• Eddavidite, a New Mineral Species, and the Murdochite (Cu12Pb2O15Cl2)-Eddavidite (Cu12Pb2O15Br2) Series

      Downs, Robert T.; Rosenblatt, Melli; Holliday, Vance; Thirumalai, Kaustubh (The University of Arizona., 2021)
      Eddavidite is a new mineral species (IMA2018-010) with ideal formula Cu12Pb2O15Br2. It has cubic Fm3m symmetry; a = 9.2407(9) Å; V = 789.1(2) Å3; Z = 2. Eddavidite is the bromide analog of murdochite, with which it forms a solid solution series. The type locality is the Southwest mine, Bisbee, Arizona, U.S.A. Eddavidite also occurs in the Ojuela mine, Mapimí, Durango, Mexico. Eddavidite forms zones within mixed murdochite-eddavidite crystals. Spot analyses of Bisbee samples show up to 67% eddavidite component while Ojuela samples show up to 62%. Eddavidite-murdochite crystals show forms {100} and {111}; the habit grades from simple cubic through cuboctahedral to unmodified octahedral. Eddavidite is black and opaque with submetallic luster, and visually indistinguishable from intergrown murdochite. Its Mohs’ hardness is 4. Eddavidite exhibits good cleavage on {111}. The empirical formula, normalized to 12 Cu apfu is Cu12(Pb1.92Fe0.06Si0.06) (O15.08F0.02) (Br0.99Cl0.89•0.12). dcalc. = 6.33 g/cm3. dmeas. = 6.45 g/cm3. The crystal structure consists of corner sharing square planar CuO4 units, arranged in Cu12O24 metal oxide clusters, which encapsulate Br atoms. PbO8 cubes share edges with Cu12O24 clusters in a continuous framework. Eddavidite is one of only 10 mineral species with essential Br. Eddavidite crystallizes from bromine enriched fluids leftover from desiccation of paleo-seawater at its two known localities.
    • Lithospheric Structure of the Ecuadorian Orogenic System and Event Location using the Seismoacoustic Wave Field

      Beck, Susan; Johnson, Roy; Koch, Clinton; Kapp, Paul; Richardson, Randall (The University of Arizona., 2021)
      Seismologists use the seismic wavefield to image the Earth’s structure at a wide range of scales, from a few meters to 1000s of km. Sources (earthquakes, explosions, etc.) of seismic waves can also be located and distinguished using the seismic wavefield. In this dissertation, I utilize both of these aspects of seismology. The major part of this dissertation focuses on the use of naturally occurring seismic sources (earthquakes) to elucidate the structure of the crust and upper mantle beneath the Ecuadorian orogenic system (Appendices A-C). In the final section, I explore the seismic location problem by combining seismic and infrasound phenomena in a Bayesian framework (Appendix D). Ecuador, the focus of the first three studies, is a complex tectonic region spanning several tectonic provinces. Offshore, the Nazca plate subducts beneath the South American plate creating major stresses that build up and result in megathrust earthquakes along the boundary between the two plates. Following a magnitude 7.8 earthquake offshore Pedernales, Ecuador in 2016, seismic instruments were deployed to study the seismicity and tectonics of the region. This collaboration between US institutions (University of Arizona and Lehigh University) and the Instituto Geofísico at the Escuela Politécnica Nacional in Ecuador also opened up a wealth of data from the Ecuadorian permanent seismic network which enabled a higher resolution study of the arc region. Appendix A presents a detailed study of the tectonics of the forearc region and the relationship with the megathrust behavior. The results indicate that the complex accretionary history of Ecuador resulted in a forearc that exhibits significant variations in the seismic velocities along the strike of the trench. These variations appear to align with the style and behavior of the seismicity in the region, suggesting that the structure of the upper plate may play an important role in controlling megathrust behavior. Appendix B shifts the focus towards the Andean region and the active volcanic arc. The Ecuadorian Andes contain a broad (~150 km wide), active, arc that extends from the Western Cordillera into the Subandean zone. Here, a map of crustal thickness beneath the Ecuadorian Andes is presented, which shows that it is largely in isostatic equilibrium at the Moho. Observed low-velocity regions are beneath several active volcanoes are interpreted as regions of long-term magma storage, consistent with crystal mush zones. To connect the arc and forearc, earthquake-generated surface waves and the Automated Surface Wave Phase Velocity Measuring System are used to measure phase velocities in Ecuador. Appendix C reports on the results of this method. Periods between 25-50 seconds show good coverage across the array and image a faster forearc region and a slower arc region, likely reflecting a thicker crust in the arc region. At periods ≥ 60 seconds coverage is limited to the arc region where a longer period of data was available. These results serve to extend the phase velocity measurements from ambient noise deeper and begin to offer constraints on the upper mantle beneath Ecuador. As more data and more stations are deployed in Ecuador it may be beneficial to revisit this analysis at a later time. In the final Appendix, the focus shifts from lithospheric structure to explore the event location problem. Here, we combine seismic and infrasound observations for locating a seismoacoustic event. A Bayesian framework is developed to better estimate the uncertainty associated with the location. This new method is tested on data from a surface explosion from the Bingham mine in Utah and shows that combining the two phenomena can improve the location beyond what either method can obtain individually.
    • Modernizing Conquest

      Waterstone, Marv; Banister, Jeff; Kinnison, Jedediah; Williams Jr., Robert A.; Perez, Emma; Oglesby, Elizabeth (The University of Arizona., 2021)
      My research leads me to the conclusion the international human rights system's separation of the “indigenous problem” from the “colonial problem” is important. It is important to the way in we understand indigenous rights today, and it is important in terms of the ways in which we understand this fundamentally statist system. First, we must first ask in what sense and to whom these "problems" are problems requiring resolution. In theory, the UN system is established to safeguard the basic rights of all peoples to a dignified existence. And yet, to believe that this represents the UN founders’ intentions for the new system would be tantamount to believing that America’s founders intended to protect the equal rights of Black peoples when they drafted Article 1, Section 2, Clause 3—the three fifths clause—of the US Constitution. The issues are further clarified if we ask why the UN posed and then bifurcated the questions of what to do with: (1) colonized peoples, and (2) Indigenous peoples. The world system continues to deem it necessary to push the discussion of the multitude of problems presented by European colonization along two discrete tracks, with neither track on course to reach any destination. As the leaders of the euro-derivative world order strive to convince everyone that they have put an end to the colonial destruction of every Indigenous culture on the planet, a primary strategy is to bifurcate the problem of European overseas colonialism and to treat both of the resulting halves of discussion as if the other half never existed.  This division permits the United Nations (UN) and the International Labour Organisation (ILO) to engage in discourse regarding Indigenous peoples that are so misrepresentative that they would qualify as farce if the actual problems were not so tragic.  It also facilitates revolutions in social consciousness, producing gaps in social memory that are filled by new narratives celebrating the new tragedies in the making, those posed by hyper-individualism-based market logics and deculturation through statist democracy building and large-scale structural integration programs.  Indigenous societies remain under attack, and post-colonialism perpetuates the status quo of colonial territoriality and neocolonial economic dependency.  The international system and its discourse plays an important role in this perpetuation. The "new" mode of thought and material production that emerged in the prelude to the “decolonization era” puts all life on an omnicidal track.
    • Transitions from Jail in the Rural Community for Adults with Mental Illness

      McEwen, Marylyn; Langley, Carrie Ann; Kahn-John, Michelle; Rainbow, Jessica (The University of Arizona., 2021)
      The purpose of this study was three-fold: 1.) to address the gap in our understanding of the factors that facilitate the use of community-based transitional support services post-release from jail when transitioning into the rural community for adults with mental illness and 2.) to address the gap in our understanding of the factors that inhibit the use of community-based transitional support services post-release from jail when transitioning into the rural community for adults with mental illness and 3.) to determine the acceptability of a biological sample to measure interlukin-6 (IL-6) for future research. Annually, nearly one million people are incarcerated in jails throughout the United States, with over 80% of them experiencing a mental illness. Rural communities have greater rates of disease burden and fewer community-based resources. These factors combined with the lack of mandated jail-to-community transition programs complicate the transitional experience for individuals living with mental illness. The transitional period, from jail to the community is filled with competing demands and can cause stress and anxiety. Acute stress has been associated with inflammation. This population often expresses resistance in providing biological samples, so aim three will allow for planning for future research involving biological specimen collection. This qualitative descriptive study provided a rich account of the inhibitors and facilitators experienced among individuals transitioning from the rural jail to the rural community while experiencing mental illness. Meleis’ Transitions Theory provided the conceptual underpinnings for this study. Data sources included interviews, a demographic data questionnaire and field notes. Data analysis was developed through qualitative content analysis through open coding, which allowed the researcher to build concepts and categories, forming themes. This iterative approach allowed for the grouping of similar codes and clusters. The results of this study illuminated several points. “Out of Jail but No Freedom” established the overarching theme for this study in which the facilitators and inhibitors of situational and health-illness transitions for adults with mental illness transitioning to the rural community is described. This research is significant for nursing practice and policy reform. Systematic reform is needed within jail medical operations, clinical models of community provided care, within policy that guides healthcare funding and delivery models, as well as court services. Mandated policies, unfunded and directed to be financially supported by communities further perpetuate disparities and social determinates of health, significantly impacting our most rural and socioeconomically depressed locations. This study illuminates the need for systematic reform within our medical divisions of rurally located jails as well as within public policy that guides healthcare funding and clinical models of care. It has become evident from this research the transition from jail is largely shaped by the experience while incarcerated. Individuals who experience jail incarceration have a right to evidenced-based standards of care, and transition programs to assist them back into the community.
    • 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.
    • 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.
    • Sensing and Arresting Corrosion of Haynes 230 Alloy in Molten Chloride Salts at 800°C

      Gervasio, Dominic; Sasaran, Vlad; Farrell, James; Guzman, Roberto (The University of Arizona., 2021)
      The corrosion of metal in molten chloride salt is studied and lowered using a power supply. A particular emphasis is on the ternary eutectic sodium chloride, potassium chloride and magnesium chloride (MgCl2-KCl-NaCl) salt with a melting point of 387°C, because it is the high temperature heat transfer fluid of choice in electrical power generators and Haynes 230 alloy (H230), because H230 is a ductile metal which retains its strength at high temperatures (800oC). A potential negative of the open circuit potential of H230 metal alloy in ternary eutectic MgCl2-KCl-NaCl is applied, the cathodic potential generates a negative (cathodic) current for the reduction any oxidants, such as metal ions, oxygen and water, in the molten salt. The magnitude of the cathodic current is a signal of the level of oxidants present in the salt. Applying the cathodic potential also arrests ionization of metal, that is, corrosion of the metal. The increasing level of oxidant impurities, (particularly water) in the molten chloride salts causes the open circuit potential (OCP) of H230 versus a silver and silver chloride reference electrode (SSE) to shift positive, which gives a warning that the molten chloride salt heat transfer fluid is corrosive to metal. The OCP is crudely measured by direct readings of H230 and SSE with a voltmeter and refined by potentiodynamic scans of current versus potential of H230 versus SSE, where the H230 potential is scanned 30 millivolts starting negative of to positive of the OCP found with the voltmeter. A linear rate equation, called the Stern-Geary method is used to find corrosion potentials and estimate corrosion rates (CR) of H230 alloy in molten salt at various relative humidity (RH) of Argon atmospheres equilibrated with the molten salt. In oxidant free ternary MgCl2-KCl-NaCl eutectic molten salt, the OCP of H230 vs SSE is -866±24 [mV] and CR of 85±9 [micron/year]. In ternary eutectic molten salt equilibrated with a 40% RH Argon flow the OCP of H230 vs SSE is -363±1 [mV] and CR of 4670±780 [micron/year]. When a negative potential, of -200mV from OCP in anaerobic salt, is applied to a H230 working electrode in eutectic molten salt at 800oC and 100% RH Argon flow is flowed over salt for 20h, it was found that this H230 working electrode (WE) was cathodically protected, because the WE (cathode) lost only 0.0552g while the counter H230 electrode (CE), a “sacrificial” anode, lost 0.4513g. This gives a preliminary assessment a cathodic potential is effective for arresting corrosion of H230 metal in oxidant-contaminated salt at temperatures up to 800oC.
    • Using Big-Data to Develop Catchment-Scale Hydrological Models for Chile

      Gupta, Hoshin V.; De la Fuente, Luis Andrés; Condon, Laura E.; Ferré, Paul Ty (The University of Arizona., 2021)
      Streamflow prediction is very important to the economic and human development of a country. For example, it is used in the quantification and distribution of the water resource, and in the design of new hydraulic infrastructure, risk quantification, rapid response to mitigate flooding, etc. For this reason, learning how to improve our estimation of streamflow must be one of the aspirations of any surface hydrologist. Chile has an extensive stream gauge network, which is part of the new CAMELS-CL database. This database also includes data about several static attributes for each of the 516 catchments represented within it, which provides us with a valuable database that can be used to develop process-based and data-based models with the ultimate goal of implementing a national hydrological model.Recent studies have shown that Machine Learning (ML) can provide better predictive performance than traditional process-based (PB) models. In hydrology, Kratzert et al. (2019), Nearing et al. (2020a), and others have reported similar results when comparing an ML-based model with the extensively studied and calibrated SAC-SMA and other benchmark models over the USA. This finding creates the opportunity to bridge the gap between ML-based and PB models by transferring insights gained via the process of developing a ML model into improvements of the PB model(s). With this in mind, we implemented the GR4J process-based catchment model as a baseline, and two ML-based models, Random Forest (RF) decision tree approach, and the Long-Short Term Memory (LSTM) dynamic state variable approach, on 322 selected Chilean catchments. The three models were compared in detail to examine their strengths and weakness, and to determine the best candidate for a national model. Our results showed that none of the three models performed “best” across the entire country, and all of them had problems in the north of Chile, indicating that additional informative attributes and variables must be incorporated into the database. Furthermore, the models showed complementary performance abilities, which opens the opportunity to develop an ensemble of the three or more models in the future to merge their respective strengths. Overall, the model performance results were found to be related to the meteorological forcings, but also with certain climatic conditions such as aridity, which emerges as an important variable to characterize the behaviors of different catchments.
    • Data Assimilation and Applications in Forecasting

      Morzfeld, Matthias; Harty, Travis Michael; Snyder, Chris; Venkataramani, Shankar C.; Arellano, Avelino F. (The University of Arizona., 2021)
      The work presented here spans two projects which are connected by data assimilationand specifically the ensemble Kalman filter (EnKF). The first explores how spatial localization, an important method commonly used in the EnKF, can be extended to multiscale problems. Rather than using a single length scale when localizing, we construct a localized covariance matrix through the estimation of eigenvectors. Specifically, we estimate the leading large-scale eigenvectors from a sample covari- ance matrix calculated from a spatially smoothed ensemble with spatial localization applied with a long localization distance. We then create projection matrices from these eigenvectors which allows us to calculate the space orthogonal to these initial large scales. This process can then be repeated for multiple scales if required. We present numerical experiments using this localization method using both simplified examples in which the correct covariance matrix is known and cycling experiments with the Lorenz Model III. The second project explores an application of the EnKF. We use the EnKF as part of a system to forecast cloud cover. Cloud cover forecasts are useful when forecasting solar power generation because clouds are the primary driver of reducing irradiance and therefore solar power generation. Our method uses satellite images, optical flow, and numerical weather prediction (NWP) in conjunction with an EnKF to estimate cloud motion vectors (CMVs) which are then used to advect cloud index (CI) fields using a 2-D advection scheme. This system produces an ensemble forecast which can be used to produce deterministic forecasts. We explore the effectiveness of these forecasts over Tucson, AZ.
    • Neural Network Algorithms for Ontology Informed Information Extraction

      Bethard, Steven; Xu, Dongfang; Cui, Hong; Surdeanu, Mihai; Miller, Timothy (The University of Arizona., 2021)
      Ontology, as a formal and explicit specification of a shared conceptualization for a particular domain, is useful in information extraction. On the one hand, since information extraction is concerned with retrieving information for a particular domain, formally and explicitly specifying the concepts of that domain through an ontology defines the boundary of what information needs to be extracted. On the other hand, an ontology, typically consisting of classes (or concepts), attributes (or properties), and relationships (or relations among class members), contains the structured information that information extraction systems aim to extract. In this thesis, we are interested in how using an ontology can improve the information extraction process. We explore two research directions that both employ ontologies in the information extraction process, temporal normalization and biomedical concept normalization. In both research directions, we show that leveraging resources in ontologies helps to build high-performance information extraction systems, and presenting the extracted output using such ontologies makes the structured information concise and interchangeable.
    • 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.
    • 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
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.