dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availabiliy Raw microarray data from preclinical samples have been deposited in GEO, access number GSE119464, and are publicly available. Patients' RNA-seq data have been deposited under EGA accession number EGAS00001003252. Access can be obtained by contacting the Institute Jules Bordet Data Access committee or Christos Sotiriou (christos. sotiriou@bordet. be). Raw clinical data are available as Supplementary Data 18. The full study protocol is available in the Supplementary Information file. The POETIC clinical trial gene expression data used in this study are available in the GEO database under accession code GSE73235. MECsWT and Rank overexpression microarray data used in this study are available in the GEO database under accession code GSE66174. The remaining data are available within the Article, Supplementary Information, or available from the authors upon request.
Acknowledgements We thank the patients who contributed to this study and acknowledge the clinical staff for their dedication. The D-BEYOND clinical trial was sponsored by  Jules Bordet Institute , which was responsible for the management of the study. This work was supported by grants to  E. González-Suárez  by  MICINN  ( SAF2014-55997-R ,  SAF2017-86117-R ) co-funded by  FEDER funds/European Regional Development Fund (ERDF) -a way to build Europe), the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  682935 ), and  Fundació La Marató  de  TV3. We thank  CERCA Programme/Generalitat de Catalunya  for institutional support. P. P. and S. B. were and A. B. is recipient of a predoctoral grant from the  MICINN. We are grateful to  William C.
Statistical analyses For comparison of the relationships between blood chemistry, gene expression and signalling pathways, each value was normalised to the average of the entire group and then log 2transformed. Prior to statistical comparison, measured values were assessed for normality using a Kolmogorov-Smirnov test. Where comparison between individual measurements was required, statistical significance was assessed by t test analysis of means allowing 5 % error. Statistical analyses were performed using a combination of StatPlus:Mac 2009 (AnalystSoft Inc. ), Statistica (StatSoft) or R-software packages (R-Core Team). For 1 H and 2 H NMR analysis of lipids, Student's two-tailed unpaired t test was used to compare means between dietary treatments. Analyses were performed in GraphPad Prism software (GraphPad Software).
Acknowledgements The authors would like to thank  Simon Tabrett ,  Natalie Habilay ,  Dean Musson ,  Nick Polymeris  and  Mike Anderson  for assistance with trial maintenance, and  Ben Maynard  for assistance with trial sampling. The authors would like to thank  João Rito  and  John Jones  for assisting the NMR analysis. This work was supported by a grant from the  Australian Centre for International Agricultural Research (ACIAR)  project  FIS-2006-141  and co-funded by  CSIRO Agriculture and Food. In addition, this work was supported by  Fundação para a Ciência e Tecnologia (FCT; Portugal)  through national funds with the co-funding by the  FEDER , within the  PT2020 Partnership Agreement , and  COMPETE 2020 (IV SFRH/BPD/ 90032/2012 ;  MP Centro2020 -ReNATURE ;  Centro-01-0145-FEDER-000007 ).
Statistical analysis The results are presented as the means with standard errors (SEM). Statistical tests were analyzed using SPSS software (IBM), considering normal (parametric tests) or non-normal distributions (nonparametric Fig. 1. Scheme of PV isolation by density gradient fractionation. From left to right: Primary macrophages infected for 24, 48 and 72 h were detached from culture plates and resuspended in Lysis buffer (see Methods) after centrifugation. Infected macrophages suspension was passaged several times through 30-gauge needles for mechanical disruption and release of PVs and intracellular parasites. The disrupted suspension was then layered by Ficoll 4% followed by Vacuole buffer solutions (see Methods) to form the density gradient. After ultracentrifugation, Isolated PVs containing parasites are found in the upper fraction of Vacuole Buffer, while free parasites are found in the middle fraction.
Acknowledgments The author gratefully acknowledges the support and advice of  Dr. Michel Rabinovitch ,  Dr. Renato Mortara  and  Dr. Morgane Bomsel. This work was supported by funds from the  Fundação de Amparo à Pesquisa do Estado de São Paulo ,  FAPESP  ( 2014/25296-2  grant),  Conselho Nacional de Desenvolvimento Científico e Tecnológico ,  CNPq , and  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).
Molecular-epidemiological data are very useful for identifying pockets of transmission, particularly in areas of declining malaria transmission, and to guide the choice of control interventions. Molecular tools also provide strategies to validate interventions, but the successful implementation of active case detection is highly dependable on the sensitivity of the diagnostics employed. Asymptomatic malaria infections and symptomatic clinical episodes have different parasite densities, and thus, the ease of detection is dependent on the diagnostic method for each form. Clinical episodes are generally detectable by blood slide LM or RDT. Differentiation of clinical malaria and nonmalaria fevers is complicated by the prevalence of lowdensity Plasmodium sp. infections. Diagnosis of bystander malaria infections may obscure viral or bacterial causes of fever and prevent adequate treatment.
In a study conducted in advanced melanoma, Jansen and colleagues reported that in comparison to patients showing partial responses (PR) or stable diseases (SD), those with a complete response (CR) seemed to have a lower risk of relapse after ICPi discontinuation  [164]. Currently, other trials aim to define the optimal duration of PD-1/PD-L1 inhibitor treatments. The Canadian STOP-GAP study (NCT02821013) is evaluating intermittent versus continuous treatment with anti-PD-1 inhibitors in 614 patients and the OS as primary endpoint. To do so, patients are being randomized in the first 16 weeks of anti-PD-1 therapy to either a standard 2 years of treatment or until the obtention of maximal tumor response followed by retreatment at the time of progression. The DANTE trial (ISRCTN15837212) is a non-inferiority trial in metastatic melanoma with the PFS as primary endpoint.
Funding: The authors are supported by grants from the  Fondation de France  (L. A. ), the  Conseil Régional de Bourgogne  and  FEDER  (L. A. ), the  Agence Nationale de la Recherche  [ ANR-11-LABX-0021 ] (L. A. ), the  Acteria Foundation  (L. A. ), the  European Research Council (ERC)  (grant agreement N • 677251), FONCER contre le cancer (M. D. ), the  National Institute of Health  (M. D. ).
Analysis and statistics Data from all participants were collected, uploaded to a protected data server and centrally analyzed in the core lab (Essen, Germany). GraphPad Prism Software (GraphPad Software, La Jolla, California, USA) was used for statistical analysis and significance was assessed with Mann-Whitney, Wilcoxon matched-pairs signed rank or Kruskal-Wallis test. Results were considered significant at *p≤0. 05, **p≤0. 01 and ***p≤0. 001.
T cell suppression is a hallmark of MDSC function. We used a previously published protocol to assess the suppression of cancer patient-derived T cells by autologous circulating MDSC. 6 This protocol was trained in the Essen core lab with selected participants and subsequently used for functional validation using patients with HNC and patients with melanoma. All these steps enabled the network to perform the study in different labs and geographical locations using a commonly agreed SOP for immunophenotyping and common assays validating MDSC function. Open access of a training school, the trained researchers performed MDSC immunophenotyping in their own Centers (phase IC, figure  1 ). In each center, a particular cancer type or infection or inflammatory disease was investigated. Data files (.
In order to test for a potential effect of the anticoagulant (sodium citrate, EDTA, heparin), we purchased the three types of blood collection tubes at the coordinating Center (lab 1) and shipped them to eight participating Centers for further use (phase IIA, figure  1 ). Centers obtained venous blood from healthy donors into three different collection tubes and used their preferred separation medium for MDSC isolation. MDSC labeling was performed according to the standardized consensus protocol and data files were sent to the coordination Center for centralized analysis. Analysis of MDSC frequencies showed that the type of anticoagulant had little influence on the frequency of M-MDSC and e-MDSC. In contrast, a clear increase in PMN-MDSC frequency was observed when heparin was compared with sodium citrate (figure  5A ).
Validation of the refined SOP and comparison with previous dataset In order to confirm the utility of standardization of the isolation procedure, five participating Centers volunteered to repeat the analysis shown in figure  3  using the refined SOP (phase IIIA, figure  1 ). Figure  6  shows the level of variance in healthy donors for the original data (figure  3 , phase IC) and the variance of data with the harmonized isolation procedure (phase IIIA). Data show that choice and standardization of blood collection tubes and separation medium reduced PMN-MDSC frequency in healthy donors, further improved intercenter comparability of results and reduced differences in MDSC counts between healthy control cohorts from different Centers. PD-L1 is primarily expressed in mononuclear MDSC and is not upregulated as a consequence of disease LOX-1 has been suggested as a marker associated with suppressive activity of PMN-MDSC.
Previous reports already highlighted the need for marker and gating harmonization  4 16  and outlined technical variables that could affect MDSC immunomonitoring. 17 A recommendation paper was published in order to suggest minimal characterization standards when researching MDSC. 4  However, also in those reports no consensus strategy for MDSC preparation, isolation and immunophenotyping is provided and an accepted gating strategy for enumeration of non-overlapping human MDSC subsets does not exist until now. These obstacles make it nearly impossible to directly compare MDSC data sets between published studies. In this study, we developed a consensus protocol for the isolation, identification and immunophenotypic characterization and analysis of human MDSC. After centralized hands-on training, this protocol was executed in the participating Centers, followed by a centralized flow cytometry analysis that utilized a uniform gating strategy.
Acknowledgements Lab1: We would like to thank  Delia Cosgrove  for her enthusiastic organizational input and for support of this project. We also thank the staff of the  ORL department  for their support during blood sample collection. Lab2: The work was supported extensively by the  Edinburgh Breast Unit team  and particularly by  Lorna Renshaw  and  Jane Keys  in this unit. We would like to thank the CIR blood resource (AMREC # 15-HV-013 ) for the recruitment of blood from normal controls and the CIR flow facility ( Shonna Johnston ,  Will Ramsay  and  Mari Pattinson ). Lab3: We would like to thank  Sandra Bossmann  en/of  Fleur Brienen  for excellent support. Lab4: We thank the staff of the  Core Facility Live Cell Imaging Mannheim  and  S. Uhlig  from  FlowCore Mannheim  for help with the cell sorting.
Lab10: This work was funded by the following grants:  PTDC/MED-IMU/30474/2017  -project cofunded by  FEDER   LISBOA-01-0145-FEDER-030474 , through  Programa Operacional Regional de Lisboa , do PORTUGAL 2020, and Fundação para a Ciencia e a Tecnologia and by  Gilead Genesis  to AES. Lab 11: This work was supported by grants from  Polish National Science Center   UMO-2011/02/A/NZ5/00337  and  UMO-2017/25/B/ NZ6/01003  (to JC). Lab12: This study was funded by the  Swedish Research Council  ( 2019-01036 ). Lab13: This work was supported by grants given by  Faculty of Engineering and Natural Sciences, Western Norway University of Applied Sciences, Norway.
The mean ± SD values of three independent experiments are plotted. (H) ChIP assays using Jurkat cells infected with VSV-G pseudotyped HIV-1 in the absence (DMSO) or presence of 1 μM nevirapin (NVP), or infected with heat-inactivated (HI) viruses. ChIP assays were performed at 24 hpi using an anti-H3 antibody, followed by qPCR analysis with the primers listed in E-G. The mean ± SD values of three independent experiments are plotted. (I-K) Quantification of total HIV DNA (I), 2LTR circles (J), and integrated HIV DNA (K) in Jurkat cells infected with VSV-G pseudotyped HIV-1 containing the two-color reporters in the absence (DMSO) or presence of raltegravir (1 μM). DNA was extracted from infected cells at 24 hpi. Data are presented as the quantification relative to control (incubation with DMSO).
ACKNOWLEDGMENTS. We thank all the members of the  Molecular Virology laboratory  for their constructive comments;  Paul A. Wade  ( Epigenetics and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences ) for his precious advice on capture-MNase-seq; and  Frank Kirchhoff  ( Institute of Molecular Virology, Ulm University Medical Center ) and  Stéphane Emiliani  ( Institut Cochin ) for providing HIV plasmids. DNA sequencing was performed at the  Montpellier GenomiX facility. This work was supported by the  Merck   Sharp and Dohme (MSD) Avenir program ,  Agence Nationale de Recherche Contre le SIDA et les Hépatites Virales (ANRS) ,  European Research Council   ERC-2018-ADG (RetroChrom 835184 ),  MSD Avenir, Labex Epi-GenMed "Investissements d'avenir"  ( ANR-10-LABX-12-01 ), and  Fondation pour la Recherche Médicale  ( LEQ20151134476 ), to M.
psittaci and C. abortus share 126 more genes outside the core genome. The C. pneumoniae genome, the largest in this dataset, possesses the highest number of unique genes (121), but also Cand. C. ibidis  (41)  and C. avium  (49)  with its relatively small genome have an elevated number of singletons. The core genome of all 33 strains comprised 784 genes (Supplementary Figure  S1 ), i. e. , they share about four-fifths of their genome. Concatenated core genes of all strains were used to reconstruct a phylogenetic tree (Figure  2 ). Two major monophyletic groups can be distinguished, the first one containing C. trachomatis, C. muridarum and C. suis, hence referred to as 'the trachomatis group', and the second one harboring the remaining 9 taxa. In the latter, C. pecorum and C. pneumoniae make up their own clade, while the other clade contains 7 species.
Discussion Many bacterial genome sequences in public databases are in a permanent draft state  [43]. Failure in achieving complete genome sequence assembly is often due to multiple sequence repeats in certain loci, which may confound assembly tools. However, incompletely assembled genome sequences pose limitations on accurate annotation and subsequent genome analysis  [44]. In the present study, we used 27 completely and 6 nearly completely assembled genomes of chlamydial strains from 12 species, 25 of which had already been in the public domain. In addition, 4 strains of C. gallinacea, 3 of C. avium and 1 of C. abortus were de novo sequenced and assembled.
Bioinformatics Tools: New and Unique Features of the RIBAP While several tools for pan-genome calculation were already available (such as Roary), we observed that the core genome size was often underestimated, in particular, when evolutionarily distinct species were compared. Thus, in the course of this comparative study, we developed a combined approach by connecting initial Roary groups based on high sequence similarity (95%) with additional parameters derived from evolutionary pairwise ILP calculations (github. com/hoelzer-lab/ribap). Using this approach, we are able to calculate a comprehensive core genome, even at genus level and for evolutionarily distinct bacteria. In addition, the RIBAP code is public domain and the pipeline is implemented using a workflow management system with containerized steps, thus allowing easy and reproducible execution on different platforms and modular pipeline adjustments including versionization in the future.
Displayed for each RIBAP group are the group size and all involved strains, as well as gene name and gene description for each strain. The corresponding assignment to Roary runs with sequence identities of 60, 70, 80, 90 and 95% is color coded in their respective groups. On the left-hand side, a phylogenetic tree is displayed, which is generated from the MSA of the CDS within this RIBAP group. Alignment and tree are provided for download. The full HTML table can be found in the OSF repository. Importantly, our approach also encompasses genes that lack a functional annotation due to assembly errors, annotation problems or incomplete reference databases (so-called 'hypothetical genes'). Thus, we are able to determine a more complete set of core genes and even assign functions a posteriori. Hypothetical genes can be added to a RIBAP group consisting of genes with an assigned function, thus allowing annotation of genes that had been overlooked by the annotation software.
The corresponding assignment to Roary runs with sequence identities of 60, 70, 80, 90 and 95% is color coded in their respective groups. On the left-hand side, a phylogenetic tree is displayed, which is generated from the MSA of the CDS within this RIBAP group. Alignment and tree are provided for download. The full HTML table can be found in the OSF repository. Importantly, our approach also encompasses genes that lack a functional annotation due to assembly errors, annotation problems or incomplete reference databases (so-called 'hypothetical genes'). Thus, we are able to determine a more complete set of core genes and even assign functions a posteriori. Hypothetical genes can be added to a RIBAP group consisting of genes with an assigned function, thus allowing annotation of genes that had been overlooked by the annotation software.
Core Genome vs. Dispensable Genome As a central parameter in comparative genomic studies, the core genome comprises the genes or CDS shared by all strains of the selected panel. However, there is no general agreement on definition of the term itself, nor on similarity cut-off values to be applied. In this situation, core genome size naturally depends on the selection of strains studied and the calculation method including sequence similarity thresholds. As standard Venn and Euler diagrams are an inadequate solution for quantitative visualization of multiple (n > 4) gene set intersections, we used the UpSetR package (Figure  1 , Supplementary Figure  S1 ), a scalable alternative for visualizing intersecting sets and their properties  [45]. The result of 784 genes shared among 33 chlamydial strains (or 791 among 12 type strains) is somewhat higher than the number reported in the study by Joseph et al.
Thus, the combination of re-annotation of all genomes based on the same database and novel functionalities of RIBAP accounts for the larger set of core genes identified in our study. Using the new RIBAP pipeline, we selected the following criteria and thresholds for core genome identification: We define a protein-coding gene to be part of the core genome when it forms a RIBAP group of 33 members (i. e. , the number of genomes included in this study). Thereby, a RIBAP group consists of at least one Roary cluster with a sequence identity of 95%. Multiple 95% Roary clusters can be connected via ILP support into larger RIBAP groups, potentially forming a core gene. We believe that these conditions take the peculiarities of chlamydial genomes into account and may represent a working compromise for core genome definition.
In an alternative approach, van Aggelen et al. [48]  recently proposed a core genome definition to be based on conserved sequences rather than conserved genes. In these circumstances, larger core genome sizes will be calculated, since also non-coding sequences are included. The elements forming the core genome are considered to be indispensable and represent an accurate dataset for phylogenetic studies  [49, 50]. Therefore, we reconstructed the phylogenetic tree of Chlamydia spp. using concatenated core gene sequences of the strains examined (Figure  2 ). While largely confirming previously published phylogenies based on rRNA  [51] , conserved proteins  [47]  and core locally collinear blocks  [32] , this tree comprises all validly published species of the genus Chlamydia and highlights intra-species genetic variability of C.
Pan-Genome and Core Genome Calculation Using RIBAP The Roary ILP Bacterial Annotation Pipeline (RIBAP) is a newly developed pipeline freely available at github. com/hoelzer-lab/ribap and implemented using the workflow management system Nextflow  [76]. For an input set of genome sequences, the pipeline performs annotation, core gene set calculation of the identified coding genes, alignments and phylogenetic reconstructions of homologous genes and the full core gene set, as well as various visualizations of the results. Each step of the pipeline is encapsulated in its own Docker container, thus, only Nextflow and Docker are required to run RIBAP. For the present study, we ran the pipeline in v0. 4 using the following command: nextflow run hoelzer-lab/ribap -r v0. 4 -fasta '*. fasta' -tree -tmlim 240.
We executed the pipeline twice: (1) on the full set of all 33 genomes and (2) only on the type strains of the 12 species included in this study. RIBAP will be described in full detail in an upcoming publication. Briefly, the pipeline combines the output of various tools, most importantly Roary and an ILP-approach, to calculate a comprehensive core gene set even in the case of evolutionarily different species. Below, we explain the core features of RIBAP to calculate a core gene set. Version numbers of all third-party tools involved in the execution of RIBAP are provided in version 04 of the GitHub release at github. com/hoelzer-lab/ribap. Complete results of the pipeline are provided at the Open Science Framework (osf. io/j9zas).
Annotation First, to ensure full comparability of annotations using the same data basis, RIBAP re-annotates all 33 genome sequences using Prokka v1. 14. 5  [77]  with default parameters. Prokka searches each candidate CDS (from start to stop codon) against a reference protein database derived from UniProtKB, while CDS with no database match are designated 'hypothetical protein'. Since Prokka is an integral part of RIBAP, the subsequent core genome calculation is based on Prokka's CDS annotations, which also include hypothetical proteins.
Integer Linear Programming and GLPK A disadvantage of pan-genomes produced by Roary at high sequence similarity thresholds is the fragmentation of clusters that likely belong together. On the other hand, a low sequence similarity threshold can result in an increased amount of false positive assignments (see Roary online FAQ). Therefore, within RIBAP, we adapted and extended an ILP approach [79] by an InDel-model to minimize the evolutionary distance between CDS based on an MMseqs2 (v10. 6d92c) all-vs-all CDS comparison  [80]. We split the initial MMseqs2 all-vs-all homology table into all pairwise genome comparisons and formatted the output to be solved with ILPs using the GNU Linear Programming Kit (GLPK, v4. 65) package  [81]. Subsequently, the solved ILPs were combined again and the resulting data was used to connect fragmented Roary clusters even below the sequence similarity threshold of 95%.
Creating a RIBAP Group A RIBAP group (set of homologous genes) is created by merging the preliminary Roary pan-genome clusters and the refined pairwise ILP results. Using pairwise ILP data, smaller Roary clusters of low sequence similarity were connected into larger RIBAP groups that potentially represent homologous core genes. For detailed documentation refer to the scripts provided at https://github. com/hoelzer-lab/ribap.