dataesr/scientific-paragraphs-categorization huggingface dataset:
| Patient population We conducted an observational cohort analysis among PLWH enrolled in the ART-CC from 1996 to 2014 (http://www. b ris. ac. uk/art-cc)  [30]. Data were contributed by 19 cohorts in Europe and North America. Patients were included if they were ART-na€ ıve, had CD4+ and HIV-1 RNA values available at ART initiation, and were ≥18 years of age at ART initiation. Ethics approval was obtained from all participating study sites, the National Health Service Health Research Authority South West-Cornwall and Plymouth Research Ethics Committee, United Kingdom (REC reference 12/SW/0253). Informed consent or a waiver of informed consent were obtained as required by local site ethics committees. Patients were assigned a random study number at the local sites and a limited dataset was transmitted to the ART-CC Data Coordinating Center.
Information on cause of death was recorded using the International Classification of Diseases Ninth Revision (ICD-9) code, the ICD-Tenth Revision (ICD-10) code, a classification based on the Coding of Death in HIV (CoDe) project (http:// www. cphiv. dk/Tools-Standards/CoDe/About), or free text. Causes of death were classified using an adapted version of the CoDe protocol into mutually exclusive categories. Clinicians classified deaths using information on death (ICD-9/ ICD-10 codes or free text), patient characteristics at ART initiation (age, sex, HIV transmission risk group, ADEs, and hepatitis C status), time from ART initiation to death, ADEs after starting ART, latest CD4 (within six months of death), and whether a patient was on ART at the time of death. A computer algorithm and a clinician classified deaths using ICD codes when these codes were available.
For both models, we did not include any IPW and thus did not account for time-dependent covariates. We also refitted the marginal structural model after excluding observations from sites with >50% missing codes of death, after excluding those with AIDS at or prior to baseline, and after limiting the cohort to only patients with a baseline CD4+ count above 500 cells/mm 3. All statistical tests were twosided. Statistical analyses were performed using R version 3. 2. Analysis code is available at http://biostat. mc. vanderbilt. edu/ ArchivedAnalyses.
7 INSERM, UMR_S  1136 ,  Institut Pierre Louis d'Epid emiologie et de Sant e Publique, Paris, France ; 8 Division of Infectious Disease,  Department of Medicine, University of Alabama, Birmingham, AL, USA; 9 National Epidemiology Center, Carlos III Health Institute, Madrid, Spain; 10 Yale University School of Medicine, New Haven, CT, USA; 11 VA Connecticut Healthcare System, West Haven, CT, USA ;  12   Hospital Cl ınic-Institut d'Investigacions Biom ediques Pi i Sunyer (IDI-BAPS), University of Barcelona, Barcelona , Spain; 13  Service of Infectious Diseases, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland ;  14 INSERM U. 1218   Bordeaux Population Health, ISPED, Bordeaux University, Bordeaux, France ; 15  Clinic of Infectious Diseases & Tropical Medicine, San Paolo Hospital, University of Milan , Milan, Italy; 16  Stichting HIV Monitoring ,  Division of Infectious Diseases, Department of Global Health, Academic Medical Center, University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands ;  17   HIV Atlanta VA Cohort Study (HAVACS) ,  Atlanta Veterans Affairs Medical Center, Decatur, GA, USA ; 18 Division of Epidemiology and Population Health,  British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada ; 19  Faculty of Medicine, University of British Columbia, Vancouver, Canada; 20 Research Department of Infection and Population Health, University College London, London, UK ; We would like to thank the following cohorts for participating in this study; the  AIDS Therapy Evaluation Project Netherlands (ATHENA) ;  ANRS CO4   French Hospital Database on HIV (FHDH) ;  ANRS CO3  Aquitaine Cohort, France; Departments of Internal Medicine at  University of Cologne and Bonn, Germany; K€ oln/Bonn Cohort, Germany; Italian Cohort of Antiretroviral-Na€ ıve Patients (ICONA) ;  CORIS, Spain ;  Proyecto para la Informatizaci on del Seguimiento Cl ınico-epidemiol ogico de la Infecci  on por HIV y SIDA (PISCIS), Spain;  Royal Free Hospital Cohort, London UK; Swiss HIV Cohort Study (SHCS) ; The Multicenter Study Group on EuroSIDA;  Southern Alberta Clinic ,  Canada ; HIV Atlanta Veterans affairs Cohort Study (HAVACS),  USA ;  UAB 1917 Clinic Cohort, Birmingham, Alabama, USA ;  Veterans Ageing Cohort Study  ( VACS8 ), USA ;  Vanderbilt-Meharry and Tennessee Center for AIDS Research, Nashville, Tennessee, USA ;  University of Washington Harborview Medical Center, USA; € Osterreichische HIV-Kohortenstudie (OEHIVKOS) ,  Austria ;  Danish HIV Cohort Study, Denmark; VACH, (Spain) ; and  HAART Observational Medical Evaluation and Research (HOMER) ,  British Columbia ,  Canada.
F U N D I N G This  work was jointly funded by the  UK Medical Research Council (MRC)  (grant number  MR/J002380/1 ) and the  UK Department for International Development (DFID)  under the  MRC/DFID  Concordat agreement and is also part of the EDCTP2 programme supported by the  European Union. Sources of funding for April Pettit: National Institutes of Health   K08 AI104352. Vanderbilt University Medical Center: Vanderbilt-Meharry CFAR  ( NIH P30 AI 54999 ),  Tennessee CFAR  ( NIH P30 AI110527 ), Sterling  K24  ( NIH K 24 AI065298 ). PISCIS was supported in part by the  Ministerio de Sanidad y Consumo, Instituto de Salud Carlos III, Madrid (Spain) , and  Spanish Network for AIDS Research  (RIS;  ISCIII-RETIC RD06/006 ). COHERE Acknowledgements: The  COHERE  study group has received unrestricted funding from:  Agence Nationale de Recherches sur le SIDA et les H epatites Virales (ANRS), France ;  HIV Monitoring Foundation , the  Netherlands ; and the  Augustinus Foundation, Denmark.
COHERE  receives funding from the  European Union Seventh Framework Programme  (FP7/2007-2013) under EuroCoord grant agreement no  260694. A list of the funders of the participating cohorts can be found on the Regional Coordinating Centre websites at http://www. cphiv. dk/COHERE/tabid/295/Default. aspx and http:// etudes. isped. u-bordeaux2. fr/cohere. Jose M. Mir  o received a personal intensification research grant # INT15/00168  during  2016  from  Instituto de Salud Carlos III, Madrid, Spain. Jonathan Sterne  is funded by  National Institute for Health Research Senior Investigator  award  NF-SI-0611-10168.
Here, we developed a mass cytometry panel, focusing on innate myeloid cells, with the aim of identifying cell subphenotypes altered by vaccination. Mass cytometry is a promising technology for discovering cell subsets. It can unravel new mechanisms of the immunization process and help to design new vaccines. Currently, longitudinal mass cytometry data analyses following immunization are scarce  17, 18. One of the main analysis challenge is the lack of appropriate pipelines. Here, we used the SPADE algorithm  19  together with SPADEVizR  20  to analyze our high-dimensional cytometry data. This analysis pipeline can be used for any kind of multidimensional cytometry data analysis, beyond the study of the dynamic of vaccine-induced immune responses or the study of vaccines modes of action. To make these complex data fully accessible to the scientific community, we publicly released them on the FlowRepository database and the Cytobank platform.
As the third analysis step, phenotypic families sharing the same abundance profiles were clustered into kinetic families. Finally, after these three successive clusterings, and as the last analysis step, discriminant analyses were used to determine which kinetic families best distinguished between post-prime and post-boost immune response, and to define the phenotypic signature of each response. (c) Mass cytometry data were analyzed using SPADE. The topology of the SPADE tree is shown. This tree was built using all samples (all macaques and all timepoints). Only the topology of the tree is displayed where each node corresponds to a cell cluster. It does not correspond to a particular sample and size of node is not related to their cell content. Clusters with similar phenotypes are linked using a minimal spanning tree approach.
3b ). Mapping the phenotypic diversity of blood granulocytes, monocytes, and DCs. We first used the SPADE algorithm  19  to identify clusters of leukocytes with similar phenotypes within the whole dataset (all animals and all timepoints). SPADE was optimally parameterized and resulted in the partition of leukocytes into 600 clusters (Supplementary Figure  S3  and Tables  S5-S6 ). The stringency of our analysis likely resulted in 'over-clustering' , generating many artificial cell subpopulations. However, we and others consider 'over-clustering' to be less misleading than 'under-clustering' , particularly as an initial step  21. We annotated each cluster based on the SPADE tree and the expression of a restricted set of markers (Fig. 3c  and Supplementary Figure  S4 ). We identified 252 clusters of neutrophils (CD66 + ), seven of basophils (CD66 -HLA-DR -CD123 + ), 76 of monocytes (HLA-DR + CD14 + ), 47 of CD11c + cDCs latter referred as cDCs (HLA-DR + CD14 -CD11c + ), four of inflammatory cDCs/non-classical monocytes (HLA-DR + CD14 + C D11c + CD16 + ), two of plasmacytoid dendritic cells (pDCs, HLA-DR + CD123 + ), and nine of uncharacterized antigen-presenting cells (APCs, HLA-DR + CD123 -CD14 -CD11c -CD16 -).
We generated two categorical heatmaps to visualize phenotype of each cell cluster at a glance, as oppposed to SPADE trees: one heatmap for the granulocytes compartment and one for the monocytes-DCs compartment (Fig. 4 ). Heatmaps represent the phenotypic diversity within the dataset, but not a particular sample and, in no case, a particular timepoint. They are not snapshots. Clusters sharing similar phenotypes as measured by a close proximity on the heatmap cluster dendrogram, were gathered into phenotypic families. This analytical strategy prevents inaccurate interpretations due to potential 'over-clustering'. Indeed, clusters may actually account for different stages of activation or maturation within a cell subpopulation, whereas phenotypic families may represent actual subpopulations. For the granulocytes compartment (Fig.
Using these settings, we may not capture the production of cytokines whose concentrations did not reach a certain threshold or that were secreted very fastly. Alternatively, blood pDCs may be non-activated and thus would not produce IFNα  42. In addition, we may not capture all the pDC diversity due to the low amount retrieved from macaque blood (Supplementary Figure  S7 ). Our data show a high degree of phenotypic diversity among blood granulocytes and monocytes-DCs with several degrees of activation/maturation. We uncovered potential novel subsets with newly described marker co-expression. Such diversity was captured because of the high number of markers specifically targeting innate myeloid cells and the high number of clusters defined. However, this diversity was also captured because our original longitudinal dataset was composed of samples collected at steady state, before or long after immunization, outnumbered by samples collected early after immunization, during acute inflammation.
Distinct subphenotypes respond to first and second immunizations. The categorical heatmaps that we generated provided good information on cell subsets phenotypic diversity within the dataset. But these still pictures did not provide information on cell subset frequencies, or their continuous or transient presence. To go beyond these pictures, we studied the impact of MVA injections on the abundance of cells from the different phenotypic families with respect to animals and time. Various abundance profiles depending on the phenotypic families were found and the post-prime and post-boost transient expansions were not always equal (Supplementary Figures  S8  and S9 ). Using a third clustering step (Fig. 3b ), the 21 phenotypic families of granulocytes and 21 phenotypic families of monocytes-DCs were gathered together into 12 kinetic families sharing similar abundance profiles (Fig.
Another challenge will be to determine predictive correlations between innate and adaptive responses in the context of prime/boost immunizations, as previously done for one-dose vaccines  88  , or yearly influenza vaccine  89. Nonetheless, the differential innate responses described here can be valuable to tailor vaccine-induced immunity. expression in the two compared cell populations. KS distance is commonly used in flow cytometry analyses  93. MSI histograms for the top 8 markers with the highest KS distance are displayed for (d) neutrophils, (e) cDCs, and (f) monocytes from kinetic families that discriminate between the post-prime (blue) and the post-boost responses (red) as defined in Fig. 7c. Histograms were built on the whole dataset and did not represent a particular sample (animal or timepoint).
Methods Ethics statement. This experiment was approved by the «Ministère de l'Éducation Nationale, de Transgene, Illkirch-Graffenstaden, France) was injected subcutaneously into five cynomolgus macaques, at 4 × 10 8 PFU, two months apart as previously described  18. It contains the full-length codon-optimized sequence of gag (encoding amino acids [aa] 1 to 512) fused with fragments from pol (encoding aa 172 to 219, 325 to 383, and 461 to 519) and nef (encoding aa 66 to 147 and 182 to 206) from the Bru/Lai isolate (Los Alamos database accession number K02013). Blood samples were collected longitudinally in EDTA, to count leukocytes, or Lithium-Heparin to measure soluble plasma factors and perform single-cell mass cytometry analyses. For Figure  S2 , six macaques were injected subcutaneously with a buffer containing 10 mM Tris-HCl, saccharose 5% (w/v), 10 mM NaGlu, 50 mM NaCl, pH8.
Quantification of plasma soluble factors. C-reactive protein (CRP) was quantified in plasma by Laboratoire Vébio (Arcueil, France) using an immunoturbidimetry assay (CRP Plus, Thermo Scientific). Cytokine, chemokine and growth factor levels were assessed with a multiplex immunoassay (MILLIPLEX MAP non-human primate cytokine magnetic bead panel, Millipore), except plasma IP-10 concentrations, which were assessed by ELISA (human CXCR10/IP-10, R&D systems). Post-prime (PP) and post-boost (PB) samples were assessed independently. Staining, mass cytometry acquisition, and data processing. Blood processing, staining, and acquisition using a CyTOF (Fluidigm), as well as initial leukocyte gating, were performed as previously described  10. Briefly, 1 mL of blood was incubated with a fixation mixture containing PFA and glycerol  10, 90  for 10 min at 4 °C.
Automatic identification of cell populations. Cell populations were identified using the Spanning-tree Progression Analysis of Density-normalized Events (SPADE) algorithm  19. Briefly, a random pre-downsampling was used to select 60,000 cells from each sample (60,000 corresponded to the number of cells contained in the smallest sample -Table  S5 ). Then the SPADE algorithm per se was applied to all samples (all macaques and all timepoints) to define the phenotype of each cluster as well as the topology of the tree. Full upsampling was eventually performed. For our dataset, the optimal SPADE settings were determined with SPADEVizR package 20 as 20 clustering markers (CD66, HLA-DR, CD3, CD64, CD8, CD123, CD11a, CD11b, CD4, CD23, CD86, CD32, CXCR4, CCR5, CD16, CD11c, CD14, CD45, CD20 and CCR7), 600 clusters, a density-based downsampling of 10%, and an outlier density parameter of 0.
Discrimination between post-prime and post-boost innate myeloid responses. The Least Absolute Shrinkage and Selection Operator (LASSO) approach was performed on R using the "lars" package. Centered and reduced abundance profiles of kinetic families were used as entry parameters. The validity of classification at each iteration was assessed by cross-validation. The best configuration was chosen as the lowest number of kinetic families used and the lowest error rate in cross-validation. Linear Discriminant Analysis (LDA) was performed on R with the "MASS" package based on the abundance profiles of kinetic families and classes (post-prime or post-boost) as entry parameters.
Finally, after these three successive clusterings, and as the last analysis step, discriminant analyses were used to determine which kinetic families best distinguished between post-prime and post-boost immune response, and to define the phenotypic signature of each response. (c) Mass cytometry data were analyzed using SPADE. The topology of the SPADE tree is shown. This tree was built using all samples (all macaques and all timepoints). Only the topology of the tree is displayed where each node corresponds to a cell cluster. It does not correspond to a particular sample and size of node is not related to their cell content. Clusters with similar phenotypes are linked using a minimal spanning tree approach. SPADE clusters were annotated and colored with respect to the expression of markers indicated in Supplementary FigureS3and as follows: neutrophils (CD66 + ), basophils (CD66 -CD123 + HLA-DR -), monocytes (CD14 + HLA-DR + ), cDCs (CD14 -HLA-DR + CD11c + CD16 + ), inflammatory cDCs/non-classical monocytes (CD14 + HLA-DR + CD11c + CD16 + ), pDCs (CD123 + HLA-DR + ), uncharacterized APCs (CD3 -CD8 -CD14 -CD20 -CD11c -CD16 -CD123 -HLA-DR + ), B cells (CD20 + HLA-DR + ), T cells (CD3 + ), and NK cells (CD3 -CD8 + ).
Acknowledgements This work was supported by the " Investissements d' Avenir " programs managed by the  ANR  under reference  ANR-10-LABX-77-01  funding the  Vaccine Research Institute (VRI) ,  Créteil  ( ImMemory research program ),  ANR-11-INBS-0008  funding the  Infectious Disease Models and Innovative Therapies  ( IDMIT, Fontenay-aux-Roses, France) infrastructure , and  ANR-10-EQPX-02-01  funding the  FlowCyTech facility (IDMIT, Fontenayaux-Roses, France) , and by the  European Union , EVHA  H2020  project (Grant  N°681032 ). We would like to thank all members of the  ASW  and  L2I  groups from IDMIT as well as  Malvina Schatz ,  Marie-Laurence Baron , and  Philippe V. Alfonso.
Data availability and interactivity. Mass cytometry data were deposited publicly. FCS files are available on the FlowRepository database through ID FR-FCM-ZYBG, and the Cytobank platform under accession numbers 68443 and 68590. The http://data. idmitcenter. fr/MVA-innate-myeloid/ website provides interactive SPADE quality control data, interactive SPADE trees, interactive heatmaps, and interactive histograms. The interactive heatmaps avoid to juggle between Fig. 4  (heatmaps), Supplementary Figures  S8-9  (phenotypic families abundance profiles), Fig. 5a  (kinetic families), Table  1  (correspondance between phenotypic and kinetic families), and Fig. 7c  (kinetic families LDA selection). They directly connect clusters and phenotypic families to their annotation, kinetic families, and their relevance as signature of the response to a first or a second immunization.
Simulation runs. We used the GNU Scientific Library (https://www. gnu. org/software/gsl/) solver with the embedded Runge-Kutta-Fehlberg (4,5) method. For each network, numerical simulations were run until steady state was reached (we set a maximum time t=1000 which we observed to be sufficient). During the dynamics, we set a species to extinction when its biomass was very small (<1e-6). To fairly compare results with and without non-trophic interactions, we used the same initial conditions in both cases. At steady state, we measured: diversity, i. e. the number of surviving species (biomass>=1e-6), total biomass (sum of the biomass of all surviving species at steady state) and total production (sum of all the positive terms of the dynamical equations of all surviving species). We also evaluated the normalized ratio of each of these metrics in the case with and without non-trophic interactions.
Clusters 1 and 4 thus have similar stress-bacterium interactions, albeit with different proportions of down-and upregulated genes (Table  S2 ). Protein classes in each functional category identified upon E. coli and OS treatment. The analytical tools in PANTHER (http://pantherdb. org/) were used for functional classification and gene set overrepresentation tests (Table  2 ). To identify the functional categories, we considered only the annotated genes within each cluster (498 in cluster 1, 735 in cluster 2, 333 in cluster 3, and 275 in cluster 4) and used the PANTHER tools for functional classification (which includes entries from E. histolytica). This database combines more than 80 genomes. The identified genes fell into seven categories, depending on their predicted activities (Fig. 3a ): catalysis, binding, structural molecules, translation, antioxidants, transporters and receptors (Table  S3 ).
In cluster 4, the genes were downregulated by OS and upregulated in the presence of LB. The set of genes was submitted to PANTHER tools (pantherdb. org) to identify the family and protein class. From there the antioxidant proteins were extracted. CL: Cluster, PR: peroxidase, DHY: dehydrogenase. the antioxidants functional category, we only found peroxiredoxin family proteins (in cluster 2). The results for all other categories were more disperse (Table  S3 ). Protein classes highlighted by a Gene Ontology terms analysis of each gene cluster. We restricted our analyses to the families of factors enriched more than 2. 5 fold in the overrepresentation test. Not all of E. histolytica's genes have an equivalent or are well annotated in the PANTHER family and protein class database. Consequently, we also considered genes that were not classified in the overrepresentation test (Fig.
(a) Heat map of gene expression values for the genes encoding LRR proteins. Variance-stabilizing transformed counts were used to draw the heat map, and row/genes values were centred on 0 but not scaled. The genes in the clusters and the corresponding changes in gene expression are listed in Supplemental Table  7. (b) Structural protein models. After selection of the genes displaying the greatest transcriptome changes, encoded proteins were fed into a two-dimensional analysis (Supplemental DOC1) and 3D structural analysis (Table  S8 ). The 3D structure for several templates (according to the Protein Data Bank) is shown, including 4fs7. 1. A (BspA protein from Bacteroides ovatus), 3cig. 1. A or 2a0z. 1. A (Toll-like receptor 3); 5gmf. 1. A (Toll-like receptor 7) and 5ijd. 1. D (Toll-like receptor 4, variable lymphocyte receptor B chimera).
The majority of LRR proteins in E. histolytica belong to the BspA family (a total of 98 genes over 137, which were probably acquired by lateral gene transfer); this first class of LRR displays homology with bacterial LRR factors  31  and those in Trichomonas vaginalis BspA  32. Entamoeba histolytica's BspA comprises 1 to 10 copies of an LRR_5 domain (IPR026906), and two BspA proteins have previously been identified  33, 34. A phosphatase domain is present in a second group of 15 amoebic LRR proteins, and other LRR motifs are found in a third group of 26 proteins). The 137 amoebic LRR proteins identified in the present study all belong to one of these three classes. The most strongly expressed LRR proteins specifically display structural homology with the TLR ectodomain needed for bacterial product recognition.