SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Transcriptional regulation of the Borrelia burgdorferi antigenically variable VlsE surface protein.

The Lyme disease agent Borrelia burgdorferi can persistently infect humans and other animals despite host active immune responses. This is facilitated, in part, by the vls locus, a complex system consisting of the vlsE expression site and an adjacent set of 11 to 15 silent vls cassettes. Segments of nonexpressed cassettes recombine with the vlsE region during infection of mammalian hosts, resulting in combinatorial antigenic variation of the VlsE outer surface protein. We now demonstrate that synthesis of VlsE is regulated during the natural mammal-tick infectious cycle, being activated in mammals but repressed during tick colonization. Examination of cultured B. burgdorferi cells indicated that the spirochete controls vlsE transcription levels in response to environmental cues. Analysis of PvlsE::gfp fusions in B. burgdorferi indicated that VlsE production is controlled at the level of transcriptional initiation, and regions of 5' DNA involved in the regulation were identified. Electrophoretic mobility shift assays detected qualitative and quantitative changes in patterns of protein-DNA complexes formed between the vlsE promoter and cytoplasmic proteins, suggesting the involvement of DNA-binding proteins in the regulation of vlsE, with at least one protein acting as a transcriptional activator.

Evaluation of residential bioaerosol concentrations and their related health effects

Limited research has been conducted on the collection of bioaerosols and their health effects on individuals in the El Paso area. A year long study was conducted in the region to evaluate indoor bioaerosol concentrations (Mota et al., unpublished data). As part of the study, air samples were collected during each season for a year from 38 homes from the El Paso area. The main objective of the study was to assess seasonality differences in bioaerosol concentrations. The air samples were then cultured and analyzed for bacterial and fungal concentrations. As a supplement to that study, a health questionnaire was given during each seasonal air sampling to the participating resident to complete regarding their health status. The aim of this study was to evaluate the health questionnaire and assess any associations between the collected bioaerosol concentrations and the self-reported respiratory symptoms of the participating home residents. Symptom frequencies were tabulated and basic descriptive statistics, along with logistic regressions, were conducted on the relationship between “High” reporters of symptoms and bioaerosol concentrations and environmental factors. The most commonly reported symptoms by homeowners were nasal symptoms and allergies. In addition, there was evidence to support an association between indoor respirable bacteria concentrations and homeowners that report greater than or equal to 8 respiratory symptoms (OR=1.10, p=0.045). Smoking status, indoor humidity and season also displayed associations with homeowners that report greater than or equal to 8 respiratory symptoms (OR=3.3, p=0.045; OR=71.0, p=0.030; OR=7.2, 3.2, p=0.001, 0.008). With such a strong association, future assessment of symptoms, bioaerosol concentrations and environmental factors is needed to further establish their relationship. ^

Alternative carbon metabolism: Virulence and regulation in Candida albicans

The interaction between C. albicans and innate immune cells is a key determinant to disease progression. Transcriptional profiling showed that C. albicans responds to macrophage phagocytosis by inducing pathways required for alternative carbon metabolism (beta-oxidation, the glyoxylate cycle, and gluconeogenesis), suggesting these pathways are important for virulence of C. albicans. ^ We have shown that deleting key genes (FOX2, FBP1) in these pathways results in virulence defects in an in vivo mouse model for systemic infection. Like icl1Δ/Δ mutants, fbp1Δ/Δ mutants are severely attenuated and fox2Δ/Δ mutants are mildly but significantly attenuated, indicating that carbon starvation is a relevant stress in vivo. ^ However, fox2Δ/Δ mutants also had unexpected phenotypes on certain carbon sources, unlike the case in Saccharomyces cerevisiae, suggesting these pathways are regulated differently in C. albicans. To test this, we identified the C. albicans regulators of these pathways based on those from S. cerevisiae and Aspergillus nidulans. ^ C. albicans has a partly conserved framework, but lacks two regulators (Oaf1p, Pip2p) controlling peroxisome biogenesis and beta-oxidation genes in yeast. Instead, C. albicans has a homolog, CTF1, of the A. nidulans fatty acid catabolism regulators FarA and FarB. We have shown that CTF1 is needed for growth on oleate (like FarA and FarB), expression of beta-oxidation and glyoxylate cycle genes, and full virulence. No function for CTF1 has previously been identified in C. albicans. Our data demonstrate a role for alternative carbon metabolism in the virulence of C. albicans and suggest that the regulation of these pathways is a mixture of the filamentous fungi and budding yeast systems. ^

The relationship between obesity, autoimmune disease, and C-reactive protein: A secondary data analysis of the National Health and Nutrition Examination Survey (NHANES) 2009--2010

Autoimmune diseases are a group of inflammatory conditions in which the body's immune system attacks its own cells. There are over 80 diseases classified as autoimmune disorders, affecting up to 23.5 million Americans. Obesity affects 32.3% of the US adult population, and could also be considered an inflammatory condition, as indicated by the presence of chronic low-grade inflammation. C-reactive protein (CRP) is a marker of inflammation, and is associated with both adiposity and autoimmune inflammation. This study sought to determine the cross-sectional association between obesity and autoimmune diseases in a large, nationally representative population derived from NHANES 2009–10 data, and the role CRP might play in this relationship. Overall, the results determined that individuals with autoimmune disease were 2.11 times more likely to report being overweight than individuals without autoimmune disease and that CRP had a mediating affect on the obesity-autoimmune relationship. ^

The SanaViz: Human Centered Geovisualization to facilitate visual exploration of Public Health data

These three manuscripts are presented as a PhD dissertation for the study of using GeoVis application to evaluate telehealth programs. The primary reason of this research was to understand how the GeoVis applications can be designed and developed using combined approaches of HC approach and cognitive fit theory and in terms utilized to evaluate telehealth program in Brazil. First manuscript The first manuscript in this dissertation presented a background about the use of GeoVisualization to facilitate visual exploration of public health data. The manuscript covered the existing challenges that were associated with an adoption of existing GeoVis applications. The manuscript combines the principles of Human Centered approach and Cognitive Fit Theory and a framework using a combination of these approaches is developed that lays the foundation of this research. The framework is then utilized to propose the design, development and evaluation of “the SanaViz” to evaluate telehealth data in Brazil, as a proof of concept. Second manuscript The second manuscript is a methods paper that describes the approaches that can be employed to design and develop “the SanaViz” based on the proposed framework. By defining the various elements of the HC approach and CFT, a mixed methods approach is utilized for the card sorting and sketching techniques. A representative sample of 20 study participants currently involved in the telehealth program at the NUTES telehealth center at UFPE, Recife, Brazil was enrolled. The findings of this manuscript helped us understand the needs of the diverse group of telehealth users, the tasks that they perform and helped us determine the essential features that might be necessary to be included in the proposed GeoVis application “the SanaViz”. Third manuscript The third manuscript involved mix- methods approach to compare the effectiveness and usefulness of the HC GeoVis application “the SanaViz” against a conventional GeoVis application “Instant Atlas”. The same group of 20 study participants who had earlier participated during Aim 2 was enrolled and a combination of quantitative and qualitative assessments was done. Effectiveness was gauged by the time that the participants took to complete the tasks using both the GeoVis applications, the ease with which they completed the tasks and the number of attempts that were taken to complete each task. Usefulness was assessed by System Usability Scale (SUS), a validated questionnaire tested in prior studies. In-depth interviews were conducted to gather opinions about both the GeoVis applications. This manuscript helped us in the demonstration of the usefulness and effectiveness of HC GeoVis applications to facilitate visual exploration of telehealth data, as a proof of concept. Together, these three manuscripts represent challenges of combining principles of Human Centered approach, Cognitive Fit Theory to design and develop GeoVis applications as a method to evaluate Telehealth data. To our knowledge, this is the first study to explore the usefulness and effectiveness of GeoVis to facilitate visual exploration of telehealth data. The results of the research enabled us to develop a framework for the design and development of GeoVis applications related to the areas of public health and especially telehealth. The results of our study showed that the varied users were involved with the telehealth program and the tasks that they performed. Further it enabled us to identify the components that might be essential to be included in these GeoVis applications. The results of our research answered the following questions; (a) Telehealth users vary in their level of understanding about GeoVis (b) Interaction features such as zooming, sorting, and linking and multiple views and representation features such as bar chart and choropleth maps were considered the most essential features of the GeoVis applications. (c) Comparing and sorting were two important tasks that the telehealth users would perform for exploratory data analysis. (d) A HC GeoVis prototype application is more effective and useful for exploration of telehealth data than a conventional GeoVis application. Future studies should be done to incorporate the proposed HC GeoVis framework to enable comprehensive assessment of the users and the tasks they perform to identify the features that might be necessary to be a part of the GeoVis applications. The results of this study demonstrate a novel approach to comprehensively and systematically enhance the evaluation of telehealth programs using the proposed GeoVis Framework.

Decomposition of $\rm R\times C$ contingency table chi -square: Application to binned DNA fragment size data and population structure analysis

The purpose of this research is to develop a new statistical method to determine the minimum set of rows (R) in a R x C contingency table of discrete data that explains the dependence of observations. The statistical power of the method will be empirically determined by computer simulation to judge its efficiency over the presently existing methods. The method will be applied to data on DNA fragment length variation at six VNTR loci in over 72 populations from five major racial groups of human (total sample size is over 15,000 individuals; each sample having at least 50 individuals). DNA fragment lengths grouped in bins will form the basis of studying inter-population DNA variation within the racial groups are significant, will provide a rigorous re-binning procedure for forensic computation of DNA profile frequencies that takes into account intra-racial DNA variation among populations. ^