A utilização de bases de dados para estudos epidemiológicos, avaliação da qualidade e quantidade dos serviços de saúde vem despertando a atenção dos pesquisadores no contexto da Saúde Pública. No Brasil, as bases de dados do Sistema Único de Saúde (SUS) são exemplos de repositórios importantes que reúnem informações fundamentais sobre a Saúde. Entretanto, apesar dos avanços em termos de coleta e de ferramentas públicas para a pesquisa nessas bases de dados, tais como o TABWIN e o TABNET, esses recursos ainda não fazem uso de técnicas mais avançadas para a produção de informação gerencial, como as disponíveis em ferramentas OLAP (On Line Analytical Processing) e de mineração de dados. A situação é extremamente agravada pelo fato dos dados da Saúde Pública, produzidos por vários sistemas isolados, não estarem integrados, impossibilitando pesquisas entre diferentes bases de dados. Consequentemente, a produção de informação gerencial torna-se uma tarefa extremamente difícil. Por outro lado, a integração dessas bases de dados pode constituir um recurso indispensável e fundamental para a manipulação do enorme volume de dados disponível nesses ambientes e, assim, possibilitar a produção de informação e conhecimento relevantes, que contribuam para a melhoria da gestão em Saúde Pública. Acompanhar o seguimento de pacientes e comparar diferentes populações são outras importantes limitações das atuais bases de dados, uma vez que não há um identificador unívoco do paciente que possibilite executar tais tarefas. Esta Tese teve como objetivo a construção de um armazém de dados (data warehouse), a partir da análise de dez anos (período de 2000 a 2009) das principais bases de dados do SUS. Os métodos propostos para coleta, limpeza, padronização das estruturas dos bancos de dados, associação de registros ao paciente e integração dos sistemas de informação do SUS permitiram a identificação e o seguimento do paciente com sensibilidade de 99,68% e a especificidade de 97,94%. / The use of databases for epidemiologic studies, quality and quantity evaluation of health services have attracted the attention of researchers in the context of Public Health. In Brazil, the databases of the Sistema Único de Saúde (SUS) are examples of important repositories, which store fundamental information about health. However, despite of the advances in terms of load and public tools for research in those databases, such as TABWIN and TABNET, these resources do not use advanced techniques to produce management information as available in OLAP (On Line Analytical Processing) and data mining tools. The situation is drastically increased for the fact that data in public health, produced for different systems, are not integrated. This makes impossible to do research between different databases. As a consequence, the production of management information is a very difficult task. On the other hand, the integration of these databases can offer an important and fundamental resource to manipulate the enormous volume of data available in those environments and, in this way, to permit the production of relevant information and knowledge to improve the management of public health. The patient follow up and the comparison of different populations are other important limitations of the available databases, due to the absence of a common patient identifier. The objective of this Thesis was the construction of a data warehouse to analyze ten years (period from 2000 to 2009) of the principal databases of SUS. The proposed methods to load, clean, database structure standardization, patient record linkage and SUS information systems integration have been permitted patient identification and follow up with sensitivity of 99.6% and specificity of 97.94%.

La recherche des signatures génétiques de l'adaptation locale est d'un grand intérêt pour de nombreuses études de génétique des populations. Les approches pour trier les loci sélectifs à partir de leur contexte génomique, se concentrent sur les valeurs extrêmes de l'indice de fixation, FST, à travers les loci. Cependant, le calcul de l'indice de fixation devient difficile lorsque la population est génétiquement continue, lorsque la prédéfinition des sous-populations est une tâche difficile et en présence d'individus mélangés dans l'échantillon. Dans cette thèse, nous présentons une nouvelle méthode pour identifier les loci sous sélection basée sur une extension de la statistique FST à des échantillons avec des individus mélangés. Considérant notre objectif d'explorer des méthodes statistiques pour identifier l'adaptation locale dans la population mélangée, nous avons inclus des données spatiales pour calculer les coefficients d'ascendance et les fréquences d'allèles. Pour enrichir notre travail, nous avons investigué les effets du déséquilibre de liaison et des méthodes d'élagage de LD dans les analyses de génomes pour la sélection. / Finding genetic signatures of local adaptation is of great interest for many population genetic studies. Common approaches to sorting selective loci from their genomic background focus on the extreme values of the fixation index, FST, across loci. However, the computation of the fixation index becomes challenging when the population is genetically continuous, when predefining subpopulations is a difficult task, and in the presence of admixed individuals in the sample. In this thesis, we present a new method to identify loci under selection based on an extension of the FST statistic to samples with admixed individuals. Considering our goal of exploring statistical methods to identify local adaptation in admixed population, we included spatial data to compute ancestry coefficients and allele frequencies. To enrich our work, we investigated the effects of linkage disequilibrium and LD-pruning methods in genome scans for selection.

Les systèmes d’échange de quotas d’émission (SEQEs) sont un instrument de régulation environnementale important et ont un rôle clef à jouer dans la réduction des émissions de gaz à effet de serre pour l’atténuation du changement climatique. Cette thèse a une double orientation : les liaisons spatiales entre SEQEs d'une part et les échanges inter-temporels au sein d’un SEQE d'autre part.Les liaisons entre SEQEs peuvent aider à établir un futur cadre de politique climatique mondiale coût-efficient. Cependant, ces liaisons sont difficiles à mettre en place et à ce jour, peu nombreuses. Dans un premier temps, à l’aide d’un modèle simple et unifié et en se basant sur des expériences réelles de SEQEs, nous comparons différentes restrictions à l’échange comme éléments facilitants une transition vers le libre échange de quotas. Dans un deuxième temps, nous construisons un modèle qui décrit et caractérise analytiquement les effets et gains associés à des liaisons multilatérales sous incertitude. Ensuite, le modèle est calibré sur émissions historiques de différentes juridictions pour illustrer les déterminants des préférences de liaison.Les SEQEs sont sujets à de l’incertitude réglementaire, ce qui peut affaiblir leurs efficience coût dynamique et signal prix de long terme. La prévalence d’une telle incertitude peut être assimilée à une situation d’ambiguïté. Nous analysons alors les décisions inter-temporelles d’entités couvertes par un SEQE et averses à l’ambiguïté puis caractérisons les distorsions induites sur le fonctionnement du système. Nous discutons enfin de l’éclairage apportés par ces résultats en rapport aux observations faites dans les SEQEs existants. / Emissions Trading Systems (ETSs) are an important instsrument in regulating pollution and have a key role to play in reducing greenhouse gas emissions to mitigate climate change. This dissertation has a twin focus: spatial linkages between ETSs at a point in time and intertemporal trading within an ETS.Linkages between ETSs are crucial for cost-effectiveness of the future climate policy architecture. Complete linkages, however, are difficult to agree and to date, few and far between. Here, our contribution is twofold. First, using a simple and unified model and drawing on experiences with real-world ETSs, we compare alternative trade restrictions on bilateral linkages in facilitating the transition to an unrestricted link. Second, we provide a general model to describe and analytically characterize the effects and gains from multilateral linkages under uncertainty. The model is then calibrated to historical emissions of real-world jurisdictions to illustrate the determinants of linkage preferences.ETSs are subject to regulatory uncertainty, which can disrupt dynamic cost-effectiveness and undermine their long-term price signal. The prevalence of regulatory uncertainty can be assimilated to a situation of ambiguity. Here, our contribution is to analyze regulated entities’ intertemporal decisions under ambiguity aversion, characterize the induced distortions in market functioning, and discuss how these can help explain observations from existing ETSs.

All the youth political organizations, that were included in his research, aimed to provide its political parties with the function of party stabilization, They acted as a mediator between political party and the defined part of society. There is an assumption, that institutionalized political party wants a strong and durable relationship with the affiliated organization because of its mutually beneficial functions. Moreover, these parties should also aim to formalize this relationship via its statutes. However, in the Czech Republic there is a strong tendency toward omitting this form of relationship as the parties prefer more informal relations. This diploma thesis discovered, that in the only youth political organization with formal ties to its maternal organization was Young Social Democrats. Even though these youth political parties act lice a support structure, they can be also perceived by political party as a potential source of instability and danger.

Atherosklerotisch bedingte Herz-Kreislauferkrankungen zählen weltweit zu den häufigsten Todesursachen. Die vorliegende Arbeit befasst sich in einem Mausmodell mit der Identifikation neuer Genorte, welche die Ausprägung der Atherosklerose und deren Kofaktoren beeinflussen. Zu diesem Zweck wurde eine Kopplungsuntersuchung in einer Kreuzung Atherosklerose-empfindlicher C57BL/6 und BALB/c Mäuse auf dem LDL-Rezeptor defizienten Hintergrund durchgeführt. Außer der Größe der atherosklerotischen Läsionen wurden 61 weitere Phänotypen bestimmt. Als Hauptergebnis konnte ein neuer Genlocus auf dem proximalen Chromosom 2 identifiziert werden, welcher einen Einfluss auf die Größe der atherosklerotischen Läsionen an der Aortenwurzel hat. Des Weiteren zeigte sich eine Co-Segregation von Lipoproteinen (Very-Low-Density Lipoprotein (VLDL) Cholesterin und High-Density Lipoprotein (HDL) Cholesterin mit diesem Locus sowie eine Korrelation dieser Lipide mit der Läsionsgrösse. Diese Ergebnisse deuten darauf hin, dass der Effekt des Chromosom 2 Lokus auf die Atherosklerose durch genetische Faktoren des Fettstoffwechsels bedingt ist. Weitere Experimente sind notwendig um den QTL weiter einzuengen und die verantwortlichen Gene zu identifizieren.

L'un des objectifs centraux de la biologie est de comprendre comment l'ADN, la séquence primaire, donne lieu à des traits observables. À cette fin, nous examinons ici des méthodes pour identifier les composants génétiques qui influencent les traits microbiens. Par « identifier », nous entendons l'élucidation à la fois l'état allélique et de la position physique de chaque variante causale d'un phénotype d'intérêt à la résolution des nucléotides de paires de bases. Nous nous sommes concentrés sur les études d'association génomique (genome-wide association studies; GWAS) en tant qu'approche générale d’étudier l'architecture génétique des traits. L'objectif global de cette thèse était d'examiner de manière critique les méthodologies GWAS et de les considérer en pratique dans des populations microbiennes fortement clonales et non- clonales (i.e. avec recombinaison fréquent). Le domaine de la GWAS microbienne est relativement nouveau par rapport aux quinze dernières années de la GWAS humaine, et en tant que tel, nous avons commencé par un examen de l'état de la GWAS microbienne. Nous avons posé deux questions principales : 1) Les méthodes GWAS humaines fonctionnent-elles facilement et sans modification pour les populations microbiennes ? 2) Et sinon, quels sont les problèmes méthodologiques centraux et les modifications nécessaires pour la GWAS microbienne? À partir de ces résultats, nous avons ensuite détaillé le déséquilibre de liaison (linkage disequilibrium; LD) comme principal obstacle dans la GWAS microbien, et nous avons présenté une nouvelle méthode, POUTINE, pour relever ce défi en exploitant les mutations homoplasiques pour briser implicitement la structure LD. Le reste de la thèse présente à la fois les méthodes traditionnelles GWAS (comptage des allèles) et POUTINE (comptage d’homoplasies) appliquées à une population hautement recombinogène de génomes de vibrions marins. Malgré une taille d'échantillon modeste, nous donnons un premier aperçu de l'architecture génétique de la résistance aux bactériophages dans une population naturelle, tout en montrant que les récepteurs des bactériophages jouent un rôle primordial. Ce résultat est en pleine cohérence avec des expériences en laboratoire de coévolution phage-bactérie. Il est important de noter que cette architecture met en évidence à quel point la sélection positive peut sculpter certains traits microbiens différemment de nombreux traits complexes humains, qui sont généralement soumis à une faible sélection purificatrice. Plus précisément, nous avons identifié des mutations à effet important à haute fréquence qui sont rarement observées dans les phénotypes complexes humains où de nombreuses mutations à faible effet contribuent à l'héritabilité. La thèse se termine par des perspectives sur les voies à suivre pour la GWAS microbienne. / One of the central goals of biology is to understand how DNA, the primary sequence, gives rise to observable traits. To this aim, we herein examine methods to identify the genetic components that influence microbial traits. By "identify" we mean the elucidation of both the allelic state and physical position of each causal variant of a phenotype of interest down to the base-pair nucleotide resolution. Our focus has been on genome-wide association studies (GWAS) as a general approach to dissecting the genetic architecture of traits. The overarching aim of this thesis was to critically examine GWAS methodologies and to consider them in practice in both strongly clonal and highly recombining microbial populations. The field of microbial GWAS is relatively new compared to the over fifteen years of human GWAS, and as such, we began this work with an examination of the state of microbial GWAS. We asked and attempted to answer two main questions: 1) Do human GWAS methods readily work without modification for microbial populations? 2) And if not, what are the central methodological problems and changes that are required for a successful microbial GWAS? Building from these findings, we then detailed linkage disequilibrium (LD) as the primary obstacle in microbial GWAS, and we presented a new method, POUTINE, to address this challenge by harnessing homoplasic mutations to implicitly break LD structure. The remainder of the thesis showcases both traditional GWAS methods (allele counting) and POUTINE applied to a highly recombining population of marine vibrio genomes. Despite a small sample size, we provide a first glimpse into the genetic architecture of bacteriophage resistance in a natural population and show that bacteriophage receptors play a primary role consistent with experimental populations of phage-bacteria coevolution. Importantly, this architecture highlights how strong positive selection can sculpt some microbial traits differently than many human complex traits, which are generally under weak purifying selection. Specifically, we identified common frequency, large-effect mutations that are rarely observed in human complex phenotypes where many low-effect mutations are thought to contribute to the bulk of heritability. The thesis concludes with perspectives on ways forward for microbial GWAS.

This thesis deals with the nature of the Left-Right scale. Theories and ideas about the Left-Right scale have been tested by a mixture of quantitative and qualitative methods. The research questions are, in short, firstly if voters’ preferences on political issues, where economic issues are tested in this specific thesis, can consistently explain voters’ Left-Right self-placement, secondly if this level of explanation can vary depending on changes in national political discourse, and finally if a high level of correlation between issue and Left-Right self-placement facilitates the matching process of parties and voters of similar ideological conviction. A regression is run on data from the World Values Survey and the European Values Study, data which consists of survey questions about attitudes towards economic preferences and self-placement on the Left-Right scale. The selection is restricted to old democracies, as there is some discrepancy between new and old democracies with regards to the capacity of the electorate to relate to the Left-Right scale. The independent variable in this regression is attitudes toward different economic issues, while the dependent variable is self-placement on the Left-Right scale. The first question is answered by the regression, which is if preferences regarding issues can explain self-placement on the Left-Right scale. The answer that is given is that there is indeed a consistent relationship between preferences on economic issues and self-placement on the Left-Right scale, over time. Albeit it is higher for some countries, like Scandinavia, and lower for other countries, like Japan and Ireland. The two remaining questions are answered by case studies, selected through the method of least likely and most likely cases. These are Netherlands, Sweden, Japan and France. In essence, these cases show that the Left-Right scale is indeed flexible, and can adapt to the current political discourse, and that a high relationship between preferences on political issues and self-placement on the Left-Right scale facilitates the matching of voters and parties of similar ideological conviction.

Der Beitrag stellt zunächst kurz die geometrischen Grundlagen der klassischen Genaulagen- Synthese von Koppelgetrieben vor und erläutert diese anschaulich für Zuhörer ohne spezielle getriebetechnische Vorkenntnisse. Dies mündet ein in die Definition und mathematische Formulierung der in der Getriebe-Software GENESYS zugrunde gelegten allgemeinen Bemessungsbausteine ,,Drei-Glieder-Gruppe“ und ,,Zweischlag mit Relativwinkel“. Dazu werden das typische Lösungsspektrum und die Lösungsvielfalt für jeweils drei, vier und fünf Lagenzuordnungen benannt, ohne jedoch näher auf mathematische Details der jeweiligen Lösungsbestimmung einzugehen. Den Kern des Beitrages bildet die anschließende Vorstellung des Konzeptes ,,Bearbeitungsstrategie“ als Ansatz für die jeweils aufgabenspezifische individuelle Entwicklung von Bearbeitungsabläufen, in denen die allgemeinen Bemessungsbausteine mit jeweils geeigneter Anpassung ihrer Eingabe- und Variationsdaten in eine Abfolge sonstiger Bearbeitungsschritte, z. B. freie Variation von Vorgaben sowie vor- und nachbereitende Analyseschritte, eingebettet werden. Abschließend wird an praktischen Beispielen demonstriert, wie dieses Konzept in GENESYS software-technisch unterstützt wird und effektiv eingesetzt werden kann.

McMaster University MASTER OF PUBLIC HEALTH (2020) Hamilton, Ontario (Health Research Methods, Evidence, and Impact) TITLE: Applying Machine Learning to Determine Nutrients Predictive of Cardiovascular Disease Using Canadian Linked Population-Based Data AUTHOR: Jason D. Morgenstern, B.Sc. (University of Guelph), M.D. (Western University) SUPERVISOR: Professor L.N. Anderson, NUMBER OF PAGES: xv, 121 / The use of big data and machine learning may help to address some challenges in nutritional epidemiology. The first objective of this thesis was to explore the use of machine learning prediction models in a hypothesis-generating approach to evaluate how detailed dietary features contribute to CVD risk prediction. The second objective was to assess the predictive performance of the models. A population-based retrospective cohort study was conducted using linked Canadian data from 2004 – 2018. Study participants were adults age 20 and older (n=12 130 ) who completed the 2004 Canadian Community Health Survey, Cycle 2.2, Nutrition (CCHS 2.2). Statistics Canada has linked the CCHS 2.2 data to the Discharge Abstracts Database and the Canadian Vital Statistics Death database, which were used to determine cardiovascular outcomes (stroke or ischemic heart disease events or deaths). Conditional inference forests were used to develop models. Then, permutation feature importance (PFI) and accumulated local effects (ALEs) were calculated to explore contributions of nutrients to predicted disease. Supplement-use (median PFI (M)=4.09 x 10-4, IQR=8.25 x 10-7 – 1.11 x 10-3) and caffeine (M=2.79 x 10-4, IQR= -9.11 x 10-5 – 5.86 x 10-4) had the highest median PFIs for nutrition-related features. Supplement-use was associated with decreased predicted risk of CVD (accumulated local effects range (ALER)= -3.02 x 10-4 – 2.76 x 10-4) and caffeine was associated with increased predicted risk (ALER= -9.96 x 10-4 – 0.035). The best-performing model had a logarithmic loss of 0.248. Overall, many non-linear relationships were observed, including threshold, j-shaped, and u-shaped. The results of this exploratory study suggest that applying machine learning to the nutritional epidemiology of CVD, particularly using big datasets, may help elucidate risks and improve predictive models. Given the limited application thus far, work such as this could lead to improvements in public health recommendations and policy related to dietary behaviours. / Thesis / Master of Public Health (MPH) / This work explores the potential for machine learning to improve the study of diet and disease. In chapter 2, opportunities are identified for big data to make diet easier to measure. Also, we highlight how machine learning could find new, complex relationships between diet and disease. In chapter 3, we apply a machine learning algorithm, called conditional inference forests, to a unique Canadian dataset to predict whether people developed strokes or heart attacks. This dataset included responses to a health survey conducted in 2004, where participants’ responses have been linked to administrative databases that record when people go to hospital or die up until 2017. Using these techniques, we identified aspects of nutrition that predicted disease, including caffeine, alcohol, and supplement-use. This work suggests that machine learning may be helpful in our attempts to understand the relationships between diet and health.

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