Amyloid is a form of abnormal protein aggregation within the living body. Massive deposits can lead to organ failure. There is also increasing evidence that smaller pre-amyloid aggregates exert direct toxic effects to cells. To date 25 different proteins are known to occur as amyloid deposition in human tissues, although not all of these conditions are known to be associated with clinical diseases. This thesis deals with the very common form of amyloid localized to the arterial media. The fibril protein called ‘medin’ was identified in 1999. Medin is a 50 amino acid residue internal fragment of the precursor protein lactadherin. Lactadherin, first found in human milk, is expressed in various tissues such as breast epithelium (including carcinomas), macrophages and aorta. The function of the protein is not known but it has several functional domains. There is an EFG like domain, including an RGD-sequence, in the N-terminal part of the molecule. The C-terminal part consists of C1 and C2 coagulation factor V and VIII like domains. Medin is from within the C2 domain. This region is suggested to be involved in phosphatidyl serine binding, important in phagocytosis of apoptotic cells. Medin amyloid was originally described from studies of the aorta. It is shown here that deposits are more widely spread and can be found in many large arteries, particularly within the upper part of the body. The prevalence of medin amyloid increases with age and deposits are found, to a certain degree, in virtually everyone above the age of 60 years. The amyloid is not only found extracellularly but intracellular deposits may also occur. Amyloid is usually associated with elastic lamina or lamellae which often show signs of fragmentation. Given the localization of amyloid to elastic structures of the arterial media, three different vascular diseases were studied: temporal (giant cell) arteritis, thoracic aortic aneurysm and thoracic aortic dissection. Medin amyloid was found in temporal arteries with and without inflammation. In inflamed arteries, amyloid was mainly located along the broken internal elastic lamina. Medin was also demonstrated within giant cells. It is suggested that medin may be an antigen triggering autoimmune giant cell arteritis. In the study of thoracic aortic aneurysms and dissections, we found significant less medin amyloid in diseased aortic tissues compared with a control material. On the other hand, immunoreactive medin, probably in the state of oligomeric aggregates, was regularly found in association with aneurysms and dissections but not in the control material. It is suggested that medin oligomers exert toxic effects on smooth muscle cells which may lead to weakening of the arterial wall with aneurysm or dissection as a consequence.

Cardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lot of training queries have to be executed during the model training phase to learn a data-dependent ML model making it very time-consuming. Many of those training or example queries use the same base data, have the same query structure, and only differ in their selective predicates. To speed up the model training phase, our core idea is to determine a predicate-independent pre-aggregation of the base data and to execute the example queries over this pre-aggregated data. Based on this idea, we present a specific aggregate-based training phase for ML-based cardinality estimation approaches in this paper. As we are going to show with different workloads in our evaluation, we are able to achieve an average speedup of 90 with our aggregate-based training phase and thus outperform indexes.