Segmental body composition determination through a bioelectrical very large scale integration (VLSI) system

The purpose of this investigation was to develop new techniques to generate segmental assessments of body composition based on Segmental Bioelectrical Impedance Analysis (SBIA). An equally important consideration was the design, simulation, development, and the software and hardware integration of the SBIA system. This integration was carried out with a Very Large Scale Integration (VLSI) Field Programmable Gate Array (FPGA) microcontroller that analyzed the measurements obtained from segments of the body, and provided full body and segmental Fat Free Mass (FFM) and Fat Mass (FM) percentages. Also, the issues related to the estimate of the body's composition in persons with spinal cord injury (SCI) were addressed and investigated. This investigation demonstrated that the SBIA methodology provided accurate segmental body composition measurements. Disabled individuals are expected to benefit from these SBIA evaluations, as they are non-invasive methods, suitable for paralyzed individuals. The SBIA VLSI system may replace bulky, non flexible electronic modules attached to human bodies. ^

Coronary heart disease risk factors among tri-ethnic students at Florida International University

The present study identified and compared Coronary Heart Disease (CHD) risk factors quantified as “CHD risk point standards” (CHDRPS) among tri-ethnic (White non-Hispanic [WNH], Hispanic [H], and Black non-Hispanic [BNH]) college students. All 300 tri-ethnic subjects completed the Cardiovascular Risk Assessment Instruments and had blood pressure readings recorded on three occasions. The Bioelectrical Impedance Analysis (BIA) was used to measure body composition. Students' knowledge of CHD risk factors was also measured. In addition, a 15 ml fasting blood sample was collected from 180 subjects and blood lipids and Homocysteine (tHcy) levels were measured. Data were analyzed by gender and ethnicity using one-way Analysis of Variance (ANOVA) with Bonferroni's pairwise mean comparison procedure, Pearson correlation, and Chi-square test with follow-up Bonferroni's Chi-square tests. ^ The mean score of CHDRPS for all subjects was 19.15 ± 6.79. Assigned to the CHD risk category, college students were below-average risk of developing CHD. Males scored significantly (p < 0.013) higher for CHD risk than females, and BNHs scored significantly (p < 0.033) higher than WNHs. High consumption of dietary fat saturated fat and cholesterol resulted in a high CHDRPS among H males and females and WNH females. High alcohol consumption resulted in a high CHDRPS among all subjects. Mean tHcy ± SD of all subjects was 6.33 ± 3. 15 μmol/L. Males had significantly (p < 0.001) higher tHcy than females. Black non-Hispanic females and H females had significantly (p < 0.003) lower tHcy than WNH females. Positive associations were found between tHcy levels and CHDRPS among females (p < 0.001), Hs (p < 0.001), H males (p < 0.049), H females (p < 0.009), and BNH females (p < 0.005). Significant positive correlations were found between BMI levels and CHDRPS in males (p < 0.001), females (p < 0.001), WNHs (p < 0.008), Hs (p < 0.001), WNH males (p < 0.024), H males (p < 0.004) and H females (p < 0.001). The mean knowledge of CHD questions of all subjects was 71.70 ± 7.92 out of 100. The mean knowledge of CHD was significantly higher for WNH males (p < 0.039) than BNH males. A significant inverse correlation (r = 0.392, p < 0.032) was found between the CHD knowledge and CHDRPS in WNH females. The researcher's findings indicate strong gender and ethnic differences in CHD risk factors among the college-age population. ^

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.