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Two-dimensional (2D) in vitro models of tissues and organs have long been used as one of the main tools to understand human physiology and for applications such as drug discovery. But there is a huge disparity between in vivo conditions and these models which has created the need for better models. It has been shown that making three-dimensional models with dynamic environments that provide proper physical and chemical cues for cells, can bridge this gap between 2D models and in vivo conditions but the toolbox for creating such models has been imperfect and rudimentary. Introduction of tissue engineering concept and advent of biofabrication tools to meet its demands has provided new possible avenues for in vitro modeling but many of these tools are specifically designed to create tissue and organ replacements and lack features such as the ability to investigate cellular behavior with ease that are necessary for in vitro modeling purposes. The objective of this doctoral thesis was to introduce a novel toolbox of biofabrication techniques, based on bioprinting and bioassembly, that together are capable of recapitulating anatomical and physiological requirements of different tissue in in vitro setups in a more relevant way while creating the possibility of investigating cellular behavior. A bioprinting technique was developed that allowed formation of large constructs with proper mechanical stability, perfusion, and direct access to cells in different locations. The second technique was based on bioassembly of collagenous grafts in micro-molds and cells from different tissues with the ability to control cell positioning and create tissue-relevant cell densities with higher degree of similarity to human tissues compared to previous techniques. The third technique was based on bioassembled stand alone and dense cell-sheets for cells capable of fusion. These techniques were subsequently used for modeling a few chosen biological phenomenon to showcase the advantages of the techniques over previously developed ones and to further shed light on possible shortcomings of each of the techniques in their application for those specific tissues. In conclusion, our techniques may serve as valuable and easy to use tools for researchers, specifically biologists to investigate different aspects of human biology and disease mechanism in more details. / Thesis / Doctor of Philosophy (PhD) / Experimentation on humans is unethical, therefore in order to understand how human body works and test new therapeutic drugs researchers have used animals and cells isolated from animals or humans. Animals are inherently different from humans and isolated cells are culture in conditions different than human body, therefore a huge gap exists between the knowledge derived from these models and what happens in human body. Since there is no one-size-fits-all technique to model all of the human tissues, the objective of current study was set to build a toolbox of techniques that each could create better environment in the lab for cells isolated from different tissues and organs with more similarity to original tissues, to bridge the gap and eliminate the need to use animal models entirely. During the course of this PhD studies, three different techniques that can be used to make such models for different tissues and organs, as well as different diseases, were developed and characterized. These techniques were also used to shed light on some of the cellular behavior that are already observed in human body but either are not explained or aren’t re-created in the lab for mechanistic studies. Certain questions regarding selected tissues were chosen and the technique most compatible with that tissue was used for the modeling purposes. For example, one investigated niche was the origin of the bone sensory cells which could be important to heal damaged bones or prevent osteoporosis. The first technique was deemed most suitable for this question while for the next question, how the fat and muscle cells are affecting each other that can be useful to better understand conditions such as diabetes and obesity, the second technique was the best option. Overall, a variety of tools were developed that can be used by biologists to create better models of human tissues in the lab as platforms to study human physiology and as media for developing treatments for different diseases.

Neste trabalho de tese, estudamos o modelo de crescimento logístico de populações biológicas utilizando a abordagem de espaço de estados. Os estados não observados são as biomassas anuais, a equação de observação é linear e a equação de estado é não linear. As distribuições de probabilidade utilizadas para os termos de erro de observação aditivos são: Normal, t-student, Skew-normal e Skew-t. As distribuições Log-normal, Log-t, Log-skew-normal e Log-skew-t são consideradas para os erros de observação multiplicativos. A inferência nos modelos é realizada considerando-se métodos Bayesianos e as distribuições a posterior de interesse são aproximadas utilizando-se algoritmos MCMC e a aproximação de Laplace. Apresentamos duas aplicações, a primeira referente a pesca de camarão marinho na costa do Chile, na qual a variável observável é o rendimento médio anual de pesca (captura por unidade de esforço média). Na segunda é considerada a pesca de lagostim vermelho na costa de Chile, na qual além do rendimento médio anual da pesca, observa-se as estimativas anuais de biomassa vulnerável, obtidas através de estudos de área varrida. Para o primeiro conjunto de dados, os modelos com erros de observação multiplicativos têm melhor performance, particularmente os modelos Log-skew-normal e Log-skew-t. Considerando estes resultados, no segundo caso utilizamos somente erros multiplicativos e a distribuição a posteriori preditiva mostra que cada variável observável parece ter sua própria família de distribuição de probabilidades. Além disso, os resultados também revelam uma crescente complexidade do modelo ao incorporar a classe mais geral de distribuições assimétricas. / We study the logistic population growth model using a state-space approach. The non observable states are the annual biomass of the population with a linear observation equation and a non-linear state equation. The probability distribution used for the additives observation error terms are Normal, Student-t, Skew-normal and Skew-t, and Log-normal, Log-t, Log-skew-normal and Log-skew-t for multiplicative observation errors terms. The inference about the parameters of the models is performed using Bayesian methods, with MCMC algorithms and Laplace approximations. We present two applications to real data sets. The first in marine shrimp population off the coast of Chile, where observable variable is the average annual fishing yield. The second application is for the population of the red squat lobster off Chile, where in addition to the average annual fishing yield, a second observable variable was included. In the first case, the multiplicative observational errors models presented the best results. Particularly the Log-skew-normal and Log-skew-t models has the better performances. Considering these results, in the second application we use only multiplicative observation errors models.

Neste trabalho de tese, estudamos o modelo de crescimento logístico de populações biológicas utilizando a abordagem de espaço de estados. Os estados não observados são as biomassas anuais, a equação de observação é linear e a equação de estado é não linear. As distribuições de probabilidade utilizadas para os termos de erro de observação aditivos são: Normal, t-student, Skew-normal e Skew-t. As distribuições Log-normal, Log-t, Log-skew-normal e Log-skew-t são consideradas para os erros de observação multiplicativos. A inferência nos modelos é realizada considerando-se métodos Bayesianos e as distribuições a posterior de interesse são aproximadas utilizando-se algoritmos MCMC e a aproximação de Laplace. Apresentamos duas aplicações, a primeira referente a pesca de camarão marinho na costa do Chile, na qual a variável observável é o rendimento médio anual de pesca (captura por unidade de esforço média). Na segunda é considerada a pesca de lagostim vermelho na costa de Chile, na qual além do rendimento médio anual da pesca, observa-se as estimativas anuais de biomassa vulnerável, obtidas através de estudos de área varrida. Para o primeiro conjunto de dados, os modelos com erros de observação multiplicativos têm melhor performance, particularmente os modelos Log-skew-normal e Log-skew-t. Considerando estes resultados, no segundo caso utilizamos somente erros multiplicativos e a distribuição a posteriori preditiva mostra que cada variável observável parece ter sua própria família de distribuição de probabilidades. Além disso, os resultados também revelam uma crescente complexidade do modelo ao incorporar a classe mais geral de distribuições assimétricas. / We study the logistic population growth model using a state-space approach. The non observable states are the annual biomass of the population with a linear observation equation and a non-linear state equation. The probability distribution used for the additives observation error terms are Normal, Student-t, Skew-normal and Skew-t, and Log-normal, Log-t, Log-skew-normal and Log-skew-t for multiplicative observation errors terms. The inference about the parameters of the models is performed using Bayesian methods, with MCMC algorithms and Laplace approximations. We present two applications to real data sets. The first in marine shrimp population off the coast of Chile, where observable variable is the average annual fishing yield. The second application is for the population of the red squat lobster off Chile, where in addition to the average annual fishing yield, a second observable variable was included. In the first case, the multiplicative observational errors models presented the best results. Particularly the Log-skew-normal and Log-skew-t models has the better performances. Considering these results, in the second application we use only multiplicative observation errors models.

<p>This thesis discusses novel developments in Bayesian analytics for high-dimensional multivariate time series. The focus is on the class of multiregression dynamic models (MDMs), which can be decomposed into sets of univariate models processed in parallel yet coupled for forecasting and decision making. Parallel processing greatly speeds up the computations and vastly expands the range of time series to which the analysis can be applied. </p><p>I begin by defining a new sparse representation of the dependence between the components of a multivariate time series. Using this representation, innovations involve sparse dynamic dependence networks, idiosyncrasies in time-varying auto-regressive lag structures, and flexibility of discounting methods for stochastic volatilities.</p><p>For exploration of the model space, I define a variant of the Shotgun Stochastic Search (SSS) algorithm. Under the parallelizable framework, this new SSS algorithm allows the stochastic search to move in each dimension simultaneously at each iteration, and thus it moves much faster to high probability regions of model space than does traditional SSS. </p><p>For the assessment of model uncertainty in MDMs, I propose an innovative method that converts model uncertainties from the multivariate context to the univariate context using Bayesian Model Averaging and power discounting techniques. I show that this approach can succeed in effectively capturing time-varying model uncertainties on various model parameters, while also identifying practically superior predictive and lucrative models in financial studies. </p><p>Finally I introduce common state coupled DLMs/MDMs (CSCDLMs/CSCMDMs), a new class of models for multivariate time series. These models are related to the established class of dynamic linear models, but include both common and series-specific state vectors and incorporate multivariate stochastic volatility. Bayesian analytics are developed including sequential updating, using a novel forward-filtering-backward-sampling scheme. Online and analytic learning of observation variances is achieved by an approximation method using variance discounting. This method results in faster computation for sequential step-ahead forecasting than MCMC, satisfying the requirement of speed for real-world applications. </p><p>A motivating example is the problem of short-term prediction of electricity demand in a "Smart Grid" scenario. Previous models do not enable either time-varying, correlated structure or online learning of the covariance structure of the state and observational evolution noise vectors. I address these issues by using a CSCMDM and applying a variance discounting method for learning correlation structure. Experimental results on a real data set, including comparisons with previous models, validate the effectiveness of the new framework.</p> / Dissertation

Ce travail de thèse s'articule autour de trois parties distinctes abordant chacune un thème précis lié à l'épidémiologie. La première partie de ces travaux s'inscrit dans le cadre de la propagation de virus via l'utilisation de modèles épidémiques. Dans cette partie, sont analysées différentes méthodes d'estimations paramétriques et y sont étudiés la qualité de ces estimateurs. Une application à des virus informatiques est proposée. La deuxième partie de cette thèse propose une méthode d'estimation de la prévalence actuelle du virus de l'hépatite C en France par l'intermédiaire d'un modèle de rétro-calcul associé à un modèle de Markov modélisant l'histoire naturelle de la maladie. Cette méthode et les résultats qui en découlent sont comparés avec les résultats obtenus via l'approche de référence en France. Enfin, la dernière partie s'intéresse à l'étude de l'impact des nouvelles thérapeutiques anti-hépatite C susceptible d'éradiquer le virus à moyen terme. En assimilant la population d'intérêt à un groupement de graphes aléatoires, la propagation du virus est modélisée à partir d'un modèle de métapopulation construit sur la base de données migratoires où les dynamiques de chaque sous-population sont régies par un ensemble d'équations différentielles déterministes. Ce travail doctoral a été réalisé dans le cadre d'une convention CIFRE avec les laboratoires Bristol-Myers Squibb. / The works undertaken in this doctoral thesis are conducted in three parts, each one dealing with a specific epidemiology-related domain. The first part of this work deals with the propagation of viruses by using well-known epidemic models. It is mainly focused on the analyze of different estimation methods and on their performance. An application on computer virus is proposed. The second part of this thesis gives an estimation method of the hepatitis C virus prevalence in France based on a back-calculation model in association with a Markov model of the disease's natural history. This method and its results are compared with those generated by the reference approach in France. The last part is focused on the study of the recent anti-hepatitis C therapeutics impact on the population since is has been stated that those could eradicate the virus at middle term. In that optic, based on published migration data and assuming that the population of interest is organized into a set of specific contact networks, a metapopulation is computed in which the dynamics of each sub-population is governed by a set of deterministic differential equations. This doctoral research has been conducted through a CIFRE industrial research agreement with the Bristol-Myers Squibb pharmaceutical company.

This thesis contains three essays in microeconometrics and applied labor economics. In the first two essays, we estimate dynamic models of schooling choices and employment contract outcomes of the French population. The first essay focuses on the comparison between second-generation immigrants from Africa and their French-natives counterparts. We show that the gap in higher education attainments between those two sub-populations is mainly explained by parents' background and that schooling investment is the main determinant of the gap in permanent employment. The second essay investigates the role played by educational attainments on the employment contract transitions in the early career. We find that a first fixed term contract has a positive impact on the probability of employment in a permanent contract, except for a limited set of the population endowed with particular schooling attainments and unobserved characteristics. Globally, schooling attainments account for around one third of the variance in the probability of permanent employment. The third essay is devoted to the analysis of intra-firm promotions of American executives. We estimate a dynamic model of promotions, in which we disentangle the spurious and the causal impacts of the speed of past advancement. We find that the principal determinant of promotions is unobserved heterogeneity and that the speed of past advancement in the firm's hierarchy (fast tracks) does not have a causal impact on promotions. Functional area has a high explanatory power in promotion outcomes. / Cette thèse présente trois essais en microéconométrie et économie du travail appliquée. Dans les deux premiers essais, nous estimons des modèles dynamiques de choix d'éducation et de contrats de travail en France. Le premier essai s'intéresse à la comparaison entre immigrés de deuxième génération originaires d'Afrique et natifs de parents français. Nous montrons que l'écart dans l'accès aux diplômes d'éducation supérieure entre ces deux sous-populations est expliqué principalement par l'environnement parental et que l'investissement en scolarité est le principal déterminant de l'écart dans l'accès à l'emploi permanent. Le deuxième essai s'intéresse au rôle joué par la scolarité dans les transitions entre contrats de travail en début de carrière. Nous trouvons qu'un premier contrat à durée fixe a un impact positif sur la probabilité d'emploi dans un contrat permanent, excepté pour une partie limitée de la population, dotée de niveaux de scolarité et de caractéristiques inobservables particulières. Globalement, le niveau de scolarité atteint explique environ un tiers de la variance de la probabilité d'emploi permanent. Le troisième essai est dédié à l'analyse des promotions intra-firme de cadres américains. Nous estimons un modèle dynamique de promotion dans lequel nous séparons l'effet causal de l'effet artificiel de la vitesse des avancements passés. Nous trouvons que le principal déterminant des promotions est l'hétérogénéité individuelle inobservable et que la vitesse antérieure de progression dans la hiérarchie de la firme (fast tracks) n'a pas d'impact causal. La division d'appartenance dans l'entreprise a un fort pouvoir explicatif dans les promotions observées.

A análise de dados de sobrevivência tem sido tradicionalmente baseada no modelo de regressão de Cox (COX, 1972). No entanto, a suposição de taxas de falha proporcionais assumida para esse modelo pode não ser atendida em diversas situações práticas. Essa restrição do modelo de Cox tem gerado interesse em abordagens alternativas, dentre elas os modelos dinâmicos que permitem efeito das covariáveis variando no tempo. Neste trabalho, foram revisados os principais modelos de sobrevivência dinâmicos com estrutura aditiva e multiplicativa nos contextos não paramétrico e semiparamétrico. Métodos gráficos baseados em resíduos foram apresentados com a finalidade de avaliar a qualidade de ajuste desses modelos. Uma versão tempo-dependente da área sob a curva ROC, denotada por AUC(t), foi proposta com a finalidade de avaliar e comparar a qualidade de predição entre modelos de sobrevivência com estruturas aditiva e multiplicativa. O desempenho da AUC(t) foi avaliado por meio de um estudo de simulação. Dados de três estudos descritos na literatura foram também analisados para ilustrar ou complementar os cenários que foram considerados no estudo de simulação. De modo geral, os resultados obtidos indicaram que os métodos gráficos apresentados para avaliar a adequação dos modelos em conjunto com a AUC(t) se constituem em um conjunto de ferramentas estatísticas úteis para o próposito de avaliar modelos de sobrevivência dinâmicos nos contextos não paramétrico e semiparamétrico. Além disso, a aplicação desse conjunto de ferramentas em alguns conjuntos de dados evidenciou que se, por um lado, os modelos dinâmicos são atrativos por permitirem covariáveis tempo-dependentes, por outro lado podem não ser apropriados para todos os conjuntos de dados, tendo em vista que estimação pode apresentar restrições para alguns deles. / Analysis of survival data has been traditionally based on the Cox regression model (COX, 1972). However, the proportionality of the hazards required by this model may not be attended for many practical situations. This restriction of the Cox model has generated interest in alternative approaches, among them dynamic models that allow covariates with time-varying effect. In this work, the main dynamic survival models with additive and multiplicative structures were revised under the nonparametric and semiparametric settings. Graphical methods based on residuals were presented in order to evaluate the goodness-of-fit of these models. A time-dependent version of the area under the ROC curve, denoted by AUC(t), was proposed to evaluate and compare the predictive accuracy of additive and multiplicative survival models. The performance of the AUC(t) was evaluated by means of a simulation study. Data from three studies described in the literature were also analyzed to illustrate or complement the scenarios that were considered in the simulation study. Overall, the results indicate that the graphical methods presented to assess the goodness-of-fit of the models together with the AUC(t) provide a useful set of statistics tools for the purpose of evaluating dynamic survival models in the nonparametric and semiparametric settings. Moreover, applying this set of tools in some data sets showed that on the one hand dynamic models are attractive because they allow time-dependent covariates, but on the other hand they may not be appropriate for all data sets since estimation may present restrictions for some of them.

O objetivo principal do presente trabalho é oferecer métodos alternativos de previsão da arrecadação tributária federal, baseados em metodologias de séries temporais, inclusive com a utilização de variáveis explicativas, que reflitam a influência do cenário macroeconômico na arrecadação tributária, com o intuito de melhorar a acurácia da previsão da arrecadação. Para tanto, foram aplicadas as metodologias de modelos dinâmicos univariados, multivariados, quais sejam, Função de Transferência, Auto-regressão Vetorial (VAR), VAR com correção de erro (VEC), Equações Simultâneas, e de modelos Estruturais. O trabalho tem abrangência regional e limita-se à análise de três séries mensais da arrecadação, relativas ao Imposto de Importação, Imposto Sobre a Renda das Pessoas Jurídicas e Contribuição para o Financiamento da Seguridade Social - Cofins, no âmbito da jurisdição do estado de São Paulo, no período de 2000 a 2007. Os resultados das previsões dos modelos acima citados são comparados entre si, com a modelagem ARIMA e com o método dos indicadores, atualmente utilizado pela Secretaria da Receita Federal do Brasil (RFB) para previsão anual da arrecadação tributária, por meio da raiz do erro médio quadrático de previsão (RMSE). A redução média do RMSE foi de 42% em relação ao erro cometido pelo método dos indicadores e de 35% em relação à modelagem ARIMA, além da drástica redução do erro anual de previsão. A utilização de metodologias de séries temporais para a previsão da arrecadação de receitas federais mostrou ser uma alternativa viável ao método dos indicadores, contribuindo para previsões mais precisas, tornando-se ferramenta segura de apoio para a tomada de decisões dos gestores. / The main objective of this work is to offer alternative methods for federal tax revenue forecasting, based on methodologies of time series, inclusively with the use of explanatory variables, which reflect the influence of the macroeconomic scenario in the tax collection, for the purpose of improving the accuracy of revenues forecasting. Therefore, there were applied the methodologies of univariate dynamic models, multivariate, namely, Transfer Function, Vector Autoregression (VAR), VAR with error correction (VEC), Simultaneous Equations, and Structural Models. The work has a regional scope and it is limited to the analysis of three series of monthly tax collection of the Import Duty, the Income Tax Law over Legal Entities Revenue and the Contribution for the Social Security Financing Cofins, under the jurisdiction of the state of São Paulo in the period from 2000 to 2007. The results of the forecasts from the models above were compared with each other, with the ARIMA moulding and with the indicators method, currently used by the Secretaria da Receita Federal do Brasil (RFB) to annual foresee of the tax collection, through the root mean square error of approximation (RMSE). The average reduction of RMSE was 42% compared to the error committed by the method of indicators and 35% of the ARIMA model, besides the drastic reduction in the annual forecast error. The use of time-series methodologies to forecast the collection of federal revenues has proved to be a viable alternative to the method of indicators, contributing for more accurate predictions, becoming a safe support tool for the managers decision making process.

<p>Today's rapid changes and major business developments in organizations increase the need for effective management teams. In management teams, there are significant demands on the members to understand how strategic, tactical and operational decisions and actions generate results. High management team effectiveness requires optimum cooperation between the members with particular emphasis on well-operated communication and ability and flexibility in working as a team. It further requires a deep understanding of the factors that influence the</p><p>management team effectiveness. The challenge to create a theoretical dynamic model to indicate development potentials regarding the effectiveness in the management teams represents the foundation for the idea behind this master thesis.</p><p>This master thesis presents a theoretical management dynamic model I have developed based on identified key factors that influence the effectiveness of management teams. For identification of these key factors, I have used literary studies and research concerning the concept of team, management team, team effectiveness, leading organizations, organization development, dynamic models and many other concepts.</p><p>I have categorized these key factors in five criteria. These criteria are engagement and dynamic leadership, team spirit, management meetings, conflict management and visions and objectives. In view of that, my definition of an effective management team is: team where high-engaged and motivated members including a strategic and dynamic leader work in a team having a good team spirit, hold effective management meetings and manage conflicts effectively to make qualified decisions that mainly are concentrated to reach welldefined bjectives and visions”.</p><p> The inspired idea behind my model is to integrate these criteria in the mechanical system called the Planetary Gear System to create a metaphoric image describing the dynamic of management teams and their effectiveness. Strategies for measuring these criteria also are identified and presented in this master thesis. These properties make the present dynamic model to a unique model in its appearance and functionality. The main function of my model is to indicate development potentials in the management teams. These development potentials are then used to give the studied management team relevant recommendations aimed at making the management team more effective. The aim of this master thesis is to investigate whether the developed model fulfill this function.</p><p>In order to investigate the ability of the model to fulfill this function the model has been applied to a real management team. The results have shown that the model has sufficient ability to indicate development potentials in the studied management team. The obtained results have been analyzed using SPSS computer program. Based on these results several recommendations are given. In this manner, the model has fulfilled stated expectations. However, a couple of additional actions aimed at increasing the qualifications of the presented dynamic model are identified at the end of this master thesis.</p><p>With the intention of verifying whether the developed model contributes to make the studied management team more effective, the performed measurement should be repeated after a period of at least six months. The re-measurement is necessary to follow up the effect of the given recommendations and also to indicate any new development potential. Such a task is recommended for further research and development of the model.</p><p> </p>