No description available.

Introduction: Classifications such as Mild Cognitive Impairment (MCI) and Cognitive Impairment, No Dementia (CIND) are thought to represent the transitory, pre-clinical phase of dementia. However, increasing research demonstrates that MCI and CIND represent nonlinear and unstable entities that do not always lead to imminent dementia. Despite an increase in research examining patterns and predictors of cognitive impairment classification stability, this concept is still poorly understood, and the research remains limited. The present study was designed to address the existing limitations within the literature by utilizing a longitudinal repeated measures design to gain a more thorough understanding of CIND classification stability patterns, as well as identify predictors of future stability. Objectives: The objectives were to i) explore patterns of longitudinal stability in cognitive status across multiple assessments, and ii) investigate whether select baseline variables could predict 6-year cognitive status stability patterns. Methods: Participants included 259 older adults from Project MIND, a six-year longitudinal repeated measures design in which participants were classified as either Normal Cognition (NC) or CIND at each assessment. A latent transition analysis approach was adapted in order to identify and characterize transitions in CIND status across annual assessments. Participants were classified as either Stable NC, Stable CIND, Progressers, Reverters, or Fluctuaters. Multinomial logistic regression was then employed to test whether baseline predictors were associated with cognitive status stability patterns. Results: The sample demonstrated high rates of reversion and fluctuation in CIND status across years of study. Additionally, premorbid IQ, total number of medications, presence of arthritis, and CIND severity at baseline were all significantly associated with select CIND stability outcomes. Conclusion: CIND status was unstable for several years following baseline assessment, and factors such as cognitive reserve may delay or protect against demonstrable cognitive impairment. Further, considering cognitive impairment severity (i.e., single versus multidomain impairment) at the time of initial classification may improve CIND classifications. Continued research on CIND stability is recommended to improve classification methodology and provide a framework for future identification and prevention. / Graduate

Electoral geographers are mostly concerned with mapping the responses of voters to different political candidates, while they also work to explain the factors that influence those responses. Yet most studies do not consider how different geographic contexts can affect the political perceptions of voters. In particular, people who live in close proximity to one another may come to embrace similar beliefs and values, while broader social and economic processes may divide these individuals into separate camps. Thus, electoral studies performed at the local level may produce different results than those done at the regional or national level. In exploring how different scales of analysis can give different interpretations of voting patterns, this research gathered data from a series of elections that took place in Virginia. These elections, which occurred between 2003 and 2006, span a variety of federal and state offices, with each presiding over a certain geographic jurisdiction. The study proceeded to map the results of each election in terms of three different types of geographic areas: precincts, counties, and legislative districts. The maps displayed the majority winners within each of these areas, giving a rough indication of the bases of support for each of the different candidates. The study then determined the number of instances where two neighboring areas both favored the same candidate, as well as the number of cases where they voted for opposing candidates. These data helped to shed light on the autocorrelation structure of voting patterns in Virginia, revealing how people in the same general vicinity tend to vote together. Overall, the results of this study demonstrate that smaller geographic units (e.g., precincts) exhibit greater autocorrelation in voting than do larger areas. This observation agrees with the concept of sectionalism, which asserts that location and culture are key influences on voting behavior. However, the data also suggest that class differences are a major source of electoral cleavage, as people from different social and economic backgrounds tend to settle in different areas. The use of multiple scales of analysis thus presents multiple explanations for the voting trend of a given location. / Master of Science

<p> Barley coleoptiles were observed to be remarkably variable in height at 72 hr of germination. The objective or the study presented here was to analyze this variation in growth among coleoptiles and to attempt to determine its cause. The first step in this analysis of the variation was to determine the growth patterns of 900 individual coleoptiles by measuring their heights periodically during their growth period. In determining the cause for the variation, genetic, environmental, hormonal, metabolic and cytoplasmic factors were considered. For example, since gibberellin and kinetin have been implicated in the control of cell division and cell elongation these growth factors were supplied exogenously to germinating seedlings in an attempt to stimulate uniform growth of all coleoptiles. In similar studies, the effect or physical conditions and co2 on coleoptile growth was determined variation in growth is shown by any one of a number of parameters. Proteins, however, because they are closer to gene activity than the others are a better indicator of whether the variation in growth is caused at a fundamental level. Variations similar to those in coleoptile height were found in the amino acid analysis of different types of coleoptiles. Experiments were also done to determine if a genetic component was responsible for the variation in coleoptile growth. There was no correlation between germination pattern of a seedling and that of its progeny; therefore, the variation in growth was attributed to differences in cytoplasmic constituents of individual coleoptiles. </p> / Thesis / Doctor of Philosophy (PhD)

Remote photoelectric monitoring of the locomotion of four freshwater teleosts in an experimental tank established that all species described non-random locomotor patterns which were affected by light intensity. A partial analysis was made of some of the characteristics of these patterns with emphasis on the direction and degree of turning. / Thesis / Doctor of Philosophy (PhD)

Abstract Not Provided. / Thesis / Candidate in Philosophy

"Same Shit, Different Day" is a collection of three VR installations portraying short, looping animations. I am exploring routines and patterns I find myself and the people around me in to draw attention to the ways in which we mindlessly go through motions in our everyday life. / Master of Fine Arts / "Same Shit, Different Day" is a collection of three VR installations portraying short, looping animations. I am exploring routines and patterns I find myself and the people around me in to draw attention to the ways in which we mindlessly go through motions in our everyday life.

No description available.

Software bugs prediction is one of the most active research areas in the software engineering community. The process of testing and debugging code proves to be costly during the software development life cycle. Software metrics measure the quality of source code to identify software bugs and vulnerabilities. Traceable code patterns are able to de- scribe code at a finer granularity level to measure quality. Micro patterns will be used in this research to mechanically describe java code at the class level. Machine learning has also been introduced for bug prediction to localize source code for testing and debugging. Deep Learning is a branch of Machine Learning that is relatively new. This research looks to improve the prediction of software bugs by utilizing micro patterns with deep learning techniques. Software bug prediction at a finer granularity level will enable developers to localize code to test and debug during the development process.

Late prehistoric to Protohistoric (ca. A.D. 1200 – 1700) agricultural settlement in the Black Prairie uplands of Mississippi may have been enabled by “seep springs,” water features fed by groundwater discharge in certain geological settings. Ceramic seriation and GIS analysis of archaeological site location shows that over time, sites clustered around areas most likely to have supported springs, a finding supported by the presence of specimens of a moist-ground snail genus at a number of sites. These data indicate that Native settlement in the Oktibbeha County area was influenced by the presence of seep springs.