Dissertation (Ph.D.) -- University of Texas Southwestern Medical Center at Dallas, 2008. / Vita. Bibliography: p. 120-129.

Thesis (Ph. D.)--University of Missouri--Columbia, 1998. / "May 1998." Typescript. Vita. Includes bibliographical references (leaves 138-147). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri--Columbia, 1998. / "May 1998." Typescript. Vita. Includes bibliographical references (leaves 325-355). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri--Columbia, 1998. / "July 1998." Typescript. Vita. Includes bibliographical references (leaves 111-119). Also available on the Internet.

Thesis (Ph.D.)--University of Alabama at Birmingham, 2009. / Title from PDF title page (viewed on Sept. 9, 2009). Includes bibliographical references.

Recent advances in information technology have enabled public organizations and corporations to collect and store huge amounts of individuals' data in data repositories. Such data are powerful sources of information about an individual's life such as interests, activities, and finances. Corporations can employ data mining and knowledge discovery techniques to extract useful knowledge and interesting patterns from large repositories of individuals' data. The extracted knowledge can be exploited to improve strategic decision making, enhance business performance, and improve services. However, person-specific data often contain sensitive information about individuals and publishing such data poses potential privacy risks. To deal with these privacy issues, data must be anonymized so that no sensitive information about individuals can be disclosed from published data while distortion is minimized to ensure usefulness of data in practice. In this thesis, we address privacy concerns in publishing longitudinal data. A data set is longitudinal if it contains information of the same observation or event about individuals collected at several points in time. For instance, the data set of multiple visits of patients of a hospital over a period of time is longitudinal. Due to temporal correlations among the events of each record, potential background knowledge of adversaries about an individual in the context of longitudinal data has specific characteristics. None of the previous anonymization techniques can effectively protect longitudinal data against an adversary with such knowledge. In this thesis we identify the potential privacy threats on longitudinal data and propose a novel framework of anonymization algorithms in a way that protects individuals' privacy against both identity disclosure and attribute disclosure, and preserves data utility. Particularly, we propose two privacy models: (K,C)^P -privacy and (K,C)-privacy, and for each of these models we propose efficient algorithms for anonymizing longitudinal data. An extensive experimental study demonstrates that our proposed framework can effectively and efficiently anonymize longitudinal data.

Yes / In a previous study we identified an extensive gating network within the inwardly rectifying Kir1.1 (ROMK) channel by combining systematic scanning mutagenesis and functional analysis with structural models of the channel in the closed, pre-open and open states. This extensive network appeared to stabilize the open and pre-open states, but the network fragmented upon channel closure. In this study we have analyzed the gating kinetics of different mutations within key parts of this gating network. These results suggest that the structure of the transition state (TS), which connects the pre-open and closed states of the channel, more closely resembles the structure of the pre-open state. Furthermore, the G-loop, which occurs at the center of this extensive gating network, appears to become unstructured in the TS because mutations within this region have a 'catalytic' effect upon the channel gating kinetics. / Deutsche Forschungsgemeinschaft, the Wellcome Trust (083547/ Z/07/Z and 092970/Z/10/Z) and the British Heart Foundation (PG/09/016/ 26992).

No / Gram-positive bacterial lipoproteins are a functionally diverse and important class of peripheral membrane proteins. Recent advances in molecular biology and the availability of whole genome sequence data have overturned many long-held assumptions about the export and processing of these proteins, most notably the recent discovery that not all lipoproteins are exported as unfolded substrates through the general secretion pathway. Here, we review recent discoveries concerning the export and processing of these proteins, their role in virulence in Gram-positive bacteria and their potential as vaccine candidates or targets for new antimicrobials. / Biotechnology and Biological Sciences Research Council (grant numbers F009224/1, F009429/1, EGH16082), the Medical Research Council (MRC), the Commission of the European Community (grant LSHG-CT-2004–005257) and The Royal Society.

The increasing amount of information makes sequential data mining an important domain of research. A vast number of data mining models and approaches have been developed in order to extract interesting and useful patterns of data. Most models are used for strategic purposes resulting in using of the time parameter. However, the extensive field of data mining applications requires new models to be introduced. The current thesis proposed models for temporal sequential data mining having as a goal the forecasting process. We focus our study on sequential temporal database analysis and on time-series data. In sequential database analysis we propose several interestingness measures for rules selection and patterns extraction. Their goal is to advantage those rules/patterns whose time-distance between the itemsets is small. The extracted information is used to predict user¿s future requests in a web log database, obtaining a higher performance in comparison to other compared models. In time-series analysis we propose a forecasting model based on Neural Networks, Genetic Algorithms, and Wavelet Transform. We apply it on a WiMAX network traffic and EUR/USD currency exchange data in order to compare its prediction performance with those obtained using other existing models. Different ways of changing parameters adapted to a given situation and the corresponding simulations are presented. It was shown that the proposed model outperforms the existing ones from the prediction point of view on the used time-series. As a whole, this thesis proposes forecasting models for different types of temporal sequential data with different characteristics and behaviour.

Although the ecological interactions and ethology of the wing-propelled diving seabirds known as the Alcidae (Aves, Charadriiformes) have been intensively studied, systematic studies of the clade have been overwhelmingly limited to extant taxa. Pan-Alcidae have the richest fossil record among Charadriiformes, with specimens representing more than 35 million years of evolutionary history. Morphometric and apomorphy-based taxonomic revision of previously named extinct pan-alcids along with description of new species of extinct pan-alcids facilitated refined estimates of species richness. Combined phylogenetic analyses of morphological and molecular sequence data including pan-alcid fossils elucidated the poorly understood evolutionary history of the clade. Divergence estimation analysis for Charadriiformes placed previously hypothesized episodes of pan-alcid radiation and extinction in context with proposed paleoclimatic drivers of alcid evolution. / text