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Mercury is a potent neurotoxin, but the clinical patterns of organic and inorganic mercury poisoning are very different. Methyl mercury produces a neurological disease; consistent signs include ataxia, dysarthria and constriction of visual fields. Inorganic mercury affects the gastro-intestinal, renal and nervous systems; gingivitis, stomatatis, tremors and psychiatric changes are common. Despite these apparent differences, recent systemic toxicity experiments suggest that the changes in dorsal root ganglia neurones are identical with the two compounds. To test this hypothesis, between 10-' and 10-~ mol of mercuric chloride and methyl mercuric acetate were injected directly into the cerebrum of rats. The histological changes were similar with the two compounds. Neuronal necrosis and cerebral oedema were the most prominent features. Control injections of sodium chloride, distilled water and buffers produced no significant changes. Ultrastructurally, neurones showed pronounced cytoplasmic swelling, suggesting a defect at the cell membrane level. Similar changes were seen with injections of ouabain, glutaraldehyde, mercury mixed with cysteine and blood from animals poisoned systemically with methyl mercury. This form of neuronal necrosis differs from that of systemic methyl mercury toxicity where the earliest changes are in the endoplasmic reticulum. This difference may be related to the higher local concentration and rate of accumulation of mercury after intracerebral injection. The comparative size of lesions was estimated anatomically, and by reference to blood brain barrier dysfunction. Inorganic lesions were only slightly larger than those produced by equimolar amounts of organic mercury. It has been suggested that the toxicity of organic mercurials is the result of biotransformation to inorganic mercury. If this is so, it is surprising that inorganic mercury lesions were not far larger.

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The purpose of this study was to measure and report the perceived stress among research postsecondary institution chief financial officers. A non-experimental descriptive approach was used in this investigation. Research questions were developed to describe and seek any differences in stress among the respondents. The population for this study was chief financial officers in research institutions based on the Carnegie Foundation for the Advancement of Teaching but excluded CFOs that had responsibility over multiple campuses, medical or professional schools and any vacant positions. The sample consisted of 90 respondents from public and private institutions. Data was collected by a self-reported survey which was a combined instrument using the Perceived Stress Scale, House & Rizzo's Tension Survey, and an amended Maslach Burnout Inventory. Demographic data was collected on the respondents and coping techniques were recorded using an open-ended self-reporting question. Responses indicated that the respondents report moderate levels of stress and parallels previously conducted research. The respondents also claim coping techniques such as exercise, relaxation with friends and family, hobbies and personal activities, spirituality and religious activities to reduce stress which also is consistent with previous research. There was no significance found among the respondents based upon the demographic makeup. Stress is an inevitable occurrence in life, especially for those who have great responsibilities, such as a university CFO. Occupational stress costs include loss of production due to absenteeism, increased medical insurance premiums and a myriad of health problems, just to name a few. For employers the best ways to help their employees cope with stress could include Employee Assistance Programs (EAPs), stress intervention programs, or mandated vacation to name a few solutions.

As machine learning has more closely interacted with optimization, the concept of convexity has loomed large. Two properties beyond simple convexity have received particularly close attention: strong smoothness and strong convexity. These properties (and their relatives) underlie machine learning analyses from convergence rates to generalization bounds --- they are central and fundamental. This thesis takes as its focus properties from operator theory that, in specific instances, relate to broadened conceptions of convexity, strong smoothness, and strong convexity. Some of the properties we consider coincide with strong smoothness and strong convexity in some settings, but represent broadenings of these concepts in other situations of interest. Our intention throughout is to take an approach that balances theoretical generality with ease of use and subsequent extension. Through this approach we establish a framework, novel in its scope of application, in which a single analysis serves to recover standard convergence rates (typically established via a variety of separate arguments) for convex optimization methods prominent in machine learning. The framework is based on a perspective in which the iterative update for each convex optimization method is regarded as the application of some operator. We establish a collection of correspondences, novel in its comprehensiveness, that exist between "contractivity- type'' properties of the iterative update operator and "monotonicity-type'' properties of the associated displacement operator. We call particular attention to the comparison between the broader range of properties that we discuss and the more restricted range considered in the contemporary literature, demonstrating as well the relationship between the broader and narrower range. In support of our discussion of these property correspondences and the optimization method analyses based on them, we relate operator theory concepts that may be unfamiliar to a machine learning audience to more familiar concepts from convex analysis. In addition to grounding our discussion of operator theory, this turns out to provide a fresh perspective on many touchstone concepts from convex analysis