Aggression is one of the most researched topics in psychology. This is understandable, since aggression behavior does a lot of harm to individuals and groups. A lot is known already about the biology of aggression, but one system that seems to be of vital importance in animals has largely been overlooked: the hypothalamic-pituitary-adrenal (HPA) axis. Menno Kruk and Jószef Haller and their research teams developed rodent models of adaptive, normal, and abnormal aggressive behavior. They found the acute HPA axis (re)activity, but also chronic basal levels to be causally relevant in the elicitation and escalation of aggressive behavior. As a mediating variable, changes in the processing of relevant social information is proposed, although this could not be tested in animals. In humans, not a lot of research has been done, but there is evidence for both the association between acute and basal cortisol levels in (abnormal) aggression. However, not many of these studies have been experimental of nature. rnrnOur aim was to add to the understanding of both basal chronic levels of HPA axis activity, as well as acute levels in the formation of aggressive behavior. Therefore, we did two experiments, both with healthy student samples. In both studies we induced aggression with a well validated paradigm from social psychology: the Taylor Aggression Paradigm. Half of the subjects, however, only went through a non-provoking control condition. We measured trait basal levels of HPA axis activity on three days prior. We took several cortisol samples before, during, and after the task. After the induction of aggression, we measured the behavioral and electrophysiological brain response to relevant social stimuli, i.e., emotional facial expressions embedded in an emotional Stroop task. In the second study, we pharmacologically manipulated cortisol levels 60min before the beginning of the experiment. To do that, half of the subjects were administered 20mg of hydrocortisone, which elevates circulating cortisol levels (cortisol group), the other half was administered a placebo (placebo group). Results showed that acute HPA axis activity is indeed relevant for aggressive behavior. We found in Study 1 a difference in cortisol levels after the aggression induction in the provoked group compared to the non-provoked group (i.e., a heightened reactivity of the HPA axis). However, this could not be replicated in Study 2. Furthermore, the pharmacological elevation of cortisol levels led to an increase in aggressive behavior in women compared to the placebo group. There were no effects in men, so that while men were significantly more aggressive than women in the placebo group, they were equally aggressive in the cortisol group. Furthermore, there was an interaction of cortisol treatment with block of the Taylor Aggression Paradigm, in that the cortisol group was significantly more aggressive in the third block of the task. Concerning basal HPA axis activity, we found an effect on aggressive behavior in both studies, albeit more consistently in women and in the provoked and non-provoked groups. However, the effect was not apparent in the cortisol group. After the aggressive encounter, information processing patterns were changed in the provoked compared to the non-provoked group for all facial expressions, especially anger. These results indicate that the HPA axis plays an important role in the formation of aggressive behavior in humans, as well. Importantly, different changes within the system, be it basal or acute, are associated with the same outcome in this task. More studies are needed, however, to better understand the role that each plays in different kinds of aggressive behavior, and the role information processing plays as a possible mediating variable. This extensive knowledge is necessary for better behavioral interventions.

Amphibians globally suffer from emerging infectious diseases like chytridiomycosis caused by the continuously spreading chytrid fungi. One is Batrachochytrium salamandrivorans (Bsal) and its disease ‒ the ‘salamander plague’ ‒ which is lethal to several caudate taxa. Recently introduced into Western Europe, long distance dispersal of Bsal, likely through human mediation, has been reported. Herein we study if Alpine salamanders (Salamandra atra and S. lanzai) are yet affected by the salamander plague in the wild. Members of the genus Salamandra are highly susceptible to Bsal leading to the lethal disease. Moreover, ecological modelling has shown that the Alps and Dinarides, where Alpine salamanders occur, are generally suitable for Bsal. We analysed skin swabs of 818 individuals of Alpine salamanders and syntopic amphibians at 40 sites between 2017 to 2022. Further, we compiled those with published data from 319 individuals from 13 sites concluding that Bsal infections were not detected. Our results suggest that the salamander plague so far is absent from the geographic ranges of Alpine salamanders. That means that there is still a chance to timely implement surveillance strategies. Among others, we recommend prevention measures, citizen science approaches, and ex situ conservation breeding of endemic salamandrid lineages.

With the advent of high­throughput sequencing (HTS), profiling immunoglobulin (IG) repertoires has become an essential part of immunological research. The dissection of IG repertoires promises to transform our understanding of the adaptive immune system dynamics. Advances in sequencing technology now also allow the use of the Ion Torrent Personal Genome Machine (PGM) to cover the full length of IG mRNA transcripts. The applications of this bench­top scale HTS platform range from identification of new therapeutic antibodies to the deconvolution of malignant B cell tumors. In the context of this thesis, the usability of the PGM is assessed to investigate the IG heavy chain (IGH) repertoires of animal models. First, an innovate bioinformatics approach is presented to identify antigendriven IGH sequences from bulk sequenced bone marrow samples of transgenic humanized rats, expressing a human IG repertoire (OmniRatTM). We show, that these rats mount a convergent IGH CDR3 response towards measles virus hemagglutinin protein and tetanus toxoid, with high similarity to human counterparts. In the future, databases could contain all IGH CDR3 sequences with known specificity to mine IG repertoire datasets for past antigen exposures, ultimately reconstructing the immunological history of an individual. Second, a unique molecular identifier (UID) based HTS approach and network property analysis is used to characterize the CLL­like CD5+ B cell expansion of A20BKO mice overexpressing a natural short splice variant of the CYLD gene (A20BKOsCYLDBOE). We could determine, that in these mice, overexpression of sCYLD leads to unmutated subvariant of CLL (U­CLL). Furthermore, we found that this short splice variant is also seen in human CLL patients highlighting it as important target for future investigations. Third, the UID based HTS approach is improved by adapting it to the PGM sequencing technology and applying a custom­made data processing pipeline including the ImMunoGeneTics (IMGT) database error detection. Like this, we were able to obtain correct IGH sequences with over 99.5% confidence and correct CDR3 sequences with over 99.9% confidence. Taken together, the results, protocols and sample processing strategies described in this thesis will improve the usability of animal models and the Ion Torrent PGM HTS platform in the field if IG repertoire research.

This dissertation includes three research articles on the portfolio risks of private investors. In the first article, we analyze a large data set of private banking portfolios in Switzerland of a major bank with the unique feature that parts of the portfolios were managed by the bank, and parts were advisory portfolios. To correct the heterogeneity of individual investors, we apply a mixture model and a cluster analysis. Our results suggest that there is indeed a substantial group of advised individual investors that outperform the bank managed portfolios, at least after fees. However, a simple passive strategy that invests in the MSCI World and a risk-free asset significantly outperforms both the better advisory and the bank managed portfolios. The new regulation of the EU for financial products (UCITS IV) prescribes Value at Risk (VaR) as the benchmark for assessing the risk of structured products. The second article discusses the limitations of this approach and shows that, in theory, the expected return of structured products can be unbounded while the VaR requirement for the lowest risk class can still be satisfied. Real-life examples of large returns within the lowest risk class are then provided. The results demonstrate that the new regulation could lead to new seemingly safe products that hide large risks. Behavioral investors who choose products based only on their official risk classes and their expected returns will, therefore, invest into suboptimal products. To overcome these limitations, we suggest a new risk-return measure for financial products based on the martingale measure that could erase such loopholes. Under the mean-VaR framework, the third article discusses the impacts of the underlying's first four moments on the structured product. By expanding the expected return and the VaR of a structured product with its underlying moments, it is possible to investigate each moment's impact on them, simultaneously. Results are tested by Monte Carlo simulation and historical simulation. The findings show that for the majority of structured products, underlyings with large positive skewness are preferred. The preferences for variance and for kurtosis are ambiguous.

This thesis considers the general task of computing a partition of a set of given objects such that each set of the partition has a cardinality of at least a fixed number k. Among such kinds of partitions, which we call k-clusters, the objective is to find the k-cluster which minimises a certain cost derived from a given pairwise difference between objects which end up the same set. As a first step, this thesis introduces a general problem, denoted by (||.||,f)-k-cluster, which models the task to find a k-cluster of minimum cost given by an objective function computed with respect to specific choices for the cost functions f and ||.||. In particular this thesis considers three different choices for f and also three different choices for ||.|| which results in a total of nine different variants of the general problem. Especially with the idea to use the concept of parameterised approximation, we first investigate the role of the lower bound on the cluster cardinalities and find that k is not a suitable parameter, due to remaining NP-hardness even for the restriction to the constant 3. The reductions presented to show this hardness yield the even stronger result which states that polynomial time approximations with some constant performance ratio for any of the nine variants of (||.||,f)-k-cluster require a restriction to instances for which the pairwise distance on the objects satisfies the triangle inequality. For this restriction to what we informally refer to as metric instances, constant-factor approximation algorithms for eight of the nine variants of (||.||,f)-k-cluster are presented. While two of these algorithms yield the provably best approximation ratio (assuming P!=NP), others can only guarantee a performance which depends on the lower bound k. With the positive effect of the triangle inequality and applications to facility location in mind, we discuss the further restriction to the setting where the given objects are points in the Euclidean metric space. Considering the effect of computational hardness caused by high dimensionality of the input for other related problems (curse of dimensionality) we check if this is also the source of intractability for (||.||,f)-k-cluster. Remaining NP-hardness for restriction to small constant dimensionality however disproves this theory. We then use parameterisation to develop approximation algorithms for (||.||,f)-k-cluster without restriction to metric instances. In particular, we discuss structural parameters which reflect how much the given input differs from a metric. This idea results in parameterised approximation algorithms with parameters such as the number of conflicts (our name for pairs of objects for which the triangle inequality is violated) or the number of conflict vertices (objects involved in a conflict). The performance ratios of these parameterised approximations are in most cases identical to those of the approximations for metric instances. This shows that for most variants of (||.||,f)-k-cluster efficient and reasonable solutions are also possible for non-metric instances.