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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Description
DNA nanotechnology has been a rapidly growing research field in the recent decades, and there have been extensive efforts to construct various types of highly programmable and robust DNA nanostructures. Due to the advantage that DNA nanostructure can be used to organize biochemical molecules with precisely controlled spatial resolution, herein we used DNA nanostructure as a scaffold for biological applications. Targeted cell-cell interaction was reconstituted through a DNA scaffolded multivalent bispecific aptamer, which may lead to promising potentials in tumor therapeutics. In addition a synthetic vaccine was constructed using DNA nanostructure as a platform to assemble both model antigen and immunoadjuvant together, and strong antibody response was demonstrated in vivo, highlighting the potential of DNA nanostructures to serve as a new platform for vaccine construction, and therefore a DNA scaffolded hapten vaccine is further constructed and tested for its antibody response. Taken together, my research demonstrated the potential of DNA nanostructure to serve as a general platform for immunological applications.
ContributorsLiu, Xiaowei (Author) / Liu, Yan (Thesis advisor) / Chang, Yung (Thesis advisor) / Yan, Hao (Committee member) / Allen, James (Committee member) / Zhang, Peiming (Committee member) / Arizona State University (Publisher)
Created2012
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
For multiple reasons, the consumption of fresh fruits and vegetables in the United States has progressively increased. This has resulted in increased domestic production and importation of these products. The associated logistics is complex due to the perishability of these products, and most current logistics systems rely on marketing and supply chains practices that result in high levels of food waste and limited offer diversity. For instance, given the lack of critical mass, small growers are conspicuously absent from mainstream distribution channels. One way to obtain these critical masses is using associative schemes such as co-ops. However, the success level of traditional associate schemes has been mixed at best. This dissertation develops decision support tools to facilitate the formation of coalitions of small growers in complementary production regions to act as a single-like supplier. Thus, this dissertation demonstrates the benefits and efficiency that could be achieved by these coalitions, presents a methodology to efficiently distribute the value of a new identified market opportunity among the growers participating in the coalition, and develops a negotiation framework between a buyer(s) and the agent representing the coalition that results in a prototype contract.There are four main areas of research contributions in this dissertation. The first is the development of optimization tools to allocate a market opportunity to potential production regions while considering consumer preferences for special denomination labels such as “local”, “organic”, etc. The second contribution is in the development of a stochastic optimization and revenue-distribution framework for the formation of coalitions of growers to maximize the captured value of a market opportunity. The framework considers the growers’ individual preferences and production characteristics (yields, resources, etc.) to develop supply contracts that entice their participation in the coalition. The third area is the development of a negotiation mechanism to design contracts between buyers and groups of growers considering the profit expectations and the variability of the future demand. The final contribution is the integration of these models and tools into a framework capable of transforming new market opportunities into implementable production plans and contractual agreement between the different supply chain participants.
ContributorsUlloa, Rodrigo (Author) / Villalobos, Jesus (Thesis advisor) / Fowler, John (Committee member) / Mac Cawley, Alejandro (Committee member) / Yan, Hao (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2022
Description
Assembly lines are low-cost production systems that manufacture similar finished units in large quantities. Manufacturers utilize mixed-model assembly lines to produce customized items that are not identical but share some general features in response to consumer needs. To maintain efficiency, the aim is to find the best feasible option to balance the lines efficiently; allocating each task to a workstation to satisfy all restrictions and fulfill all operational requirements in such a way that the line has the highest performance and maximum throughput. The work to be done at each workstation and line depends on the precise product configuration and is not constant across all models. This research seeks to enhance the subject of assembly line balancing by establishing a model for creating the most efficient assembly system. Several realistic characteristics are included into efficient optimization techniques and mathematical models to provide a more comprehensive model for building assembly systems. This involves analyzing the learning growth by task, employing parallel line designs, and configuring mixed models structure under particular constraints and criteria. This dissertation covers a gap in the literature by utilizing some exact and approximation modeling approaches. These methods are based on mathematical programming techniques, including integer and mixed integer models and heuristics. In this dissertation, heuristic approximations are employed to address problem-solving challenges caused by the problem's combinatorial complexity. This study proposes a model that considers learning curve effects and dynamic demand. This is exemplified in instances of a new assembly line, new employees, introducing new products or simply implementing engineering change orders. To achieve a cost-based optimal solution, an integer mathematical formulation is proposed to minimize the production line's total cost under the impact of learning and demand fulfillment. The research further creates approaches to obtain a comprehensive model in the case of single and mixed models for parallel lines systems. Optimization models and heuristics are developed under various aspects, such as cycle times by line and tooling considerations. Numerous extensions are explored effectively to analyze the cost impact under certain constraints and implications. The implementation results demonstrate that the proposed models and heuristics provide valuable insights.
ContributorsAlhomaidi, Esam (Author) / Askin, Ronald G (Thesis advisor) / Yan, Hao (Committee member) / Iquebal, Ashif (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2023
Description
Peptide-based vaccines represent a promising strategy to develop personalized treatments for cancer immunotherapy. Despite their specificity and low cost of production, these vaccines have had minimal success in clinical studies due to their lack of immunogenicity, creating a need for more effective vaccine designs. Adjuvants can be incorporated to enhance their immunogenicity by promoting dendritic cell activation and antigen cross-presentation. Due to their favorable size and ability to incorporate peptides and adjuvants, nanoparticles represent an advantageous platform for designing peptide vaccines. One prime example is RNA origami (RNA-OG) nanostructures, which are nucleic acid nanostructures programmed to assemble into uniform shapes and sizes. These stable nanostructures can rationally incorporate small molecules giving them a wide array of functions. Furthermore, RNA-OG itself can function as an adjuvant to stimulate innate immune cells. In the following study, self-adjuvanted RNA-OG was employed as a vaccine assembly platform, incorporating tumor peptides onto the nanostructure to design RNA-OG-peptide nanovaccines for cancer immunotherapy. RNA-OG-peptide was found to induce dendritic cell activation and antigen cross-presentation, which mobilized tumor-specific cytotoxic T cells to elicit protective anti-tumor immunity in tumor-bearing mice. These findings demonstrate the therapeutic potential of RNA-OG as a stable, carrier-free nanovaccine platform. In an attempt to further enhance the efficacy by optimizing the amount of peptides assembled, RNA-OG was complexed with polylysine-linked peptides, a simple strategy that allowed peptide amounts to be varied. Interestingly, increasing the peptide load led to decreased vaccine efficacy, which was correlated with an ineffective CD8+ T cell response. On the other hand, the vaccine efficacy was improved by decreasing the amount of peptide loaded onto RNA-OG, which may have attributed to greater complex stability compared to the high peptide load. These results highlight a simple strategy that can be used to optimize vaccine efficacy by altering the load of assembled peptides. These studies advance our understanding of RNA-OG as a peptide vaccine platform and provide various strategies to improve the design of peptide vaccines for translation into cancer immunotherapy.
ContributorsYip, Theresa (Author) / Chang, Yung (Thesis advisor) / Borges Florsheim, Esther (Committee member) / Lake, Douglas (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2024
Description
Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to model building and their inclusion will increase training time and potentially hurt the model performance. The objective of this research is to develop novel SSL models to balance data inclusivity and usability. My dissertation research focuses on applications of SSL in healthcare, driven by problems in brain cancer radiomics, migraine imaging, and Parkinson’s Disease telemonitoring.
The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain.
The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models.
The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.
The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain.
The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models.
The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.
ContributorsGaw, Nathan (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Hu, Leland (Committee member) / Arizona State University (Publisher)
Created2019
Description
There are many biological questions that require single-cell analysis of gene sequences, including analysis of clonally distributed dimeric immunoreceptors on lymphocytes (T cells and B cells) and/or the accumulation of driver/accessory mutations in polyclonal tumors. Lysis of bulk cell populations results in mixing of gene sequences, making it impossible to know which pairs of gene sequences originated from any particular cell and obfuscating analysis of rare sequences within large populations. Although current single-cell sorting technologies can be used to address some of these questions, such approaches are expensive, require specialized equipment, and lack the necessary high-throughput capacity for comprehensive analysis. Water-in-oil emulsion approaches for single cell sorting have been developed but droplet-based single-cell lysis and analysis have proven inefficient and yield high rates of false pairings. Ideally, molecular approaches for linking gene sequences from individual cells could be coupled with next-generation high-throughput sequencing to overcome these obstacles, but conventional approaches for linking gene sequences, such as by transfection with bridging oligonucleotides, result in activation of cellular nucleases that destroy the template, precluding this strategy. Recent advances in the synthesis and fabrication of modular deoxyribonucleic acid (DNA) origami nanostructures have resulted in new possibilities for addressing many current and long-standing scientific and technical challenges in biology and medicine. One exciting application of DNA nanotechnology is the intracellular capture, barcode linkage, and subsequent sequence analysis of multiple messenger RNA (mRNA) targets from individual cells within heterogeneous cell populations. DNA nanostructures can be transfected into individual cells to capture and protect mRNA for specific expressed genes, and incorporation of origami-specific bowtie-barcodes into the origami nanostructure facilitates pairing and analysis of mRNA from individual cells by high-throughput next-generation sequencing. This approach is highly modular and can be adapted to virtually any two (and possibly more) gene target sequences, and therefore has a wide range of potential applications for analysis of diverse cell populations such as understanding the relationship between different immune cell populations, development of novel immunotherapeutic antibodies, or improving the diagnosis or treatment for a wide variety of cancers.
ContributorsSchoettle, Louis (Author) / Blattman, Joseph N (Thesis advisor) / Yan, Hao (Committee member) / Chang, Yung (Committee member) / Lindsay, Stuart (Committee member) / Arizona State University (Publisher)
Created2017
Description
Recent advances in manufacturing system, such as advanced embedded sensing, big data analytics and IoT and robotics, are promising a paradigm shift in the manufacturing industry towards smart manufacturing systems. Typically, real-time data is available in many industries, such as automotive, semiconductor, and food production, which can reflect the machine conditions and production system’s operation performance. However, a major research gap still exists in terms of how to utilize these real-time data information to evaluate and predict production system performance and to further facilitate timely decision making and production control on the factory floor. To tackle these challenges, this dissertation takes on an integrated analytical approach by hybridizing data analytics, stochastic modeling and decision making under uncertainty methodology to solve practical manufacturing problems.
Specifically, in this research, the machine degradation process is considered. It has been shown that machines working at different operating states may break down in different probabilistic manners. In addition, machines working in worse operating stage are more likely to fail, thus causing more frequent down period and reducing the system throughput. However, there is still a lack of analytical methods to quantify the potential impact of machine condition degradation on the overall system performance to facilitate operation decision making on the factory floor. To address these issues, this dissertation considers a serial production line with finite buffers and multiple machines following Markovian degradation process. An integrated model based on the aggregation method is built to quantify the overall system performance and its interactions with machine condition process. Moreover, system properties are investigated to analyze the influence of system parameters on system performance. In addition, three types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems with the coupling between machine condition degradation and productivity given the real-time signals.
Specifically, in this research, the machine degradation process is considered. It has been shown that machines working at different operating states may break down in different probabilistic manners. In addition, machines working in worse operating stage are more likely to fail, thus causing more frequent down period and reducing the system throughput. However, there is still a lack of analytical methods to quantify the potential impact of machine condition degradation on the overall system performance to facilitate operation decision making on the factory floor. To address these issues, this dissertation considers a serial production line with finite buffers and multiple machines following Markovian degradation process. An integrated model based on the aggregation method is built to quantify the overall system performance and its interactions with machine condition process. Moreover, system properties are investigated to analyze the influence of system parameters on system performance. In addition, three types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems with the coupling between machine condition degradation and productivity given the real-time signals.
ContributorsKang, Yunyi (Author) / Ju, Feng (Thesis advisor) / Pedrielli, Giulia (Committee member) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2020
Description
Modern manufacturing systems are part of a complex supply chain where customer preferences are constantly evolving. The rapidly evolving market demands manufacturing organizations to be increasingly agile and flexible. Medium term capacity planning for manufacturing systems employ queueing network models based on stationary demand assumptions. However, these stationary demand assumptions are not very practical for rapidly evolving supply chains. Nonstationary demand processes provide a reasonable framework to capture the time-varying nature of modern markets. The analysis of queues and queueing networks with time-varying parameters is mathematically intractable. In this dissertation, heuristics which draw upon existing steady state queueing results are proposed to provide computationally efficient approximations for dynamic multi-product manufacturing systems modeled as time-varying queueing networks with multiple customer classes (product types). This dissertation addresses the problem of performance evaluation of such manufacturing systems.
This dissertation considers the two key aspects of dynamic multi-product manufacturing systems - namely, performance evaluation and optimal server resource allocation. First, the performance evaluation of systems with infinite queueing room and a first-come first-serve service paradigm is considered. Second, systems with finite queueing room and priorities between product types are considered. Finally, the optimal server allocation problem is addressed in the context of dynamic multi-product manufacturing systems. The performance estimates developed in the earlier part of the dissertation are leveraged in a simulated annealing algorithm framework to obtain server resource allocations.
This dissertation considers the two key aspects of dynamic multi-product manufacturing systems - namely, performance evaluation and optimal server resource allocation. First, the performance evaluation of systems with infinite queueing room and a first-come first-serve service paradigm is considered. Second, systems with finite queueing room and priorities between product types are considered. Finally, the optimal server allocation problem is addressed in the context of dynamic multi-product manufacturing systems. The performance estimates developed in the earlier part of the dissertation are leveraged in a simulated annealing algorithm framework to obtain server resource allocations.
ContributorsJampani Hanumantha, Girish (Author) / Askin, Ronald (Thesis advisor) / Ju, Feng (Committee member) / Yan, Hao (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2020
Description
Nonalcoholic Steatohepatitis (NASH) is a severe form of Nonalcoholic fatty liverdisease, that is caused due to excessive calorie intake, sedentary lifestyle and in the
absence of severe alcohol consumption. It is widely prevalent in the United States
and in many other developed countries, affecting up to 25 percent of the population.
Due to being asymptotic, it usually goes unnoticed and may lead to liver failure if
not treated at the right time.
Currently, liver biopsy is the gold standard to diagnose NASH, but being an
invasive procedure, it comes with it's own complications along with the inconvenience
of sampling repeated measurements over a period of time. Hence, noninvasive
procedures to assess NASH are urgently required. Magnetic Resonance Elastography
(MRE) based Shear Stiffness and Loss Modulus along with Magnetic Resonance
Imaging based proton density fat fraction have been successfully combined to predict
NASH stages However, their role in the prediction of disease progression still remains
to be investigated.
This thesis thus looks into combining features from serial MRE observations to
develop statistical models to predict NASH progression. It utilizes data from an experiment
conducted on male mice to develop progressive and regressive NASH and
trains ordinal models, ordered probit regression and ordinal forest on labels generated
from a logistic regression model. The models are assessed on histological data collected
at the end point of the experiment. The models developed provide a framework
to utilize a non-invasive tool to predict NASH disease progression.
ContributorsDeshpande, Eeshan (Author) / Ju, Feng (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2021