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Investigating the Role of ILV1 on Stress Response in Saccharomyces cerevisiae
Full text linkSaccharomyces cerevisiae, a model organism within molecular genetics, is also known for its broad role within baking, brewing, biofuel, and pharmaceutical industries. An unpublished study at The University of Tennessee at Chattanooga observed decreased cell viability in an ILV1 Knockout strain of S. cerevisiae when exposed to environmental stressors. A subsequent study at The University of Tennessee at Chattanooga found expression levels of the candidate genes to be significantly altered within the ILV1 Knockout strain relative to the BY4743 Wildtype strain, demonstrating the reality of a pleiotropic role within ILV1. Now, this study aims to further investigate this peculiar characteristic of ILV1 and role on stress response in S. cerevisiae by analyzing eight candidate genes involved in various metabolic processes. Two strains of S. cerevisiae, a BY4743 Wildtype and ILV1 Knockout, were subjected to identical stress conditions (salinity, osmotic, oxidative, and heat) and RNA was individually extracted from each trial of yeast cells, converted into cDNA, and analyzed through quantitative Real Time Polymerase Chain Reaction (qRT-PCR). The results of this study demonstrate minimal significant variation in candidate gene expression levels between the ILV1 Knockout and the BY4743 Wildtype strains. These results do not entirely support the pleiotropic nature of ILV1 and instead challenge the pleiotropic hypothesis of ILV1, warranting further investigation into the nature of ILV1
The effect of catalyst choice on biodiesel yield and quality using waste cooking oil as a feedstock
Full text linkBiodiesel is one current area of interest as a replacement for traditional diesel fuel. Benefits of biodiesel include that it can be derived from renewable resources, and it possesses properties similar to that of traditional diesel fuel. Biodiesel is often produced through a catalyzed transesterification process, which involves the use of a catalyst to aid in the conversion of triglycerides into alkyl esters. A method called the transesterification double step process (TDSP) was researched and used in this project due to the success seen using this method for the conversion of waste cooking oil to biodiesel. In this project, four basic catalysts were used at two different reaction temperatures to produce biodiesel, with waste cooking oil being used as the feedstock of interest. New cooking oil was also used to produce biodiesel under the same conditions used for the conversion of waste cooking oil. Three experiments were performed for each set of unique reaction conditions, for a total of 48 experiments conducted. Various testing methods were used for characterization of the final biodiesel products, with results allowing for the relationships between biodiesel quality and catalyst choice, temperature, and feedstock to be determined
Larry Sultan: Photography\u27s Role in Shaping Perceptions
Full text linkThis paper and presentation provide an overview and analysis of photographer, Larry Sultan. They explore his background and career providing insights into his practice and projects. The 2 main projects discussed are titled Pictures From Home and The Valley.https://scholar.utc.edu/global-contemporary-artists/1027/thumbnail.jp
Toward explainable machine learning methods for stroke patient outcomes in Tennessee
No full textStroke is one of the leading causes of long-term disability and death in the United States. Stroke patients often face severe health consequences, significantly impacting their lives and placing a substantial financial burden on their families and the wider healthcare system. Therefore, reliable predictions of various patient outcomes, such as early hospital readmission, length of stay (LOS) in the hospital, and risk of mortality, can help patients and healthcare providers in various aspects. Furthermore, successful modeling of such phenomena can help identify the influential factors affecting the patient outcomes, and, by this, improve the quality of care for patients. In this research, we have combined statistical analysis and machine learning (ML) algorithms to enhance the prediction of three patient outcomes — i.e. 30-day readmission, LOS, and mortality — for stroke patients in Tennessee. Since typically such a dataset is imbalanced, due to a small fraction of those events, various ML algorithms, suitable for imbalanced data, such as XGBoost, LightGBM, and CatBoost, were employed in this work. To further improve the performance of the models, various data-level approaches were used to overcome the imbalanced nature of the data. These methods include cluster centroids, NearMiss, and Instant Hardness Threshold. It was shown that such a combination of data modification, especially with under-sampling methods, and suitable ML algorithms can lead to high model performance, measured in terms of Recall and other metrics. Furthermore, based on the features of the data available in our work, using SHAP explainable ML method, the influential factors affecting these outcomes were identified; higher age and mostly the vital signs at the time of admission play an important role in LOS. For 30-day readmission peripheral artery disease, sleep disorders, as well as prescribed medicine such as anticoagulant and antibiotic agents were among the most influential features. For mortality, static patient health conditions were the most influential factors. A simple Graphical User Interface (GUI) was also developed for one of the LOS outcomes, which can be extended to other outcomes, to demonstrate the capability of this work for practical applications
Assessing a police department\u27s reliance on the National Integrated Ballistic Information Network (NIBIN) to solve shooting cases: Can NIBIN increase shooting clearance rates?
Full text linkOften, police response to shooting incidents lacks the investigative leads needed for successful follow-up and prosecution. This may result from a lack of surveillance video or witnesses investigators rely on to solve such cases quickly. To overcome these obstacles, law enforcement has turned to forensic science to provide police with the information and clues to be successful. Ballistic evidence is one such tool that can provide police with detailed information regarding linked shooting cases that can help identify possible new witnesses, surveillance videos, potential suspects and criminal groups, motive, and account for the number of firearms used at the scene. This is all made possible with access to the National Integrated Ballistic Information Network (NIBIN) database, which is overseen by the Bureau of Alcohol, Tobacco, Firearms, and Explosives (ATF). Though such a database is useful, law enforcement agencies that do not have access must rely on consolidated state or local labs for ballistic evidence processing. This results in delayed lab reports by weeks or even months, failing to provide the timely information investigators need. To overcome such delays, police departments across the United States have begun purchasing the technology to enter and compare ballistic evidence in the NIBIN database in hopes of identifying other linked shooting cases. Based on previous research, this may not always mean the agency will obtain timely information if they fail to have a plan to push NIBIN leads generated, when there is a match, out to investigators whose job is to follow up on such information. This study examined one department’s use of the NIBIN database for leads and how investigators used them as part of the investigative process. Using a mixed-methods research approach, data was obtained from the agency and investigators to determine if the agency saw a reduction in fatal and nonfatal shootings and improved clearance rates after becoming a NIBIN site compared to previous periods when they did not have direct access to the NIBIN database. Furthermore, information obtained from investigators sought to understand their perceptions of using the NIBIN database as a tool in the investigative process
The Silent Strain: Workplace Hazing as a Hidden Organizational Stressor.
Full text linkBetween 25% and 75% of working Americans report experiencing hazing at some point in their careers (Thomas et al., 2021). This wide range is partly due to underreporting, often driven by fears of retaliation, termination, or stalled career progression (Thomas et al., 2021; Tolfer et al., 2016). Despite its frequency, research has historically focused on non-workplace contexts, leaving a critical gap in understanding its organizational impact.Although definitions of hazing vary across disciplines, four consistent characteristics emerge: it is temporary, unidirectional, coercive, and coalitional (Thomas et al., 2021). These elements form the conceptual foundation for this study. In this study hazing is defined as the short term socialization process in which new employees within an organization are faced with high induction costs that are unrelated to their new roles and act as a barrier to their recognition as a legitimate organizational member (Mawritz et al, 2020; Cimino et al., 2019; and Cimino, 2011) To address this gap, this study investigates workplace hazing through the Job Demands-Resources (JD-R) model, positioning hazing as a unique organizational stressor. We hypothesize that hazing exposure will significantly predict psychological strain. Additionally, we hypothesize that employee personal resources and individual characteristics will moderate this relationship, weakening the link between hazing and strain. A cross-sectional survey design will be used to examine this relationship. Participants will be recruited via Prolific, with inclusion criteria requiring individuals to be over 18 and employed full-time. The Job Demands-Resources (JD-R) model (Demerouti et al., 2001) will guide the theoretical framework, and the Workplace Hazing Scale (WHS) (Mawritz et al., 2020) will be used to measure hazing experiences. Understanding workplace hazing is a critical step toward identifying its harmful effects on employees, informing organizational strategies to reduce its occurrence, and exploring its long-term implications for both individuals and institutions. These findings will contribute to the research by clarifying how personal resources buffer the stressor-strain relationship of hazing and employee psychological well being. These findings will allow for future research that investigates organizational interventions and the effectiveness in mitigating potential negative effects of hazing
Machine Learning for Reparameterization of Molecular Dynamics Water Model to Accurately Capture Electrical and Thermal Behavior
Full text linkWater is an important component in many scientific and engineering applications, but precisely modeling its thermal, electrical, and physical properties remains difficult in molecular dynamics (MD) simulations. The widely used TIP4P water model, while useful in many circumstances, requires parameterization to better agreement with experimental results under various conditions. In this study, interpretable machine learning techniques were used to systematically tune the TIP4P model parameters where mathematical relationship between features and targets has been developed using deep symbolic optimization (DSO), resulting in a more accurate depiction of water behavior in molecular dynamics simulation. A data-driven approach was created to optimize critical model parameters while maintaining physical interpretability by combining molecular dynamics simulations and neural network-based modeling. A new optimized TIP4P water model has been developed to reproduce the thermal conductivity, diffusion coefficient, density and dielectric constant with accurate dipole calculations
Pretty Dangerous: The Romanticization of Grooming in Pretty Little Liars
No full textSexual grooming is the deliberate manipulation of a minor by an adult to reduce their sensitivity to sexual contact, with the specific aim of perpetrating child or adolescent sexual abuse (Winters, 2022). Prevalence rates for sexual grooming vary; however, a recent study by Winters and Jeglic (2022) found that approximately 24% of their sample of undergraduate student sample reported experiencing sexual grooming as a minor. These predatory behaviors were prominently featured between lead adolescent and adult characters throughout the popular television show, Pretty Little Liars. This raises concerns about how such behaviors might be perceived when portrayed in popular media targeted at teens and young adults. When Pretty Little Liars first aired, it was ranked number one in TV programming in Female Teens in 2010 (ABC Family, 2010). This strong viewership highlights how the show was effectively marketed towards a young audience despite its portrayal of problematic – and illegal - romantic relationships between adults and teens. The goal of our study was to examine how sexual grooming behaviors were portrayed in both the original series and its reboot, exploring changes in how sexual grooming behaviors were framed over time.
We conducted a content analysis study to examine grooming behavior in Pretty Little Liars, the original series, as well as Pretty Little Liars: Original Sin, the reboot. For our study, we developed a codebook to track the manifest and latent content of each episode, with two raters independently scoring each episode for the following content: victim and perpetrator demographics, grooming behaviors, how grooming behaviors were portrayed in the episode, and stage of grooming. There are five basic stages of the Sexual Grooming Model (SGM) that were coded: 1) victim selection, 2) gaining access and isolating a child, 3) trust development, 4) desensitization to sexual content and physical contact, and 5) maintenance following the abuse (Winters et al., 2022). This was done for the first and second seasons of the original series and the first season of the reboot. For season one of the original series, we found that 90.9% of grooming behavior was portrayed as romantic, as for season two it decreased to 84%. Grooming behaviors were normalized and often seen as positive or romantic throughout the original series. The reboot had a dramatic change; there was only 10% that believed grooming behavior was being portrayed as romantic. The writers for the reboot reframed the portrayal of grooming in a negative light, often having the adolescents confront their perpetrators for their behaviors. Additional manifest and latent content will be presented and described.
Understanding how grooming behaviors are depicted in media is essential for public awareness; this study contributes to a deeper understanding of grooming behaviors portrayed in the media. Teachers and educators can use these findings to help students recognize and critique problematic behaviors in television shows and movies, fostering critical discussions about the differences between reality and media portrayals of grooming and other sensitive topics. Ultimately, our findings could spark essential conversations about how the media portrays sensitive topics and encourage future creators to handle these topics more thoughtfully.
References
ABC Family. (2010, June 16). Pretty Little Liars retains 100 percent of premiere audience. BroadwayWorld. https://www.broadwayworld.com/article/ABC-Familys-PRETTY-LITTLE-LIARS-Retains-100-Percent-Of-Premiere-Audience-20100616
Gámez-Guadix, M., Mateos-Pérez, E., Alcázar, M. A., Martínez-Bacaicoa, J., & Wachs, S. (2023). Stability of the online grooming victimization of minors: Prevalence and association with shame, guilt, and mental health outcomes over one year. Journal of adolescence, 95(8), 1715–1724. https://doi.org/10.1002/jad.12240
Winters, G. M., Kaylor, L. E., & Jeglic, E. L. (2022). Toward a universal definition of child sexual grooming. Deviant Behavior, 43(8), 926–938. https://doi.org/10.1080/01639625.2021.1941427
Winters, G. M., & Jeglic, E. L. (2022). The Sexual Grooming Scale–Victim Version: The development and pilot testing of a measure to assess the nature and extent of child sexual grooming. Victims & Offenders, 17(6), 919-940. https://doi.org/10.1080/15564886.2021.197499
Course registration and student success: A mixed-methods study of closed classes, student enrollment, and graduation
Full text linkThe purpose of this study was to explore if closed classes related to credit hour enrollment and graduation for undergraduate students at a mid-size public institution. A mixed-methods study was guided by four research questions: • Research Question 1 (RQ1): How, if at all, do closed class encounters predict credit hour enrollment while controlling for student demographics and academic preparation for first-time, full-time students at a public university? • Research Question 2 (RQ2): How, if at all, do closed class encounters predict time to graduation while controlling for student demographics and academic preparation for first-time, full-time students at a public university? • Research Question 3 (RQ3): How do undergraduate students perceive their experiences with course registration and closed classes as it relates to overall enrollment? • Research Question 4 (RQ4): How do undergraduate students perceive their experiences with course registration and closed classes as it relates to their graduation? The quantitative portion of the study analyzed historical student demographic and registration data for 6,418 students from three freshmen cohorts. Hierarchical regression analyses produced a model with student demographic variables and closed class encounters that predicted between 2% to 6% of variance in enrollment hours and 11% of variance in graduation. Closed class encounters had limited predictive power in the regression models, adding between .2% to .4% of explanation in all regression models. The qualitative elements of the study used survey data to gather student perspectives regarding registration and closed classes, and 189 students participated in the survey. Their responses were analyzed and categorized through a thematic coding process, with emergent themes being reported. The findings suggested the majority of students encountered closed classes, but they used many different strategies to navigate enrollment and graduation planning, including enrolling in alternative courses over preferred classes. The enrollment changes had differing impacts on students, including conflicts with other personal, work, and academic obligations. The importance of faculty and academic advisors in supporting student success was also surfaced. Potential recommendations include revised class scheduling and registration practices, proactive registration support for students, and emphasizing the value of the courses to students
Density Functional Theory modeling of heterogeneous reactions of hydrocarbon intermediates on silicon carbide: surrogate kinetic model development for Chemical Vapor Infiltration
Full text linkThis study investigates the decomposition mechanism of Methyl trichlorosilane during Silicon Carbide deposition from Chemical Vapor Infiltration to produce SiC-based ceramic matrix composites. High-performance applications require SiC-based materials; however, producing them presents difficulties due to limited deposition rates, high energy consumption, and uneven coatings. To overcome these challenges, the mechanism of SiC formation needed to be understood completely. Modeling surface reactions of decomposition on the substrate using Density Functional Theory is the key point of current research. By focusing on the adsorption, reaction, and desorption mechanisms that control SiC development, our method incorporates quantum mechanical models. Using Transition State Theory, the study examines reaction routes and identifies key intermediates, including methyl and other hydrocarbon species. The findings expand our knowledge of the rate-limiting steps in MTS breakdown and offer guidance for refining CVI/CVD procedures, which could increase material quality and deposition efficiency for cutting-edge engineering applications