Sparsity with sign-coherent groups of variables via the cooperative-Lasso

We consider the problems of estimation and selection of parameters endowed with a known group structure, when the groups are assumed to be sign-coherent, that is, gathering either nonnegative, nonpositive or null parameters. To tackle this problem, we propose the cooperative-Lasso penalty. We derive the optimality conditions defining the cooperative-Lasso estimate for generalized linear models, and propose an efficient active set algorithm suited to high-dimensional problems. We study the asymptotic consistency of the estimator in the linear regression setup and derive its irrepresentable conditions, which are milder than the ones of the group-Lasso regarding the matching of groups with the sparsity pattern of the true parameters. We also address the problem of model selection in linear regression by deriving an approximation of the degrees of freedom of the cooperative-Lasso estimator. Simulations comparing the proposed estimator to the group and sparse group-Lasso comply with our theoretical results, showing consistent improvements in support recovery for sign-coherent groups. We finally propose two examples illustrating the wide applicability of the cooperative-Lasso: first to the processing of ordinal variables, where the penalty acts as a monotonicity prior; second to the processing of genomic data, where the set of differentially expressed probes is enriched by incorporating all the probes of the microarray that are related to the corresponding genes.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS520 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Weighted-Lasso for Structured Network Inference from Time Course Data

We present a weighted-Lasso method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own a prior internal structure of connectivity which drives the inference method. This prior structure can be either derived from prior biological knowledge or inferred by the method itself. We illustrate the performance of this structure-based penalization both on synthetic data and on two canonical regulatory networks, first yeast cell cycle regulation network by analyzing Spellman et al's dataset and second E. coli S.O.S. DNA repair network by analysing U. Alon's lab data

Inference of gene regulatory networks from non independently and identically distributed transcriptomic data

Cette thèse étudie l'inférence de modèles graphiques Gaussiens en grande dimension à partir de données du transcriptome non indépendamment et identiquement distribuées dans l'objectif d'estimer des réseaux de régulation génétique. Dans ce contexte de données en grande dimension, l'hétérogénéité des données peut être mise à profit pour définir des méthodes de régularisation structurées améliorant la qualité des estimateurs. Nous considérons tout d'abord l'hétérogénéité apparaissant au niveau du réseau, fondée sur l'hypothèse que les réseaux biologiques sont organisés, ce qui nous conduit à définir une régularisation l1 pondérée. Modélisant l'hétérogénéité au niveau des données, nous étudions les propriétés théoriques d'une méthode de régularisation par bloc appelée coopérative-Lasso, définie dans le but de lier l'inférence sur des jeux de données distincts mais proches en un certain sens. Pour finir, nous nous intéressons au problème central de l'incertitude des estimations, définissant un test d'homogénéité pour modèle linéaire en grande dimension.This thesis investigates the inference of high-dimensional Gaussian graphical models from non identically and independently distributed transcriptomic data in the objective of recovering gene regulatory networks. In the context of high-dimensional statistics, data heterogeneity paves the way to the definition of structured regularizers in order to improve the quality of the estimator. We first consider heterogeneity at the network level, building upon the assumption that biological networks are organized, which leads to the definition of a weighted l1 regularization. Modelling heterogeneity at the observation level, we provide a consistency analysis of a recent block-sparse regularizer called the cooperative-Lasso designed to combine observations from distinct but close datasets. Finally we address the crucial question of uncertainty, deriving homonegeity tests for high-dimensional linear regression.EVRY-Bib. électronique (912289901) / SudocSudocFranceF

Monitoring of the heart movements using a FMCW radar and correlation with an ECG

Monitoring the heart activity is an important task to prevent and diagnose cardiovascular diseases. An electrocardio-gram (ECG) is the gold standard for such task. It monitors the heart electrical activity, and while the later is highly correlated to the cardiac mechanical activity, it does not provide all the information. Other sensors such as echo-cardiograph allow to monitor the heart movements, but such tools are hard to operate and expensive. Therefore, contact-less monitoring of the heart using RF sensing has gained interest over the past years. In this paper, we provide a process to extract the movement of the heart with a high accuracy from a millimeter wave radar, i.e. we describe a non invasive and affordable way to monitor cardiac movements. We then demonstrate the correlation between the observed movements and the ECG. Furthermore, we propose an algorithm to synchronize the ECG signal and the processed signal from the radar sensor. The results we obtained provide insights on the mechanical activity of the heart, which could assist cardiologists in their diagnosisComment: 10 pages, 19 figure

Role of Impurities on CO2 Injection: Experimental and Numerical Simulations of Thermodynamic Properties of Water-salt-gas Mixtures (CO2 + Co-injected Gases) Under Geological Storage Conditions

International audienceRole of impurities on CO 2 injection: experimental and numerical simulations of thermodynamic properties of water-salt-gas mixtures (CO 2 + co-injected gases) under geological storage conditions Abstract Regarding the hydrocarbon source and CO 2 capture processes, fuel gas from boilers may be accompanied by so-called "annex gases" which could be co-injected in a geological storage. These gases, such as SOx, NOx, or oxygen for instance, are likely to interact with reservoir fluids and rocks and well materials (casing and cement) and could potentially affect the safety of the storage. However, there are currently only few data on the behaviour of such gas mixtures, as well as on their chemical reactivity, especially in the presence of water. One reason for this lack comes from the difficulty in handling because of their dangerousness and their chemical reactivity. Therefore, the purpose of the Gaz Annexes was to develop new experimental and analytical protocols in order to acquire new thermodynamic data on these annex gases, in fine for predicting the behaviour of a geological storage of CO 2 + co-injected gases in the short, medium and long terms. This paper presents Gaz Annexes concerning acquisition of PVT experimental and pseudo-experimental data to adjust and validate thermodynamic models for water / gas / salts mixtures as well as the possible influence of SO 2 and NO on the geological storage of CO 2. The Gaz Annexes s new insights for the establishment of recommendations concerning acceptable content of annex gases

Histoire des sciences au Moyen Âge

Programme de l’année 2010-2011 : I. Les intérêts scientifiques dans les commentaires bibliques (suite). — II. Les transformations de la matière et leurs théories médiévales (suite)

Weighted-LASSO for Structured Network Inference from Time Course Data

We present a weighted-LASSO method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own prior internal structures of connectivity which drive the inference method. This prior structure can be either derived from prior biological knowledge or inferred by the method itself. We illustrate the performance of this structure-based penalization both on synthetic data and on two canonical regulatory networks (the yeast cell cycle regulation network and the E. coli S.O.S. DNA repair network).

Longitudinal Effect of Bariatric Surgery on Retinal Microcirculation and Target Organ Damage: the BASTOD Study

International audiencePurpose: Obesity is associated with increased cardiovascular risk. Bariatric surgery (BS) improves the clinical and metabolic profile. Retinal caliber changes could precede cardiovascular events. Different studies have shown an improvement in retinal caliber after BS. The aim of this study was to examine retinal caliber and other cardiovascular target organ damage before and after BS.Materials and methods: Monocentric, prospective cohort study at the Montpellier University Hospital. Biologic features, vessel stiffness, echocardiograph variables, and retinal caliber at baseline and 6 and 12 months were assessed in consecutive patients with class 2 or 3 obesity undergoing BS. A mixed linear model adjusted for age and sex was used.Results: We included 88 patients (75 women). The mean (SD) age was 43 years (11) and mean (SD) baseline weight 117 (21) Kg. Mean changes in the first year after BS were - 5.1 µm in central retinal vein equivalent (CRVE) (p < 0.0001), + 0.02 in arteriole-to-venule ratio (AVR) (p < 0.0001), - 1.4 mmol/L in glycemia (p < 0.0001), - 1.0 mg/L in natural logarithm of C-reactive protein (p < 0.0001), and - 54.0 g in left ventricular mass (p = 0.0005). We observed no significant improvement in arterial stiffness markers. Predictors of improvement in CRVE were high baseline weight (p = 0.030), male sex (p = 0.025), and no diabetes history (p Dynamic links between variations = 0.047).Conclusion: The retinal microvascular phenotype improved during the first year after bariatric surgery, with decreased CRVE and increased AVR. Factors associated with retinal microvascular plasticity were male sex, high baseline weight, and absence of diabetes. Longitudinal assessment of retinal vascular calibers may offer new insights into the pathophysiology of subclinical vascular processes

Geological gas‐storage shapes deep life

International audienceAround the world, several dozen deep sedimentary aquifers are being used for storage of natural gas. Ad hoc studies of the microbial ecology of some of them have suggested that sulfate reducing and methanogenic microorganisms play a key role in how these aquifers' communities function. Here, we investigate the influence of gas storage on these two metabolic groups by using high-throughput sequencing and show the importance of sulfate-reducing Desulfotomaculum and a new monophyletic methanogenic group. Aquifer microbial diversity was significantly related to the geological level. The distance to the stored natural gas affects the ratio of sulfate-reducing Firmicutes to deltaproteobacteria. In only one aquifer, the methanogenic archaea dominate the sulfate-reducers. This aquifer was used to store town gas (containing at least 50% H2 ) around 50 years ago. The observed decrease of sulfates in this aquifer could be related to stimulation of subsurface sulfate-reducers. These results suggest that the composition of the microbial communities is impacted by decades old transient gas storage activity. The tremendous stability of these gas-impacted deep subsurface microbial ecosystems suggests that in situ biotic methanation projects in geological reservoirs may be sustainable over time

A Quantitative Multivariate Model of Human Dendritic Cell-T Helper Cell Communication

International audienceCell-cell communication involves a large number of molecular signals that function as words of a complex language whose grammar remains mostly unknown. Here, we describe an integrative approach involving (1) protein-level measurement of multiple communication signals coupled to output responses in receiving cells and (2) mathematical modeling to uncover input-output relationships and interactions between signals. Using human dendritic cell (DC)-T helper (Th) cell communication as a model, we measured 36 DC-derived signals and 17 Th cytokines broadly covering Th diversity in 428 observations. We developed a data-driven, computationally validated model capturing 56 already described and 290 potentially novel mechanisms of Th cell specification. By predicting context-dependent behaviors, we demonstrate a new function for IL-12p70 as an inducer of Th17 in an IL-1 signaling context. This work provides a unique resource to decipher the complex combinatorial rules governing DC-Th cell communication and guide their manipulation for vaccine design and immunotherapies