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Free-stream water wheels running on floating river installations may contribute to hydropower production as part of a decentralized network meeting the highest ecological standards. While such devices are certainly not novel, their dynamics are complex and a need exists for an optimization of their power-producing characteristics. In this work, a parametrized two-dimensional computational fluid dynamics simulation is coupled to a genetic optimizer seeking to maximize the generated shaft power within a large domain of design parameters. Two objectives are pursued simultaneously: maximize the hydraulic efficiency, and maximize the power density of the device. After nearly 2000 individuals are evaluated, a Pareto front is identified; a family of designs is created to cover the trade-off between the two objectives. The results indicate that compared to operators constrained by the flow-exposed area, operators constrained by the rotor size would trade a 40% reduction in hydraulic performance in order to gain a 50% increase in power per unit rotor area. This optimization of the free-stream water wheel, the first in published literature to our knowledge, allows for the quantification of this trade-off and the publication of broadly-applicable design guidelines for the corresponding optimal blade geometry, number of blades, radius, and depth.

International audience; Vertical-axis water turbines (VAWT), the target of the present study, provide a higher area-based power density when used in arrays, and are a promising alternative to horizontal-axis hydro-kinetic turbines (HAWT). Nevertheless, they operate under highly dynamic conditions near or even beyond dynamic stall at their best-efficiency-point. The abrupt loss of lift and strong increase of drag associated with hydrofoil stall can produce cyclic loads and possible damage of turbomachines due to material fatigue. The effect of flexible structures in a highly dynamic flow regime including separation and stall is here studied systematically in an experimental setup which permits observations of all regimes ranging from quasi-static state up to the occurrence of deep dynamic stall and beyond. The process is studied using a surrogate model consisting of an oscillating NACA0018 hydrofoil in a closed water channel, following a motion law comparable to the real angle of incidence of a Darrieus turbine blade along its rotation. The investigated parameters are the oscillation frequency and tip speed ratio, for one rigid as well as for three flexible hydrofoils of different stiffnesses. The coupling process is therefore investigated for multiple machine designs and working points. Lift and drag measurements have been carried out in a systematic manner. Results show that at tip speed ratios for which highly dynamic flow regimes occur, flexible blades provide not only higher thrust, but also reduced normal forces and reduced peak-to-peak cyclic normal force variations. This reduction of stress loads would translate into significantly increased turbine lifetime. This supports the need for further investigations in order to identify optimal blade flexibility and check further turbine designs.

Summary We propose a new three-step methodology to perform an automated mineralogical inversion from wellbore logs. The approach is derived from a Bayesian linear-regression model with no prior knowledge of the mineral composition of the rock. The first step makes use of approximate Bayesian computation (ABC) for each depth sample to evaluate all the possible mineral proportions that are consistent with the measured log responses. The second step gathers these candidates for a given stratum and computes through a density-based clustering algorithm the most probable mineralogical compositions. Finally, for each stratum and for the most probable combinations, a mineralogical inversion is performed with an associated confidence estimate. The advantage of this approach is to explore all possible mineralogy hypotheses that match the wellbore data. This pipeline is tested on both synthetic and real data sets.

To the editor: Multiple myeloma (MM) is characterized by a large diversity of genetic abnormalities.[1][1][⇓][2]-[3][3] They can be classified in 3 categories: copy number changes, mutations, and translocations involving mainly the IGH gene at 14q32. Translocations are usually balanced

Tumor-promoting inflammation and avoiding immune destruction are hallmarks of cancer. Here, we demonstrate that the pro-inflammatory cytokine interleukin (IL)-18 is critically involved in these hallmarks in multiple myeloma (MM). Mice deficient for IL-18 were remarkably protected from Vk∗MYC MM progression in a CD8+ T cell-dependent manner. The MM-niche-derived IL-18 drove generation of myeloid-derived suppressor cells (MDSCs), leading to accelerated disease progression. A global transcriptome analysis of the immune microenvironment in 73 MM patients strongly supported the negative impact of IL-18-driven MDSCs on T cell responses. Strikingly, high levels of bone marrow plasma IL-18 were associated with poor overall survival in MM patients. Furthermore, our preclinical studies suggested that IL-18 could be a potential therapeutic target in MM.

International audience; Although long intergenic non-coding RNAs (lincRNA) role in various cancers is described, their significance in Multiple Myeloma (MM) remains poorly defined. Here we have studied the lincRNA profile and their clinical impact in MM. We performed RNA-seq on MM cells from 308 newly diagnosed and uniformly treated patients, 16 normal plasma cells and utilized RNA-seq data from 532 newly diagnosed patients from CoMMpass study to analyze for lincRNAs. We observed 869 differentially expressed lincRNAs in MM compared to normal plasma cells. We identified 14 lincRNAs associated with PFS and calculated a risk score to stratify patients. The median PFS between high vs low-risk groups was 17 months vs not-reached (NR); and OS 30 months vs NR, respectively (p < 0.0001 for both). In the independent validation dataset between high and low-risk groups, PFS was 27 vs 42 months (HR 2.06 [1.44−2.96]; p < 0.0005); and 4-year OS 62% vs 86% (HR 2.76 [1.51–5.05]; p < 0.0005) confirming significant clinical relevance of lincRNA in MM. Importantly, lincRNA signature was able to further identify patients with significant differential outcomes within each low and high-risk categories identified using standard risk categorization including cytogenetic/FISH, ISS, and MRD negative or positive. Our results suggest that lincRNAs have an independent effect on MM outcome and provide a rationale to evaluate its molecular and biological impact.

International audience; We consider a problem of change point detection for a continuous-time stochastic process in the family of piecewise deterministic Markov processes introduced by MHA Davis in the 80's. The process is only observed in discrete time and with noise, and the aim is to accurately detect the (random) time when its dynamics change and also select the new dynamics among a finite number of possibilities as soon as possible after the change-point. To do so, we turn the detection problem into an optimal stopping problem for partially observed Markov decision process with values in a continuous state space, and provide a discretization of the state space to be able to solve the problem numerically. Applications include for instance maintenance optimization where the change-point corresponds to a failure time of some equipment. Another application concerns treatment optimization for cancer patients. The change-point then corresponds to a sudden deterioration of the health of the patient. It must be detected early so that the treatment can be adapted.

Despite the development of novel drugs, alkylating agents remain an important component of therapy in multiple myeloma (MM). DNA repair processes contribute towards sensitivity to alkylating agents and therefore we here evaluate the role of nucleotide excision repair (NER), which is involved in the removal of bulky adducts and DNA crosslinks in MM. We first evaluated NER activity using a novel functional assay and observed a heterogeneous NER efficiency in MM cell lines and patient samples. Using next-generation sequencing data, we identified that expression of the canonical NER gene, excision repair cross-complementation group 3 (ERCC3), significantly impacted the outcome in newly diagnosed MM patients treated with alkylating agents. Next, using small RNA interference, stable knockdown and overexpression, and small-molecule inhibitors targeting xeroderma pigmentosum complementation group B (XPB), the DNA helicase encoded by ERCC3, we demonstrate that NER inhibition significantly increases sensitivity and overcomes resistance to alkylating agents in MM. Moreover, inhibiting XPB leads to the dual inhibition of NER and transcription and is particularly efficient in myeloma cells. Altogether, we show that NER impacts alkylating agents sensitivity in myeloma cells and identify ERCC3 as a potential therapeutic target in MM.

International audience

International audience; We consider the segmentation problem of univariate distributions from the exponential family with multiple parameters. In segmenta-tion, the choice of the number of segments remains a difficult issue due to the discrete nature of the change-points. In this general exponential family distribution framework, we propose a penalized log-likelihood esti-mator where the penalty is inspired by papers of L. Birgé and P. Massart. The resulting estimator is proved to satisfy some oracle inequalities. We then further study the particular case of categorical variables by comparing the values of the key constants when derived from the specification of our general approach and when obtained by working directly with the characteristics of this distribution. Finally, simulation studies are conducted to assess the performance of our criterion and to compare our approach to other existing methods, and an application on real data modeled using the categorical distribution is provided.

We consider the segmentation problem of Poisson and negative binomial (i.e. overdispersed Poisson) rate distributions. In segmentation, an important issue remains the choice of the number of segments. To this end, we propose a penalized log-likelihood estimator where the penalty function is constructed in a non-asymptotic context following the works of L. Birg\'e and P. Massart. The resulting estimator is proved to satisfy an oracle inequality. The performances of our criterion is assessed using simulated and real datasets in the RNA-seq data analysis context.

International audience

We are interested in the comparison of the positions of the change-points in the segmentation of independent series. We consider a Bayesian framework with conjugate priors to perform exact inference on the change-point model. This work is motivated by the comparison of transcript boundaries in yeast grown under different conditions. When comparing two series, we derive the posterior credibility interval of the shift between the locations. When comparing more than two series, we compute the posterior probability for a given change-point to have the same location in all series. All calculations are made in an exact manner in a quadratic time. The performances of those approaches are assessed via a simulation study. When applied to yeast genes, this approach reveals different behavior between internal and external exon boundaries .

Hidden Markov models (HMMs) are intensively used in various fields to model and classify data observed along a line (e.g. time). The fit of such models strongly relies on the choice of emission distributions that are most often chosen among some parametric family. In this paper, we prove that finite state space non parametric HMMs are identifiable as soon as the transition matrix of the latent Markov chain has full rank and the emission probability distributions are linearly independent. This general result allows the use of semi- or non-parametric emission distributions. Based on this result we present a series of classification problems that can be tackled out of the strict parametric framework. We derive the corresponding inference algorithms. We also illustrate their use on few biological examples, showing that they may improve the classification performances.

In this paper, we consider the Integrated Completed Likelihood (ICL) as a useful criterion for estimating the number of changes in the underlying distribution of data, specifically in problems where detecting the precise location of these changes is the main goal. The exact computation of the ICL requires O(Kn(2)) operations (with K the number of segments and n the number of data-points) which is prohibitive in many practical situations with large sequences of data. We describe a framework to estimate the ICL with O(K(2)n) complexity. Our approach is general in the sense that it can accommodate any given model distribution. We checked the run-time and validity of our approach on simulated data and demonstrate its good performance when analyzing real Next-Generation Sequencing (NGS) data using a negative binomial model. Our method is implemented in the R package postCP and available on the CRAN repository.

Transcriptome sequencing (RNA-Seq) yields massive data sets, containing a wealth of information on the expression of a genome. While numerous methods have been developed for the analysis of differential gene expression, little has been attempted for the localization of transcribed regions, that is, segments of DNA that are transcribed and processed to result in a mature messenger RNA. Our understanding of genomes, mostly annotated from biological experiments or computational gene prediction methods, could benefit greatly from re-annotation using the high precision of RNA-Seq. We consider five classes of genome segmentation methods to delineate transcribed regions, including intron/exon boundaries, based on RNA-Seq data. The methods provide different functionality and include both exact and heuristic approaches, using diverse models, such as hidden Markov or Bayesian models, and diverse algorithms, such as dynamic programming or the forward-backward algorithm. We evaluate the methods in a simulation study where RNA-Seq read counts are generated from parametric models as well as by resampling of actual yeast RNA-Seq data. The methods are compared in terms of criteria that include global and local fit to a reference segmentation, Receiver Operator Characteristic (ROC) curves, and coverage of credibility intervals based on posterior change-point distributions. All compared algorithms are implemented in packages available on the Comprehensive R Archive Network (CRAN, http://cran.r-project.org). The data set used in the simulation study is publicly available from the Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/sra). While the different methods each have pros and cons, our results suggest that the EBS Bayesian approach of Rigaill, Lebarbier, and Robin (2012) performs well in a re-annotation context, as illustrated in the simulation study and in the application to actual yeast RNA-Seq data. This article has supplementary material online.

Objective Altered microbiota composition, changes in immune responses and impaired intestinal barrier functions are observed in IBD. Most of these features are controlled by proteases and their inhibitors to maintain gut homeostasis. Unrestrained or excessive proteolysis can lead to pathological gastrointestinal conditions. The aim was to validate the identified protease IBD candidates from a previously performed systematic review through a genetic association study and functional follow-up. Design We performed a genetic association study in a large multicentre cohort of patients with Crohn's disease (CD) and UC from five European IBD referral centres in a total of 2320 CD patients, 2112 UC patients and 1796 healthy controls. Subsequently, we did an extensive functional assessment of the candidate genes to explore their causality in IBD pathogenesis. Results Ten single nucleotide polymorphisms (SNPs) in four genes were significantly associated with CD: CYLD, USP40, APEH and USP3. CYLD was the most significant gene with the intronically located rs12324931 the strongest associated SNP (p(FDR)=1.74e-17, OR=2.24 (1.83 to 2.74)). Five SNPs in four genes were significantly associated with UC: USP40, APEH, DAG1 and USP3. CYLD, as well as some of the other associated genes, is part of the ubiquitin proteasome system (UPS). We therefore determined if the IBD-associated adherent-invasive Escherichia coli (AIEC) can modulate the UPS functioning. Infection of intestinal epithelial cells with the AIEC LF82 reference strain modulated the UPS turnover by reducing poly-ubiquitin conjugate accumulation, increasing 26S proteasome activities and decreasing protein levels of the NF-kappa B regulator CYLD. This resulted in I kappa B-alpha degradation and NF-kappa B activation. This activity was very important for the pathogenicity of AIEC since decreased CYLD resulted in increased ability of AIEC LF82 to replicate intracellularly. Conclusions Our results reveal the UPS, and CYLD specifically, as an important contributor to IBD pathogenesis, which is favoured by both genetic and microbial factors.

We propose to model the output of transcriptome sequencing technologies (RNA-Seq) using the negative binomial distribution, as well as build segmentation models suited to their study at different biological scales, in the context of these technologies becoming a valuable tool for genome annotation, gene expression analysis, and new-transcript discovery. We develop a fast segmentation algorithm to analyze whole chromosomes series, and we propose two methods for estimating the number of segments, a key feature related to the number of genes expressed in the cell, should they be identified from previous experiments or discovered at this occasion. Research on precise gene annotation, and in particular comparison of transcription boundaries for individuals, naturally leads us to the statistical comparison of change-points in independent series. To address our questions, we build tools, in a Bayesian segmentation framework, for which we are able to provide uncertainty measures. We illustrate our models, all implemented in R packages, on an RNA-Seq dataset from a study on yeast, and show for instance that the intron boundaries are conserved across conditions while the beginning and end of transcripts are subject to differential splicing.; Nous proposons de modéliser les données issues des technologies de séquençage du transcriptome (RNA-Seq) à l'aide de la loi binomiale négative, et nous construisons des modèles de segmentation adaptés à leur étude à différentes échelles biologiques, dans le contexte où ces technologies sont devenues un outil précieux pour l'annotation de génome, l'analyse de l'expression des gènes, et la détection de nouveaux transcrits. Nous développons un algorithme de segmentation rapide pour analyser des séries à l'échelle du chromosome, et nous proposons deux méthodes pour l'estimation du nombre de segments, directement lié au nombre de gènes exprimés dans la cellule, qu'ils soient précédemment annotés ou détectés à cette même occasion. L'objectif d'annotation précise des gènes, et plus particulièrement de comparaison des sites de début et fin de transcription entre individus, nous amène naturellement à nous intéresser à la comparaison des localisations de ruptures dans des séries indépendantes. Nous construisons ainsi dans un cadre de segmentation bayésienne des outils de réponse à nos questions pour lesquels nous sommes capable de fournir des mesures d'incertitude. Nous illustrons nos modèles, tous implémentés dans des packages R, sur des données RNA-Seq provenant d'expériences sur la levure, et montrons par exemple que les frontières des introns sont conservées entre conditions tandis que les débuts et fin de transcriptions sont soumis à l'épissage différentiel.

International audience

Background: Change point problems arise in many genomic analyses such as the detection of copy number variations or the detection of transcribed regions. The expanding Next Generation Sequencing technologies now allow to locate change points at the nucleotide resolution. Results: Because of its complexity which is almost linear in the sequence length when the maximal number of segments is constant, and as its performance had been acknowledged for microarrays, we propose to use the Pruned Dynamic Programming algorithm for Seq-experiment outputs. This requires the adaptation of the algorithm to the negative binomial distribution with which we model the data. We show that if the dispersion in the signal is known, the PDP algorithm can be used, and we provide an estimator for this dispersion. We describe a compression framework which reduces the time complexity without modifying the accuracy of the segmentation. We propose to estimate the number of segments via a penalized likelihood criterion. We illustrate the performance of the proposed methodology on RNA-Seq data. Conclusions: We illustrate the results of our approach on a real dataset and show its good performance. Our algorithm is available as an R package on the CRAN repository.

International audience

International audience