In this thesis, we applied local analysis tools (eigenvalue and eigenvalue elasticity analysis, global function elasticity/sensitivity analysis), and global analysis tools (deriving the location and stability of fixed points) to both aggregate and individual-level dynamic models of infectious diseases. We sought to use these methods to gain insight into the models and to evaluate the use of these methods to study their short-term and long-term dynamics and the influences of arameters on the models.<p> We found that eigenvalues are effective for understanding short-term behaviours of a nonlinear system, but less effective in providing insights of the long-term impacts of a parameter change on its behaviours. In term of disease control, local changes of behaviours, yielded from the changes of parameters based on eigenvalue elasticity, are able to alter behaviours in a short-term, especially in the period of a disease outbreak. While eigenvalue elasticity analysis can be helpful for understanding the impact of parameter changes for simple aggregate models, such analyses prove unwieldy and complicated, particularly for models with large number of state variables; and easily fall prey to eigenvalue multiplicity problems for large individual-based models, and istracting artifacts associated with small denominators. In response to these concerns, we introduced other local methods (global function elasticity/sensitivity analyses) that capture many of the advantages of eigenvalue elasticity methods with much greater simplicity. Unfortunately, parameter changes guided by these local analysis techniques are often insufficient to alter behaviours in the longer-term, such as when system behaviours approach stable endemic equilibria. By contrast, the global analytic tools, such as fixed point location and stability analysis, are effective for providing insights into the global behaviours of disease spread in the long-term, as well as their dependence on parameters. Using all of the above analysis as a toolset, we gained some possible insights into combination of local and global approaches. Choice of applying local or global analysis tools to infectious disease models is dependent on the specific target of policy makers as well as model type.

In this thesis, we applied local analysis tools (eigenvalue and eigenvalue elasticity analysis, global function elasticity/sensitivity analysis), and global analysis tools (deriving the location and stability of fixed points) to both aggregate and individual-level dynamic models of infectious diseases. We sought to use these methods to gain insight into the models and to evaluate the use of these methods to study their short-term and long-term dynamics and the influences of arameters on the models.<p> We found that eigenvalues are effective for understanding short-term behaviours of a nonlinear system, but less effective in providing insights of the long-term impacts of a parameter change on its behaviours. In term of disease control, local changes of behaviours, yielded from the changes of parameters based on eigenvalue elasticity, are able to alter behaviours in a short-term, especially in the period of a disease outbreak. While eigenvalue elasticity analysis can be helpful for understanding the impact of parameter changes for simple aggregate models, such analyses prove unwieldy and complicated, particularly for models with large number of state variables; and easily fall prey to eigenvalue multiplicity problems for large individual-based models, and istracting artifacts associated with small denominators. In response to these concerns, we introduced other local methods (global function elasticity/sensitivity analyses) that capture many of the advantages of eigenvalue elasticity methods with much greater simplicity. Unfortunately, parameter changes guided by these local analysis techniques are often insufficient to alter behaviours in the longer-term, such as when system behaviours approach stable endemic equilibria. By contrast, the global analytic tools, such as fixed point location and stability analysis, are effective for providing insights into the global behaviours of disease spread in the long-term, as well as their dependence on parameters. Using all of the above analysis as a toolset, we gained some possible insights into combination of local and global approaches. Choice of applying local or global analysis tools to infectious disease models is dependent on the specific target of policy makers as well as model type.

The effect of a single genetic mutation can vary greatly between different types of cells. The mutated gene may not be expressed in one tissue but may cause a devastating loss of function in another. To learn about disease mechanisms and generate novel therapies, genetic disorders must be studied in the types of cells where the mutations are most deleterious. Recently, scientists have begun manipulating cellular identity to create the cell types most affected by various genetic diseases. This dissertation describes the experience of generating reprogramming models for three genetic disorders: Ring 14 syndrome, Pearson syndrome, and Fanconi anemia.

This thesis focuses on whether we can create a categorizational model for foreign intelligence communities and if we can identify factors that will help us predict those foreign intelligence communities' effectiveness in counterterrorism. In order to determine this, the author based one of her main factors on the 1995 Sederberg article that separates counterterrorism models into three main categories: (1) War Model, (2) Criminal Model, and (3) Disease Model. She also relates these models to three of the main geopolitical schools of thought as follows: (1) War Model - Realism, (2) Criminal Model - Liberalism, and (3) Disease Model - Constructivism. The second main factor analyzed here is the centralization of the intelligence community. This clearly affects the consistency of the counterterrorism message being encouraged by the state, as well as the speed with which the Intelligence Community can respond to an emergency situation. The key contributions of this thesis lie in the creation of the comparison model and the subsequent analysis of the six case studies. This model combines and acknowledges theories of counterterrorism effectiveness that have not been previously studied together and is also a useful comparison tool for foreign intelligence agencies without running into many of the same...

京都大学 / 0048 / 新制・課程博士 / 博士(医科学) / 甲第20811号 / 医科博第82号 / 新制||医科||6(附属図書館) / 京都大学大学院医学研究科医科学専攻 / (主査)教授 長船 健二, 教授 妻木 範行, 教授 村井 俊哉 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20930号 / 理博第4382号 / 新制||理||1630(附属図書館) / 京都大学大学院理学研究科化学専攻 / (主査)教授 杉山 弘, 教授 三木 邦夫, 教授 秋山 芳展 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 新制・論文博士 / 博士(農学) / 乙第13497号 / 論農博第2901号 / 新制||農||1093(附属図書館) / 学位論文||R4||N5403(農学部図書室) / (主査)教授 谷 史人, 教授 佐々木 努, 教授 保川 清 / 学位規則第4条第2項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Using a novel, adipose-liver, two-organ, human-on-a-chip system, the metabolic disease non-alcoholic fatty liver disease was modeled. This model was then used to test the effects of the gut microbiome on NAFLD progression. Two products of the gut microbiome, Trimethylamine-n-oxide and butyrate, were selected as representatives of potentially harmful and potentially beneficial compounds. A dose response, adipocyte and hepatocyte monocultures controls, and HoaC systems were run for 14 days. Through this experimentation, it was found that a dysbiosis of the gut microbiome could be influencing NAFLD progression. Additionally, further development and discovery regarding adipose-liver systems was added to the ongoing conversation of HoaC systems and their usages.

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 word addiction is almost immediately associated with notions of drug dependency and alcoholism, and drug addiction is often referred to as a pandemic that affects individuals, families, communities and society at large. Aetiological approaches to understanding and treating addiction have changed dramatically throughout history, and currently the most contemporary approach is that of the disease model which views addiction as an illness rather than as a ‘badness’. While the underpinnings of Narcotics Anonymous’ 12 step philosophy employs non-specific drug language as it views all drugs as having the capacity to become addictive, and while it does not distinguish between the capacity for substances and certain behaviours to become addictive, activities such as overeating, having sex and gambling are yet to be classified as legitimate addictions by the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR). As a result, it appears as if some confusion exists as to whether these behaviours should be classified as impulse control disorders or as genuine addictions due to the various similarities they share in common with substance based disorders. The research conducted explored how people recovering from addiction, as well as how people working with addiction understand addiction and multiple dependency, together with the factors that contribute to relapse and the ability to abstain. Various 12 Step meetings from a variety of 12 Step Fellowships were attended and members were invited to participate in the study. Ultimately seventy eight participants completed a self developed questionnaire which was utilised to assess how people recovering from addiction understood addiction and multiple dependency and the factors that contribute to relapse and the ability to abstain. Quantitative data were analysed via descriptive and inferential statistics. Furthermore twenty participants working with addiction were interviewed with the use of a semi structured interview schedule in order to explore their perceptions around addiction and the factors that contribute to relapse and sobriety. Qualitative data were analysed using thematic content analysis. Results indicated that the majority of recovering addicts and professionals working with addiction understand addiction as a disease. However, discrepancy was apparent with regards to whether or not all recovering addicts have the same disease and subsequently whether all addictions can be treated in the same manner. The above result suggested that there was no standardised, uniform way in which the disease model is understood and interpreted. Factors such as cross addiction, resistance to change and issues relating to the maintenance of change were identified as issues that contribute to relapse, while factors such as aftercare, following the 12 step programme and support were identified as the main aspects that contribute to sobriety. No statistical significance was noted between participants who had relapsed as opposed to those who had not for variables of sensation seeking, impulsivity and perceived stress (which may have been as a result of small sample size). Deeper understanding of the disease model together with broader application of it, and a focus on appropriate training and more comprehensive assessment could perhaps see a reduction in high rates of relapse and recidivism more commonly known as the ‘revolving door syndrome’.