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Description
DNA methylation, a subset of epigenetics, has been found to be a significant marker associated with variations in gene expression and activity across the entire human genome. As of now, however, there is little to no information about how DNA methylation varies between different tissues inside a singular person's body. By using research data from a preliminary study of lean and obese clinical subjects, this study attempts to put together a profile of the differences in DNA methylation that can be observed between two particular body tissues from this subject group: blood and skeletal muscle. This study allows us to start describing the changes that occur at the epigenetic level that influence how differently these two tissues operate, along with seeing how these tissues change between individuals of different weight classes, especially in the context of the development of symptoms of Type 2 Diabetes.
ContributorsRappazzo, Micah Gabriel (Author) / Coletta, Dawn (Thesis director) / Katsanos, Christos (Committee member) / Dinu, Valentin (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / Department of Psychology (Contributor)
Created2013-12
Description
X-ray free-electron lasers provide novel opportunities to conduct single particle analysis on nanoscale particles. Coherent diffractive imaging experiments were performed at the Linac Coherent Light Source (LCLS), SLAC National Laboratory, exposing single inorganic core-shell nanoparticles to femtosecond hard-X-ray pulses. Each facetted nanoparticle consisted of a crystalline gold core and a differently shaped palladium shell. Scattered intensities were observed up to about 7 nm resolution. Analysis of the scattering patterns revealed the size distribution of the samples, which is consistent with that obtained from direct real-space imaging by electron microscopy. Scattering patterns resulting from single particles were selected and compiled into a dataset which can be valuable for algorithm developments in single particle scattering research.
ContributorsLi, Xuanxuan (Author) / Chiu, Chun-Ya (Author) / Wang, Hsiang-Ju (Author) / Kassemeyer, Stephan (Author) / Botha, Sabine (Author) / Shoeman, Robert L. (Author) / Lawrence, Robert (Author) / Kupitz, Christopher (Author) / Kirian, Richard (Author) / James, Daniel (Author) / Wang, Dingjie (Author) / Nelson, Garrett (Author) / Messerschmidt, Marc (Author) / Boutet, Sebastien (Author) / Williams, Garth J. (Author) / Hartman, Elisabeth (Author) / Jafarpour, Aliakbar (Author) / Foucar, Lutz M. (Author) / Barty, Anton (Author) / Chapman, Henry (Author) / Liang, Mengning (Author) / Menzel, Andreas (Author) / Wang, Fenglin (Author) / Basu, Shibom (Author) / Fromme, Raimund (Author) / Doak, R. Bruce (Author) / Fromme, Petra (Author) / Weierstall, Uwe (Author) / Huang, Michael H. (Author) / Spence, John (Author) / Schlichting, Ilme (Author) / Hogue, Brenda (Author) / Liu, Haiguang (Author) / ASU Biodesign Center Immunotherapy, Vaccines and Virotherapy (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Molecular Sciences (Contributor) / Department of Physics (Contributor) / School of Life Sciences (Contributor)
Created2017-04-11
Description
Viral protein U (Vpu) is a type-III integral membrane protein encoded by Human Immunodeficiency Virus-1 (HIV- 1). It is expressed in infected host cells and plays several roles in viral progeny escape from infected cells, including down-regulation of CD4 receptors. But key structure/function questions remain regarding the mechanisms by which the Vpu protein contributes to HIV-1 pathogenesis. Here we describe expression of Vpu in bacteria, its purification and characterization. We report the successful expression of PelB-Vpu in Escherichia coli using the leader peptide pectate lyase B (PelB) from Erwinia carotovora. The protein was detergent extractable and could be isolated in a very pure form. We demonstrate that the PelB signal peptide successfully targets Vpu to the cell membranes and inserts it as a type I membrane protein. PelB-Vpu was biophysically characterized by circular dichroism and dynamic light scattering experiments and was shown to be an excellent candidate for elucidating structural models.
ContributorsDeb, Arpan (Author) / Johnson, William (Author) / Kline, Alexander (Author) / Scott, Boston (Author) / Meador, Lydia (Author) / Srinivas, Dustin (Author) / Martin Garcia, Jose Manuel (Author) / Dorner, Katerina (Author) / Borges, Chad (Author) / Misra, Rajeev (Author) / Hogue, Brenda (Author) / Fromme, Petra (Author) / Mor, Tsafrir (Author) / ASU Biodesign Center Immunotherapy, Vaccines and Virotherapy (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Biodesign Institute (Contributor) / School of Molecular Sciences (Contributor) / Applied Structural Discovery (Contributor) / Personalized Diagnostics (Contributor)
Created2017-02-22
Description
Colorectal cancer is the third most common type of cancer that affects both men and women and the second leading cause of death in cancer related deaths[1, 2]. The most common form of treatment is chemotherapy followed by radiation, which is insufficient to cure stage four cancers[3]. Salmonella enteric has long been shown to have inherent tumor targeting properties and have been able to penetrate and exist in all aspects of the tumor environment, something that chemotherapy is unable to achieve. This lab has developed a genetically modified Salmonella typhimurium (GMS) which is able to deliver DNA vaccines or synthesized proteins directly to tumor sites. These GMS strains have been used to deliver human TNF-related apoptosis inducing ligand (TRAIL) protein directly to tumor sites, but expression level was limited. It is the hope of the experiment that codon optimization of TRAIL to S. typhimurium preferred codons will lead to increased TRAIL expression in the GMS. For preliminary studies, BALB/c mice were subcutaneously challenged with CT-26 murine colorectal cancer cells and treated with an intra-tumor injection with either PBS, strain GMS + PCMV FasL (P2), or strain GMS + Pmus FasL). APC/CDX2 mutant mice were also induced to develop human colon polyps and treated with either PBS, strain GMS + vector (P1), P2, or P3. The BALB/c mouse showed statistically significant levels of decreased tumor size in groups treated with P2 or P3. The APC/CDX2 mouse study showed statistically significant levels of decreased colon polyp numbers in groups treated with P3, as expected, but was not significantly significant for groups treated with P1 and P2. In addition, TRAIL was codon optimized for robust synthesis in Salmonella. The construct will be characterized and evaluated in vitro and in vivo. Hopefully, the therapeutic effect of codon optimized TRAIL will be maximal while almost completely minimizing any unintended side effects.
ContributorsCrawford, Courtney Rose (Co-author) / Crawford, Courtney (Co-author) / Kong, Wei (Thesis director) / Shi, Yixin (Committee member) / Fu, Lingchen (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Many pathogens are bacteria and antibiotic resistance is increasing. The development of novel treatments is hampered by a poor understanding of the mechanisms of their regulation. Specifically, non-coding RNAs play an important role in the internal regulation of bacteria. To further the investigation of non-coding RNA and pathogenicity, RNA sequencing data for PorX/PorY dependent regulation in P. gingivalis, a Gram negative oral pathogen was studied. The PorX/PorY two component regulatory system controls phenotypes for this bacteria's virulence including an important type IX secretion system for gingipain proteases, which degrades host cytokines, down regulating the host response by reducing inflammation. This study compared transcription of non-coding RNA in wild type and PorX knockout mutant strain, in the 33277 strain and the more virulent W83 strain in both liquid and solid cultures to identify and categorize loci of genomic sequence for further study of porX/porY regulation.
ContributorsHoenack, Michael Anthony (Author) / Kong, Wei (Thesis director) / Shi, Yixin (Committee member) / Lingchen, Fu (Committee member) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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
Obesity and related health disparities including type 2 diabetes disproportionately impact Latino youth. These health disparities may be the result of gene-environment interactions, but limited research has examined these interactions in the pediatric age group. Lifestyle intervention is the cornerstone for preventing diabetes among high-risk populations and epigenetic and genetic factors may help explain the biological mechanisms underlying diabetes risk reduction following lifestyle changes. MicroRNAs (miRNAs) are small, non-coding RNA’s that regulate gene expression and have emerged as potential biomarkers for predicting type 2 diabetes risk in adults but have yet to be applied to youth. Therefore, the purpose of this study was to identify changes in miRNA expression among Latino youth with prediabetes (4 female/2 male, ages 14-16, BMI percentile 99 ±.2) who participated in a 12-week lifestyle intervention focused on increasing physical activity and improving nutrition-related behaviors.
ContributorsKarch, Jamie (Co-author) / Day, Samantha (Co-author) / Shaibi, Gabriel (Thesis director) / Coletta, Dawn (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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