La composition corporelle est importante pour évaluer l'état de santé et le statut nutritionnel d'individus. Le surpoids et l'obésité deviennent des problèmes de santé à l'échelle mondiale. L'accroissement de la masse grasse, notamment celle du tronc, a été associée à une augmentation du risque de maladies métaboliques, telles que le diabète de type 2 et les maladies cardiovasculaires. La masse musculaire, en particulier appendiculaire, est également un indice de santé, et est liée au taux de mortalité. En outre, le vieillissement s'accompagne de changements importants dans la composition corporelle. La masse maigre diminue (Kyle et al., 2001) et la masse grasse augmente, liée à une accumulation de tissus adipeux, en particulier dans la région abdominale (Kuk et al., 2009). Il est donc important d'étudier ces changements en fonction de l'âge pour tenter d'établir un pré-diagnostic et aider à la prévention de la morbidité et de mortalité. La composition corporelle se mesure par différentes méthodes, telles que le pesage sous l'eau ou l'absorption bi-photonique à rayons X (DXA). Cependant, ces méthodes de mesure ne sont pas adaptées pour des populations de taille très grande, car elles nécessitent un équipement fixe, demandent des manipulations longues et sont coûteuses. En revanche, le potentiel de méthodes de prédiction statistique a été mis en évidence pour estimer la composition corporelle (Snijder et al., 2006), et plusieurs modèles ont été proposés pour prédire la composition corporelle, notamment le pourcentage de la masse grasse (BF%) (Gallagher et al.,2000a; Jackson et al., 2002; Mioche et al., 2011b). Le premier objectif de cette thèse est de développer un modèle multivarié à partir de covariables anthropométriques pour prédire simultanément les masses grasse et maigre de différents segments du corps. Pour cela, nous avons proposé une régression linéaire multivariable publiée dans le British Journal of Nutrition. Notre proposition multivariée présente deux avantages principaux. Le premier avantage consiste à utiliser les covariables très simples que sont l'âge, le poids et la taille dont la mesure est facile et peu coûteuse. L'utilité d'ajouter comme covariable le tour de taille a été évaluée. Le deuxième avantage est que l'approche multivariée prend en compte la structure de corrélation entre les variables, ce qui est utile pour certaines études d'inférence où on s'intéresse à des fonctions des variables prédites. La qualité de la précision multivariée a été évaluée par comparaison avec celle des modèles univariés déjà publiés. Nous avons montré que la prédiction multivariée est bonne et que notre approche peut donc être utilisée pour des études de risques métaboliques en grandes populations. Le second objectif de cette thèse est d'étudier l'évolution de la composition corporelle au cours du vieillissement, en tenant compte des covariables anthropométriques. Deux modélisations bayésiennes ont été retenues et développées. Un des avantages principaux de nos propositions est, grâce à une modélisation, de réaliser une analyse longitudinale à partir de données transversales. En outre, la modélisation bayésienne permet de fournir une distribution prédictive, et non pas une simple valeur prédite, ce qui permet d'explorer l'incertitude de la prédiction. Également, des résultats antérieurs ou publiés peuvent être incorporés dans la distribution priore, ce qui conduit à des conclusions plus précises. Les prédictions précédentes sont fondées sur des modèles où la structure de corrélation entre les variables est laissée libre, le troisième objectif de notre travail a été d'imposer une structure de corrélation particulière adaptée au problème. L'avantage est l'utilisation d'un sous-modèle parcimonieux du modèle multivarié précédent. Cette structure est décrite au moyen d'un réseau bayésien gaussien (GBN). [...] Suite et fin du résumé dans la thèse. / The assessment of human body composition is important for evaluating health and nutritional status. Among health issues, overweight and obesity are worldwide problems. Increased fat mass, especially in the trunk location, has been associated with an increased risk of metabolic diseases, such as type 2 diabetes and cardiovascular disease. The lean body mass, especially appendicular muscle mass, is also directly related to health and particularly with the mortality rate. Also, aging is associated with substantial changes in body composition. Reduction in body lean or body fat-free mass occurs during aging (Kyle et al., 2001) together with an increase of body fat related to accumulation of adipose tissues, particularly in abdominal region (Kuk et al., 2009); therefore assessing these changes in segmental body composition may be important because the study will lead to a pre-diagnosis for the prevention of morbidity and mortality risk. Accurate measurements of body composition can be obtained from different methods, such as underwater weighing and dual-energy X-ray absorptiometry (DXA). However, their applications are not always convenient, because they require fixed equipment and they are also time consuming and expensive. As a result, they are not convenient for use as a part of routine clinical examinations or population studies. The potential uses of statistical methods for body composition assessment have been highlighted (Snijder et al., 2006), and several attempts to predict body composition, particularly body fat percentage (BF%), have been made (Gallagher et al., 2000a; Jackson et al., 2002; Mioche et al., 2011b).The first aim in this thesis was to develop a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured anthropometric covariables. We proposed a linear solution published in the British Journal of Nutrition. There are two main advantages in our proposed multivariate approach. The first consists in using very simple covariables, such as body weight and height, because these measurements are easy and not expensive. The usefulness of waist circumference is also investigated and combined with age, height and weight as predictor variables. The second advantage is that the multivariate approach enables to take into account the correlation structure between the responses into account, which is useful for a number of inference tasks, e.g., to give simultaneous confidence regions for all the responses together. Then the prediction accuracy of the multivariate approach is justified by comparing with that of the available univariate models that predict body fat percentage (BF%). With a good accuracy, the multivariate outcomes might then be used in studies necessitating the assessment of metabolic risk factors in large populations.The second aim in this thesis was to study age-related changes in segmental body compositions, associated with anthropometric covariables. Two Bayesian modeling methods are proposed for the exploration of age-related changes. The main advantage of these methods is to propose a surrogate for a longitudinal analysis from the cross-sectional datasets. Moreover, the Bayesian modeling enables to provide a prediction distribution, rather than a simple estimate, this is more relevant for exploring the uncertainty or accuracy problems. Also we can incorporate the previous findings in the prior distribution, by combining it with the datasets, we could obtain more suitable conclusions.The previous predictions were based on models supposing any correlation structure within the variables, the third aim in this thesis was to propose a parsimonious sub-model of the multivariable model described by a Gaussian Bayesian network (GBN), more precisely Crossed Gaussian Bayesian Networks (CGBN). Last and final summary in the thesis.

Vegetation phenology controls seasonal variation in ecosystem processes and exerts important controls on land-atmosphere exchanges of carbon, water, and energy. However, the ecological processes and interactions between climate and vegetation that control phenology and associated feedbacks to the atmosphere are not fully understood. In this dissertation, I use remote sensing in combination with climate and ecological data to improve understanding of biophysical controls and feedbacks between vegetation phenology and the atmosphere in temperate forest ecosystems of North America. In the first part of this dissertation, I evaluate the agreement and characterize the similarities and differences between land surface phenology products from two remote sensing instruments (MODIS and VIIRS) that are designed to provide long-term continuity of land surface phenology measurements at global scale. Results from this analysis indicate that the VIIRS land surface phenology product provides excellent continuity with the MODIS record despite subtle differences between each instrument and the algorithms used to generate each product. In the second part of this dissertation, a state-space Bayesian modeling framework is applied to seventeen years of MODIS and daily weather data to improve understanding of what controls the timing of springtime phenology in deciduous forests of temperate and boreal North America. Results show that photoperiod is more important in warmer regions than in colder regions, which contradicts a widely held hypothesis that photoperiod provides a key safety mechanism preventing early leaf-out during springtime. In the final part of this dissertation, I use a physically-based attribution method to quantify the relative importance of covarying surface biophysical and atmospheric variables in modifying the surface energy balance during springtime. Results show that the widely observed decrease in the Bowen ratio that occurs with leaf emergence is not solely attributable to changes in surface resistance caused by increasing leaf area during spring. Rather, observed changes in the Bowen ratio reflect the combined effects of changes in surface properties and atmospheric conditions. The results from this dissertation provide an improved foundation for long-term studies focused on observing and modeling springtime vegetation phenology and associated feedbacks to the atmosphere in deciduous forest ecosystems at local-to-continental scales.

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When working with a complex AI or robotics system in a specific application, users often need to incorporate their special domain knowledge into the autonomous system. Such needs call for the ability to manage autonomy. However, managing autonomy can be a difficult task because the internal mechanisms and algorithms of the autonomous components may be beyond the users' understanding. We propose an approach where users manage autonomy indirectly by managing information provided to the intelligent system hierarchically at three different temporal scales: strategic, between-episodes, and within-episode. Information management tools at multiple temporal scales allow users to influence the autonomous behaviors of the system without the need for tedious direct/manual control. Information fed to the system can be in the forms of areas of focus, representations of task difficulty, and the amount of autonomy desired. We apply this approach to using an Unmanned Aerial Vehicle (UAV) to support Wilderness Search and Rescue (WiSAR). This dissertation presents autonomous algorithms/components and autonomy management tools/interfaces we designed at different temporal scales, and provides evidence that the approach improves the performance of the human-robot team and the experience of the human partner.

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Computational biology currently faces challenges in a big data world with thousands of data samples across multiple disease types including cancer. The challenging problem is how to extract biologically meaningful information from large-scale genomic data. Next-generation Sequencing (NGS) can now produce high quality data at DNA and RNA levels. However, in cells there exist a lot of non-specific (background) signals that affect the detection accuracy of true (foreground) signals. In this dissertation work, under Bayesian framework, we aim to develop and apply approaches to learn the distribution of genomic signals in each type of NGS data for reliable identification of specific foreground signals. We propose a novel Bayesian approach (ChIP-BIT) to reliably detect transcription factor (TF) binding sites (TFBSs) within promoter or enhancer regions by jointly analyzing the sample and input ChIP-seq data for one specific TF. Specifically, a Gaussian mixture model is used to capture both binding and background signals in the sample data; and background signals are modeled by a local Gaussian distribution that is accurately estimated from the input data. An Expectation-Maximization algorithm is used to learn the model parameters according to the distributions on binding signal intensity and binding locations. Extensive simulation studies and experimental validation both demonstrate that ChIP-BIT has a significantly improved performance on TFBS detection over conventional methods, particularly on weak binding signal detection. To infer cis-regulatory modules (CRMs) of multiple TFs, we propose to develop a Bayesian integration approach, namely BICORN, to integrate ChIP-seq and RNA-seq data of the same tissue. Each TFBS identified from ChIP-seq data can be either a functional binding event mediating target gene transcription or a non-functional binding. The functional bindings of a set of TFs usually work together as a CRM to regulate the transcription processes of a group of genes. We develop a Gibbs sampling approach to learn the distribution of CRMs (a joint distribution of multiple TFs) based on their functional bindings and target gene expression. The robustness of BICORN has been validated on simulated regulatory network and gene expression data with respect to different noise settings. BICORN is further applied to breast cancer MCF-7 ChIP-seq and RNA-seq data to identify CRMs functional in promoter or enhancer regions. In tumor cells, the normal regulatory mechanism may be interrupted by genome mutations, especially those somatic mutations that uniquely occur in tumor cells. Focused on a specific type of genome mutation, structural variation (SV), we develop a novel pattern-based probabilistic approach, namely PSSV, to identify somatic SVs from whole genome sequencing (WGS) data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with a heterozygous status in the normal sample and a homozygous status in the tumor sample. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer patient WGS data for identifying somatic SVs of key factors associated with breast cancer development. In this dissertation research, we demonstrate the advantage of the proposed distributional learning-based approaches over conventional methods for NGS data analysis. Distributional learning is a very powerful approach to gain biological insights from high quality NGS data. Successful applications of the proposed Bayesian methods to breast cancer NGS data shed light on underlying molecular mechanisms of breast cancer, enabling biologists or clinicians to identify major cancer drivers and develop new therapeutics for cancer treatment. / Ph. D.

Computational systems biology is an inter-disciplinary field that aims to develop computational approaches for a system-level understanding of biological systems. Advances in high-throughput biotechnology offer broad scope and high resolution in multiple disciplines. However, it is still a major challenge to extract biologically meaningful information from the overwhelming amount of data generated from biological systems. Effective computational approaches are of pressing need to reveal the functional components. Thus, in this dissertation work, we aim to develop computational approaches for differential analysis of RNA-seq and methylation data to detect aberrant events associated with cancers. We develop a novel Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. BayesIso features a joint model of the variability of RNA-seq data and the differential state of isoforms. BayesIso can not only account for the variability of RNA-seq data but also combines the differential states of isoforms as hidden variables for differential analysis. The differential states of isoforms are estimated jointly with other model parameters through a sampling process, providing an improved performance in detecting isoforms of less differentially expressed. We propose to develop a novel probabilistic approach, DM-BLD, in a Bayesian framework to identify differentially methylated genes. The DM-BLD approach features a hierarchical model, built upon Markov random field models, to capture both the local dependency of measured loci and the dependency of methylation change. A Gibbs sampling procedure is designed to estimate the posterior distribution of the methylation change of CpG sites. Then, the differential methylation score of a gene is calculated from the estimated methylation changes of the involved CpG sites and the significance of genes is assessed by permutation-based statistical tests. We have demonstrated the advantage of the proposed Bayesian approaches over conventional methods for differential analysis of RNA-seq data and methylation data. The joint estimation of the posterior distributions of the variables and model parameters using sampling procedure has demonstrated the advantage in detecting isoforms or methylated genes of less differential. The applications to breast cancer data shed light on understanding the molecular mechanisms underlying breast cancer recurrence, aiming to identify new molecular targets for breast cancer treatment. / Ph. D.