Matching Items (81)
Description
Hispanic youth have the highest risk for obesity, making this population a key priority for early childhood interventions to prevent the development of adult obesity and its consequences. Involving parents in these interventions is essential to support positive long-term physical activity and nutrition habits. Interventions in the past have engaged parents by providing information about nutrition and fruit and vegetable intake through written materials or text such as newsletters and text messages. The Sustainability via Active Garden Education (SAGE) intervention used gardening and interactive activities to teach preschool children ages 3-5 about healthy eating and physical activity. It aimed to increase physical activity and fruit and vegetable intake in preschool children as well as improve related parenting practices. The intervention utilized newsletters to engage parents by promoting opportunities to increase physical activity and fruit and vegetable intake for their children at home. The newsletters also encouraged parents to discuss what was learned during the SAGE lessons with their children. The purpose of this paper is to describe the content of the newsletters and determine the parent perception of the newsletters through parent survey responses. This can help inform future childhood obesity interventions and parent engagement.
ContributorsVi, Vinny (Author) / Lee, Rebecca (Thesis director) / Martinelli, Sarah (Committee member) / Edson College of Nursing and Health Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
This document presents a new implementation of the Smoothed Particles Hydrodynamics algorithm using DirectX 11 and DirectCompute. The main goal of this document is to present to the reader an alternative solution to the largely studied and researched problem of fluid simulation. Most other solutions have been implemented using the NVIDIA CUDA framework; however, the proposed solution in this document uses the Microsoft general-purpose computing on graphics processing units API. The implementation allows for the simulation of a large number of particles in a real-time scenario. The solution presented here uses the Smoothed Particles Hydrodynamics algorithm to calculate the forces within the fluid; this algorithm provides a Lagrangian approach for discretizes the Navier-Stockes equations into a set of particles. Our solution uses the DirectCompute compute shaders to evaluate each particle using the multithreading and multi-core capabilities of the GPU increasing the overall performance. The solution then describes a method for extracting the fluid surface using the Marching Cubes method and the programmable interfaces exposed by the DirectX pipeline. Particularly, this document presents a method for using the Geometry Shader Stage to generate the triangle mesh as defined by the Marching Cubes method. The implementation results show the ability to simulate over 64K particles at a rate of 900 and 400 frames per second, not including the surface reconstruction steps and including the Marching Cubes steps respectively.
ContributorsFigueroa, Gustavo (Author) / Farin, Gerald (Thesis advisor) / Maciejewski, Ross (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2012
Description
Acne scarring can negatively affect individuals’ lives long after active acne has resolved. An online survey analyzed the public’s acne history and knowledge of acne scar prevention to determine acne scar risk factors and public awareness of acne scar prevention and yielded 209 complete data sets. Though types of acne scars vary in how long they persist on one’s skin, all forms were found to be equal in the negative psychological impact they inflict. Acne severity, acne duration, individual age, and family history of scarring were found to have associations with atrophic scarring The findings suggest a need for implementing a structured and standardized way for communicating acne scar prevention information to the general public. Practical implications of these findings are discussed further for increasing public awareness of acne scarring and prevention knowledge.
ContributorsJone, Jillian Louise (Author) / Lee, Rebecca (Thesis director) / Redden, Tamara (Committee member) / Edson College of Nursing and Health Innovation (Contributor) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Cultural Perceptions of Leisure and Well-being in Rock Climbing Communities of Peru and Arizona, USA
Description
In December of 2015, I made my way to rural Peru for a few weeks, my first visit to South America. While I was there, I observed a devotion to family and leisure activity, topics that were not heavily prioritized in my experience in Arizona. Upon my return, I became more involved in leisure activities, particularly running, hiking, yoga, and climbing. These involvements noticeably benefitted my health and well-being. The way the Peruvians I met prioritized these subjects fascinated me, and I wanted to study this difference between Arizona and Peru. In July of 2017, I returned to Peru for a semester abroad with my bags packed and the following research questions: 1) Are differences in motivation for rock climbing between Arizona and Peruvian climbers associated with cultural values? 2) Do leisure activities and the amount of time spent on them have an effect on quality of life? 3) Does the degree of climbing specialization impact perceptions of well-being? 4) What characteristics impact perceptions of quality of life among climbers? Are these characteristics affected by country of origin? My prediction was that Peruvians had higher quality of life due to their emphasis on leisure. Through this study, I learned that this conclusion was not as simple as I anticipated.
ContributorsMatta, Samantha Tania (Author) / Hultsman, Wendy (Thesis director) / Sampson, David (Committee member) / Lee, Rebecca (Committee member) / College of Integrative Sciences and Arts (Contributor) / School of Molecular Sciences (Contributor) / Division of Teacher Preparation (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
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