The University of Texas at Tyler
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EFFECTS OF FIBROGENIC AND NON FIBROGENIC NANOMATERIALS ON CELLULAR AND MOLECULAR RESPONSES IN THP-1 MACROPHAGE
Full text linkTraditional toxicological testing of nanomaterials is often low-throughput and time-consuming. Here, we aimed to develop an economically viable in vitro assay to prescreen nanomaterials with fibrogenic potential. To this end, we tested the generation of reactive oxygen species (ROS) as well as cytokines (pro- and anti-inflammatory) by macrophages following treatment with fibrogenic and non-fibrogenic nanomaterials. Both classes of nanomaterials induced ROS indistinguishably, suggesting that ROS production doesn’t predict nanomaterial fibrogenicity. We observed higher levels of IL1β and IL6 in macrophages treated with fibrogenic nanomaterials. Analysis of single-cell RNA-Sequencing data of fibrotic lungs from rodents and humans revealed overexpression of Brain Abundant membrane attached signal protein 1 and Plexin D1 in alveolar macrophages. Notably, BASP1 exhibited overexpression in macrophages when subjected to fibrogenic NPs, a response not observed with non-fibrogenic NPs. This high-throughput in-vitro assay leveraging the expression of BASP1 could potentially help in prescreening toxic nanomaterials with fibrogenic potential
IMPLEMENTING MACHINE LEARNING DISTRESS PREDICTION MODELS TO ASSESS THE EFFECT OF BINDER MODIFICATION ON ASPHALT PAVEMENT PERFORMANCE
No full textAsphalt pavement endures a decline owing to the repeated stress from vehicles, shifts in environmental conditions, and binder degradation, which causes distress. This study develops a comprehensive framework that employs machine learning to forecast pavement impairment and sustainable binder modification techniques to enhance both infrastructure durability and environmentally friendly practices. Long Term Pavement Performance (LTPP) data, encompassing FWD deflection indices, traffic load, environmental, and material factors, were employed to train Artificial Neural Network (ANN) models and various classification frameworks, which precisely predicted rut depth (R² \u3e 0.88), fatigue crack area (R² \u3e 0.9), and classified distress types with nearly 90% accuracy. Laboratory examination of PG 64-22, PG 70-22, and PG 76-22 binders augmented with reclaimed facemasks (FM) and sugarcane bagasse fiber (SBF) demonstrated substantial advancements in rutting and fatigue resistance. Integrating binder rheology with predictive modeling established a robust connection between laboratory advancements and their practical applications, providing a unified, data-driven approach for sustainable pavement design and management
POPULATION STRUCTURE ANALYSIS OF FOUR BASAL HIGHER-ATTINES USING BIOINFORMATIC APPROACHES
No full textMetapopulations within the family Formicidae are unique among other animals due to the large colonies they build, and the mating strategies required to fertilize a specialized reproductive caste that will often produce young for the life of the colony. One group of ants, the fungus-gardeners (tribe Attini). The population structure of North American non-leafcutting, fungus-gardening ants has been understudied, especially in the southwest of the United States. Additionally, not much is known about their dispersal biology, so the dynamics of dispersal of these species and how they affect population structure is likewise not well known. To shed light on the structure in these species, four species of non-leaf-cutting ants in the genus Trachymyrmex and Mycetomoellerius were obtained from two other studies for population analysis. The GBS reads of two species sampled in Texas and Oklahoma, Trachymyrmex septentrionalis and Mycetomoellerius turrifex, and two from Arizona, Trachymyrmex arizonensis and Trachymyrmex pomonae, were analyzed using a custom pipeline of bioinformatic software. This pipeline separated the GBS read into two paths: one calling SNPs from the nuclear genome, and the second extracting the mitochondrial genome with MitoFinder. The SNP markers were used to answer the question of population structure by using common tests of structure such as STRUCTURE, FST, AMOVA, and PCA. The extracted mitochondrial markers were used to compare with the nuclear SNPs to determine if there is a discordance between the two-genome suggesting one sex disperses more than the other. The results showed that there is significant structure in T. septentrionalis, M. turrifex, and T. arizonensis; however, no structure was found in T. pomonae. Further, comparison of the nuclear and mitochondrial genomes found evidence of male-biased dispersal within the same T. septentrionalis, M. turrifex, and T. arizonensis
USING IMPROVEMENT SCIENCE TO EXAMINE THE EFFECTS OF THE EUREKA MATH ON TEACHER PERCEPTIONS AND STUDENT ACHIEVEMENT IN GRADES 3–5
No full textThis Improvement Science Dissertation in Practice (ISDiP) investigates teacher perceptions of implementing Eureka Math TEKS Edition in grades three through five within a rural Central Texas district and the resulting impact on student achievement. The study employed a mixed methods design incorporating surveys, observations, and performance data collected over two academic years (2023-2025). Participants over the two years included 16 teachers, one instructional coach, and one principal at a campus of approximately 500 students in grades three through five. Teacher perceptions were analyzed to understand factors influencing curriculum fidelity, instructional practices, and classroom implementation. Concurrently, student mathematics performance was monitored to examine the relationship between teacher implementation and learning outcomes. Findings revealed that structured approaches to professional learning through weekly lesson internalization protocols improved teacher confidence and fidelity of curriculum implementation. Trends in student achievement showed growth in the percentage of students scoring “Meets Grade Level” on STAAR in each grade level. The implications for this study underscore the importance of sustained, job-embedded professional learning, collaborative PLC structures, and systematic feedback loops for enhancing high-quality instructional materials implementation. The results suggest that intentional coaching, data-informed reflection, and ongoing evaluation practices can improve teacher efficacy and promote measurable gains in mathematics achievement
FACTORS THAT INFLUENCE THE IMPLEMENTATION OF EFFECTIVE SUCCESSION PLANNING IN NONPROFIT ORGANIZATIONS
No full textThe purpose of this qualitative study was to explore factors influencing the implementation of effective succession planning (SP) in nonprofit organizations within a specific traditional industry—the electric utility industry. This study adopted the concept of SP as a process or system, and systems theory was selected to examine the SP process. Rothwell’s (2016) seven- pointed star model for systematic SP and management provided the conceptual framework for this study. This study used an inductive, qualitative approach to collect information from participants, with semi-structured, open-ended interviews. Face-to-face interviews were preferred, but some online interviews via Zoom were necessary. Interviews were conducted with 13 employees from various Texas nonprofit electric cooperatives. Following an analysis of the qualitative data, Rothwell’s (2016) seven-pointed star model was applied as a structured framework for organizational SP programs. The raw data were systematically organized into seven categories corresponding to each step of the SP process outlined by the model: (a) make the commitment, (b) assess present work/people requirements, (c) appraise individual performance, (d) assess future work/people requirements, (e) assess future individual potential, (f) close the development gap, and (g) evaluate the SP program. The findings revealed 14 total factors. The most significant outcome of the study was updating Rothwell’s model and making it more complete by explaining how each step can be more effective in the SP process. Recognizing and understanding these factors will be important not only for organizations but also for human resource development scholarship
Developing Clinical Judgment in Nursing Students Using Discourse with Meaningful Feedback, Explanatory Mixed Methods Design
No full textThe competency of clinical judgment in new nurses leading to safe practice has declined. The integrated clinical education theory posits discourse with meaningful feedback between nurse educators and pre-licensure students during clinical experiences enhances the development of clinical judgment. Quantitative evidence is needed for efficacy of clinical teaching using discourse with meaningful feedback. The hypothesis for this study was implementation of discourse with meaningful feedback between the academic nurse educator and the pre-licensure student nurse in direct patient care will improve the development of clinical judgment in students compared to the development of clinical judgment in students exposed to other clinical teaching practices. An explanatory sequential mixed methods design was used. The quantitative phase was a quasi-experimental pretest-posttest control group design using a convenience sample. The qualitative phase followed a narrative approach to provide deeper understanding of quantitative results. Academic nurse educators measured clinical judgment in students on the first and last day of clinical. Educators in the intervention group used discourse with meaningful feedback during the clinical course. The control group educators used their own teaching practices. A repeated measure analysis of covariance (RM-ANCOVA) was used to assess differences in clinical judgment development. Intervention educators participated in a focus group following collection of posttest scores. Results showed a significant mean difference in the Total LCJR scores with a large effect size with discourse and meaningful feedback. Key facets in the study intervention were consistent use of a clinical judgment model and providing space for students to think and reflect
INTEGRATING SERVICE-LEARNING PEDAGOGY IN ETHICS EDUCATION FOR HRD STUDENTS: A QUALITATIVE CASE STUDY
No full textThis dissertation explores the role of Service-Learning (S-L) as an instructional strategy for fostering ethical reasoning among Human Resource Development learners. Using a qualitative case study of a master’s-level Business Ethics course, I analyzed student reflections and course artifacts (Case A) alongside instructors’ survey responses (Case B) via iterative two-level coding (themes and nested subcodes). Findings converge on a consistent pathway: Authentic task → Collaboration/Engagement → Reflection → Ethical Reasoning → Workplace Transfer. This pathway is moderated by an equity lens and bounded by logistics and represented by the Service-Learning Pathway Model. For students (RQ1–RQ3), S-L deepened ethical reasoning and awareness, prompted explicit application of course frameworks to partner tasks, increased moral sensitivity, and decision quality as real-world dilemmas mirrored class content. Students perceived S-L effective because reflection and authentic engagement made ethics concrete and more easily digested. Instructors judged S-L most effective when authenticity and structured reflection operated together; “partnership practice” (clear scopes, regular check-ins, partner-facing deliverables) enabled smoother implementation and tangible community impact. The chief barriers were operational rather than conceptual: time/scheduling and workload pressures, uneven institutional support and professional development, and ongoing concerns regarding equitable access. Overall, results align with experiential and transformational learning traditions and map onto AHRD principles (integrity, welfare, respect, competence, professional responsibility, and personal responsibility). Practical implications include scaffolded course design (milestones, rubrics, reflection cycles), program supports (liaisons, templates), and equity-centered accommodations
IMPROVING THE SCORES OF THE MATHEMATICS TEXAS SUCCESS INITIATIVE ASSESSMENT 2.0 (TSIA2)
No full textThe purpose of this improvement science study was to increase mathematics readiness for the Texas Success Initiative Assessment 2.0 (TSIA2) among high school seniors in a South Texas border community. The study addressed persistently low TSIA2 mathematics passing rates, particularly among Hispanic and economically disadvantaged students. Guided by the Plan-Do-Study-Act (PDSA) framework, two improvement cycles were conducted using an embedded mixed-methods design. In Cycle 1, the Texas College Bridge (TCB), a self-paced platform built on NROC’s EdReady and the only Texas Education Agency–approved program for TSIA preparation, was implemented as the baseline intervention. Quantitative analysis indicated an 11.5% pass rate for the 2023–2024 senior cohort. Qualitative teacher survey data reported challenges with student engagement, instructional alignment, and fidelity of use. In Cycle 2, ChalkTalk, an interactive, teacher-mediated platform with embedded diagnostics, was introduced to address the challenges identified in Cycle 1. Quantitative results showed a statistically significant increase in pass rates to 33.2% for the 2024–2025 senior cohort, X²(1, N = 457) = 30.70, p \u3c .001, Cramer’s V = .26. Teacher feedback emphasized the role of daily integration, adaptive features, and structured facilitation in supporting mathematics readiness. Integration of quantitative and qualitative findings indicated that intervention effectiveness was closely associated with implementation fidelity and teacher mediation. Although findings are not generalizable to all Texas schools, they are transferable to schools along the U.S.–Mexico border that serve majority Hispanic, low-income, and newcomer populations. This study contributes to improvement science research by documenting how iterative testing and mixed-methods integration were applied to refine mathematics readiness interventions and address local TSIA2 outcomes
DNP Final Report: ENHANCING SURGICAL PREPAREDNESS: THE IMPACT OF PREOPERATIVE PHONE CALLS TO REDUCE CANCELLATIONS
No full textSame-day surgery cancellations disrupt operating room efficiency, delay timely care, and contribute to patient dissatisfaction. At a vascular surgery clinic in an urban academic medical center, a retrospective review revealed a same-day cancellation rate of 20.83%, many of which were preventable. The project focused on identifying strategies to reduce cancellations, with the literature highlighting interventions such as standardized preprocedural protocols, screening tools, reminder phone calls, patient education, and centralized surgical processes. Among these, preoperative phone calls emerged as the most consistently effective approach for reducing preventable cancellations. Guided by this evidence, the project implemented a structured phone call protocol for patient’s schedule for vascular surgery. Outcomes were evaluated by comparing cancellation rates before and after implementation. This intervention showed a positive impact on operating room utilization, patient satisfaction, and cost efficiency. Sustainability is supported by integrating standardized phone calls into routine preoperative workflows, staff training, and ongoing quality monitoring to maintain long-term improvements in efficiency and patient centered care
Exploring 2D Geometric Shape Classification Using AI-Driven Feature Tables in Mathematics
No full textThis study explored the effectiveness of an AI-integrated instructional task designed to enhance preservice teachers\u27 understanding of the features and hierarchical relationships of 2D geometric shapes. Originally developed and tested in online K-12 professional development settings, this intervention was adapted for in-person preservice teacher education context in this study. Data were collected from 17 preservice teachers through demographic surveys, pre- and posttests using the Van Hiele geometry framework, hierarchical diagram tasks, feature table creation during the intervention, and postintervention reflections. Findings indicated a statistically significant improvement in the accuracy and complexity of postintervention hierarchical diagrams, along with a descriptively higher mean score on the posttest of Van Hiele geometry content knowledge. Postintervention diagram classifications revealed a greater number of participants achieving the highest level of understanding of hierarchical relationships. Thematic analysis of participants\u27 reflections suggested an increased awareness of AI integration in teaching and a deeper conceptual understanding of classification and hierarchy. This study highlights a practical approach for future educators to incorporate AI concepts into mathematics instruction, supporting the connection between abstract geometric ideas and real world applications