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This is the study of Acute Impact of Ujjayi Yogic Pranayama vs Aerobic Exercise on Cognitive Performance and Short Term Memory. The purpose of this research was to compare two forms of exercise and their effects on someone's cognitive performance and short term memory. The research was performed in an acute setting were both exercises was conducted in under 15 minutes of active participation. The research question was; will aerobic exercise or the Pranayama breathing exercise provide better results and demonstrate a more effective way to increase the cognitive performance and short term memory for a college student aged 18-30. This was accomplished by using an aerobic exercise on an elliptical machine and then participating in the breathing exercise for 10 minutes in both scenarios. This study had two scenarios. Each scenario had a preliminary cognitive performance and short-term memory, post-Ujjayi exercise had a cognitive performance and short-term memory and a post-aerobic exercise had a cognitive performance and short-term memory. There was an hour break between Ujjayi exercise and aerobic exercise in both scenarios to prevent any type of bias. Scenario 1 had these three settings but the students were not given a breakfast supplement. In Scenario 2 the students were given a break supplement and followed the same procedures as scenario 1. There were 25 students for scenario 1 and 25 students for scenario 2. The students were allowed to participate in scenario 1 and 2 but it had to be a week after their first participation. All participants were originally signed up for scenario 1 and they could come back to perform scenario 2 a week later. The first scenario was completing the tests in the absence of food. Scenario two was completing the tests after having been given a Clif Bar to consume. The results of both of these scenarios showed that for cognitive performance and short term memory aerobic exercise had a beneficial impact on their performance. However, students who had a breakfast performed better on the preliminary tests and scored better after the yogic Ujjayi Pranayama exercise on their cognitive performance and short term memory tests. There was also a negligible difference between the test results after the preliminary tests and yogic Ujjayi Pranayama. However, in scenario one the overall tests scores for preliminary and yogic Ujjayi Pranayama were less than those in scenario two. Students who recorded that they were more actively engaging in regular physical exercise 3-7 days a week also did worse in scenario 1, but when presented with scenario 2 they scored equal with those who did not perform regular exercise. The overall purpose for this research was to find out how to increase cognitive performance and short term memory ability in college age students 18-30 in a short amount of time. The results of this study will be impactful for the future studies that will be focused on when comparing aerobic exercise and yogic pranayama.
ContributorsKopecky, Zachary (Co-author) / Enright, Roan (Co-author) / McILwraith, Heide (Thesis director) / Lee, Rebecca (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This study confirms that there is stigma attached to how Somali-Americans perceive mental and emotional impairments compared to the perception of physical disabilities and impairments. More Somali-Americans are willing to seek help regarding their mental and physical health which is a positive step in improving the perceptions of Somali-Americans towards mental or emotional impairments and physical disabilities. Findings can contribute to the knowledge of health care professionals (i.e. nurses) in caring for patients identifying as Somali to promote culturally competent care.
ContributorsAden, Amina (Author) / Hosley, Brenda (Thesis director) / Lee, Rebecca (Committee member) / Lyles, Annmarie (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging and diagnosis. The tools have been extensively used in a number of medical studies including brain tumor, breast cancer, liver cancer, Alzheimer's disease, and migraine. Recognizing the need from users in the medical field for a simplified interface and streamlined functionalities, this project aims to democratize this pipeline so that it is more readily available to health practitioners and third party developers.
ContributorsBaer, Lisa Zhou (Author) / Wu, Teresa (Thesis director) / Wang, Yalin (Committee member) / Computer Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Research supports that music therapy can be used in multiple aspects of care for patients living within different environments. There is a gap in the literature when it comes to the impact of music sessions for older adults who do not have a diagnosed disease, therefore this study analyzes this population specifically. This study examines music therapy and its effects on anxiety and depression in adults aged 65 or older living in independent living homes. The adults participated in a mixed-methods study over the span of one month examining music as an intervention to decrease anxiety and depression. Each subject consented into the study, completed a demographic survey, answered open-ended questions regarding their experience with anxiety/sadness and ways to cope, as well as Profile of Moods Scale (POMS) during the first session. On the last week of the study, the participants were asked to fill out the same POMS scale to evaluate whether music influenced anxiety and depression. There was limited evidence found in this study to support the use of music therapy as an intervention to decrease anxiety and depression in adults over the age of 65.
ContributorsWolfus, Sarah Ilyssa (Author) / Lee, Rebecca (Thesis director) / Larkey, Linda (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The Centers for Disease Control and Prevention in the United States announced that there has been roughly a 50% increase in the prevalence of food allergies among people between the years of 1997 - 2011. A food allergy can be described as a medical condition where being exposed to a certain food triggers a harmful immune response in the body, known as an allergic reaction. These reactions can range from mild to fatal, and they are caused mainly by the top 8 major food allergens: dairy, eggs, peanuts, tree nuts, wheat, soy, fish, and shellfish. Food allergies mainly plague children under the age of 3, as some of them will grow out of their allergy sensitivity over time, and most people develop their allergies at a young age, and not when they are older. The rise in prevalence is becoming a frightening problem around the world, and there are emerging theories that are attempting to ascribe a cause. There are three well-known hypotheses that will be discussed: the Hygiene Hypothesis, the Dual-Allergen Exposure Hypothesis, and the Vitamin-D Deficiency Hypothesis. Beyond that, this report proposes that a new hypothesis be studied, the Food Systems Hypothesis. This hypothesis theorizes that the cause of the rise of food allergies is actually caused by changes in the food itself and particularly the pesticides that are used to cultivate it.
ContributorsCromer, Kelly (Author) / Lee, Rebecca (Thesis director) / MacFadyen, Joshua (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Hematopoietic stem cell transplantation (HSCT) is a unique but intense procedure used to save the lives of patients with hematopoietic malignancies. However, patients and caregivers undergoing HSCT can experience prolonged psychological distress due to an intense and distinctive transplant process. Types of psychological distress include anxiety, depression, social isolation, and post-traumatic stress disorder. Although this a significant healthcare problem, limited research has been conducted within the HSCT patient and caregiver population to investigate ways to improve their mental health. The purpose of this study was to examine the effects of an educational video intervention about post-transplant recovery in decreasing emotional distress and promoting emotional well-being in HSCT patients and caregivers. This pilot study utilized a quantitative single-group pretest-posttest design to examine the effect of educational videos on participant's emotional well-being. Four educational videos were developed using information gathered from several reliable bone marrow transplant and cancer websites. A convenience sampling method was used to recruit HSCT patient and caregiver participants. Eleven Caucasian, English-speaking individuals (6 patients, 5 caregivers; 54.5% female; M age= 43.7 years) across the United States were enrolled in the 60-90 minute online intervention. Participant responses were measured using pretest and posttest questionnaires. Results from the study found that the educational videos were effective in decreasing levels of depression and anxiety. Implications for nursing practice include the need to educate HSCT patients and caregivers about transplant recovery to decrease emotional distress. This study demonstrates the impact post-transplant education has on decreasing depression and anxiety in HSCT patients and caregivers.
ContributorsBosselman, Kate Elizabeth (Author) / Kim, Sunny (Thesis director) / Lee, Rebecca (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation constructs a new computational processing framework to robustly and precisely quantify retinotopic maps based on their angle distortion properties. More generally, this framework solves the problem of how to robustly and precisely quantify (angle) distortions of noisy or incomplete (boundary enclosed) 2-dimensional surface to surface mappings. This framework builds upon the Beltrami Coefficient (BC) description of quasiconformal mappings that directly quantifies local mapping (circles to ellipses) distortions between diffeomorphisms of boundary enclosed plane domains homeomorphic to the unit disk. A new map called the Beltrami Coefficient Map (BCM) was constructed to describe distortions in retinotopic maps. The BCM can be used to fully reconstruct the original target surface (retinal visual field) of retinotopic maps. This dissertation also compared retinotopic maps in the visual processing cascade, which is a series of connected retinotopic maps responsible for visual data processing of physical images captured by the eyes. By comparing the BCM results from a large Human Connectome project (HCP) retinotopic dataset (N=181), a new computational quasiconformal mapping description of the transformed retinal image as it passes through the cascade is proposed, which is not present in any current literature. The description applied on HCP data provided direct visible and quantifiable geometric properties of the cascade in a way that has not been observed before. Because retinotopic maps are generated from in vivo noisy functional magnetic resonance imaging (fMRI), quantifying them comes with a certain degree of uncertainty. To quantify the uncertainties in the quantification results, it is necessary to generate statistical models of retinotopic maps from their BCMs and raw fMRI signals. Considering that estimating retinotopic maps from real noisy fMRI time series data using the population receptive field (pRF) model is a time consuming process, a convolutional neural network (CNN) was constructed and trained to predict pRF model parameters from real noisy fMRI data
ContributorsTa, Duyan Nguyen (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Hansford, Dianne (Committee member) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Retinotopic map, the map between visual inputs on the retina and neuronal activation in brain visual areas, is one of the central topics in visual neuroscience. For human observers, the map is typically obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Biological evidences show the retinotopic mapping is topology-preserving/topological (i.e. keep the neighboring relationship after human brain process) within each visual region.
Unfortunately, due to limited spatial resolution and the signal-noise ratio of fMRI, state of art retinotopic map is not topological.
The topic was to model the topology-preserving condition mathematically, fix non-topological retinotopic map with numerical methods, and improve the quality of retinotopic maps.
The impose of topological condition, benefits several applications.
With the topological retinotopic maps, one may have a better insight on human retinotopic maps, including better cortical magnification factor quantification, more precise description of retinotopic maps, and potentially better exam ways of in Ophthalmology clinic.
ContributorsTu, Yanshuai (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Crook, Sharon (Committee member) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2022