Les maladies cardiovasculaires sont l'une des principales causes de mortalité chez l'homme. Une façon de diagnostiquer des maladies cardiaques et des anomalies est le traitement de signaux cardiaques tels que le ECG. Dans beaucoup de ces traitements, des caractéristiques inter-battements et intra-battements de signaux ECG doivent être extraites. Ces caractéristiques comprennent les points de repère des ondes de l’ECG (leur début, leur fin et leur point de pic), les intervalles significatifs et les segments qui peuvent être définis pour le signal ECG. L'extraction des points de référence de l'ECG consiste à identifier l'emplacement du pic, de début et de la fin de l'onde P, du complexe QRS et de l'onde T. Ces points véhiculent des informations cliniquement utiles, mais la segmentation precise de chaque battement de l'ECG est une tâche difficile, même pour les cardiologues expérimentés.Dans cette thèse, nous utilisons un cadre bayésien basé sur le modèle dynamique d'ECG proposé par McSharry. Depuis ce modèle s'appuyant sur la morphologie des ECG, il peut être utile pour la segmentation et l'analyse d'intervalles d'ECG. Afin de tenir compte de la séquentialité des ondes P, QRS et T, nous utiliserons également l'approche de Markov et des modèles de Markov cachés (MMC). En bref dans cette thèse, nous utilisons un modèle dynamique (filtre de Kalman), un modèle séquentiel (MMC) et leur combinaison (commutation de filtres de Kalman (SKF)). Nous proposons trois méthodes à base de filtres de Kalman, une méthode basée sur les MMC et un procédé à base de SKF. Nous utilisons les méthodes proposées pour l'extraction de points de référence et l'analyse d'intervalles des ECG. Le méthodes basées sur le filtrage de Kalman sont également utilisés pour le débruitage d'ECG, la détection de l'alternation de l'onde T, et la détection du pic R de l'ECG du foetus.Pour évaluer les performances des méthodes proposées pour l'extraction des points de référence de l'ECG, nous utilisons la base de données "Physionet QT", et une base de données "Swine" qui comprennent ECG annotations de signaux par les médecins. Pour le débruitage d'ECG, nous utilisons les bases de données "MIT-BIH Normal Sinus Rhythm", "MIT-BIH Arrhythmia" et "MIT-BIH noise stress test". La base de données "TWA Challenge 2008 database" est utilisée pour la détection de l'alternation de l'onde T. Enfin, la base de données "Physionet Computing in Cardiology Challenge 2013 database" est utilisée pour la détection du pic R de l'ECG du feotus. Pour l'extraction de points de reference, la performance des méthodes proposées sont évaluées en termes de moyenne, écart-type et l'erreur quadratique moyenne (EQM). Nous calculons aussi la sensibilité des méthodes. Pour le débruitage d'ECG, nous comparons les méthodes en terme d'amélioration du rapport signal à bruit. / Cardiovascular diseases are one of the major causes of mortality in humans. One way to diagnose heart diseases and abnormalities is processing of cardiac signals such as ECG. In many of these processes, inter-beat and intra-beat features of ECG signal must be extracted. These features include peak, onset and offset of ECG waves, meaningful intervals and segments that can be defined for ECG signal. ECG fiducial point (FP) extraction refers to identifying the location of the peak as well as the onset and offset of the P-wave, QRS complex and T-wave which convey clinically useful information. However, the precise segmentation of each ECG beat is a difficult task, even for experienced cardiologists.In this thesis, we use a Bayesian framework based on the McSharry ECG dynamical model for ECG FP extraction. Since this framework is based on the morphology of ECG waves, it can be useful for ECG segmentation and interval analysis. In order to consider the time sequential property of ECG signal, we also use the Markovian approach and hidden Markov models (HMM). In brief in this thesis, we use dynamic model (Kalman filter), sequential model (HMM) and their combination (switching Kalman filter (SKF)). We propose three Kalman-based methods, an HMM-based method and a SKF-based method. We use the proposed methods for ECG FP extraction and ECG interval analysis. Kalman-based methods are also used for ECG denoising, T-wave alternans (TWA) detection and fetal ECG R-peak detection.To evaluate the performance of proposed methods for ECG FP extraction, we use the "Physionet QT database", and a "Swine ECG database" that include ECG signal annotations by physicians. For ECG denoising, we use the "MIT-BIH Normal Sinus Rhythm", "MIT-BIH Arrhythmia" and "MIT-BIH noise stress test" databases. "TWA Challenge 2008 database" is used for TWA detection and finally, "Physionet Computing in Cardiology Challenge 2013 database" is used for R-peak detection of fetal ECG. In ECG FP extraction, the performance of the proposed methods are evaluated in terms of mean, standard deviation and root mean square of error. We also calculate the Sensitivity for methods. For ECG denoising, we compare methods in their obtained SNR improvement.

Given current rates of anthropogenic environmental change, combined with the increasing lethal and non-lethal mortality threat that human activities pose, there is a vital need to understand wildlife movement and behaviour in human-dominated landscapes to help inform conservation efforts and wildlife management. As long-term monitoring of wildlife populations using Global Positioning System (GPS) telemetry increases, there are new opportunities to quantify change in wildlife movement and behaviour. The objective of this PhD research is to develop novel methodological approaches for quantifying change in spatial-temporal patterns of wildlife movement and habitat by leveraging long time series of GPS telemetry and remotely sensed data. Analyses were focused on the habitat and movement of individuals in the threatened grizzly bear (Ursus arctos) population of Alberta, Canada, which occupies a human-dominated and heterogeneous landscape. Using methods in functional data analysis, a multivariate regionalization approach was developed that effectively summarizes complex spatial-temporal patterns associated with landscape disturbance, as well as recovery, which is often left unaccounted in studies quantifying patterns associated with disturbance. Next, the quasi-experimental framework afforded by a hunting moratorium was used to compare the influence of lethal (i.e., hunting) and non-lethal (i.e., anthropogenic disturbance) human-induced risk on antipredator behaviour of an apex predator, the grizzly bear. In support of the predation risk allocation hypothesis, male bears significantly decrease risky daytime behaviours by 122% during periods of high lethal human-induced risk. Rapid behavioural restoration occurred following the end of the hunt, characterized by diel bimodal movement patterns which may promote coexistence of large predators in human-dominated landscapes. A multi-scale approach using hierarchical Bayesian models, combined with post hoc trend tests and change point detection, was developed to test the influence of landscape disturbance and conditions on grizzly bear home range and movement selection over time. The results, representing the first longitudinal empirical analysis of grizzly bear habitat selection, revealed selection for habitat security at broad scales and for resource availability and habitat permeability at finer spatial scales, which has influenced potential landscape connectivity over time. Finally, combining approaches in movement ecology and conservation physiology, a body condition index was used to characterize how the physiological condition (i.e., internal state) of grizzly bears influences behavioral patterns due to costs and benefits associated with risk avoidance and resource acquisition. The results demonstrated individuals in poorer condition were more likely to engage in risky behaviour associated with anthropogenic disturbance, which highlights complex challenges for carnivore conservation and management of human-carnivore conflict. In summary, this dissertation contributes 1) a multivariate regionalization approach for quantifying spatial-temporal patterns of landscape disturbance and recovery applicable across diverse natural systems, 2) support for the growing theory that apex predators modify behavioural patterns to account for temporal overlap with lethal and non-lethal human-induced risk associated with humans, 3) an integrated approach for considering multi-scale spatial-temporal change in patterns of wildlife habitat selection and landscape connectivity associated with landscape change, 4) a cross-disciplinary framework for considering the impacts of the internal state on behavioural patterns and risk tolerance. / Graduate

Cette thèse porte sur le diagnostic et le pronostic pour l’aide à la maintenance de systèmes complexes. Elle présente deux approches de diagnostic/pronostic qui permettent de générer les indicateurs utiles pour l’optimisation de la stratégie de maintenance. Plus précisément, ces approches permettent d’évaluer l’état de santé et de prédire la durée de vie résiduelle du système. Les approches présentées visent en particulier à pallier le problème d’absence d’indicateurs de dégradation. Les développements sont fondés sur l’utilisation d’observateurs, de formalisme de Modèle de Markov Caché, des méthodes d’inférences statistiques et des méthodes de prédiction de séries temporelles à base d’apprentissage afin de caractériser et prédire les modes de fonctionnement du système. Les deux approches sont illustrées sur des exemples de dégradation d’un système de régulation de niveau d’eau, d’une machine asynchrone et d’une batterie Li-Ion. / The research presented in this thesis deals of diagnosis and prognosis of complex systems. It presents two approaches that generate useful indicators for optimizing maintenance strategies. Specifically, these approaches are used to assess the level of degradation and estimate the Remaining Useful Life of the system. The aim of these approaches is to overcome for the lack of degradation indicators. The developments are based on observers, Hidden Markov Model formalism, statistical inference methods and learning-based methods in order to characterize and predict the system operating modes. To illustrate the proposed failure diagnosis/prognosis approaches, a simulated tank level control system, an induction motor and a Li-Ion battery were used.

Nous nous intéressons à des modèles statistiques pour les données indexées par des arborescences. Dans le contexte de l'équipe Virtual Plants, équipe hôte de cette thèse, les applications d'intérêt portent sur le développement de la plante et sa modulation par des facteurs environnementaux et génétiques. Nous nous restreignons donc à des applications issues du développement de la plante, à la fois au niveau microscopique avec l'étude de la lignée cellulaire du tissu biologique servant à la croissance des plantes, et au niveau macroscopique avec le mécanisme de production de branches. Le catalogue de modèles disponibles pour les données indexées par des arborescences est beaucoup moins important que celui disponible pour les données indexées par des chemins. Cette thèse vise donc à proposer un cadre de modélisation statistique pour l'étude de patterns pour données indexées par des arborescences. À cette fin, deux classes différentes de modèles statistiques, les modèles de Markov et de détection de ruptures, sont étudiées. / We address statistical models for tree-indexed data.Tree-indexed data can be seen as a generalization of path-indexed data since directed path graphs are directed tree graphs where there is at most one child per vertex.In the context of the Virtual Plants team, host team of this thesis, applications of interest focus on plant development and its modulation by environmental and genetic factors.We thus focus on plant developmental applications, both at the microscopic level with the study of the cell lineage in the biological tissue responsible for the plant growth, and at the macroscopic level with the mechanism of production of branches. The catalog of models available for tree-indexed data is far less important than the one available for path-indexed data.This thesis therefore aims at proposing a statistical modeling framework for studying patterns in tree-indexed data.To this end, two different classes of statistical models, Markov and change-point models, are investigated.

Depuis une dizaine d'années, une part croissante de fonds de pension rencontrent des difficultés financières. Cette détérioration a soulevé des questions sur la gestion de ces institutions et sur l'efficacité du cadre réglementaires. Cette thèse a pour objet d'analyser les comportements financiers et la gestion des risques opérés par les fonds de pension à prestation définies et les institutions assimilées. En premier lieu, nous relions les choix d'investissement à la question du contrôle managériale. Notre analyse suggère que la bonne gestion des fonds de pension nécessite un partage optimal des droits de contrôle entre les participants du plan et l'entreprise sponsor. Nous montrons alors comment cette répartition affecte les décisions d'investissement. Notre seconde analyse étudie l'impact des fluctuations financières sur la gestion des fonds de pension. Nos résultats suggèrent que le cadre réglementaire actuel conduit à de larges effets pro-cycliques, en particulier sur les exigences de capital et les décisions d'investissement. Finalement, nous analysons comment les changement structurels de la mortalité affectent le risque et les politiques de risque des fonds de pension. / Since ten years, an increasing proportion of pension funds faces to severe financial difficulties, addressing some questions about the management of these institutions and the effectiveness of the regulatory framework. This thesis aims to analyze the investment decisions and financial risk management made by the pension fund defined benefit and assimilated institutions, in order to address some advances for the regulation purpose. First, we address the question of the pension funds management by analyzing the implications of the managerial control problem. Our analysis suggests that the efficient management may require an optimal splitting of control rights between plan participants and the sponsoring company. We then show how this splitting of right controls can affects investment decisions in pension funds. Second, we analyze the implications of financial cycles for pension fund management. Our results suggest that the regulatory framework produces large pro-cyclical, including regime-dependent capital requirement and regime-dependent investment decisions. Finally, we analyze how the structural change in mortality affect the risk and the risk management of pension funds.

La marche est définie par des séquences de gestes cycliques et répétées. Il a été déjà montré que la vitesse et la variabilité de ces séquences peuvent révéler des aptitudes ou des défaillances motrices. L’originalité de ce travail est alors d’analyser et de caractériser les foulées de sujets âgés à partir des signaux de pression issus de semelles instrumentées lors de la marche, au moyen d’outils de traitement du signal. Une étude préliminaire, sur les signaux de pression générés lors de la marche, nous a permis de mettre en évidence le caractère cyclo-stationnaire de ces signaux. Ces paramètres sont testées sur une population de 47 sujets. Tout d'abord, nous avons commencé par un prétraitement des signaux et nous avons montré dans la première de cette thèse que le filtrage peut éliminer une partie vitale du signal. C’est pourquoi un filtre adaptatif basé sur la décomposition en mode empirique a été conçu. Les points de retournement ont été filtrés ensuite en utilisant une technique temps-fréquence appelée «synochronosqueezing». Nous avons également montré que le contenu des signaux de force de marche est fortement affecté par des paramètres inquantifiables tels que les tâches cognitives qui les rendent difficiles à normaliser. C’est pourquoi les paramètres extraits de nos signaux sont tous dérivées par une comparaison inter-sujet. Par exemple, nous avons assimilé la différence dans la répartition de poids entre les pieds. Il est également recommandé dans ce travail de choisir le centre des capteurs plutôt que de compter sur la somme des forces issues du réseau de capteurs pour la classification. Ensuite, on a montré que l’hypothèse de la marche équilibrée et déséquilibrée peut améliorer les résultats de la classification. Le potentiel de cette hypothèse est montré à l'aide de la répartition du poids ainsi que le produit de l'âge × vitesse dans le premier classificateur et la corrélation dans le second classificateur. Une simulation de la série temporelle de VGRF basé sur une version modifiée du modèle de Markov non stationnaire, du premier ordre est ensuite dérivée. Ce modèle prédit les allures chez les sujets normaux et suffisamment pour les allures des sujets de Parkinson. On a trouvé que les trois modes: temps, fréquence et espace sont très utiles pour l’analyse des signaux de force, c’est pourquoi l’analyse de facteurs parallèles est introduite comme étant une méthode de tenseur qui peut être utilisée dans le futur / Walking is defined as sequences of repetitive cyclic gestures. It was already shown that the speed and the variability of these sequences can reveal abilities or motorskill failures. The originality of this work is to analyze and characterize the steps of elderly persons by using pressure signals. In a preliminary study, we showed that pressure signals are characterized by cyclostationarity. In this study, we intend to exploit the nonstationarity of the signals in a search for new indicators that can help in gait signal classification between normal and Parkinson subjects in the elderly population. These parameters are tested on a population of 47 subjects. First, we started with preprocessing the vertical ground reaction force (VGRF) signals and showed in this first part of the thesis that filtering can remove a vital part of the signal. That is why an adaptive filter based on empirical mode decomposition (EMD) was built. Turning points are filtered using synochronosqueezing of time-frequency representations of the signal. We also showed that the content of gait force signals is highly affected by unquantifiable parameter such as cognitive tasks which make them hard to be normalized. That is why features being extracted are derived from inter-subject comparison. For example we equated the difference in the load distribution between feet. It is also recommended in this work to choose the mid-sensor rather than relying on summation of forces from array of sensors for classification purposes. A hypothesis of balanced and unbalanced gait is verified to be potential in improving the classification accuracy. The power of this hypothesis is shown by using the load distribution and Age×Speed in the first classifier and the correlation in the second classifier. A time series simulation of VGRF based on a modified version of nonstationary- Markov model of first order is derived. This model successfully predict gaits in normal subjects and fairly did in Parkinson’s gait. We found out that the three modes: time, frequency and space are helpful in analyzing force signals that is why parallel factor analysis is introduced as a tensor method to be used in a future work

Cette thèse est une collaboration entre le LAboratoire des Sciences et Matériaux pour l’Électronique et d’Automatique (LASMEA) de Clermont-Ferrand et le Laboratoire Vision et Ingénierie des Contenus (LVIC) du CEA LIST à Saclay. La première moitié de la thèse a été accomplie au sein de l’équipe ComSee (1) du LASMEA et la deuxième au LVIC. L’objectif de ces travaux est de concevoir un système de vidéo-assistance temps réel pour la détection d’évènements dans des scènes possiblement denses.La vidéosurveillance intelligente de scènes denses telles que des foules est particulièrement difficile, principalement à cause de leur complexité et de la grande quantité de données à traiter simultanément. Le but de cette thèse consiste à élaborer une méthode de détection d’évènements rares dans de telles scènes, observées depuis une caméra fixe. La méthode en question s’appuie sur l’analyse automatique de mouvement et ne nécessite aucune information à priori. Les mouvements nominaux sont déterminés grâce à un apprentissage statistique non supervisé. Les plus fréquemment observés sont considérés comme des évènements normaux. Une phase de classification permet ensuite de détecter les mouvements déviant trop du modèle statistique, pour les considérer comme anormaux. Cette approche est particulièrement adaptée aux lieux de déplacements structurés, tels que des scènes de couloirs ou de carrefours routiers. Aucune étape de calibration, de segmentation de l’image, de détection d’objets ou de suivi n’est nécessaire. Contrairement aux analyses de trajectoires d’objets suivis, le coût calculatoire de notre méthode est invariante au nombre de cibles présentes en même temps et fonctionne en temps réel. Notre système s’appuie sur une classification locale du mouvement de la scène, sans calibration préalable. Dans un premier temps, une caractérisation du mouvement est réalisée, soit par des méthodes classiques de flot optique, soit par des descripteurs spatio-temporels. Ainsi, nous proposons un nouveau descripteur spatio-temporel fondé sur la recherche d’une relation linéaire entre les gradients spatiaux et les gradients temporels en des zones où le mouvement est supposé uniforme. Tout comme les algorithmes de flot optique, ce descripteur s’appuie sur la contrainte d’illumination constante.Cependant en prenant en compte un voisinage temporel plus important, il permet une caractérisation du mouvement plus lisse et plus robuste au bruit. De plus, sa faible complexité calculatoire est bien adaptée aux applications temps réel. Nous proposons ensuite d’étudier différentes méthodes de classification : La première, statique, dans un traitement image par image, s’appuie sur une estimation bayésienne de la caractérisation du mouvement au travers d’une approche basée sur les fenêtres de Parzen. Cette nouvelle méthode est une variante parcimonieuse des fenêtres de Parzen. Nous montrons que cette approche est algorithmiquement efficace pour approximer de manière compacte et précise les densités de probabilité. La seconde méthode, basée sur les réseaux bayésiens, permet de modéliser la dynamique du mouvement. Au lieu de considérer ce dernier image par image, des séquences de mouvements sont analysées au travers de chaînes de Markov Cachées. Ajouté à cela, une autre contribution de ce manuscrit est de prendre en compte la modélisation du voisinage d’un bloc afin d’ajouter une cohérence spatiale à la propagation du mouvement. Ceci est réalisé par le biais de couplages de chaînes de Markov cachées.Ces différentes approches statistiques ont été évaluées sur des données synthétiques ainsi qu’en situations réelles, aussi bien pour la surveillance du trafic routier que pour la surveillance de foule.Cette phase d’évaluation permet de donner des premières conclusions encourageantes quant à la faisabilité de la vidéosurveillance intelligente d’espaces possiblement denses. / The automatic analysis of crowded areas in video sequences is particularly difficult because ofthe large amount of information to be processed simultaneously and the complexity of the scenes. We propose in this thesis a method for detecting abnormal events in possibly dense scenes observed from a static camera. The approach is based on the automatic classification of motion requiring no prior information. Motion patterns are encoded in an unsupervised learning framework in order to generate a statistical model of frequently observed (aka. normal) events. Then at the detection stage, motion patterns that deviate from the model are classified as unexpected events. The method is particularly adapted to scenes with structured movement with directional flow of objects or people such as corridors, roads, intersections. No camera calibration is needed, nor image segmentation, object detection and tracking. In contrast to approaches that rely on trajectory analysis of tracked objects, our method is independent of the number of targets and runs in real-time. Our system relies on a local classification of global scene movement. The local analysis is done on each blocks of a regular grid. We first introduce a new spatio-temporal local descriptor to characterize the movement efficiently. Assuming a locally uniform motion of space-time blocks of the image, our approach consists in determining whether there is a linear relationship between spatial gradients and temporal gradients. This spatio-temporal descriptor holds the Illumination constancy constraint like optical flow techniques, but it allows taking into account the spatial neighborhood and a temporal window by giving a smooth characterization of the motion, which makes it more robust to noise. In addition, its low computational complexity is suitable for real-time applications. Secondly, we present two different classification frameworks : The first approach is a static (frame by frame) classification approach based on a Bayesian characterization of the motion by using an approximation of the Parzen windowing method or Kernel Density Estimation (KDE) to model the probability density function of motion patterns.This new method is the sparse variant of the KDE (SKDE). We show that the SKDE is a very efficient algorithm giving compact representations and good approximations of the density functions. The second approach, based on Bayesian Networks, models the dynamics of the movement. Instead of considering motion patterns in each block independently, temporal sequences of motion patterns are learned by using Hidden Markov Models (HMM). The second proposed improvement consists in modeling the movement in one block by taking into account the observed motion in adjacent blocks. This is performed by the coupled HMM method. Evaluations were conducted to highlight the classification performance of the proposed methods,on both synthetic data and very challenging real video sequences captured by video surveillance cameras.These evaluations allow us to give first conclusions concerning automatic analyses of possibly crowded area.

[pt] A indústria de óleo e gás utiliza a sísmica para investigar a distribuição de tipos de rocha (facies) em subsuperfície. Por outro lado, apesar de seu corriqueiro uso em geociências, medidas sísmicas costumam ser ruidosas, e a inversão do dado sísmico para a distribuição de facies é um problema mal posto. Por esta razão, diversos autores estudam esta inversão sob o ponto de vista probabilístico, para ao menos estimar as incertezas da solução do problema inverso. O objetivo da presente dissertação é desenvolver método quantitativo para estimar a probabilidade de reservatório com hidrocarboneto, dado um traço sísmico de reflexão, integrando modelagem sísmica direta, e conhecimento geológico a priori. Utiliza-se, um dos métodos mais recentes para resolver o problema inverso: Modelo de Markov Oculto com Efeito Convolucional (mais especificamente, a Aproximação por Projeção de (1)). É demonstrado que o método pode ser reformulado em termos do Modelo de Markov Oculto (MMO) ordinário. A teoria de sísmica de AVA é apresentada, e usada conjuntamente com MMO com Efeito Convolucional para resolver a inversão de sísmica para facies. A técnica de inversão é avaliada usando-se medidas difundidas em Aprendizado de Máquina, em um conjunto de experimentos variados e realistas. Apresenta-se uma técnica para medir a capacidade do algoritmo em estimar valores confiáveis de probabilidade. Pelos testes realizados a aproximação por projeção apresenta distorções de probabilidade inferiores a 5 por cento, tornando-a uma técnica útil para a indústria de óleo e gás. / [en] Oil and Gas Industry uses seismic data in order to unravel the distribution of rock types (facies) in the subsurface. But, despite its widespread use, seismic data is noisy and the inversion from seismic data to the underlying rock distribution is an ill-posed problem. For this reason, many authors have studied the topic in a probabilistic formulation, in order to provide uncertainty estimations about the solution of the inversion problem. The objective of the present thesis is to develop a quantitative method to estimate the probability of hydrocarbon bearing reservoir, given a seismic reflection profile, and, to integrate geological prior knowledge with geophysical forward modelling. One of the newest methods for facies inversion is used: Convolved Hidden Markov Model (more specifically the Projection Approximation from (1)). It is demonstrated how Convolved HMM can be reformulated as an ordinary Hidden Markov Model problem (which models geological prior knowledge). Seismic AVA theory is introduced, and used with Convolved HMM theory to solve the seismic to facies problem. The performance of the inversion technique is measured with common machine learning scores, in a broad set of realistic experiments. The technique capability of estimating reliable probabilities is quantified, and it is shown to present distortions smaller than 5 percent. As a conclusion, the studied Projection Approximation is applicable for risk management in Oil and Gas applications, which integrates geological and geophysical knowledge.

Significant advancements in genome sequencing techniques and other high-throughput initiatives have resulted in the availability of complete sequences of genomes of a large number of organisms, which provide an opportunity to study detailed biological information encoded therein. Identification of functional roles of proteins can aid in comprehension of various cellular activities in an organism, which is traditionally achieved using techniques pertaining to the field of molecular biology, protein chemistry and macromolecular crystallography. The established experimental methods for protein structure and function determination, although accurate and resourceful, are laborious and time consuming. Computational analyses of sequences of gene products and exploration of evolutionary relationships can give clues on protein structure and/or function with reasonable accuracy which can be used to direct experimental studies on proteins of interest, effectively. Moreover, with growing volumes of data, there has been a growing disparity in the number of well-characterized and uncharacterized proteins, further necessitating the use of computational methods for investigating evolutionary and structure-function relationships. The remarkable progress made in the development of computational techniques (Chapter 1) has immensely contributed to the state-of-the-art biological sequence analysis and recognition of protein structure and function in a reliable manner. These methods have largely influenced the exploration of protein sequence-structure-function space. One of the relevant applications of computational approaches is in the understanding of functional make-up of human pathogens, their complex interplay with the host and implications in pathogenesis. In this thesis, sensitive profile-based search procedures have been utilized to address various aspects in the context of three pathogens- Mycobacterium tuberculosis, Plasmodium falciparum and Trypanosoma brucei, which are causative agents of potentially life- threatening diseases. The existing drugs approved for the diseases, although of immense value in controlling the disease, have several shortcomings, the most important of them being the emergence of drug resistance that render the current treatment regimens futile. Thus, the identification of practicable targets and new drugs or new combination therapies become an important necessity. Analyses on structural and functional repertoire of proteins encoded in the pathogenic genomes can provide means for rational identification of therapeutic intervention strategies. This thesis begins with the computational analyses of proteins encoded in M. tuberculosis genome. M. tuberculosis is a primary aetiological agent of tuberculosis in humans, and is o responsible for an estimated 1.5 million deaths every year. The complete genome of the pathogen was sequenced and made available more than a decade ago, which has been valuable in determination of functional roles of its gene products. Yet, functions of many M. tuberculosis proteins remain unknown. Computational prediction of protein function is an on- going process based on ever growing information made available in public databases as well as the introduction of powerful homology recognition techniques. Hence, a continuous refinement is essential to make the most of the sequence data, ensuring its accuracy and relevance. With the use of multiple sequence and structural profile-based search procedures, an enhanced structural and functional characterization of M. tuberculosis proteins, totalling to 95% of the genome was achieved (Chapter 2). Following are the key findings. o Domain definitions were obtained for a total of 3566 of 4018 proteins. Amino acid residue coverage of >70% was achieved for 2295 proteins which constitute more than half of the proteome. o Domain assignments were newly identified for 244 proteins with domain-unassigned regions. Structure prediction for these proteins corroborated all the remote homologyrelationships recognized using profile-based methods, enhancing the reliability of the predictions. o Comparison on domain compositions of proteins between M. tuberculosis and human host, revealed presence of pathogen-specific domains that are not homologous to proteins in human. Such proteins in M. tuberculosis are mainly virulence factors involved in host-pathogen interactions such as immune-dominance and aiding entry and survival in human host macrophages, hence forming attractive targets for drug discovery. o Putative structural and functional information for proteins with no recognizable domains were inferred by means of fold recognition and an iterative profile-based search against sequence database. o Attributing putative structures and functions to 955 conserved hypothetical proteins in M. tuberculosis, 137 of which are reportedly essential to the pathogen, provide a basis to re-investigate their involvement in pathogenesis and survival in the host. Proteins with no detectable homologues were recognized as M. tuberculosis H37Rv-specific, which can serve as promising drug targets. An attempt was made to identify porin-like proteins in M. tuberculosis, considering MspA porin from M. smegmatis as a template. The difficulty in recognition of putative porins in M. tuberculosis is indicative of novel outer membrane channel proteins, not characterized yet, or high representation of ion-channels, symporters and transporters to compensate for the functional role of porins. In addition, MspA-like proteins were not readily recognized in other slow-growing mycobacterial pathogens that are known to infect human host, apart from M. tuberculosis. This indicates probable acquisition of physiological adaptations, i.e. absence of porins, to confer drug-resistance, in the course of their co-evolution with human hosts. Evolutionary relationships recognized between sequence (Pfam) and structural (SCOP) families aided in association of potential structures and/or functions for 55 uncharacterized Pfam domains recognized in M. tuberculosis. Such associations deliver useful insights into the structure and function of a protein housing the uncharacterized domain. The functional inferences drawn for M. tuberculosis proteins based on the predictions can provide valuable basis for experimental endeavours in understanding mechanisms of pathogenesis and can significantly impact anti-tubercular drug discovery programmes. An interesting outcome benefitted from the exercise of exploring relationships between Pfam and SCOP families, was the identification of evolutionary relationship between a Pfam domain of unknown function DUF2652 and class III nucleotidyl cyclases. A detailed investigation was undertaken to assess this relationship (Chapter 3). Nucleotidyl cyclases synthesize cyclic nucleotides which are critical second messengers in signalling pathways. The DUF2652 family predominantly comprises of bacterial proteins belonging to three lineages- Actinobacteria, Bacteroidetes and Proteobacteria. Thus, recognition of evolutionary relationship between these bacterial proteins and nucleotide cyclases is of particular interest due to the indispensability of cyclic nucleotides in regulation of varied biological activities in bacteria. Use of fold recognition program suggested presence of nucleotide cyclase-characteristic topological motif (βααββαβ) in all the members of the DUF2652 family. Detailed analyses on structural and functional features of the uncharacterized set of bacterial proteins corresponding to 50 bacterial genomes, using profile- based alignments, revealed presence of key features typical of nucleotidyl cyclases, including metal-binding aspartates, substrate-specifying residues and transition-state stabilizing residues. Depending on the features, 20 proteins of Actinobacteria lineage, predominantly mycobacteria, of unknown structure and function were identified as putative nucleotide cyclases, 23 proteins of Bacteroidetes lineage were associated with guanylyl cyclases, while 8 uncharacterized proteins of Proteobacteria were recognized as nucleotide cyclase-like proteins (7 adenylyl and one guanylyl cyclase). Sequence similarity-based clustering of the predicted nucleotide cyclase-like proteins with established nucleotide cyclases indicated the apparent evolutionarily distinctness of the subfamily of class III nucleotidyl cyclases predicted. Furthermore, analysis of evolutionarily conserved gene clusters of the predicted nucleotide cyclase-like proteins indicated functional associations that support the predictions on their participation in cellular signalling events. The inferences made can be experimentally investigated further to ascertain the involvement of the uncharacterized bacterial proteins in signalling pathways, which can help in understanding the pathobiology of pathogenic species of interest. The next objective was the recognition of biologically relevant protein-protein interactions across M. tuberculosis and human host (Chapter 4). M. tuberculosis is well known for its ability to successfully co-evolve with human host in terms of establishing infection, survival and persistence. The current knowledge on the mechanisms of host invasion, immune evasion and persistence in the host environment can be attributed, and is limited, to the experimental studies pursued by numerous groups. Chapter 4 presents an approach for computational identification of biologically feasible protein-protein interactions across M. tuberculosis and human host. The approach utilizes crystal structures of intra-organism protein-protein complexes which are transient in nature. Identification of homologues of host and pathogen proteins in the database of known protein-protein interactions, formed the initial step, followed by identification of conserved interfacial patch and integration of information on tissue-specific expression of human proteins and subcellular localization of human and M. tuberculosis proteins. In addition, appropriate filters were used to extract biologically feasible host-pathogen protein-protein interactions. This resulted in recognition of 386 interactions potentially mediated by 59 M. tuberculosis proteins and 90 human proteins. A predominance of host-pathogen interactions (193 protein-protein interactions) brought about by M. tuberculosis proteins participating in cell wall processes, was observed, which is in concurrence with the experimental studies on immuno-modulatory activities brought about by such proteins. These set of mycobacterial proteins were predicted to interact with diverse set of host proteins such as those involved in ubiquitin conjugation pathways, metabolic pathways, signalling pathways, regulation of cell proliferation, transport, apoptosis and autophagy. The predictions have the potential to complement experimental observations at the molecular level. Details on couple of interesting cases are presented in the chapter, one of which is the probable mechanism of immune evasion adopted by M. tuberculosis to inhibit lysozyme activity in macrophages, and second is the mechanism of nutrient uptake from host. The set of M. tuberculosis proteins predicted to mediate interactions with host proteins have the potential to warrant an experimental follow-up on probable mechanisms of pathogenesis and also serve as attractive targets for chemotherapeutic interventions. proteins known to participate in P. falciparum metabolism. Pathway holes, where evidence on metabolic step exists but the catalysing enzyme is not known, have also been addressed in the study, several of which have been suggested to play an important role in growth and development of the parasite during its intra-erythrocytic stages in human host. A subsequent objective was the recognition P. falciparum proteins potentially capable of remodelling erythrocytes to suit their niche (Chapter 7). Exploitative mechanisms are brought about by the parasite to remodel erythrocytes for growth and survival during intra-erythrocytic stages of its life-cycle, the understanding of which is limited to experimental studies. To achieve physicochemically viable protein-protein interactions potentially mediated by proteins of human erythrocytes and P. falciparum proteins, a structure-influenced protocol, similar to the one demonstrated in Chapter 4, was employed. Information on subcellular localization and protein expression is crucial especially for parasites like P. falciparum, which reside in One of the major shortcomings with current treatment regimen for tuberculosis is the emergence of multidrug (MDR) and extensively drug-resistant (XDR) strains that render first-line and second-line drug treatments futile. This entails a need to explore target space in M. tuberculosis as well as explore the potential of existing drugs for repurposing against tuberculosis. A drug repurposing strategy i.e. exploring within-target-family selectivity of small molecules, has been implemented (Chapter 5) to contribute towards time and cost-saving anti-tubercular drug development efforts. With the use of profile-based search procedures, evolutionary relationships between targets (other than proteins of M. tuberculosis) of FDA-approved drugs and M. tuberculosis proteins were investigated. A key filter to exclude drugs capable of acting on human proteins substantially reduced the chances of obtaining anti-targets. Thus, total of 130 FDA-approved drugs were recognized that can be repurposed against 78 M. tuberculosis proteins, belonging to the functional categories- intermediary metabolism and respiration, information pathways, cell wall and cell processes and lipid metabolism. The catalogue of structure and function of M. tuberculosis proteins and their involvement in host-pathogen protein-protein interactions compiled from chapters 2 and 4 served as a guiding tool to explore the functional importance of targets identified. Many of the potential targets identified have been experimentally shown to be essential for growth and survival of the pathogen earlier, thus gaining importance in terms of pharmaceutical relevance. Polypharmacological drugs or drugs capable of acting of multiple targets were also identified (92 drugs) in the study. These drugs have the potential to stand tolerance against development of drug resistance in the pathogen. Comparative sequence and structure-based analysis of M. tuberculosis proteins homologous to known targets yielded credible inferences on putative binding sites of FDA-approved drugs in potential targets. Instances where information on binding sites could not be readily inferred from known targets, potentially druggable sites have been predicted. Comparison with earlier experimental studies that report anti-tubercular potential of several approved drugs enhanced the credibility of 74 of 130 FDA-approved drugs that can be readily prioritized for clinical studies. An additional exercise was pursued to identify prospective anti-tubercular agents by means of structural comparison between ChEMBL compounds and 130 FDA-approved drugs. Only those compounds were retained that showed considerably high structural similarity with approved drugs. Such compounds with minor changes in terms of physicochemical properties provide a basis for exploration of compounds that may exhibit higher affinities to bind to M. tuberculosis targets. The set of approved drugs recognized as repurpose-able candidates against tuberculosis, in concert with the structurally similar compounds, can significantly impact anti-tubercular drug development and drug discovery. The next part of the thesis focuses on Plasmodium falciparum, an obligate intracellular protozoan parasite responsible for malaria. The parasite genome features unusual characteristics including abundance of low complexity regions and pronounced sequence divergence that render protein structure and function recognition difficult. The parasite also manifests remarkable plasticity in its metabolic organization throughout its developmental stages in two hosts-human and mosquito; thus obtaining an exhaustive list of metabolic proteins in the parasite gains importance. Considering the utility of multiple sensitive profile-based search approaches in enhanced annotation of M. tuberculosis genome, a similar exercise was employed to recognize potential metabolic proteins in P. falciparum (Chapter 6). A total of 172 metabolic proteins were identified as participants of 78 metabolic pathways, over and above 609heterogeneous environmental conditions at different stages in their lifecycle. Inclusion of such data aided in extraction of 208 biologically relevant protein-protein interactions potentially mediated by 59 P. falciparum proteins and 30 erythrocyte proteins. Host-parasite protein-protein interactions were predicted pertaining to several major strategies spanning intra-erythrocytic stages in P. falciparum pathogenesis including- gaining entry into the host erythrocytes (category: RBC invasion, protease), redirecting parasitic proteins to erythrocyte membrane (category: protein traffic), modulating erythrocyte machinery (category: rosette formation, putative adhesin, chaperone, kinase), evading immunity (category: immune evasion) and eventually egress (category: merozoite egress) to infect other uninfected erythrocytes. Elaborate means to analyse and evaluate the functional viability of a predicted interaction in terms of geometrical packing at the interfacial region, electrostatic complementarity of the interacting surfaces and interaction energies is also demonstrated. The protein-protein interactions, thus predicted between human erythrocytes and P. falciparum, have the potential to provide a useful basis in understanding probable mechanisms of pathogenesis, and indeed in pinning down attractive targets for antimalarial drug discovery. The emergence of drug resistance against all known antimalarial agents, currently in use, necessitates discovery and development of either new antimalarial agents or unexplored combination of drugs that may not only reduce mortality and morbidity of malaria, but also reduce the risk of resistance to antimalarial drugs. In an attempt to contribute towards the same, Chapter 8 explores the established concept of within-target-family selectivity of small molecules to recognize antimalarial potential of the approved drugs. Eighty six FDA-approved drugs, predominantly constituted by antibacterial agents, were identified as feasible candidates for repurposing against 90 P. falciparum proteins. Most of the potential parasite targets identified are known to participate in housekeeping machinery, protein biosynthesis, metabolic pathways and cell growth and differentiation, and thus are pharmaceutically relevant. During intra-erythrocytic growth of P. falciparum, the parasite resides within the erythrocyte, within a protective encasing, known as parasitophorous vacuole. Hence a drug, intended to target a parasite protein residing in an organelle, must be sufficiently hydrophilic or hydrophobic to be able to permeate cell membranes and reach its site of activity. On the basis of lipophilicity of the drugs, a physical property determined experimentally, 57 of 86 FDA-approved drugs were recognized as feasible candidates for use against P. falciparum during the course of blood-stages of infection, which can be prioritized for antimalarial drug development programmes. The final section of the thesis focuses on the protozoan parasite Trypanosoma brucei, a causative agent of African sleeping sickness (Chapter 9). This disease is endemic to sub-Saharan regions of Africa. Despite the availability of completely sequenced genome of T. brucei, structure and function for about 50% of the proteins encoded in the genome remain unknown. Absence of prophylactic chemotherapy and vaccine, compounded with emergence of drug-resistance renders anti-trypanosomal drug discovery challenging. Thus, considering the utility of frameworks established in earlier chapters for recognition of protein structure, function and drug-targets, similar steps were undertaken to understand functional repertoire of the parasite and use drug repurposing methods to accelerate anti-trypanosomal drug discovery efforts. Structures and functions were reliably recognized for 70% of the gene products (5894) encoded in T. brucei genome, with the use of multiple profile-based search procedures, coupled with information on presence of transmembrane domains and signal peptide cleavage sites. Consequently, a total of 282 uncharacterized T. brucei proteins could be newly coined as potential metabolic proteins. Integration of information on stage-specific expression profiles with Trypanosoma-specific and T-.brucei-specific proteins identified in the study, aided in pinning down potential attractive targets. Additionally, exploration of evolutionary relationships between targets of FDA-approved drugs and T. brucei proteins, 68 FDA-approved drugs were predicted as repurpose-able candidates against 42 potential T. brucei targets which primarily include proteins involved in regulatory processes and metabolism. Several targets predicted are reportedly essential in assisting the parasite to switch between differentiation forms (bloodstream and procyclic) in the course of its lifecycle. These targets are of high therapeutic relevance, hence the corresponding drug-target associations provide a useful resource for experimental endeavours. In summary, this thesis presents computational analyses on three pathogenic genomes in terms of enhancing the understanding of functional repertoire of the pathogens, addressing metabolic pathway holes, exploring probable mechanisms of pathogenesis brought about by potential host-pathogen protein-protein interactions, and identifying feasible FDA-approved drug candidates to repurpose against the pathogens. The studies are pursued primarily by taking advantage of powerful homology-detection techniques and the ever-growing biological information made available in public databases. Indeed, the inferences drawn for the three pathogenic genomes serve an excellent resource for an experimental follow-up. The set of protocols presented in the thesis are highly generic in nature, as demonstrated for three pathogens, and can be utilized for genome-wide analyses on many other pathogens of interest. The supplemental data associated with the chapters is provided in a compact disc attached with this thesis.

O desafio para a área de monitoramento de processos, em indústrias químicas, ainda é a etapa de detecção, com a necessidade de desenvolvimento de sistemas confiáveis. Pode-se resumir que um sistema é confiável, ao ser capaz de detectar as situações anormais, de modo precoce, e, ao mesmo tempo, de minimizar a geração de alarmes falsos. Ao se ter um sistema confiável, pode-se empregá-lo para auxiliar o operador, de fábricas, no processo de tomada de decisões. O objetivo deste estudo é apresentar uma metodologia, baseada na técnica, modelo oculto de Markov (HMM, acrônimo de ?Hidden Markov Model?), para se detectar situações anormais em caldeiras de recuperação química. As aplicações de maior sucesso de HMM são na área de reconhecimento de fala. Pode-se citar como aspectos positivos: o raciocínio probabilístico, a modelagem explícita, e a identificação a partir de dados históricos. Fez-se duas aplicações. O primeiro estudo de caso é no ?benchmark? de um sistema de evaporação múltiplo efeito de uma fábrica de produção de açúcar. Identificou-se um HMM, característico de operação normal, para se detectar cinco situações anormais no atuador responsável por regular o fluxo de xarope de açúcar para o primeiro evaporador. A detecção, para as três situações abruptas, é imediata, uma vez que o HMM foi capaz de detectar alterações, abruptas, no sinal da variável monitorada. Em relação às duas situações incipientes, foi possível detectá-las ainda em estágio inicial; ao ser o valor de f (vetor responsável por representar a intensidade de um evento anormal, com o tempo), no instante da detecção, próximo a zero, igual a 2,8% e 2,1%, respectivamente. O segundo estudo de caso é em uma caldeira de recuperação química, de uma fábrica de produção de celulose, no Brasil. O objetivo é monitorar o acúmulo de depósitos de cinzas sobre os equipamentos da sessão de transferência de calor convectivo, através de medições de perda de carga. Este é um dos principais desafios para se aumentar a eficiência operacional deste equipamento. Após a identificação de um HMM característico de perda de carga alta, pôde-se verificar a sua capacidade de informar o estado atual e, por consequência, a tendência do sistema, de modo similar à um preditor. Pôde-se demonstrar também a utilidade de se definir limites de controle, com o objetivo de se ter a informação sobre a distância entre o estado atual e os níveis de alarme de perda de carga. / The greatest challenge faced by the area of process monitoring in chemical industries still resides in the fault detection task, which aims at developing reliable systems. One may say that a system is reliable if it is able to perform early fault detection and, at the same time, to reduce the generation of false alarms. Once there is a reliable system available, it can be employed to help operators, in factories, in the decisionmaking process. The aim of this study is presenting a methodology, based on the Hidden Markov Model (HMM) technique, suggesting its use in the detection of abnormal situations in chemical recovery boilers. The most successful applications of HMM are in the area of speech recognition. Some of its advantages are: probabilistic reasoning, explicit modeling and the identification based on process history data. This study discusses two applications. The first one is on a benchmark of a multiple evaporation system in a sugar factory. A HMM representative of the normal operation was identified, in order to detect five abnormal situations at the actuator responsible for controlling the syrup flow to the first evaporator. The detection result for the three abrupt situations was immediate, since the HMM was capable of detecting the statistical changes on the signal of the monitored variable as soon as they occurred. Regarding to the two incipient situations, the detection was done at an early stage. For both events, the value of vector f (responsible for representing the strength of an abnormal event over time), at the time it occurred, was near zero, equal to 2.8 and 2.1%, respectively. The second case study deals with the application of HMM in a chemical recovery boiler, belonging to a cellulose mill, in Brazil. The aim is monitoring the accumulation of ash deposits over the equipments of the convective heat transfer section, through pressure drop measures. This is one of the main challenges to be overcome nowadays, bearing in mind the interest that exists in increasing the operational efficiency of this equipment. Initially, a HMM for high values of pressure drop was identified. With this model, it was possible to check its capacity to inform the current state, and consequently, the tendency of the system (similarly as a predictor). It was also possible to show the utility of defining control limits, in order to inform the operator the relative distance between the current state of the system and the alarm levels of pressure drop.