dataesr/scientific-paragraphs-categorization huggingface dataset:
Collected data and evaluation criteria The cross-sectional survey took place from October to December 2019 (2019 study). Samples from a previous study on similar population in 2015 were added for measuring leptospirosis seroprevalence and its evolution over 4 years. The data presented here are primarily from the 2019-study. A questionnaire collected sociodemographic information as well as previous yellow fever vaccination (both self-reported and verified by vaccination record if available, number of injections) and use of antileishmanial treatment in the last year and how they obtained it (health facility or self-medication). Medical examination looked for the presence of suspected active mucocutaneous leishmaniasis lesions. A blood sample was taken at the time of inclusion, centrifuged in the evening, stored at 4˚C.
Acknowledgments The authors thank the  Centre de Ressources Biologiques Amazonie (Biobank Amazonie), Cerba lab , and the team who collected data on the field:  Mylène Ce  ´be,  Alan Ribeiro  and  Florine Corlin. The authors also thank the technicians of the National Reference Center for  Leptospirosis (Institut Pasteur)  for the serological analysis of serum samples.
We present ePeak, a Snakemake-based pipeline for the identification and quantification of reproducible peaks from raw ChIP-seq, CUT&RUN and CUT&Tag epigenomic profiling techniques. It also includes a statistical module to perform tailored differential marking and binding analysis with state of the art methods. ePeak streamlines critical steps like the quality assessment of the immunoprecipitation, spike-in calibration and the selection of reproducible peaks between replicates for both narrow and broad peaks. It generates complete reports for data quality control assessment and optimal interpretation of the results. We advocate for a differential analysis that accounts for the biological dynamics of each chromatin factor. Thus, ePeak provides linear and nonlinear methods for normalisation as well as conservative and stringent models for variance estimation and significance testing of the observed marking/binding differences.
Such is the case of the metrics to assess the quality of the immunoprecipitation and the Irreproducible Discovery Rate (IDR)  (5, 7)  for the estimation of replicate reproducibility, whose automation has proven to be difficult. For CUT&RUN and CUT&Tag protocols, general pipelines are being developed that incorporate essential adjustments like the spike-in calibration and different peak calling strategies  (8, 9). Standards for the differential analysis of RNA-seq datasets have been widely studied and implemented allowing users to do an informed choice of the methods to optimise sensitivity and/or specificity  (10, 11). This is not yet the case for ChIP-seq, CUT&RUN and CUT&Tag, where most researchers are not aware of the effect that a statistical setting can have on their comparisons, depending on the dynamics of the chromatin factor under scrutiny.
We present ePeak, a pipeline that streamlines the complete analysis process from raw ChIP-seq, CUT&RUN and CUT&Tag data to peak calling with the identification of reproducible peaks if replicates are available. ePeak is compatible with any chromatin factor, e. g. histone modification or transcription factor (TF), and can process at once multiple chromatin factors with different number of biological replicates measured in one or more biological conditions. It is designed to facilitate the analysis when the optimal parameters for each tool are known. However, it also permits to efficiently estimate the optimal parameter values by iter-ating the analysis without repeating time consuming steps such as mapping and deduplication. Following peak calling, ePeak also offers a downstream analysis module to evaluate the differential marking or binding between multiple biological conditions.
This implies an additional level of complexity that is unknown for RNA-seq experiments. Epigenomic profiling is performed for histone modifications with different nucleation, spreading and maintenance kinetics, as well as proteins with variable affinity to tightly or loosely compacted chromatin. As a consequence, the proper quantification of the marking/binding dynamics needs to consider these differences. ePeak provides a flexible set of statistical settings with multiple options to adjust the quantification and subsequent comparison. These include: linear, nonlinear and spike-in methods for the library size normalisation as well as two different variance estimation approaches, limma  (12)  and DESeq2  (13). As an example, we apply the various statistical settings proposed by ePeak on a published dataset (  14 ) of three histone modifications (H3K4me3, H3K27ac and H3K4me1) and two transcription factors (Oct4 and Klf4) profiled with ChIP-seq.
Workflow description ePeak is a modular pipeline designed to deal with various chromatin factors, e. g. histone modifications, transcription factors (TFs), profiled in multiple biological conditions with/without replicates. It is built in five modules that execute specific and interdependent tasks as illustrated in Figure  1A  (Supplementary material for detailed description). The bioinformatic pre-processing module aims at obtaining the deduplicated aligned reads. It includes: filtering, trimming and quality assessment of the raw data; mapping to the reference genome, correction of PCR amplification biases and, when available, removal of biased regions (i. e. blacklisted regions) that display artificially high coverage. For experiments where global changes of histone modifications is supposed to take place a spike-in control is added  (15) , this module executes the same steps for the exogenous chromatin on the corresponding genome.
This module is intended to provide a wide range of statistical settings to obtain a comprehensive quantitative description of the epigenomic-mediated process. Given a proper experimental design, without confounding factors, this module permits to correct unwanted technical variability (i. e. batch effects) and to account for systematic technical variability such as different library sizes. It includes linear and nonlinear methods for normalisation and different variance estimation models as implemented in the DESeq2 and limma packages. For experiments with spike-in control this module performs a linear normalisation using the scaling factor calculated from the mapping coverage of the exogenous chromatin (Supplementary Figure  S2 ).
ePeak different running modes ePeak proposes two main running modes, depending on the specific needs of the user: (i) a production mode for users who want to analyse their datasets using standard, predefined parameters for the peak calling (narrow or broad peaks) and for the differential analysis (library size normalisation method and variance estimation approach) and (ii) an exploratory mode for users dealing with epigenomic projects where the behaviour of the chromatin factor under study is uncertain. Depending on the specific question or the available data, ePeak allows for an analysis at different levels, as illustrated in Figure  1A. A basic analysis stops at the peak calling step when replicates could not be obtained. The outcome in this case will be a list of peaks that can be ranked according to the adjusted P-value or the log fold change of the observed enrichment.
The importance of providing multiple differential analysis settings Unlike RNA-seq reads that measure the transcriptional output of a given locus, ChIP-seq, CUT&RUN and CUT&Tag reads represent both the probability of marking/binding and its spatial distribution at a given genomic region, i. e. the peak. It follows that these samples have two sources of variability: one related to the read coverage of the peak, and the other to the position of the maximum enrichment or summit. Despite this additional layer of complexity, the differential analysis of marking/binding is traditionally performed using the methods developed for RNA-seq like DESeq2 and limma. To illustrate the above, we used ePeak to re-analyse a published dataset of three histone modifications (H3K4me3, H3K27ac and H3K4me1) and two TFs (Oct4 and Klf4) profiled using ChIP-seq in two biological conditions (shControl and shUbc9-treated reprogrammable mouse embryonic fibroblasts)  (14).
More recently, ChIP-seq pipelines that take care of the intermediate steps from the raw data to the peak calling have been implemented. The GenePipes pipeline  (32)  includes the calculation of quality control metrics like crosscorrelation and sequence bias as well as the functional characterisation of peaks according to their genomic localisation or enriched sequence motifs. However it does not cover the differential analysis. The snakePipes ChIP-seq pipeline  (33)  does not perform any quality control step but does the differential analysis with the R package CSAW. The nf-core (nextflow) pipeline  (34)  comprises both quality control metrics calculation and differential analysis using the R package DESeq2 and can be applied on narrow and broad peaks. Regarding CUT&RUN and CUT&Tag, the nf-core (nextflow) pipeline (  8 ) and the CUT&RUNTools 2.
0 (9) are currently the most complete protocols. None of them, however, incorporate the differential marking/binding step. To our knowledge, there is no current pipeline that, like ePeak, can perform all the following tasks for ChIP-seq, CUT&RUN and CUT&Tag datasets: (i) peak calling starting from the raw sequencing data for both narrow and broad peaks (ii) identification of reproducible peaks between replicates for both narrow and broad peaks, using respectively the Irreproducible Discovery Rate (IDR) and the intersection approach (iii) differential analysis with multiple statistical settings. Last but not least, ePeak produces (i) a customised Mul-tiQC with quality report of all the pre-processing steps for all datasets (Supplementary Figure  S3 ), (ii) an XML session readable by the Integrative Genomic Viewer (IGV) and (iii) a complete report with the results of the differential analysis.
Beyond an analysis pipeline On top of being a complete and general ChIP-seq, CUT&RUN and CUT&Tag analysis pipeline, ePeak is a flexible tool to systematically explore the epigenomic landscape underlying a dynamic biological system. It permits the exhaustive exploration of the marking/binding changes that follow or drive the perturbation (e. g. mutation, treatment, pathogen infection) of a steady or control state. Indeed, the various statistical settings of the differential analysis module enable a quantitative estimation of the heterogeneity of the regulatory response. The distinct populations of differentially marked/bound peaks can be interpreted as groups of genomic regions with differing regulatory roles, e. g. related to leaky or not synchronised transcriptional events, or to changes occurring only in a cell subpopulation.
ACKNOWLEDGEMENTS Authors would like to thank  Victoire Baillet  for help in code debugging and critical reading of the manuscript,  Thomas Bigot  and  Blaise Li  for advice on the Snakemake development and the singularity packaging,  Julien Guglielmini  for guidance on Bash and all members of the  Hub of Bioinformatics and Biostatistics  for useful discussions. This work used the computational and storage services (MAESTRO cluster) provided by the  IT department at Institut Pasteur, Paris , and the Core Cluster of the  Institut Francais de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ).
Systematic tools developed for epidemiological studies of disease prevalence in human populations are rarely applied to the study of wildlife disease prevalence; however, such tools can be used to capture the scale and contrast required in macroecological studies to quantify disease burdens regionally. Furthermore, while recent research has shown the impact of deforestation on the distribution of macaques in the context of P. knowlesi  15, 18  , associations between LULCC and variation in the prevalence of simian Plasmodium spp. in primates have not been explored. This study is the first to systematically assess P. knowlesi prevalence in NHPs at a regional scale, and across a wide range of habitats. We aimed to 1) systematically assemble a georeferenced dataset of P. knowlesi in NHPs; 2) evaluate variation in NHP P.
(b) Data assembly A systematic literature review was conducted in Medline, Embase and Web of Science under the CoCoPop framework (Condition, Context, Population)  16, 45  to identify articles reporting prevalence of naturally-acquired P. knowlesi in NHPs. Key authors were contacted to request unpublished or supplementary datasets identified from articles and reference lists. All studies were screened for data on NHPs with a confirmed P. knowlesi diagnosis or absence data (zero counts of P. knowlesi with appropriate diagnostic methods). Exclusion criteria included (a) studies exclusively relying on microscopy  46  (b) laboratory, animal model or experimental infection studies (c) data from outside of Southeast Asia. No limit was set on the temporal range. Duplicate records reporting results from the same surveys were removed, with one record per survey retained.
Critical appraisal of the studies was conducted using the Joanna Briggs Institute (JBI) checklist for prevalence studies  45  (see Supplementary Information (SI) for details and criteria). A flowchart of the selection process is illustrated in Figure  S3 , with a full list of articles included provided in Table  S2. Primary outcome was defined as P. knowlesi prevalence (p, proportion positive for P. knowlesi infection from n sampled NHPs). For each independent primate study, the following variables were extracted: year of data collection, primate species sampled, primate status (wild/captive), diagnostic test (PCR/sequencing) and target gene(s), sampling method (routine/purposive), number of P. knowlesi positive samples, number of Plasmodium spp. positive samples, total number of primates tested and geographical information.
For the majority of samples identified, study site was geolocated to a geographic area. Geolocation was assigned at the lowest available level of administrative polygon (district/state/country) by cross-referencing reported sampling location with GADM (v3. 6) administrative boundaries. If specific location was given, GPS coordinates were assigned via Google Maps. For the purpose of data visualisation, centroids were extracted per polygon and point coordinates were plotted in QGIS (3. 10. 14) and R (4. 1. 0) software.
(c) Meta-analysis of NHP prevalence Using methodology standardly used for analysis of human disease prevalence in individual participant datasets (IDP)  47, 48  , a meta-analysis of P. knowlesi prevalence (number positive out of the number sampled) was conducted on primate malaria survey data. Data were disaggregated by geographic location and primate species, to illustrate variance in prevalence by sampling site  48. One-stage meta-analysis is considered appropriate for studies where the outcome may be infrequent, so data was included in a single model under the 'DerSimonian and Laird' variance estimator  45. For studies where prevalence estimates tend towards 0% or 100%, variance tends towards 0. To stabilise the variance and accommodate for the inclusion of a high number of zero prevalence records, the Freeman-Tukey double arcsine method was applied in the transformation of prevalence  45.
Selection of random points was validated by visual inspection of the stability of model coefficients with the inclusion of an increasing number of points. Number of points was selected conservatively at the point where coefficients stabilised (n=10), minimising the risk of retaining spurious covariates in model selection due to artificially inflated sample sizes. Environmental covariates were extracted at each sampling point from satellite-derived remote-sensing datasets (Table  1 ). Gridded UN-adjusted human population estimates were assembled at 1km resolution from WorldPop  51. A threshold for high population density was set at 300 persons/km 2 pixel according to recent urban classifications  52. Elevation data was obtained from NASA SRTM 90m Digital Elevation Database v4. 1 (CGIAR-CSI)  53  with a spatial resolution of 1km x 1km.
RESULTS (a) Descriptive analysis 18 articles were retained in in the study  17, 22, [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73] [74] [75]  , containing 123 unique primate survey records to form the dataset for analyses (see SI for details of JBI Critical Assessment, Table  S5 )  45. Year of sampling ranges from 2004-2019. No primatological studies were identified from Vietnam, Brunei or Timor-Leste. Full characteristics of the articles and individual study methodologies are reported in Supplementary Information (Table  S2 ). Within the 123 records identified, survey data had been collected from a total of 92 unique sites. Spatial resolution of the survey sites varied from GPS point coordinates to GID0 (country) classification (Supplementary Table  S7 ). Geographic distribution of sampling is illustrated in Figure  1.
Studies have also posited that vector species could adapt to match changes in macaque-human host availability  85. Changes in relative host density, vector density and wildlife parasite prevalence in nascent forest fringes may enhance the spillover potential of this disease system into human populations in fragmented habitats. Overall, this finding offers an insight to understanding the mechanisms that underpin the increased force of infection of P. knowlesi that is associated with LULCC. There are limitations to consider in the interpretation of these findings. 'Small-study effects' were observed in the dataset, suggestive of a bias toward positive effect estimates  48. This may be a result of data disaggregation and small samples creating artefactually higher estimates or may reflect true bias in data collection toward areas known to be endemic for P.
g. primary or agroforest). Strong links have been identified between land use changes and ecosystem perturbation that favours the transmission of vector-borne diseases 2. Prevalence of P. knowlesi in macaques is likely to be a crucial determinant of human infection risk, and more representative estimates of P. knowlesi prevalence derived here might better inform regionally-specific transmission risk models. This study characterises risk factors for heightened prevalence of P. knowlesi in NHPs. Findings provide evidence in support of the premise that P. knowlesi in primate hosts is partly driven by LULCC across Southeast Asia. While the full complexity is not captured by the covariates used, it is clear that P. knowlesi infection in NHPs is not restricted to densely forested areas. This study also demonstrates the utility of systematic meta-analysis tools and remote-sensing datasets in the investigation of macroecological disease trends, in conjunction with methods to standardise a spatially heterogeneous dataset and data-driven selection of spatial scales.
SOT101 acts as a selective and potent agonist of the IL-15 pathway through the IL-2/IL-15Rbg thereby inducing proliferation and activation of CD8 + T cells, memory CD8 + T cells, NK cells, gd T cells and NKT cells in vitro and in vivo without the expansion of T regulatory cells. SOT101 was shown to promote NK cell development and differentiation in human immune system (HIS) mice  (9) , to stimulate T cell reconstitution and T cell-dependent antibody responses in HIS mice  (10). SOT101 mediated a stronger anti-metastatic activity and antitumor efficacy in various mouse cancer models over IL-15  (11). SOT101 was shown to synergize with an anti-PD-1 antibody in mouse colorectal cancer models  (12). Finally, SOT101 has been investigated in a dose escalation Phase I clinical trial as monotherapy, or in combination with pembrolizumab for treatment of patients with advanced solid tumors (NCT04234113) and is currently under efficacy and safety evaluation in Phase II clinical trial in combination with pembrolizumab in patients with selected advanced solid tumors (NCT05256381).
Cells were centrifuged 2200 rpm, 2 min, supernatant discarded and stained with intracellular antibody (Ki67 or IFN-g detection antibodies) (50 μl/sample) in PBS containing 2 ul of rat serum/50 μl (Sigma Aldrich, USA). Cells were incubated for 20 min at 4°C. After a washing step, 120 μl/well of FACS buffer was added to the cells for measurement. Data were collected using Flow cytometer LSR Fortessa (Becton Dickinson, USA) and Software BD DiVA. FlowJo (Tree Star, Inc. , USA) software was used for the cytometric data evaluation.
The dead cells detected by LIVE/DEAD Fixable Dye780 stain (eBioscience, USA) were excluded from the analyses. The gating strategy was as follows: singlets/live lymphocytes/CD3 -/DX5 + /Ki67. Data were collected using Flow cytometer BD LSR II (Becton Dickinson, USA) and Software BD DiVA. FlowJo (Tree Star, Inc. , USA) software was used for the cytometric data evaluation. To determine the vascular leakage syndrome (VLS) in mice after the SOT101 treatment, lungs were gently taken out from mouse, placed into a microcentrifuge tube and the lung weight was determined. Then lungs were lyophilized for 10 h. The net weight difference between the wet and dry lungs was used to determine VLS.
Plates were washed and 100 μl/ well of detection solution consisting of 0. 5 μg/ml human/primate anti-IL-15 biotinylated antibody BAM247 (R&D systems, USA) in Low Cross Buffer was added and incubated at RT for 90 min (± 5 min) with agitation. Plates were washed, 100 μl/well of streptavidin-HRP (BD Biosciences, USA) solution was added and incubated RT for 30 min (± 5 min) with agitation. After a washing step, 100 μl/ well of TMB Substrate (Sigma Aldrich, USA) was added and incubated at RT until the samples turned blue for max. 15 min (usually 2 -4 min). Then 100 μl/well of stop solution (Invitrogen, USA) was added and optical density was measured using the ELISA spectrophotometer Spark (Tecan, Switzerland) at 450 nm and at 620 nm as a reference wavelength. Data were analyzed by AccelPharm, Switzerland to derive pharmacokinetic parameters.
1 ml of PBS mixed with matrigel (1:1) for tumor development. All animals were randomly allocated to the different study groups (8 mice per group, 9-11 weeks of age). The mean tumor size at randomization was ~100 mm 3. Randomization was performed based on "Matched distribution" randomization method (StudyDirector ™ software, version 3. 1. 399. 19 ). The date of randomization was denoted as day 1. Treatment was initiated on the randomization day 1 (early therapeutic treatment) or 5 days after randomization (late therapeutic treatment). After tumor cell inoculation, the animals were checked daily for morbidity and mortality. Tumor volumes were measured two times per week in two dimensions using a caliper, and the volume was expressed in mm 3.
This would be fully compatible with the clinical schedule of SOT101 employed in the ongoing Phase II clinical trial in combination with pembrolizumab in patients with selected advanced solid tumors (NCT05256381). Since the SOT101 doses in mice needed to be higher due to a lower homology of the IL-15Rbg compared to the human receptors, the SOT101 doses and schedule were adapted and optimized in a dose escalation Phase I clinical trial (NCT04234113). Our preclinical data shown in this study would support such trial design also with other therapeutic monoclonal antibodies. Given the fact that ADCC is also the major mechanism of other therapeutic antibodies such as Cetuximab, Obinutuzumab, Rituximab etc. similar schedule of their combination with SOT101 could be proposed. Whereas the combination therapy was very effective inducing tumor regressions in comparison to respective monotherapies, in the early therapeutic treatment of the established tumors no dependence on the sequencing of both drugs was observed.