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Description
The RNA editing enzyme adenosine deaminase acting on double stranded RNA 2 (ADAR2) converts adenosine into inosine in regions of double stranded RNA. Here, it was discovered that this critical function of ADAR2 was dysfunctional in amyotrophic lateral sclerosis (ALS) mediated by the C9orf72 hexanucleotide repeat expansion, the most common genetic abnormality associated with ALS. Typically a nuclear protein, ADAR2 was localized in cytoplasmic accumulations in postmortem tissue from C9orf72 ALS patients. The mislocalization of ADAR2 was confirmed using immunostaining in a C9orf72 mouse model and motor neurons differentiated from C9orf72 patient induced pluripotent stem cells. Notably, the cytoplasmic accumulation of ADAR2 coexisted in neurons with cytoplasmic accumulations of TAR DNA binding protein 43 (TDP-43). Interestingly, ADAR2 overexpression in mammalian cell lines induced nuclear depletion and cytoplasmic accumulation of TDP-43, reflective of the pathology observed in ALS patients. The mislocalization of TDP-43 was dependent on the catalytic activity of ADAR2 and the ability of TDP-43 to bind directly to inosine containing RNA. In addition, TDP-43 nuclear export was significantly elevated in cells with increased RNA editing. Together these results describe a novel cellular mechanism by which alterations in RNA editing drive the nuclear export of TDP-43 leading to its cytoplasmic mislocalization. Considering the contribution of cytoplasmic TDP-43 to the pathogenesis of ALS, these findings represent a novel understanding of how the formation of pathogenic cytoplasmic TDP-43 accumulations may be initiated. Further research exploring this mechanism will provide insights into opportunities for novel therapeutic interventions.
ContributorsMoore, Stephen Philip (Author) / Sattler, Rita (Thesis advisor) / Zarnescu, Daniela (Committee member) / Brafman, David (Committee member) / Van Keuren-Jensen, Kendall (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2021
Description
Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more.
Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it.
Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning
techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
ContributorsMuglikar, Omkar Dushyant (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
Description
Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand, the outer surface along with its enclosed internal volume can be taken as a three-dimensional embedding of interests. Most studies focus on the surface-based perspective by leveraging the intrinsic features on the tangent plane. But a two-dimensional model may fail to fully represent the realistic properties of shapes with both intrinsic and extrinsic properties. In this thesis, severalStochastic Partial Differential Equations (SPDEs) are thoroughly investigated and several methods are originated from these SPDEs to try to solve the problem of both two-dimensional and three-dimensional shape analyses. The unique physical meanings of these SPDEs inspired the findings of features, shape descriptors, metrics, and kernels in this series of works. Initially, the data generation of high-dimensional shapes, here, the tetrahedral meshes, is introduced. The cerebral cortex is taken as the study target and an automatic pipeline of generating the gray matter tetrahedral mesh is introduced. Then, a discretized Laplace-Beltrami operator (LBO) and a Hamiltonian operator (HO) in tetrahedral domain with Finite Element Method (FEM) are derived. Two high-dimensional shape descriptors are defined based on the solution of the heat equation and Schrödinger’s equation. Considering the fact that high-dimensional shape models usually contain massive redundancies, and the demands on effective landmarks in many applications, a Gaussian process landmarking on tetrahedral meshes is further studied. A SIWKS-based metric space is used to define a geometry-aware Gaussian process. The study of the periodic potential diffusion process further inspired the idea of a new kernel call the geometry-aware convolutional kernel. A series of Bayesian learning methods are then introduced to tackle the problem of shape retrieval and classification. Experiments of every single item are demonstrated. From the popular SPDE such as the heat equation and Schrödinger’s equation to the general potential diffusion equation and the specific periodic potential diffusion equation, it clearly shows that classical SPDEs play an important role in discovering new features, metrics, shape descriptors and kernels. I hope this thesis could be an example of using interdisciplinary knowledge to solve problems.
ContributorsFan, Yonghui (Author) / Wang, Yalin (Thesis advisor) / Lepore, Natasha (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Alzheimer's disease (AD) is a neurodegenerative disease that damages the cognitive abilities of a patient. It is critical to diagnose AD early to begin treatment as soon as possible which can be done through biomarkers. One such biomarker is the beta-amyloid (Aβ) peptide which can be quantified using the centiloid (CL) scale. For identifying the Aβ biomarker, A deep learning model that can model AD progression by predicting the CL value for brain magnetic resonance images (MRIs) is proposed. Brain MRI images can be obtained through the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets, however a single model cannot perform well on both datasets at once. Thus, A regularization-based continuous learning framework to perform domain adaptation on the previous model is also proposed which captures the latent information about the relationship between Aβ and AD progression within both datasets.
ContributorsTrinh, Matthew Brian (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Su, Yi (Committee member) / Arizona State University (Publisher)
Created2022
Description
The GGGGCC (G4C2) hexanucleotide repeat expansion (HRE) in the C9orf72 gene is the most common genetic abnormality associated with both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), two devastatingly progressive neurodegenerative diseases. The discovery of this genetic link confirmed that ALS and FTD reside along a spectrum with clinical and pathological commonalities. Historically understood as diseases resulting in neuronal death, the role of non-neuronal cells like astrocytes is still wholly unresolved. With evidence of cortical neurodegeneration leading to cognitive impairments in C9orf72-ALS/FTD, there is a need to investigate the role of cortical astrocytes in this disease spectrum. Here, a patient-derived induced pluripotent stem cell (iPSC) cortical astrocyte model was developed to investigate consequences of C9orf72-HRE pathogenic features in this cell type. Although there were no significant C9orf72-HRE pathogenic features in cortical astrocytes, transcriptomic, proteomic and phosphoproteomic profiles elucidated global disease-related phenotypes. Specifically, aberrant expression of astrocytic-synapse proteins and secreted factors were identified. SPARCL1, a pro-synaptogenic secreted astrocyte factor was found to be selectively decreased in C9orf72-ALS/FTD iPSC-cortical astrocytes. This finding was further validated in human tissue analyses, indicating that cortical astrocytes in C9orf72-ALS/FTD exhibit a reactive transformation that is characterized by a decrease in SPARCL1 expression. Considering the evidence for substantial astrogliosis and synaptic failure leading to cognitive impairments in C9orf72-ALS/FTD, these findings represent a novel understanding of how cortical astrocytes may contribute to the cortical neurodegeneration in this disease spectrum.
ContributorsBustos, Lynette (Author) / Sattler, Rita (Thesis advisor) / Newbern, Jason (Committee member) / Zarnescu, Daniela (Committee member) / Brafman, David (Committee member) / Mehta, Shwetal (Committee member) / Arizona State University (Publisher)
Created2023
Description
The purpose of the overall project is to create a simulated environment similar to Google map and traffic but simplified for education purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environment used in the project is ASU VIPLE (Visual IoT/Robotics Programming Language Environment). It is a visual programming environment for Computer Science education. VIPLE supports a number of devices and platforms, including a traffic simulator developed using Unity game engine. This thesis focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithm in VIPLE. The traffic data is generated from the recorded real traffic data published at Arizona Maricopa County website. Based on the generated traffic data, VIPLE programs are developed to implement the traffic simulation based on dynamic changing traffic data.
ContributorsZhang, Zhemin (Author) / Chen, Yinong (Thesis advisor) / Wang, Yalin (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2022
Description
Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults with ASD may experience accelerated cognitive decline and neurodegeneration as they age, although studies are limited by their cross-sectional design in a population with strong age-cohort effects. Studying aging in ASD and identifying biomarkers to predict atypical aging is important because the population of older individuals with ASD is growing. Understanding the unique challenges faced as autistic adults age is necessary to develop treatments to improve quality of life and preserve independence. In this study, a longitudinal design was used to characterize cognitive and brain aging trajectories in ASD as a function of autistic trait severity. Principal components analysis (PCA) was used to derive a cognitive metric that best explains performance variability on tasks measuring memory ability and executive function. The slope of the integrated persistent feature (SIP) was used to quantify functional connectivity; the SIP is a novel, threshold-free graph theory metric which summarizes the speed of information diffusion in the brain. Longitudinal mixed models were using to predict cognitive and brain aging trajectories (measured via the SIP) as a function of autistic trait severity, sex, and their interaction. The sensitivity of the SIP was also compared with traditional graph theory metrics. It was hypothesized that older adults with ASD would experience accelerated cognitive and brain aging and furthermore, age-related changes in brain network topology would predict age-related changes in cognitive performance.
For both cognitive and brain aging, autistic traits and sex interacted to predict trajectories, such that older men with high autistic traits were most at risk for poorer outcomes. In men with autism, variability in SIP scores across time points trended toward predicting cognitive aging trajectories. Findings also suggested that autistic traits are more sensitive to differences in brain aging than diagnostic group and that the SIP is more sensitive to brain aging trajectories than other graph theory metrics. However, further research is required to determine how physiological biomarkers such as the SIP are associated with cognitive outcomes.
ContributorsSullivan, Georgia (Author) / Braden, Blair (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Schaefer, Sydney (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2022
Description
APOE encodes for a lipid transport protein and has three allelic variants-APOE ε2, ε3 and ε4 each of which differentially modulate the risk for Alzheimer’s disease (AD). The presence of the ε4 allele of APOE greatly increases AD risk compared to the presence of the more prevalent and risk neutral ε3 allele. An imbalance in the generation and clearance of amyloid beta (Aβ) peptides has been hypothesized to play a key role in driving the disease. APOE4 impacts several AD-relevant cellular processes. However, it is unclear whether these effects represent a gain of toxic function or a loss of function, specifically as it relates to modulating amyloid beta (Aβ) levels. Here, a set of APOE knockout (KO) and APOE4 isogenic human induced pluripotent stem cells (hiPSCs) were generated from a parental APOE3 hiPSC line with a highly penetrant familial AD (fAD) mutation to investigate this with respect to Aβ secretion in neural cultures and Aβ uptake in monocultures of microglia-like cells (iMGLs). Conversion of APOE3 to E4 as well as functionally knocking APOE out from the APOE3 parental line, result in elevated Aβ levels in neural cultures, likely through multiple mechanisms including the altered processing of the precursor protein to Aβ called amyloid precursor protein (APP). In pure neuronal cultures, a shift in the processing of APP was observed with the Aβ-generating amyloidogenic pathway being favored in both APOE3 as well as APOE4 neurons compared to APOE KO neurons, with APOE4 neurons exhibiting a greater shift. In iMGLs derived from the isogenic hiPSC lines, expression of APOE, regardless of the isoform, lowered the uptake of Aβ. Overall, APOE4 modulates Aβ levels through distinct loss of protective and gain of function effects. Dissecting these effects would contribute towards a better understanding of the design of potential APOE-targeted therapeutics in the future.
ContributorsRajaram Srinivasan, Gayathri (Author) / Brafman, David (Thesis advisor) / Plaisier, Christopher (Committee member) / Newbern, Jason (Committee member) / Stabenfeldt, Sarah (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2024