the host comparisons, but used more stringent thresholds as commonly observed in similar studies  [8] , and considered significant differences when FDR < 0. 01 and FC > 4. Gene ontology (GO) enrichment was similar to the methods used for the host.
Species-specific weighted co-network gene expression analyses in the host We built a signed weighted co-expression networks for the host compartment to cluster co-expressed genes and identify putative driver genes using the WGCNA R package  [49]. Variation in normalised counts were previously controlled using the 'vst' method implemented in the DESeq2 R package  [37]. We reduced the expression noise in the dataset by keeping only transcripts with minimum overall variance (> 5%). Briefly, we fixed a soft threshold power of 14 using the scale-free topology criterion to reach a model fit (|R|) of 0. 80. The modules were defined using the 'cutreeDynamic' function (minimum 30 genes by module and default cutting-height = 0. 99) based on the topological overlap matrix, a module eigengene distance threshold of 0.
The genetic bases of fish resistance We further explored the putative genetic variation between resistant and infected fish by focusing on resistant fish because of their established phenotype, (i. e. survivors with no signs of lesions after bacterial challenge). We followed GATK recommendations for SNP identification based on RNAseq data. Briefly, BAM files were pre-treated using the 'CleanSam' function, duplicates were picked out with the 'MarkDuplicates' function, and cigar string split with the 'SplitNCigarReads' function. All functions were implemented in GATK v4. 0. 3. 0 software  [50, 51]. Final SNP calling was conducted with Freebayes v1. 1. 0 (https://github. com/ekg/freebayes) requiring minimum coverage of 15 and minimum mapping quality of 20, forcing ploidy at 2 and removing indels ('-no-indels') and complex polymorphisms ('-no-complex').
The raw VCF file was filtered for minimum allele frequency ('-min_maf = 0. 2'), minimum coverage ('-minDP = 20') and using Vcftools v0. 1. 14 [52]  to allow no missing data. We computed relatedness ('-relatedness2') within and among groups with Vcftools v0. 1. 14 [52]. We further used distance-based redundant discriminant analysis (db-RDA) to document genetic variation among groups and correlation, with cortisol, treatment and weight as the explanatory variables. Briefly, we computed Euclidean distances and PcoA using the 'daisy' and 'pcoa' functions, respectively, implemented in the ape R package  [45]. Pco factors (n = 6) were selected based on a broken-stick approach  [46, 47]  and used to produce a db-RDA. We tested the model significance using 999 permutations, effects were considered significant when P < 0.
Dynamics of host transcriptomic response to infection and search for genomic bases of resistance Mean number of paired-end raw reads reached 65. 44 M ± 23. 1 sd and 25. 46 ± 4. 31 sd, for infected24h and for control24h, control96h and resistant96h, respectively. Global mean unique mapping rate for skin smear samples reached 71. 64 ± 2. 99% relative to a combined reference for host and microbe compartments. Datasets were predominantly composed of host-origin sequences (mean 86. 57 ± 13. 48%), with the infected 24h group showing a significantly higher proportion of reads of nonhost origin [mean 30. 70% ± 0. 03 se] than other groups (Dunn's test; Benjamini-Hochberg adj. P < 0. 05; Figure  S1 ). Details of the host transcriptome and individual mapping are provided in Tables  S1  and S3.
We then used co-expression network analysis (WGCN A) to draw clusters of co-regulated genes associated with discrete (treatment) or continuous variables (weight and cortisol) and to identify putative hub genes. No gene module correlated significantly with fish mass, suggesting that mass had no significant effect on gene expression profiles. A total of three modules showed negative correlations (P < 0. 01) with disease status (coded 1 for control 24h, control 96h and resistant 96h groups and 2 for infected 24h ), namely module turquoise-host (r = -0. 97, P < 0. 001), module black-host (r = -0. 5, P < 0. 001) and modulegreen-host (r = -0. 49, P < 0. 001; Fig. 3 ). Inversely, two modules showed positive correlation with the treatment, namely module blue-host (r = 0. 97, P < 0. 001) and modulepink-host (r = 0.
48; P = 0. 001). Almost all of these modules (with the exception of module black-host ) also correlated Individual cortisol [log (pg. mg -1 )] and weight (g) are continuous values. Time (24 hpi and 96 hpi) and Status (coded 1 for control 24h, control 96h and resistant 96h groups and 2 for infected 24h ) are discrete numeric values. b Heatmaps of the top 30 genes in module blue-host and module turquoise-host. Scales represent Log2 (prior 2) of the individual expression levels. Individuals were clustered using hierarchical clustering procedures implemented in the pheatmap R package  [53]  significantly with cortisol levels. The genes found upregulated in control 24h clustered mostly in module turquoi- se-host (n = 3468; 95. 5%), module black-host (n = 72; 2. 0%) and module green-host (n = 39; 1.
Acknowledgements We thank the  Direction des Ressources Marines (DRM)  for their help with fish rearing and especially  Moana Maamaatuaiahutapu  and  Mike Teissier  for the production of live preys. We thank  Chedia Hamouna  for her help with fish maintenance and monitoring during the experimental infection. We thank  Céline Reisser  for providing additional gonads tissues for the platax transcriptome assembly. We finally thank  Eric Duchaud  and  Pierre Boudinot  for providing valuable comments on previous version of the manuscript.
Introduction Cancer immunotherapies lead to specific and durable anti-cancer responses, overcoming traditional cancer treatment limitations. Successful immunotherapy approach aims to restore an immune response by either stimulating or suppressing the immune system  1, 2. Thanks to the recent advancement (progress?) of novel monoclonal antibodies (mAbs) and immune cell-based therapies, the field of immunotherapy has oriented our perception of medicine towards a personalized approach. Hence, different therapeutic modalities have been evaluated clinically to improve cancer immunotherapy by harnessing the immune system. These include, amongst others, the use of mAbs blocking pro-angiogenic factors or immune checkpoints inhibitors (e. g. , NCT01274338, NCT02252042, NCT02125461). Targeting immune-checkpoints proteins with anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) and anti-programmed cell death receptor-1 (PD-1) antibodies demonstrated outstanding results when compared to cytotoxic chemotherapies or to targeted therapies  3, 4.
As such, a paramount need in improving efficacy as well as safety in cancer immunotherapy remains. While monoclonal antibodies have demonstrated to be powerful therapeutic modalities, they are yet limited in efficacy and raised some safety issues (see box 1). Cellbased therapies have recently emerged (e. g. , dendritic cell (DC)-based vaccines, chimeric antigen receptor (CAR) T and CAR NK cells, etc. ) and lead to promising results in several clinical trials (e. g. , NCT03274219, NCT02498912, NCT02408016, NCT02311621, etc. )  [15] [16] [17] [18]. DC-based vaccines use nanoparticles (NPs) to pulse cancer cell lysates, DNA or mRNA into DCs to prime tumor-specific T cells  19, 20  while CAR T and CAR NK cells act in a HLAindependent mechanism without requiring antigen presentation  21. These recent approaches allow to overcome the low immunogenicity of tumors that might hinder the tumor-associated antigen presentation, and thus the cytotoxic response.
Authority for alterations could, for example, be given only for data that the party has provided. Additionally, or alternatively, "party-over-party" checks could be required before accepting changes. For example, if the veterinarian provides the number of medication packages delivered and the farmer is permitted to make changes to that number, then the system might require final approval from the veterinarian. By imposing quality checks upon data input, the need to alter data at a later stage-and possible problems associated with such changes (e. g. , changed benchmarking results)-might be avoided or minimized. This can be done by defining mandatory information to be included and running plausibility checks before the dataset can be submitted. Requiring active confirmation of a farmer that their data are correct might also be an option.
" By processing the data into the anticipated result or by crosschecking with corresponding information in other databases or previous submissions, suspected mistakes can also be identified. After this checking step, validation should be requested, for example, by requiring additional (manual) confirmation or requiring input of the corrected data instead. For example, in the Netherlands, quality systems are notified of outliers by the Netherlands Veterinary Medicines Institute, and confirmation or correction is requested. - - Y Farmers Y Czech Republic Q VET pigs - ADD - Y Farmers Y Denmark VetStat - ADD - Y Farmers Y Finland AH ETT poultry - - Flocks N N Finland SIKAVA - - - (Y) N France CLIPP - - Days Y Farmers Y France GVET mg UDD•UCD•DDD (vet) •DCD(vet) Days/animals Y Farmers Y France INAPORC mg DDD(vet)•DCD (vet) - N Y France RefA²vi - DDD FR •DCD FR - N Y France VEAL mg/animal DCD FR Days/animals N Y Germany HIT - - Days/animals Y Farmers Y Germany QS - - Days/animals Y Farmers Y Germany VetCAb-ID - - Days/animals N N Germany VetCAb(-S) - - Days/animals N Y Ireland Teagasc mg/kg - - Y Farmers Y Ireland Nat.
" The variables collected for the use data are dependent on the AMU indicator being calculated (see section "Data analysis"). As an example, the variables required in the Netherlands for the use data include the farm identification number, the delivery date of the antimicrobials, the European Article Number (EAN) of the antimicrobial and the number of packages delivered. The EAN is an identification number which is unique for every antimicrobial sold. In a database all antimicrobials used in livestock are recorded. This database also contains information on dosages (by animal species), administration routes, antimicrobial class, active ingredient(s), etc.
Animal Population Data Depending on the AMU indicator being calculated, different types of animal population data can be collected (see section "Data analysis"). Within animal species, several animal categories can be distinguished to further refine AMU monitoring. For example, in Denmark and the Netherlands, three animal categories are defined for pigs: sows and piglets, weaned piglets and finishing pigs  (21, 22). Animal population data per farm can be obtained internally or externally. Internally means that the data are collected specifically for the purpose of analyzing AMU data. For example, the monitoring system might require a farmer to provide the number of animals present (by production category) on his or her farm or the veterinarian might be required to record the number of animals on the farms visited.
Regular inspections to obtain animal counts are also a possibility. Externally acquired population data originate from sources established for purposes other than analyzing AMU data, typically not owned or managed by the AMU monitoring system, such as animal population data collected for epidemiological surveillance, for allocation of grants or for manure accounting. In the French GVET, for example, data from "GTE" is used, another French system whose goal is to yield technical-economic results to farmers. Data of produced biomass might also be obtained from slaughterhouses, which would have to register the number of animals slaughtered by farm. To improve data quality and for data management in general, it is advisable to minimize the number of "external" data sources. However, in many of the existing systems, animal population data are provided from external sources.