Matching Items (102)
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 partitioning of photosynthates between their sites of production (source) and their sites of utilization (sink) is a major determinant of crop yield and the potential of regulating this translocation promises substantial opportunities for yield increases. Ubiquitous overexpression of the plant type I proton pyrophosphatase (H+-PPase) in crops improves several valuable traits including salt tolerance and drought resistance, nutrient and water use efficiencies, and increased root biomass and yield. Originally, type I H+-PPases were described as pyrophosphate (PPi)-dependent proton pumps localized exclusively in vacuoles of mesophyll and meristematic tissues. It has been proposed that in the meristematic tissues, the role of this enzyme would be hydrolyzing PPi originated in biosynthetic reactions and favoring sink strength. Interestingly, this enzyme has been also localized at the plasma membrane of companion cells in the phloem which load and transport photosynthates from source leaves to sinks. Of note, the plasma membrane-localized H+-PPase could only function as a PPi-synthase in these cells due to the steep proton gradient between the apoplast and cytosol. The generated PPi would favor active sucrose loading through the sucrose/proton symporter in the phloem by promoting sucrose hydrolysis through the Sucrose Synthase pathway and providing the ATP required to maintain the proton gradient. To better understand these two different roles of type I H+-PPases, a series of Arabidopsis thaliana transgenic plants were generated. By expressing soluble pyrophosphatases in companion cells of Col-0 ecotype and H+-PPase mutants, impaired photosynthates partitioning was observed, suggesting phloem-localized H+-PPase could generate the PPi required for sucrose loading. Col-0 plants expressed with either phloem- or meristem-specific AVP1 overexpression cassette and the cross between the two tissue specific lines (Cross) were generated. The results showed that the phloem-specific AVP1-overexpressing plants had increased root hair elongation under limited nutrient conditions and both phloem- and meristem-overexpression of AVP1 contributed to improved rhizosphere acidification and drought resistance. It was concluded that H+-PPases localized in both sink and source tissues regulate plant growth and performance under stress through its versatile enzymatic functions (PPi hydrolase and synthase).
ContributorsLi, Lin (Author) / Park, Yujin (Thesis advisor) / Mangone, Marco (Committee member) / Roberson, Robert (Committee member) / Vermaas, Willem (Committee member) / Arizona State University (Publisher)
Created2022
Description
The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the visual cortex of the brain). However, using the pRF model is very time consuming. Past research has focused on the creation of Convolutional Neural Networks (CNN) to mimic the pRF model in a fraction of the time, and they have worked well under highly controlled conditions. However, these models have not been thoroughly tested on real human data. This thesis focused on adapting one of these CNNs to accurately predict the retinotopy of a real human subject using a dataset from the Human Connectome Project. The results show promise towards creating a fully functioning CNN, but they also expose new challenges that must be overcome before the model could be used to predict the retinotopy of new human subjects.
ContributorsBurgard, Braeden (Author) / Wang, Yalin (Thesis director) / Ta, Duyan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
Description
MicroRNAs (miRNAs) are 17-22 nucleotide non-coding RNAs that regulate gene expression by targeting non-complementary elements in the 3’ untranslated regions (3’UTRs) of mRNAs. miRNAs, which form complex networks of interaction that differ by tissue and developmental stage, display conservation in their function across metazoan species. Yet much remains unknown regarding their biogenesis, localization, strand selection, and their absolute abundance due to the difficulty of detecting and amplifying such small molecules. Here, I used an updated HT qPCR-based methodology to follow miRNA expression of 5p and 3p strands for all 190 C. elegans miRNAs described in miRBase throughout all six developmental stages in triplicates (total of 9,708 experiments), and studied their expression levels, tissue localization, and the rules underlying miRNA strand selection. My study validated previous findings and identified novel, conserved patterns of miRNA strand expression throughout C. elegans development, which at times correlate with previously observed developmental phenotypes. Additionally, my results highlighted novel structural principles underlying strand selection, which can be applied to higher metazoans.
Though optimized for use in C. elegans, this method can be easily adapted to other eukaryotic systems, allowing for more scalable quantitative investigation of miRNA biology and/or miRNA diagnostics.
ContributorsMeadows, Dalton Alexander (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Joshua (Committee member) / Murugan, Vel (Committee member) / Wilson-Rawls, Jeanne (Committee member) / Arizona State University (Publisher)
Created2023
Description
Precise modulation of gene expression is essential for proper tissue and cell-specific differentiation and function. Multiple distinct post-transcriptional regulatory mechanisms, such as miRNA (microRNA)-based regulation and alternative polyadenylation (APA), are an intrinsic part of this modulation and orchestrate intricate pathways to achieve and maintain balanced gene expression.MiRNA-based regulation and APA function through sequence motifs located in the 3’ Untranslated Region (3’UTR) of mRNA transcripts. MiRNAs are short (~22 nt) non-coding RNA molecules that bind target sequences within the 3’UTR of an mRNA transcript, inhibiting its translation or promoting its degradation. APA occurs during RNA transcription termination and leads to the preparation of mature mRNAs with different 3’UTR lengths, allowing shorter 3’UTRs to bypass miRNA regulation.
In addition to these two post-transcriptional forms of regulation, co-transcriptional mechanisms such as alternative RNA splicing, which produces distinct gene products from a precursor mRNA, are also important in controlling gene expression.
While miRNA-based regulation, APA, and alternative RNA splicing are important regulatory mechanisms, there is a lack of comprehensive understanding of how they interact and communicate with each other. This thesis studies these three forms of gene regulation in the nematode C. elegans, with the goal of extracting rules and mechanisms used by each of them in development to establish and maintain somatic tissue identity.
After isolating miRNA targets in multiple C. elegans somatic tissues, it was found that miRNAs can modulate the abundance of hnRNPs and SR proteins, which are known to control alternative RNA splicing in a dosage-dependent manner.To identify tissue-specific miRNAs, a nuclear fluorescent cell sorting (FACS)-based methodology named Nuc-Seq, was developed to isolate and sequence tissue-specific miRNAs from body muscle tissue. Nuc-Seq identified 2,848 muscle-specific protein-coding genes and 16 body muscle-specific miRNAs. This data was used to develop a high-quality body muscle-specific miRNA-APA Interactome which allows studies in regulatory processes in detail.
Taken together, this work highlights some of the complexity of pre- and post-transcriptional gene regulation and sheds light on how miRNA-based regulation, APA, and alternative RNA splicing are interconnected and are responsible for the establishment and maintenance of tissue identity.
ContributorsSchorr, Anna L (Author) / Mangone, Marco (Thesis advisor) / Harris, Robin (Committee member) / Sharma, Shalini (Committee member) / Varsani, Arvind (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