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The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.
ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2011
Description
The health benefits of physical activity are widely accepted. Emerging research also indicates that sedentary behaviors can carry negative health consequences regardless of physical activity level. This dissertation explored four projects that examined measurement properties of physical activity and sedentary behavior monitors. Project one identified the oxygen costs of four other care activities in seventeen adults. Pushing a wheelchair and pushing a stroller were identified as moderate-intensity activities. Minutes spent engaged in these activities contribute towards meeting the 2008 Physical Activity Guidelines. Project two identified the oxygen costs of common cleaning activities in sixteen adults. Mopping a floor was identified as moderate-intensity physical activity, while cleaning a kitchen and cleaning a bathtub were identified as light-intensity physical activity. Minutes spent engaged in mopping a floor contributes towards meeting the 2008 Physical Activity Guidelines. Project three evaluated the differences in number of minutes spent in activity levels when utilizing different epoch lengths in accelerometry. A shorter epoch length (1-second, 5-seconds) accumulated significantly more minutes of sedentary behaviors than a longer epoch length (60-seconds). The longer epoch length also identified significantly more time engaged in light-intensity activities than the shorter epoch lengths. Future research needs to account for epoch length selection when conducting physical activity and sedentary behavior assessment. Project four investigated the accuracy of four activity monitors in assessing activities that were either sedentary behaviors or light-intensity physical activities. The ActiGraph GT3X+ assessed the activities least accurately, while the SenseWear Armband and ActivPAL assessed activities equally accurately. The monitor used to assess physical activity and sedentary behaviors may influence the accuracy of the measurement of a construct.
ContributorsMeckes, Nathanael (Author) / Ainsworth, Barbara E (Thesis advisor) / Belyea, Michael (Committee member) / Buman, Matthew (Committee member) / Gaesser, Glenn (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Arizona State University (Publisher)
Created2012
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
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
Purpose: The purpose of this study was to examine the acute effects of two novel intermittent exercise prescriptions on glucose regulation and ambulatory blood pressure. Methods: Ten subjects (5 men and 5 women, ages 31.5 ± 5.42 yr, height 170.38 ± 9.69 cm and weight 88.59 ± 18.91 kg) participated in this four-treatment crossover trial. All subjects participated in four trials, each taking place over three days. On the evening of the first day, subjects were fitted with a continuous glucose monitor (CGM). On the second day, subjects were fitted with an ambulatory blood pressure monitor (ABP) and underwent one of the following four conditions in a randomized order: 1) 30-min: 30 minutes of continuous exercise at 60 - 70% VO2peak; 2) Mod 2-min: twenty-one 2-min bouts of walking at 3 mph performed once every 20 minutes; 3) HI 2-min: eight 2-min bouts of walking at maximal incline performed once every hour; 4) Control: a no exercise control condition. On the morning of the third day, the CGM and ABP devices were removed. All meals were standardized during the study visits. Linear mixed models were used to compare mean differences in glucose and blood pressure regulation between the four trials. Results: Glucose concentrations were significantly lower following the 30-min (91.1 ± 14.9 mg/dl), Mod 2-min (93.7 ± 19.8 mg/dl) and HI 2-min (96.1 ± 16.4 mg/dl) trials as compared to the Control (101.1 ± 20 mg/dl) (P < 0.001 for all three comparisons). The 30-min trial was superior to the Mod 2-min, which was superior to the HI 2-min trial in lowering blood glucose levels (P < 0.001 and P = 0.003 respectively). Only the 30-min trial was effective in lowering systolic ABP (124 ± 12 mmHg) as compared to the Control trial (127 ± 14 mmHg; P < 0.001) for up to 11 hours post exercise. Conclusion: Performing frequent short (i.e., 2 minutes) bouts of moderate or high intensity exercise may be a viable alternative to traditional continuous exercise in improving glucose regulation. However, 2-min bouts of exercise are not effective in reducing ambulatory blood pressure in healthy adults.
ContributorsBhammar, Dharini Mukeshkumar (Author) / Gaesser, Glenn A (Thesis advisor) / Shaibi, Gabriel (Committee member) / Buman, Matthew (Committee member) / Swan, Pamela (Committee member) / Lee, Chong (Committee member) / Arizona State University (Publisher)
Created2013
Description
The purpose of this pilot randomized control trial was to test the initial efficacy of a 10 week social cognitive theory (SCT)-based intervention to reduce workplace sitting time (ST). Participants were currently employed adults with predominantly sedentary occupations (n=24) working in the Greater Phoenix area in 2012-2013. Participants wore an activPAL (AP) inclinometer to assess postural allocation (i.e., sitting vs. standing) and Actigraph accelerometer (AG) to assess sedentary time for one week prior to beginning and immediately following the completion of the 10 week intervention. Self-reported measures of sedentary time were obtained via two validated questionnaires for overall (International Physical Activity Questionnaire [IPAQ]) and domain specific sedentary behaviors (Sedentary Behavior Questionnaire [SBQ]). SCT constructs were also measured pre and post via adapted physical activity questionnaires. Participants were randomly assigned to receive either (a) 10 weekly social cognitive-based e-newsletters focused on reducing workplace ST; or (b) similarly formatted 10 weekly e-newsletters focusing on health education. Baseline adjusted Analysis of Covariance statistical analyses were used to examine differences between groups in time spent sitting (AP) and sedentary (AG) during self-reported work hours from pre- to post- intervention. Both groups decreased ST and AG sedentary time; however, no significant differences were observed. SCT constructs also did not change significantly between pretest and post test in either group. These results indicate that individualized educational approaches to decreasing workplace sitting time may not be sufficient for observing long term change in behaviors. Future research should utilize a larger sample, measure main outcomes more frequently, and incorporate more environmental factors throughout the intervention.
ContributorsGordon, Amanda (Author) / Buman, Matthew (Thesis advisor) / Der Ananian, Cheryl (Committee member) / Swan, Pamela (Committee member) / Arizona State University (Publisher)
Created2013
Description
INTRODUCTION: Exercise performed at moderate to vigorous intensities has been shown to generate a post exercise hypotensive response. Whether this response is observed with very low exercise intensities is unclear. PURPOSE: To compare post physical activity ambulatory blood pressure (ABP) response to a single worksite walking day and a normal sedentary work day in pre-hypertensive adults. METHODS: Participants were 7 pre-hypertensive (127 + 8 mmHg / 83 + 8 mmHg) adults (3 male, 4 female, age = 42 + 12 yr) who participated in a randomized, cross-over study that included a control and a walking treatment. Only those who indicated regularly sitting at least 8 hours/day and no structured physical activity were enrolled. Treatment days were randomly assigned and were performed one week apart. Walking treatment consisted of periodically increasing walk time up to 2.5 hours over the course of an 8 hour work day on a walking workstation (Steelcase Company, Grand Rapids, MI). Walk speed was set at 1 mph. Participants wore an ambulatory blood pressure cuff (Oscar 2, SunTech Medical, Morrisville, NC) for 24-hours on both treatment days. Participants maintained normal daily activities on the control day. ABP data collected from 9:00 am until 10:00 pm of the same day were included in statistical analyses. Linear mixed models were used to detect differences in systolic (SBP) and diastolic blood pressure (DBP) by treatment condition over the whole day and post workday for the time periods between 4 -10 pm when participants were no longer at work. RESULTS:BP was significantly lower in response to the walking treatment compared to the control day (Mean SBP 126 +7 mmHg vs.124 +7 mmHg, p=.043; DBP 80 + 3 mmHg vs. 77 + 3 mmHg, p = 0.001 respectively). Post workday (4:00 to 10:00 pm) SBP decreased 3 mmHg (p=.017) and DBP decreased 4 mmHg (p<.001) following walking. CONCLUSION: Even low intensity exercise such as walking on a walking workstation is effective for significantly reducing acute BP when compared to a normal work day.
ContributorsZeigler, Zachary (Author) / Swan, Pamela (Thesis advisor) / Buman, Matthew (Committee member) / Gaesser, Glenn (Committee member) / Arizona State University (Publisher)
Created2013
Description
Eccentric muscle action (ECC) occurs when the force exerted by a working muscle is less than that of an outside resistance. This is characterized by muscle lengthening, despite actin-myosin crossbridge formation. Research has indicated that muscles acting eccentrically are capable of producing more force when compared to muscles acting concentrically. Further, research has shown ECC muscle actions may have different fatigue patterns that CON actions. The purpose of this study was to determine if a) ECC bench press yields greater strength than concentric (CON) as measured by one-repetition maximum (1RM), b) there is a difference between the number of repetitions that can be completed concentrically and eccentrically under the same relative intensities of 1RM (90%, 80%, 70%, 60%), c) a prediction model may be able to predict ECC 1RM from CON 1RM or CON repetitions to fatigue. For this study, 30 healthy males (age = 24.63 + 5.6 years) were tested for 1RM in CON and ECC bench press, as well as the number of repetitions they were able to complete at various intensities of mode-specific 1RM. A mechanical hoist was affixed to a gantry crane and placed over a standard weightlifting bench. The hoist was connected to 45lb plates that were loaded on a standard barbell, which allowed for mechanical raising and lowering of the barbell. For CON repetitions, the weight was mechanically lowered to the chest and the participant pressed it up. For ECC repetitions, the weight was mechanically raised and the participant lowered it. Paired t-tests showed that ECC 1RM was significantly (p < 0.05) greater than CON 1RM (ECC =255.17 + 68.37lbs, CON = 205.83 + 58.43lbs). There was a significant difference (p < 0.05) between the number of repetitions completed at 90% 1RM (CON = 4.57 + 2.21 repetitions, ECC = 7.67 + 3.24 repetitions). There were no differences in repetitions completed at any other intensity 1RM. CON 1RM and the number of repetitions completed with two different absolute loads (130-150lbs and 155-175lbs) concentrically and eccentrically were valid predictors of ECC 1RM. These data indicate that ECC actions yield increased force capabilities than CON actions, there is no difference in the rate of the fatigue, and ECC 1RM may be predicted from various CON tests.
ContributorsKelly, Stephen B., Jr (Author) / Hooker, Steven (Thesis advisor) / Brown, Lee (Committee member) / Buman, Matthew (Committee member) / Gaesser, Glenn (Committee member) / Swan, Pamela (Committee member) / Arizona State University (Publisher)
Created2013
Description
A P-value based method is proposed for statistical monitoring of various types of profiles in phase II. The performance of the proposed method is evaluated by the average run length criterion under various shifts in the intercept, slope and error standard deviation of the model. In our proposed approach, P-values are computed at each level within a sample. If at least one of the P-values is less than a pre-specified significance level, the chart signals out-of-control. The primary advantage of our approach is that only one control chart is required to monitor several parameters simultaneously: the intercept, slope(s), and the error standard deviation. A comprehensive comparison of the proposed method and the existing KMW-Shewhart method for monitoring linear profiles is conducted. In addition, the effect that the number of observations within a sample has on the performance of the proposed method is investigated. The proposed method was also compared to the T^2 method discussed in Kang and Albin (2000) for multivariate, polynomial, and nonlinear profiles. A simulation study shows that overall the proposed P-value method performs satisfactorily for different profile types.
ContributorsAdibi, Azadeh (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Li, Jing (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Rapid advance in sensor and information technology has resulted in both spatially and temporally data-rich environment, which creates a pressing need for us to develop novel statistical methods and the associated computational tools to extract intelligent knowledge and informative patterns from these massive datasets. The statistical challenges for addressing these massive datasets lay in their complex structures, such as high-dimensionality, hierarchy, multi-modality, heterogeneity and data uncertainty. Besides the statistical challenges, the associated computational approaches are also considered essential in achieving efficiency, effectiveness, as well as the numerical stability in practice. On the other hand, some recent developments in statistics and machine learning, such as sparse learning, transfer learning, and some traditional methodologies which still hold potential, such as multi-level models, all shed lights on addressing these complex datasets in a statistically powerful and computationally efficient way. In this dissertation, we identify four kinds of general complex datasets, including "high-dimensional datasets", "hierarchically-structured datasets", "multimodality datasets" and "data uncertainties", which are ubiquitous in many domains, such as biology, medicine, neuroscience, health care delivery, manufacturing, etc. We depict the development of novel statistical models to analyze complex datasets which fall under these four categories, and we show how these models can be applied to some real-world applications, such as Alzheimer's disease research, nursing care process, and manufacturing.
ContributorsHuang, Shuai (Author) / Li, Jing (Thesis advisor) / Askin, Ronald (Committee member) / Ye, Jieping (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2012