Thesis (M. Ed.)--Kutztown State College. / Source: Masters Abstracts International, Volume: 45-06, page: 2745. Typescript. Includes bibliographical references (leaves 77-79).

Students’ career decisions can impact the global economy through brain drain and gain. This study investigates factors affecting international students’ preferences regarding where to start their careers. It is an ex ante study, conducted before final career decisions are made, and uses micro-level primary data. Information was collected from international graduate students at Texas A&M University, using a computer-assisted survey. Multinomial probit and logit models were used to analyze the data. Unique to this study is that students could indicate they are not sure where they prefer to start their careers. An inference from the statistical test based on the inverse Mills ratio is that there are no significant differences between two groups, those who are not sure and those with defined preferences. Fifty-one percent of the students surveyed indicated they preferred to begin their careers in the U.S., 22 percent preferred their home countries, and 27 percent were not sure. Of the students who preferred their home country over the U.S., significant influencers are political and career indices, number of years lived in the U.S., enrollment in Engineering and Business, and gender. A change in either the political or career indices to favor the U.S. systems and more years lived in the U.S. both increased the likelihood of students’ preferring to start their careers in the U.S. Both female students and students enrolled in Engineering and Business were more likely to prefer the U.S. For students unsure of their preferences, significant variables are career and civil indices, number of years lived in the U.S., degree level, source of funding, and marital status. Results are consistent with previous studies, but with notable differences. For students who are unsure of their preferences, salary differences between the U.S. and their home countries are not statistically significant. Contrary to earlier literature, regional differences do not have a statistically significant effect on students’ preferences. Consistency between this study of preferences and previous studies on actual decisions indicates students act on their preferences when starting their careers. Hence, this study’s results provide insights for policies to deter brain drain or to enhance brain gain.

The molecular mechanisms that regulate survival of embryonic neural precursors are still relatively ill-defined. Here, we have asked whether the p53 family member p63 plays any role during this developmental window, focusing upon the embryonic cerebral cortex. We show that genetic knockdown of p63 either in culture or in the embryonic telencephalon causes apoptosis of cortical precursors and newly-born cortical neurons, and that this can be rescued by expression of ΔNp63, but not TAp63 isoforms. This cortical precursor apoptosis is the consequence of deregulated p53 activity, since both basal precursor apoptosis and that induced by loss of p63 are rescued by coincident genetic silencing of p53. Finally, we demonstrate that the third p53 family member, ΔNp73, does not regulate survival of cortical precursor cells, but that it collaborates with ΔNp63 to ensure the survival of newly-born cortical neurons. Thus, the balance of ΔNp63 versus p53 determines the life versus death of embryonic cortical precursors. To assess if p63 plays a similar role in the adult brain, we examined mice haploinsufficient in p63 (p63+/-); we observed that aging, but not young, mice show deficits in short term memory and vertical exploratory behaviour. These results establish a foundation upon which the role of p63 in aging can be further characterized. Given the importance of p73 in aging and in preventing neurodegeneration, we aimed to create a conditional p73 knock out mouse (floxed p73). This mouse will allow for lineage-specific characterization of p73 function and will circumvent the reduced life span displayed by 4 mice lacking p73 (p73-/-). The work presented in this thesis has advanced our knowledge on the role of the p53 family members (p53, p63, and p73) in regulating survival and death in the nervous system. This knowledge will be important to understand neuropathology and to design appropriate therapeutic interventions.

The molecular mechanisms that regulate survival of embryonic neural precursors are still relatively ill-defined. Here, we have asked whether the p53 family member p63 plays any role during this developmental window, focusing upon the embryonic cerebral cortex. We show that genetic knockdown of p63 either in culture or in the embryonic telencephalon causes apoptosis of cortical precursors and newly-born cortical neurons, and that this can be rescued by expression of ΔNp63, but not TAp63 isoforms. This cortical precursor apoptosis is the consequence of deregulated p53 activity, since both basal precursor apoptosis and that induced by loss of p63 are rescued by coincident genetic silencing of p53. Finally, we demonstrate that the third p53 family member, ΔNp73, does not regulate survival of cortical precursor cells, but that it collaborates with ΔNp63 to ensure the survival of newly-born cortical neurons. Thus, the balance of ΔNp63 versus p53 determines the life versus death of embryonic cortical precursors. To assess if p63 plays a similar role in the adult brain, we examined mice haploinsufficient in p63 (p63+/-); we observed that aging, but not young, mice show deficits in short term memory and vertical exploratory behaviour. These results establish a foundation upon which the role of p63 in aging can be further characterized. Given the importance of p73 in aging and in preventing neurodegeneration, we aimed to create a conditional p73 knock out mouse (floxed p73). This mouse will allow for lineage-specific characterization of p73 function and will circumvent the reduced life span displayed by 4 mice lacking p73 (p73-/-). The work presented in this thesis has advanced our knowledge on the role of the p53 family members (p53, p63, and p73) in regulating survival and death in the nervous system. This knowledge will be important to understand neuropathology and to design appropriate therapeutic interventions.

Brain injuries (BI) are the leading cause of death and disability among people under the age of 45 (Ontario Brain Injury Association, 2004). With improved survival rates, more individuals each year return to the community with impairments and disabilities caused by their injury (Smith, Magill–Evans, and Brintnell, 1998). Adjusting to these impairments may affect the individual’s subjective well being; therefore, attention to community reintegration by researchers, policy developers, and health care providers is important. Using qualitative research methods and systems theory as the theoretical framework, the purpose of the study was to examine community reintegration from the perspectives of three key groups: individuals with BI, community based agencies, and primary care physicians regarding the meaning attributed to “successful reintegration”, as well as the key characteristics and barriers experienced during reintegration. “Successful” reintegration appears to be an individually derived concept. Participants consistently identified the need for information about the process of community reintegration, and resources available both during rehabilitation and after discharge from the hospital as being both a key aspect of community reintegration, as well as a barrier experienced during the return to community. / October 2006

Recent research has suggested disrupted interactions between brain regions may contribute to some of the symptoms of motor disorders such as Parkinson’s Disease (PD). It is therefore important to develop models for inferring brain functional connectivity from data obtained through non-invasive imaging technologies, such as functional magnetic resonance imaging (fMRI). The complexity of brain activities as well as the dynamic nature of motor disorders require such models to be able to perform complex, large-scale, and dynamic system computation. Traditional models proposed in the literature such as structural equation modeling (SEM), multivariate autoregressive models (MAR), dynamic causal modeling (DCM), and dynamic Bayesian networks (DBNs) have all been suggested as suitable for fMRI data analysis. However, they suffer from their own disadvantages such as high computational cost (e.g. DBNs), inability to deal with non-linear case (e.g. MAR), large sample size requirement (e.g. SEM), et., al. In this research, we propose applying Probabilistic Boolean Network (PBN) for modeling brain connectivity due to its solid stochastic properties, computational simplicity, robustness to uncertainty, and capability to deal with small-size data, typical for fIVIRI data sets. Applying the proposed PBN framework to real fMRI data recorded from PD subjects enables us to identify statistically significant abnormality in PD connectivity by comparing it with normal subjects. The PBN results also suggest a mechanism of evaluating the effectiveness of L-dopa, the principal treatment for PD. In addition to PBNs’ promising application in inferring brain connectivity, PBN modeling for brain ROTs also enables researchers to study dynamic activities of the system under stochastic conditions, gaining essential information regarding asymptotic behaviors of ROTs for potential therapeutic intervention in PD. The results indicate significant difference in feature states between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamic network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention. Results identified a target ROT with the best intervention performance, and that ROl is a potential candidate for therapeutic exercise.

Students’ career decisions can impact the global economy through brain drain and gain. This study investigates factors affecting international students’ preferences regarding where to start their careers. It is an ex ante study, conducted before final career decisions are made, and uses micro-level primary data. Information was collected from international graduate students at Texas A&M University, using a computer-assisted survey. Multinomial probit and logit models were used to analyze the data. Unique to this study is that students could indicate they are not sure where they prefer to start their careers. An inference from the statistical test based on the inverse Mills ratio is that there are no significant differences between two groups, those who are not sure and those with defined preferences. Fifty-one percent of the students surveyed indicated they preferred to begin their careers in the U.S., 22 percent preferred their home countries, and 27 percent were not sure. Of the students who preferred their home country over the U.S., significant influencers are political and career indices, number of years lived in the U.S., enrollment in Engineering and Business, and gender. A change in either the political or career indices to favor the U.S. systems and more years lived in the U.S. both increased the likelihood of students’ preferring to start their careers in the U.S. Both female students and students enrolled in Engineering and Business were more likely to prefer the U.S. For students unsure of their preferences, significant variables are career and civil indices, number of years lived in the U.S., degree level, source of funding, and marital status. Results are consistent with previous studies, but with notable differences. For students who are unsure of their preferences, salary differences between the U.S. and their home countries are not statistically significant. Contrary to earlier literature, regional differences do not have a statistically significant effect on students’ preferences. Consistency between this study of preferences and previous studies on actual decisions indicates students act on their preferences when starting their careers. Hence, this study’s results provide insights for policies to deter brain drain or to enhance brain gain.

Traditional fMRI utilizes blood oxygenation level dependent (BOLD) contrast to map brain activity. BOLD signal is sensitive to the hemodynamic changes associated with brain activity, and gives an indirect measure of brain activity. Low frequency fluctuations (LFFs) have been observed in the BOLD signal even in the absence of any anesthetic agent, and the correlations between the fluctuations from different brain regions has been used to map functional connectivity in the brain. Most studies involving spontaneous fluctuations in the BOLD signal extract connectivity patterns that show relationships between brain areas that are maintained over the length of the scanning session. The research presented in this document investigates the spatiotemporal dynamics of the BOLD fluctuations to identify common spatiotemporal patterns within a scan. First, the presence of a visually detectable spatiotemporal propagation pattern is demonstrated by utilizing single-slice data with high spatial and temporal resolution. The pattern consists of lateral-medial propagation of BOLD signal, demonstrating the presence of time-varying features in spontaneous BOLD fluctuations. Further, a novel pattern finding algorithm is developed for detecting repeated spatiotemporal patterns in BOLD fMRI data. The algorithm is applied to high temporal resolution T2*-weighted multislice images obtained from rats and humans in the absence of any task or stimulation. In rats, the primary pattern consists of waves of high signal intensity, propagating in a lateral-medial direction across the cortex, replicating the results obtained using visual observation. In humans, the most common spatiotemporal pattern consisted of an alteration between activation of areas comprising the "default-mode" (e.g., posterior cingulate and anterior medial prefrontal cortices) and the "task-positive" (e.g., superior parietal and premotor cortices) networks. Signal propagation from focal starting points is also observed. The pattern finding algorithm is shown to be reasonably insensitive to the variation in user-defined parameters, and the results are consistent within and between subjects. This novel approach for probing the spontaneous network activity of the brain has implications for the interpretation of conventional functional connectivity studies, and may increase the amount of information that can be obtained from neuroimaging data.

Thesis (Ph. D.)--Open University.

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 55-65). Also available in print.