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Introduction Guadeloupe, a French West Indies island, has been fiercely affected by two large waves of COVID.Therapeutic approach was different between the two waves in the intensive care unit (ICU). We aimed to compare the two different periods in terms of characteristics and outcomes and to evaluate risk factors associated with 60-day mortality in our overall cohort. Methods All consecutive patients with laboratory confirmed COVID-19 pneumonia and requiring oxygen support admitted in our ICU unit of University Hospital of Guadeloupe were prospectively included. Patients were treated during the first wave with a combination of Hydroxychloroquine and Azithromycin and during the second wave with dexamethasone and reinforced anticoagulation. Results In our cohort, 187 patients were included, 31 during the first one and 156 during the second. Patients were mostly male (69%) with a median age of 64years old. Patients tend to be younger during the second wave and body mass index was higher (respectively 31 vs 27kg/m2, p=0.01). Overall mortality at Day 60 was high (45%) and not different between the two waves. Among patients under mechanical ventilation risk factors associated with death in a multivariate analysis were a high number of comorbidities, a high level of SOFA score and the delay of invasive mechanical ventilation (IMV) onset after admission in ICU (OR=1.6 (95% CI 1.2 – 2.4). Conclusion Although therapeutics approach evolve, COVID-19 severe pneumonia is still associated with a high mortality rate in ICU.

An 18-gene RNA signature detects severe cases among young Cambodian secondary-infected dengue patients and yields insights into the underlying pathogenesis. We present evidence that the detection is robust for peripheral blood mononuclear cells and whole blood and different experimental techniques., Background Early detection of severe dengue can improve patient care and survival. To date, no reliable single-gene biomarker exists. We hypothesized that robust multigene signatures exist. Methods We performed a prospective study on Cambodian dengue patients aged 4 to 22 years. Peripheral blood mononuclear cells (PBMCs) were obtained at hospital admission. We analyzed 42 transcriptomic profiles of patients with secondary dengue infected with dengue serotype 1. Our novel signature discovery approach controls the number of included genes and captures nonlinear relationships between transcript concentrations and severity. We evaluated the signature on secondary cases infected with different serotypes using 2 datasets: 22 PBMC samples from additional patients in our cohort and 32 whole blood samples from an independent cohort. Results We identified an 18-gene signature for detecting severe dengue in patients with secondary infection upon hospital admission with a sensitivity of 0.93 (95% confidence interval [CI], .82–.98), specificity of 0.67 (95% CI, .53–.80), and area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI, .75–.97). At validation, the signature had empirical AUCs of 0.85 (95% CI, .69–1.00) and 0.83 (95% CI, .68–.98) for the PBMCs and whole blood datasets, respectively. Conclusions The signature could detect severe dengue in secondary-infected patients upon hospital admission. Its genes offer new insights into the pathogenesis of severe dengue.

International audience; Clinical symptoms of dengue virus (DENV) infection, the most prevalent arthropod-borne viral disease, range from classical mild dengue fever to severe, life-threatening dengue shock syndrome. However, most DENV infections cause few or no symptoms. Asymptomatic DENV-infected patients provide a unique opportunity to decipher the host immune responses leading to virus elimination without negative impact on an individual's health. We used an integrated approach of transcriptional profiling and immunological analysis to compare a Cambodian population of strictly asymptomatic viremic individuals with clinical dengue patients. Whereas inflammatory pathways and innate immune response pathways were similar between asymptomatic individuals and clinical dengue patients, expression of proteins related to antigen presentation and subsequent T cell and B cell activation pathways was differentially regulated, independent of viral load and previous DENV infection history. Feedback mechanisms controlled the immune response in asymptomatic viremic individuals, as demonstrated by increased activation of T cell apoptosis-related pathways and FcγRIIB (Fcγ receptor IIB) signaling associated with decreased anti-DENV-specific antibody concentrations. Together, our data illustrate that symptom-free DENV infection in children is associated with increased activation of the adaptive immune compartment and proper control mechanisms, leading to elimination of viral infection without excessive immune activation, with implications for novel vaccine development strategies.

International audience; The detection of change-points in a spatially or time ordered data sequence is an important problem in many fields such as genetics and finance. We derive the asymptotic distribution of a statistic recently suggested for detecting change-points. Simulation of its estimated limit distribution leads to a new and computationally efficient change-point detection algorithm, which can be used on very long signals. We assess the algorithm via simulations and on previously benchmarked real-world data sets.

Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks. Availability: C++ source code is available at: http://bioinfo.curie.fr/projects/freec/ Contact: freec@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.

International audience; Visual Predictive Checks (VPC) are graphical tools to help decide whether a given model could have plausibly generated a given set of real data. Typically, time-course data is binned into time intervals, then statistics are calculated on the real data and data simulated from the model, and represented graphically for each interval. Poor selection of bins can easily lead to incorrect model diagnosis. We propose an automatic binning strategy that improves reliability of model diagnosis using VPC. It is implemented in version 4 of the Monolix software.

Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of 'experts' and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalized cumulative prediction error., Comment: article + 2 figures

Background DNA barcoding aims to assign individuals to given species according to their sequence at a small locus, generally part of the CO1 mitochondrial gene. Amongst other issues, this raises the question of how to deal with within-species genetic variability and potential transpecific polymorphism. In this context, we examine several assignation methods belonging to two main categories: (i) phylogenetic methods (neighbour-joining and PhyML) that attempt to account for the genealogical framework of DNA evolution and (ii) supervised classification methods (k-nearest neighbour, CART, random forest and kernel methods). These methods range from basic to elaborate. We investigated the ability of each method to correctly classify query sequences drawn from samples of related species using both simulated and real data. Simulated data sets were generated using coalescent simulations in which we varied the genealogical history, mutation parameter, sample size and number of species. Results No method was found to be the best in all cases. The simplest method of all, "one nearest neighbour", was found to be the most reliable with respect to changes in the parameters of the data sets. The parameter most influencing the performance of the various methods was molecular diversity of the data. Addition of genetically independent loci - nuclear genes - improved the predictive performance of most methods. Conclusion The study implies that taxonomists can influence the quality of their analyses either by choosing a method best-adapted to the configuration of their sample, or, given a certain method, increasing the sample size or altering the amount of molecular diversity. This can be achieved either by sequencing more mtDNA or by sequencing additional nuclear genes. In the latter case, they may also have to modify their data analysis method.

Background Nucleotides are trimmed from the ends of variable (V), diversity (D) and joining (J) genes during immunoglobulin (IG) and T cell receptor (TR) rearrangements in B cells and T cells of the immune system. This trimming is followed by addition of nucleotides at random, forming the N regions (N for nucleotides) of the V-J and V-D-J junctions. These processes are crucial for creating diversity in the immune response since the number of trimmed nucleotides and the number of added nucleotides vary in each B or T cell. IMGT® sequence analysis tools, IMGT/V-QUEST and IMGT/JunctionAnalysis, are able to provide detailed and accurate analysis of the final observed junction nucleotide sequences (tool "output"). However, as trimmed nucleotides can potentially be replaced by identical N region nucleotides during the process, the observed "output" represents a biased estimate of the "true trimming process." Results A probabilistic approach based on an analysis of the standardized tool "output" is proposed to infer the probability distribution of the "true trimmming process" and to provide plausible biological hypotheses explaining this process. We collated a benchmark dataset of TR alpha (TRA) and TR gamma (TRG) V-J rearranged sequences and junctions analysed with IMGT/V-QUEST and IMGT/JunctionAnalysis, the nucleotide sequence analysis tools from IMGT®, the international ImMunoGeneTics information system®, http://imgt.cines.fr. The standardized description of the tool output is based on the IMGT-ONTOLOGY axioms and concepts. We propose a simple first-order model that attempts to transform the observed "output" probability distribution into an estimate closer to the "true trimming process" probability distribution. We use this estimate to test the hypothesis that Poisson processes are involved in trimming. This hypothesis was not rejected at standard confidence levels for three of the four trimming processes: TRAV, TRAJ and TRGV. Conclusion By using trimming of rearranged TR genes as a benchmark, we show that a probabilistic approach, applied to IMGT® standardized tool "outputs" opens the way to plausible hypotheses on the events involved in the "true trimming process" and eventually to an exact quantification of trimming itself. With increasing high-throughput of standardized immunogenetics data, similar probabilistic approaches will improve understanding of processes so far only characterized by the "output" of standardized tools.

Motivation: Inference and reconstruction of biological networks from heterogeneous data is currently an active research subject with several important applications in systems biology. The problem has been attacked from many different points of view with varying degrees of success. In particular, predicting new edges with a reasonable false discovery rate is highly demanded for practical applications, but remains extremely challenging due to the sparsity of the networks of interest. Results: While most previous approaches based on the partial knowledge of the network to be inferred build global models to predict new edges over the network, we introduce here a novel method which predicts whether there is an edge from a newly added vertex to each of the vertices of a known network using local models. This involves learning individually a certain subnetwork associated with each vertex of the known network, then using the discovered classification rule associated with only that vertex to predict the edge to the new vertex. Excellent experimental results are shown in the case of metabolic and protein–protein interaction network reconstruction from a variety of genomic data. Availability: An implementation of the proposed algorithm is available upon request from the authors. Contact: Jean-Philippe.Vert@ensmp.fr

Summary: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs. Availability: Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/. Contact: freec@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.

International audience; We develop a methodological approach to identify and prioritize psychosocial factors (stressors) requiring priority action to reduce stress levels. Data analysis was carried out on a random sample of 10 000 French employees who completed, during a routine interview with the occupational physician, a 25-item questionnaire about stress levels, as well as a questionnaire about 58 stressors grouped into 5 latent variables: job control, job context, relationships at work, tasks performed and recognition. Our method combines Importance-Performance Analysis, a valuable approach for prioritizing improvements in the quality of services, with Partial Least Squares-Path modeling, a Structural Equation Modeling approach widely applied in psychosocial research. Findings on our data suggest two areas worthy of attention: one with five stressors on which decision makers should concentrate, and another with five stressors that managers should leave alone when acting to reduce stress levels. We show that IPA is robust when answers to questions are dichotomized, as opposed to the initial 6-point Likert scale. We believe that our approach will be a useful tool for experts and decision-makers in the field of stress management and prevention.