The Influence of the Dopamine Transporter Genotype and Childhood Family Relations on Depressiveness and Depression Based on a Longitudinal Study

Käesolev uurimistöö käsitleb dopamiini rolli depressiooni puhul, keskendudes dopamiini transporteri geeni 3’ mittetransleeritavas piirkonnas asuva varieeruva arvuga tandemkorduse (DAT1 3'UTR VNTR) polümorfismi, lapsepõlve peresuhete kvaliteedi ja depressiivsuse vahelise seose uurimisele. Uurimistöö meetodiks on longituudne terviseuuring, mis sisaldab genotüübiandmete, enesekohaste küsimustike ja psühhiaatriliste intervjuudega kogutud info analüüsi. Uuring põhineb Eesti laste isiksuse, käitumise ja terviseuuringu (ELIKTU) noorema kohordi andmetel. Selgus, et polümorfismi genotüüp, mille puhul mõlemad geenialleelid sisaldavad 10 kordust, suurendab oluliselt kõrgema depressiivsuse tõenäosust võrreldes genotüübiga, kus üks alleelidest sisaldab 9 kordust. Parem peresuhete kvaliteet vähendas depressiivsuse riski, halvem suurendas seda märkimisväärselt. Halvemad peresuhted suurendasid ka kliinilise depressiooni diagnoosi omamise tõenäosust 25ndaks eluaastaks. Statistilised testid ei kinnitanud DAT1 3'UTR VNTR ja peresuhete kvaliteedi koosmõju depressiivsusele ja depressiooni kujunemisele

Information Structure in Discourse

Institute for Communicating and Collaborative SystemsThe present dissertation proposes integrating Discourse Representation Theory (DRT), information structure (IS) and Combinatory Categorial Grammar (CCG) into a single framework. It achieves this by making two new contributions to computational treatment of information structure. First, it presents an uncomplicated approach to incorporating information structure in DRT. Second, it shows how the new DRT representation can be integrated into a unification-based grammar framework in a straightforward manner. We foresee the main application of the new formalism to be in spoken language systems: the approach presented here has the potential to considerably facilitate spoken language systems benefiting from insights derived from information structure. The DRT representation with information structure which is proposed in this dissertation is simpler than the previous attempts to include information structure in DRT. We believe that the simplicity of the Information-Structure-marked Discourse Representation Structure (IS-DRS) is precisely what makes it attractive and easy to use for practical tasks like determining the intonation in spoken language applications. The IS component in ISDRS covers a range of aspects of information structural semantics. A further advantage of IS-DRS is that in its case a single semantic representation is suitable for both the generation of context-appropriate prosody and automatic reasoning. A semantic representation on its own is useful for describing and analysing a language. However, it is of even greater utility if it is accompanied by a mechanism that allows one to directly infer the semantic representation from a natural language expression. We incorporated the IS-DRS into the Categorial Grammar (CG) framework, developing a unification based realisation of Combinatory Categorial Grammar, which we call Unification-based Combinatory Categorial Grammar (UCCG). UCCG inherits elements from Combinatory Categorial Grammar and Unification Categorial Grammar. The UCCG framework is developed gradually throughout the dissertation. The information structural component is included as the final step. The IS-DRSs for linguistic expressions are built up compositionally from the IS-DRSs of their sub-expressions. Feature unification is the driving force in this process. The formalism is illustrated by numerous examples which are characterised by different levels of syntactic complexity and diverse information structure. We believe that the main assets of both the IS-DRSs as well as the Unification-based Combinatory Categorial Grammar framework are their simplicity, transparency, and inherent suitability for computational implementation. This makes them an appealing choice for use in practical applications like spoken language systems

Most small cerebral cortical veins demonstrate significant flow pulsatility: a human phase contrast MRI study at 7T

Phase contrast MRI has been used to investigate flow pulsatility in cerebral arteries, larger cerebral veins and the cerebrospinal fluid. Such measurements of intracranial pulsatility and compliance are beginning to inform understanding of the pathophysiology of conditions including normal pressure hydrocephalus, multiple sclerosis and dementias. We demonstrate the presence of flow pulsatility in small cerebral cortical veins, for the first time using phase contrast MRI at 7 Tesla, with the aim of improving our understanding of the haemodynamics of this little-studied vascular compartment. A method for establishing where venous flow is pulsatile is introduced, revealing significant pulsatility in 116 out of 146 veins, across 8 healthy participants, assessed in parietal and frontal regions. Distributions of pulsatility index and pulse waveform delay were characterized, indicating a small, but statistically significant (p<0.05), delay of 59±41 ms in cortical veins with respect to the superior sagittal sinus, but no differences between veins draining different arterial supply territories. Measurements of pulsatility in smaller cortical veins, a hitherto unstudied compartment closer to the capillary bed, could lead to a better understanding of intracranial compliance and cerebrovascular (patho)physiology

Artificial Intelligence

Discourse connectives can show sense ambiguities, in that they can signal more than one possible rhetorical relation. The aim of this study is discover how to disambiguate such discourse connectives using a statistical model. Six discourse connectives (after, as soon as, before, once, since and while) which show am-biguities in the sdrt (Segmented Discourse Representation Theory (Asher &amp; Lascarides, 2003)) relation that they signal are considered. Maximum entropy based models using different combinations of linguistic features derived from the connective’s context are trained and tested on a corpus of examples containing these connectives, which has been annotated with the correct rhetorical relation. The best performing model achieves an average of 70.4 % accuracy across all the connectives, as compared to a most common sense baseline of 57.2%. There is a wide variation in performance between the different connectives, with the models for since and while at 30 percentage points above the baseline, and the models for after and as soon as failing to beat the baseline by a statistically signficant margin. The most informative features in the model were found to be those de-rived from the main verbs in the text spans connected by the rhetorical relation, and the words and parts of speech collocated with the connective. i Acknowledgements I would like to thank my supervisor, Alex Lascarides, for introducing me to the study of discourse and for all her help, encouragement and timely feedback throughout this project. I would also like to thank Mirella Lapata for extracting the examples which formed the corpus for this study, and the annotators; Alex