Hafiz M. R. Khan

White Matter Deterioration May Foreshadow Impairment of Emotional Valence Determination in Early-Stage Dementia of the Alzheimer Type

In Alzheimer Disease (AD), non-verbal skills often remain intact for far longer than verbally mediated processes. Four (1 female, 3 males) participants with early-stage Clinically Diagnosed Dementia of the Alzheimer Type (CDDAT) and eight neurotypicals (NTs; 4 females, 4 males) completed the emotional valence determination test (EVDT) while undergoing BOLD functional magnetic resonance imaging (fMRI). We expected CDDAT participants to perform just as well as NTs on the EVDT, and to display increased activity within the bilateral amygdala and right anterior cingulate cortex (r-ACC). We hypothesized that such activity would reflect an increased reliance on these structures to compensate for on-going neuronal loss in frontoparietal regions due to the disease. We used diffusion tensor imaging (DTI) to determine if white matter (WM) damage had occurred in frontoparietal regions as well. CDDAT participants had similar behavioral performance and no differences were observed in brain activity or connectivity patterns within the amygdalae or r-ACC. Decreased fractional anisotropy (FA) values were noted, however, for the bilateral superior longitudinal fasciculi and posterior cingulate cortex (PCC). We interpret these findings to suggest that emotional valence determination and non-verbal skill sets are largely intact at this stage of the disease, but signs foreshadowing future decline were revealed by possible WM deterioration. Understanding how non-verbal skill sets are altered, while remaining largely intact, offers new insights into how non-verbal communication may be more successfully implemented in the care of AD patients and highlights the potential role of DTI as a presymptomatic biomarker.

Impact of Exercise and Education in Adults of Lubbock, Texas: Implications for Better Lifestyle

The objective of our study was to evaluate the exercise and educational intervention in the city of Lubbock via GET FiT Lubbock (GFL) program. The GFL program was designed to increase exercise and educational opportunities, which positively impact health risk factors in Lubbock residents. The GFL program design included the recruitment of subjects to participate on a team that consisted of four individuals, each subject tracked their exercise minutes, and their educational session attendance. The tracking of exercise and educational sessions was done on the GFL website. Biometric testing was conducted pre- and post- intervention. The program was located within the Lubbock community in places that were close to their place of residence. The intervention included walking and educational sessions, including goal setting lectures, nutrition information, and exercise demonstrations. Study participants, included male and female adults who tracked their exercise time and educational sessions. Exercise minutes and educational session attendance were self-reported. Our data analysis revealed that significant difference was found between pre- and post- intervention measures, including weight, body mass index (BMI), high-density lipoprotein (HDL). Significant difference was found for weight, BMI, and HDL in females. Based on these findings, we conclude that the intervention showed positive effects on exercise and lifestyle.

Inference from the Exponentiated Weibull Model with Applications to Real Data

In this paper, a novel Bayesian framework is used to derive the posterior density function, predictive density for a single future response, a bivariate future response, and several future responses from the exponentiated Weibull model (EWM). We study three related types of models, the exponentiated exponential, exponentiated Weibull, and beta generalized exponential, which are all utilized to determine the goodness of fit of two real data sets. The statistical analysis indicates that the EWM best fits both data sets. We determine the predictive means, standard deviations, highest predictive density intervals, and the shape characteristics for a single future response. We also consider a new parameterization method to determine the posterior kernel densities for the parameters. The summary results of the parameters are calculated by using the Markov chain Monte Carlo method.

A Preliminary Investigation with Twitter to Augment CVD Exposome Research

This project focuses on analyzing the sentiment of tweets in order to find a correspondence to health issues and to gain a new perspective in analyzing health data. Twitter social media is a huge source of information that can augment data about health in particular geographic locations. For this project, analyzing tweets is an attempt to find some relation between the sentiment of tweets and Cardiovascular Disease (CVD) in the counties along Interstate 20 (I-20) in Texas. Only geo-tagged tweets that are mapped to the counties of interest are used in the main analysis. The sentiment of the text of the Tweet is determined as being either positive or negative. Using the Natural Language Toolkit (NLTK), several classifiers are trained to determine the sentiment of the tweet. Each of the classifiers results are compared to measure the confidence of the sentiment declared. After all the tweets are classified, then the results are used to calculate the following for each county: Positive-to-Negative ratio, Positive-to-Population ratio, and Negative-to-Population ratio. This data is then separated into quintiles and compared to the Cardiovascular Disease map of I-20 in order to determine if a relationship may exist between CVD and the tweets. The preliminary results show that a correspondence exists between the low CVD rate in a county to the Positive-to-Negative ratio of that same county.

Bayesian prediction from the inverse rayleigh distribution based on type-II trim censoring

Abstract In this paper, we consider predictive inference for the parameter assuming the given type-II censored sample follows an inverse Rayleigh distribution. Predictive density functions for a single future response, a bivariate future response, and several future responses are obtained by incorporating the posterior density function. To derive the posterior and predictive distributions on the basis of the type-II censored sample, a Bayesian framework was used in conjunction with an informative prior distribution for the parameters. A numerical example is considered to illustrate the results. A simulated type-II censored sample from an inverse Rayleigh distribution is utilized where the sample contains upper extreme values. Several scenarios of the predictive distribution are determined with 95% confidence intervals for a single future response. The findings will be worthwhile to obtain predictive inference for responses when type-II censored samples carry upper extreme values.

Lower activation in frontal cortex and posterior cingulate cortex observed during sex determination test in early-stage dementia of the Alzheimer type

Face-labeling refers to the ability to classify faces into social categories. This plays a critical role in human interaction as it serves to define concepts of socially acceptable interpersonal behavior. The purpose of the current study was to characterize, what, if any, impairments in face-labeling are detectable in participants with early-stage clinically diagnosed dementia of the Alzheimer type (CDDAT) through the use of the sex determination test (SDT). In the current study, four (1 female, 3 males) CDDAT and nine (4 females, 5 males) age-matched neurotypicals (NT) completed the SDT using chimeric faces while undergoing BOLD fMRI. It was expected that CDDAT participants would have poor verbal fluency, which would correspond to poor performance on the SDT. This could be explained by decreased activation and connectivity patterns within the fusiform face area (FFA) and anterior cingulate cortex (ACC). DTI was also performed to test the association of pathological deterioration of connectivity in the uncinate fasciculus (UF) and verbally-mediated performance. CDDAT showed lower verbal fluency test (VFT) performance, but VFT was not significantly correlated to SDT and no significant difference was seen between CDDAT and NT for SDT performance as half of the CDDAT performed substantially worse than NT while the other half performed similarly. BOLD fMRI of SDT displayed differences in the left superior frontal gyrus and posterior cingulate cortex (PCC), but not the FFA or ACC. Furthermore, although DTI showed deterioration of the right inferior and superior longitudinal fasciculi, as well as the PCC, it did not demonstrate significant deterioration of UF tracts. Taken together, early-stage CDDAT may represent a common emerging point for the loss of face labeling ability.

Effects of some design factors on the distribution of similarity indices in cluster analysis

ABSTRACT This article investigates the effects of number of clusters, cluster size, and correction for chance agreement on the distribution of two similarity indices, namely, Jaccard and Rand indices. Skewness and kurtosis are calculated for the two indices and their corrected forms then compared with those of the normal distribution. Three clustering algorithms are implemented: complete linkage, Ward, and K-means. Data were randomly generated from bivariate normal distributions with specified means and variance covariance matrices. Three-way ANOVA is performed to assess the significance of the design factors using skewness and kurtosis of the indices as responses. Test statistics for testing skewness and kurtosis and observed power are calculated. Simulation results showed that independent of the clustering algorithms or the similarity indices used, the interaction effect cluster size x number of clusters and the main effects of cluster size and number of clusters were found always significant for skewness and kurtosis. The three way interaction of cluster size x correction x number of clusters was significant for skewness of Rand and Jaccard indices using all clustering algorithms, but was not significant using Wards method for both Rand and Jaccard indices, while significant for Jaccard only using complete linkage and K-means algorithms. The correction for chance agreement was significant for skewness and kurtosis using Rand and Jaccard indices when complete linkage method is used. Hence, such design factors must be taken into consideration when studying distribution of such indices.

Posterior Inference for White Hispanic Breast Cancer Survival D ata

The purpose of this paper is to develop a statistical probability model and to obtain posterior inference for the parameters given the survival times of the White Hispanic female cancer patients. Stratified random sample of White Hispanic female patients’ survival data was used to derive a best fit statistical probability model. The study sample was extracted from the Surveillance Epidemiology and End Results (SEER) cancer registry database. Three model building criterions were utilized; Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. We found that the Exponentiated Weibull model fits the survival times better as compared to other widely known statistical probability models. The Bayesian approach is employed to derive the posterior inference for the parameters.