Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

Rigid and non-rigid point-based medical image registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Medical advice, diabetes self-management, and health outcomes of a multi-ethnic population from the National Health and Nutrition Examination Survey 2007--2008

Diabetes self-management, an essential component of diabetes care, includes weight control practices and requires guidance from providers. Minorities are likely to have less access to quality health care than White non-Hispanics (WNH) (American College of Physicians-American Society of Internal Medicine, 2000). Medical advice received and understood may differ by race/ethnicity as a consequence of the patient-provider communication process; and, may affect diabetes self-management. ^ This study examined the relationships among participants’ report of: (1) medical advice given; (2) diabetes self-management, and; (3) health outcomes for Mexican-Americans (MA) and Black non-Hispanics (BNH) as compared to WNH (reference group) using data available through the National Health and Nutrition Examination Survey (NHANES) for the years 2007–2008. This study was a secondary, single point analysis. Approximately 30 datasets were merged; and, the quality and integrity was assured by analysis of frequency, range and quartiles. The subjects were extracted based on the following inclusion criteria: belonging to either the MA, BNH or WNH categories; 21 years or older; responded yes to being diagnosed with diabetes. A final sample size of 654 adults [MA (131); BNH (223); WNH (300)] was used for the analyses. The findings revealed significant statistical differences in medical advice reported given. BNH [OR = 1.83 (1.16, 2.88), p = 0.013] were more likely than WNH to report being told to reduce fat or calories. Similarly, BNH [OR = 2.84 (1.45, 5.59), p = 0.005] were more likely than WNH to report that they were told to increase their physical activity. Mexican-Americans were less likely to self-monitor their blood glucose than WNH [OR = 2.70 (1.66, 4.38), p<0.001]. There were differences among ethnicities for reporting receiving recent diabetes education. Black, non-Hispanics were twice as likely to report receiving diabetes education than WNH [OR = 2.29 (1.36, 3.85), p = 0.004]. Medical advice reported given and ethnicity/race, together, predicted several health outcomes. Having recent diabetes education increased the likelihood of performing several diabetes self-management behaviors, independent of race. ^ These findings indicate a need for patient-provider communication and care to be assessed for effectiveness and, the importance of ongoing diabetes education for persons with diabetes.^

Scheduling medical application workloads on virtualized computing systems

This dissertation presents and evaluates a methodology for scheduling medical application workloads in virtualized computing environments. Such environments are being widely adopted by providers of "cloud computing" services. In the context of provisioning resources for medical applications, such environments allow users to deploy applications on distributed computing resources while keeping their data secure. Furthermore, higher level services that further abstract the infrastructure-related issues can be built on top of such infrastructures. For example, a medical imaging service can allow medical professionals to process their data in the cloud, easing them from the burden of having to deploy and manage these resources themselves. In this work, we focus on issues related to scheduling scientific workloads on virtualized environments. We build upon the knowledge base of traditional parallel job scheduling to address the specific case of medical applications while harnessing the benefits afforded by virtualization technology. To this end, we provide the following contributions: (1) An in-depth analysis of the execution characteristics of the target applications when run in virtualized environments. (2) A performance prediction methodology applicable to the target environment. (3) A scheduling algorithm that harnesses application knowledge and virtualization-related benefits to provide strong scheduling performance and quality of service guarantees. In the process of addressing these pertinent issues for our target user base (i.e. medical professionals and researchers), we provide insight that benefits a large community of scientific application users in industry and academia. Our execution time prediction and scheduling methodologies are implemented and evaluated on a real system running popular scientific applications. We find that we are able to predict the execution time of a number of these applications with an average error of 15%. Our scheduling methodology, which is tested with medical image processing workloads, is compared to that of two baseline scheduling solutions and we find that it outperforms them in terms of both the number of jobs processed and resource utilization by 20–30%, without violating any deadlines. We conclude that our solution is a viable approach to supporting the computational needs of medical users, even if the cloud computing paradigm is not widely adopted in its current form.

Predictors of success: Medical Laboratory Associate in Science Degree Program

Hospitals and healthcare facilities in the United States are facing serious shortages of medical laboratory personnel, which, if not addressed, stand to negatively impact patient care. The problem is compounded by a reduction in the numbers of academic programs and resulting decrease in the number of graduates to keep up with the increase in industry demands. Given these challenges, the purpose of this study was to identify predictors of success for students in a selected 2-year Medical Laboratory Technology Associate in Science Degree Program. ^ This study examined five academic factors (College Placement Test Math and Reading scores, Cumulative GPA, Science GPA, and Professional [first semester laboratory courses] GPA) and, demographic data to see if any of these factors could predict program completion. The researcher examined academic records for a 10-year period (N =158). Using a retrospective model, the correlational analysis between the variables and completion revealed a significant relationship (p < .05) for CGPA, SGPA, CPT Math, and PGPA indicating that students with higher CGPA, SGPA, CPT Math, and PGPA were more likely to complete their degree in 2 years. Binary logistic regression analysis with the same academic variables revealed PGPA was the best predictor of program completion (p < .001). ^ Additionally, the findings in this study are consistent with the academic part of the Bean and Metzner Conceptual Model of Nontraditional Student Attrition which points to academic outcome variables such as GPA as affecting attrition. Thus, the findings in this study are important to students and educators in the field of Medical Laboratory Technology since PGPA is a predictor that can be used to provide early in-program intervention to the at-risk student, thus increasing the chances of successful timely completion.^

FIU Medical Library Stacks and Computers

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FIU Medical Library Computers

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Relationship of Lifestyle Medical Advice and Non-HDL Cholesterol Control of a Nationally Representative US Sample with Hypercholesterolemia by Race/Ethnicity

Objective. The main purpose of this study was to evaluate the associations of lifestyle medical advice and non-HDL cholesterol control of a nationally representative US sample of adults with hypercholesterolemia by race/ethnicity. Methods. Data were collected by appending sociodemographic, anthropometric, and laboratory data from two cycles of the National Health and Nutrition Survey (2007-2008 and 2009-2010). This study acquired data from male and female adults aged ≥ 20 years (N = 11,577), classified as either Mexican American (MA), (), other Hispanic (OH) (), Black non-Hispanic (BNH) (), or White non-Hispanic (WNH) (). Results. Minorities were more likely to report having received dietary, weight management, and exercise recommendations by healthcare professionals than WNH, adjusting for confounders. Approximately 80% of those receiving medical advice followed the recommendation, regardless of race/ethnicity. Of those who received medical advice, reporting “currently controlling or losing weight” was associated with lower non-HDL cholesterol. BNH who reported “currently controlling or losing weight” had higher non-HDL cholesterol than WNH who reported following the advice. Conclusion. The results suggest that current methods of communicating lifestyle advice may not be adequate across race/ethnicity and that a change in perspective and delivery of medical recommendations for persons with hypercholesterolemia is needed.

Medical advice and diabetes self-management reported by Mexican-American, Black- and White-non-Hispanic adults across the United States

Background Diabetes has reached epidemic proportions in the United States, particularly among minorities, and if improperly managed can lead to medical complications and death. Healthcare providers play vital roles in communicating standards of care, which include guidance on diabetes self-management. The background of the client may play a role in the patient-provider communication process. The aim of this study was to determine the association between medical advice and diabetes self care management behaviors for a nationally representative sample of adults with diabetes. Moreover, we sought to establish whether or not race/ethnicity was a modifier for reported medical advice received and diabetes self-management behaviors. Methods We analyzed data from 654 adults aged 21 years and over with diagnosed diabetes [130 Mexican-Americans; 224 Black non-Hispanics; and, 300 White non-Hispanics] and an additional 161 with 'undiagnosed diabetes' [N = 815(171 MA, 281 BNH and 364 WNH)] who participated in the National Health and Nutrition Examination Survey (NHANES) 2007-2008. Logistic regression models were used to evaluate whether medical advice to engage in particular self-management behaviors (reduce fat or calories, increase physical activity or exercise, and control or lose weight) predicted actually engaging in the particular behavior and whether the impact of medical advice on engaging in the behavior differed by race/ethnicity. Additional analyses examined whether these relationships were maintained when other factors potentially related to engaging in diabetes self management such as participants' diabetes education, sociodemographics and physical characteristics were controlled. Sample weights were used to account for the complex sample design. Results Although medical advice to the patient is considered a standard of care for diabetes, approximately one-third of the sample reported not receiving dietary, weight management, or physical activity self-management advice. Participants who reported being given medical advice for each specific diabetes self-management behaviors were 4-8 times more likely to report performing the corresponding behaviors, independent of race. These results supported the ecological model with certain caveats. Conclusions Providing standard medical advice appears to lead to diabetes self-management behaviors as reported by adults across the United States. Moreover, it does not appear that race/ethnicity influenced reporting performance of the standard diabetes self-management behavior. Longitudinal studies evaluating patient-provider communication, medical advice and diabetes self-management behaviors are needed to clarify our findings.

Context -aware data caching for mobile computing environments

The deployment of wireless communications coupled with the popularity of portable devices has led to significant research in the area of mobile data caching. Prior research has focused on the development of solutions that allow applications to run in wireless environments using proxy based techniques. Most of these approaches are semantic based and do not provide adequate support for representing the context of a user (i.e., the interpreted human intention.). Although the context may be treated implicitly it is still crucial to data management. In order to address this challenge this dissertation focuses on two characteristics: how to predict (i) the future location of the user and (ii) locations of the fetched data where the queried data item has valid answers. Using this approach, more complete information about the dynamics of an application environment is maintained. ^ The contribution of this dissertation is a novel data caching mechanism for pervasive computing environments that can adapt dynamically to a mobile user's context. In this dissertation, we design and develop a conceptual model and context aware protocols for wireless data caching management. Our replacement policy uses the validity of the data fetched from the server and the neighboring locations to decide which of the cache entries is less likely to be needed in the future, and therefore a good candidate for eviction when cache space is needed. The context aware driven prefetching algorithm exploits the query context to effectively guide the prefetching process. The query context is defined using a mobile user's movement pattern and requested information context. Numerical results and simulations show that the proposed prefetching and replacement policies significantly outperform conventional ones. ^ Anticipated applications of these solutions include biomedical engineering, tele-health, medical information systems and business. ^

Dietary Quality and Comorbidities of Adults at Medical Risk for Diabetes

Background and aims: The current study evaluates following a special diet with diet quality and comorbidities (hypertension, hypercholesterolemia, and obesity) in four racial/ethnic groups diagnosed with prediabetes or “at risk for diabetes”. Methods and results: This is a cross-sectional analysis of data from the National Health and Nutrition Examination Surveys (NHANES), 2007- 2008 and 2009-2010. Sample weights were used to achieve a representative sample. Data were available for N = 2666 adults, aged ≥20 years (508 Mexican American, 294, Other Hispanic, 616 Black non-Hispanic, and 1248 White non-Hispanic) who were medically diagnosed with either prediabetes or “at risk for diabetes”. Those reporting following a special diet had greater odds of meeting saturated fat guidelines (

Relationship of Lifestyle Medical Advice and Non-HDL Cholesterol Control of a Nationally Representative US Sample with Hypercholesterolemia by Race/Ethnicity

Objective: The main purpose of this study was to evaluate the associations of lifestyle medical advice and non-HDL cholesterol control of a nationally representative US sample of adults with hypercholesterolemia by race/ethnicity. Methods: Data were collected by appending sociodemographic, anthropometric, and laboratory data from two cycles of the National Health and Nutrition Survey (2007-2008 and 2009-2010). This study acquired data from male and female adults aged ≥ 20 years (N = 11,577), classified as either Mexican American (MA), (), other Hispanic (OH) (), Black non-Hispanic (BNH) (), or White non-Hispanic (WNH) (). Results: Minorities were more likely to report having received dietary, weight management, and exercise recommendations by healthcare professionals than WNH, adjusting for confounders. Approximately 80% of those receiving medical advice followed the recommendation, regardless of race/ethnicity. Of those who received medical advice, reporting “currently controlling or losing weight” was associated with lower non-HDL cholesterol. BNH who reported “currently controlling or losing weight” had higher non-HDL cholesterol than WNH who reported following the advice. Conclusion: The results suggest that current methods of communicating lifestyle advice may not be adequate across race/ethnicity and that a change in perspective and delivery of medical recommendations for persons with hypercholesterolemia is needed.

Medical Advice and Diabetes Self-Management Reported by Mexican-American, Black- and White-Non-Hispanic Adults Across the United States

Background: Diabetes has reached epidemic proportions in the United States, particularly among minorities, and if improperly managed can lead to medical complications and death. Healthcare providers play vital roles in communicating standards of care, which include guidance on diabetes self-management. The background of the client may play a role in the patient-provider communication process. The aim of this study was to determine the association between medical advice and diabetes self care management behaviors for a nationally representative sample of adults with diabetes. Moreover, we sought to establish whether or not race/ethnicity was a modifier for reported medical advice received and diabetes self-management behaviors. Methods: We analyzed data from 654 adults aged 21 years and over with diagnosed diabetes [130 MexicanAmericans; 224 Black non-Hispanics; and, 300 White non-Hispanics] and an additional 161 with ‘undiagnosed diabetes’ [N = 815(171 MA, 281 BNH and 364 WNH)] who participated in the National Health and Nutrition Examination Survey (NHANES) 2007-2008. Logistic regression models were used to evaluate whether medical advice to engage in particular self-management behaviors (reduce fat or calories, increase physical activity or exercise, and control or lose weight) predicted actually engaging in the particular behavior and whether the impact of medical advice on engaging in the behavior differed by race/ethnicity. Additional analyses examined whether these relationships were maintained when other factors potentially related to engaging in diabetes self management such as participants’ diabetes education, sociodemographics and physical characteristics were controlled. Sample weights were used to account for the complex sample design. Results: Although medical advice to the patient is considered a standard of care for diabetes, approximately onethird of the sample reported not receiving dietary, weight management, or physical activity self-management advice. Participants who reported being given medical advice for each specific diabetes self-management behaviors were 4-8 times more likely to report performing the corresponding behaviors, independent of race. These results supported the ecological model with certain caveats. Conclusions: Providing standard medical advice appears to lead to diabetes self-management behaviors as reported by adults across the United States. Moreover, it does not appear that race/ethnicity influenced reporting performance of the standard diabetes self-management behavior. Longitudinal studies evaluating patient-provider communication, medical advice and diabetes self-management behaviors are needed to clarify our findings.