Aims: To investigate the natural history of CHC, estimate the disease burden of CHC, and assess the cost-effectiveness of screening for CHC among immigrants living in Canada. Methods: A retrospective cohort study compared the prognosis of CHC between immigrant patients and native-born patients who had advanced fibrosis. A cross-sectional study assessed the association between obesity and hepatitis C viremia. The disease burden of CHC among immigrants was estimated through Markov cohort model. The cost-effectiveness of screening for CHC was assessed among immigrants. Results: The retrospective cohort study including 318 patients demonstrated that immigrant patients had significantly higher risk of hepatocellular carcinoma than Canadian-born patients (p=0.005). The hazard ratio associated with ‘immigrant’ for hepatocellular carcinoma in multivariate Cox proportional-hazards analyses reduced to the least and non-significant (p=0.318) after adjusting age and type 2 diabetes. The prevalence of obesity in 1118 individuals tested positive for hepatitis C antibody was 28.8%. Multiple regression analyses and propensity score methods suggested a significant association between obesity and hepatitis C viremia. The disease burden study estimated that immigrants with CHC had much shorter average life years (26.9 years vs. 39.1 years) and quality adjusted life years (20.6 years vs. 32.4 years) than the age matched immigrants without CHC. The cost-effectiveness study indicated that screening for CHC among immigrants from 183 countries (72.1% of immigrant population in Canada) had an incremental cost-effectiveness ratio less than $50,000 per quality adjusted life year gained. Conclusion: Immigrant patients with CHC could have a higher risk of HCC than native-born patients. The significant association between obesity and hepatitis C viremia could explain the observed high prevalence of type 2 diabetes in patients with CHC. CHC reduces the average life expectancy of immigrants with CHC more than 10 years. Screening for CHC is cost-effective among over 70% of immigrants living in Canada.

In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.

Understanding the behavior of pedestrians in road transportation is critical to maintain a safe en- vironment. Accidents on road transportation are one of the most common causes of death today. As autonomous vehicles start to become a standard in our society, safety on road transportation becomes increasingly important. Road transportation is a complex system with a lot of dierent factors. Identifying risky behaviors and preventing accidents from occurring requires better under- standing of the behaviors of the dierent persons involved. In this thesis the activities and behavior of a pedestrian is analyzed. Using sensor data from phones, eight dierent events of a pedestrian are classied using machine learning algorithms. Features extracted from phone sensors that can be used to model dierent pedestrian activities are identied. Current state of the art literature is researched to nd relevant machine learning algorithms for a classication model. Two models are implemented using two dierent machine learning algorithms: Articial Neural Network and Hid- den Markov Model. Two dierent experiments are conducted where phone sensor data is collected and classied using the models, achieving a classication accuracy of up to 93%.

O movimento dos lábios é um recurso visual relevante para a detecção da atividade de voz do locutor e para o reconhecimento da fala. Quando os lábios estão se movendo eles transmitem a idéia de ocorrências de diálogos (conversas ou períodos de fala) para o observador, enquanto que os períodos de silêncio podem ser representados pela ausência de movimentações dos lábios (boca fechada). Baseado nesta idéia, este trabalho foca esforços para detectar a movimentação de lábios e usá-la para realizar a detecção de atividade de voz. Primeiramente, é realizada a detecção de pele e a detecção de face para reduzir a área de extração dos lábios, sendo que as regiões mais prováveis de serem lábios são computadas usando a abordagem Bayesiana dentro da área delimitada. Então, a pré-segmentação dos lábios é obtida pela limiarização da região das probabilidades calculadas. A seguir, é localizada a região da boca pelo resultado obtido na pré-segmentação dos lábios, ou seja, alguns pixels que não são de lábios e foram detectados são eliminados, e em seguida são aplicados algumas operações morfológicas para incluir alguns pixels labiais e não labiais em torno da boca. Então, uma nova segmentação de lábios é realizada sobre a região da boca depois de aplicada uma transformação de cores para realçar a região a ser segmentada. Após a segmentação, é aplicado o fechamento das lacunas internas dos lábios segmentados. Finalmente, o movimento temporal dos lábios é explorado usando o modelo das cadeias ocultas de Markov (HMMs) para detectar as prováveis ocorrências de atividades de fala dentro de uma janela temporal. / Lips motion are relevant visual feature for detecting the voice active of speaker and speech recognition. When the lips are moving, they carries an idea of occurrence of dialogues (talk) or periods of speeches to the watcher, whereas the periods of silences may be represented by the absence of lips motion (mouth closed). Based on this idea, this work focus efforts to obtain the lips motion as features and to perform visual voice activity detection. First, the algorithm performs skin segmentation and face detection to reduce the search area for lip extraction, and the most likely lip regions are computed using a Bayesian approach within the delimited area. Then, the pre-segmentation of the lips is obtained by thresholding the calculated probability region. After, it is localized the mouth region by resulted obtained in pre-segmentation of the lips, i.e., some nonlips pixels detected are eliminated, and it are applied a simple morphological operators to include some lips pixels and non-lips around the mouth. Thus, a new segmentation of lips is performed over mouth region after transformation of color to enhance the region to be segmented. And, is applied the closing of gaps internal of lips segmented. Finally, the temporal motion of the lips is explored using Hidden Markov Models (HMMs) to detect the likely occurrence of active speech within a temporal window.

Made available in DSpace on 2014-06-11T19:26:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-08-01Bitstream added on 2014-06-13T18:54:08Z : No. of bitstreams: 1 tezza_rid_me_jabo.pdf: 2155126 bytes, checksum: 33a07381689b0811f980f19b4c0fc487 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Com o seqüenciamento completo do genoma do fitopatógeno Xanthomonas axonopodis pv. citri (Xac), em 2002, inúmeras possibilidades de estudo foram viabilizadas, dando margem à busca de novas formas de controle do cancro cítrico, baseadas em alvos moleculares. Estudos dessa natureza têm mostrado a existência de genes que somente são expressos quando a bactéria está se desenvolvendo in planta. Sabe-se que essa regulação é dependente da região promotora e sua identificação pode possibilitar avanços significativos na busca do controle dessa doença. Apesar do crescente avanço das técnicas experimentais in vitro em biologia molecular, identificar um número significante de promotores ainda é uma tarefa difícil e dispendiosa. Os métodos computacionais existentes enfrentam a falta de um número adequado de promotores conhecidos para identificar padrões conservados entre as espécies. Logo, um método para predizê-Ios em qualquer organismo procariótico ainda é um desafio. O Modelo Oculto de Markov é um modelo estatístico aplicável a muitas tarefas em biologia molecular. Entre elas, predição e mapeamento de seqüências promotoras no genoma de um procarioto. Neste trabalho, estudou-se o mapeamento in silico de promotores gênicos de todo o genoma da Xac e em 68% dos genes a localização de um promotor foi indicada. A análise comparativa com dados experimentais de proteômica mostrou que 72% das proteínas expressas identificaram-se com elementos desta lista de promotores, o que corresponde a 27% do total de genes do genoma. À partir destes dados foi possível levantar um rol de informações sobre estes candidatos a promotores incitando a novos estudos moleculares. / With the complete genome sequencing of the phytopathogen Xanthomonas axonopodis pv. Citri (Xac), in 2002, several study possibilities were made practical and then creating the search of new citrus canker control ways, based in molecular aims. This kind of studies has shown the genes existences that are only expressed when the bacteria are developing itself in plant. It has been known that this regulation is promoter region dependent and its identification can allow significant advances in the search of this disease control. Although increasing advance of in vitro experimental techniques in molecular biology, identifying a meaningful number of promoters is still a hard and expensive task. The existents computer science methods face the need of a proper number of known promoters to identify conserved patterns among the species. Therefore, a method to predict them in any prokaryote organism is still a challenge. The Hidden Markov Model (HMM) is a statistic model applicable in many tasks in molecular biology. Among them, prediction and mapping of the promoters sequences in prokaryotic genome. In this work, which has studied the genic promoters in silico mapping of the whole Xac genome, in 68% of the genes the promoter localization was indicated. The proteomic experimental data comparative analysis showed that 72% of the expressed proteins identified with elements from the promoters list, which corresponds 27% of the genome genes total. According to these data it was possible to generate an information roll about these promoters candidates inciting new molecular studies.

Resumo: Com o seqüenciamento completo do genoma do fitopatógeno Xanthomonas axonopodis pv. citri (Xac), em 2002, inúmeras possibilidades de estudo foram viabilizadas, dando margem à busca de novas formas de controle do cancro cítrico, baseadas em alvos moleculares. Estudos dessa natureza têm mostrado a existência de genes que somente são expressos quando a bactéria está se desenvolvendo in planta. Sabe-se que essa regulação é dependente da região promotora e sua identificação pode possibilitar avanços significativos na busca do controle dessa doença. Apesar do crescente avanço das técnicas experimentais in vitro em biologia molecular, identificar um número significante de promotores ainda é uma tarefa difícil e dispendiosa. Os métodos computacionais existentes enfrentam a falta de um número adequado de promotores conhecidos para identificar padrões conservados entre as espécies. Logo, um método para predizê-Ios em qualquer organismo procariótico ainda é um desafio. O Modelo Oculto de Markov é um modelo estatístico aplicável a muitas tarefas em biologia molecular. Entre elas, predição e mapeamento de seqüências promotoras no genoma de um procarioto. Neste trabalho, estudou-se o mapeamento in silico de promotores gênicos de todo o genoma da Xac e em 68% dos genes a localização de um promotor foi indicada. A análise comparativa com dados experimentais de proteômica mostrou que 72% das proteínas expressas identificaram-se com elementos desta lista de promotores, o que corresponde a 27% do total de genes do genoma. À partir destes dados foi possível levantar um rol de informações sobre estes candidatos a promotores incitando a novos estudos moleculares. / Abstract: With the complete genome sequencing of the phytopathogen Xanthomonas axonopodis pv. Citri (Xac), in 2002, several study possibilities were made practical and then creating the search of new citrus canker control ways, based in molecular aims. This kind of studies has shown the genes existences that are only expressed when the bacteria are developing itself in plant. It has been known that this regulation is promoter region dependent and its identification can allow significant advances in the search of this disease control. Although increasing advance of in vitro experimental techniques in molecular biology, identifying a meaningful number of promoters is still a hard and expensive task. The existents computer science methods face the need of a proper number of known promoters to identify conserved patterns among the species. Therefore, a method to predict them in any prokaryote organism is still a challenge. The Hidden Markov Model (HMM) is a statistic model applicable in many tasks in molecular biology. Among them, prediction and mapping of the promoters sequences in prokaryotic genome. In this work, which has studied the genic promoters in silico mapping of the whole Xac genome, in 68% of the genes the promoter localization was indicated. The proteomic experimental data comparative analysis showed that 72% of the expressed proteins identified with elements from the promoters list, which corresponds 27% of the genome genes total. According to these data it was possible to generate an information roll about these promoters candidates inciting new molecular studies. / Orientadora: Maria Inês Tiraboschi Ferro / Coorientador: Marcelo Luiz de Laia / Banca: Manoel Victor Franco Lemos / Banca: Poliana Fernanda Giachetto / Mestre

O movimento dos lábios é um recurso visual relevante para a detecção da atividade de voz do locutor e para o reconhecimento da fala. Quando os lábios estão se movendo eles transmitem a idéia de ocorrências de diálogos (conversas ou períodos de fala) para o observador, enquanto que os períodos de silêncio podem ser representados pela ausência de movimentações dos lábios (boca fechada). Baseado nesta idéia, este trabalho foca esforços para detectar a movimentação de lábios e usá-la para realizar a detecção de atividade de voz. Primeiramente, é realizada a detecção de pele e a detecção de face para reduzir a área de extração dos lábios, sendo que as regiões mais prováveis de serem lábios são computadas usando a abordagem Bayesiana dentro da área delimitada. Então, a pré-segmentação dos lábios é obtida pela limiarização da região das probabilidades calculadas. A seguir, é localizada a região da boca pelo resultado obtido na pré-segmentação dos lábios, ou seja, alguns pixels que não são de lábios e foram detectados são eliminados, e em seguida são aplicados algumas operações morfológicas para incluir alguns pixels labiais e não labiais em torno da boca. Então, uma nova segmentação de lábios é realizada sobre a região da boca depois de aplicada uma transformação de cores para realçar a região a ser segmentada. Após a segmentação, é aplicado o fechamento das lacunas internas dos lábios segmentados. Finalmente, o movimento temporal dos lábios é explorado usando o modelo das cadeias ocultas de Markov (HMMs) para detectar as prováveis ocorrências de atividades de fala dentro de uma janela temporal. / Lips motion are relevant visual feature for detecting the voice active of speaker and speech recognition. When the lips are moving, they carries an idea of occurrence of dialogues (talk) or periods of speeches to the watcher, whereas the periods of silences may be represented by the absence of lips motion (mouth closed). Based on this idea, this work focus efforts to obtain the lips motion as features and to perform visual voice activity detection. First, the algorithm performs skin segmentation and face detection to reduce the search area for lip extraction, and the most likely lip regions are computed using a Bayesian approach within the delimited area. Then, the pre-segmentation of the lips is obtained by thresholding the calculated probability region. After, it is localized the mouth region by resulted obtained in pre-segmentation of the lips, i.e., some nonlips pixels detected are eliminated, and it are applied a simple morphological operators to include some lips pixels and non-lips around the mouth. Thus, a new segmentation of lips is performed over mouth region after transformation of color to enhance the region to be segmented. And, is applied the closing of gaps internal of lips segmented. Finally, the temporal motion of the lips is explored using Hidden Markov Models (HMMs) to detect the likely occurrence of active speech within a temporal window.

The safety of miners is of interest to all countries. In the event of a coal mine disaster, how to locate the miners remains the biggest and most urgent issue. The aim of this study is to propose a precise positioning method for underground mine environments to a low cost and with acceptable accuracy. During the research work, in-depth learning and analysis of current geolocation methods for indoor areas have been carried out: advantages, disadvantages and the level of suitability of each method for mine environment have been presented. A layered two-step Hidden Markov Model has been proposed to simulate human walking in underground mine environments and an improved Viterbi algorithm suitable for the model has been implemented. The result of the positioning accuracy is quite satisfying compared to other positioning methods in the same category. A small modification to the proposed model has been illustrated in the future work which makes it more suitable for different situations rather than that limited by assumptions. The proposed positioning method can be claimed to be quite suitable for underground mine environments to a low cost and with acceptable accuracy.

Vehicular networks are an emerging technology that offer potential for providing a variety of new services. However, extending vehicular networks to include IP connections is still problematic, due in part to the incompatibility of mobile IP handovers with the increased mobility of vehicles. The handover process, consisting of discovery, registration, and packet forwarding, has a large overhead and disrupts connectivity. With increased handover frequency and smaller access point dwell times in vehicular networks, the handover causes a large degradation in performance. This thesis proposes a predictive handover solution, using a combination of a Kalman filter and an online hidden Markov model, to minimize the effects of prediction errors and to capitalize on advanced handover registration. Extensive simulated experiments were carried out in NS-2 to study the performance of the proposed solution within a variety of traffic and network topology scenarios. Results show a significant improvement to both prediction accuracy and network performance when compared to recent proposed approaches.

Survival analysis is a statistical discipline that analyzes the time to occurrence of certain events. The aim of this thesis is to describe the possibilities of survival analysis in the environment of statistical software R. Theoretical knowledge is applied to real data, parametric and nonparametric estimates of survival functions are evaluated by different methods and compared with each other. In the section focusing on nonparametric models Kaplan-Meier and Nelson-Aalen functions are described. Among the parametric estimates there were included well-known probability distributions, survival functions and risk functions derived from these distributions are presented and there is discussed their usefulness in survival analysis. Another aim is to show the possibility of deriving transition probabilities from estimates and building a Markov chain model to capture the changes of studied cohort over time. The second part of the work contains a description of the applications of the theory of survival analysis. In this section there are shown possibilities of statistical modeling in the field of survival analysis using the software R. Outputs from R were used to create Markov model. There are presented possibilities of pharmacoeconomic models and description of the basic concepts of HTA. Cost-effectiveness calculations using ICER were conducted in accordance with the methodology of SUKL. It was shown that the statistical modelling of survival plays an important role in the evaluation of the cost-effectiveness of medicines.