Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 83
Description
Background: Evidence about the purported hypoglycemic and hypolipidemic effects of nopales (prickly pear cactus pads) is limited. Objective: To evaluate the efficacy of nopales for improving cardiometabolic risk factors and oxidative stress, compared to control, in adults with hypercholesterolemia. Design: In a randomized crossover trial, participants were assigned to a 2-wk intervention with 2 cups/day of nopales or cucumbers (control), with a 2 to 3-wk washout period. The study included 16 adults (5 male; 46±14 y; BMI = 31.4±5.7 kg/m2) with moderate hypercholesterolemia (low density lipoprotein cholesterol [LDL-c] = 137±21 mg/dL), but otherwise healthy. Main outcomes measured included: dietary intake (energy, macronutrients and micronutrients), cardiometabolic risk markers (total cholesterol, LDL-c, high density lipoprotein cholesterol [HDL-c], triglycerides, cholesterol distribution in LDL and HDL subfractions, glucose, insulin, homeostasis model assessment, and C-reactive protein), and oxidative stress markers (vitamin C, total antioxidant capacity, oxidized LDL, and LDL susceptibility to oxidation). Effects of treatment, time, or interactions were assessed using repeated measures ANOVA. Results: There was no significant treatment-by-time effect for any dietary composition data, lipid profile, cardiometabolic outcomes, or oxidative stress markers. A significant time effect was observed for energy, which was decreased in both treatments (cucumber, -8.3%; nopales, -10.1%; pTime=0.026) mostly due to lower mono and polyunsaturated fatty acids intake (pTime=0.023 and pTime=0.003, respectively). Both treatments significantly increased triglyceride concentrations (cucumber, 14.8%; nopales, 15.2%; pTime=0.020). Despite the lack of significant treatment-by-time effects, great individual response variability was observed for all outcomes. After the cucumber and nopales phases, a decrease in LDL-c was observed in 44% and 63% of the participants respectively. On average LDL-c was decreased by 2.0 mg/dL (-1.4%) after the cucumber phase and 3.9 mg/dL (-2.9%) after the nopales phase (pTime=0.176). Pro-atherogenic changes in HDL subfractions were observed in both interventions over time, by decreasing the proportion of HDL-c in large HDL (cucumber, -5.1%; nopales, -5.9%; pTime=0.021) and increasing the proportion in small HDL (cucumber, 4.1%; nopales, 7.9%; pTime=0.002). Conclusions: These data do not support the purported benefits of nopales at doses of 2 cups/day for 2-wk on markers of lipoprotein profile, cardiometabolic risk, and oxidative stress in hypercholesterolemic adults.
ContributorsPereira Pignotti, Giselle Adriana (Author) / Vega-Lopez, Sonia (Thesis advisor) / Gaesser, Glenn (Committee member) / Keller, Colleen (Committee member) / Shaibi, Gabriel (Committee member) / Sweazea, Karen (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables.
ContributorsKoh, Derek (Author) / Runger, George C. (Thesis advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2013
Effect of a Wii Fit® intervention on balance, muscular fitness, and bone health in middle-aged women
Description
Sustaining a fall can be hazardous for those with low bone mass. Interventions exist to reduce fall-risk, but may not retain long-term interest. "Exergaming" has become popular in older adults as a therapy, but no research has been done on its preventative ability in non-clinical populations. The purpose was to determine the impact of 12-weeks of interactive play with the Wii Fit® on balance, muscular fitness, and bone health in peri- menopausal women. METHODS: 24 peri-menopausal-women were randomized into study groups. Balance was assessed using the Berg/FICSIT-4 and a force plate. Muscular strength was measured using the isokinetic dynamometer at 60°/180°/240°/sec and endurance was assessed using 50 repetitions at 240°/sec. Bone health was tracked using dual-energy x-ray absorptiometry (DXA) for the hip/lumbar spine and qualitative ultrasound (QUS) of the heel. Serum osteocalcin was assessed by enzyme immunoassay. Physical activity was quantified using the Women's Health Initiative Physical Activity Questionnaire and dietary patterns were measured using the Nurses' Health Food Frequency Questionnaire. All measures were repeated at weeks 6 and 12, except for the DXA, which was completed pre-post. RESULTS: There were no significant differences in diet and PA between groups. Wii Fit® training did not improve scores on the Berg/FICSIT-4, but improved center of pressure on the force plate for Tandem Step, Eyes Closed (p-values: 0.001-0.051). There were no significant improvements for muscular fitness at any of the angular velocities. DXA BMD of the left femoral neck improved in the intervention group (+1.15%) and decreased in the control (-1.13%), but no other sites had significant changes. Osteocalcin indicated no differences in bone turnover between groups at baseline, but the intervention group showed increased bone turnover between weeks 6 and 12. CONCLUSIONS: Findings indicate that WiiFit® training may improve balance by preserving center of pressure. QUS, DXA and osteocalcin data confirm that those in the intervention group were experiencing more bone turnover and bone formation than the control group. In summary, twelve weeks of strength /balance training with the Wii Fit® shows promise as a preventative intervention to reduce fall and fracture risk in non-clinical middle aged women who are at risk.
ContributorsWherry, Sarah Jo (Author) / Swan, Pamela D (Thesis advisor) / Adams, Marc (Committee member) / Der Ananian, Cheryl (Committee member) / Sweazea, Karen (Committee member) / Vaughan, Linda (Committee member) / Arizona State University (Publisher)
Created2014
Description
ABSTRACT
Asthma is a high-stress, chronic medical condition; 1 in 12 adults in the United States combat the bronchoconstriction from asthma. However, there are very few strong studies indicating any alternative therapy for asthmatics, particularly following a cold incidence. Vitamin C has been proven to be effective for other high-stress populations, but the asthmatic population has not yet been trialed. This study examined the effectiveness of vitamin C supplementation during the cold season on cold incidence and asthmatic symptoms. Asthmatics, otherwise-healthy, who were non-smokers and non-athletes between the ages of 18 and 55 with low plasma vitamin C concentrations were separated by anthropometrics and vitamin C status into two groups: either vitamin C (500 mg vitamin C capsule consumed twice per day) or control (placebo capsule consumed twice per day). Subjects were instructed to complete the Wisconsin Upper Respiratory Symptom Survey-21 and a short asthma symptoms questionnaire daily along with a shortened vitamin C Food Frequency Questionnaire and physical activity questionnaire weekly for eight weeks. Blood samples were drawn at Week 0 (baseline), Week 4, and Week 8. Compliance was monitored through a calendar check sheet. The vitamin C levels of both groups increased from Week 0 to Week 4, but decreased in the vitamin C group at Week 8. The vitamin C group had a 19% decrease in plasma histamine while the control group had a 53% increase in plasma histamine at the end of the trial, but this was not statistically significant (p>0.05). Total symptoms recorded from WURSS-21 were 129.3±120.7 for the vitamin C and 271.0±293.9, but the difference was not statistically significant (p=0.724). Total asthma symptoms also slightly varied between the groups, but again was not statistically significant (p=0.154). These results were hindered by the low number of subjects recruited. Continued research in this study approach is necessary to definitively reject or accept the potential role of vitamin C in asthma and cold care.
Asthma is a high-stress, chronic medical condition; 1 in 12 adults in the United States combat the bronchoconstriction from asthma. However, there are very few strong studies indicating any alternative therapy for asthmatics, particularly following a cold incidence. Vitamin C has been proven to be effective for other high-stress populations, but the asthmatic population has not yet been trialed. This study examined the effectiveness of vitamin C supplementation during the cold season on cold incidence and asthmatic symptoms. Asthmatics, otherwise-healthy, who were non-smokers and non-athletes between the ages of 18 and 55 with low plasma vitamin C concentrations were separated by anthropometrics and vitamin C status into two groups: either vitamin C (500 mg vitamin C capsule consumed twice per day) or control (placebo capsule consumed twice per day). Subjects were instructed to complete the Wisconsin Upper Respiratory Symptom Survey-21 and a short asthma symptoms questionnaire daily along with a shortened vitamin C Food Frequency Questionnaire and physical activity questionnaire weekly for eight weeks. Blood samples were drawn at Week 0 (baseline), Week 4, and Week 8. Compliance was monitored through a calendar check sheet. The vitamin C levels of both groups increased from Week 0 to Week 4, but decreased in the vitamin C group at Week 8. The vitamin C group had a 19% decrease in plasma histamine while the control group had a 53% increase in plasma histamine at the end of the trial, but this was not statistically significant (p>0.05). Total symptoms recorded from WURSS-21 were 129.3±120.7 for the vitamin C and 271.0±293.9, but the difference was not statistically significant (p=0.724). Total asthma symptoms also slightly varied between the groups, but again was not statistically significant (p=0.154). These results were hindered by the low number of subjects recruited. Continued research in this study approach is necessary to definitively reject or accept the potential role of vitamin C in asthma and cold care.
ContributorsEarhart, Kathryn Michelle (Author) / Johnston, Carol (Thesis advisor) / Sweazea, Karen (Committee member) / Lespron, Christy (Committee member) / Arizona State University (Publisher)
Created2015
Description
No-confounding designs (NC) in 16 runs for 6, 7, and 8 factors are non-regular fractional factorial designs that have been suggested as attractive alternatives to the regular minimum aberration resolution IV designs because they do not completely confound any two-factor interactions with each other. These designs allow for potential estimation of main effects and a few two-factor interactions without the need for follow-up experimentation. Analysis methods for non-regular designs is an area of ongoing research, because standard variable selection techniques such as stepwise regression may not always be the best approach. The current work investigates the use of the Dantzig selector for analyzing no-confounding designs. Through a series of examples it shows that this technique is very effective for identifying the set of active factors in no-confounding designs when there are three of four active main effects and up to two active two-factor interactions.
To evaluate the performance of Dantzig selector, a simulation study was conducted and the results based on the percentage of type II errors are analyzed. Also, another alternative for 6 factor NC design, called the Alternate No-confounding design in six factors is introduced in this study. The performance of this Alternate NC design in 6 factors is then evaluated by using Dantzig selector as an analysis method. Lastly, a section is dedicated to comparing the performance of NC-6 and Alternate NC-6 designs.
To evaluate the performance of Dantzig selector, a simulation study was conducted and the results based on the percentage of type II errors are analyzed. Also, another alternative for 6 factor NC design, called the Alternate No-confounding design in six factors is introduced in this study. The performance of this Alternate NC design in 6 factors is then evaluated by using Dantzig selector as an analysis method. Lastly, a section is dedicated to comparing the performance of NC-6 and Alternate NC-6 designs.
ContributorsKrishnamoorthy, Archana (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2014
Description
Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
ContributorsAzarnoush, Bahareh (Author) / Runger, George C. (Thesis advisor) / Bekki, Jennifer (Thesis advisor) / Pan, Rong (Committee member) / Saghafian, Soroush (Committee member) / Arizona State University (Publisher)
Created2014
Description
For animals that experience annual cycles of gonad development, the seasonal timing (phenology) of gonad growth is a major adaptation to local environmental conditions. To optimally time seasonal gonad growth, animals use environmental cues that forecast future conditions. The availability of food is one such environmental cue. Although the importance of food availability has been appreciated for decades, the physiological mechanisms underlying the modulation of seasonal gonad growth by this environmental factor remain poorly understood.
Urbanization is characterized by profound environmental changes, and urban animals must adjust to an environment vastly different from that of their non-urban conspecifics. Evidence suggests that birds adjust to urban areas by advancing the timing of seasonal breeding and gonad development, compared to their non-urban conspecifics. A leading hypothesis to account for this phenomenon is that food availability is elevated in urban areas, which improves the energetic status of urban birds and enables them to initiate gonad development earlier than their non-urban conspecifics. However, this hypothesis remains largely untested.
My dissertation dovetailed comparative studies and experimental approaches conducted in field and captive settings to examine the physiological mechanisms by which food availability modulates gonad growth and to investigate whether elevated food availability in urban areas advances the phenology of gonad growth in urban birds. My captive study demonstrated that energetic status modulates reproductive hormone secretion, but not gonad growth. By contrast, free-ranging urban and non-urban birds did not differ in energetic status or plasma levels of reproductive hormones either in years in which urban birds had advanced phenology of gonad growth or in a year that had no habitat-related disparity in seasonal gonad growth. Therefore, my dissertation provides no support for the hypothesis that urban birds begin seasonal gonad growth because they are in better energetic status and increase the secretion of reproductive hormones earlier than non-urban birds. My studies do suggest, however, that the phenology of key food items and the endocrine responsiveness of the reproductive system may contribute to habitat-related disparities in the phenology of gonad growth.
Urbanization is characterized by profound environmental changes, and urban animals must adjust to an environment vastly different from that of their non-urban conspecifics. Evidence suggests that birds adjust to urban areas by advancing the timing of seasonal breeding and gonad development, compared to their non-urban conspecifics. A leading hypothesis to account for this phenomenon is that food availability is elevated in urban areas, which improves the energetic status of urban birds and enables them to initiate gonad development earlier than their non-urban conspecifics. However, this hypothesis remains largely untested.
My dissertation dovetailed comparative studies and experimental approaches conducted in field and captive settings to examine the physiological mechanisms by which food availability modulates gonad growth and to investigate whether elevated food availability in urban areas advances the phenology of gonad growth in urban birds. My captive study demonstrated that energetic status modulates reproductive hormone secretion, but not gonad growth. By contrast, free-ranging urban and non-urban birds did not differ in energetic status or plasma levels of reproductive hormones either in years in which urban birds had advanced phenology of gonad growth or in a year that had no habitat-related disparity in seasonal gonad growth. Therefore, my dissertation provides no support for the hypothesis that urban birds begin seasonal gonad growth because they are in better energetic status and increase the secretion of reproductive hormones earlier than non-urban birds. My studies do suggest, however, that the phenology of key food items and the endocrine responsiveness of the reproductive system may contribute to habitat-related disparities in the phenology of gonad growth.
ContributorsDavies, Scott (Author) / Deviche, Pierre (Thesis advisor) / Sweazea, Karen (Committee member) / McGraw, Kevin (Committee member) / Orchinik, Miles (Committee member) / Warren, Paige (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this era of fast computational machines and new optimization algorithms, there have been great advances in Experimental Designs. We focus our research on design issues in generalized linear models (GLMs) and functional magnetic resonance imaging(fMRI). The first part of our research is on tackling the challenging problem of constructing
exact designs for GLMs, that are robust against parameter, link and model
uncertainties by improving an existing algorithm and providing a new one, based on using a continuous particle swarm optimization (PSO) and spectral clustering. The proposed algorithm is sufficiently versatile to accomodate most popular design selection criteria, and we concentrate on providing robust designs for GLMs, using the D and A optimality criterion. The second part of our research is on providing an algorithm
that is a faster alternative to a recently proposed genetic algorithm (GA) to construct optimal designs for fMRI studies. Our algorithm is built upon a discrete version of the PSO.
exact designs for GLMs, that are robust against parameter, link and model
uncertainties by improving an existing algorithm and providing a new one, based on using a continuous particle swarm optimization (PSO) and spectral clustering. The proposed algorithm is sufficiently versatile to accomodate most popular design selection criteria, and we concentrate on providing robust designs for GLMs, using the D and A optimality criterion. The second part of our research is on providing an algorithm
that is a faster alternative to a recently proposed genetic algorithm (GA) to construct optimal designs for fMRI studies. Our algorithm is built upon a discrete version of the PSO.
ContributorsTemkit, M'Hamed (Author) / Kao, Jason (Thesis advisor) / Reiser, Mark R. (Committee member) / Barber, Jarrett (Committee member) / Montgomery, Douglas C. (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2014
Description
The common cold is a significant cause of morbidity world-wide, with human rhinovirus infections accounting for a majority colds suffered each year. While the symptoms of the common cold are generally mild and self-limiting, vulnerable populations such as individuals with asthma can experience severe secondary complications including acute asthma exacerbation which can result in severe morbidity. Most human rhinovirus types utilize Intercellular Adhesion Molecule-1 (ICAM-1) as a receptor to enter cells and initiate infection. Expression of this cell-surface protein is elevated in the respiratory tract of asthma patients. The theoretical basis for this research is the observation that plasma measures of the soluble form of Intercellular Adhesion Molecule-1 (sICAM-1) decrease in response to vitamin C supplementation. As rhinovirus infection occurs in the upper respiratory tract, the primary aim of this study was to evaluate change in sICAM-1 concentration in nasal lavage of asthmatic individuals in response to vitamin C supplementation. Otherwise healthy asthmatic adults between the ages of 18-65 years who were not currently using steroidal nasal sprays, smoking, or actively training for competitive sports were recruited from a university community and surrounding area to participate in an 18-day double-blind randomized placebo-controlled supplement study with a parallel arm design. 13 subjects were stratified based on age, gender, BMI and baseline plasma vitamin C level to receive either 500 mg vitamin C twice daily (VTC, n=7) or placebo (PLC, n=6). Biochemical measures included nasal lavage sICAM-1, plasma sICAM-1, plasma histamine, and plasma vitamin C. Survey measures included Wisconsin Upper Respiratory Symptom Survey-21 to assess colds, Daytime Symptom Diary Scale to assess asthma symptoms, and measures of diet quality including a vitamin C food frequency questionnaire and Rapid Eating Assessment for Participants. No between group comparison of means reached significance (Mann-Whitney U test, p>0.05). Nasal lavage sICAM-1 levels were decreased in VTC group by 37% at study day 4, although this finding did not reach significance. Findings in this study can be used to develop future investigations into the response of nasal lavage sICAM-1 to vitamin C supplementation.
ContributorsGnant, Lindsay (Author) / Johnston, Carol (Thesis advisor) / Sweazea, Karen (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2014