Alzheimer's disease (AD) is becoming more prevalent as the world population gets older. The formation of Amyloid-beta (\AB) plaques is one of the pathologies related to AD. Recent work has shown that the \ab load in brain tissue has a negative correlation with cognitive performance in cognitively healthy centenarians.
This work aims to expand this research by investigating whether the types of \ab plaque present are linked to cognition and by comparing the types of plaques in the centenarian cohort with an AD cohort.
For this task, a system is developed that can identify \ab plaques in images of brain tissue. It first automatically segments the grey matter using a fine-tuned U-net. Then the plaques are located using traditional image processing techniques. Lastly, shape and size features are extracted from the plaque in addition to a feature vector made by a pre-trained AlexNet. K-means clustering is used on AlexNet features to find categories for the plaques.
The clustering approach failed to yield good results. However, the area and roundness are differently distributed between the AD and centenarian cohorts, but the differences are small. Correlations have been found between the area and roundness of plaques in the occipital pole and cognitive performance in centenarians. They indicate that cognitively stronger individuals have smaller and less round \ab plaques in their brains. More research is necessary to reveal the true extent of the impact of plaque types on cognition.

To validate the quality of software, test cases are used. These test cases are often manually-written, which is labor-intensive. To avoid this problem, automated software testing was invented. Search-based software testing is a useful tool for developers to automatically generate test cases. However, improvements are still needed to create test cases that compete with manually-written ones.
EvoMaster is a tool that generates system-level test cases for RESTful APIs using the MIO algorithm. An important aspect of this algorithm is sampling new test cases. Currently, EvoMaster employs random and smart sampling to achieve this goal. This paper aims to improve the coverage of the generated tests by expanding the sampling methods with seeded sampling. This method consists of extracting sequences of HTTP requests from manually-written tests and using these to sample new test cases.
Seeded sampling is evaluated on two RESTful APIs with 7 different parameter sets. We show that the addition of seeded sampling can improve the coverage achieved by EvoMaster compared to the current combination of sampling techniques. Nonetheless, this paper has some limitations. It only takes two RESTful APIs into account and has a small amount of benchmark runs to back its findings.