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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
Colorectal cancer (CRC) is one of the most highly diagnosed cancers in the United States and accounts for 9.5% of all new cancer cases worldwide. With a 50% five-year prognosis, it is the second highest cancerous cause of death in the U.S. CRC tumors express antigens that are capable of inducing an immune response. The identification of autoantibodies (AAb) against tumor-associated antigens (TAA) may facilitate personalized tumor treatment in the form of targeted immunotherapy. The objective of this study was to observe the AAb expression raised against a 2000 human gene survey in late-stage colorectal cancer using the Nucleic Acid Programmable Protein Arrays (NAPPA). AAbs from serum samples were collected from 80 patients who died within 24 months of their last blood draw and 80 age and gender matched healthy control were profiled using NAPPA. TAA p53, a well-established protein that is one of the most highly mutated across a variety of cancers, was one of the top candidates based on statistical analysis, which, along with its family proteins p63 and p73 (which showed inverse AAb response profiles) warranted further testing via RAPID ELISA. Statistical analysis from these results revealed an inverse differential relationship between p53 and p63, in which p53 seropositivity was higher in patients than in controls, while the opposite was unexpectedly the case for p63. This study involving the AAb immunoprofiling of advanced stage CRC patients is one of the first to shed light on the high-throughput feasibility of immunoproteomic experiments using protein arrays as well as the identification of immunotherapy targets in a more rapid move towards specialized treatment of advanced CRC.
ContributorsSzeto, Emily (Author) / LaBaer, Joshua (Thesis director) / Qiu, Ji (Committee member) / Demirkan, Gokhan (Committee member) / Barrett, The Honors College (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor)
Created2014-12
Description
The pathogenesis of type 1 diabetes (T1D) is still not fully understood in the scientific community. Evidence has shown that viral infections are one of the important environmental factors associated with the disease development. Seven of the top T1D related viruses were selected to study the prevalence of viral humoral response in T1D patients using our innovative protein array platform called Nucleic Acid Programmable Protein Array (NAPPA). In this study, each viral gene was individually captured using various PCR based techniques, cloned into a protein expression vector, and assembled as the first version of T1D viral protein array. Humoral responses of IgG, IgA, and IgM were examined. Although each class of immunoglobulin generated a wide-range of reactivity, responses to various viral proteins from different proteins were observed. In summary, we captured most of the T1D related viral genes, established viral protein expression on the protein array, and displayed the serum response on the viral protein array. The successful progress will help to fulfill the long term goal of testing the viral infection hypothesis in T1D development.
ContributorsDavis, Amy Darlene (Author) / LaBaer, Joshua (Thesis director) / Qiu, Ji (Committee member) / Desi, Paul (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2013-05
Description
AMPylation is a post-translation modification that has an important role in the survival of many bacterial pathogens by affecting the host cell's molecular signaling. In the course of studying this intercellular manipulation, there has only been modest progression in the identification of the enzymes with AMPylation capabilities (AMPylators) and their respective targets. The reason for these minimal developments is the inability to analyze a large subset of these proteins. Therefore, to increase the efficiency of the identification and characterization of the proteins, Yu et al developed a high-throughput non-radioactive discovery platform using Human Nucleic Acid Programmable Protein Arrays (NAPPA) and a validation platform using bead-based assays. The large-scale unbiased screening of potential substrates for two bacterial AMPylators containing Fic domain, VopS and IbpAFic2, had been performed and dozens of novel substrates were identified and confirmed. With the efficiency of this method, the platform was extended to the identification of novel substrates for a Legionella virulence factor, SidM, containing a different adenylyl transferase domain. The screening was performed using NAPPA arrays comprising of 10,000 human proteins, the active AMPylator SidM, and its inactive D110/112A mutant as a negative control. Many potential substrates of SidM were found, including Rab GTPases and non-GTPase proteins. Several of which have been confirmed with the bead-based AMPylation assays.
ContributorsGraves, Morgan C. (Author) / LaBaer, Joshua (Thesis director) / Qiu, Ji (Committee member) / Yu, Xiaobo (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2013-05
Description
CREB3L1 has been previously shown to auto-acetylate itself when prepared from HeLa cell based in vitro protein expression lysates. To circumvent the concerns of the contamination of co-purified human proteins from HeLa lysates, the protein was purified through insect cell transfection in vitro. The objective of this study was to assay the auto-acetylation activity of CREB3L1 prepared from insect cells using the baculovirus expression vector system (BEVS). To this end, His-tagged CREB3L1 was affinity purified from Hi5 cells using an IMAC column and used for acetylation assay. Samples were taken different time points and auto-acetylation was by western using antibodies specific to acetylated lysines. Auto-acetylation activity was observed after overnight incubation. Future experiments will focus on the improvement of purification yield and the identification of the substrates and interacting proteins of CREB3L1 to better understand the biological functions of this novel acetyltransferase.
ContributorsSchwab, Anna (Author) / LaBaer, Joshua (Thesis director) / Qiu, Ji (Committee member) / Barrett, The Honors College (Contributor)
Created2017-05