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We introduce new methods for preprocessing and normalization of data acquired by gas/liquid chromatography coupled with mass spectrometry (GC/LC-MS). Normalization is desired prior to subsequent statistical analysis to adjust variabilities in ion intensities that are not caused by biological differences. There are different sources of experimental bias including variabilities in sample collection, sample storage, poor experimental design, noise, etc. Also, instrument variability in experiments involving a large number of runs leads to a significant drift in intensity measurements. We propose new normalization methods based on bootstrapping, Gaussian process regression, non-negative matrix factorization (NMF), and Bayesian hierarchical models. These methods model the bias by borrowing information across runs and features. Another novel aspect is utilizing scan-level data to improve the accuracy of quantification. We evaluated the performance of our method using simulated and experimental data. In comparison with several existing methods, the proposed methods yielded significant improvement. Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software tools specifically designed for analysis of GS-SIM-MS data. We introduce SIMAT, a new R package for quantitative analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping peaks based on a pre-specified library of background analytes. The tool also allows visualization of the total ion chromatogram (TIC) of runs and extracted ion chromatogram (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS data can be imported in netCDF or NIST mass spectral library (MSL) formats. We evaluated the performance of SIMAT using several experimental data sets. Our results demonstrate that SIMAT performs better than AMDIS and MetaboliteDetector in terms of finding the correct targets in the acquired GC-SIM-MS data and estimating their relative levels. / Ph. D.

Motor vehicle crashes are one of the leading causes of death in the United States. Traffic safety research targets at understanding the cause of crash, preventing the crash, and mitigating crash severity. This dissertation focuses on the driver-related traffic safety issues, in particular, on developing and implementing contemporary statistical modeling techniques on driving risk research on Naturalistic Driving Study data. The dissertation includes 5 chapters. In Chapter 1, I introduced the backgrounds of traffic safety research and naturalistic driving study. In Chapter 2, the state-of-practice statistical methods were implemented on individual driver risk assessment using NDS data. The study showed that critical-incident events and driver demographic characteristics can serve as good predictors for identifying risky drivers. In Chapter 3, I developed and evaluated a novel Bayesian random exposure method for Poisson regression models to account for situations where the exposure information needs to be estimated. Simulation studies and real data analysis on Cellphone Pilot Analysis study data showed that, random exposure models have significantly better model fitting performances and higher parameter coverage probabilities as compared to traditional fixed exposure models. The advantage is more apparent when the values of Poisson regression coefficients are large. In Chapter 4, I performed comprehensive simulation-based performance analyses to investigate the type-I error, power and coverage probabilities on summary effect size in classical meta-analysis models. The results shed some light for reference on the prospective and retrospective performance analysis in meta-analysis research. In Chapter 5, I implemented classical- and Bayesian-approach multi-group hierarchical models on 100-Car data. Simulation-based retrospective performance analyses were used to investigate the powers and parameter coverage probabilities among different hierarchical models. The results showed that under fixed-effects model context, complex secondary tasks are associated with higher driving risk. / Ph. D.

In this thesis, a dynamic spatial model for the spread of Chronic Wasting Disease in Colorado mule deer is derived from a system of differential equations that captures the qualitative spatial and temporal behaviour of the disease. These differential equations are incorporated into an empirical Bayesian hierarchical model through the unusual step of deterministic autoregressive updates. Spatial effects in the model are described directly in the differential equations rather than through the use of correlations in the data. The use of deterministic updates is a simplification that reduces the number of parameters that must be estimated, yet still provides a flexible model that gives reasonable predictions for the disease. The posterior distribution generated by the data model hierarchy possesses characteristics that are atypical for many Markov chain Monte Carlo simulation techniques. To address these difficulties, a new MCMC technique is developed that has qualities similar to recently introduced tempered Langevin type algorithms. The methodology is used to fit the CWD model, and posterior parameter estimates are then used to obtain predictions about Chronic Wasting Disease.

The ever-growing size and complexity of molecular network models makes them difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hundreds of reactions can seem nearly impossible. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine together smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces, called ports. In this work, we enhance the port concept by defining different types of ports. Not all modules connect together the same way, therefore multiple connection options need to exist. / Master of Science / Genes and proteins interact to control the functions of a living cell. In order to better understand these interactions, mathematical models can be created. A model is a representation of a cellular function that can be simulated on a computer. Results from the simulations can be used to gather insight and drive the direction of new laboratory experiments. As new discoveries are made, mathematical models continue to grow in size and complexity. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine together smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces, called ports. In this work, we enhance the port concept by defining different types of ports. Not all modules connect together the same way, therefore multiple connection options need to exist.

<div>Prediction of player performance is a key component in the construction of baseball team rosters. Traditionally, the problem of predicting seasonal plate appearance outcomes has been approached univariately. That is, focusing on each outcome separately rather than jointly modeling the collection of outcomes. More recently, there has been a greater emphasis on joint modeling, thereby accounting for the correlations between outcomes. However, most of these state of the art prediction models are the proprietary property of teams or industrial sports entities and so little is available in open publications.</div><div><br></div><div>This dissertation introduces a joint modeling approach to predict seasonal plate appearance outcome vectors using a mixed-effects multinomial logistic-normal model. This model accounts for positive and negative correlations between outcomes both across and within player seasons. It is also applied to the important, yet unaddressed, problem of predicting performance for players moving between the Japanese and American major leagues.</div><div><br></div>This work begins by motivating the methodological choices through a comparison of state of the art procedures followed by a detailed description of the modeling and estimation approach that includes model t assessments. We then apply the method to longitudinal multinomial count data of baseball player-seasons for players moving between the Japanese and American major leagues and discuss the results. Extensions of this modeling framework to other similar data structures are also discussed.<br>

Submitted by Verena Bastos (verena@uefs.br) on 2015-07-31T13:39:35Z No. of bitstreams: 1 DISSERTACAO DE BIANKA 2015.pdf: 1088519 bytes, checksum: 3efca07e3a24a9e6cadd7b95dbe70562 (MD5) / Made available in DSpace on 2015-07-31T13:39:35Z (GMT). No. of bitstreams: 1 DISSERTACAO DE BIANKA 2015.pdf: 1088519 bytes, checksum: 3efca07e3a24a9e6cadd7b95dbe70562 (MD5) Previous issue date: 2014-03-21 / Objective: To assess factors related to the occurrence of gender violence in a population of northeastern Bahia in 2007 and identify risk behaviors in women who have witnessed family violence during their childhood and were victims of violence in adulthood. Methods: This was a cross-sectional study conducted with 4170 individuals, of both sexes, aged 15 years and living in the city of Feira de Santana , Bahia. A probabilistic sample of clusters derived from census tracts was used. Data were collected during home visits with use of household and individual questionnaire record. Bivariate and the Chi square test analyzes were performed considering IC95 % and p ? 0.05 for statistically significant association. To verify the factors associated with violence , we used a hierarchical logistic regression analysis. Results: The prevalence of physical and / or emotional violence was 18,63%. Regarding the history of violence in childhood prevalence was equal to 12,14%. Women had a higher prevalence (19,7%) than men (16,5%) with 1,31 times higher prevalence of victimization. It was observed that women who have never been to school (15,08%), non-white (12,61%) and had an income of up to 1 minimum wage (14.17%) had a higher incidence of physical violence in childhood. The women drinkers had 1,43 times higher prevalence of experiencing violence in childhood and, in relation to smoking, this prevalence increased to 1,56. Adjusted by hierarchical logistic regression analysis showed a positive association between women suffer physical and / or emotional violence with household type (RP = 1,28; IC95%: 1,10; 1,54), type of building (RP = 1,66; IC95%: 1,14; 2,41), smoking (RP = 1,36; IC95%: 1,10; 1,70) and violence in childhood (RP = 2,13; IC95%: 1,79; 2,53). Conclusions: Gender violence is a complex problem with social roots and that deserves to be addressed as a public health problem . Thus , it is urgent policies to combat poverty , interpersonal conflicts, especially those from the interior of the family system , substance use , particularly alcohol measures as well as preparation in the care of victims of violence and the deployment of a service to protect women victimized because many remain silent for fear of reprisals from their attackers. / Objetivo: Analisar os fatores relacionados ? ocorr?ncia da viol?ncia de g?nero em uma popula??o do nordeste da Bahia no ano de 2007 e identificar os comportamentos de risco de mulheres que presenciaram viol?ncia na fam?lia durante sua inf?ncia e foram v?timas de viol?ncia na vida adulta. M?todos: Trata-se de um estudo de corte transversal realizado com 4170 indiv?duos, de ambos os sexos, com idade acima de 15 anos e residentes no munic?pio de Feira de Santana-BA. Foi utilizada uma amostra probabil?stica de conglomerados derivados de setores censit?rios. Os dados foram coletados em visitas domiciliares com uso de ficha domiciliar e question?rio individual. Foram realizadas an?lises bivariadas e Teste do Qui Quadrado de Pearson, considerando IC95% e p ? 0,05 para associa??o estatisticamente significante. Para verificar os fatores associados ? viol?ncia, empregou-se a an?lise de regress?o log?stica hierarquizada. Resultados: A preval?ncia de viol?ncia f?sica e/ou emocional foi de 18,63%. Em rela??o ? hist?ria de viol?ncia na inf?ncia a preval?ncia foi igual a 12,14%. As mulheres apresentaram preval?ncia superior (19,7%) aos homens (16,5%) com preval?ncia 1,31 vezes maior de vitimiza??o. Foi poss?vel observar que mulheres que nunca foram ? escola (15,08%), n?o brancas (12,61%) e que tinham renda de at? 1 sal?rio m?nimo (14,17%) apresentaram maior ocorr?ncia de viol?ncia f?sica na inf?ncia. As mulheres etilistas tiveram preval?ncia 1,43 vezes maior de ter sofrido viol?ncia na inf?ncia e, em rela??o ao h?bito de fumar, esta preval?ncia aumentou para 1,56. A an?lise ajustada por regress?o log?stica hierarquizada mostrou uma associa??o positiva entre a mulher sofrer viol?ncia f?sica e/ou emocional com tipo de domic?lio (RP = 1,28; IC95%: 1,10; 1,54), tipo de edifica??o (RP = 1,66; IC95%: 1,14; 2,41), tabagismo (RP = 1,36; IC95%: 1,10; 1,70) e viol?ncia na inf?ncia (RP = 2,13; IC95%: 1,79; 2,53). Conclus?es: A viol?ncia de g?nero ? um problema complexo com ra?zes sociais e que merece ser abordada como um problema de sa?de p?blica. Assim, urge medidas pol?ticas para o combate da pobreza, conflitos interpessoais, sobretudo os oriundos do interior do sistema familiar, ao consumo de subst?ncias, principalmente o ?lcool, bem como o preparo no atendimento das v?timas de viol?ncia e a implanta??o de um servi?o de prote??o ?s mulheres vitimizadas, pois muitas se calam por medo de sofrer repres?lias por parte de seus agressores.

The present study aims to explore the relationship between proficiency level and semantic retrieval in the second language. A group of Korean bilinguals who speak English with high proficiency performed semantic relatedness judgement tasks of two hundred English word pairs. Unbeknownst to the participants, half of the words in both the related and the unrelated categories contained a "hidden prime"—a common first syllable shared by the two words, if translated into Korean. Each participant's event-related potential (ERP) was recorded while reading the words. While a former study by Thierry and Wu (2007) found that Chinese-English bilinguals were affected by the hidden primes, thus causing a "N400 reduction effect" in their averaged ERP, the bilingual group of the present study was unaffected by the hidden primes. The difference between the bilingual groups' performance between Thierry and Wu's study and the present study is likely caused by the higher English proficiency of the bilingual group in the present study. This provides additional evidence supporting the Revised Hierarchical Model of semantic retrieval proposed by Kroll and Steward (1994), which suggests that increased proficiency leads to reduced reliance on the first language during second language semantic retrieval.