Tierärztliche Hochsch., Diss., 2000--Hannover.

Diss. Univ. Köln, 2008.

Zugl.: Berlin, Techn. Universiẗat, Diss.

<p> Depression is associated with higher severity of memory disorders and has been shown to predict lower levels of cognitive functioning in those diagnosed with Mild Cognitive Impairment (MCI) or dementia. Yet, little is known about this association cross-culturally, particularly between Hispanics and European Americans.</p><p> This study demonstrates that although levels of depression differed significantly across diagnostic group, Hispanics and European Americans were similar in levels of depression at each diagnosis. However, only for the European American group did depression levels predict lower scores in confrontational naming and semantic memory. Additionally, exploratory analyses of the entire sample demonstrated that lower depression predicted less likelihood of MCI or dementia diagnoses. This could indicate that there is a need for intervention and treatment of depression, in particular for later stages of MCI and dementia, that should be culturally catered to individual ethnicities. </p><p>

An extensive review of past benchmarking literature showed that there have been a substantial number of both conceptual and empirical attempts to formulate a benchmarking approach, particularly in the manufacturing industry. However, there has been limited investigation and application of benchmarking in tourism and particularly in tourist destinations. The aim of this research is to further develop the concept of benchmarking for application within tourist destinations and to evaluate its potential impact on destination performance. A holistic model for destination benchmarking was developed using the three main types of benchmark: internal, external and generic. Internal benchmarking aimed at improving a destination's internal performance by evaluating quantitative and qualitative measures. External benchmarking used tourist motivation, satisfaction and expenditure scores to investigate how one destination may perform better than another. Generic benchmarking aimed at evaluating and improving a destination's performance using quality and eco-label standards. This study developed four hypotheses to test the possible measures and methods to be used in carrying out destination benchmarking research and investigate how cross-cultural differences between tourists and between destinations might influence its formulation and application. These hypotheses and the model were tested utilising both primary and secondary data collection methods. The primary data was collected from eight different groups of British and German tourists visiting Mallorca and Turkey in the summer of 1998 (n=2,582). Findings were analysed using content analysis and a series of statistical procedures such as chisquare, mean difference (t-test), factor analysis and multiple regression. Personal observations were also recorded. The secondary data included statistical figures on tourism in Mallorca and Turkey. This research provides a discussion of findings and their implications for benchmarking theory and practitioners. The relevance of benchmarking to tourist destinations was examined through the measurement of performance, types of destination benchmarking and taking action. It is apparent that specific measures could be developed for destinations. Both internal and external benchmarking could be applied to benchmarking of destinations. However, in the case of external benchmarking, this research indicated that each destination might have its own regional differentiation and unique characteristics in some respects. Crosscultural differences between tourists from different countries also need to be considered. Given these findings, it is possible to suggest that this research makes a fresh and innovative contribution to the literature not only on tourism but also on benchmarking. The contribution of this study's findings to knowledge exists in the methods and techniques used to identify the factors influencing selected destination performance variables and in the methods to be employed for comparison between the two destinations. Caution should be used in generalising the results to apply to other destinations.

<p>RL has been transformative for neuroscience by providing a normative anchor for interpreting neural and behavioral data. End-to-end RL methods have scored impressive victories with minimal compromises in autonomy, hand-engineering, and generality. The cost of this minimalism in practice is that model-free RL methods are slow to learn and generalize poorly. Humans and animals exhibit substantially improved flexibility and generalize learned information rapidly to new environment by learning invariants of the environment and features of the environment that support fast learning rapid transfer in new environments. An important question for both neuroscience and machine learning is what kind of ``representational objectives'' encourage humans and other animals to encode structure about the world. This can be formalized as ``representation feature learning,'' in which the animal or agent learns to form representations with information potentially relevant to the downstream RL process. We will overview different representational objectives that have received attention in neuroscience and in machine learning. The focus of this overview will be to first highlight conditions under which these seemingly unrelated objectives are actually mathematically equivalent. We will use this to motivate a breakdown of properties of different learned representations that are meaningfully different and can be used to inform contrasting hypotheses for neuroscience. We then use this perspective to motivate our model of the hippocampus. A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity, and policy dependence in place cells suggests that the representation is not purely spatial. We approach the problem of understanding hippocampal representations from a reinforcement learning perspective, focusing on what kind of spatial representation is most useful for maximizing future reward. We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. We go on to argue that entorhinal grid cells encode a low-dimensional basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.

<p> The main goal of this project was to develop and provide validity evidence for a diseasespecific quality of life survey to be used with pediatric localized scleroderma (LS) patients. This new survey, called the Localized Scleroderma Quality of Life Instrument (LoSQI), incorporated unique features associated with the disease, not captured by current surveys. As a secondary goal, the feasibility and usefulness of anchoring vignettes with pediatric patients were examined. The project included three phases; content domain development and item generation, a pilot study, and a field test. Validity evidence was gathered from multiple sources including test content, internal structure, and in relation to other variables. Overall, there was initial support for use of the LoSQI with pediatric LS patients. Patients indicated general understanding and readability of the items, and there was qualitative evidence for content validity. Exploratory factor analysis suggested the utility of reporting a total score along with two subscale scores, (1) Pain and Physical Functioning and (2) Body Image and Social Support. Reliability of both the subscale and total scores was acceptable. There was less evidence for use of anchoring vignettes in this context, as there was a high frequency of ties in rankings, which limited the utility of statistical models. Despite limitations from a small sample size and skewed response distributions, the pilot study and the field test provided promising initial evidence that the LoSQI can be used to capture HRQoL in LS patients ages 10-20 years. Future studies should examine responsiveness of the scores to change and optimal capture of HRQoL in patients &lt;10 years of age.</p><p>

<p> This study will help to clarify how interactions with dolphins affect people. It examines human well-being and experiential responses to scuba diving with bottlenose dolphins, <i>Tursiops truncatus</i>, and compares this to participation in a scuba dive without this interaction. Ninety-nine adults were split between an intervention and a control group in a mixed methods convergent parallel quasi-experimental design. Before and after the activity participants completed an emotional well-being scale (Positive and Negative Affect Schedule; PANAS) and only afterward received a researcher-designed qualitative questionnaire targeting peak experiences. For the PANAS, a <i> t</i>-test found a significant difference in negative affect change scores between groups, <i>t</i>(97) = &ndash;2.135, <i>p</i> = .035, <i>d</i> = 0.43. The intervention group experienced a larger decrease in negative affect than the control group at a small-medium effect size. Qualitative themes endorsed more by the control group are self-confidence; level of difficulty; novelty; transformation or overcoming; nature; and physical, mental, or emotional stress or discomfort. For the intervention group, more participants expressed tranquility; numinosity; and connection to nature, themselves, or the Divine. Themes mentioned approximately equally include ineffability, presence in the moment, comfort and safety, desiring to continue, good or extraordinary experience, and freedom. Twelve intervention and 9 control group participants appeared to have a peak experience, as defined by Maslow. This indicates that a peak experience during a scuba dive, with and without dolphins, is a relatively common occurrence. This study demonstrates the importance of rigorous studies in human-dolphin interaction research. Studies on human-dolphin interaction published to date have neglected to impose appropriate controls, which has led to the misattribution of all pre- to postintervention differences to dolphin interaction. Both groups have intriguing results; the presence of dolphins led to a larger decrease in negative affect and greater likelihood of tranquility, numinosity, connection, and peak experiences.</p><p>

A body of work and ensuing publications covering 2000–2012 are presented, predominantly concerning studies of various cohorts of people exposed to ionising radiation. The major areas cover epidemiological and statistical studies on the Life span study (LSS) cohort of Japanese survivors of the World War II atomic-bomb attacks on Hiroshima and Nagasaki and the mortality follow-up of German uranium miners. Following the presentation of a very brief history of the effects of radiation exposure on humans, the background and context of the advances achieved by the candidate are described. The LSS provides the most studied cohort and a range of topics from cancer risks related to neutron and γ-ray doses, organ specific doses, and carcinogenesis have been explored covering about half of the candidate’s publications. The cohort of German “Wismut” uranium miners exposed to radon and other potential carcinogens, which is the largest one of its kind, has enabled the development of epidemiological models for lung and extra-pulmonary cancers. The third distinct topic relates to analyses of data on cellular radiation damage relevant to the evaluation of both diagnostic radiation characteristics and the effects on cancer patients. Other studies have considered the incidence of malignant diseases in humans injected with radium-224 and development of epidemiological models for thyroid cancer risk in areas affected by the 1986 Chernobyl accident. Fundamental contributions have been published in the application of mathematical methods for data analysis. The candidate has succeeded in going beyond the traditional statistical methods in radiation epidemiology by introduction of numerical techniques deriving from the field of information science and novel to the field. These methods, such as techniques for model selection and mitigation of strongly correlated quantities, have been presented as general tools and have demonstrated powerful results, such as in applications to data from LSS. The impact and relevance for public health of the epidemiological results is indicated by their frequent citation in recent reports by international bodies such as by the International Commission on Radiological Protection, the United Nations Scientific Committee on the Effects of Atomic Radiation, the World Health Organization (WHO) and WHO-International Agency for Research on Cancer.Several topics, from among this broad coverage of radiation epidemiological themes, the development of novel statistical techniques and their application, are highlighted. Work on distinguishing the effect of neutrons and γ-rays in the Japanese LSS data has led to progress on quantifying their relative biological effectiveness with important consequences for the health effects of modern radiation diagnostics. A technique for combining risks from several risk models, called multi-model inference, has been shown to ease the dilemma of selecting between models with very different consequences, with particular relevance for major issues of public health concern connected with radiation exposure. The Wismut cohort has revealed for the first time the response characteristics of a significant effect of working underground on prostate cancer incidence, suggesting a relation with lack of exposure to light which remains unexplained.

Cancer is a deadly, complex disease with 14 million new cases diagnosed every year and the endeavour to develop a cure is a global multidisciplinary effort. The complexity of cancer and the resulting vast volume of data derived from its research necessitates a robust and cutting-edge system of mathematical and statistical modelling. This thesis proposes novel mathematical models of quantification and modelling applied to heterogeneous preclinical cancers, focusing on the translation of animal studies into patients with particular emphasis on tumour stroma. The first section of this thesis (quantification) will present different techniques of extracting and quantifying data from bioanalytical assays. The overall aim will be to present and discuss potential methods of obtaining data regarding tumour volume, stromal morphology, stromal heterogeneity, and oxygen distribution. Firstly, a 3D scanning technique will be discusses. This technique aims to assess tumour volume in mice more precisely than the current favoured method (callipers) and record any cutaneous symptoms as well, with the potential to revolutionise tumour growth analysis. Secondly, a series of image processing methods will be presented which, when applied to tumour histopathology, demonstrate that tumour stromal morphology and its microenvironment play a key role in tumour physiology. Lastly, it will be demonstrated through the integration of in-vitro data from various sources that oxygen and nutrient distribution in tumours is very irregular, creating metabolic niches with distinct physiologies within a single tumour. Tumour volume, oxygen, and stroma are the three aspects central to the successful modelling of tumour drug responses over time. The second section of this thesis (modelling) will feature a mathematical oxygen-driven model - utilising 38 cell lines and 5 patient-derived animal models - that aims to demonstrate the relationship between homogeneous oxygen distribution and preclinical tumour growth. Finally, all concepts discussed will be merged into a computational tumour-stroma model. This cellular automaton (stochastic) model will demonstrate that tumour stroma plays a key role in tumour growth and has both positive (at a molecular level) and negative (at both a molecular and tissue level) effects on cancers. This thesis contains a useful set of algorithms to help visualise, quantify, and understand tissue phenomena in cancer physiology, as well as providing a series of platforms to predict tumour outcome in the preclinical setting with clinical relevance.