Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 152
Description
Cancer diseases are among the leading cause of death in the United States. Advanced cancer diseases are characterized by genetic defects resulting in uncontrollable cell growth. Currently, chemotherapeutics are one of the mainstream treatments administered to cancer patients but are less effective if administered in the later stages of metastasis, and can result in unwanted side effects and broad toxicities. Therefore, current efforts have explored gene therapy as an alternative strategy to correct the genetic defects associated with cancer diseases, by administering genes which encode for proteins that result in cell death. While the use of viral vectors shows high level expression of the delivered transgene, the potential for insertion mutagenesis and activation of immune responses raise concern in clinical applications. Non-viral vectors, including cationic lipids and polymers, have been explored as potentially safer alternatives to viral delivery systems. These systems are advantageous for transgene delivery due to ease of synthesis, scale up, versatility, and in some cases due to their biodegradability and biocompatibility. However, low efficacies for transgene expression and high cytotoxicities limit the practical use of these polymers. In this work, a small library of twenty-one cationic polymers was synthesized following a ring opening polymerization of diglycidyl ethers (epoxides) by polyamines. The polymers were screened in parallel and transfection efficacies of individual polymers were compared to those of polyethylenimine (PEI), a current standard for polymer-mediated transgene delivery. Seven lead polymers that demonstrated higher transgene expression efficacies than PEI in pancreatic and prostate cancer cells lines were identified from the screening. A second related effort involved the generation of polymer-antibody conjugates in order to facilitate targeting of delivered plasmid DNA selectively to cancer cells. Future work with the novel lead polymers and polymer-antibody conjugates developed in this research will involve an investigation into the delivery of transgenes encoding for apoptosis-inducing proteins both in vitro and in vivo.
ContributorsVu, Lucas (Author) / Rege, Kaushal (Thesis advisor) / Nielsen, David (Committee member) / Sierks, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation presents a systematic study of the sorption mechanisms of hydrophobic silica aerogel (Cabot Nanogel®) granules for oil and volatile organic compounds (VOCs) in different phases. The performance of Nanogel for removing oil from laboratory synthetic oil-in-water emulsions and real oily wastewater, and VOCs from their aqueous solution, in both packed bed (PB) and inverse fluidized bed (IFB) modes was also investigated. The sorption mechanisms of VOCs in the vapor, pure liquid, and aqueous solution phases, free oil, emulsified oil, and oil from real wastewater on Nanogel were systematically studied via batch kinetics and equilibrium experiments. The VOC results show that the adsorption of vapor is very slow due to the extremely low thermal conductivity of Nanogel. The faster adsorption rates in the liquid and solution phases are controlled by the mass transport, either by capillary flow or by vapor diffusion/adsorption. The oil results show that Nanogel has a very high capacity for adsorption of pure oils. However, the rate for adsorption of oil from an oil-water emulsion on the Nanogel is 5-10 times slower than that for adsorption of pure oils or organics from their aqueous solutions. For an oil-water emulsion, the oil adsorption capacity decreases with an increasing proportion of the surfactant added. An even lower sorption capacity and a slower sorption rate were observed for a real oily wastewater sample due to the high stability and very small droplet size of the wastewater. The performance of Nanogel granules for removing emulsified oil, oil from real oily wastewater, and toluene at low concentrations in both PB and IFB modes was systematically investigated. The hydrodynamics characteristics of the Nanogel granules in an IFB were studied by measuring the pressure drop and bed expansion with superficial water velocity. The density of the Nanogel granules was calculated from the plateau pressure drop of the IFB. The oil/toluene removal efficiency and the capacity of the Nanogel granules in the PB or IFB were also measured experimentally and predicted by two models based on equilibrium and kinetic batch measurements of the Nanogel granules.
ContributorsWang, Ding (Author) / Lin, Jerry Y.S. (Thesis advisor) / Pfeffer, Robert (Thesis advisor) / Westerhoff, Paul (Committee member) / Nielsen, David (Committee member) / Lind, Mary Laura (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Cancer is the second leading cause of death in the United States and novel methods of treating advanced malignancies are of high importance. Of these deaths, prostate cancer and breast cancer are the second most fatal carcinomas in men and women respectively, while pancreatic cancer is the fourth most fatal in both men and women. Developing new drugs for the treatment of cancer is both a slow and expensive process. It is estimated that it takes an average of 15 years and an expense of $800 million to bring a single new drug to the market. However, it is also estimated that nearly 40% of that cost could be avoided by finding alternative uses for drugs that have already been approved by the Food and Drug Administration (FDA). The research presented in this document describes the testing, identification, and mechanistic evaluation of novel methods for treating many human carcinomas using drugs previously approved by the FDA. A tissue culture plate-based screening of FDA approved drugs will identify compounds that can be used in combination with the protein TRAIL to induce apoptosis selectively in cancer cells. Identified leads will next be optimized using high-throughput microfluidic devices to determine the most effective treatment conditions. Finally, a rigorous mechanistic analysis will be conducted to understand how the FDA-approved drug mitoxantrone, sensitizes cancer cells to TRAIL-mediated apoptosis.
ContributorsTaylor, David (Author) / Rege, Kaushal (Thesis advisor) / Jayaraman, Arul (Committee member) / Nielsen, David (Committee member) / Kodibagkar, Vikram (Committee member) / Dai, Lenore (Committee member) / Arizona State University (Publisher)
Created2013
Description
The processing power and storage capacity of portable devices have improved considerably over the past decade. This has motivated the implementation of sophisticated audio and other signal processing algorithms on such mobile devices. Of particular interest in this thesis is audio/speech processing based on perceptual criteria. Specifically, estimation of parameters from human auditory models, such as auditory patterns and loudness, involves computationally intensive operations which can strain device resources. Hence, strategies for implementing computationally efficient human auditory models for loudness estimation have been studied in this thesis. Existing algorithms for reducing computations in auditory pattern and loudness estimation have been examined and improved algorithms have been proposed to overcome limitations of these methods. In addition, real-time applications such as perceptual loudness estimation and loudness equalization using auditory models have also been implemented. A software implementation of loudness estimation on iOS devices is also reported in this thesis. In addition to the loudness estimation algorithms and software, in this thesis project we also created new illustrations of speech and audio processing concepts for research and education. As a result, a new suite of speech/audio DSP functions was developed and integrated as part of the award-winning educational iOS App 'iJDSP." These functions are described in detail in this thesis. Several enhancements in the architecture of the application have also been introduced for providing the supporting framework for speech/audio processing. Frame-by-frame processing and visualization functionalities have been developed to facilitate speech/audio processing. In addition, facilities for easy sound recording, processing and audio rendering have also been developed to provide students, practitioners and researchers with an enriched DSP simulation tool. Simulations and assessments have been also developed for use in classes and training of practitioners and students.
ContributorsKalyanasundaram, Girish (Author) / Spanias, Andreas S (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2013
Description
A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems. Under this common theme, this dissertation can be broadly organized into three parts based on the system environments. The first part investigates stochastic optimization in real-time wireless systems, with the focus on the deadline-aware scheduling for real-time traffic. The optimal solution to such scheduling problems requires to explicitly taking into account the coupling in the deadline-aware transmissions and stochastic characteristics of the traffic, which involves a dynamic program that is traditionally known to be intractable or computationally expensive to implement. First, real-time scheduling with adaptive network coding over memoryless channels is studied, and a polynomial-time complexity algorithm is developed to characterize the optimal real-time scheduling. Then, real-time scheduling over Markovian channels is investigated, where channel conditions are time-varying and online channel learning is necessary, and the optimal scheduling policies in different traffic regimes are studied. The second part focuses on the stochastic optimization and real-time scheduling involved in energy systems. First, risk-aware scheduling and dispatch for plug-in electric vehicles (EVs) are studied, aiming to jointly optimize the EV charging cost and the risk of the load mismatch between the forecasted and the actual EV loads, due to the random driving activities of EVs. Then, the integration of wind generation at high penetration levels into bulk power grids is considered. Joint optimization of economic dispatch and interruptible load management is investigated using short-term wind farm generation forecast. The third part studies stochastic optimization in distributed control systems under different network environments. First, distributed spectrum access in cognitive radio networks is investigated by using pricing approach, where primary users (PUs) sell the temporarily unused spectrum and secondary users compete via random access for such spectrum opportunities. The optimal pricing strategy for PUs and the corresponding distributed implementation of spectrum access control are developed to maximize the PU's revenue. Then, a systematic study of the nonconvex utility-based power control problem is presented under the physical interference model in ad-hoc networks. Distributed power control schemes are devised to maximize the system utility, by leveraging the extended duality theory and simulated annealing.
ContributorsYang, Lei (Author) / Zhang, Junshan (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Xue, Guoliang (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2012
Description
The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.
ContributorsZhang, Rui (Author) / Zhang, Yanchao (Thesis advisor) / Duman, Tolga Mete (Committee member) / Xue, Guoliang (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
To further the efforts producing energy from more renewable sources, microbial electrochemical cells (MXCs) can utilize anode respiring bacteria (ARB) to couple the oxidation of an organic substrate to the delivery of electrons to the anode. Although ARB such as Geobacter and Shewanella have been well-studied in terms of their microbiology and electrochemistry, much is still unknown about the mechanism of electron transfer to the anode. To this end, this thesis seeks to elucidate the complexities of electron transfer existing in Geobacter sulfurreducens biofilms by employing Electrochemical Impedance Spectroscopy (EIS) as the tool of choice. Experiments measuring EIS resistances as a function of growth were used to uncover the potential gradients that emerge in biofilms as they grow and become thicker. While a better understanding of this model ARB is sought, electrochemical characterization of a halophile, Geoalkalibacter subterraneus (Glk. subterraneus), revealed that this organism can function as an ARB and produce seemingly high current densities while consuming different organic substrates, including acetate, butyrate, and glycerol. The importance of identifying and studying novel ARB for broader MXC applications was stressed in this thesis as a potential avenue for tackling some of human energy problems.
ContributorsAjulo, Oluyomi (Author) / Torres, Cesar (Thesis advisor) / Nielsen, David (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Popat, Sudeep (Committee member) / Arizona State University (Publisher)
Created2013