INTRODUÇÃO: O subgrupo Anopheles strodei é pouco estudado apesar de sua importância epidemiológica potencial. Espécies desse subgrupo foram encontradas naturalmente infectadas por parasitos que causam malária em humanos, Plasmodium falciparum, Plasmodium vivax e Plasmodium malariae, no Brasil. O subgrupo Anopheles strodei compreende oito espécies: An. rondoni (Neiva & Pinto), An. albertoi Unti, An. arthuri Unti, An. strodei Root, An. strodei CP, e três outras espécies que foram propostas por Bourke et al. (2013), mas não foram descritas: An. arthuri B, An. arthuri C e An. arthuri D. OBJETIVOS: A definição e delimitação precisa de espécies que atuam como vetores de agentes infecciosos é um dos objetivos da entomologia da saúde pública. Os objetivos deste estudo foram: 1) Estabelecer as relações filogenéticas entre espécies do Subgrupo Strodei; 2) Estimar a distribuição espacial potencial das espécies do Subgrupo Strodei; 3) Confirmar a presença de quatro espécies sob o nome An. arthuri. MÉTODOS: Sequências de DNA de um gene mitocondrial (fragmento de 658 pares de bases do código de barras do gene COI, citocromo oxidase subunidade I) e de três genes nucleares codificadores de proteínas (White, CAD e CAT) foram empregadas para estabelecer as relações filogenéticas potenciais entre as espécies que compõe o subgrupo An. strodei. As análises filogenéticas foram conduzidas utilizando abordagem Bayesiana das sequências de DNA dos quatro genes. Para estabelecer a distribuição espacial potencial das espécies, utilizou-se abordagem de máxima entropia de nichos ecológicos. Para isso as localidades das coletas, juntamente com os dados climáticos e geográficos foram introduzidos no programa MAXENT. RESULTADOS: Os resultados das análises filogenéticas demonstraram e, portanto, confirmaram o monofiletismo do Subgrupo Strodei, a presença de pelo menos sete espécies sob o nome An. strodei, ou seja, corroborou a validade de An. albertoi, An. arthuri, An. strodei, An. strodei CP, além das espécies denominadas, preliminarmente, como An. arthuri B, An. arthuri C e An. arthuri D. Portanto, como definida atualmente, An. arthuri não representa grupo monofilético, pois inclui táxons que deverão ser formalmente descritos em estudos futuros. CONCLUSÃO: As distribuições potenciais de espécies do Subgrupo Strodei foram propostas pela primeira vez. Cinquenta e cinco sequências do gene nuclear CAT e outras 46 sequências do gene nuclear CAD únicas foram recentemente caracterizadas para espécies do Subgrupo Strodei de Anopheles (Nyssorhynchus), confirmando a presença de pelos menos sete espécies, além de An. rondoni que não foi alvo deste estudo, mas de outros anteriores que confirmaram a validade da mesma. / Introduction Anopheles strodei sensu lato is an understudied subgroup of potential epidemiological importance, having been found naturally infected in Brazil with Plasmodium falciparum, Plasmodium vivax and Plasmodium malariae. An. strodei s.l. is currently composed on 8 species: An. albertoi Unti, An. CP Form, An. rondoni (Neiva & Pinto), An. strodei Root, An. arthuri Unti and three other unnamed species that have been proposed by Bourke et al. (2013): An. arthuri B, An. arthuri C and An. arthuri D. Objectives As delineating species accurately is an essential goal of public health entomology, the objectives of this study were to: 1) Determine the phylogenetic relationships within the Strodei Subgroup and reaffirm or reject the hypothesis of the 3 new species (An. arthuri B, An. arthuri C and An. arthuri D) 2) Address the potential spatial distribution of species of the An. strodei subgroup to provide support for the candidate species in the Strodei Subgroup Methods Bayesian inference, which included DNA sequences of one mitochondrial and three nuclear protein coding genes: CO1, white, CAD and CAT, was used to determine the phylogenetic relationship within the group. To propose a species distribution, collection localities, along with climatic and geographic data were input into MAXENT. Results When analyzing the four molecular markers employed, support was found for allopatry in the Strodei Subgroup. The paraphyletic clade of An. arthuri was supported. Conclusion Potential species distributions of the Strodei Subgroup were addressed for the first time. Fifty-five unique CAT sequences and 46 unique CAD sequences were newly characterized.

Being in the era of Big data, the applicability and importance of data-driven models like artificial neural network (ANN) in the modern statistics have increased substantially. In this dissertation, our main goal is to contribute to the development and the expansion of these ANN models by incorporating Bayesian learning techniques. We have demonstrated the applicability of these Bayesian ANN models in interdisciplinary research including health and cybersecurity. Breast cancer is one of the leading causes of deaths among females. Early and accurate diagnosis is a critical component which decides the survival of the patients. Including the well known ``Gail Model", numerous efforts are being made to quantify the risk of diagnosing malignant breast cancer. However, these models impose some limitations on their use of risk prediction. In this dissertation, we have developed a diagnosis model using ANN to identify the potential breast cancer patients with their demographic factors and the previous mammogram results. While developing the model, we applied the Bayesian regularization techniques (evidence procedure), along with the automatic relevance determination (ARD) prior, to minimize the network over-fitting. The optimal Bayesian network has 81\% overall accuracy in correctly classifying the actual status of breast cancer patients, 59\% sensitivity in accurately detecting the malignancy and 83\% specificity in correctly detecting non-malignancy. The area under the receiver operating characteristic curve (0.7940) shows that this is a moderate classification model. We then present a new Bayesian ANN model for developing a nonlinear Poisson regression model which can be used for count data modeling. Here, we have summarized all the important steps involved in developing the ANN model, including the forward-propagation, backward-propagation and the error gradient calculations of the newly developed network. As a part of this, we have introduced a new activation function into the output layer of the ANN and error minimizing criterion, using count data. Moreover, we have expanded our model to incorporate the Bayesian learning techniques. The performance our model is tested using simulation data. In addition to that, a piecewise constant hazard model is developed by extending the above nonlinear Poisson regression model under the Bayesian setting. This model can be utilized over the other conventional methods for accurate survival time prediction. With this, we were able to significantly improve the prediction accuracies. We captured the uncertainties of our predictions by incorporating the error bars which could not achieve with a linear Poisson model due to the overdispersion in the data. We also have proposed a new hybrid learning technique, and we evaluated the performance of those techniques with a varying number of hidden nodes and data size. Finally, we demonstrate the suitability of Bayesian ANN models for time series forecasting by using an online training algorithm. We have developed a vulnerability forecast model for the Linux operating system by using this approach.

Doctor of Philosophy / Department of Diagnostic Medicine/Pathobiology / Michael W. Sanderson / Shiga-toxin-producing Escherichia coli (STEC), of which enterohemorrhagic E. coli (EHEC) are a pathogenic sub-group, are foodborne pathogens of significant public health importance in the United States. STEC belong to the family Enterobacteriaceae commonly found in the large intestine of humans and other warm-blooded animals. EHEC harbors shiga toxin (stx1 and/or stx2) and eae genes which confers the ability to cause human illnesses. The U.S. Department of Agriculture Food Safety and Inspection Service declared seven STEC (O26, O45, O103, O111, O121, O145, and O157) as adulterants in ground beef and non-intact beef products to reduce/eliminate the burden of the pathogens in the beef production chain. STEC control efforts in the U.S. include the development of quantitative microbial risk assessment (QMRA) to identify mitigation strategies that are effective and economical in reducing exposure and reduce occurrence and public health risk from STEC in the beef chain. Collection of accurate and unbiased data is critical for the development of a QMRA that is valid for decision making. Determining the prevalence and concentration of the seven STEC in the different cattle types and seasons is valuable for the development a valid QMRA for STEC in beef production in the U.S. Our systematic review and meta-analysis study of the prevalence and concentration of E. coli O157 along the beef production chain indicated differences in the fecal prevalence of E. coli O157 among cattle types and seasons, revealed decreasing prevalence and concentration of E. coli O157 on cattle hides and carcass surfaces from pre-evisceration to the final chilled carcass stage, and identified study setting, detection method, hide or carcass swab area, and study design as significant sources of heterogeneity among studies reporting prevalence of E. coli O157 along the beef production chain. Bayesian estimation of the diagnostic performance of three laboratory methods (culture, conventional PCR [cPCR], and multiplex quantitative PCR [mqPCR]) used for the detection of the seven STEC in the feces of cattle is necessary to estimate true prevalence of EHEC in cattle. The analysis revealed highest sensitivity of mqPCR, followed by cPCR, and culture for the detection of E. coli O157; the cPCR and mqPCR had comparable specificity, but specificity of mqPCR method was heavily dependent on prior specification. The mqPCR method was the most sensitive for the detection O26, O45, and O103 serogroups. The cPCR method was more sensitive than the culture method for serogroups O26, and O121, but comparable for serogroups O45, O103, O111, and O145. The cPCR method showed higher specificity than mqPCR within serogroups O45, O121, and O145 but no apparent differences within serogroups O26, O103, and O111. A second order quantitative microbial risk assessment was developed to quantify the prevalence and concentration of the seven STEC on pre-evisceration beef carcasses and evaluate the impact of peri-harvest interventions. Simulation scenarios of current industry peri-harvest intervention practices showed variable effectiveness in reducing STEC contamination on pre-evisceration beef carcass, however, a scenario of increased adoption of peri-harvest interventions was more effective at reducing STEC contamination. Fecal-to-hide transfer and hide-to-carcass transfer had a large effect on prevalence and concentration of STEC on pre-evisceration carcasses.

abstract: Information exists in various forms and a better utilization of the available information can benefit the system awareness and response predictions. The focus of this dissertation is on the fusion of different types of information using Bayesian-Entropy method. The Maximum Entropy method in information theory introduces a unique way of handling information in the form of constraints. The Bayesian-Entropy (BE) principle is proposed to integrate the Bayes’ theorem and Maximum Entropy method to encode extra information. The posterior distribution in Bayesian-Entropy method has a Bayesian part to handle point observation data, and an Entropy part that encodes constraints, such as statistical moment information, range information and general function between variables. The proposed method is then extended to its network format as Bayesian Entropy Network (BEN), which serves as a generalized information fusion tool for diagnostics, prognostics, and surrogate modeling. The proposed BEN is demonstrated and validated with extensive engineering applications. The BEN method is first demonstrated for diagnostics of gas pipelines and metal/composite plates for damage diagnostics. Both empirical knowledge and physics model are integrated with direct observations to improve the accuracy for diagnostics and to reduce the training samples. Next, the BEN is demonstrated in prognostics and safety assessment in air traffic management system. Various information types, such as human concepts, variable correlation functions, physical constraints, and tendency data, are fused in BEN to enhance the safety assessment and risk prediction in the National Airspace System (NAS). Following this, the BE principle is applied in surrogate modeling. Multiple algorithms are proposed based on different type of information encoding, such as Bayesian-Entropy Linear Regression (BELR), Bayesian-Entropy Semiparametric Gaussian Process (BESGP), and Bayesian-Entropy Gaussian Process (BEGP) are demonstrated with numerical toy problems and practical engineering analysis. The results show that the major benefits are the superior prediction/extrapolation performance and significant reduction of training samples by using additional physics/knowledge as constraints. The proposed BEN offers a systematic and rigorous way to incorporate various information sources. Several major conclusions are drawn based on the proposed study. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2020

No description available.

One of the most significant changes in the evolution of modern business management is that organizations no longer compete as individual entities in the market, but as interlocking supply chains. Markets are no longer simply trading desks but dynamic ecosystems where people, organizations and the environment interact. Products and associated materials and resources are links that bridge supply chains from upstream (sourcing and manufacturing) to downstream (delivering and consuming). The lifecycle of a product plays a critical role in supply chains. Supply chains may be composed by, designed around, and modified for products. Product-related issues greatly impact supply chains. Existing studies have advanced product management and product lifecycle management literature through dimensions of product innovation, product growth, product line extensions, product efficiencies, and product acquisition. Product deletion, rationalization, or reduction research is limited but is a critical issue for many reasons. Sustainability is an important reason for this managerial decision. This study, grounded from multiple literature streams in both marketing and supply chain fields, identified relations and propositions to form a firm-level analysis on the role of supply chains in organizational product deletion decisions. Interviews, observational and archival data from international companies (i.e.: Australia, China, India, and Iran) contributed to the empirical support as case studies through a grounded theory approach. Bayesian analysis, an underused empirical analysis tool, was utilized to provide insights into this underdeveloped research stream; and its relationship to qualitative research enhances broader methodological understanding. Gibbs sampler and reversible jump Markov chain Monte Carlo (MCMC) simulation were used for Bayesian analysis based on collected data. The integrative findings are exploratory but provide insights for a number of research propositions.

From creation to its degradation, the RNA molecule is the action field of many binding proteins with different roles in regulation and RNA metabolism. Since these proteins are involved in a large number of processes, a variety of diseases are related to abnormalities occurring within the binding mechanisms. One of the experimental methods for detecting the binding sites of these proteins is PAR-CLIP built on the next generation sequencing technology. Due to its size and intrinsic noise, PAR-CLIP data analysis requires appropriate pre-processing and thorough statistical analysis. The present work has two main goals. First, to develop a modular pipeline for preprocessing PAR-CLIP data and extracting necessary signals for further analysis. Second, to devise a novel statistical model in order to carry out inference about presence of protein binding sites based on the signals extracted in the pre-processing step.

Time series data is very common in our daily life. Since they are related to time, most of them show a periodicity. The existence of this periodic in uence leads to our research problem, seasonal adjustment. Seasonal adjustment is generally applied around us, especially in areas of economy and nance. Over the last few decades, scholars around the world made a lot of contributions in this area, and one of the latest methods is X-13ARIMA-SEATS, which is built on ARIMA models and linear lters. On the other hand, state space modelling (abbreviated to SSM) is also a popular method to solve this problem and researchers including J. Durbin, S.J. Koopman and and A. Harvery have contributed a lot of work to it. Unlike linear lters and ARIMA models, the study on SSM starts relatively late, thus it has not been studied and developed widely for the seasonal adjustment problem. And SSMs have a lot advantages over those ARIMA-based and lter-based methods such as exibility, the understandable structure and the potential to do partial pooling, but in practice, its default decomposition result behaves bad in some cases, such as excessively spiky trend series; on the contrary, X-13ARIMA-SEATS could output good decomposition result for us to analyze, but it can't be tweaked or combined as easily as generative models and behaves like a black-box. In this paper, we shall use Bayesian inference to combine both methods' characteristics together. Simultaneously, to show the advantage of using SSMs concretely, we shall give a simple application in partial pooling and talk about how to apply the Bayesian analysis to partial pooling.

Neutrinoless Double Beta Decay(0𝜈𝛽𝛽) is one of the major research interests in neutrino physics. The discovery of 0𝜈𝛽𝛽 would answer persistent puzzles in the Standard Model of Elementary Particles. KamLAND-Zen is one of the leading efforts in the search of 0𝛽𝛽 and has acquired data from 745 kg of ^{136}Xe over 224 live-days. This data is analyzed using a Bayesian approach consisting of a Markov Chain Monte Carlo (MCMC) algorithm. The implementation of the Bayesian analysis, which is the focal point of this dissertation, yields a 90\% Credible Interval at T^{0𝜈}_{1/2} = 7.03 × 10^{25} years. Finally, a machine learning event classification algorithm, based on a spherical convolutional neural network (spherical CNN) was developed to increase the T^{0𝜈}_{1/2} sensitivity. The classification power of this algorithm was demonstrated on a Monte Carlo detector simulation, and a data driven classifier was trained to reject crucial backgrounds in the 0𝜈𝛽𝛽 analysis. After implementing the spherical CNN, an increase in T^{0𝜈}_{1/2} sensitivity of 11.0% is predicted. These early studies pave the way for substantial improvements in future 0𝜈𝛽𝛽 analyses.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21080号 / 工博第4444号 / 新制||工||1691(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 KIM Chul-Woo, 教授 杉浦 邦征, 教授 八木 知己 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM