Inaug.-Diss.--Zürich. / Curriculum vitae. "Notenbeilage": 3 p. at end. "Literatur": p. [7].

Inaug.-Diss.--Zürich. / Curriculum vitae. "Notenbeilage": 3 p. at end. "Literatur": p. [7].

No description available.

Yes / Medical Research Council, Innovate UK and Epistem Ltd.

This part of the thesis is confidential.

No description available.

Inaug.-Diss.--Berlin. / Lebenslauf. "Mozarts opern in der Gesamtausgabe": p. 78. "Literatur": p. 79.

This dissertation thesis describes an advanced method of knowledge discovery in databases (KDD), implemented in system LISp-Miner. The goal is to show the possibilities of coordinated use of analytical tools and complex procedures GUHA in this system. The thesis uses methodology CRISP-DM, which is firstly described and work is proceeded using this methodology in the following sections. The author firstly introduces readers domain area and then the data itself, which are processed to the analysis needs. Analytical questions that are answered at, are drawn from the literature, which is focused on domain area. The work should be used as a guide to LISp-Miner users, using analytical tools and procedures GUHA is therefore described the easiest way to understand.

Denna studie fördjupar sig i effektiviteten av generativa modeller, nämligen Variational Autoencoders (VAEs), Diffusion Models (DMs) och Generative Adversarial Networks (GANs), i bildkomprimering. Forskningen fokuserar på att utvärdera dessa modeller utifrån deras förmåga att komprimera bilder med bibehållen visuell trohet. Utvärderingsmått som Peak Signal-to-Noise Ratio (PSNR) och Bits Per Pixel (bpp) används för att bedöma modellernas prestanda. Genom en jämförelse av olika tillvägagångssätt och förtränade modeller identifierades en praktisk och effektiv metod för bildkomprimering som belyste potentialen hos generativa modeller inom detta område. Metoden som användes var att låta modellerna komprimera en uppsättning av fem bilder från kodak-dataset, de komprimerade bilderna var desamma längs alla modeller. De rekonstruerade bilderna analyserades sedan genom att mäta deras Peak signal-to-noise ratio (PSNR) och värdet för bitar per pixel (BPP) för varje modell. Resultaten visar att VAE tenderar att ge bättre återgivningsbilder med ett PSNR-medelvärde 37 jämfört med GAN(28) och DM(30) medan GAN:er minskar bildstorleken mest med och ett medelvärde på 0,2 bitar per pixel jämfört med 0,7 för DM och 0,9 för VAE. / This study delves into the effectiveness of generative models, namely Variational Autoencoders (VAEs), Diffusion Models (DMs) and Generative Adversarial Networks (GANs), in image compression. The research focuses on evaluating these models based on their ability to compress images while maintaining visual fidelity. Evaluation metrics such as the Peak Signal-to-Noise Ratio (PSNR) and Bits Per Pixel (bpp) are utilized to assess the performance of the models. Through a comparison of different approaches and pre-trained models, a practical and efficient method for image compression was identified, shedding light on the potential of generative models in this domain. The method used was letting the models compress a set of five images from the kodak dataset, the images compressed were the same along all models. The reconstructed images were then analyzed by measuring their their Peak signalto-noise ratio(PSNR) and the bits per pixel(BPP) value of each model. The results show that the VAE tends to give better fidelity images with a PSNR average value 37 compared to GAN(28) and DM(30) while GANs reduce the image size the most with and average value of 0.2 bits per pixel compared with 0.7 for DM and 0.9 for VAE.

Hypertension and type two diabetes mellitus (Type 2 DM) are serious chronic illnesses that impact on the lives of millions of people around the world. Various epidemiological studies have shown a relationship between early life events such as intrauterine growth retardation (IUGR) resulting in low birth weight and the development of these chronic illnesses in adult life. To explain the link between these two events, it has been suggested that an ‘insult’ at a critical time point of development can ‘program’ alterations in gene expression, organ size, and cell number. This has been termed “the early life origins of disease’. There is also evidence that these programmed effects are not limited to the first generation but can also be passed to subsequent generations. With changes in lifestyle in modern society, the prevalence of obesity is increasing, in association with problems such as type 2 DM, hypertension, fatty liver, atherosclerosis and the metabolic syndrome. Obesity during pregnancy is linked to problems such as gestational diabetes, hypertension and early miscarriage as well as a higher risk of congenital malformations. Maternal obesity has also been recognised as one of the factors capable of ‘programming’ the offspring, increasing the risk of childhood and adult disorders such as obesity and hypertension. In this thesis I have used two animal models to explore the underlying mechanisms of programming and its intergenerational effects: i) a rat model of prenatal glucocorticoid over-exposure (the dexamethasone-programmed rat) and ii) a mouse model of obesity during pregnancy. Using the dexamethasone-programmed rat, I have shown that prenatal glucocorticoid overexposure reduces fetal and placental weight in the first generation (F1) offspring, in association with alterations in gene expression in placenta and liver. In addition, I have shown effects on fetal and placental weights and gene expression in the second generation (F2) offspring. The observed changes in gene expression in the F2 offspring differ from those in the first generation. Thus, although effects on fetal growth are seen in both generations, the underlying mechanisms appear to be different. We also observed marked parent of origin effects on fetal and placental growth and gene expression in the second generation. In the mouse model of maternal obesity, birth weight was decreased in the F1 offspring. At weaning, the offspring of obese mothers were heavier than controls, however this difference in weight was not persistent. At three months of age, F1 female offspring of obese mothers showed altered expression of hepatic genes important in lipid regulation and metabolism. More striking changes were seen in the F2 generation in which there was an effect of paternal exposure to maternal obesity to decrease birth weight. There were also parent of origin effects on organ weights and insulin levels at six months of age. These results provide evidence for the transmission of programming effects to a second generation in two different programming models and suggest that the mechanisms leading to these effects differ between generations.