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Abstract Excess pore water pressure can be generated in subgrades of both railway and pavement sub-structures under cyclic loading caused by heavy traffic. When saturated subgrades are subjected to cyclic loading, excess pore water pressures accumulate over time which then could lead to migration of particles into overlying layers. The migration of subgrade soil particles to the upper layers would lead to clogging of pores and reducing the upper layers' drainage capacity. Both excess pore water pressure accumulation and migration of fine particles could negatively affect the long-term performance and service life of the sub-structures and eventually may lead to failure. Understanding the mechanism of both excess pore water pressure and migration of fine particles under cyclic loading is, therefore, essential for not only designing but also further proposing efficient and economical maintenance methods. The main objectives of this research are to (1) investigate excess pore water pressure generation in fine granular materials under cyclic loading and (2) evaluate migration of fine granular materials into overlying layers under cyclic loading.  A series of undrained cyclic triaxial tests were performed to study the excess pore water pressure generation in fine granular materials. Two types of fine granular materials, i.e., railway sand (natural granular material) and tailings (artificial granular material), were selected for this investigation. The cyclic characteristics of these materials, e.g., accumulated strain and excess pore water pressure, were evaluated in terms of number of cycles and applied cyclic stress ratios (CSR). As a result, axial strain and excess pore water pressure accumulated over time due to cyclic loading. However, its accumulations were significantly dependent on CSR values and material types. Finally, a relationship between excess pore water pressure and accumulated strain of the fine granular materials was discovered based on all outputs from the undrained cyclic triaxial tests (both tailings and railway sand samples).  In order to evaluate the migration of fine granular materials into overlying layers under cyclic loading, a modified large-scale triaxial system was used as a physical model test. Samples prepared for the modified large-scale triaxial system composed of a 60 mm thick gravel layer overlying a 120 mm thick subgrade layer (tailings and railway sand). The quantitative analysis on migration of the fine granular materials was based on the mass percentage and grain size of migrated materials collected at the gravel layer. In addition, the cyclic responses (strain and pore water pressure) were evaluated. As a result, the total migration rate of the railway sand sample was found to be small. There were no migrated sand particles pumped up to the gravel surface, i.e., no mud pumping, after the test terminated. The migrated sand particles were observed and collected at the bottom half of the gravel layer. The total migration rate of the tailing sample was much higher than that of the railway sand sample. In addition, the migration analysis revealed that finer tailings particles tended to be migrated into the upper gravel layer easier than coarser ones under cyclic loading. The migrated tailings particles were observed at the surface of the gravel layer after the test ended. It could be involved in significant increases in excess pore water pressure at the last cycles of the physical model test. The findings obtained in this research may provide an additional contribution to the literature dealing with the excess pore water pressure accumulation and its effects on the migration of fine particles under cyclic loading.

This study examines the jobs-housing balance and the excess commute of Saturn employees in Spring Hill. Until recently, the number of jobs has greatly exceeded the number of houses. In 2005 the balance is about even, however many newer residents are believed to be people who work in Nashville. Many Saturn employees live on the outskirts of Spring Hill and must commute longer distances to work. A spatial analysis using GIS of employee home locations shows that 74% of employees commute is in excess compared to if employees actually lived within Spring Hill. Surveys of Saturn employees shows traffic en route to work is a major frustration. It is suggested that future housing development in Spring Hill occur closer to Saturn. This will equilibrate the jobs-housing balance and will reduce the excess commute. Other Southern towns may wish to consider these results when planning for the development of automobile manufacturers.

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<p><strong>Background and Objectives: </strong>There are methodological challenges with research in osteoporosis. The first is to predict the lifetime risk of hip fracture incorporating trends in the rates of hip fracture and mortality. The second is to identify optimum pharmacotherapy to reduce fractures in the absence of active-comparator trials. A third is to isolate the costs for incident and prevalent fractures. The objective of this thesis is to investigate these issues.</p> <p><strong> </strong><strong>Methods: </strong></p> <p>Project 1: From national administrative data, we estimated the lifetime risk of hip fracture for age 50 years to end of life using life tables.</p> <p>Project 2: A literature review identified randomized placebo-controlled trials with nine drugs for post-menopausal women to estimate odds ratios between drugs for fractures.</p> <p>Project 3: From provincial administrative data from Manitoba excess costs relative to matched controls were estimated for incident fractures, prevalent fractures and non-fracture osteoporosis. .</p> <p><strong>Results and Conclusions:</strong></p> <p>Project 1:<strong> </strong>For women and men, the crude lifetime risks of hip fracture was 12.1% and 4.6% respectively, and lower after incorporating trends, 8.9% and 6.7%. The risk is expected to continue to fall for both women and men.</p> <p>Project 2: Three drugs, zoledronic acid, teriparatide and denosumab, had the highest odds of reducing fractures and the largest effect sizes. Estimates were consistent between Bayesian and classical approaches.</p> <p>Project 3: All incident fracture types and most prevalent fractures had significant excess costs, and the results were robust to assessment of missing variances. Excluding prevalent fractures underestimates the cost of illness of fractures.</p> / Doctor of Philosophy (PhD)

Memory of chirality (MOC) is an emerging strategy in asymmetric synthesis. It has been applied to enolate chemistry, reactions involving carbocation intermediates, and to radical systems. In this strategy the chirality of an enantiopure reactant is transferred to the dynamic chirality of a reactive intermediate to produce stereospecific product. 1,4-Benzodiazepin-2-ones have been described as a "privileged" structure in medicinal chemistry. In addition to their uses as anxiolytics (Valium ®) and anti-epileptic agents (Clonopin ®), they have shown activity as HIV Tat antagonist, ras farnesyltransferase inhibitors in cancer cells, and antiarrhythmic agents. Because of the utility of this scaffold in the area of medicinal chemistry, it has served as a template in libraries for tens of thousands of compounds. Despite the vast diversity of 1,4-benzodiazepin-2-ones, there are few routes to enantiomerically enriched 3,3-disubstituted benzodiazepines containing a "quaternary" stereogenic center. This research will discuss the stereochemical properties of 1,4-benzodiazepin-2-ones, and provide a novel approach to synthesize enantiomerically enriched "quaternary" benzodiazepines with stereogenic centers through MOC, without the use of external chiral sources. / Ph. D.

Gene network modeling is one of the major goals of systems biology research. Gene network modeling targets the middle layer of active biological systems that orchestrate the activities of genes and proteins. Gene network modeling can provide critical information to bridge the gap between causes and effects which is essential to explain the mechanisms underlying disease. Among the network construction tasks, the rewiring of relevant network structure plays critical roles in determining the behavior of diseases. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential dependency networks cannot be constructed by existing knowledge alone, effective incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. Existing studies on protein-protein interactions and biological pathways provide constantly accumulated rich domain knowledge. Though novel incorporation of biological prior knowledge into network learning algorithms can effectively leverage domain knowledge, biological prior knowledge is neither condition-specific nor error-free, only serving as an aggregated source of partially-validated evidence under diverse experimental conditions. Hence, direct incorporation of imperfect and non-specific prior knowledge in specific problems is prone to errors and theoretically problematic. To address this challenge, we propose a novel mathematical formulation that enables incorporation of prior knowledge into structural learning of biological networks as Gaussian graphical models, utilizing the strengths of both measurement data and prior knowledge. We propose a novel strategy to estimate and control the impact of unavoidable false positives in the prior knowledge that fully exploits the evidence from data while obtains "second opinion" by efficient consultations with prior knowledge. By proposing a significance assessment scheme to detect statistically significant rewiring of the learned differential dependency network, our method can assign edge-specific p-values and specify edge types to indicate one of six biological scenarios. The data-knowledge jointly inferred gene networks are relatively simple to interpret, yet still convey considerable biological information. Experiments on extensive simulation data and comparison with peer methods demonstrate the effectiveness of knowledge-fused differential dependency network in revealing the statistically significant rewiring in biological networks, leveraging data-driven evidence and existing biological knowledge, while remaining robust to the false positive edges in the prior knowledge. We also made significant efforts in disseminating the developed method tools to the research community. We developed an accompanying R package and Cytoscape plugin to provide both batch processing ability and user-friendly graphic interfaces. With the comprehensive software tools, we apply our method to several practically important biological problems to study how yeast response to stress, to find the origin of ovarian cancer, and to evaluate the drug treatment effectiveness and other broader biological questions. In the yeast stress response study our findings corroborated existing literatures. A network distance measurement is defined based on KDDN and provided novel hypothesis on the origin of high-grade serous ovarian cancer. KDDN is also used in a novel integrated study of network biology and imaging in evaluating drug treatment of brain tumor. Applications to many other problems also received promising biological results. / Ph. D.