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Applying Multi-Scale Computational Approaches to Study Disease Related Biomolecules
No full textComputational biophysics plays a significant role in understanding biological processes in various biology systems and provides new sights to investigate disease-related biomolecules. During the doctoral research, I utilized multi-scale computational approaches, including structural modeling, Molecular Dynamics (MD) simulation, electrostatic analysis (DelPhi, DelPhiForce), and machine learning based Hybridizing Ions Treatment-2 (HIT-2) program, to investigate biomolecules. My research includes bound ions effects on kinesin Ncd binding to microtubule, ion concentration effects on kinesin BimC binding affinity, microtubule dynamics, etc.Ions are crucial for biomolecular interactions, especially for highly charged biomolecules. Bound ion effects are difficult to study in implicit solvent models. Based on the machine learning approach, a hybrid solvent method was developed to combine the explicit solvent model with implicit solvent model to study protein-protein interactions. The hybrid approach treats the bound ions explicitly and the free ions implicitly. The work applies the hybrid approach to a kinesintubulin complex, which demonstrates that the bound ions, especially the interfacial bound ions, play significant roles in kinesin-microtubule binding. The hybrid approach is not only capable of handling kinesin-tubulin complexes, but also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc.Microtubules are key players in several stages of the cell cycle and are also involved in transportation of cellular organelles. Therefore, understanding the interactions among tubulins is crucial for characterizing microtubule dynamics. Studying microtubule dynamics can help researchers make advances in the treatment of neurodegenerative diseases and cancer. A series of computational approaches were utilized to study the electrostatic interactions at the binding interfaces of tubulin monomers. The calculations explained that due to the electrostatic interactions, the tubulins always preferred to form α/β tubulin dimmers. The interactions between two protofilaments are the weakest, thus the protofilaments are easily separated from each other. The study elucidates some mechanistic details of microtubule dynamics and also identifies important residues at the binding interfaces as potential drug targets for the inhibition of cancer cells.BimC family proteins are bipolar motor proteins belonging to the kinesin superfamily which promote mitosis by crosslinking and sliding apart antiparallel microtubules. Understanding the binding mechanism between BimC and the microtubule is crucial for researchers to make advances in the treatment of cancer and other malignancies. By combining molecular dynamics (MD) simulations with a series of computational approaches, the electrostatic interactions at the binding interfaces of BimC and the microtubule under three different potassium chloride (KCl) concentrations were studied. We found the electrostatic features on the motor domains of BimC provide the strongest attractive interactions to the microtubule at 0 mM KCl compared to the complex at 50 and 150 mM KCl concentrations, which is validated by experimental conclusions. Furthermore, important salt bridges and residues at the binding interfaces of the complexes were identified, which illustrate the details of the BimC/microtubule interactions. The identified important residues involved in salt bridges are potential hot spots of drug targets for designing new drugs to cancer therapy
Exploring the Barriers to Mental Health Treatment Among Justice-Involved Women Living in the U.S.-México Border Region
No full textFor women in the criminal justice system, mental illness is a complicated and prevalent factor, with rates ranging from 49.2% to 67.9% (Bronson & Berzofsky, 2017). To address the mental health care that justice-involved women need, scholars have called for a closer examination of barriers that hinder access to mental health and substance use treatment among this population (Winham et al., 2015; Wilfong et al., 2021). Barriers to treatment can be attitudinal (i.e., stigma and fear) or structural (i.e., transportation, cost of treatment), yet few, if any, studies have examined how these barriers present in a group of justice-involved women living in the U.S.-México border region. The current project explored the relationship of barriers to treatment among 85 justice-involved women living in the Paso del Norte border region located in El Paso, Texas, and surrounding areas. The project investigated (1) what barriers women frequently identified as hindering their treatment, (2) how the barriers contributed to receipt of services, and (3) whether internalized stigma mediated a relationship between endorsed attitudinal barriers and perceived public stigma. Findings show attitudinal barriers were more frequently endorsed as barriers to seeking treatment compared to structural barriers. For the second aim, attitudes and public stigma were at decreased odds for past treatment seeking, yet internalized stigma was associated with increased odds of past treatment seeking and future treatment seeking. However, structural barriers did not emerge as significant predictors for past, present, or future treatment seeking. Finally, internalized stigma was a significant mediator between public stigma and attitude barriers. Results suggest that stigma and attitudes, while complex, are associated with help-seeking and should be addressed to increase utilization of treatment services for justice-involved women in the Paso del Norte border region
Smart Public Transportation and Mobility Hub Design in El Paso
No full textThis thesis explores the potential of a Smart Public Transportation concept and mobility hub design as tools to mitigate the growing trend of transportation issues, such as traffic congestion, reliance on privately owned vehicles, or unattractive public transportation. Consequently, residents’ lives are negatively affected by excessive noise, air pollution, and travel delays, leading to increased safety risks, lost productivity, and a lower quality of life. Considering these challenges, the study begins by analyzing the current state of public transportation in El Paso. Next, it introduces a toolkit from the Smart City concept, followed by case studies of best practices of public transit and mobility hub implementation from American and European conditions. Lastly, the core of this thesis lies in the design of a mobility hub in Downtown El Paso, accompanied by implementation recommendations. Hopefully, this work will serve as a roadmap and contribute to more sustainable and accessible transportation for the benefit of El Paso
Workplace Bullying in K-12 Education
No full textWorkplace bullying within K-12 education settings has become an increasingly recognized concern, with potentially profound implications for educators\u27 well-being. This quantitative research study aims to explore the phenomenon of workplace bullying among teachers and administrators in K-12 educational institutions. Findings from this study will contribute to the existing literature by shedding light on the prevalence and manifestations of workplace bullying in K-12 education, as well as its implications for individual well-being, organizational culture, and educational outcomes. The study aims to inform the development of targeted interventions and policies to prevent and address workplace bullying, thereby fostering healthier and more supportive educational environments for educators
The Cost of Student Mandatory Fees: An Examination of the Longitudinal Growth in Student Mandatory Fees at Four-Year Public Universities in Texas and Their Impact on Student Retention Rates
No full textThe ever-increasing costs and debt incurred by US college students is a hotly contested issue. In March 2022, The Texas Higher Education Coordinating Board (THECB) released a refreshed strategic plan for the state’s higher education: Building a Talent Strong Texas. The refreshed plan concentrated on access for minority populations, enhanced student completion goals, and expanded the focus on reducing student debt. Texas set out to lead the nation in the least college student debt reported.One of the least examined or understood college costs is student mandatory fees (Arnott, 2012; Black & Taylor, 2018; Kelchen, 2016; Reinagel & Cooper, 2020). This study sought to examine the rising costs associated with student mandatory fees at four-year public institutions in Texas. The quantitative study used six years of publicly available national panel data to determine a) by what magnitude required fees were increasing, b) whether fees were becoming an increasing proportion of the price of attendance (PoA) for in-state and out-of-state students living off campus without family, and c) to examine the relationship between student fees and institutional fall-to-fall retention rates. Findings included a 591 mean average increase (24%) out-of-state fees at public, four-institutions in Texas (n=32). Despite increases over the six years, fees did not represent a greater proportion of the price of attendance reported. The study also found that rate term for both in-state and out-of-state fees did explain statistically significant variance in institutional student retention rates, but upon further examination no specific variables in the proposed models were significant. Institutional variability did account for over 90% of variance in the proposed model which has implications for the THECB to intervene around institutional flexibility in determining student fees as a component of higher education costs
Using Genetic Algorithm and Geographic Information System for Equity-Driven Transportation Planning and Analysis
No full textEfforts to address equitable access in public transit planning have gained momentum, spurred by incentives for Metropolitan Planning Organizations (MPOs) and State Departments of Transportation (DOTs). However, traditional strategies often fall short of meeting the needs of disadvantaged communities, particularly in underserved areas. This study presents a pioneering methodology leveraging Genetic Algorithms (GA) and Geographic Information Systems (GIS) to optimize bus stop placement, aiming to enhance equitable access in public transit systems. Focusing on Route 16 of Sun Metro in El Paso, Texas—a critical feeder route linking the Westside Transit Center to Upper Valley neighborhoods—the research commences with a comprehensive analysis of demographic data and existing transit conditions to pinpoint disparities and accessibility challenges. By harnessing GA and GIS, the study proposes solutions tailored to equity factors, resulting in notable improvements in accessibility metrics. The research underscores the imperative of modernizing evaluation methodologies and integrating emerging technologies. Despite encountering challenges such as data availability constraints, computational demands, and the dynamic nature of urban environments, the study advocates for developing adaptive models. This research contributes significantly to advancing equitable transit systems and practices, offering valuable insights and a replicable methodology to enhance accessibility and equity in public transportation networks
Mechanical Stability of Body-Centered Cubic Iron in Born-Von Kármán Parameter Space Using Evolutionary Algorithms
No full textThis study aims to assess the mechanical stability of body-centered cubic (BCC) structure, focusing on pure iron as a representative case. The primary approach involves utilizing phonon dispersion curves analysis to gain insights into the vibrational properties and over-all stability of the crystal lattice through a multi-faceted computational approach. The goal of the study is to find the regions (pockets) in Born-von Kármán (BvK) parameter space where the crystal is stable for certain temperature and pressure.This involves setting up the crystal parameters, creating a body-centered cubic (BCC) crystal with specific lattice parameters (2.86 for iron) establishing force constants, and employing Phonopy for phonon dispersion analysis. We developed a genetic algorithm to investigate the mechanical stability of body-centered cubic (BCC) iron in reduced BvK pa- rameter space. For each genetic solution, we calculate the phonon dispersion relations by computing the dynamical matrix at various grid points across the Brillouin Zone. We then evaluate the stability of each solution by measuring deviations from predefined mechanical stability conditions.Introduction of the RMSE metric provides a quantitative assessment of the accuracy of the computational model. The imaginary frequencies identified during the phonon dispersion analysis highlight potential areas of instability in the BvK space, and application of a ge- netic algorithm serves as an innovative approach to optimize the model and enhance its accuracy.The comprehensive nature of this analysis positions it as a foundational study for further exploration into the mechanical stability of metallic crystals, with potential applications in materials science and engineering
Examining the Relationships between Social Media Use Constructs and Mental and Sleep Health in Hispanic University Emerging Adults
Get PDFThe number of social media users and platforms have increased dramatically in recent years. Several different social media use constructs have developed from past research to assess social media engagement, including overall social media use, nighttime in-bed social media use, social media addiction, social media self-control failure, and emotional investment in social media. Such constructs are distinct from each other and may adversely impact mental and sleep health, especially during emerging adulthood. This study investigated the relationships between such social media use constructs and mental and sleep health in Hispanic university emerging adults through a framework of Uses and Gratifications Theory. Three hundred and fifty-eight Hispanic university emerging adults completed a cross-sectional online survey assessing sociodemographics, overall social media use frequency, nighttime in-bed social media use, social media addiction, social media self-control failure, emotional investment in social media, depression, anxiety, stress, and sleep quality. It was hypothesized that the above social media use constructs of interest would be positively associated with depression, anxiety, stress, and poor sleep quality after controlling for participant age and sex. Four multiple linear regression models were performed to test hypotheses. Hypotheses were partially supported such that social media addiction was positively associated with depression, anxiety, stress, and poor sleep quality, and social media self-control failure was positively associated with anxiety, stress, and poor sleep quality. These findings indicate that Hispanic university emerging adults may use social media problematically to cope with pre-existing poor mental and sleep health or that poor mental and sleep health stem from using social media problematically. Assessing problematic forms of social media use seems vital in clinical settings. Future studies may wish to investigate these observed relationships longitudinally to establish temporality
Characterizing The Mentoring Landscape In An Online Biology Cure: An Examination Of Student And Graduate Teaching Assistant Perspectives And Outcomes
Get PDFBoth undergraduate research experiences (UREs) and course-based undergraduate research experiences (CUREs) provide students with benefits and opportunities such as improving technical skills and making connections that provide resources. Where these two experiences differ is accessibility. Similar to their URE counterpart, CUREs entail mentorship on the part of the faculty/graduate teaching assistant (GTA) instructor (Dolan, 2016). There is general agreement that mentoring has many benefits for undergraduate STEM students, such as an improvement in retention rates, increased confidence and self-efficacy, and a more established sense of belonging and science identity (Dolan, 2016). CURE instructors often adopt the role of “mentor” in addition to the “teacher” role. Although evidence suggests that mentoring improves students’ success, in-depth understanding of mentorship in contexts such as online learning and CUREs remains limited. Before and after the COVID-19 crisis, advances in technology facilitated the implementation of online programs, which have led to mentoring interactions occurring in online contexts with increasing frequency. To make higher education more accessible and inclusive, online CUREs could provide valuable research and mentorship opportunities to non-traditional students who otherwise might have a harder time being considered and accessing opportunities such as UREs and face-to-face CUREs due to various logistical constraints (e.g., time, distance). While the benefits of mentoring have been well documented, the complexities and pathways that lead to said benefits are still not well understood. Additionally, online mentoring presents different dynamics and challenges that have not yet been explored and therefore understood, especially in the context of CUREs. This project aims to characterize said pathways in order to give instructors a better understanding of students’ outcomes as they relate to the components that make up an effective mentoring relationship within the context of an online CURE. Specifically, this project explores student and GTA perceptions of mentoring within an online CURE adopting the national SEA-PHAGES model (Jordan et al., 2014). Further, it aims to understand how students’ perceptions of mentoring supports relate to a suite of latent and observed variables (e.g., demographics, science identity, instructor trust) as well as how being in an online environment affects said perceptions
