dataesr/scientific-paragraphs-categorization huggingface dataset:
| Unsupervised hierarchical clustering For the sorted cells, DESEQ2 VST normalized expression data were directly used for unsupervised hierarchical clustering. Cluster purity was used as an external validation criterion and was calculated by first creating a confusion matrix between assigned cluster number and annotated cell type before adding the maximum values from each row (i. e. assigned cluster) and dividing by the total number of samples. A benchmark of distances (euclidean, maximum, and pearson) and methods (ward. D2, single, complete, average and centroid) was performed to identify the optimal method leading to the best cluster purity using a pre-defined number of clusters according to the original annotation. For bulk data sets, DESEQ2 VST normalized expression data were independently calculated and further center-scaled for each data set to correct the potential batch effect.
| HERVs, genes, and copy number variation correlations HERVs located in extended AHR (peaks +/À 20 000 bp of a HERV TSS) were selected for correlation analysis. For each HERV, a list of surrounding genes located at +/À 50 000 bp of their TSS was established. Pearson's correlations were calculated between the RNA expression of each HERV and each of its surrounding gene, independently. P-values were corrected with the FDR method. Genes were then annotated using a published list of cancer-related genes from the Cancer Gene Census. 41 The same list of HERVs was then used to perform correlations with CNV from the same cytoband. TCGA LAML CNV data were retrieved from the NCI GDC portal and used as is to calculate Pearson's correlations with HERVs from the same cytoband.
| Survival analysis Intensively treated patients with available survival data were retained for the survival analysis. Patients receiving hypomethylating agent therapies or supportive care were discarded from this analysis. Overall survival curves were estimated with the Kaplan-Meier method. Only known prognostic factors (Age, ELN2017, white blood count), batch and clusters were integrated in the final multivariate cox model. For the second survival analysis (shown in Figure  S6 ), intensively treated HLA-A*02 patients were selected and stratified according to the P1 expression level, cut in terciles. The same covariates were included in a multivariate cox model of overall survival. Survival analyses were performed using the R survival and survminer packages. Cox model proportional-hazards assumptions were assessed using Schoenfeld's test and inspection of residual plots.
| Cancer hallmark and immune signatures GSVA For each cancer hallmark,  42  a unique gene signature was established (Table  S3 ) based on The Molecular Signatures Database (MSigDb) Hallmark Gene Set Collection. 43 When not available in MSigDb, hallmark signatures were established from Gene Ontology (GO) signatures, as previously described. 44 The signature for the immune evasion hallmark was retrieved from Hubert et al. ,  45  Individual enrichment scores were calculated from each patient by single sample gene-set variation analysis (ssGSVA),  46  and scaled by study. The mean score for each cluster was then calculated and shown in a radar plot. Immune signatures were obtained from Thorsson et al. 47  and calculated by ssGSVA for each sample. Unsupervised hierarchical clustering was then performed on study-scaled ssGSVA scores in each cluster.
| HERV-LSC signature To establish the HERV-LSC signature, correlations between the expression of each unique HERV and the validated LSC17 score  48  were computed independently in the 4 bulk RNA-seq data sets. 47 HERVs showing a significant correlation (FDR-adjusted p-value <0. 05) with the LSC17 score in at least 2 data sets and with concordant results (i. e. correlated in the same direction in each data set) were retained to build signatures. Signatures were calculated as the mean expression of all HERVs pondered (multiplied) by the sum of their Pearson's correlation coefficients. To select a minimal number of HERVs, the resulting 47 HERVs were ranked according to the absolute sum of their Pearson's correlation coefficients, and signatures were iteratively built by removing the last candidate (i.
| Differential HERV expression analysis Differential expression analysis was performed using DESEQ2. 40 Raw counts of HERVs and genes from all normal and AML data sets were merged and integrated into the same DESEQ object, using study (i. e. batch) as a covariate in the design formula. Differential expression analysis was performed for all the 4 independent bulk AML data sets and the sorted LSC and pHSC populations against each of the 42 normal tissues. Fold change were shrunk with the apeglm method. 49 atures with a fold change superior to 4 (log2FC >2) and a base mean of at least 1 normalized count per million were considered to be overexpressed.
| Additive model Differential expression at the peptide level between normal bone marrow and AML cells was estimated by relocating each peptide in all the potential HERVs with predicted ORFs coding for one of the 262 peptides. Each peptide could thus have one or several differential expression data according to the number of HERVs they were found in. This total number of HERVs was considered by considering the total sum of fold change of each HERV containing the peptide of interest, leading to an estimation of the cumulated fold change per peptide. To avoid prioritization of targets with artificially high foldchanges due to very low expression levels, each fold change was pondered by the base mean of its respective HERV, leading to the final additive model.
3% with a corrected Rand Index of 0. 47 (Figure  1A ). We then sought to improve the clustering using ATAC-seq data through the analysis of peaks from open chromatin regions. Using the HOMER package, we applied a classic human genome annotation from gencode (v33) to annotate the set of 590 650 significant nonoverlapping peaks from open chromatin regions previously defined in bone marrow and peripheral blood cells sorted from healthy donors (n = 80 samples). 32 As previously described, unsupervised hierarchical clustering based on promoter elements (peaks between À100 bp and 1000 bp away from a transcription start-site [TSS]) and intergenic elements (peaks more than 1000 bp away from any other feature) significantly improved cluster classification, with a purity reaching 81. 8% (Figure  S1B ).
We then re-annotated these significant peaks with a custom reference consisting of the same Gencode annotation concatenated with the previously used 14 968 HERV loci from Repeatmasker. This new annotation showed that 16% of the significant peaks correspond to HERV regions (Figure  1B ). One important previously reported finding is that classification based exclusively on intergenic elements (the so-called "distal regulatory elements") is sufficient to classify normal hematopoietic cell populations. 32 Enhanced annotation of these distal regulatory elements revealed an enrichment in HERVs, with up to 37. 6% of the top 500 variable intergenic peaks corresponding to a HERV region (Figure  1B ). A plot of the total aggregated count from these regions showed a gaussian distribution surrounding HERV TSS, confirming the good quality of the ATAC-seq signal (Figure  S1C ).
32 Cluster purity based on the original categories of cells did not consider these similarities and was thus a poor indicator of clustering performance in this case. Differential ATAC-count analysis centered on extended AHR (+/À 20 000 bp from a HERV TSS) revealed distinct profiles between AML LSCs, blasts and pHSCs compared to their normal counterparts, with globally a more open chromatin in blasts and a more closed chromatin in LSCs and pHSCs (Figure  2B ). To further characterize the role of HERVs in these AHR, we computed correlations between RNA expression of each HERV present in an AHR and its respective surrounding genes located at +/À 50 000 bp. Strikingly, we mostly found positive correlations between HERV expression and their surrounding genes (Figure  S2B ). Annotation of the genes with a pre-established list of cancerassociated genes from the Cancer Gene Census database  41  revealed several genes positively correlated with HERVs expressed in AHR (Figure  2C ).
| HERV expression defines subtypes of AML with distinct cancer hallmarks and outcomes Having established that the HERV retrotranscriptome characterizes particular cell types, we then wondered whether HERV expression could define distinct AML profiles in bulk RNA-seq data. We explored HERV expression in 4 independent RNA-seq data sets (TCGA, AMLCG, LEUCEGENE, and BEAT), retaining only bone marrow samples from AML patients at diagnosis (n = 788) (Table  S2 ). For each data set, we selected the top 2000 most variable HERVs based on the scaled DESEQ2 VST normalized count (Figure  S3A ). We merged the 4 data sets, keeping only the intersect between each top 2000 candidate HERVs, resulting in 961 variable HERVs conserved across the 4 data sets. Unsupervised hierarchical clustering guided by the average silhouette (Figure  S3B ) and Bayesian Information Criterion (BIC) evolution defined 9 clusters that were not dependent on the study (Figure  3A  and Figure  S3C ).
To further demonstrate the value of HERV expression as a promising biomarker, we next established a LSC signature based on HERV expression. We calculated correlations between each individual HERV and the previously published LSC17 score  48  in the 4 independent data sets. Forty-seven HERVs with a significant correlation with the LSC17   S4 , see methods). This 47-HERV LSC signature was further refined to select the minimal number of HERVs allowing a good LSC discrimination. The resulting LSC signature based on 25 different HERVs showed a very good discrimination in the independent validation set of sorted AML cells, separating LSCs from all other cells with an area under the curve (AUC) of 0. 80 versus 0. 64 for the non-refined signature and 0. 76 for the LSC17 score (Figure  3G ). Both the full 47-HERV and the 25-HERV LSC signatures showed a significant impact on overall survival, mirroring the LSC17 signature's performances (Figure  S3G ).
| AML-specific HERVs contain several openreading frames representing a source of shared tumor antigens Considering the specific expression of some HERVs in AML, we evaluated if they may represent a source of shared tumor antigens for immunotherapeutic approaches. To define leukemia-specific targets, we used a data set of more than 1000 normal samples from 42 different sites extracted from the Genotype-Tissue Expression (GTEX) database in addition to the normal bone marrow and peripheral blood samples used above. We performed differential HERV expression analysis between AML and normal tissues, considering transcripts with a shrunken fold change >4 and a base mean of at least 1 normalized count per million as overexpressed. To be considered as AML-specific, each HERV candidate had to be overexpressed in at least 2 independent data sets of AML bone marrow compared to normal bone marrow cells and not overexpressed in any of the 42 normal tissues compared to any AML dataset.
To reduce the number of false positives, ORFs of at least 10 amino-acids were then aligned against a manually annotated database of 71 known existing HERV proteins derived from curated Uniprot references. ORFs with at least a 75% identity with an existing HERV protein and an E-value < 0. 01 were selected for epitope screening. Using these criteria, we found that only 33/125 AML-specific HERVs possessed at least 1 ORF (Figure  S4C ). Two independent recent tools trained on mass spectrometry data were then used for epitope prediction: netMHCpan 4. 1  53  and MHC flurry 2. 0. 52 HLA-A*02 epitope screening revealed 262 unique peptides predicted as strong HLA-A*02 binders with both independent tools. To set up a list of peptides to prioritize, we further ranked these results. Considering that epitope immunogenicity is linked to its tumor abundance,  54  we established an additive model considering (i) the mean expression of HERVs containing the peptide sequence, (ii) the fold change compared to normal bone marrow and (iii) the number of different HERVs containing the peptide sequence.
| DISCUSSION Representing 8% of the human genome, HERVs are a great source of genetic information. However, as the results of their analysis are largely dependent on the chosen analytical strategy, this signal can often be missed or misinterpreted. For instance, using a partial database limited to HERVs corresponding to known Uniprot references (56) can occult a major part of the HERV retrotranscriptome, and restricting HERV signal to uniquely mapped reads strongly reduces the signal from conserved, recently integrated families prone to multimap. 57 Consistently, we used a custom-designed pipeline based on Telescope,  36  a recently developed tool allowing the accurate quantification of a complete base of 14 968 HERV loci. One of the main advantages of Telescope resides in its reassignment algorithm to correctly reassign a high number of multimaps and thus keep the highest possible signal from recently integrated and conserved HERV families.
DATA AVAILABILITY STATEMENT The data analyzed in this study were obtained from GEO at GSE74246, GSE49642, GSE52656, GSE62190, GSE66917, GSE67039, and GSE106272, and from the NCI Genomic Data Commons (GDC) data portal (https://portal. gdc. cancer. gov/). THP-1 cell line data were accessed from the CCLE portal (https://portals. broadinstitute. org/ccle).
Analyses of lymphocyte subpopulations Lymphocyte subpopulations were directly identified from EDTA-treated blood samples using two types of whole-blood staining with fluorochrome-conjugated monoclonal antibodies: (1) FITC-conjugated anti-pig CD4 (Acrys Antibodies, Herford, Germany), PE-conjugated anti-pig CD8α (BD Biosciences) and PerCPconjugated anti-pig CD3 (Bio-Techne, Lille, France) antibodies, or (2) PE-conjugated anti-pig CD21 (Acrys Antibodies) and PerCP-conjugated anti-pig CD3 in the dark for 15 min at room temperature. Erythrocytes were removed using a lysis solution (BD Biosciences, Le Pont de Claix, France) and the DNA marker DRAQ5 ™ (1 μM final; Biostatus, Leicestershire, UK) was added. Samples were analysed with a FACS Aria flow cytometer (BD Biosciences, Le Pont de Claix, France) and data computed using the FlowJo software (FlowJo, Ashland, USA).
Statistical analyses Statistical analyses were performed using R software (version 4. 1. 2, R Core Team, 2016) while charts were graphed using Graph Pad Prism (version 9. 3. 1). To describe the consequences of the farm health status and/or the age upon immune and endocrine systems in piglets, linear mixed-effects models were performed using the lme4 package  [27]. Whenever required, data were initially transformed. We used logarithm (neutrophil, γδ T lymphocyte, Ag-experienced Th cell and cytotoxic lymphocyte counts, blood IgM, IFNα, urea and free fatty acid concentrations) or square root (monocyte, total lymphocyte, naive CD4 + T and B lymphocyte counts). The goodness-of-fit of all models (normality and homoscedasticity of residuals and distribution of random effects) was checked by graphic procedures and by Shapiro-Wilk tests.
For quantitative variables measured only at t1, only the HS and the sow parity were included in the model as fixed effects, and the farm as a random one. For this model, metabolic parameters (glycemia, free-fatty acidaemia, uraemia and creatininemia) were firstly processed using a Principal Component Analysis (PCA)  [28]  with the FactoMineR package in the R software  [29]  in order to reduce the number of variables while minimizing information loss. TNFα and IL-8 variables were analysed as qualitative variables (< or > 15. 6 for TNFα and (< or > 62. 5 for IL-8) using mixed logistic regression models included the HS, the farm and the sow parity as fixed effects and the farm as a random effect. To assess the effect of the HS on growth (ADG), the mixed linear model included the health status, the sow parity and the t0 weight as fixed effects, and the farm as a random one.
OS was de ned as the time between the start of the nivolumabipilimumab combination and death or last news. Median follow-up was estimated with the reverse Kaplan Meier method. Differences between groups were tested with Fisher's test for qualitative variables. PFS and OS were estimated by the Kaplan Meier method. For the calculation of PFS, patients without any event were censored at the date of last news; for the calculation of OS, patients still alive were censored at the date of last news. The survival data were compared with a log-rank test. Adverse events were assessed according NCI CTCAE version 4. 03. All statistical tests were two-sided, and p-values less than 0. 05 were considered statistically signi cant. Statistical analysis were performed using R® software.
Funding M. R. was supported by the  Interface INSERM program Competing Interests Sophie Piperno-Neumann  declares consultant fees from  Immunocore, Pierre-Fabre  and travel grants from  PharmaMar. Manuel Rodrigues  received a travel grant from  Astra-Zeneca , honoraria for lectures from  Astra-Zeneca ,  Merck ,  Immunocore  and  GlaxoSmithKline , research grants from  BMS  and  MSD. All other authors have no relevant nancial or non-nancial interests to disclose.
Database searches and article inclusion We used the Medline and Embase databases (Ovid SP) to identify relevant studies. In consultation with a health sciences librarian, a search strategy was initially developed for Medline and then adapted for Embase (Appendix 1 in supplementary material, see last page of the document -Medline only). The database search took place on 27 May 2021. The search strategy included terms related to vaccination (vaccine, immunization, immunisation), its psychosocial determinants (knowledge, attitudes, beliefs, confidence, intentions, and behaviors) and HCP types/groups (nurses, midwives, medical students, physicians, pediatricians, and pharmacists) for articles in English and French published in the peer-reviewed literature from 2015 through the search date. Articles were included if they met all of the following criteria: (a) focused on VH (and/or several vaccines) and assessed the psychosocial determinants of vaccine acceptance and recommendation (studies measuring only socioprofessional characteristics associated with vaccination behaviors were not included); (b) questioned HCPs or medical trainees about their willingness to recommend vaccines for themselves, their family, and/or their patients; (c) used any study design (quantitative or qualitative); (d) were conducted in one or more Western countries; (e) were peer-reviewed; and (f) their full text was available in English or in French.
We excluded articles that were systematic reviews or literature reviews, conference abstracts, editorials, dissertations, commentaries, book reviews, or abstracts not accompanied by a full text, as well as articles that focused only on influenza, HPV, or COVID-19 vaccines (as various reviews have already explored them  [21] [22] [23] [24] [25] [26] ), or that considered mainly unauthorized vaccines. The database search generated 7,269 articles (Figure  1 ). After removing duplicates and reviewing titles and abstracts, we assessed eligibility for 57 full-text articles and rejected 20 that did not meet the inclusion criteria. Disagreements between authors were resolved by consensus. After the addition of two more articles from the reference lists of included articles, this review finally included 39 articles.
Acknowledgments We warmly thank  Jo Ann Cahn , who revised our English,  Roxane Lépine , librarian at the  Institut national de santé publique du Québec , and  Mélissa Picard-Filiatrault  for their assistance with the search and screening of articles, and  Lisa Fressard  who formatted the references. We have these persons' permission to mention their names in the manuscript for publication.
Cells and cell culture The mouse IgG + B lymphoma cell line IIA1. 6 (derived from the A20 cell line [ATCC# TIB-208], listed in Cellosaurus as A20. IIA CVCL_0J27) was cultured as previously reported  (Yuseff et al. , 2011)  in CLICK Medium (RPMI 1640 -GlutaMax-I + 10% fetal calf serum, 1% penicillin-streptomycin, 0. 1% β-mercaptoethanol, and 2% sodium pyruvate). Fetal calf serum was decomplemented for 40 min at 56°C. All cell culture products were purchased from Gibco/Life Technologies. All experiments were conducted in CLICK + 25 mM HEPES (15630080, Gibco). The cell line was confirmed to be free of mycoplasma contamination. The transgenic LifeAct-GFP mouse line has been described elsewhere  (Riedl et al. , 2008)  and was kept in the C57BL/B6 background. The experiments were performed on 8-12-week-old male or female mice.
Live imaging of IIA1. 6 cell polarization in microfluidic chips Live imaging of polarization was performed using an inverted spinning disk confocal microscope (Eclipse Ti Nikon/Roper spinning head) equipped with a Nikon ×40, NA 1. 3, Plan Fluor oil immersion objective, a CMOS BSI Photometrics camera (pixel size 6. 5 µm), and controlled with the Metamorph software (Molecular Devices, France). Stacks of 21 images (δz = 0. 7 µm) were taken every 30 s during 40 min, with a binning of 2. Auto Focus was implemented in Metamorph using the bright-field image, then applied to fluorescent channels with a z-offset at each time point. On the day of the experiment, droplets were functionalized and cells were resuspended at 1. 5×10 6 cells/mL in CLICK + 25 mM HEPES. Microfluidic chips, cells, and media were kept in an incubator at 37°C with 5% CO 2 until imaging.
Multiple synapse experiments and imaging For multiple synapse experiments of Figure  8A , B, E and F, 2. 5×10 5 cells in 25 µL media were mixed with 4 µL of concentrated droplets (droplet solution washed with media from which the undernatant has been removed as much as possible) and left to interact 2 min at 37°C, before adding 400 µL media to limit new encounters between cells and droplets. This suspension was then added on a FluoroDish coated with 100 mg/mL BSA and left at 37°C. After 45 min, cell-droplet pairs were imaged all over the dish using an inverted spinning disk confocal microscope (Eclipse Ti Nikon/Roper spinning head) equipped with a Nikon ×60, NA 1. 4, Plan Fluor oil immersion objective, a CMOS BSI Photometrics camera (pixel size 6. 5 µm), and controlled with the Metamorph software (Molecular Devices).
2% BSA 1 hr before being washed and mounted using Fluoromount-G. 3D SIM imaging was performed using a Delta Vision OMX v4 microscope, equipped with an Olympus ×100, NA 1. 42, Plan Apo N, oil immersion objective, and EMCCD cameras. Image reconstruction was performed using the SoftWoRx image software under Linux. 3D visualization for figures was performed using the Imaris Viewer software. Laser scanning confocal imaging was performed using a Leica SP8 laser scanning microscope equipped with a ×40 NA 1. 3 oil immersion objective.
Image and statistical analysis Image analysis was performed on the Fiji software  (Schindelin et al. , 2012)  using custom macros, unless stated otherwise. All codes are available upon request. Single kinetic curves analysis were performed using RStudio (RStudio, 2020). Graphs and statistical analysis were made using GraphPad PRISM version 9. 2. 0 for Windows (GraphPad Software, San Diego, CA, https://www. graphpad. com). All replicates are biological replicates, and the number of replicates is indicated in each figure legend. For graphs of polarization in time of BSA vs. αIgG (Figure  2 ), a moving-average filter of length 3 was applied on the mean and SEM before plotting. The non-smoothed mean curve is superimposed to the graphs. For image analysis of live imaging, cell-droplet doublets were cropped from original acquisitions and were cut so that cells arrive at the second frame (marked as 0 s in figures).