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

<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.

The mammalian brain is, with its numerous neural elements and structured complex connectivity, one of the most complex systems in nature. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex networks. Here, we try to shed some light on the relationship between structural and functional connectivities by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the cortical areas by a subnetwork of interacting excitable neurons (multilevel model) and by a neural mass model (population model). With weak couplings, the multilevel model displays biologically plausible dynamics and the synchronization patterns reveal a hierarchical cluster organization in the network structure. We can identify a group of brain areas involved in multifunctional tasks by comparing the dynamical clusters to the topological communities of the network. With strong couplings of multilevel model and by using neural mass model, the dynamics are characterized by well-defined oscillations. The synchronization patterns are mainly determined by the node intensity (total input strengths of a node); the detailed network topology is of secondary importance. The biologically improved multilevel model exhibits similar dynamical patterns in the two regimes. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks. / Das Gehirn von Säugetieren stellt mit seinen zahlreichen, hochgradig vernetzten Neuronen ein natürliches Netzwerk von immenser Komplexität dar. In der jüngsten Vergangenheit sind die großflächige kortikale Konnektivitäten, sowohl unter strukturellen wie auch funktionalen Gesichtspunkten, in den Fokus der Forschung getreten. Die Verwendung von komplexe Netzwerke spielt hierbei eine entscheidende Rolle. In der vorliegenden Dissertation versuchen wir, das Verhältnis von struktureller und funktionaler Konnektivität durch Untersuchung der Synchronisationsdynamik anhand eines realistischen Modells der Konnektivität im Kortex einer Katze näher zu beleuchten. Wir modellieren die Kortexareale durch ein Subnetzwerk interagierender, erregbarer Neuronen (multilevel model) und durch ein Modell von Neuronenensembles (population model). Bei schwacher Kopplung zeigt das multilevel model eine biologisch plausible Dynamik und die Synchronisationsmuster lassen eine hierarchische Organisation der Netzwerkstruktur erkennen. Indem wir die dynamischen Cluster mit den topologischen Einheiten des Netzwerks vergleichen, sind wir in der Lage die Hirnareale, die an der Bewältigung komplexer Aufgaben beteiligt sind, zu identifizieren. Bei starker Kopplung im multilevel model und unter Verwendung des Ensemblemodells weist die Dynamik klare Oszillationen auf. Die Synchronisationsmuster werden hauptsächlich durch die Eingangsstärke an den einzelnen Knoten bestimmt, während die genaue Netzwerktopologie zweitrangig ist. Eine Erweiterung des Modells auf andere biologisch relevante Faktoren bestätigt die vorherigen Ergebnisse. Die Untersuchung der Synchronisation in einem multilevel model des Kortex ermöglicht daher tiefere Einblicke in die Zusammenhänge zwischen Netzwerktopologie und funktionaler Organisation in komplexen Hirn-Netzwerken.

Proteomics serves an important role at the systems-level in understanding of biological functioning. Mass spectrometry proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. In the most widely used bottom-up approach to MS-based high-throughput quantitative proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and then analyzed using a mass spectrometer. The three fundamental challenges in the analysis of bottom-up MS-based proteomics are as follows: (i) Identifying the proteins that are present in a sample, (ii) Aligning different samples on elution (retention) time, mass, peak area (intensity) and etc, (iii) Quantifying the abundance levels of the identified proteins after alignment. Each of these challenges requires knowledge of the biological and technological context that give rise to the observed data, as well as the application of sound statistical principles for estimation and inference. In this dissertation, we present a set of statistical methods in bottom-up proteomics towards protein identification, alignment and quantification. We describe a fully Bayesian hierarchical modeling approach to peptide and protein identification on the basis of MS/MS fragmentation patterns in a unified framework. Our major contribution is to allow for dependence among the list of top candidate PSMs, which we accomplish with a Bayesian multiple component mixture model incorporating decoy search results and joint estimation of the accuracy of a list of peptide identifications for each MS/MS fragmentation spectrum. We also propose an objective criteria for the evaluation of the False Discovery Rate (FDR) associated with a list of identifications at both peptide level, which results in more accurate FDR estimates than existing methods like PeptideProphet. Several alignment algorithms have been developed using different warping functions. However, all the existing alignment approaches suffer from a useful metric for scoring an alignment between two data sets and hence lack a quantitative score for how good an alignment is. Our alignment approach uses "Anchor points" found to align all the individual scan in the target sample and provides a framework to quantify the alignment, that is, assigning a p-value to a set of aligned LC-MS runs to assess the correctness of alignment. After alignment using our algorithm, the p-values from Wilcoxon signed-rank test on elution (retention) time, M/Z, peak area successfully turn into non-significant values. Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical mass spectrometry-based proteomics data sets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of "presence / absence", we enable the selection of proteins not typically amendable to quantitative analysis; e.g., "one-state" proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence / absence analysis of a given data set in a principled way, resulting in a single list of selected proteins with a single associated FDR.

Ce mémoire porte sur l’étude des maxima de champs gaussiens. Plus précisément, l’étude portera sur la convergence en loi, la convergence du premier ordre et la convergence du deuxième ordre du maximum d’une collection de variables aléatoires gaussiennes. Les modèles de champs gaussiens présentés sont le modèle i.i.d., le modèle hiérarchique et le champ libre gaussien. Ces champs gaussiens diffèrent par le degré de corrélation entre les variables aléatoires. Le résultat principal de ce mémoire sera que la convergence en probabilité du premier ordre du maximum est la même pour les trois modèles. Quelques résultats de simulations seront présentés afin de corroborer les résultats théoriques obtenus. / In this study, results about maxima of different Gaussian fields will be presented. More precisely, results for the convergence of the first order of the maximum of a set of Gaussian variables will be presented. Some results on the convergence of the second order, and of the law will also be explained. The models presented here are the Gaussian field of i.i.d. variables, the hierarchical model and the Gaussian free fields model. These fields differ from one another by their correlation structure. The main result of this study is that the first order convergence in probability of the maximum is the same for the three models. Finally, numerical simulations results will be presented to confirm theoretical results.

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Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration originates in evolutionary principles in nature. Parallelization of genetic algorithms provides not only faster processing but also new and better solutions. Parallel genetic algorithms are also closer to real nature than their sequential counterparts. This paper describes the most used models of parallelization of genetic algorithms. Moreover, it provides the design and implementation in programming language Python. Finally, the implementation is verified in several test cases.

No description available.