In this dissertation we investigate the emotional and psychiatric effects of glucocorticoids (e.g. cortisol, dexamethasone and prednisone). Glucocorticoids are widely used and their possible... Show moreIn this dissertation we investigate the emotional and psychiatric effects of glucocorticoids (e.g. cortisol, dexamethasone and prednisone). Glucocorticoids are widely used and their possible psychiatric side effects are well known. It is still unclear who is susceptible to side effects and what mechanism is behind this. In this thesis we try to better understand the underlying causes. For example, we use a relatively new static method in this research field, which can analyze changes over time better than the usual methods. In a study, we show that in depressed study participants, changes in certain affect items preceded changes in cortisol levels, while in control participants this was the other way around. Furthermore, we explore a hypothesis that may explain the mechanism of the psychiatric side effects of glucocorticoids. This mechanism is also used for a strategy to prevent the psychiatric side effects. The strategy is based on the fact that synthetic glucocorticoids are very similar to the body's own hormone cortisol, but their binding to the two cortisol receptor proteins and their effects may be different. By achieving a balanced activation of the two cortisol proteins when using glucocorticoids, the psychiatric side effects could possibly be prevented. In summary, this thesis provides further insight into the emotional and psychiatric side effects of glucocorticoids, but the search for a better understanding of the neuropsychiatric side effects remains a pressing concern, which will hopefully benefit patient care in the future. Show less
Sign language lexica are a useful resource for researchers and people learning sign languages. Current implementations allow a user to search a sign either by its gloss or by selecting its primary... Show moreSign language lexica are a useful resource for researchers and people learning sign languages. Current implementations allow a user to search a sign either by its gloss or by selecting its primary features such as handshape and location. This study focuses on exploring a reverse search functionality where a user can sign a query sign in front of a webcam and retrieve a set of matching signs. By extracting different body joints combinations (upper body, dominant hand's arm and wrist) using the pose estimation framework OpenPose, we compare four techniques (PCA, UMAP, DTW and Euclidean distance) as distance metrics between 20 query signs, each performed by eight participants on a 1200 sign lexicon. The results show that UMAP and DTW can predict a matching sign with an 80\% and 71\% accuracy respectively at the top-20 retrieved signs using the movement of the dominant hand arm. Using DTW and adding more sign instances from other participants in the lexicon, the accuracy can be raised to 90\% at the top-10 ranking. Our results suggest that our methodology can be used with no training in any sign language lexicon regardless of its size. Show less
