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To evaluate the impact of genetic polymorphisms of UGT enzymes (UGT1A4, UGT2B7, UGT2B15 and UGT 2B17) and the transporter protein ABCB1 on Lamotrigine (LTG) metabolism.Single nucleotide polymorphisms UGT1A4*2 (P24T, c.70CA), UGT1A4*3 (L48V c.142TG), UGT2B7*2 (H802Y, c.802CT), UGT2B15*2 (Y85D, c.253GT), UGT2B17 deletion and transporters ABC 1236CT and 3435CT were determined in 337 Caucasian patients with epilepsy treated with LTG in Denmark. The prospectively collected data included LTG dosage, LTG plasma concentration, 2-N-GLU concentration, sex, smoking habits, concomitant medicine, oral contraceptives (OC).The non-smokers with LTG monotherapy and LTG polytherapy with other non-interacting drugs NIAEDs (n = 199) were analyzed separately in univariant analyses. LTG ratios (LTG plasma concentration/ (LTG dose/weight)) in patients carrying wild type UGT1A4*2 C-allele were 22% lower than in heterozygous C-carriers (p = 0.013). Patients with UGT2B7*2 polymorphism TT genotype had 1.2-fold higher LTG ratios (p = 0.0078) and 0.78-fold lower GLU/LTG ratio (p = 0.0275) than patients homozygous for the C allele. The similar significant findings were also seen comparing homozygotes (TT) with heterozygotes patients (CT). Individuals homozygous for the UGT2B15*2 T allele displayed 18% lower LTG ratio concentrations than individuals homozygous for the G allele (p = 0.014),while significant difference in GLU/LTG ratio was only seen comparing wild type with homozygous patients (GG versus TT, p = 0.031). A copy number variation gene deletion polymorphism of UGT2B17 showed that individuals devoid of the gene (del/del) exhibited 1.3-fold higher LTG ratio (p = 0.015). For ABCB1c.1236 CT and ABC1B1c.3435 CT no associations with LTG and GLU ratios were found. Sex specific differences in enzyme activity (most prominent effect in women) on LTG metabolism were found for UGT2B15, UGT2B17, UGT1A4 and UGT2B7 polymorphisms. Multiple regression analysis confirmed the significant effect of OC, VPA and UGT1A4 * 2 and UGT2B7 * 2 on LTG metabolism.Our study confirms the previous findings that genetic variations in UGT2B7 and UGT1A4 genes are associated with serum LTG concentrations. Furthermore, our results indicate that it is possible that different UGT genotypes may exert larger impact on LTG metabolism in women than in men.

Komplexe Netzwerke sind mächtige Werkzeuge, die die Untersuchung komplexer Systeme unterstützen. In vielen Bereichen werden komplexe Netzwerke eingesetzt, um die Dynamik interagierender Entitäten wie Neuronen, Menschen oder sogar Wettersysteme zu verstehen. Darüber hinaus erweitern sich die Anwendungsbereiche mit der stetigen Entwicklung neuer theoretischer Ansätze. In dieser Arbeit wollen wir sowohl den theoretischen Rahmen der Netzwerkwissenschaften weiterentwickeln als auch komplexe Netzwerke in der Klimatologie und der computergestützten Sozialwissenschaft anwenden. Complex networks are powerful tools enabling the study of complex systems. In many fields, complex networks are used as a tool to gain an understanding of the dynamics of interacting entities such as neurons in a brain, humans on social media, or global weather systems. At the same time, new theoretical frameworks that extend the toolbox of Network Science promote the application of network tools in new research fields. In this thesis, we aim for both, advancing the theoretical framework of Network Science as well as applying complex networks in Climatology and Computational Social Science.

Searches for new physics beyond the Standard Model are mostly driven by theories predicting new particles or processes. Due to the theoretical bias of these searches, the investigated phase space is very narrow. Thus, a new signal might not be found even if it occupies a phase space close to the investigated one. Autoencoders offer a semi supervised and bias-free method to search for new signals in any region. They are a special type of neural networks, that reproduces the input using a lower dimensional representation. Autoencoders are trained in a semi supervised manner, which allows them to detect anomalies while only being trained on known processes of the Standard Model. This master thesis evaluates autoencoders as a method to search for new physics signatures, using data from $pp$ collisions at $\sqrt{s}=13$ TeV measured by the ATLAS detector. To evaluate autoencoders, a region that is dominated by tt events, with a minor fraction of single top-quark $tW$ events is used. For that region a discriminant called mini-max $m(lb)$ already exists. A detailed comparison between the two algorithms is carried out. The autoencoder learns a partial representation of mini-max $m(lb)$ and additionally selects a region with increased discrepancy, that is not included by mini-max $m(lb)$. Furthermore, an autoencoder is applied to a region with a discrepancy between data and simulation, where the origin of the discrepancy is unknown. The autoencoder suggests that the discrepancy originates from a shape mismatch and/or a scaling error.

Eur J Clin Invest 2012; 42 (9): 10271036 Abstract While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare.