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- Creators: Gaesser, Glenn A
- Creators: Li, Jing
Description
Cardiovascular disease (CVD) is the number one cause of death in the United States and type 2 diabetes (T2D) and obesity lead to cardiovascular disease. Obese adults are more susceptible to CVD compared to their non-obese counterparts. Exercise training leads to large reductions in the risk of CVD and T2D. Recent evidence suggests high-intensity interval training (HIT) may yield similar or superior benefits in a shorter amount of time compared to traditional continuous exercise training. The purpose of this study was to compare the effects of HIT to continuous (CONT) exercise training for the improvement of endothelial function, glucose control, and visceral adipose tissue. Seventeen obese men (N=9) and women (N=8) were randomized to eight weeks of either HIT (N=9, age=34 years, BMI=37.6 kg/m2) or CONT (N=8, age=34 years, BMI=34.6 kg/m2) exercise 3 days/week for 8 weeks. Endothelial function was assessed via flow-mediated dilation (FMD), glucose control was assessed via continuous glucose monitoring (CGM), and visceral adipose tissue and body composition was measured with an iDXA. Incremental exercise testing was performed at baseline, 4 weeks, and 8 weeks. There were no changes in weight, fat mass, or visceral adipose tissue measured by the iDXA, but there was a significant reduction in body fat that did not differ by group (46±6.3 to 45.4±6.6%, P=0.025). HIT led to a significantly greater improvement in FMD compared to CONT exercise (HIT: 5.1 to 9.0%; CONT: 5.0 to 2.6%, P=0.006). Average 24-hour glucose was not improved over the whole group and there were no group x time interactions for CGM data (HIT: 103.9 to 98.2 mg/dl; CONT: 99.9 to 100.2 mg/dl, P>0.05). When statistical analysis included only the subjects who started with an average glucose at baseline > 100 mg/dl, there was a significant improvement in glucose control overall, but no group x time interaction (107.8 to 94.2 mg/dl, P=0.027). Eight weeks of HIT led to superior improvements in endothelial function and similar improvements in glucose control in obese subjects at risk for T2D and CVD. HIT was shown to have comparable or superior health benefits in this obese sample with a 36% lower total exercise time commitment.
ContributorsSawyer, Brandon J (Author) / Gaesser, Glenn A (Thesis advisor) / Shaibi, Gabriel (Committee member) / Lee, Chong (Committee member) / Swan, Pamela (Committee member) / Buman, Matthew (Committee member) / Arizona State University (Publisher)
Created2013
Description
The effects of aging on muscular efficiency are controversial. Proponents for increased efficiency suggest that age-related changes in muscle enhance efficiency in senescence. Exercise study results are mixed due to varying modalities, ages, and efficiency calculations. The present study attempted to address oxygen uptake, caloric expenditure, walking economy, and gross
et cycling efficiency in young (18-59 years old) and older (60-81 years old) adults (N=444). Walking was performed at three miles per hour by 86 young (mean = 29.60, standard deviation (SD) = 10.50 years old) and 121 older adults (mean = 66.80, SD = 4.50 years old). Cycling at 50 watts (60-70 revolutions per minute) was performed by 116 young (mean= 29.00, SD= 10.00 years old) and 121 older adults (m = 67.10 SD = 4.50 years old). Steady-state sub-maximal gross
et oxygen uptake and caloric expenditures from each activity and rest were analyzed. Net walking economy was represented by net caloric expenditure (kilocalories/kilogram/min). Cycling measures included percent gross
et cycling efficiency (kilo-calorie derived). Linear regressions were used to assess each measure as a function of age. Differences in age group means were assessed using independent t-tests for each modality (alpha = 0.05). No significant differences in mean oxygen uptake nor walking economy were found between young and older walkers (p>0.05). Older adults performing cycle ergometry demonstrated lower gross
et oxygen uptakes and lower gross caloric expenditures (p< 0.05).
et cycling efficiency in young (18-59 years old) and older (60-81 years old) adults (N=444). Walking was performed at three miles per hour by 86 young (mean = 29.60, standard deviation (SD) = 10.50 years old) and 121 older adults (mean = 66.80, SD = 4.50 years old). Cycling at 50 watts (60-70 revolutions per minute) was performed by 116 young (mean= 29.00, SD= 10.00 years old) and 121 older adults (m = 67.10 SD = 4.50 years old). Steady-state sub-maximal gross
et oxygen uptake and caloric expenditures from each activity and rest were analyzed. Net walking economy was represented by net caloric expenditure (kilocalories/kilogram/min). Cycling measures included percent gross
et cycling efficiency (kilo-calorie derived). Linear regressions were used to assess each measure as a function of age. Differences in age group means were assessed using independent t-tests for each modality (alpha = 0.05). No significant differences in mean oxygen uptake nor walking economy were found between young and older walkers (p>0.05). Older adults performing cycle ergometry demonstrated lower gross
et oxygen uptakes and lower gross caloric expenditures (p< 0.05).
ContributorsFlores, Michelle (Author) / Gaesser, Glenn A (Committee member) / Campbell, Kathryn D (Committee member) / Angadi, Siddhartha S (Committee member) / Arizona State University (Publisher)
Created2014
Description
Discriminative learning when training and test data belong to different distributions is a challenging and complex task. Often times we have very few or no labeled data from the test or target distribution, but we may have plenty of labeled data from one or multiple related sources with different distributions. Due to its capability of migrating knowledge from related domains, transfer learning has shown to be effective for cross-domain learning problems. In this dissertation, I carry out research along this direction with a particular focus on designing efficient and effective algorithms for BioImaging and Bilingual applications. Specifically, I propose deep transfer learning algorithms which combine transfer learning and deep learning to improve image annotation performance. Firstly, I propose to generate the deep features for the Drosophila embryo images via pretrained deep models and build linear classifiers on top of the deep features. Secondly, I propose to fine-tune the pretrained model with a small amount of labeled images. The time complexity and performance of deep transfer learning methodologies are investigated. Promising results have demonstrated the knowledge transfer ability of proposed deep transfer algorithms. Moreover, I propose a novel Robust Principal Component Analysis (RPCA) approach to process the noisy images in advance. In addition, I also present a two-stage re-weighting framework for general domain adaptation problems. The distribution of source domain is mapped towards the target domain in the first stage, and an adaptive learning model is proposed in the second stage to incorporate label information from the target domain if it is available. Then the proposed model is applied to tackle cross lingual spam detection problem at LinkedIn’s website. Our experimental results on real data demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsSun, Qian (Author) / Ye, Jieping (Committee member) / Xue, Guoliang (Committee member) / Liu, Huan (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2015
Description
Cardiovascular disease and diabetes are major health burdens. Diabetes is a primary risk factor of cardiovascular disease, and there is a strong link between obesity and risk of developing diabetes. With the prevalence of prediabetes highest among overweight/obese individuals, investigation into preventative strategies are needed. Aerobic exercise is a potent stimulus for both insulin and non-insulin dependent glucose uptake into the skeletal muscle. A single exercise session can improve insulin sensitivity within hours after exercise. The effects of intensity, type, and volume of exercise on glucose homeostasis have been studied extensively; however, controlling for muscle contraction frequency with a constant exercise intensity and workload has not been examined. The purpose of this study was to compare muscle contraction frequency during aerobic exercise by altering cycling cadence on insulin sensitivity and vascular health. Eleven obese males (age=28yr, BMI=35kg/m2) completed three conditions in random order: 1) control-no exercise; 2) 45-min cycling at 45 revolutions per minute (45RPM) at 65-75%VO2max; 3) 45-min cycling at 90RPM at 65-75%VO2max. Glucose control and insulin sensitivity were assessed with oral glucose tolerance tests (OGTT) 4 hours post-exercise. Vascular health was assessed via flow-mediated dilation (FMD) pre-exercise, 1-hr and 2-hr post exercise and ambulatory blood pressure was assessed pre-exercise, and continually every 15 min post-exercise. Linear mixed models were used to compare the mean differences in outcome variables. There were no significant differences found between control and both exercise conditions for all OGTT outcomes and no differences were found between control and exercise in FMD (all, p>0.05). Significant effects for exercise were found for both brachial and central blood pressure measures. Brachial systolic blood pressures were lower at 2- and 4-hr post-exercise by approximately -10 and -8mmHg, respectively (p<0.001 and p=0.004) versus control. Central systolic blood pressures were lower at 2-, 3-, and 4-hr post-exercise by approximately -8, -9 and -6mmHg, respectively (p<0.001, p=0.021 and p=0.004) versus control. In conclusion, aerobic exercise, regardless of muscle contraction frequency, were unable to effect glucose control and insulin sensitivity. Similarly, there was no effect on vascular function. However, there was a significant effect of aerobic exercise on reducing post-exercise blood pressure.
ContributorsJarrett, Catherine Lee (Author) / Gaesser, Glenn A (Thesis advisor) / Angadi, Siddhartha S (Committee member) / Dickinson, Jared M (Committee member) / Whisner, Corrie M (Committee member) / Todd, Michael W (Committee member) / Arizona State University (Publisher)
Created2017
Description
Transfer learning is a sub-field of statistical modeling and machine learning. It refers to methods that integrate the knowledge of other domains (called source domains) and the data of the target domain in a mathematically rigorous and intelligent way, to develop a better model for the target domain than a model using the data of the target domain alone. While transfer learning is a promising approach in various application domains, my dissertation research focuses on the particular application in health care, including telemonitoring of Parkinson’s Disease (PD) and radiomics for glioblastoma.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
ContributorsYoon, Hyunsoo (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Hu, Leland S. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Transfer learning refers to statistical machine learning methods that integrate the knowledge of one domain (source domain) and the data of another domain (target domain) in an appropriate way, in order to develop a model for the target domain that is better than a model using the data of the target domain alone. Transfer learning emerged because classic machine learning, when used to model different domains, has to take on one of two mechanical approaches. That is, it will either assume the data distributions of the different domains to be the same and thereby developing one model that fits all, or develop one model for each domain independently. Transfer learning, on the other hand, aims to mitigate the limitations of the two approaches by accounting for both the similarity and specificity of related domains. The objective of my dissertation research is to develop new transfer learning methods and demonstrate the utility of the methods in real-world applications. Specifically, in my methodological development, I focus on two different transfer learning scenarios: spatial transfer learning across different domains and temporal transfer learning along time in the same domain. Furthermore, I apply the proposed spatial transfer learning approach to modeling of degenerate biological systems.Degeneracy is a well-known characteristic, widely-existing in many biological systems, and contributes to the heterogeneity, complexity, and robustness of biological systems. In particular, I study the application of one degenerate biological system which is to use transcription factor (TF) binding sites to predict gene expression across multiple cell lines. Also, I apply the proposed temporal transfer learning approach to change detection of dynamic network data. Change detection is a classic research area in Statistical Process Control (SPC), but change detection in network data has been limited studied. I integrate the temporal transfer learning method called the Network State Space Model (NSSM) and SPC and formulate the problem of change detection from dynamic networks into a covariance monitoring problem. I demonstrate the performance of the NSSM in change detection of dynamic social networks.
ContributorsZou, Na (Author) / Li, Jing (Thesis advisor) / Baydogan, Mustafa (Committee member) / Borror, Connie (Committee member) / Montgomery, Douglas C. (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2015
Description
The winter holiday period has been highlighted as a major risk period for weight gain due to excess caloric intake in the form of fat and sugar. Furthermore, diets high in fat and sugar have been implicated in the pathogenesis of diabetes and cardiovascular disease. Exercise aids in the prevention of weight/fat gain, and prevents deleterious changes in cardiometabolic function. The objective of this study was to examine the effects of a fat-sugar supplemented diet, with and without two different exercise training protocols, on body composition, glycemic control and other markers of cardiovascular disease in an at-risk population of overweight and obese males. Twenty-seven, healthy overweight/obese (BMI >25 kg/m2) males were fed 2 donuts per day, 6 days/week, for four weeks, while maintaining their current diet. In addition, all subjects were randomized to one of the following conditions: sedentary control, 1,000 kcal/week moderate-intensity continuous training (MICT) (50% of peak oxygen consumption), or 1,000 kcal/week high-intensity interval training (HIIT) (90-95% of peak heart rate). Supervised exercise training was performed 4 days/week on a cycle ergometer. Changes in body weight and composition, endothelial function, arterial stiffness, glycemic control, blood lipids and cardiorespiratory fitness (CRF) were assessed before and after the intervention. Body weight, lean mass and visceral fat increased significantly in HIIT (p<0.05) and were unchanged in MICT. There was a trend for a significant increase in body weight (p=0.07) and lean mass (p=0.11) in control. Glycemic control during the 2-h OGTT improved significantly in MICT and control, with no change in HIIT. Hepatic insulin resistance index (IRI) and 30-min insulin during the OGTT improved significantly after MICT and worsened following control (p=0.03), while HIIT was unchanged. CRF increased significantly in both HIIT and MICT, with no change in control (p<0.001). There were no significant changes in other markers of cardiovascular disease. The addition of a fat-sugar supplement (~14,500 kcal) over a 4-week period was not sufficient to induce deleterious changes in body composition and cardiometabolic health in overweight/obese young males. Exercise training did not afford overweight/obese males additional health benefits, with the exception of improvements in fitness and hepatic IRI.
ContributorsTucker, Wesley Jack (Author) / Gaesser, Glenn A (Thesis advisor) / Angadi, Siddhartha S (Committee member) / Whisner, Corrie M (Committee member) / Buman, Matthew P (Committee member) / Shaibi, Gabriel (Committee member) / Arizona State University (Publisher)
Created2016
Description
Hepatocellular carcinoma (HCC) is a malignant tumor and seventh most common cancer in human. Every year there is a significant rise in the number of patients suffering from HCC. Most clinical research has focused on HCC early detection so that there are high chances of patient's survival. Emerging advancements in functional and structural imaging techniques have provided the ability to detect microscopic changes in tumor micro environment and micro structure. The prime focus of this thesis is to validate the applicability of advanced imaging modality, Magnetic Resonance Elastography (MRE), for HCC diagnosis. The research was carried out on three HCC patient's data and three sets of experiments were conducted. The main focus was on quantitative aspect of MRE in conjunction with Texture Analysis, an advanced imaging processing pipeline and multi-variate analysis machine learning method for accurate HCC diagnosis. We analyzed the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Along with this we studied different machine learning algorithms and developed models using them. Performance metrics such as Prediction Accuracy, Sensitivity and Specificity have been used for evaluation for the final developed model. We were able to identify the significant features in the dataset and also the selected classifier was robust in predicting the response class variable with high accuracy.
ContributorsBansal, Gaurav (Author) / Wu, Teresa (Thesis advisor) / Mitchell, Ross (Thesis advisor) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2013
Description
Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.
ContributorsGao, Fei (Author) / Wu, Teresa (Thesis advisor) / Li, Jing (Committee member) / Yan, Hao (Committee member) / Patel, Bhavika (Committee member) / Arizona State University (Publisher)
Created2019
Description
Background: Postprandial hyperglycemia can increase levels of oxidative stress and is an independent risk factor for complications associated with type 2 diabetes.
Purpose: To evaluate the acute effects of a 15-min postmeal walk on glucose control and markers of oxidative stress following a high-carbohydrate meal.
Methods: Ten obese subjects (55.0 ± 10.0 yrs) with impaired fasting glucose (107.1 ± 9.0 mg/dL) participated in this repeated measures trial. Subjects arrived at the laboratory following an overnight fast and underwent one of three conditions: 1) Test meal with no walking or fiber (CON), 2) Test meal with 10g fiber and no walking (FIB), 3) Test meal with no fiber followed by a 15-min treadmill walk at preferred walking speed (WALK). Blood samples were taken over four hours and assayed for glucose, insulin, thiobarbituric reactive substances (TBARS), catalase, uric acid, and total antioxidant capacity (TAC). A repeated measures ANOVA was used to compare mean differences for all outcome variables.
Results: The 2hr and 4hr incremental area under the curve (iAUC) for glucose was lower in both FIB (2hr: -93.59 mmol∙120 min∙L-1, p = 0.006; 4hr: -92.59 mmol∙240 min∙L-1; p = 0.041) and WALK (2hr: -77.21 mmol∙120 min∙L-1, p = 0.002; 4hr: -102.94 mmol∙240 min∙L-1; p = 0.005) conditions respectively, compared with CON. There were no differences in 2hr or 4hr iAUC for glucose between FIB and WALK (2hr: p = 0.493; 4hr: p = 0.783). The 2hr iAUC for insulin was significantly lower in both FIB (-37.15 μU ∙h/mL; p = 0.021) and WALK (-66.35 μU ∙h/mL; p < 0.001) conditions, compared with CON, and was significantly lower in the WALK (-29.2 μU ∙h/mL; p = 0.049) condition, compared with FIB. The 4hr iAUC for insulin in the WALK condition was significantly lower than both CON (-104.51 μU ∙h/mL; p = 0.001) and FIB (-77.12 μU ∙h/mL; p = 0.006) conditions. Markers of oxidative stress were not significantly different between conditions.
Conclusion: A moderate 15-minute postmeal walk is an effective strategy to reduce postprandial hyperglycemia. However, it is unclear if this attenuation could lead to improvements in postprandial oxidative stress.
Purpose: To evaluate the acute effects of a 15-min postmeal walk on glucose control and markers of oxidative stress following a high-carbohydrate meal.
Methods: Ten obese subjects (55.0 ± 10.0 yrs) with impaired fasting glucose (107.1 ± 9.0 mg/dL) participated in this repeated measures trial. Subjects arrived at the laboratory following an overnight fast and underwent one of three conditions: 1) Test meal with no walking or fiber (CON), 2) Test meal with 10g fiber and no walking (FIB), 3) Test meal with no fiber followed by a 15-min treadmill walk at preferred walking speed (WALK). Blood samples were taken over four hours and assayed for glucose, insulin, thiobarbituric reactive substances (TBARS), catalase, uric acid, and total antioxidant capacity (TAC). A repeated measures ANOVA was used to compare mean differences for all outcome variables.
Results: The 2hr and 4hr incremental area under the curve (iAUC) for glucose was lower in both FIB (2hr: -93.59 mmol∙120 min∙L-1, p = 0.006; 4hr: -92.59 mmol∙240 min∙L-1; p = 0.041) and WALK (2hr: -77.21 mmol∙120 min∙L-1, p = 0.002; 4hr: -102.94 mmol∙240 min∙L-1; p = 0.005) conditions respectively, compared with CON. There were no differences in 2hr or 4hr iAUC for glucose between FIB and WALK (2hr: p = 0.493; 4hr: p = 0.783). The 2hr iAUC for insulin was significantly lower in both FIB (-37.15 μU ∙h/mL; p = 0.021) and WALK (-66.35 μU ∙h/mL; p < 0.001) conditions, compared with CON, and was significantly lower in the WALK (-29.2 μU ∙h/mL; p = 0.049) condition, compared with FIB. The 4hr iAUC for insulin in the WALK condition was significantly lower than both CON (-104.51 μU ∙h/mL; p = 0.001) and FIB (-77.12 μU ∙h/mL; p = 0.006) conditions. Markers of oxidative stress were not significantly different between conditions.
Conclusion: A moderate 15-minute postmeal walk is an effective strategy to reduce postprandial hyperglycemia. However, it is unclear if this attenuation could lead to improvements in postprandial oxidative stress.
ContributorsKnurick, Jessica (Author) / Johnston, Carol S (Thesis advisor) / Sweazea, Karen L (Committee member) / Gaesser, Glenn A (Committee member) / Shaibi, Gabriel Q (Committee member) / Lee, Chong D (Committee member) / Arizona State University (Publisher)
Created2015