UA Theses and Dissertations
ABOUT THE COLLECTIONS
More than 40,000 theses and dissertations produced at the University of Arizona are included in the UA Theses and Dissertations collections. These items are publicly available and full-text searchable. A small percentage of items are under embargo (restricted).
We have digitized the entire backfile of UA master's theses and
doctoral dissertations that were held in the University
of Arizona Libraries.
- Submitting master's theses to the UA Libraries was optional for many decades; as a result, we do not have all master's theses that were written at the University of Arizona.
- A small number of historical theses containing culturally sensitive material are not available online.
You can also refer to the Theses & Dissertations - frequently asked questions guide to find materials that are not available online.
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Utilizing a Reminder To Improve Childhood Obesity Screening in Native American ChildrenImproving the screening and management of childhood obesity among Native American children is critical in addressing the significant health disparities in this population. This quality improvement project implemented a pediatric obesity screening reminder and provided targeted education to healthcare providers. Childhood obesity is a significant public health concern, with the Native American population being disproportionately affected. The quality improvement project measured the impact of the two interventions through pre- and post- intervention self-administered questionnaires and chart audits. The findings showed a slight increase in the detection rate of obesity at well-child visits and increased referral to the registered dietitian (RD). However, there were issues, such as culture, practicality, and patients’ involvement. The outcomes of the project demonstrated that the interventions were effective in increasing the rate of obesity screening and RD referral, but highlighted the need for another PDSA cycle with modifications. The results of the project revealed that childhood obesity is not easily manageable, especially where the community is culturally diverse. A higher rate of declined RD referrals further necessitates enhanced engagement strategies such as improved patient education, motivational interviewing, and family involvement. Subsequent endeavors should include collaborating with obesity specialists and community resources. Providing walk-in or same day access to the RD and increased telehealth services can also assist in navigating logistical challenges that may hinder families from receiving initial and ongoing nutritional education.
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Understanding the Stellar Mass Growth and Quenching of Massive GalaxiesOne of the first remarkable studies in the field of galaxy evolution determined that there are two distinct types of galaxies based on their morphologies: spirals and ellipticals. We’ve since found that spirals tend to be blue, lower-mass, gas-rich, star-forming galaxies. On the other hand, ellipticals tend to be red, massive, gas-poor, quiescent galaxies. This “galaxybimodality” is still being studied today, as we have many unanswered questions about the origin of its existence. For example, how do star-forming galaxies grow in stellar mass? And what physical processes are responsible for the cessation of star formation in quiescent galaxies? In this dissertation, I explore the stellar mass growth and quenching of massive galaxies. I use a sample of high redshift (6.7 < z < 13.2) galaxies to study how varying the initial mass function (IMF) changes their inferred stellar masses, showing that a redshift-dependent IMF infers reduced stellar masses in the high redshift universe. Next I explore the heterogeneity ofmolecular gas reservoirs in quiescent galaxies, showing that quiescent galaxies with detectable gas reservoirs have evidence of secondary bursts of star formation, likely driven by gas-rich minor mergers. Furthermore, I explore the connection between active galactic nuclei (AGN) activity and suppressed star formation, and show that even with high quality data and gold-standard star formation history (SFH) modeling, it is difficult to find observational evidence of AGN-driven quenching. Finally, I investigate the star formation and chemical enrichment histories of massive, quiescent galaxies as a function of their structural and environmental properties, finding that galaxies are quenched through a complex interplay of physical mechanisms.
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Undergraduate Research and Students with Learning Disabilities: Tensions Between the Exclusivity And Promise Of The ExperienceUndergraduate Research (UR) is a high-impact practice that results in positive outcomes for participants, such as improved academic skills and intellectual development as well as higher graduation rates and graduate school attendance. This research project compares the UR participation patterns of students with learning disabilities (LD) and their experiences engaging in UR settings to the signals displayed on UR websites. The project utilizes a mixed-methods design comprising four data sources and several analytical methods: content analysis of UR websites, quantitative analysis of institutional UR course data, and quantitative and qualitative analysis of survey data from 50 LD students and interview data from 4 LD students. UR websites feature text and imagery associated with prestige (achievement and competition), personal investment (commitment and unique benefits), and STEM fields. Students’ perceptions of UR participants and their course taking patterns mirrored those signals in some ways but not others. Students viewed UR as an activity for smart students who are involved and good at school, and the majority of actual participants were high achieving. Survey and interview respondents corroborated the benefits listed on UR websites. While participants with LD were underrepresented as UR participants, they reported positive experiences in UR settings, Their LD impacted their work to some extent, yet none disclosed their disability status or requested accommodations to avoid the stigma associated with learning disabilities. Non-participants with LD reported a lack of awareness, time, and confidence as the main reasons for not participating. These findings indicate the need to revise and expand the signals about UR to define it as an active learning process for students in all fields of study, not just STEM. Website content and outreach should specifically include representation of students who do not fit the traditional, high achieving norm. Additionally, faculty and staff should receive training on bias related to disability, Universal Design for Learning to create a more inclusive experience for LD students. Lastly, other university personnel and peer ambassadors should be trained to promote UR experience to a wider range of students.
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Transitional Shock Wave Boundary Layer Interactions and Surface Heat Transfer on a Hollow-Cylinder/Flare at Mach 5A hollow-cylinder model with a 15◦ half-angle flare is tested at Mach 5 for fivedifferent Reynolds numbers ranging from 4.10 × 105 < ReL < 1.62 × 106 . The laminar boundary layer separates due to the shock-induced pressure rise and reattaches downstream of the flare corner. Reattachment is associated with transition to turbulence and the formation of hot streaks. Surface heat transfer is measured using a FLIR infrared camera and five custom Ahmic thin-film gauges while pressure fluctuations are measured with two Kulites and three PCBs. For sufficiently transitional cases, heat transfer reaches a maximum near reattachment and moves upstream for increasing Reynolds number. Steady heat transfer trends along x/L between IR and thin-film gauges match qualitatively and the discrepancies between each diagnostic are discussed. In the premultiplied pressure spectra, two peaks were identified at f ≈ 14 kHz and f ≈ 100 kHz and their potential sources are discussed. Unsteady heat transfer measurements are discussed in regards to their viability for detecting reattachment and transition.
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Traditional and Novel Hippocampally Mediated Cognitive Tasks in Typically and Atypically Developing YouthThe hippocampus is known to help in the recall of associations and episodes from the past. This is a form of mental representation, and other forms of mental representation may be associated with the hippocampus as well. Five candidate complex cognitive functions involving suspected hippocampal involvement are discussed in the theoretical part of this dissertation, and a case is made for integration of the hippocampus into developmental theory. As the hippocampus is a gradually developing structure, we must consider how its slow course affects the development of cognitive functions that seem to involve it, at various stages. Following on this call to update developmental theory with integration of the hippocampus to account for a broad range of representational cognitive processes in Chapter 2, I introduce preliminary models in Chapter 3 in which I seek to test this for the case of creativity. Forty-three youth participated in cognitive data collection (21 with Down syndrome), and 38 of these participants (18 with Down syndrome) also underwent MR imaging. In modeling creativity as a function of memory and executive control, I seek to discover the extent to which memory contributes to creativity. Significant results were found for the group with Down syndrome, with associative memory and executive function emerging as predictors of creative performance. Surprisingly, these associations were absent in the typically developing group, for which the model and the variables were not significant. I also predicted that creativity and adaptive behavior would be positively correlated as representational functions that seem to have mnemonic contributions. Results included a positive correlation for creativity and adaptive behavior in the group with Down syndrome, while these functions were negatively correlated in the typically developing sample. In Chapter 4 I turn to examining the hippocampus itself. Previous studies have examined the hippocampus at the level of its subfields in adults with Down syndrome but not in youth, and while automated segmentation studies have been done with typically developing youth, there is little information on whether automated and manual methods agree for this age group. In the current study, subfield segmentations were made for youth with Down syndrome and typically developing youth using both methods in order to determine what group differences characterize the development of subfields, how well the methods concur with typical and atypical samples, and whether specific subfields relate to specific cognitive functions. The two methods showed small to moderate correlations across the subfields tested. The anterior hippocampus was correlated with associative memory in both groups and the CA1 subfield with adaptive behavior in both groups. Creativity did not show a correlation with hippocampal subfield volumes. Altogether group differences were more profound than expected in the studies described in chapters 3 and 4. Creativity, putatively linked to the hippocampus in Chapter 3, was well described by memory and executive control in the DS sample only. Possible explanations for this difference and the directionality difference between groups in the creativity to adaptive behavior study are offered. In Chapter 4, volumetric results largely supported hypotheses, but the methods produced less similar segmentations than anticipated, suggesting that continued caution is warranted in using automated methods with youth and special populations. Memory was similarly correlated with anterior hippocampus in both groups, but CA1 was only significantly related to CA1 in the DS group after correction, and creativity as a whole bore no significant relationships to subfields, although the creativity domain of flexibility was significantly related to CA and DG subfields in typically developing youth. In total, this dissertation explored hippocampal development and the concurrent development of “hippocampal” representational skills. More work is needed in order to understand how the developing hippocampal subfields interact with other brain regions and networks, how this changes across developmental time and how it may differ in various models of hippocampal impairment.
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The Voices of American Female Composers: A Study of Daughters by Lori Laitman and Love Sweet by Jennifer Higdon for Voice and Piano TrioLori Laitman and Jennifer Higdon are esteemed American female composers in contemporary classical music. The two composers’ works include modern compositional techniques, such as unexpected harmonic progressions, unconventional structures, and the use of highly specific musical directions in areas such as dynamics and pedaling. Their works also contrast with each other in several ways: Laitman frequently changes meter to fit the natural flow of the text in vocal works, while Higdon uses different meters to delineate sections; Higdon’s textures tend to be thin compared to Laitman’s; and in creating their unique sound worlds, Laitman tends to focus more on harmonic complexity, while Higdon invests in extended techniques. This document provides an analysis of two works by these composers: Daughters by Laitman and Love Sweet by Higdon. The two compositions are selected for their unusual instrumentation of piano trio and female voice, putting into relief the similarities and differences between the two composers and the compositional tools each employs. This document offers detailed analyses of these works, with an emphasis on how they reflect a distinctly feminine context and perspective through stories of love and motherhood. The analysis focuses on recurring thematic material, text painting, structure, texture, and specific musical directions. This study thus provides context and insight for future performances of these works. The resulting comparison of these two composers’ vocal chamber works paves the way to a deeper understanding of their styles in a contemporary context.
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The Impact of Extreme Weather on Winter Wheat: Evidence from the United StatesThe impact of climate on winter wheat yields is analyzed across the United States, using county level data spanning from 1974 to 2023 for 25 states. Using county and time fixed effect models, we studied the effect of temperature and precipitation for the planting, growing and harvesting phases of winter wheat. By using standardized z-scores, the study measures the impact of extreme climate deviations on crop yield variability. Findings highlight that moderate temperatures and precipitation are beneficial for winter wheat growth. The extreme climatic conditions, especially extreme heat, cold and rainfall have a significant effect on yields. Warmer conditions during the planting season increase the yields but excessive heat during the planting and harvesting seasons reduces yield. Moderate rainfall increases crop production in all stages but extreme rainfall results in yield reductions. The analysis also found that excluding outlier counties in California and Washington did not significantly alter the signs of the estimated coefficients.Key words: extreme climate, winter wheat yield, Z-score
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The Optimal Transfer Pathway Problem: Optimizing Course Equivalency and Prerequisite Compliance for Seamless Transfer Between Community Colleges and UniversitiesThis thesis delves into the Optimal Transfer Pathway (OTP) problem, a pivotal computational challenge in higher education that aims to create efficient transfer roadmaps for students transitioning from community colleges to universities. The OTP problem seeks to minimize total credit hours while ensuring students can earn both associate’s and bachelor’s degrees. We begin by analyzing the computational complexity of the OTP problem, revealing it to be N P -complete and highlighting the inherent difficulty of solving large-scale instances.To tackle this complexity, we first examine the Integer Quadratic Programming (IQP) ap- proach. This approach guarantees an optimal solution but often requires substantial com- putational time, especially for large-scale datasets. This limitation makes the IQP algorithm impractical for real-time applications in student advising and academic planning. In response to these challenges, we introduce the Iterative Course Swapping (ICS) algorithm, a heuristic approach that efficiently generates near-optimal solutions. The ICS algorithm seamlessly integrates degree requirements from both institutions, available courses, articula- tion agreements, and prerequisite information. It produces valid transfer pathways that sat- isfy all degree requirements, respect course prerequisites, and maintain the proper sequence of community college and university courses. Importantly, the ICS algorithm’s time com- plexity is significantly improved over the IQP approach. To evaluate the performance and computational efficiency of both the ICS and IQP algo- rithms, we conducted experiments using real-world data from Pima Community College and the University of Arizona. The results demonstrate that both algorithms significantly improve credit transfer efficiency, leading to higher credit retention rates and reduced time to degree completion. The ICS algorithm excels in computational performance, generating transfer pathways more quickly and effectively than previous approaches, particularly for large datasets. While the IQP algorithm guarantees an optimal solution, developing a trans- fer plan often requires more time, rendering it impractical for large-scale datasets. This trade- off between optimality and computational efficiency highlights the potential of the ICS algo- rithm for real-world applications, where timely solutions are crucial for student advising and academic planning. This research contributes to developing more equitable and efficient transfer systems, ulti- mately facilitating smoother transitions between community colleges and universities. The broader implications include promoting higher graduation rates and reducing student finan- cial burdens. This work lays the foundation for future research in optimizing educational pathways and demonstrates the value of applying computational approaches to complex ed- ucational challenges.
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The Image of the Seminarian in Nineteenth-Century Russian LiteratureNational literature has long been considered a reflection of a state’s significance andlegitimacy; indeed, fiction participates in and contributes to the myth of the nation. As Benedict Anderson has shown, national literature helps shape the nation. In his scholarship, he defines the nation as an imagined community of people who seem to represent a homogenous group despite the actual inequality and diversity of the population. In line with Anderson, Sarah Corse remarks that a nation is “marked by a distinctive set of values, tensions, myths, and psychological foci, that produces in turn a certain readily identifiable national character.” Julia Wright argues that national literature becomes one of the most effective spreaders of such images. However, instead of portraying a nation as a heterogeneous and diverse community of various social classes, religions, and cultures, national literature deprives the population of its diversity and its complexities. The Russian writers of the 1830s onward struggled to define “Russianness” and Russian identity, and their debates on the subject took place through the medium of their fiction. Preoccupied with probing the essence of “Russianness,” literary elites began to examine national “types,” giving particular attention to the nation’s nascent middle classes: merchants, governesses, teachers, and seminarians. The lattermost, seminarians, would come to constitute a complex source of Russianness. Though eventually ascended to a place among the Russian intelligentsia, their origins were of the middle classes. Reflecting the tensions and social upheaval inherent in the rise of Russia’s new middle-class estate, the portrayal of seminarians in the literature of the day was often of a disparaging tone. In spite of this, the significance of the marginalized estate continued to its rise to prominence in the nineteenth century. The evolution in depictions of seminarians in Russian literature throughout the period reflects the course of public opinion, with some significant exceptions. This thesis analyzes selected works by two influential writers of the period, Gogolʹ’s Vii (1835) and Khvoshchinskaia’s The Baritone (1859). These works were chosen due to their extensive and opposing descriptions of seminarians. Close reading of these selected works allows one to scrutinize the shift in image of seminarians in Russian literature and culture between 1830 and 1860. Chapter I of this thesis delves into Nikolai Gogolʹ’s novella and explores how the writer depicted seminarians in demeaning and derogatory terms and examines the characteristics he ascribes to these students. Accordingly, this thesis posits that Gogolʹ illustrated a perceived clash between the seminarians’ expected religiosity and their failure to meet these expectations via their personal behavior. Chapter II will then investigate a depiction of the estate as portrayed by Nadezhda Khvoshchinskaia (pseudonym V. Krestovskii) in The Baritone, written two decades later. In her work, the estate is portrayed in an arguably idealized manner, reflecting the shift in seminarians’ image as they increasingly came to be viewed as a reflection of potential futures for a changing Russian society. Thus, it becomes possible to trace certain changes in the portrayal of the seminarians against the backdrop of the historical events.
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The Hidden Costs of Complexity: Using Causal Inference and Double Machine Learning to Uncover Important Relationships in Higher Education Data SetsGraduation rates are a critical performance metric for higher education institutions, reflecting both student success and the effectiveness of educational programs and policies. Among various influencing factors, curricular complexity has emerged as a significant determinant. This study rigorously estimates the causal effect of curricular complexity on four-year graduation rates across 26 universities in the United States. To achieve this, we employ a multifaceted methodological framework integrating advanced causal inference techniques. We calculate the Generalized Propensity Score (GPS) to adjust for confounding variables and predict the treatment variable using Hierarchical Linear Modeling (HLM), accounting for the nested data structure (students within universities). The data is stratified into quintiles based on GPS values to ensure balanced comparison groups. Within each quintile, Double Machine Learning (DML) is utilized to estimate the causal effect of curricular complexity on four-year graduation rates, leveraging logistic regression for the binary outcome variable (four-year graduation) and linear regression for the continuous treatment variable (curricular complexity). Additionally, we construct a causal network using the PC Algorithm, refined by domain experts for plausibility and relevance. The Bayesian Information Criterion (BIC) score is used to select the optimal adjusted network. Sensitivity analysis assesses the robustness of our findings against potential unmeasured confounding factors. Our results indicate a significant causal relationship between curricular complexity and four-year graduation rates. Specifically, higher curricular complexity is associated with lower graduation rates, with an estimated causal effect of -3.879% per unit increase in complexity. Sensitivity analysis confirms the robustness of these findings, with a new effect estimate of -3.763% per unit increase in complexity after accounting for potential unobserved confounders. Detailed analysis across quintiles showed consistent results, indicating that higher curricular complexity within each stratified group reduces the likelihood of graduating in four years. The Average Treatment Effect (ATE) across quintiles ranged from -7.5% to -21.5% per unit increase in complexity. The implications of this study are far-reaching. By highlighting the impact of curricular complexity, our findings can inform university policies aimed at optimizing curricula to enhance student success. Moreover, the methodological framework presented here offers a comprehensive approach to causal inference in educational research, combining GPS, HLM, DML, and network analysis to provide robust and actionable insights.
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The Evolution of Chinese Violin Music in the 20th CenturyChinese violin music experienced significant development in the 20th century, mirroring the nation's cultural, political, and social transformations. My aim is to explore and highlight this evolution through four representative works: Sicong Ma's Rondo No. 1, Yongcheng Qin's Tone Poem at the Seashore, Gang Chen's Sunshine on Tashkurgan, and Pei Lu's Flute and Drum at Sunset. The study of these works provides insight into how Chinese composers adapted and transformed Western musical influences to create a distinctive Chinese violin repertoire. By analyzing these works, one can gain a deeper understanding of the impact of historical events and cultural changes on Chinese composers and their music
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The Feasibility of Virtual Tai Chi Easy for Registered NursesBackground: Registered nurses (RNs) are at risk of experiencing elevated levels of stress and burnout and are leaving the profession in droves. Considering the ever-expanding nursing shortage and the aging of the population in the United States (US), high turnover rates and nurses leaving the profession are highly problematic. Nurses experience numerous barriers to self-care or attending wellness classes due to work schedules, working off-shifts, and competing demands. Tai Chi Easy™ (TCE) is a safe, adaptable, and low-barrier form of mind-body exercise that can be delivered virtually, which increases accessibility. Aims: This dissertation study aimed to determine whether a virtual TCE exercise program was feasible, acceptable, and appropriate self-care intervention for RNs and to describe within-group changes in occupational stress, posttraumatic stress, somatic symptoms, burnout, transition shock, and intention to quit. Methods: Several nursing and non-nursing theories and concepts underpinned the single-group pre-post-intervention study design. RNs were recruited via postcards and emailed study flyers. Participants engaged in an asynchronous, virtual 1-hour TCE class twice a week for six weeks and practiced 10 minutes four days per week for six weeks. Study measures were collected using REDCap and included recruitment, retention, intervention adherence and safety, demographics, Life Events Checklist for DSM-5 (LEC-5), Adverse Childhood Experiences Questionnaire (ACE-Q), PTSD Checklist for DSM-V (PCL-5), Somatic Symptom Questionnaire (SSQ-8), Maslach Burnout Inventory-Health Services Survey (MBI-HSS), and the English version of the Nurses’ Intention to Quit Scale (NITQ). Data analysis using Microsoft Excel software included descriptive statistics and paired t-tests. 14 Results: A total of 18 RNs enrolled, and 14 RNs (mean age=51±16, 86% female, 71% employed full-time) completed the study. Participants reported that the TCE intervention was acceptable (75%), appropriate (75%) and feasible (73%). However, intervention adherence was inadequate (65% TCE classes, 74% independent practice). No safety issues were reported during the study. Pre-post intervention changes in symptoms for PCL-5 (p=0.32), SSQ-8 (p=0.22), MBI (p>0.50, all domains), and NITQ (p=0.49) were not statistically significant. Study attrition was 22% (n=4) due to participant-reported time constraints. Conclusion: With some modifications, virtual TCE training may be a feasible mind-body self-care intervention for RNs.
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The Effect of Extreme Weather on Mortality: Evidence from the United StatesThis paper estimates the impact of extreme weather on mortality rates associated with 5 specific causes and total mortality across the counties in the United States, period of 1979-2002. Using the county-level panel data I explored how significantly the deviations in the temperatures and precipitation impact mortality rates specifically cardiovascular, respiratory, neoplasms, transport injuries, self-harm and interpersonal mortalities. By using a comprehensive methodological framework that points to the standardized z-scores to identify the significant weather anomalies, uses average temperature bins to explore the non-linear effects and sets the temperature thresholds to see the consequences of extreme heat and cold. This study enhances our understanding of climate health. Key findings show that average temperature ranges between <0◦F -60◦F have a significant impact on total and cardiovascular mortalities. The extreme maximum and minimum temperatures are significantly associated with motor vehicle accidents, likely due to tire blowouts and wet ice road conditions. This research contributes to the understanding of how extreme weather affects health, offering important insights into how such conditions impact mortality. .
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SRSaRa: A SaRa-Inspired Modification of Pettitt's Test for Non-Parametric Change-Point DetectionThe Signed-Rank Screening and Ranking Algorithm or SRSaRa is a non-parametric change-point detection technique that is based on a SaRa-like process with a diagnostic function inspired by Pettitt's test. Possessing two modes, `LM' and `MAX' for single and multiple change-point detection respectively, the SRSaRa is flexible and robust to outliers through its diagnostic function. The SRSaRa's `MAX' mode for single change-point detection outperforms Pettitt's test in several scenarios while maintaining Type-I error control, while the SRSaRa's `LM' mode is capable of controlling FDR at the desired level and shows promise as a non-parametric multiple change-point detection technique.
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Risk Factors of Helicobacter Pylori for Hispanics Living in Southern ArizonaThis thesis investigated the occurrence of Helicobacter pylori (H. pylori) and other riskfactors associated with gastric health in Hispanic communities as part of The Southern Arizona Healthy Stomach Project (SoAZHSP). This thesis report assessed tap water samples for culturable microorganism and metals, H. pylori infection using the urea breath test (UBT), and household and individual surveys to statistically assess predictors of known and identifiable risk factors of H. pylori infections in this subpopulation. Thirty-three percent (n=14 of 42) of the subpopulation tested positive for H. pylori. In the United States (US), H. pylori infection prevalence estimates range between 17.6% and 36.0%, while H. pylori prevalence rates for Hispanics living in the US range between 38% and 62%. Factors such as lack of health insurance (p=0.02), low consumption of restaurant and/or fast food (p =0.02), older age (p=0.04), lack of alcohol consumption (p=0.01), and tap water quality (p=0.04 for total coliforms detection) were predictors of H. pylori infections. Furthermore, only 41.9% of participants had heard of H. pylori, indicating low awareness. Despite sample size and reporting bias limitations, this report highlights the multifaceted nature of H. pylori infections and the importance of improving H. pylori awareness, water quality, health care access, and food hygiene practices. These findings underscore the need for targeted, comprehensive interventions to address H. pylori infections in Hispanic communities to improve overall health outcomes.
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Resolving the Disordered C-Terminal Tail of Cardiac Troponin T and the Impact of Cardiomyopathic MutationsIn 1994, it was demonstrated that mutations in the genes that encode thin filament proteins were identified in causing familial hypertrophic cardiomyopathy (FHC) (1). This added to the prior knowledge that mutations in the motor protein myosin had been linked to genetic cardiomyopathy (2). Since then, many descriptions of both hypertrophic and dilated cardiomyopathy (HCM and DCM respectively) have been given. Generally, cardiomyopathies are described as a heterogenous group of diseases of the myocardium (3, 4). These diseases are often associated with either systolic or diastolic dysfunction, sometimes both. Since the 1990’s, the genetic basis of HCM and DCM is widely recognized, however, although clinically described as one disease, these cardiomyopathies are often quite variable in clinical manifestation and phenotype (3, 4). This suggests the underlying mechanisms of disease are just as variable and diverse. To better understand the mechanisms that result in disease we must not only understand the late-stage clinical phenotype but also understand the natural history of disease trajectories as well as the structure and function of not only the myofilament but also its role on the larger scale of whole heart function with the cardiovascular system. As my focus in this dissertation lies on the cardiac thin filament and the regulatory proteins that impart calcium regulation to cardiac contraction, I will examine, from a structural perspective, how a relatively short segment of one of the regulatory troponin subunits regulates cardiac thin filament activation by investigating its structure, which is known to be disordered and flexible. I hope to convince you this protein segment is not only important to cardiac thin filament regulation but is also implicated in disease as a known hotspot for cardiomyopathy causing mutations and variants of unknown significance and that by understanding its disordered structure, we can better understand the mechanism(s) by which different mutations alter this typical structure and alter intermolecular interactions thus altering cardiac thin filament regulation and thus cardiac contraction.
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Provider Education on Ketamine Effects and Administration Guidelines in an Outpatient ClinicPurpose: Increase provider knowledge of the risks and benefits of using ketamine, increase provider understanding of racemic ketamine’s mechanism of action, increase provider comfort educating patients about ketamine, and improve provider comfort during administration of ketamine in an outpatient clinic.Background: Racemic ketamine, used off-label for psychiatric purposes, is a novel treatment and has demonstrated a significant reduction in both treatment-resistant depression and suicidal ideation. No formal guidelines exist for its administration in mental health clinics, and many providers lack the knowledge to adequately prescribe, administer, and educate patients regarding its risks and benefits. Methods: A quality improvement project that recruited three PMHNPs to watch a 30-minute asynchronous recorded presentation and participate in a pre-and post-survey self-assessing their ketamine understanding, confidence in patient education, and comfort during administration. A pre- and post-10-question multiple choice and true/false knowledge tests were administered to assess learning. Results: Three PMHNPs watched the presentation and completed the surveys/tests. All providers self-reported a change in their understanding of ketamine’s risks/benefits, their confidence in educating patients about its effects, and their comfort with administration. The domain with the highest self-reported improvement was patient education. Provider knowledge test scores increased by 36.7% from pre-intervention to post-intervention. None of the changes were deemed statistically significant. Conclusions: Post-intervention survey demonstrated a self-reported increase across all project purpose domains, with the highest change noted in the domain of patient education. The post-knowledge test score yielded a 36.7% increase.
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Phenomenological Model of Glass Dynamics: Effect of Spatial Heterogeneity in Glass and Supercooled LiquidEnergy consumption is exponentially increasing every year in our society. This is caused by the rapid growth of information that needs to be transferred, processed, and stored on a global scale. Therefore, the materials science community must study and develop materials that can serve as building blocks for more energy-efficient and powerful information processing devices. Glasses play a pivotal role in achieving this goal because of their technological relevance, such as silica fibers for information transport across the globe and chalcogenide glasses for electronic and photonic devices for information processing and storage. Designing efficient and effective glasses for such applications requires accurate control of their physical properties. However, predicting the dynamics of glasses is known to be extremely challenging. Thus, developing a model that can predict glass dynamics under various temperature conditions is of great interest to the glass science community and industry. In this dissertation, we propose a new phenomenological model that can predict glass dynamics over a wider range of temperatures and time scales, which exceeds the capability of existing phenomenological models.Various organic and inorganic systems are used to validate the proposed model which correctly predicts the temperature-dependence of non-exponentiality in supercooled liquids, where the non-exponentiality controls the microscopic spatial heterogeneous dynamics of supercooled liquids. The model is also used to investigate the relaxation dynamics of two silicate glasses far below their glass transition temperatures T_g. The proposed model successfully simulate their relaxation behavior after complex thermal histories while existing models such as Tool-Narayanaswamy-Moynihan (TNM) have failed to correctly describe their relaxation dynamics. This study reveals the importance of accounting for the temperature dependence of non-exponentiality, as the temperature dependence of spatial heterogeneity directly affects the glass dynamics far from T_g. Kovacs’ expansion gap paradox is reproduced for the first time in the literature using the proposed phenomenological model. Further, the paradox is investigated over a wide range of temperatures to reveal that the paradox as well as the non-linear relaxation vanishes at high temperatures. It is found that the vanishing of the non-linear relaxation and the expansion gap are caused by the significant narrowing of the distribution of relaxation time at high temperatures, leading to homogenous relaxation regardless of the initial temperature before annealing. Additionally, the model is used to simulate the microscopic fluctuation of density as a function of temperature and time, and the model correctly describes the dynamics of the fluctuation that are observed in experiments. This is the first demonstration of modeling the density fluctuation with a phenomenological model in the literature. Thus, these studies show the importance of accounting for the dynamics of microscopic spatial heterogeneity to successfully predict complex relaxation with a phenomenological model. The proposed model is shown to exceed the capability of existing models and should therefore serve as an important new tool to understand glass dynamics and allows the development of glasses with desired physical properties for a range of applications such as efficient and powerful information processing devices.
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Predicting Host-Pathogen Interactions Between C. Difficile 630 and MouseClostridioides difficile (C. difficile) is the causative organism of hospital-acquired infectious diarrhea. C. difficile infection (CDI) causes more than 500,000 infections and 12,800 deaths. Antibiotic ablation of the commensal microbiome leads to colonization and infection in CDI patients. Symptomatic diarrheal infection requires the release of C. difficile toxins, TcdA and TcdB. Together, these toxins destabilize the colon epithelial cell membrane, causing fluid secretion into the colon, inflammation, and tissue damage. Molecular interaction databases describe C. difficile toxin interactions with host cell receptors, establishing these toxins as crucial virulence factors. However, there needs to be more curated and annotated information about non-toxin colonization and infection of these interactions in databases. Comprehensive identification of C. difficile toxin interactions and adhesion and colonization processes with host proteins will advance our understanding of Clostridioides difficile infection (CDI) and facilitate the design of targeted rational therapies.In this study, we predict host-pathogen interactions (HPI) between mouse and C. difficile strain 630. We select mice as the host to predict interactions as these animal models provide genetic similarity to humans and can predict human disease responses. Selecting C. difficile 630 allows for a comprehensive study due to its well-characterized genome, clinical relevance, and representation of prevalent, virulent strains. The interolog-based approach is leveraged to provide host-pathogen interactions (HPI) rapidly. This method relies on sequence-based homology to transfer experimentally validated interactions to a mouse and C. difficile 630 model. The interolog approach predicts protein-protein interactions based on the assumption that if two proteins interact between species, their orthologs will likely interact in another species' system. PSICQUIC interaction data is used to identify homologs in mouse-C. difficile. Mouse orthologs are found in human proteins using Ensembl BioMart. Next, pathogenic bacterial orthologs in C. difficile strain 630 are identified using reciprocal BLAST. This approach allows for the inference of potential interactions by mapping known interactions from one organism to another, yielding a set of 1,281 interologs; for extracellular pathogens such as C. difficile strain 630, extracellular or secreted proteins are expected to interact with host surface proteins. This pruning results in 100 interologs. CDI occurs in the colon, leading us to our next step: studying the colonic expression of host proteins. We exclude proteins not expressed in the colon as they do not contribute to the disease. Next, we manually assess C. difficile proteins in the predicted HPI for biological function and annotation. This results in 37 predicted HPIs. Network analysis of these HPIs further identifies 13 interactions with edge clustering for further investigation. One predicted HPI shows an interaction between the C. difficile strain 630 sortase protein, CD630_27180, which cleaves bacterial surface proteins for adhesion, interacting with mouse protein, Ninjurin-1, an outer surface protein in the host implicated in immune functioning. This interaction is likely as these proteins are present externally in the bacteria. Another prediction identifies an interaction between CD630_03860, a secreted sortase protein, and Col12a1, a collagen protein expressed in the colon and implicated in maintaining cell junction stability. This HPI is likely to occur as CD630_03860 is a secreted protein that may allow for the breakdown of the epithelial tight junction, giving C. difficile access to the basolateral layer of colon epithelial cells. While the interolog approach is rapid and cost-effective, it has some limitations. The interolog method relies on sequence homology to predict interactions. The predicted HPI quality in this study depends on the experimental data available on PSICQUIC. Experimental data on PSICQUIC is sourced from databases that are manually annotated. It only represents some of the available experimental data in the literature that could lead to meaningful predictions. Additionally, predicted proteins may have different functions than those in the experimental data, leading to biologically incongruent predictions in CDI. For example, C. difficile strain 630 protein flagellin (FliC) interacts with TLR5 in humans. However, this interaction cannot be predicted in mice as TLR5 in mice binds different ligands in bacterial proteins. This research advances the application of interologs to HPI prediction by using pruning methods to refine interactions specific to C. difficile infection. However, alternative approaches to predict HPIs for C. difficile should also be investigated, such as machine learning. Ensemble machine learning in predicting HPI using protein sequence has been used to predict accurate host-pathogen interaction.
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Peridynamics with Strain Gradient Elasticity to Account for Microstructural Size EffectThis study proposes the development of a peridynamic (PD) model with strain gradient elasticity (SGE) for size effect on scaling of structural strength. Peridynamic theory introduces damage into the constitutive relations in a natural way. It will enable the investigation of the combined effect of PD and SGE length scale parameters on the stiffness and strength of the material. The primary challenges of general gradient elasticity are the vast number of constitutive parameters. Also, it requires two additional non-classical boundary conditions arising from the presence of fourth order spatial derivatives in the equation of motion. Considering a simplified SGE model with commonly accepted length parameters, the PD form of the equilibrium equations are established for one- and two-dimensional analysis. The PD with SGE (PDSG) equation of motion is without any spatial derivatives and allows for the imposition of displacement constraints and non-zero tractions in the form of a body force density. The PD equations are derived in their bond-based and state-based forms. This derivation presents a novel approach to write the bond-based and ordinary state-based force density vectors for PD and PDSG in terms of the PD functions provided by the Peridynamic differential operator (PDDO). The resulting equations present two length parameters: the horizon of a material point in PD and the characteristic length in SGE theory. The PDSG is first applied to study the deformation response of a single-walled carbon nanotube (SWCNT) subjected to an axial load, and subsequently its longitudinal vibration. To verify the two-dimensional PDSG formulation a thin film is modeled to mimic the one-dimensional SWCNT problem and subsequently compared with the one-dimensional analytical solution. Another benchmark problem of a thin film subjected to tangential displacement is compared to its analytical solution. The dynamic response is compared to a point-wise computational solution with nonclassical boundary conditions. Finally, a plate with and without a crack is modeled to showcase the capability of the PDSG model.