More than 30,000 theses and dissertations produced at the University of Arizona are included in the UA Theses and Dissertations collections. These items are available open access, and are full-text searchable. A small percentage of items are under embargo (restricted).

We have digitized the entire backfile of master's theses and doctoral dissertations that have been submitted to the University of Arizona Libraries - since 1895!

If you can't find the item you want in the repository and would like to check its digitization status, please email us at

You can also refer to the Dissertations and Theses in the UA Libraries guide to find materials that are not available online.

Collections in this community

Recent Submissions

  • Mitigation of Communication Blackout Arising from Plasma Sheath Formation at Plasmasonic Speeds

    Ziolkowski, Richard W.; Dvorak, Steven L.; Webb, Bruce; Cao, Siyang; Weidman, Charles D. (The University of Arizona., 2021)
    When a vehicle moves at plasmasonic speeds, i.e., at 10 timesthe speed of sound or more, within an atmosphere, the com- pression of the air in front of this plasmasonic vehicle creates a signicant rise in the temperature of the air region surrounding it. When the heat level becomes high enough, the gases in that region ionize and create a plasma, i.e., a medium consisting of mainly charged particles. It exhibits a permittivity that has a negative real part and permeability with a positive real part. It is consequently termed as an epsilon negative (ENG) medium. The EM waves in an ENG medium are evanescent, i.e., they are decaying elds rather than being propagating ones. When such an ENG layer consists of a large enough density of charged particles, the radio frequency (RF) electromagnetic (EM) waves radiated from an antenna within the vehicle are re ected from the plasma surrounding it; they cannot propagate through the plasma to reach a receiver on the other side of it. Similarly, EM waves from a source trying to reach the vehicle from out- side of the plasma region are also re ected and, hence, cannot reach it. Since these EM waves are unable to penetrate through the plasma region, a RF blackout situation occurs, i.e., commu- nications and telemetry signal exchanges with the vehicle are lost. This RF blackout problem puts the success of the vehicle's mission in jeopardy. Recent metamaterial research has shown that a medium ex- hibiting both a negative permittivity and a negative permeabil- ity, i.e., a double negative (DNG) medium, supports propagat- ing EM waves. The research reported in this dissertation has explored the possibility of designing and realizing a metamate- rial that has a negative permeability and a positive permittivity, i.e., a mu-negative (MNG) articial medium, that is conjugate matched to the ENG plasma. It demonstrates that when it is combined with the ENG medium, it creates an eective DNG composite medium that allows EM waves to penetrate through it. A composite MNG-ENG meta-structure is rst developed and integrated with both dipole and Huygens dipole antenna systems to demonstrate an electromagnetic solution to the RF blackout problem. The characteristics of this solution are pre- sented. However, when very thick plasma regions are encoun- tered, it is shown that this simple solution becomes inadequate. It is further demonstrated that a RF blackout solution still exists by engineering the physical and material properties of a multilayered MNG-ENG meta-structure to match it to the thicker plasma regions. Practical MNG metamaterials are de- signed based on split ring resonators (SRRs); practical ENG metamaterials are designed based on capacitively loaded strips (CLSs). Moreover, they are developed as elements whose fre- quency responses are tunable for applications when the plasma properties vary along the plasmasonic vehicle's trajectory. The propagation characteristics of EM signals through plasma re- gions with dierent thicknesses were explored when ideal and the associated practical MNG-ENG multilayer meta-structures are matched to them. It is demonstrated that a practical metamaterials- inspired EM solution to the RF blackout problem faced by plas- masonic vehicles surrounded by plasmas of various thicknesses is possible.
  • Contextual Influences in Perceptual Decision Making

    Wilson, Robert; Cook, Sarah; Cowen, Steven; Allen, John; Isham, Eve (The University of Arizona., 2021)
    Perceptual Decision Making involves making a simple decision about some feature of a perceptual stimulus. How do different contextual influences change the underlying dynamics of this task? Here I use the Clicks task, a simple perceptual decision-making task where participants must report which side they heard more clicks, in order to study how different contextual influences can shape the underlying dynamics of this decision-making process. This task is subject to a number of different suboptimalities which can drive errors, such as side bias, integration shape, choice kernel, reinforcement learning, and noise. Here I address how various contextual influences can modulate these suboptimalities in order to change decision-making behavior. In the first study, I show that when participants are under higher motivation to perform the task accurately, noise in the decision process is subject to rapid modulation in order to achieve higher performance. However, no other suboptimalities were modulated in order to achieve this higher performance. The second study aims to find other sources of contextual influence that can modulate the other suboptimalities, such as the integration shape. Results show that through changing the underlying statistics of the stimulus, participants can indeed modulate their integration shape, or the way they weight information over time, to match these stimulus statistics. These two studies provide evidence that noise in the decision process, and integration shape are two underlying dynamics of the decision process that are subject to modulation. Future research can address further contextual influences that may be able to modulate the remaining suboptimalities such as side bias, reinforcement learning, and choice kernel.
  • From the Back to the Front Burner: Clinical Supervisor Training and Evaluation

    Hartley, Michael T.; Shaheed, Camelia; Tashjian, Amanda; Johnson, Phillip R. (The University of Arizona., 2021)
    Despite agreement on the importance of satisfactory and quality clinical supervision, evaluation models of supervisory competence have received little attention in the professional literature. The purpose of this multiple baseline design was to evaluate whether, following a training intervention, supervisors with six or more months of supervision experience increased their use of three essential supervisory practices during dyadic supervision: (a) supervisee goal review, (b) client progress review, and (c) provision of formative feedback. Each of three is necessary to the promotion of supervisee professional development. Following its introduction, the intervention in this study increased the frequency and accuracy of each supervisor’s use of the three practices during observations of dyadic supervision. Furthermore, the intervention resulted in supervisor and supervisee changes in perceptions of delivered supervision; specifically, greater alignment of perceptions. Implications will focus on the need to further define and evaluate specific, not assumed, supervisor needs.
  • Multimodal Optical Imaging for Tissue Characterization and Disease Diagnosis

    Barton, Jennifer K.; Sawyer, Travis William; Schwiegerling, James T.; Kang, Dongkyun; Gordon, George (The University of Arizona., 2021)
    Epithelial cancers are among the most dangerous forms of cancer. Of this broad group of disease, ovarian and esophageal cancer are particularly deadly, with five-year survival rates of less than 50% and 20% respectively. The primary cause of this low survival rate is due to predominantly late diagnosis. Diagnosis at early stages leads to over 90% 5-year survival rates for ovarian cancer and over 40% for esophageal cancer, but fewer than 15% of cases for these two cancers are detected early. Screening is complicated by non-specific or complete lack of symptoms, as well as the heterogeneity of the diseases. For both esophageal and ovarian cancers, many screening tests including imaging, physical examination, and blood markers tests have been investigated; however, at this time no routine screening is recommended in average-risk patients. This study evaluates the feasibility and design of instruments to use multimodal optical imaging to improve ovarian and esophageal cancer screening. This includes optical coherence tomography (OCT), multiphoton microscopy (MPM), and wide-field fluorescence imaging. The study is subdivided into four sections. In the first two sections, advanced algorithms and processing techniques are presented for rapid analysis and quantitative diagnostic evaluation for optical coherence tomography images of ovarian cancer. The results show promise for automatic processing of OCT images using segmentation, combined with highly accurate diagnostic performance in identifying diseased tissue using texture features of OCT images. The third section details the application of MPM and wide-field fluorescence imaging using exogenous contrast agents to evaluate and classify tissue health. Two tissue studies using a mouse model of ovarian cancer are presented: one ex vivo and one in vivo study. The results of both demonstrate that these modalities provide high contrast for identifying diseased tissue and that the combination of these two modalities show an improvement in diagnostic performance over a single modality. Finally, the study concludes with the design for a multimodal forward-viewing esophageal endoscope using optical coherence tomography and autofluorescence imaging. The design implements a piezo-scanning fiber to deliver the light, and spatially separates the OCT and fluorescence return signal. The design provides high resolution and is compatible with working channels in existing gastroscopes to enable easy clinical translation.
  • Algorithms for Scalability and Security in Adversarial Environments

    Ditzler, Gregory; Liu, Heng; Tandon, Ravi; Hariri, Salim (The University of Arizona., 2021)
    The scalability and security characteristics are central to modern machine learning and data science pipelines. The scalability issue of machine learning pipelines is that many real-world applications encounter large-scale datasets that are almost unimaginable. As a result, the ever-increasing data scale makes many of the classical machine learning algorithms obsolete in the face of big data. Meanwhile, the security issue emerged from the assumption that a machine learning model does not consider an adversary’s existence to subvert a classifier’s objective. Therefore, machine learning pipelines exhibit vulnerabilities in an adversarial environment. In this thesis, we investigate the scalability and security issues of machine learning with a focus on Feature Selection (FS) and Automatic Speech Recognition (ASR). FS is a critical preprocessing stage that helps avoid the “curse of dimensionality” and overfitting issues by identifying the feature subset that is both relevant and non-redundant. In the past few decades, FS has been driven by exploring “big data” and the extensive development of high-performance computing. Nevertheless, the implementation of scalable FS remains an under-explored topic. Moreover, although the research community has made extensive efforts to promote the classifiers’ security and develop countermeasures against adversaries, only a few contributions investigated the FS’s behaviors in an adversarial environment. Given that machine learning pipelines are increasingly relying on FS to combat the “curse of dimensionality” and overfitting issues, insecure FS can be the “Achilles heel” of data pipelines. In this thesis, we address the scalability and security issues of information-theoretic filter FS algorithms. In our contributions, we revisit the greedy forward optimization in information-theoretic filter FS and propose a semi-parallel optimizing paradigm that can provide an equivalent feature subset as the greedy forward optimization but in a fraction of the time. We next explore weaknesses of information-theoretic filter FS algorithms by designing a generic FS poisoning algorithm. We also demonstrate the transferability of the proposed poisoning algorithm across seven information-theoretic FS algorithms. The remainder of this thesis examines the security issues for Deep Neural Networks (DNNs) based ASR applications. Recently, DNNs have achieved remarkable success in numerous real-world applications. However, recent contributions have shown that DNNs can be easily fooled by adversarial inputs that appear legitimate. In the past decade, the majority of adversarial machine learning research focused on image domains; however, far fewer work on audio exist. Thus, developing novel audio attack algorithms and adversarial audio detection methods both remain under-explored. Further, we revisit the structure of LSTMs that are used in ASR then propose a new audio attack algorithm that evades the state-of-the-art temporal dependency-based detection by explicitly controlling the temporal dependency in generated adversarial audios. Finally, we leverage the DNN quantization techniques and propose a novel adversarial audio detection method by incorporating the DNN’s activation quantization error.
  • Machine Learning for Channel Estimation and Hybrid Beamforming in Millimeter-Wave Wireless Networks

    Bose, Tamal; Tandon, Ravi; Peken, Ture; Ditzler, Gregory; Djordjevic, Ivan (The University of Arizona., 2021)
    The continuous growth of mobile users and high-speed wireless applications drives the demand for using the abundant bandwidth of mmWave (millimeter-wave) frequencies. On one hand, a massive number of antennas can be supported due to small wavelengths of mmWave signals, which allow using antennas with small form factors. On the other hand, the free space path loss increases with the square of the frequency, which implies that the path loss would be severe in mmWave frequencies. Fortunately, one can compensate for the performance degradation due to the path loss by using directional beamforming (BF) along with the high gain large antenna array systems (massive MIMO). This dissertation tackles three distinct problems, namely channel estimation in massive MIMO, signal detection in massive MIMO, and efficient design of hybrid BF algorithms. In the first part of this dissertation, we focus on the effective channel estimation for massive MIMO systems to overcome the pilot contamination problem. We present an adaptive independent component analysis (ICA)-based channel estimation method, which outperforms conventional ICA as well as other conventional methods for channel estimation. We also make use of compressive sensing (CS) methods for channel estimation and show the advantages in terms of channel estimation accuracy and complexity. In the second part of this dissertation, we consider the problem of signal detection specifically focusing on the scenarios when non-Gaussian signals need to be detected and the receiver may be equipped with a large number of antennas. We show that for the case of non-Gaussian signal detection it turns out the conventional Neyman-Pearson (NP) detector does not perform well for the low signal-to-noise-ratio (SNR) regime. Motivated by this, we propose a bispectrum detector, which is able to better detect the corresponding non-Gaussian information in the signal. We also present the theoretical analysis for the asymptotic behavior of Probability of False Alarm and Probability of Detection. We show the performance of signal detection (for both Gaussian and non-Gaussian signals) as a function of the number of antennas and sampling rate. We also obtain the scaling behavior of the performance in the massive antenna regime. The third part of this dissertation covers the efficient design of hybrid BF algorithms with a specific focus on massive MIMO systems in mmWave networks. The key challenge in the design of hybrid BF algorithms in such networks is that the computational complexity can be prohibitive. We start by focusing on the fundamental approach of finding BF solutions through singular value decomposition (SVD) and explore the role of ML techniques to perform SVD. The first part of this contribution focuses on the data-driven approach to SVD. We propose three deep neural network (DNN) architectures to approximate the SVD, with varying levels of complexity. The methodology for training these DNN architectures is inspired by the fundamental property of SVD, i.e., it can be used to obtain low-rank approximations. We next explicitly take the constraints of hybrid BF into account (such as quantized phase shifters, power constraints), and propose a novel DNN-based approach for the design of hybrid BF systems. Our results show that DNNs can be an attractive and efficient solution for both estimating the SVD as well as hybrid beamformers. Furthermore, we provide time complexity and memory requirement analyses for the proposed DNN-based and state-of-the-art hybrid BF approaches. We then propose a novel reinforcement learning-based hybrid BF algorithm that applies Q-learning in a supervised manner. We analyze the computational complexity of our algorithm as a function of iteration steps and show that a significant reduction in computational complexity is achieved compared to the exhaustive search. In addition to exploring supervised approaches, in the remaining part of this contribution we also explore unsupervised methods for SVD and hybrid BF. These methods are particularly attractive for scenarios when channel conditions change too fast and we may not have a pre-existing dataset of channels and the corresponding optimal BF solutions, which are required for supervised learning. For unsupervised learning, we explore two techniques namely autoencoders and generative adversarial networks (GANs) for both the SVD and hybrid BF. We first propose a linear autoencoder-based approach for the SVD, and then provide a linear autoencoder-based hybrid BF algorithm, which incorporates the constraints of the hybrid BF. In the last part of this contribution, we focus on two different generative models: variational autoencoders (VAEs) and GANs to reduce the number of training iterations compared to the linear autoencoder-based approach. We first propose VAE and Wasserstein GAN (WGAN) based algorithms for the SVD. We then present a VAE and a novel GAN architecture to find the hybrid BF solutions.
  • Protected Triazabutadienes in Chemical Biology

    Jewett, John C.; Guzman, Lindsay Eileen; Ghosh, Indraneel; Glass, Richard; Montfort, Bill; Riehle, Michael (The University of Arizona., 2021)
    Benzene diazonium (BDz) ions are electrophiles reactive to aromatic amino acids such as tyrosine and histidine. Protected triazabutadienes are chemical probes that generate BDz ions for selective protein labeling. This dissertation summarizes the design and synthesis of protected triazabutadiene molecules. The reactivity of protected triazabutadienes was explored. A functionalized protected triazabutadiene was synthesized. Experiments were performed to determine in vivo reactivity of the protected triazabutadiene in mosquito larvae, Ae. aegypti.
  • Interpretable and Robust Machine Learning for Precision Medicine

    Lussier, Yves A.; Zhang, Hao H.; Rachid Zaim, Samir; Piegorsch, Walter W.; Subbian, Vignesh (The University of Arizona., 2021)
    This dissertation represents the unification of the body of research produced throughoutmy doctoral training, highlighting three major articles. These projects revolved around how refining and advancing algorithmic methodologies and frameworks in statistics and machine learning (ML) can improve experimental designs and analyses in genomics and transcriptomics for paving the road to interpretable and robust machine learning for precision medicine. The challenges in the Omics field of ML lies with noisy signal-tonoise ratio and a curse of dimensionality. Throughout this dissertation, one constant theme is demonstrating how feature reductions and improved signal to noise ratio with the use of gene sets (ontologies). This dissertation can be succinctly described as ontology-anchored dimension reduction, combined with single subject (N-of-1) analytics and machine learning applied to transcriptomics. The culmination of these projects is a final pilot study that brings together these concepts to create robust and interpretable machine learning classifiers for precision medicine that can be enriched to identify pathways and their interactions. In precision medicine, the goal is to deliver: The right treatment, at the right time, for the right person. The aim of my doctoral research is to continue advancing precision medicine bydeveloping cutting-edge statistical and machine learning software and frameworks to improve the state-of-the-art technology available. Building upon the works of colleagues, advisors, and others, this dissertation represents comprehensive efforts from a variety of scientific domains such as informatics, computer science, biology, genetics, mathematics, and last but not least, statistics. Common themes include experimental designs and evaluations, ontologies and knowledge graphs, large-scale significance testing, correlation structures, ensemble learners, and random forests. The first chapter introduces the logistics of the scientific dissertation structure. In the second chapter, a numerical study illustrates the increased ability to detect individualized differential gene expression when we aggregate signal using gene ontologies to group genes by their biological processes. The third chapter borrows from machine learning and mathematics to optimize small-sample and single-subject studies in genomics, while a third study is presented in Chapter 4, introducing a novel, effective, and scalable feature selection machine learning algorithm to identify differential gene products and interactions by combining random forests and correlated Bernoulli trials for large-scale hypothesis testing. The final chapter presents a pilot study that combines all these projects into a proof-of-concept of how to create robust and interpretable machine learning classifiers in small-sample studies for precision medicine. These techniques were all developed and applied to analyze Next Generation Sequencing (NGS) and RNA-sequencing data derived from samples in cohort studies, and their biological mechanisms were incorporated from gene ontologies. As is implicit in these works, they represent an interdisciplinary effort that is only possible in team science, allowing for creative solutions when the best minds in statistics, computer science, mathematics, biology, and medicine come together to work on the same problem. Statistical & Machine Learning Advisor: Helen H. ZhangBio- and Clinical Informatics Advisor: Yves A. Lussier
  • Toxoplasma Injected Neurons Are Depolarized While Surrounding Neurons Remain Unaltered

    Koshy, Anita A.; Alexander, Oscar Mendez; Zarnescu, Daniela; Miller, Julie; Khanna, Rajesh (The University of Arizona., 2020)
    Toxoplasma gondii is a neurotropic intracellular parasite that causes a life-long latent infection by encysting in the brain. Toxoplasma’s ability to asymptomatically persist within the central nervous system (CNS) is quite unusual, leading some investigators to investigate how this chronic infection might change baseline physiology. These investigations have been limited to understanding global changes, such as global neurotransmitter release or electrophysiology via electroencephalography as no mechanism existed to interrogate in vivo how Toxoplasma might change infected neurons. At the single cell level only one in vitro study demonstrated hyper and hypoexcitable states of neuron firing, but even this study failed to look at the individual neuron. We have overcome this barrier by developing a novel mouse model that allows us to permanently mark and track CNS host cells that have been injected with parasite effector proteins, parasite proteins that are secreted into host cells and manipulate host cells processes. Using this novel system, we have determined that: i) Toxoplasma persists in neurons because parasites primarily interact with neurons; ii) the vast majority of these Toxoplasma-injected neurons (TINs) (~95%) do not harbor parasites; and iii) TINs are not homogenously distributed throughout the brain. Based upon these findings and prior work, I hypothesize, that neurons injected with T. gondii effector proteins will be significantly changed such that they will show altered physiology. Given how widely neuron physiology can vary by region and neuron subtype, I used a stepwise approach to address this hypothesis. First, I developed a MATLAB-based mapping program to neuroanatomically map TINs. Second, using this program, I determine that the isocortex and striatum are enriched for TINs. Third, within these regions, I used immunofluorescence assays to identify specific neuron subtypes commonly injected by Toxoplasma, including medium spiny neurons (MSNs) in the striatum. Finally, I leveraged the well-described electrophysiology of MSNs to compare single cell recordings of striatal TINs and nearby non-injected MSNs. Based upon these studies, I determined that the electrophysiology of striatal TINs substantially differs from surrounding striatal non-injected neurons, suggesting that interacting with Toxoplasma is sufficient to alter the electrophysiology of neurons possibly through a TINs-directed immune response or direct manipulation of the TIN from injected Toxoplasma proteins or active infection.
  • Restoring Degraded Drylands – An Exploration of the Biotic and Abiotic Factors That Support Desirable Plant Communities

    Gornish, Elise S.; Farrell, Hannah Lucia; Fehmi, Jeffrey S.; Blankinship, Joseph (The University of Arizona., 2020)
    With ecological restoration, land managers seek to reestablish desirable ecosystem processes andservices to degraded landscapes, commonly by adding plants. The outcome of restoration practices is not solely determined by the methods; rather, the outcome is dependent on a web of interacting ecosystem factors. This dissertation explores some of the biotic and abiotic factors that influence plant survival and thus restoration outcomes in the drylands of Southern Arizona. In the first section, I investigate the management, ecology, and competitive interactions of buffelgrass (Pennisetum ciliare; an invasive, drought tolerant bunchgrass) with a) a review of treatment methods and b) a greenhouse competition experiment. I found buffelgrass to be highly competitive against native grasses due to its plasticity, where it can create self-reinforcing feedback loops through its use of resources. Buffelgrass was found to require multiple treatment strategies used in tandem to increase treatment efficacy. Fortunately, my results also show that active restoration through seeding native drought tolerant species after buffelgrass treatment shows potential to suppress buffelgrass regrowth. In the second section, I examine how soil and precipitation interact with restoration practices to determine vegetation communities on a disturbed pipeline corridor. Five years after the pipeline was restored, I found that the seeded and unseeded plant communities converged in terms of plant cover, species richness, and functional groups (but not species). Additionally, I found that the soil treatment/manipulation and soil organisms had major implications for the plant communities, regardless of seeding practices. I hope that this research helps inform restoration solutions that incorporate the complexities, feedbacks, and non-linearities of dryland ecosystems.
  • Virtue of Character in Aristotle's Eudemian Ethics

    Annas, Julia; Wastvedt, Bjorn Sether; Buddensiek, Friedemann; Smit, Houston; Timmons, Mark (The University of Arizona., 2021)
    Aristotle’s Eudemian Ethics (EE) is much less well understood than his Nicomachean Ethics. Between Schleiermacher (1817) and Kapp (1912), the treatise was not even recognized as Aristotle’s, and translations only became widely available a century later (Fermani 2008, Solopova 2011, Kenny 2011). A real improvement on Susemihl's 1884 Greek text is just now being produced (Rowe). In the dissertation, I examine the EE’s unique conception of the development and maturity of virtue of character. I argue that, on Aristotle’s view (1) habituation results in the ability to act in a certain way through repetitive movement, (2) the pleasures and pains that accompany virtuous and vicious action guide the development of virtuous and vicious character, and (3) the virtuous person’s non-rational desiderative faculties set correct ends (4) in pursuit of which deliberation then identifies appropriate action—excellent deliberation produces action conducive to a contemplative yet active life. The virtuous person chooses fine actions qua fine but also because they aim effectively at her mundane desires. On all four of these points, previous research consists entirely of a handful of largely philological essays focusing on individual passages, recent unpublished drafts of a generally synoptic nature, and a single chapter of Buddensiek’s 1999 book. For example, the only prior work on habituation in the EE consists of two exegeses of a key passage (Chamberlain 1984, Ferreira 2017) and unpublished essays from both the 2017 Symposium Aristotelicum on EE II and a 2018 workshop on the treatises’ relationship. My work is the first sustained examination of ethical virtue in Aristotle’s Eudemian Ethics.
  • Art for the New Masses: Participation and the Institution of Art in Post-Socialist China

    Ren, Hai; Zhou, Yanhua; Lanza, Fabio; Busbea, Larry; Du, Heng (The University of Arizona., 2021)
    In the past few decades, contemporary Chinese art has witnessed a tendency emphasizing the importance of artists’ participation in society. Artists conducted their projects by collaborating with local residents in both marginal communities in metropolitan cities and rural villages. These participatory practices engage with a wide range of social issues in China, including ideology criticism, environmental activism, cultural and creative industries in urban regeneration, rural reconstruction, migrant workers, women, and left-behind children in undeveloped rural regions. I term this type of art “new mass art.” New mass art suggests the involvement of many people in art practices and the connection to the history of “art for the masses” in socialist China. It moreover echoes cultural transformations of post-socialist China in which neoliberalism and socialism coexist and have a strong influence on the complex dynamics of the categories of the collective and the individual in aesthetic experiences. Centered around the “new mass art” movement in post-socialist China, this dissertation explores how new mass art is created; who created it; for what purpose; who are the intended audiences; how it moves and interacts in different social milieus; and how it responds to emerging political identity in post-socialist China. Focusing on three overarching concepts—“the new masses,” “participation” and “the institution”—this work examines their variations in different historical moments of China. It specifically explores how historical, socio-political and cultural contexts of post-socialist China affect the changing aesthetic value of new mass art, and how this aesthetic value redefines the connotation of the three concepts. It also captures the complex dynamics of the categories of the collective and the individual in contemporary China within the phrase “the new masses,” and examines how new mass art practices have become integral to the emergence of these people—enabling them to define themselves as individuals as well as members of a political collective; as consumers and producers simultaneously; as both the audience and the medium of new forms of participation in art. This dissertation argues that new mass art in post-socialist China demonstrates a hybrid aesthetic value that is profoundly associated with the social, political and cultural characteristics of China’s post-socialism in which socialist and capitalist systems exist alongside each other and mutually inform one another. In post-socialist China, the hybrid aesthetic value is embodied in the visual formats of both “art for the masses” and “art as the avant-garde.” The former is a type of art which uses mass participation as means to work in, with and for the institution. The latter emphasizes civic participation as a subversive power to attack the institution and challenge authoritarian discourses. The two visual formats are ever-changing in new mass art practices. They have been shown in different approaches of participation, the “art-institution” relation they deal with, and the new identity of the people the artworks engender. The main contribution of the project lies in seeking alternative ways of conceptualizing and configuring our conventional understanding of participation and “art-institution” relation through the lens of new mass art. By concentrating on the hybrid political and aesthetic meanings of “the new masses” in art practices of post-socialist China, I attempt to provide an alternative perspective to examine how new mass art have become central to the negotiation of the newly established political identity.
  • Two Different Approaches Towards Development of Therapeutics for Neurological Disorders

    Khanna, May; Mollasalehi, Niloufar; Montfort, William R.; Horton, Nancy C.; Jewett, John C.; Tomasiak, Thomas M. (The University of Arizona., 2021)
    The research focuses on two different approaches towards drug discovery for neurodegenerative diseases Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS). Neurodegeneration is a progressive deterioration of neural structures leading to cognitive or motor impairment. There is still no effective therapy for any of the most common neurodegenerative diseases and the available therapies only lower the rate of the disease progression or manage the symptoms. Although neurodegenerative diseases exhibit distinct clinical characteristics, they usually present aggregation of specific protein(s) along with neuroinflammation among others. The first investigated approach towards discovering therapeutics is the use of small molecules targeting the N-terminal domain of transactive response (TAR) DNA binding protein-43 (TDP-43), a critical factor in neurodegenerative diseases including Amyotrophic Lateral Sclerosis (ALS) and Alzheimer’s disease (AD) to disrupt the aggregation of TDP-43. Targeting the N-terminal domain (NTD) of TDP-43 by in silico docking, a small molecule called nTRD022 have been discovered binding to the NTD of TDP-43 that allosterically affects the RNA binding of the protein leading to disruption of RNA binding to TDP-43 and improving the muscle strength, as an ALS related phenotype, in Drosophila fly MS models which could potentially be further developed as ALS therapeutic. The second approach focuses on developing a systematic evolution of ligands by exponential enrichment (SELEX) to generate therapeutic aptamers. Aptamers are short nucleic acid ligands that are able to specifically recognize a target with high specificity and high affinity. They have been widely used in therapeutic and diagnostic applications for neurodegenerative diseases over the last two decades. However, due to their relatively recent development, there are not as many aptamers in clinical trials as expected. In this study, aptamers have been developed against autoantibodies targeting myelin oligodendrocyte glycoprotein (MOG), a component of myelin sheath necessary for its structural integrity. The aptamer was developed to specifically target MOG antibody to disrupt the interaction between the antibody and MOG protein to protect the myelin sheath degeneration in motor neurons in MS patients. The developed aptamer, NM02, showed binding affinity towards MOG antibody in low micro molar range.
  • Wavelet Analysis as a Non-Stationary Approach to Validate a Simulated Mosquito Model

    Brown, Heidi E.; Dixon, Ginger; Cazelles, Bernard; Pettygrove, Sydney (The University of Arizona., 2020)
    IntroductionThe Arizona Department of Health Services, Pinal County Public Health Services District, and the University of Arizona collaborated to reform current surveillance practices for mosquitos and West Nile Virus (WNV) with strategies that are resilient to changes in climate. The Dynamic Mosquito Simulated Model developed by Morin and Comrie (2010) uses daily precipitation and temperatures to estimate mosquito abundance and was adapted and validated by Brown et al. (2015) for both Culex quinquefasciatus and Cx. tarsalis mosquito vectors. We sought to verify that the simulated mosquito model can be used as a reliable substitute for observed data, and that wavelet analysis can be applied to verify simulated model data as a substitute or supplement for observed data. To our knowledge, wavelet analysis has rarely been used with WNV data and none have used wavelet analysis to validate simulated mosquito abundance data. MethodsData were collected in Pinal County from 2012 to 2018 and restricted to 2015 to 2018 when trap locations were sampled greater than 5 nights per year for analysis. Daily weather data time series values were collected from the PRISM Climate Group at Oregon State University. The dataset was analyzed using the Wavelet_EETS wavelet analysis program developed by Dr. Cazelles and Dr. Chavez (2003) for time series, power spectrum, global spectrum, and coherency plots. ResultsTime series plots of the simulated and observed mosquito abundance show similar patterns. The simulated mosquito season begins prior to and ends after the observed season and except for the beginning of 2018 when the observed season began later in May, the observed season begins within two weeks of late-March and ends within two weeks of early-November. An extended simulated season compared to observed season was noted around November 2017 for both species, which corresponded with warmer than average temperatures compared to 2016. Comparison of wavelet power spectrums and wavelet coherency for the observed and simulated data show similar spectral patterns at 1-year periods. The plot of the phase differences between the two time series demonstrates an average lag period of 3-6 weeks between season changes of the observed and simulated data, with more variability in phase differences noted for Cx. tarsalis. DiscussionThe similar patterns in the time series, power spectrum, and global spectrum plots as well as strong association in coherency analyses demonstrate that the simulated mosquito abundance model can reliably be used as a substitute or supplement for observed data, validating it’s use as a meteorologically-based early warning system for the Pinal County region and surrogate for surveillance data in further analyses. Based on the extended simulated season and average instantaneous lag, the recommended extension for surveillance trapping is from mid-February to mid-December.
  • A Risk Assessment for 1,4-dioxane in Cosmetics and Drinking Water

    Beamer, Paloma; Ng, Natasha Sonia; Griffin, Stephanie; Burgess, Jeff (The University of Arizona., 2020)
    1,4-Dioxane is a potential carcinogen that is a contaminant of drinking water and cosmetics. The present study addressed route-specific and aggregate exposure from drinking water and cosmetics. Exposure rates were generated by modeling both exposure pathways in ConsExpo Web Version 1.0.7, 31-03-2020.The generation of exposure rates allowed for the examination of 1,4-dioxane exposure in individual cosmetic products, drinking water, total cosmetic exposure, and aggregate exposure. Water had the highest exposure rates with values ranging from 1.006 x 10 -5 – 3.781 x 10-4 mg/kg-day. Cosmetic exposure rates ranged from 1.30 x 10-10 – 2.239 x 10-4 mg/kg-day. Cosmetic categories with harsh ingredients that require ethoxylation had the highest exposure rates. Route-specific cosmetic and drinking water exposure were not of concern at all exposure percentiles. Aggregated exposure started to become of concern at the 99th percentile of exposure with an aggregate risk index (ARI) of 1.85.

    Pine, Gerald; Husband, Nathaniel Alexander (The University of Arizona., 2016)
    Nasogastric tubes are hollow thermoplastic tubes used to deliver nutrition to the stomachs of patients who cannot ingest food orally. A common medical malpractice event is the introduction of liquid via these tubes into the respiratory tract instead of the stomach, which can result in fluid aspiration that can lead to patient harm or death. Current standard of practice verifies tube placement in a hospital via a chest X-ray or stomach acid pH test. While these procedures are effective, they are not conducive to repeat verification and require the skills of medical professionals. The goal of the project is to develop a cost-efficient and easy-to-use device that informs the user when the tube has been placed in the stomach, not in the airway. The device is small enough for use within existing tubes and can withstand the corrosive gastric environment for up to 30 days. This design uses an open circuit that is closed by ions present in the acidic fluid of the stomach. The closure of the circuit results in a differential voltage signal that provides the user with a “safe to feed” message.

    Duncan, Burris; Pottinger, Heidi; Chavez, Alexis Ariana (The University of Arizona., 2020-08)
    This research is a sub-study of the original ‘Intense Physiotherapies to Improve Function in Young Children with Cerebral Palsy’ study conducted by my advisors Dr. Burris Duncan and Dr. Heidi Pottinger. The sub-study was created to obtain qualitative data from the parents of children who participated in their study at Tucson Medical Center (TMC). The processes for this work included obtaining human subjects-related training to be able to interview the families, recruitment of subjects by Dr. Pottinger, preparation for interviewing parents, conducting live interviews, and analyzing qualitative data with key findings/themes identified. These findings will help to identify areas for improvement for future clinical trials/research with TMC families.
  • The Association Between Perceived Resiliency and Change in Income from Childhood to Early Adulthood

    Killgore, William D.; Gutierrez, Giovanna (The University of Arizona., 2020)
    Background: It is well known that majority of children growing up in low income families will experience some type of adversity and as a result of their environment many will not adapt well when entering adulthood. Yet, there is a small percentage of children that overcome their childhood hardships, adapt well, and develop into successful adults. That small percentage of children are assumed to have some form of childhood resilience which might account for their subsequent success as adults. Specifically, perceived resilience may account for growth and success in adapting to the challenges and expectations of adulthood. Objective: To determine if changing from low income as a child to high income as an adult correlates with a higher perceived resilience. Methods: A correlation study using between subject design was conducted. The Socioeconomic Status (SES) Questionnaire and Dispositional Resiliency Scale-15 (DRS-15) Questionnaire were distributed to 48 healthy participants. The resulting data were analyzed using Pearson r correlation in SPSS 24. Results: As a whole, the sample did not show significant association between the total DRS-15 scores and change in income from childhood to early adulthood (r = .123, p = .404). A subgroup having shown an increase in income (n=7) had a mean total DRS-15 score of 22.86 (SD = 3.36) and four individuals of this subgroup that only came from low-income families had a mean total DRS-15 score of 25.5 (SD = 1.29). According to the total DRS-15 scoring scale, both group's mean total DRS-15 scores can be found in the “low” range of the total Hardiness score. However, the subgroup with increased income (n=7) had a significant correlation with total DRS-15 scores and change in income from childhood to early adulthood, suggesting that as these individual’s total DRS-15 scores increased they were more likely to make more money (r= .727, p = .032). For individuals with declining income from childhood to early adulthood (n=26) there was no significant correlation (r = .089, p = .666). After controlling for childhood income as a possible confounding variable, we still found no significant correlation between individuals with decreased income after leaving home and total DRS-15 scores indicating childhood income had no effect on this association (r = .013, p = .949). On the other hand, after removing childhood income from the correlational study between individuals with increased income after leaving home and total DRS-15 scores there was no longer a significance, suggesting that childhood household income significantly influenced the correlation between income change and total DRS 15 scores (r = -.278, p = .594). Conclusion: These results suggest that coming from low-income families and obtaining higher income as early adults doesn’t correlate with perceived resilience. Yet, for a subset of low-income individuals show a positive correlation between changed income and their total DRS-15 scores. Further studies are recommended to see if the results found in this study are replicable but should take into account the limitations as mentioned in the study or take into account other measurements.
  • Reclaiming Mining Lands in Southern Arizona: A Scientific and Policy Inquiry towards Resiliency

    Maier, Raina M.; Neilson, Julia W.; Jennings, Lydia Luisa; Gornish, Elise; Rasmussen, Craig; Colombi, Benedict J. (The University of Arizona., 2020)
    Copper is critical to the sustainability of the modern lifestyle; however, mining creates massive land disturbance and generates large quantities of unstable waste materials that need to be managed for the long-term environmental and public health of neighboring communities (Lima et al., 2016). One waste challenge associated with modern mining is managing mine tailings. Mine tailing reclamation is site specific but often has a long-term goal towards ecological restoration and is impacted by stakeholder and rights holder associations (i.e. private, government or tribal) (Keller et al, 2011). Mine waste reclamation aims to stabilize waste to support self-sustaining plant communities. A critical limitation to vegetation success in mining waste is that these “soils” are deficient of the microbial communities and nutrients necessary to support plant establishment. The costs associated with these restoration activities can also be substantial, and vary with mine size, regulatory regimes, technology, presence of legacy reclamation costs, or cultural resources within the area (Mudd, 2009). Long-term ecological management of mine waste is an essential and problematic component of efficient mine site reclamation throughout the Southwest. Soil health properties essential to successful phytostabilization of reclaimed mine sites are poorly understood. This study found that total nitrogen and DNA biomass show promise as potential indicators of soil fertility that correlate with revegetation improvement. Our studies suggest that capping material source significantly influences the rate of plant establishment. Quantitative metrics must be further investigated to improve methods for screening potential capping material sources for mine tailings reclamation. Longer term studies are needed, particularly for mine recovery in the Southwest, where plants grow slower. Future work should consider how below ground fertility metrics reflect ecosystem stability and plant structure, and how the quality and application method of soil capping may impact future plant community structure.
  • Comparison of Mixed Models and Paired T-Test for Analyzing Crossover Clinical Trials in the Presence of Missing Data

    Bell, Melanie; Vicenti, Anthony; Watkins, Joe; Zhou, Jin (The University of Arizona., 2020)
    AB/BA crossover clinical trials are popular designs that can achieve high power with a lower number of subjects than other randomized control trial designs. They are often analyzed using paired t-test or mixed models, and like many clinical trials, are often impacted by missing data. Mixed models have been shown to produced more powerful and unbiased results in the presence of missing data than t-tests for other designs, but these two approaches have not been compared in crossover trials. We conducted a simulation study to compare the bias and power of paired t-tests and mixed models when analyzing an AB/BA crossover clinical trial in the presence of missing data. Several different missing structures were simulated under two within-subject correlations, ρ =0.3 and ρ =0.7. Both methods performed similarly when analyzing complete data, but the mixed model produced both equal or less bias estimates and higher power than the paired t-test under all simulation scenarios. In the worst-case scenario we considered, the t-tests resulted in percent bias up to -105% and power as low as 5% compared the mixed model’s percent bias of 1% and 57% power. In less severe cases, both methods had 0% bias, but mixed models still achieved an absolute power gain of 2%-6%. In the presence of missing data, the mixed model achieved higher power than the paired t-test under all simulated scenarios. The mixed model also achieved equal or less bias under all simulated scenarios. Therefore, mixed models should be used over paired t-test when analyzing AB/BA crossover clinical trial in the face of missing data.

View more