Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas

Russia’s natural resources in the world economy : history, review and reassessment

Russia’s role in the global economic system today, and the Soviet Union’s in the past, is dominated by the export of natural resources, particularly oil and gas. The rents earned from these exports are both a source of strength and weakness, as they link the fortunes of Russia’s domestic economy to the volatility of global resource markets. This paper returns to a major research project conducted through the offices of the Association of American Geographers that resulted in Soviet Natural Resources in the World Economy, published in 1983. The project was first conceived in the aftermath of the resource crisis in the 1970s and concluded in the early 1980s as the Soviet Union sought to increase resource exports to support a failing domestic economy. This paper examines the origins, evolution, and management of this seminal work and presents a re-reading of the book in a contemporary context. We develop some of the key themes of the original project and conclude that it has contemporary relevance, as a reliance upon the resource sector remains a defining characteristic of Russia’s political economy and continues to shape Russia’s role in the global economy. We find that the regional dimension that was so important in the original project remains critical as Russia seeks to extend the resource frontier into new regions in the Arctic and the East and, at the same time, reduce its reliance on European markets – that are both stagnant and hostile – by developing new markets in Asia

Material Well-being, Social Relationships and Children’s Overall Life Satisfaction in Hong Kong

There has been growing research interest into child poverty and child well-being in Asia. However the development of qualitative and quantitative data in the field predominately adopts ‘expert-led’ or adult-derived measures of child poverty. This article aims to explore variations in children’s overall life satisfaction by their socio-demographic characteristics and social relationships in Hong Kong. Data used in this article is drawn from the first wave of the Strategic Public Policy Research (SPPR) project – ‘Trends and Implications of Poverty and Social Disadvantages in Hong Kong: A Multi-disciplinary and Longitudinal Study’. This article reports, for the first time evidence based on a child-derived material deprivation index - thereby addressing the limitations in traditional adult-derived child poverty measures. The study found that child deprivation explained more of the variation in children’s overall life satisfaction than traditional adult-reported income poverty. Further analyses showed that children’s perceived positive relationships with family and teachers, perceived strong social support from family, and experience of being bullied were associated with their life satisfaction

Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field

Hydration free energy (HFE) calculations are often used to assess the performance of biomolecular force fields and the quality of assigned parameters. The AMOEBA polarizable force field moves beyond traditional pairwise additive models of electrostatics and may be expected to improve upon predictions of thermodynamic quantities such as HFEs over and above fixed point charge models. The recent SAMPL4 challenge evaluated the AMOEBA polarizable force field in this regard, but showed substantially worse results than those using the fixed point charge GAFF model. Starting with a set of automatically generated AMOEBA parameters for the SAMPL4 dataset, we evaluate the cumulative effects of a series of incremental improvements in parameterization protocol, including both solute and solvent model changes. Ultimately the optimized AMOEBA parameters give a set of results that are not statistically significantly different from those of GAFF in terms of signed and unsigned error metrics. This allows us to propose a number of guidelines for new molecule parameter derivation with AMOEBA, which we expect to have benefits for a range of biomolecular simulation applications such as protein ligand binding studie

Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field

The effects of electronic polarization in biomolecular interactions will differ depending on the local dielectric constant of the environment, such as in solvent, DNA, proteins, and membranes. Here the performance of the AMOEBA polarizable force field is evaluated under nonaqueous conditions by calculating the solvation free energies of small molecules in four common organic solvents. Results are compared with experimental data and equivalent simulations performed with the GAFF pairwise-additive force field. Although AMOEBA results give mean errors close to “chemical accuracy,” GAFF performs surprisingly well, with statistically significantly more accurate results than AMOEBA in some solvents. However, for both models, free energies calculated in chloroform show worst agreement to experiment and individual solutes are consistently poor performers, suggesting non-potential-specific errors also contribute to inaccuracy. Scope for the improvement of both potentials remains limited by the lack of high quality experimental data across multiple solvents, particularly those of high dielectric constant. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc