Metagenomics is the study of a collective microbial genetic content recovered directly from natural (e.g., soil, ocean, and freshwater) or host-associated (e.g., human gut, skin, and oral) environmental communities that contain microorganisms, i.e., microbiomes. The rapid technological developments in next generation sequencing (NGS) technologies, enabling to sequence tens or hundreds of millions of short DNA fragments (or reads) in a single run, facilitates the studies of multiple microorganisms lived in environmental communities. Metagenomics, a relatively new but fast growing field, allows us to understand the diversity of microbes, their functions, cooperation, and evolution in a particular ecosystem. Also, it assists us to identify significantly different metabolic potentials in different environments. Particularly, metagenomic analysis on the basis of functional features (e.g., pathways, subsystems, functional roles) enables to contribute the genomic contents of microbes to human health and leads us to understand how the microbes affect human health by analyzing a metagenomic data corresponding to two or multiple populations with different clinical phenotypes (e.g., diseased and healthy, or different treatments). Currently, metagenomic analysis has substantial impact not only on genetic and environmental areas, but also on clinical applications. In our study, we focus on the development of computational and statistical methods for functional metagnomic analysis of sequencing data that is obtained from various environmental microbial samples/communities.

Motor vehicle crashes have increasingly become a serious concern for highway safety engineers and transportation agencies over the past few decades. This serious concern has led to a great deal of research activities. One of these activities is to develop safety analysis tools, specifically crash prediction models, for the purpose of reducing crashes and enhancing highway safety.Crash prediction models based on statistical or econometric modeling techniques are used for a variety of purposes; most commonly to estimate the expected crash frequencies from various roadway entities (highways, intersections, interstates, etc.) and also to identify geometric, environmental, and operations factors that are associated with crashes. A comprehensive review of prior literature indicates that many researchers have mainly focused on the development of aggregate crash prediction models based on single equation estimation techniques to identify the influences of geometric, environmental, and traffic variables on a single counted outcome. In some cases, however, more than one dependent variable might be of interest and hence several equations are formulated at the same time. Such a multiple equation structure may require simultaneous (or joint) estimation techniques under some situations.This dissertation research develops simultaneous estimation approaches to account for contemporaneous correlation and endogeneity problems in crash data. Specifically, seemingly unrelated negative binomial models and simultaneous equation models are developed to account for contemporaneous correlation between the disturbance terms across crash type models and to control for the endogenous relationship between the presence of left-turn lanes and angle crashes.Modeling crash types may provide certain advantages to gain insights as to 1) identification of high-risk sites with respect to specific types of crashes, which is not revealed through crash totals, and 2) the differences between conditions that lead to various crash types, but the disturbance terms across crash types might be contemporaneously correlated due to the unobserved common characteristics. Therefore, individual and simultaneous crash type models were estimated and the results of both models were compared. The results showed that a simultaneous estimation approach provides more efficient estimators relative to a single equation estimation technique.The presence of left-turn lanes has been treated as exogenous in crash prediction models, but in fact they are affecting each other. The bi-directional relationship between left-turn lanes and crashes results in endogeneity. This research investigated the endogenous relationship between left-turn lanes and crashes and developed simultaneous equation models to control for the endogeneity. The findings indicated that the presence of left-turn lanes is endogenously associated with crashes and the real effect of left-turn lanes on crashes can be obtained by controlling for endogeneity.

<p>The first part of the thesis focuses on the development of Bayesian modeling motivated by geophysics applications. In Chapter 2, we model the frequency of pyroclastic flows collected from the Soufriere Hills volcano. Multiple change points within the dataset reveal several limitations of existing methods in literature. We propose Bayesian hierarchical models (BBH) by introducing an extra level of hierarchy with hyper parameters, adding a penalty term to constrain close consecutive rates, and using a mixture prior distribution to more accurately match certain circumstances in reality. We end the chapter with a description of the prediction procedure, which is the biggest advantage of the BBH in comparison with other existing methods. In Chapter 3, we develop new statistical techniques to model and relate three complex processes and datasets: the process of extrusion of magma into the lava dome, the growth of the dome as measured by its height, and the rockfalls as an indication of the dome's instability. First, we study the dynamic Negative Binomial branching process and use it to model the rockfalls. Moreover, a generalized regression model is proposed to regress daily rockfall numbers on the extrusion rate and dome height. Furthermore, we solve an inverse problem from the regression model and predict extrusion rate based on rockfalls and dome height.</p><p>The other focus of the thesis is adaptive Markov chain Monte Carlo (MCMC) method. In Chapter 4, we improve upon the Wang-Landau (WL) algorithm. The WL algorithm is an adaptive sampling scheme that modifies the target distribution to enable the chain to visit low-density regions of the state space. However, the approach relies heavily on a partition of the state space that is left to the user to specify. As a result, the implementation and the use of the algorithm are time-consuming and less automatic. We propose an automatic, adaptive partitioning scheme which continually refines the initial partition as needed during sampling. We show that this overcomes the limitations of the input user-specified partition, making the algorithm significantly more automatic and user-friendly while also making the performance dramatically more reliable and robust. In Chapter 5, we consider the convergence and autocorrelation aspects of MCMC. We propose an Exploration/Exploitation (XX) approach to constructing adaptive MCMC algorithms, which combines adaptation schemes of distinct types. The exploration piece uses adaptation strategies aiming at exploring new regions of the target distribution and thus improving the rate of convergence to equilibrium. The exploitation piece involves an adaptation component which decreases autocorrelation for sampling among regions already discovered. We demonstrate that the combined XX algorithm significantly outperforms either original algorithm on difficult multimodal sampling problems.</p> / Dissertation

The planet is experiencing a dramatic loss of species. The majority of species are unknown to science, and it is usually infeasible to conduct a census of a region to acquire a complete inventory of all life forms. Therefore, it is important to estimate and conduct statistical inference on the total number of species in a region based on samples obtained from field observations. Such estimates may suggest the number of species new to science and at potential risk of extinction. In this thesis, we develop novel methodology to conduct statistical inference, based on abundance-based data collected from multiple sampling locations, on the number of species within a taxonomic group residing in a region. The primary contribution of this work is the formulation of novel statistical methodology for analysis in this setting, where abundances of species are recorded at multiple sampling units across a region. This particular area has received relatively little attention in the literature. In the first chapter, the problem of estimating the number of species is formulated in a broad context, one that occurs in several seemingly unrelated fields of study. Estimators are commonly developed from statistical sampling models. Depending on the organisms or objects under study, different sampling techniques are used, and consequently, a variety of statistical models have been developed for this problem. A review of existing estimation methods, categorized by the associated sampling model, is presented in the second chapter. The third chapter develops a new negative binomial mixture model. The negative binomial model is employed to account for the common tendency of individuals of a particular species to occur in clusters. An exponential mixing distribution permits inference on the number of species that exist in the region, but were in fact absent from the sampling units. Adopting a classical approach for statistical inference, we develop the maximum likelihood estimator, and a corresponding profile-log-likelihood interval estimate of species richness. In addition, a Gaussian-based confidence interval based on large-sample theory is presented. The fourth chapter further extends the hierarchical model developed in Chapter 3 into a Bayesian framework. The motivation for the Bayesian paradigm is explained, and a hierarchical model based on random effects and discrete latent variables is presented. Computing the posterior distribution in this case is not straight-forward. A data augmentation technique that indirectly places priors on species richness is employed to compute the model using a Metropolis-Hastings algorithm. The fifth chapter examines the performance of our new methodology. Simulation studies are used to examine the mean-squared error of our proposed estimators. Comparisons to several commonly-used non-parametric estimators are made. Several conclusions emerge, and settings where our approaches can yield superior performance are clarified. In the sixth chapter, we present a case study. The methodology is applied to a real data set of oribatid mites (a taxonomic order of micro-arthropods) collected from multiple sites in a tropical rainforest in Panama. We adjust our statistical sampling models to account for the varying masses of material sampled from the sites. The resulting estimates of species richness for the oribatid mites are useful, and contribute to a wider investigation, currently underway, examining the species richness of all arthropods in the rainforest. Our approaches are the only existing methods that can make full use of the abundance-based data from multiple sampling units located in a single region. The seventh and final chapter concludes the thesis with a discussion of key considerations related to implementation and modeling assumptions, and describes potential avenues for further investigation. / Graduate

Thesis (M. Sc.)--Carleton University, 2008. / Includes bibliographical references (p. 57-62). Also available in electronic format on the Internet.

Resumo: O psilídeo Diaphorina citri Kuwayama tornou-se nos últimos anos, uma das mais importantes pragas na cultura de citros, principalmente pelos prejuízos causados às plantas devido à transmissão da bactéria causadora da doença Huanglongbing (HLB) ou Greening. Com a finalidade de estudar a distribuição espacial de ninfas e adultos desta praga, instalou-se um experimento em 2 áreas de citros com histórico de ocorrência de HLB, no município de Matão, região central do Estado de São Paulo, uma com plantas de 4 anos e outra com plantas de 12 anos de idade. Para estudo da agregação da população nas plantas, foram utilizados os seguintes índices de dispersão: razão variância/média (I), índice de Morisita (Id), coeficiente de Green (Cx) e expoente k da distribuição binomial negativa para cada amostragem. A distribuição binomial negativa foi o modelo mais adequado para representar a distribuição espacial do psilídeo na cultura de citros, tanto para ninfas como para adultos. Através da análise destes índices, verificou-se que, na maioria das amostragens, as ninfas encontradas nas brotações e os adultos capturados nas armadilhas apresentaram distribuição agregada. Foram desenvolvidos planos de amostragem seqüencial para ninfas e adultos em região com e sem HLB, e os números máximos de amostras esperados para se tomar a decisão foram de 264 e 83 para ninfas, em regiões com e sem a doença, e de 184 e 150 amostras para adultos, em regiões com e sem a doença. / Abstract: The psyllid Diaphorina citri Kuwayama is one of the most important pests of citrus, mostly because of plant damage due to transmission of the bacterium that causes Huanglongbing (HLB) or Greening disease. The experiment was carried out in 2 sweet orange orchards with previous HLB occurrence in Matão (central region of the State of São Paulo, Brazil), in plants with 4 and 12 years of age, in order to study the spatial distribution of nymphs and adults of this pest. The following dispersion indices were used to study pest aggregation in the citrus plants: variance/mean relationship (I), index of Morisita (Id), coefficient of Green (Cx), and the k exponent of negative binomial distribution for each sampling. The negative binomial distribution was the most representative spatial distribution of this psyllid in citrus, for both nymphs and adults. The analysis of these indices showed that, for most samplings, psyllid nymphs found in branches and adults caught in traps presented an aggregated distribution. Sequential sampling plans were developed for nymphs and adults in regions with and without HLB, and the maximum number of samples for decision making was 264 and 83 samples for nymphs in regions with and without the disease, and, 184 and 150 samples for adults, in regions with and without the disease, respectively. / Orientador: José Carlos Barbosa / Coorientador: Pedro Takao Yamamoto / Banca: Odair Aparecido Fernandes / Banca: Wilson Itamar Maruyama / Banca: Antonio Baldo Geraldo Martins / Banca: Eduardo Sanches Stuchi / Doutor

One of the greatest problems for sub-Saharan Africa is shortage of epidemiological data to support planning for provision of adequate public and animal health services. The overriding challenge is to provide the necessary resources to facilitate the process of regular data collection in support of disease surveillance and vector monitoring across target regions. Due to such circumstances, there is currently an increasing interest towards devising cheaper but yet significantly reliable means for availing the needed epidemiological and vector data for planning purpose. This study comes as a contribution towards solving such challenges. The study has three research components starting with a review of past Uganda national tsetse and trypanosomiasis control efforts as a means towards appreciating the dynamics of controlling the vector and disease. This is an analysis of what was applied, what worked, what didn't, and why it didn’t as linked to the broader vector and disease control system. Secondly through the use of remote sensing, geographical information systems and global positioning technologies tsetse species were sampled within Lake Victoria Basin. Only two species of tsetse were trapped, G. f. fG. f. fuscipes which was widely distributed across the surveyed area, and G. Pallidipes which was detected in a few isolated locations close to the border with Kenya in Eastern Uganda. The analysis of land cover with tsetse findings showed an important association between G. f. fuscipes and particular vegetation mosaics. Unfortunately, while the results are highly informative, approaches for data collection such as this one are costly and unlikely to be sustained by the already over-burdened health systems in the low developed countries of Africa. The third and main part of this study investigates, demonstrates and delivers the possibilities of applying spatial epidemiological modelling techniques to produce both tsetse distribution and abundance maps. Four spatial and non-spatial regression models (Logistic, Autologistic, Negative binomial and Auto-negative binomial), were constructed and used to predict tsetse fly presence and tsetse fly abundance for the study area. The product is an improved understanding of association between environmental variables and tsetse fly distribution/abundance and maps providing continuous representations of the probability of tsetse occurrence and predicted tsetse abundance across the study area. The results indicate that tsetse presence and abundance are influenced differently. Tsetse abundance is highly determined by river systems while tsetse presence is majorly influenced by forested landscapes. Therefore, efforts to control trypanosomiasis through vector control in the Lake Victoria basin will call for delineation of such clearly identified high tsetse accumulation zones for targeted tsetse control operations. This will ensure optimum utilization of the scarce resources and above all contribute to the protection of humans and animals against trypanosomiasis infection.

Promover a segurança viária no entorno escolar é uma estratégia que contribui para que sejam construídas cidades seguras, saudáveis e sustentáveis. Nesse sentido, este estudo é dedicado a identificar a influência das características da estrutura urbana na frequência e na severidade dos acidentes no entorno de escolas de educação básica de Porto Alegre. A análise da frequência e da severidade de acidentes foi conduzida através da estimação de modelos econométricos: binomial negativo e logit ordenado, respectivamente. Para esses, foram calculados os efeitos marginais, permitindo a observação da magnitude dos impactos das variáveis explicativas sobre as variáveis dependentes. As variáveis dependentes frequência e severidade foram extraídas dos acidentes registrados em Porto Alegre entre 2012 e 2014. Foram incluídas, simultaneamente, variáveis da estrutura urbana, das escolas, socioeconômicas e dos acidentes (para a severidade). A partir do geoprocessamento dos dados existentes, os entornos escolares puderam ser caracterizados para três diferentes áreas circulares de análise (buffer ring) de raios de 100, 150 e 200 metros, permitindo a comparação do uso das diferentes áreas. O conjunto de estimativas indica que áreas menores produzem modelos de melhor desempenho para ambas as técnicas empregadas. No entanto, áreas maiores permitem a análise de maior quantidade de variáveis relativas à estrutura urbana. Essa relação sugere os benefícios da escolha a partir do trade-off entre ajuste do modelo e sua capacidade de propiciar análises de variáveis. Foi identificado que a frequência e a severidade de acidentes podem estar relacionadas a uma única variável explicativa de formas opostas – a partir de sinais contrários. Essa diferença de resultados para frequência e severidade de acidentes indica que há maiores benefícios em analisá-las em conjunto. Identificou-se ainda que existem benefícios para a segurança viária em áreas de estrutura urbana com quarteirões menores e maior quantidade de interseções de quatro vias, em frequência e severidade, respectivamente. Já as áreas mais arborizadas tendem a apresentar acidentes de menor severidade nos casos de usuários de modos ativos. / Fostering road safety nearby schools is a strategy that contributes to build safe, healthy and sustainable cities. The aim of this study is to identify the influence of the built environment characteristics in the frequency and severity of accidents nearby elementary and secondary schools in Porto Alegre. The frequency and severity of the accidents were analyzed using econometric models: negative binomial and ordered logit, respectively. The evaluation of their marginal effects allowed the magnitude of the impact caused by the explanatory variable on the dependent variables to be observed. The measured variables frequency and severity were extracted from accidents registered in Porto Alegre from 2012 and 2014. Built environment, socioeconomic and school variables were also included, as well as accident data (for severity). Data geoprocessing allowed school surroundings to be characterized for three different buffer rings, measuring 100, 150 and 200 meters of radius. Thereby it was possible to compare the inclusion of different areas in the study. The estimations indicates that models based on smaller areas have better performances for both employed techniques, whereas larger areas allow the study of a bigger quantity of urban infrastructure variables. That indicates the benefits of choosing based on a trade-off between model adjustment and capacity to engender the analysis of variables. It was shown that frequency and severity of accidents could be related to a single explanatory variable in opposite ways – based on contrary signs. This difference in the results found for frequency and severity indicates that there are more benefits when analyzing them together. Moreover, there are benefits for road safety in areas where the city blocks are shorter and where there are more four-way intersections, in frequency and severity of accidents, respectively. Also, areas of more important afforestation tend to decrease the severity of accidents involving users of active modes.

This thesis focuses on a well-known topic in sports betting, predicting the number of goals in soccer games.The data set used comes from the top English soccer league: Premier League, and consists of games played in the seasons 2015/16 to 2017/18.This thesis approaches the prediction with the auxiliary support of the odds from the betting exchange Betfair. The purpose is to find a model that can create an accurate goal distribution. %The other purpose is to investigate whether Negative binomial distribution regressionThe methods used are Bayesian Negative Binomial regression and Bayesian Poisson regression. The results conclude that the Poisson regression is the better model because of the presence of underdispersion.We argue that the methods can be used to compare different sportsbooks accuracies, and may help creating better models.

Made available in DSpace on 2014-06-11T19:28:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-06-05Bitstream added on 2014-06-13T20:37:32Z : No. of bitstreams: 1 toledo_fr_me_jabo.pdf: 503818 bytes, checksum: 0c25df81cfee4dc01b57b77e3a7b22a0 (MD5) / Fundecitrus / O estudo da distribuição espacial de pragas é fundamental para elaboração de planos de amostragem para uso no manejo integrado de pragas. Para Toxoptera citricida foi estudada a distribuição espacial em talhões de pomares de citros comerciais de laranja (Citrus sinensis) da variedade Pêra, com 5, 9 e 15 anos de idade, durante o período de setembro de 2004 a abril de 2005. Foram realizadas 14 amostragens de número de pulgões-preto em intervalos aproximados de 15 dias entre as mesmas, utilizando-se armadilhas adesivas de cor amarela (0,11 x 0,11 m) fixadas à planta a 1,5 m de altura aproximadamente. As armadilhas foram distribuídas na área, a cada cinco plantas na linha, em linhas alternadas, totalizando 137 armadilhas no talhão com 5 anos, 140 no talhão com 9 anos e 80 no talhão com 15 anos. A lei de Taylor e a distribuição binomial negativa foram os modelos que melhor representaram a distribuição da população. Foram com construídos planos de amostragens para levantamento desta praga com base na lei de Taylor e na distribuição binomial negativa. / The study of spatial distribution of insects is fundamental to elaborate sampling plans with potential to use in integrated pest management. The spatial distribution of Toxoptera citricida was studied in plots of commercial orchards of orange (Citrus sinensis) of the variety 'Pêra' with 5, 9 and 15 years of age, during the period of September of 2004 and April of 2005. Fourteen samples of the number of Toxoptera citricida was performed each 15d approximately, using yellow adhesive traps fixed at 1,5 m of height each 5 plants in alternated lines, summing 137 traps in the 5-years plot, 140 traps in the 9-years plot and 80 traps in the 15-years plot. The best models fitted the distribution of population were the Taylor Law and negative binomial distribution, which were used to elaborate the sampling plans.