Tip growth is a ubiquitous process throughout the plant kingdom in which a single cell elongates in one direction in a self-similar manner. To sustain tip growth in plants, the cell must regulate the extensibility of the wall to promote growth and avoid turgor-induced rupture. This process is heavily dependent on the cytoskeleton, which is thought to coordinate the delivery and recycling of vesicles containing cell wall materials at the cell tip. Although significant work has been done to elucidate the various molecular players in this process, there remains a need for a more mechanistic understanding of the cytoskeletonÂ’s role in tip growth. For this reason, specific emphasis should be placed on understanding the dynamics of the cytoskeleton, its associated motors, and their cargo. Since the advent of fluorescence fusion technology, various quantitative fluorescence dynamics techniques have emerged. Among the most prominent of these techniques is fluorescence recovery after photobleaching (FRAP). Despite its prominence, it is unclear how to interpret fluorescence recoveries in confined cellular geometries such as tip-growing cells. Here we developed a digital confocal microscope simulation of FRAP in tip-growing cells. With this simulation, we determined that fluorescence recoveries are significantly influenced by cell boundaries. With this FRAP simulation, we then measured the diffusion of VAMP72-labeled vesicles in the moss Physcomitrella patens. Using finite element modeling of polarized cell growth, and the measured VAMP72-labeled vesicle diffusion coefficient, we were able to show that diffusion alone cannot support the required transport of wall materials to the cell tip. This indicates that an actin-based active transport system is necessary for vesicle clustering at the cell tip to support growth. This provides one essential function of the actin cytoskeleton in polarized cell growth. After establishing the requirement for actin-based transport, we then sought to characterize the in vivo binding interactions of myosin XI, vesicles, and filamentous actin. Particle tracking evidence from P. patens protoplasts suggests that myosin XI and VAMP72-labeled vesicles exhibit fast transient interactions. Hidden Markov modeling of particle tracking indicates that myosin XI and VAMP72- labeled vesicles move along actin filaments in short-lived linear trajectories. These fast transient interactions may be necessary to achieve the rapid dynamics of the apical actin, important for growth. This work advances the fieldÂ’s understanding of fluorescence dynamics, elucidates a necessary function of the actin cytoskeleton, and provides insight into how the components of the cytoskeleton interact in vivo.

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Traditionally robots are controlled using devices like joysticks, keyboards, mice and other similar human computer interface (HCI) devices. Although this approach is effective and practical for some cases, it is restrictive only to healthy individuals without disabilities, and it also requires the user to master the device before its usage. It becomes complicated and non-intuitive when multiple robots need to be controlled simultaneously with these traditional devices, as in the case of Human Swarm Interfaces (HSI). This work presents a novel concept of using human bio-signals to control swarms of robots. With this concept there are two major advantages: Firstly, it gives amputees and people with certain disabilities the ability to control robotic swarms, which has previously not been possible. Secondly, it also gives the user a more intuitive interface to control swarms of robots by using gestures, thoughts, and eye movement. We measure different bio-signals from the human body including Electroencephalography (EEG), Electromyography (EMG), Electrooculography (EOG), using off the shelf products. After minimal signal processing, we then decode the intended control action using machine learning techniques like Hidden Markov Models (HMM) and K-Nearest Neighbors (K-NN). We employ formation controllers based on distance and displacement to control the shape and motion of the robotic swarm. Comparison for ground truth for thoughts and gesture classifications are done, and the resulting pipelines are evaluated with both simulations and hardware experiments with swarms of ground robots and aerial vehicles.

A demanda por abordagens eficientes para o problema de reconhecer a estrutura de cada gene numa sequência genômica motivou a implementação de um grande número de programas preditores de genes. Fizemos uma análise dos programas de sucesso com abordagem probabilística e reconhecemos semelhanças na implementação dos mesmos. A maior parte desses programas utiliza a cadeia oculta generalizada de Markov (GHMM - generalized hiddenMarkov model) como um modelo de gene. Percebemos que muitos preditores têm a arquitetura da GHMM fixada no código-fonte, dificultando a investigação de novas abordagens. Devido a essa dificuldade e pelas semelhanças entre os programas atuais, implementamos o sistema MYOP (Make Your Own Predictor) que tem como objetivo fornecer um ambiente flexível o qual permite avaliar rapidamente cada modelo de gene. Mostramos a utilidade da ferramenta através da implementação e avaliação de 96 modelos de genes em que cada modelo é formado por um conjunto de estados e cada estado tem uma distribuição de duração e um outro modelo probabilístico. Verificamos que nem sempre um modelo probabilísticomais sofisticado fornece um preditor melhor, mostrando a relevância das experimentações e a importância de um sistema como o MYOP. / The demand for efficient approaches for the gene structure prediction has motivated the implementation of different programs. In this work, we have analyzed successful programs that apply the probabilistic approach. We have observed similarities between different implementations, the same mathematical framework called generalized hidden Markov chain (GHMM) is applied. One problem with these implementations is that they maintain fixed GHMM architectures that are hard-coded. Due to this problem and similarities between the programs, we have implemented the MYOP framework (Make Your Own Predictor) with the objective of providing a flexible environment that allows the rapid evaluation of each gene model. We have demonstrated the utility of this tool through the implementation and evaluation of 96 gene models in which each model has a set of states and each state has a duration distribution and a probabilistic model. We have shown that a sophisticated probabilisticmodel is not sufficient to obtain better predictor, showing the experimentation relevance and the importance of a system as MYOP.

Les modèles de Markov cachés (MMC) par exemple ceux de la librairie Pfam sont des outils très populaires pour l'annotation des domaines protéiques. Cependant, ils ne sont pas toujours adaptés aux protéines les plus divergentes. C'est notamment le cas avec Plasmodium falciparum (principal agent du paludisme chez l'Homme), où les MMC de Pfam identifient peu de familles distinctes de domaines, et couvrent moins de 50% des protéines de l'organisme. L'objectif de cette thèse est d'apporter des méthodes nouvelles pour affiner la détection de domaines dans les protéines divergentes.Le premier axe développé est une approche d'identification de domaines utilisant leurs propriétés de co-occurrence. Différentes études ont montré que la majorité des domaines apparaissent dans les protéines avec un ensemble très réduits d'autres domaines favoris. Notre méthode exploite cette propriété pour détecter des domaines trop divergents pour être identifiés par l'approche classique. Cette détection s'accompagne d'une estimation du taux d'erreur par une procédure de ré-échantillonnage. Chez P. falciparum, elle permet d'identifier, avec un taux d'erreur estimé inférieur à 20%, 585 nouveaux domaines dont 159 familles étaient inédites dans cet organisme ce qui représente 16% du nombre de domaines connus.Le second axe de mes recherches présente plusieurs méthodes de corrections statistiques et évolutives des MMC pour l'annotation d'organismes divergents. Deux types d'approches ont été proposées. D'un côté, nous intégrons aux alignements d'apprentissage des MMC, les séquences précédemment identifiés dans l'organisme cible ou ses proches relatifs. La limitation de cette solution est que seules des familles de domaines déjà connues dans le taxon peuvent ainsi être identifiées. Le deuxième type d'approche contourne cette limitation en corrigeant tous les modèles par une prise en compte de l'évolution des séquences d'apprentissage. Pour cela, nous faisons appel à des techniques classiques de la bioinformatique et de l'apprentissage statistique. Les résultats obtenus offrent un ensemble de prédictions complémentaires totalisant 663 nouveaux domaines supplémentaires dont 504 familles inédites soit une augmentation de 18% à ajouter aux précédents résultats. / Hidden Markov Models (HMMs) from Pfam database for example are popular tools for protein domain annotation. However, they are not well suited for studying highly divergent proteins. This is notably the case with Plasmodium falciparum (main causal agent of human malaria), where Pfam HMMs identify few distinct domain families and cover less than 50% of its proteins. This thesis aims at providing new methods to enhance domain detection in divergent proteins.The first axis of this work is an approach of domain identification based on domain co-occurrence. Several studies shown that a majority of domains appear in proteins with a small set of other favourite domains. Our method exploits this tendency to detect domains escaping to the classical procedure because of their divergence. Detected domains come along with an false discovery rate (FDR) estimation computed with a shuffling procedure. In P. falciparum proteins, this approach allows us identify, with an FDR below 20%, 585 new domains with 159 families that were previously unseen in this organism which account for 16% of the known domains.The second axis of my researches involves the development of statistical and evolutionary methods of HMM correction to improve the annotation of divergent organisms. Two kind of approaches are proposed. On the one hand, the sequences previously identified in the target organism and its close relatives are integrated in the learning alignments. An obvious limitation of this solution is that only new occurrences of previously known families in the taxon can be discovered. On the other hand, we evade this limitation by adjusting HMM parameters by simulating the evolution of the learning sequences. To this end, classical techniques from bioinformatics and statistical learning were used. Alternative libraries offer a complementary set of predictions summing 663 new domains with 504 previously unseen families corresponding to an improvement of 18% to add to the previous results.

Modelos de Markov ocultos (MMOs) são uma ferramenta importante em matemática aplicada e estatística. Eles se baseiam em dois processos estocásticos. O primeiro é uma cadeia de Markov, que não é observada diretamente. O segundo é observável e sua distribuição depende do estado na cadeia de Markov. Supomos que os processos são discretos no tempo e assumem um número finito de estados. Para extrair informações dos MMOs, é necessário estimar seus parâmetros. Diversos algoritmos locais têm sido utilizados nas últimas décadas para essa tarefa. Nosso trabalho estuda a estimação de parâmetros em modelos de Markov ocultos, do ponto de vista da otimização global. Desenvolvemos algoritmos capazes de encontrar, em uma execução bem sucedida, todos os estimadores de máxima verossimilhança globais de um modelo de Markov oculto. Para tanto, usamos aritmética intervalar. Essa aritmética permite explorar sistematicamente o espaço paramétrico, excluindo regiões que não contém soluções. O cálculo da função objetivo é feito através da recursão \\textit, descrita na literatura estatística. Modificamos a extensão intervalar natural dessa recursão usando programação linear. Nossa abordagem é mais eficiente e produz intervalos mais estreitos do que a implementação padrão. Experimentos mostram ganhos de 16 a 250 vezes, de acordo com a complexidade do modelo. Revisamos os algoritmos locais, tendo em vista sua aplicação em métodos globais. Comparamos os algoritmos de Baum-Welch, pontos interiores e gradientes projetados espectrais. Concluímos que o método de Baum-Welch é o mais indicado como auxiliar em otimização global. Modificamos o \\textit{interval branch and bound} para resolver a estimação de modelos com eficiência. Usamos as condições KKT e as simetrias do problema na construção de testes para reduzir ou excluir caixas. Implementamos procedimentos de aceleração da convergência, como o método de Newton intervalar e propagação de restrições e da função objetivo. Nosso algoritmo foi escrito em \\textit{C++}, usando programação genérica. Mostramos que nossa implementação dá resultados tão bons quanto o resolvedor global BARON, porém com mais eficiência. Em média, nosso algoritmo é capaz de resolver $50\\%$ mais problemas no mesmo período de tempo. Concluímos estudando aspectos qualitativos dos MMOs com mistura Bernoulli. Plotamos todos os máximos globais detectados em instâncias com poucas observações e apresentamos novos limitantes superiores da verossimilhança baseados na divisão de uma amostra grande em grupos menores. / Hidden Markov models(HMMs) are an important tool in statistics and applied mathematics. Our work deals with processes formed by two discrete time and finite state space stochastic processes. The first process is a Markov chain and is not directly observed. On the other hand, the second process is observable and its distribution depends on the current state of the hidden component. In order to extract conclusions from a Hidden Markov Model we must estimate the parameters that defines it. Several local algorithms has been used to handle with this task. We present a global optimization approach based on interval arithmetic to maximize the likelihood function. Interval arithmetic allow us to explore parametric space systematically, discarding regions which cannot contain global maxima. We evaluate the objective function and its derivatives by the so called backward recursion and show that is possible to obtain sharper interval extensions for such functions using linear programming. Numerical experiments shows that our approach is $16$ to $250$ times more efficient than standard implementations. We also study local optimization algorithms hidden Markov model estimation. We compare Baum-Welch procedure with interior points and spectral projected gradients. We conclude that Baum-Welch is the best option as a sub-algorithm in a global optimization framework. We improve the well known interval branch and bound algorithm to take advantages on the problem structure. We derive new exclusion tests, based on its KKT conditions and symmetries. We implement our approach in C++, under generic programming paradigm. We show that our implementation is compatible with global optimization solver BARON in terms of precision. We also show that our algorithm is faster than BARON. In average, we can handle with $50\\%$ more problems within the same amount of time. We conclude studying qualitative aspects of Bernoulli hidden Markov models. We plot all global maxima found in small observations instances and show a new upper bound of the likelihood based on splitting observations in small groups.

For young animals, surviving the first year of nutritional independence requires rapid development of effective foraging behaviour before the onset of terminal starvation. Grey seal (Halichoerus grypus) pups are abandoned on the natal colony after a brief (15-21 days) suckling period and must learn to dive and forage without parental instruction. Regional and sex-specific differences in diet and foraging behaviour have been described for adults and juveniles, but the early-life behaviour of pups during the critical first months at sea remains poorly understood. This thesis investigates sources of intrinsic and extrinsic variation in the development of foraging behaviour and resource selection in grey seal pups. The studies presented here feature tracking and dive data collected from 52 recently-weaned pups, tagged at six different breeding colonies in two geographically-distinct regions of the United Kingdom (UK). Original aspects of this thesis include: (Chapter I) a comprehensive review of analytical methods for inferring foraging behaviour from tracking and dive data in pinnipeds; (Chapter II) description and comparison of regional and sex differences in movements and diving characteristics of recently-weaned pups during their first trips at sea; (Chapter III) implementation of a novel generalized hidden Markov modelling (HMM) technique to investigate the development of foraging movement patterns whilst accounting for sources of intrinsic (age, sex) and extrinsic (regional) variation; and (Chapter IV) the first analysis of grey seal pup foraging habitat preference, incorporating behavioural inferences from HMMs and investigating changes in preference through time.

O Processamento de Linguagem Natural (PLN) é uma área da Ciência da Computação, que vem tentando, ao longo dos anos, aperfeiçoar a comunicação entre o homem e o computador. Varias técnicas tem sido utilizadas para aperfeiçoar esta comunicação, entre elas a aplicação de métodos estatísticos. Estes métodos tem sido usados por pesquisadores de PLN, com um crescente sucesso e uma de suas maiores vantagens é a possibilidade do tratamento de textos irrestritos. Em particular, a aplicação dos métodos estatísticos, na marcação automática de "corpus" com categorias morfo-sintáticas, tem se mostrado bastante promissora, obtendo resultados surpreendentes. Assim sendo, este trabalho descreve o processo de marcação automática de categorias morfo-sintáticas. Inicialmente, são apresentados e comparados os principais métodos aplicados a marcação automática: os métodos baseados em regras e os métodos estatísticos. São descritos os principais formalismos e técnicas usadas para esta finalidade pelos métodos estatísticos. E introduzida a marcação automática para a Língua Portuguesa, algo até então inédito. O objetivo deste trabalho é fazer um estudo detalhado e uma avaliação do sistema rotulador de categorias morfo-sintáticas, a fim de que se possa definir um padrão no qual o sistema apresente a mais alta precisão possível. Para efetuar esta avaliação, são especificados alguns critérios: a qualidade do "corpus" de treinamento, o seu tamanho e a influencia das palavras desconhecidas. A partir dos resultados obtidos, espera-se poder aperfeiçoar o sistema rotulador, de forma a aproveitar, da melhor maneira possível, os recursos disponíveis para a Língua Portuguesa. / Natural Language Processing (NLP) is an area of Computer Sciences, that have been trying to improve communication between human beings and computers. A number of different techniques have been used to improve this communication and among them, the use of stochastic methods. These methods have successfully being used by NLP researchers and one of their most remarkable advantages is that they are able to deal with unrestricted texts. Namely, the use of stochastic methods for part-of-speech tagging has achieving some extremely good results. Thus, this work describes the process of part-of-speech tagging. At first, we present and compare the main tagging methods: the rule-based methods and the stochastic ones. We describe the main stochastic tagging formalisms and techniques for part-of-speech tagging. We also introduce part-of-speech tagging for the Portuguese Language. The main purpose of this work is to study and evaluate a part-of-speech tagger system in order to establish a pattern in which it is possible to achieve the greatest accuracy. To perform this evaluation, several parameters were set: the corpus quality, its size and the relation between unknown words and accuracy. The results obtained will be used to improve the tagger, in order to use better the available Portuguese Language resources.

Distance sampling and spatial capture-recapture are statistical methods to estimate the number of animals in a wild population based on encounters between these animals and scientific detectors. Both methods estimate the probability an animal is detected during a survey, but do not explicitly model animal movement. The primary challenge is that animal movement in these surveys is unobserved; one must average over all possible paths each animal could have travelled during the survey. In this thesis, a general statistical model, with distance sampling and spatial capture-recapture as special cases, is presented that explicitly incorporates animal movement. An efficient algorithm to integrate over all possible movement paths, based on quadrature and hidden Markov modelling, is given to overcome the computational obstacles. For distance sampling, simulation studies and case studies show that incorporating animal movement can reduce the bias in estimated abundance found in conventional models and expand application of distance sampling to surveys that violate the assumption of no animal movement. For spatial capture-recapture, continuous-time encounter records are used to make detailed inference on where animals spend their time during the survey. In surveys conducted in discrete occasions, maximum likelihood models that allow for mobile activity centres are presented to account for transience, dispersal, and heterogeneous space use. These methods provide an alternative when animal movement causes bias in standard methods and the opportunity to gain richer inference on how animals move, where they spend their time, and how they interact.

Mapas genéticos são arranjos lineares que indicam a ordem e distância entre locos nos cromossomos de uma determinada espécie. Recentemente, a grande disponibilidade de marcadores moleculares tem tornado estes mapas cada vez mais saturados, sendo necessários métodos eficientes para sua construção. Uma das etapas que merece mais atenção na construção de mapas de ligação é a ordenação dos marcadores genéticos dentro de cada grupo de ligação. Tal ordenação é considerada um caso especial do clássico problema do caixeiro viajante (TSP), que consiste em escolher a melhor ordem entre todas as possíveis. Entretanto, a estratégia de busca exaustiva torna-se inviável quando o número de marcadores é grande. Nesses casos, para que esses mapas possam ser construídos uma alternativa viável é a utilização de algoritmos que forneçam soluções aproximadas. O objetivo desse trabalho foi avaliar a eficiência dos algoritmos Try (TRY), Seriation (SER), Rapid Chain Delineation (RCD), Recombination Counting and Ordering (RECORD) e Unidirectional Growth (UG), além dos critérios PARF (produto mínimo das frações de recombinação adjacentes), SARF (soma mínima das frações de recombinação adjacentes), SALOD (soma máxima dos LOD scores adjacentes) e LMHC (verossimilhança via cadeias de Markov ocultas), usados juntamente com o algoritmo de verificação de erros RIPPLE, para a construção de mapas genéticos. Para tanto, foi simulado um mapa de ligação de uma espécie vegetal hipotética, diplóide e monóica, contendo 21 marcadores com distância fixa entre eles de 3 centimorgans. Usando o método Monte Carlo, foram obtidas aleatoriamente 550 populações F2 com 100 e 400 indivíduos, além de diferentes combinações de marcadores dominantes e codominantes. Foi ainda simulada perda de 10% e 20% dos dados. Os resultados mostraram que os algoritmos TRY e SER tiveram bons resultados em todas as situações simuladas, mesmo com presença de elevado número de dados perdidos e marcadores dominantes ligados em repulsão, podendo ser então recomendado em situações práticas. Os algoritmos RECORD e UG apresentaram bons resultados na ausência de marcadores dominantes ligados em repulsão, podendo então ser recomendados em situações com poucos marcadores dominantes. Dentre todos os algoritmos, o RCD foi o que se mostrou menos eficiente. O critério LHMC, aplicado com o algoritmo RIPPLE, foi o que apresentou melhores resultados quando se deseja fazer verificações de erros na ordenação. / Genetic linkage maps are linear arrangements showing the order and distance between loci in chromosomes of a particular species. Recently, the availability of molecular markers has made such maps more saturated and efficient methods are needed for their construction. One of the steps that deserves more attention in the construction of genetic linkage maps is the ordering of genetic markers within each linkage group. This ordering is considered a special case of the classic traveling salesman problem (TSP), which consists in choosing the best order among all possible ones. However, the strategy of exhaustive search becomes unfeasible when the number of markers is large. One possible alternative to construct such maps is to use algorithms that provide approximate solutions. Thus, the aim of this work was to evaluate the efficiency of algorithms Try (TRY), Seriation (SER), Rapid Chain Delineation (RCD), Recombination Counting and Ordering (RECORD) and Unidirectional Growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent lod scores) and LMHC (likelihood via hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. For doing so, a linkage map of a hypothetical diploid and monoecious plant species was simulated, containing 21 markers with fixed distance of 3 centimorgans between them. Using Monte Carlo methods, 550 F2 populations were randomly simulated with 100 and 400 individuals, together with different combinations of dominant and codominant markers. 10 % and 20 % of missing data was also included. Results showed that the algorithms TRY and SER gave good results in all situations, even with presence of a large number of missing data and dominant markers linked in repulsion phase. Thus, these can be recommended for analyzing real data. The algorithms RECORD and UG gave good results in the absence of dominant markers linked in repulsion phase and can be used in this case. Among all algorithms, RCD was the least efficient. The criterion LHMC, applied with the RIPPLE algorithm, showed the best results when the goal is to check ordering errors.