Matching Items (181)
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
Culture informs ideas about healthy and acceptable body types. Through globalization the U.S.-European body model has become increasingly significant in local contexts, influencing local body models. While Puerto Ricans have historically valued plump bodies - a biocultural legacy of a historically food scarce environment - this dissertation investigated shifts in these ideals across generations to a stronger preference for thinness. A sample of 23 intergenerational family triads of women, and one close male relative or friend per woman, were administered quantitative questionnaires. Ethnographic interviews were conducted with a sub-sample of women from 16 triads and 1 quintet. Questions about weight history and body sizes were used to address cultural changes in body models. Findings indicate the general trend for all generations has been a reduction in the spectrum of acceptable bodies to an almost singular idealized thin body. Female weight gain during puberty and influence of media produced varied responses across age groups. Overall, Puerto Ricans find it acceptable to gain weight with ageing, during a divorce, and postpartum. Thin bodies are associated with beauty and health, but healthy women that do not resemble the thin ideal, submit themselves to dangerous weight loss practices to achieve self and social acceptance. Further research and direct interventions need to be conducted to alter perceptions that conflate beauty with health in order to address the `normative discontent' women of all ages experience. Weight discrimination and concern with being overweight were evident in Puerto Rican everyday life, indicated by the role of media and acculturation in this study. Anti-fat attitudes were stronger for individuals that identified closely with United States culture. Exposure to drama and personal transformation television programs are associated with increased body image dissatisfaction, and increased exposure to variety shows and celebrity news shows is associated with increased anti-fat attitudes and body dissatisfaction. In sum, the positive valuation of fat in the Puerto Rican cultural body size model in the 1970s has shifted toward a negative valuation of fat and a preference for thin body size.
ContributorsRodriguez-Soto, Isa (Author) / Maupin, Jonathan (Thesis advisor) / Wutich, Amber (Committee member) / Walters-Pacheco, Kattia (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
Despite the arid climate of Maricopa County, Arizona, vector-borne diseases have presented significant health challenges to the residents and public health professionals of Maricopa County in the past, and will continue to do so in the foreseeable future. Currently, West Nile virus is the only mosquitoes-transmitted disease actively, and natively, transmitted throughout the state of Arizona. In an effort to gain a more complete understanding of the transmission dynamics of West Nile virus this thesis examines human, vector, and environment interactions as they exist within Maricopa County. Through ethnographic and geographic information systems research methods this thesis identifies 1) the individual factors that influence residents' knowledge and behaviors regarding mosquitoes, 2) the individual and regional factors that influence residents' knowledge of mosquito ecology and the spatial distribution of local mosquito populations, and 3) the environmental, demographic, and socioeconomic factors that influence mosquito abundance within Maricopa County. By identifying the factors that influence human-vector and vector-environment interactions, the results of this thesis may influence current and future educational and mosquito control efforts throughout Maricopa County.
ContributorsKunzweiler, Colin (Author) / Boone, Christopher (Thesis advisor) / Wutich, Amber (Committee member) / Brewis-Slade, Alexandra (Committee member) / Arizona State University (Publisher)
Created2013
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
Food deserts are the collection of deprived food environments and limit local residents from accessing healthy and affordable food. This dissertation research in San Lorenzo, Paraguay tests if the assumptions about food deserts in the Global North are also relevant to the Global South. In the Global South, the recent growth of supermarkets is transforming local food environments and may worsen residential food access, such as through emerging more food deserts globally. This dissertation research blends the tools, theories, and frameworks from clinical nutrition, public health, and anthropology to identify the form and impact of food deserts in the market city of San Lorenzo, Paraguay. The downtown food retail district and the neighborhood food environment in San Lorenzo were mapped to assess what stores and markets are used by residents. The food stores include a variety of formal (supermarkets) and informal (local corner stores and market vendors) market sources. Food stores were characterized using an adapted version of the Nutrition Environment Measures Survey for Stores (NEMS-S) to measure store food availability, affordability, and quality. A major goal in this dissertation was to identify how and why residents select a type of food store source over another using various ethnographic interviewing techniques. Residential store selection was linked to the NEMS-S measures to establish a connection between the objective quality of the local food environment, residential behaviors in the local food environment, and nutritional health status. Using a sample of 68 households in one neighborhood, modeling suggested the quality of local food environment does effect weight (measure as body mass index), especially for those who have lived longer in poorer food environments. More generally, I find that San Lorenzo is a city-wide food desert, suggesting that research needs to establish more nuanced categories of poor food environments to address how food environments emerge health concerns in the Global South.
ContributorsGartin, Meredith (Author) / Brewis Slade, Alexandra (Thesis advisor) / Boone, Christopher (Committee member) / Wutich, Amber (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
The study examines cross-cultural perceptions of wastewater reuse from 282 participants from four global sites representing varied levels of socio-economic and political development from the Global North and Global South: Spain, New Zealand, Fiji, and Guatemala. The data comes from the Global Ethnohydrology Survey conducted by the School of Human Evolution and Social Change during the summer of 2013. The Global Ethnohydrology Study is a transdisciplinary multi-year research initiative that examines the range of variation in local ecological knowledge of water issues, also known as "ethnohydrology." Participants were asked about their willingness, level of disgust, and concern with using treated wastewater for various daily activities. Additionally, they were asked to draw schematic representations of how wastewater should be treated to become drinkable again. Using visual content analysis, the drawings were coded for a variety of treatment levels and specific treatment processes. Conclusions about the perceived health implications from wastewater reuse that can stem from drinking treated wastewater were made. The relationship between humans and wastewater is one that has many direct social and health impacts on communities at large. In reaction to global limitations of freshwater, wastewater serves as a valuable resource to tap into. This research examines the cross-cultural public health concerns about treated wastewater in order to draw conclusions that can aid in strategic implementation of advocacy and public education about wastewater reuse.
ContributorsPatel, Sarah Shakir (Author) / Wutich, Amber (Thesis director) / Rice, Jacelyn (Committee member) / Barrett, The Honors College (Contributor) / School of Politics and Global Studies (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2015-05