CYP2D6, a member of the cytochrome P450 enzyme system, has a central role in drug metabolism, it metabolizes 25 % of clinically used drugs. The gene that codes for the enzyme displays a high degree of polymorphism, which effects enzyme functions to various degrees. Aside from smaller mutations like SNPs, alleles may also feature duplications or deletion of the whole gene. Due to the clinical relevance of these mutations, a simple and precise method for genotyping is needed. In this study, a method based on pyrosequencing for copy number analysis was evaluated, wherein the copy number was determined by relative quantification to a reference gene CYP2D8P. During evaluation of the method, several adjustments were tried for optimization, including adjustments of annealing temperature and primer concentration. The results showed a difficulty in distinguishing between copy numbers using the method, as well as a high coefficient of variation. Therefore, further optimization is required before the method could be implemented into clinical practice.

Abstract The mitochondrial oxidative phosphorylation system (OXPHOS) generates energy but also deleterious reactive oxygen species (ROS). Changes in the cytoskeleton, composed mainly of microfilaments, microtubules and intermediate filaments, have been observed in OXPHOS deficiency. The 3243A>G point mutation in mitochondrial DNA (mtDNA) leads to mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes (MELAS), which is the most common mitochondrial disease. Interestingly, mitochondrial aberrations have been demonstrated in patients with a mutation in NOTCH3, the genetic cause of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Randomization of vimentin intermediate filament direction and length together with slower population growth was observed in myoblasts with 3243A>G, with no difference in the amount of apoptotic cell death. Upon complex IV inhibition (with or without the microtubule-depolymerizing compound nocodazole) or a lack of mtDNA (ρ0) in osteosarcoma cells the vimentin network collapsed perinuclearly, forming thick bundles, whereas complex I inhibition led to thinner vimentin network bundles. Furthermore, the amount of vimentin was increased in ρ0 cells. Mitochondria accumulated around the nucleus upon complex IV inhibition and in ρ0 cells. Analysis of the total proteome revealed that specific OXPHOS deficiencies led to changes in the expression of cytoskeletal proteins and proteins involved in apoptosis, OXPHOS, glycolysis and oxidative stress response. Muscle histochemical and genetic analysis showed ragged red fibres and cytochrome c oxidase-negative fibres to be associated with 5650G>A in a patient with R133C in NOTCH3 and 5650G>A in MTTA. Immunolabelling of cells with R133C and 5650G>A revealed a sparse tubulin network with asters and less abundant mitochondria by comparison with control cell lines. Comparison of nucleotide diversity between CADASIL pedigrees and controls showed increased mtDNA sequence variation in the CADASIL patients. Also maternal relatives in two CADASIL pedigrees differed from each other in their mtDNA. These findings suggest that defects in OXPHOS lead to selective changes in the vimentin network, which may have a role in the pathophysiology of mitochondrial diseases. They also suggest a relationship between NOTCH3 and mtDNA, and establish the pathogenicity of 5650G>A. The overall results emphasize that a deficiency in the energy converting system together with oxidative stress can lead to cytoskeletal changes.

The classification of protein sequences is a subfield in the area of Bioinformatics that attracts a substantial interest today. Machine Learning algorithms are here believed to be able to improve the performance of the classification phase. This thesis considers the application of different Machine Learning algorithms to the classification problem of a data set of short-chain dehydrogenases/reductases (SDR) proteins. The classification concerns both the division of the proteins into the two main families, Classic and Extended, and into their different subfamilies. The results of the different algorithms are compared to select the most appropriate algorithm for this particular classification problem. / Klassificeringen av proteinsekvenser är ett område inom Bioinformatik, vilket idag drar till sig ett stort intresse. Maskininlärningsalgoritmer anses här kunna förbättra utförandet av klassificeringsfasen. Denna uppsats rör tillämpandet av olika maskininlärningsalgoritmer för klassificering av ett dataset med short-chain dehydrogenases/reductases (SDR) proteiner. Klassificeringen rör både indelningen av proteinerna i två huvudklasser, Classic och Extended, och deras olika subklasser. Resultaten av de olika algoritmerna jämförs för att välja ut den mest lämpliga algoritmen för detta specifika klassificeringsproblem. / Sandra Olandersson Blåbärsvägen 27 372 38 Ronneby home: 0457-12084

La thèse décrit l’ensemble hétérogène et flou de produits et de situations de travail qu'embrasse le monde des pâtissiers en France de la fin des années 1970 au début des années 2010 à la lumière d'une question de sociologie : dans quelle mesure les marques de considération portées aux travailleurs et à leur travail participent-elles à stabiliser des formes professionnelles, qu'il s'agisse d’un groupe professionnel, de segments élitaires, de communautés de praticiens ou encore de trajectoires individuelles ? Les matériaux mobilisés dans les trois parties de la thèse sont issus d’un travail sur archives et d’une enquête ethnographique. Une base prosopographique de première main est exploitée à partir de méthodes d’analyse de séquences. Dans la première partie, les revendications de statut portées au nom du groupe des pâtissiers par plusieurs collectifs et les conventions esthétiques convoquées et explicitées dans ce cadre sont étudiées. Dans la deuxième partie, les rhétoriques d’excellence mises en œuvre sur trois arènes de définition du travail pâtissier (le système des concours pâtissiers, une association élitaire et les sélections critiques) sont comparées au regard des sanctions qu’elles sécrètent et des segments élitaires qu'elles concourent à stabiliser. La troisième partie est consacrée à la stabilisation de trajectoires individuelles sur les marchés du travail et sur les marchés boutiquiers, à l’appui notamment de ces marques de considération. Ce faisant, les modèles d’avantage cumulatif sont mis à l’épreuve. En filigrane, la conduite d’entreprise et les compétences décoratives apparaissent comme deux principes récurrents de considération du travail pâtissier. / The thesis describes the heterogenous world of pâtissiers – in terms of products and work situations – in France from the late 1970’s to the early 2010’s in the light of a sociological question : to what extent do marks of consideration sent to workers and to their work contribute to stabilize professional forms, be it professional groups, professional segments (elitarian or not) or individual trajectories ? The three parts of the thesis are based on empirical research, interweaving archive and ethnographic materials. An original prosopographical database is exploited through sequence analysis. The first part of the thesis presents an analysis of competing jurisdictional claims describing a group of pâtissiers and explicitly aesthetic choices that these claims reveal. In a second part, three arenas of definition of pâtissier work (pâtissier competitions, an elitarian association and critics) are compared with regard to rhetorics of excellence they convey and the elitarian segments they contribute to stabilize. The third part looks at individual trajectories that are partly built on these marks of consideration. In so doing, the cumulative advantage models are discussed. The thesis shows that entrepreneurship and decoration skills are recurrent principles of consideration of pâtissier work.

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Background: Game analytics is a field that aims to analyze games and help in the enhancement of game development. Data mining is a prominent technique for game analytics. Recent advances in the field of process mining have motivated users to apply process mining to real-world scenarios in order to derive process-oriented insights. In this study, We provide a discussion on how process mining can be used in game analytics. Objective: The goal of this study is to apply process mining to player data from a live game, analyze the results, and determine whether these results can be interpreted, whether we can derive any patterns or insights that can be useful for game designers, and whether process mining can be used in-game analytics and, if so, what kind of versatility it can offer. Also, this study provides approaches on how process mining can be used in player churn prediction and determination of abnormal player activity sequences. Method: Firstly, a literature review is performed to comprehend all of the process mining techniques and metrics used to evaluate the discovered process models. Then experiments are conducted by applying process mining on data from a live game, determine a churn predictor using process mining and determining a technique to identify abnormal player sequences. Results: Process discovery algorithms are applied on data from a live game, the results are analyzed. Several process models are discovered to identify player churn and it is compared with a baseline machine learning churn predictor trained on the same data to that of process mining. Abnormal player activity sequences of the gameare determined using process mining and compared with expected player sequences and analyzed with the help of game designers. Conclusion: Process mining can be utilized in game analytics to discover new process-oriented insights. When compared to typical data mining techniques, the results gained by process mining are more versatile. It also has other capabilities such as detecting unusual sequences in data.

This work presents comprehensive analytics of trajectories of functional loss and recovery using sequence analysis and clustering techniques. The study focuses on a large dataset consisting of assessments of activities of daily living conducted among nursing home residents. The first main part of this research involves converting the assessments into sequences of disability combinations and utilizing graphical tools and various indicators to gain valuable insights into the trajectories of functional disabilities over time. In the second part of the research, a novel clustering approach is introduced that combines Markov models with distance-based techniques. This hybrid methodology results in 13 distinct clusters of trajectories. The clusters are thoroughly examined, and representative sets are carefully selected based on various criteria. This selection process ensures that the chosen sets accurately represent the characteristics of each cluster. The findings of this study have significant implications for healthcare systems, including developing predictive models which can be utilized to forecast the trajectory of individual patients based on their cluster membership. This enables healthcare providers to anticipate disease progression, tailor treatments, and dynamically adjust care plans, resulting in improved patient outcomes and the overall quality of care. Moreover, the information derived from the analytics can aid in optimizing healthcare systems by facilitating resource allocation and cost optimization. The insights gained can also guide policymakers and families in planning appropriate care for patients. This research advances healthcare decision-making and ensures appropriate support. / Thesis / Master of Science (MSc) / The ability to independently perform activities of daily living (ADLs) is a crucial indicator of an individual's health status, and the loss of this ability can have a profound impact on their overall quality of life. Our research focuses on analyzing the trajectories of patients as they experience functional decline and recovery. While various techniques have been utilized to explore ADL trajectories, this study stands out by employing clustering and sequence analysis approaches to examine different groups of trajectories. To overcome the computational challenges involved, we propose a combined clustering approach. This hybrid approach consists of two phases: applying a Markov model prior to distance-based algorithms. The findings derived from our research hold significant applications in optimizing healthcare systems, improving health outcomes, facilitating the development of targeted and effective interventions that support patients in preserving their independence, and enhancing the quality of care.

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