• 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.
    • 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.
    • Testing the Intrinsic Benefit Model of the Signaling Theory

      Galaskiewicz, Joseph; Okada, Sosuke; Breiger, Ronald; Kugler, Tamar (The University of Arizona., 2021)
      This study proposes the intrinsic benefit model of the signaling theory for sociology. The signaling theory is a subtheory of the game theory. It was developed independently within Evolutionary Biology and Economics, and it is concerned with the communications under the situations with asymmetrical information. Although the signaling theory have been widely adapted across social science, its influence within Sociology has been limited so far. This study proposes the argument that the signaling theory can achieve the increased relevance within Sociology by focusing on the role of (perceived) intrinsic benefit obtained from the signal production. The focus on the intrinsic benefit would allow the signaling theory to be applied on the broader range of phenomena which are of sociological interests, while at the same time analytically integrating additional social and symbolic contexts of the signals. Based on this argument, the propositions were developed about the role of the signal visibility and the intentionality of the signal. The three experiments were conducted to test the propositions. The two vignette experiments were conducted to test the effect of signal visibility on the signaling of environmental commitment through the purchases of electronic vehicles. A laboratory experiments was conducted to test the effect of the intentionality of the signal on the signaling of trustworthiness through donations. The first experiment gave the strong support to the propositions, whereas the second and the third experiment produced the mixed results. The author suggests that the overall findings are consistent with the main argument underlying the intrinsic benefit model.
    • Differential Selection and Schizophrenia Orthologues: The Roles of Life History, Neuroanatomy, and Socioecology

      Jacobs, William J.; Penaherrera Aguirre, Mateo; Figueredo, Aurelio J.; Steklis, Netzin G.; Steklis, Horst D. (The University of Arizona., 2020)
      The evolution of major psychopathologies remains a point of intense debate within the scientific community. Counter to classical Darwinian perspectives, these disorders persist in human populations despite their high fitness costs (shorter lifespan and fewer surviving offspring reaching sexual maturity). Schizophrenia, an umbrella term referring to a cluster of positive (delusions, hallucinations, disorganized behaviors) and negative signs (flattened affect, limited motility, anhedonia, asociality, among others), is no exception. Over the past three decades, researchers have developed various theories to address this evolutionary conundrum. Comparative researchers have addressed this subject by investigating numerous empirically based animal models. These investigations have detected potential mechanisms involved in the development of schizophrenia and explored the effectiveness of various interventions aimed towards reducing the pathology’s severity. Even though this approach offers a unique approach to this subject, the current literature could benefit from a comparative phylogenetic perspective. For example, the molecular genetic literature has identified a positive association between life history indicators, such as maximum longevity, with the persistence of ancestral variants of genes (orthologues) across non-human species. Given that slow-life history species are generally subject to weaker selective pressures, this pattern is expected. Hence, deleterious or near deleterious mutations are harder to eliminate. The present dissertation aims to complement the molecular genetic literature by considering the roles of life history, neuroanatomy, and socioecology in this pathology's evolution. The current manuscript describes three comparative phylogenetic studies that outline the evolution of schizophrenia. Study 1 explored the connection between life history and the (dN) ̅⁄(dS) ̅ values (a metric of orthologue persistence) of this pathology. Building on the previous result, Study 2 proposed a comparative phylogenetic examination of schizophrenia, autism, and bipolar disorder orthologues, as a potential complementary avenue for understanding the evolutionary dynamics of comorbidity among these disorders. As predicted, the analyses detected an underlying common genetic factor loading into the orthologue estimates for each disorder. Moreover, a Higher-Order Life History factor exhibited significant positive associations with the (dN) ̅⁄(dS) ̅ values of autism and bipolar disorder. Similarly, this latent variable positively and significantly predicted the Genetic Psychopathology factor. Study 3 explored the connections among substrate use, diet, sociality, life history, neuroanatomy, and their respective relations with the persistence of schizophrenia orthologues in eutherian mammals. The model hypothesized that a Higher-Order Life History factor should positively associate with neuroanatomical indicators such as the species’ total number of neurons. Lastly, the number of neurons predicted the persistence of schizophrenia orthologues. Life history mediated the association between sociality and neuroanatomy. Moreover, the total number of neurons mediated the connection between life history and the estimates for schizophrenia orthologues. The phylogenetic model demonstrated that sociality, a Higher-Order Life History, and the total number of neurons had significant positive associations with schizophrenia ancestral alleles' persistence.
    • Beyond Goodbye: Daily Emotion Regulation from Network Members and from Thoughts of Deceased Loved Ones

      O'Connor, Mary-Frances; Stelzer, Eva-Maria; Butler, Emily; Greenberg, Jeff; Mehl, Matthias; Sbarra, David (The University of Arizona., 2021)
      Background: The present daily diary study tested the ERROSS model (Stelzer & O'Connor, under review), examining whether conjugally bereaved individuals benefit from a diverse repertoire of social interaction partners and daily emotion regulation (ER) strategies. Beyond living supportive others, the study investigated associations between daily ER from thoughts of the deceased loved one and mental health, and the potential role of attachment moderators.Method: Participants were 156 community-dwelling adults (86% females) who experienced the death of a spouse or romantic partner up to five years prior. In a structured two-week long daily-diary, participants listed their daily interaction partners and the ER strategies provided by them as well as their daily mental wellbeing and grief. In addition, participants reported on their mental interactions with their deceased spouse and described the felt ER evoked by those interactions. Results: Multilevel modeling analyses found that at the within-person level, daily repertoire was positively related to positive affect, and daily network size was negatively related to life satisfaction. At the between-person level, greater averaged repertoire and network size were positively associated with mental wellbeing (i.e., greater positive affect and life satisfaction, lower negative affect). For ER from the deceased, ER strategies from the deceased were associated with increased negative affect on a daily level, but positively associated with positive affect and life satisfaction on the between-person level. No significant mental health associations emerged for daily grief. Conclusion: These results provide the first evidence of the ERROSS model in a naturalistic setting, and highlight the benefits or a diverse repertoire of ER during the transition to widowhood.
    • Factors Shaping Endophyte Communities Associated with Selected Cultivated Plants in Arizona

      Arnold, Anne E.; Hamzazai, Aasiya; Schuch, Ursula; Ray, Dennis T.; Pessarakli, Mohammad; Orbach, Marc J. (The University of Arizona., 2020)
      Symbiotic microbial communities can be found in all plant species in all major terrestrial ecosystems including wild, horticultural, agricultural and residential garden settings. Microorganisms such as some bacteria and fungi that colonize the interior of a plant tissue without causing apparent disease – endophytes -- can be acquired through horizontal transmission or via vertical inheritance. Studies have shown that endophytes inhabit all tissue types of host plants and play vital roles in plant health and productivity, providing tolerance against biotic and abiotic stresses. However, the diversity of endophytes occurring in different plant tissues such as leaves, stems and seeds, and the factors that influence the endophytic diversity in those tissues is not well known. The knowledge gap is especially large for semi-arid areas such as Arizona, where endophytes may be especially important for plant survival under environmental stress.In the first part of this study, I evaluated colonization of three different types of crop seeds by soilborne fungi under different agricultural, residential and wild grassland settings in central and southern Arizona, USA. I found that fungal colonization differed among plant varieties, even when placed into the same soil. Seeds recruited distinct fungal communities in different locations, and fungal communities differed among agricultural, residential, and grassland settings. Variation in fungal communities was consistent with variation in soil chemistry. This work provides a case study regarding the diversity of fungal endophytes that can infect seeds in Arizona soils and highlights the prevalence of certain taxa, such as Fusarium and Alternaria, that are especially common and may be beneficial for plants, despite their reputations more broadly as undesirable pathogens or producers of mycotoxins. In the second part of this study, I provide a first perspective on endophytic biodiversity associated with common plants in a residential garden setting (i.e., the garden endobiome). I surveyed leaf and stem tissue of 17 different plant varieties late in the growing season in a garden environment in Tucson, Arizona. I found that the isolation frequency of bacterial and fungal endophytes from leaves, and bacterial endophytes from stems, varied among plant families. Different plant species in a given family showed similar isolation frequencies of endophytes. In general, bacteria were more common in leaves, and fungi were more common in stems. Edible and inedible leaves harbored fungi with similar frequency overall, but I did not observe bacteria in edible leaves. Because this study used inexpensive methods, I develop it further as the basis for an educational module to be used for students at the high school or college level, with limited resources, to study endomicrobiomes of cultivated plants in developing countries. Ultimately, my dissertation provides an overview of the endophytic communities that colonize various tissues and plant species in a variety of settings in Arizona. Such studies can help in mapping the distributions of endophytic microbes important for plants and provide a road-map for identifying the drivers of their community composition. These studies also can provide a basis for selecting potentially beneficial fungi and bacteria to aid in plant resilience in a changing world. Finally, this body of work provides a basis for teaching students in the developing world about biodiversity, ecology, mycology, and plant biology through endophytes, Earth’s most widespread symbionts of plants.
    • Calculation of the Resummed Radiation Reaction to Order 1/M Using Heavy Fermion Effective Theory

      Fleming, Sean P.; Hill, Andrew; Rutherfoord, John; Su, Shufang; Gralla, Samuel E.; Meinel, Stefan (The University of Arizona., 2020)
      The Abraham-Lorentz-Dirac equation, which is the widely-accepted expression for the recoil force experienced by a radiating charge (known as the \textit{radiation reaction}), displays strange, unphysical behavior. As such, it has been rife with controversy and confusion for over a century. For most of that time, these issues were treated mostly as curiosities and left to the musings of theoreticians. But the advent of high-intensity pulsed tabletop lasers in recent decades has made the radiation reaction relevant to modern experimental physics, which has led to a resurgence of research into the topic. In this dissertation, we calculate the radiation reaction to order $m^{-1}$ experienced by a charge of mass $m$ in an external electromagnetic field resulting from emission of a single photon. To accomplish this, we use heavy fermion effective theory (HFET), which is an effective field theory of QED, and model the total electromagnetic field as the superposition of a quantized self-field associated with the charge and a classical external field. HFET is a novel approach that greatly simplifies the calculation compared to full QED. The simplified calculations allow us to resum our force expression to all orders in $e A_{\text{cl}}$, where $A_{\text{cl}}$ is the background field; this is a novel result. Wilson lines arise in our expressions as a result of resummation.
    • Beyond the Standard Model Higgses at Future Colliders

      Su, Shufang; Li, Shuailong; Meinel, Stefan; Varnes, Erich; Johns, Kenneth; Zhang, Shufeng (The University of Arizona., 2020)
      Motivated by several long-standing puzzles confronting the Standard Model (SM) in particle physics, many Beyond Standard Models (BSM) with extended Higgs sectors were proposed. The Two-Higgs-Doublet Model (2HDM) is a prototype model with two doublets in the Higgs sector. Other than the SM-like Higgs h, the low energy spectrum of 2HDM contains four BSM Higgs states, the neutral CP-even Higgs H, the neutral CP-odd Higgs A and a pair of charged Higgs H±. Along with the four incarnations, namely type-I, type-II, type-L and type-F, it provides rich phenomenologies for exploration. In this thesis, we explore four types of 2HDMs at several future colliders as well as the opportunities to distinguish them. Two general methods are employed: the direct search at a future 100 TeV pp collider and a multi-TeV muon collider and the indirect search at several proposed Higgs factories and Z-factories. With direct search, we study the exotic charged Higgs decay H± -> HW± in a hierarchical Type-II 2HDM at a 100 TeV pp collider and find that almost the entire space can be probed after combining with other exotic Higgs decay modes. In addition, due to the clean environment at a muon collider, it allows the probe of heavy BSM Higgses at an unprecedentedly high scale and offers remarkable chances for discrimination among the four types. With an indirect search, BSM Higgses are explored by accurately studying their corrections to the SM Higgs and Z-pole precision observables. For illustration, we study the impact on Type-I 2HDM and find the parameter space can be tightly constrained. The discovery potential and the extent to which the four types of 2HDMs could be distinguishable are also examined. We find that most of the currently allowed parameter regions permit a 5σ discovery at future Higgs factories and the four types of 2HDMs can be largely distinguishable once a 5σ discovery is made.