ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: nucleic acid nanotechnology
Description
Originally conceived as a way to scaffold molecules of interest into three-dimensional (3D) crystalline lattices for X ray crystallography, the field of deoxyribonucleic acid (DNA) nanotechnology has dramatically evolved since its inception. The unique properties of DNA nanostructures have promoted their use not only for X ray crystallography, but for a suite of biomedical applications as well. The work presented in this dissertation focuses on both of these exciting applications in the field: 1) Nucleic acid nanostructures as multifunctional drug and vaccine delivery platforms, and 2) 3D DNA crystals for structure elucidation of scaffolded guest molecules.Chapter 1 illustrates how a wide variety of DNA nanostructures have been developed for the delivery of drugs and vaccine components. However, their applications are limited under physiological conditions due to their lack of stability in low salt environments, susceptibility to enzymatic degradation, and tendency for endosomal entrapment. To address these issues, Chapter 2 describes a PEGylated peptide coating molecule was designed to electrostatically adhere to and protect DNA origami nanostructures and to facilitate their cytosolic delivery by peptide-mediated endosomal escape. The development of this molecule will aid in the use of nucleic acid nanostructures for biomedical purposes, such as the delivery of messenger ribonucleic acid (mRNA) vaccine constructs. To this end, Chapter 3 discusses the fabrication of a structured mRNA nanoparticle for more cost-efficient mRNA vaccine manufacture and proposes a multi-epitope mRNA nanostructure vaccine design for targeting human papillomavirus (HPV) type 16-induced head and neck cancers.
DNA nanotechnology was originally envisioned to serve as three-dimensional scaffolds capable of positioning proteins in a rigid array for their structure elucidation by X ray crystallography. Accordingly, Chapter 4 explores design parameters, such as sequence and Holliday junction isomeric forms, for efficient crystallization of 3D DNA lattices. Furthermore, previously published DNA crystal motifs are used to site-specifically position and structurally evaluate minor groove binding molecules with defined occupancies. The results of this study provide significant advancement towards the ultimate goal of the field.
ContributorsHenry, Skylar J.W. (Author) / Stephanopoulos, Nicholas (Thesis advisor) / Anderson, Karen (Thesis advisor) / Blattman, Joseph (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2023
Description
Transfer learning is a sub-field of statistical modeling and machine learning. It refers to methods that integrate the knowledge of other domains (called source domains) and the data of the target domain in a mathematically rigorous and intelligent way, to develop a better model for the target domain than a model using the data of the target domain alone. While transfer learning is a promising approach in various application domains, my dissertation research focuses on the particular application in health care, including telemonitoring of Parkinson’s Disease (PD) and radiomics for glioblastoma.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
ContributorsYoon, Hyunsoo (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Hu, Leland S. (Committee member) / Arizona State University (Publisher)
Created2018