Open Research Oklahoma (Oklahoma State Univ.)
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2024 Grain sorghum performance trials
No full textThe Oklahoma Cooperative Extension Service periodically issues revisions to its publications. The most current edition is made available. For access to an earlier edition, if available for this title, please contact the Oklahoma State University Library Archives by email at [email protected] or by phone at 405-744-6311
Emotional response to exercise
No full textIt is commonly said that "Adding exercise to your daily routine can positively affect your life." For example, exercise can reduce the risk of numerous health issues such as certain forms of cancer, high blood pressure, osteoporosis, diabetes, and obesity. However, in 2020, according to the Centers for Disease Control and Prevention, 24.2% of adults reached the guidelines for both muscle-strengthening activities and aerobic activities. If exercise is so important, why do over¾ of adults not meet the physical activity guidelines? According to Harvard Medical School, exercise is easy to avoid. To help determine a way to get people to exercise, we conducted a study that used a program called FaceReader to analyze an individual's facial expressions either while exercising or viewing exercise. By doing this, we can tell the emotion that they are feeling which can help medical professionals prescribe exercise specific to the individual's taste which would result in more positive long-term results. Our objective of this study is to more accurately determine how exercise can be used as a prescription to ensure that patients stick to their treatment.For the video portion of this study, participants watched a two-minute video with short clips of several exercises that increased in intensity as the video played, and, using FaceReader, a program that examines the micro-expressions in someone's face, we analyzed their facial expressions to see their varying emotions during the different exercises. For the exercise portion of this study, participants were given the option to choose from the treadmill, the exercise bike, or, if they had a device that could track their heart rate, the rowing machine. A camera was set up on the machine and FaceReader was used to analyze the emotional response to exercise. Exercise was conducted in two-minute intervals with 2 minutes of a warm-up, low-intensity, medium-intensity, high-intensity, and a cool down.The data collected suggested that a majority of the participants had a less negative response to the moderate-intensity workouts. During the video portion of the study, we found that 42.86% of women's peak interest of a certain intensity matched the intensity level of their preferred exercises, this was only true for 25% of men. From this we concluded that women's facial expressions matched their preferred form of exercise 18% more than men's. The workout portion of this study allowed us to analyze facial expressions while conducting exercises chosen by participants, according to the data collected we were able to better prescribe exercise regimens that are both physiologically and mentally beneficial for participants
Assessing AI integration in psychiatry research: Evaluating policies across leading journals
No full textIntroduction/Objectives: By refining systematic reviews, enhancing data analysis, and discovering clinical applications, AI is revolutionizing psychiatry and mental health research. However, the rise and mounting reliability of AI in research bodes significant concerns about transparency, ethics, and reproducibility. With the advent of generative AI, human prompting is no longer necessary in creating, reviewing, and drawing conclusions from data. This study examines how leading psychiatry and mental health journals address these challenges through their author instructions and editorial policies.Methods: A cross-sectional review of the top 100 peer-reviewed psychiatry and mental health journals, ranked by the 2023 SCImago SJR indicator, was conducted. Data from each journal's "Instructions for Authors" were extracted to assess AI-related policies, including authorship criteria, guidelines for reporting AI use, and the acceptance of AI-generated content (e.g., manuscript preparation and image generation). Correlational analyses were then performed to examine associations between these policies and journal characteristics.Results: 100 journals were assessed and 91% of them included AI use in their author instructions. Most prohibited AI authorship, but required disclosure of AI involvement in submissions. AI-generated content was allowed by 22% of journals, and 13% permitted AI-generated images. Journals with higher impact factors were more likely to have detailed AI policies. However, significant gaps in standardization and guidance persist.Conclusions: While many psychiatry and mental health journals recognize AI's growing role in research, few have implemented specific reporting guidelines (RGs) for its use. This glaring absence of standardized guidelines threatens both the transparency and the integrity of AI-driven research. Without comprehensive regulations, the risks of unethical practices and irreproducible results loom large. To safeguard ethical and high-quality research in this rapidly evolving era, the adoption of robust AI guidelines is urgently needed
Wellness together: Fostering a culture of wellness in hybrid work environments
No full textWellness Together is a session designed to foster a supportive and inclusive culture of wellness at work, whether employees are hybrid, in-person, or remote. The session will begin with a brief meditation and move into a discussion of ways in which we can foster a wellness culture with our colleagues, within our departments, and within our larger organizations. Participants will be encouraged to share their own ideas and wellness activities towards the end of the session and will walk away with lots of tips and tools to take back to their workplace communities.falseLibrar
Association of adolescent obesity and poor mental health: An assessment of the Youth Risk Behavior Surveillance System
No full textIntroduction: Adolescent obesity and mental health are two significant public health concerns. While studies suggest a relationship between the two, it remains unclear if this is due to elevated body mass index (BMI) or the psychosocial role of weight perception. Therefore, our study aims to assess the association between BMI and mental health, and the role of weight perception and demographic factors.Methods: Using data from the 2021 Youth Risk Behavior Surveillance System, we determined population estimates, demographics, and rates of BMI, weight perception, and mental health groups. We constructed logistic regression models to assess associations between BMI and mental health, weight perception and mental health, and the effects of sex, age, and ethnicity/race.Results: Adolescents with BMI’s classified as overweight or obese had higher rates of poor mental health (34.4% and 32.89%, respectively). The odds ratios also found that they were 1.43 (95% CI: 1.24-1.66) and 1.53 (95% CI: 1.31-1.79) times more likely to experience poor mental health, compared to those with a healthy weight. Adolescents who perceived themselves as obese, regardless of actual BMI, were significantly more likely to experience poor mental health (BMI 95th percentile AOR: 1.82, 95% CI: 1.47-2.27) while those with obesity who perceived themselves as healthy weight were significantly less likely to experience poor mental health (AOR: 0.65, 95% CI: 0.45-0.93), compared to those who had healthy weight and their perception was congruent.Conclusions: The association between BMI and poor mental health was significant, however, the association between weight perception and mental health was even stronger. Additionally, the additive effects of ethnicity/race, sex, and age highlight the need for personalized interventions in addressing adolescent mental health
Discovering matrix metallopeptidase 25 mechanisms against C. neoformans
No full textC. neoformans is an opportunistic fungal pathogen that attacks the immune system’s first line of defense, known as the innate immune system. It is one of the leading causes of death in patients with HIV and other immunocompromising diseases. Every year, an estimated 112,000 AIDS patients die from C. neoformans meningitis infections. Currently, there are only three drugs that can treat the infection: Amphotericin B, Fluconazole, and 5-Flucytosine. All are highly cytotoxic and need to be used in a three-drug regime for best results. Dendritic cells (DCs), are innate immune cells that can kill C. neoformans, and specifically, lysosomal contents are responsible. Previous studies in our lab have found that DC lysosomal components can be used to kill the pathogen. One of these components is matrix metallopeptidase 25 (MMP25); it has an MIC value of 0.78µg/ml and had the lowest cytotoxicity. It is able to kill the pathogen at 12.5µg/ml in vitro within 48 hours. In our study, we aim to discover the mechanisms used by MMP25 to kill C. neoformans. We performed minimum inhibitory testing and determined the minimum inhibitory concentration needed to kill C. neoformans at a concentration of 1x106 cryptococcal cells. We are also using cryptococcal mutant libraries to test how the compound affects strains with mutations in specific genes encoding kinases and phosphatases in the fungal cells. We expect our results to show that genes associated with the cell wall and membrane will be affected the most by MMP25. Our lab will continue to conduct experiments to determine the mechanisms of action and provide insight to future researchers.Microbiology and Molecular Genetic
Nutritional concerns for exercising horses
No full textThe Oklahoma Cooperative Extension Service periodically issues revisions to its publications. The most current edition is made available. For access to an earlier edition, if available for this title, please contact the Oklahoma State University Library Archives by email at [email protected] or by phone at 405-744-6311
Hidden statistics: Examining the obesity epidemic across American Indians and Alaska Natives using self-reported identity compared to imputed racial categories
No full textIntroduction/Objectives: To identify obesity rates among high school students who identify solely as non-Hispanic, American Indian/Alaska Native (AI/AN) in comparison to a disaggregated approach that includes all youth identifying as AI/AN—alone or in combination with other ethnoracial groups using the 2021 Youth Risk Behavior Surveillance System (YRBSS).Design Methods: We conducted a cross-sectional analysis of the Youth Risk Behavior Surveillance System (YRBSS) to assess obesity rates among high school students in the United States, self-reporting as AI/AN alone or in combination, compared to the imputed raceeth variable in YRBSS.Results: According to the imputed raceeth variable, 119 high school students were classified as AI/AN only, with 30 being classified as obese (30; 29.43%). In contrast, 664 participants identified as AI/AN alone or in combination with other racial groups, with 149 students classified as obese (149; 22.11%). The self-report data yielded a total of 128 AI/AN-only high school students, with 31 students being classified as obese (31; 25.7%). Obesity rates varied among the other AI/AN subgroups: AI/AN and White/Caucasian (15.23%), AI/AN and Black (21.72%), AI/AN alone with Hispanic/Latino ethnicity (23.52%), or AI/AN in combination with 1 or more race (24.25%).Discussion/Conclusion: Disaggregation of ethnic groups into smaller subgroups by allowing individuals to self-report ethnoracial status limits bias and provides a more accurate dataset. Accurate data representation is crucial for adequately reporting obesity and other metabolic disorders in conjunction with race/ethnicity in medicine. Classifying AI/AN populations as non-Hispanic/Latino single-race limits the population size and hinders the amount of public resources that are allocated towards AI/AN health and well-being
Endorsement of artificial intelligence guidelines across leading orthopaedic and sports medicine journals: A cross-sectional study
No full textBackground: Artificial intelligence (AI) is increasingly integrated into orthopaedic research and practice, offering transformative capabilities in diagnostics, treatment planning, and systematic data analysis. However, its adoption raises ethical, methodological, and policy challenges, particularly regarding transparency, authorship, and reproducibility.Objective: This study aims to evaluate the current policies of orthopaedic and sports medicine (OSM) journals concerning AI use in research, focusing on transparency requirements, ethical considerations, and reporting standards.Methods: We conducted a cross-sectional review of the “Instructions for Authors” from the top 100 orthopaedic and sports medicine journals ranked by the 2023 SCImago Journal Rank indicator. Data on AI-specific reporting guidelines, policies on AI-generated content, images, and authorship were extracted. Descriptive statistics and correlational analyses were performed to assess trends and associations.Results: Of the 100 journals analyzed, 78% referenced AI in their guidelines, primarily addressing authorship criteria and disclosure requirements. Only 2% endorsed AI-specific reporting guidelines, while 22% lacked any AI-related policy. Journals were more likely to permit AI for content generation (66%) than for image generation (43%). Transparency regarding AI use during manuscript preparation was required by 78% of journals, aligning with International Committee of Medical Journal Editors (ICMJE) recommendations.Conclusion: Despite widespread acknowledgment of AI’s role in research, the adoption of AI-specific reporting guidelines remains rare, underscoring a critical need for standardized policies. OSM journals should establish clear and comprehensive AI-related guidelines to ensure transparency, reproducibility, and ethical rigor in AI-integrated research
Clive Barker and the Monstrous Patriarch: Gendered power in the modern horror film
No full textGender in the horror film is a particularly nascent subject. Considering the function of horror as an exploration of fear in a mass-appeal medium, the subversions and adhesions in these films are uniquely illustrative of rigidity and performativity of gender. Thus, explicitly queer gender subversions in horror are a tumultuous subject, the function of fear allowing for queer and feminist self expression, but often making a villain of said queer or feminist actor. This project explores the gender expression of Clive Barker’s characters in Hellraiser (1987) and The Hellbound Heart (1986). His work, both heralded and maligned for its portrayal of non-mainstream experiences of pleasure, also portrays a non-mainstream experience of patriarchy. Here the reader sees something of a masculinized feminine, and the patriarchal experience of power. Through the characters of Frank, Julia, and Kirsty, Barker outlines a character type this project names as the “rogue patriarch,” and sets this powerful but irresponsible man against the actions against the women he wishes to control and possess.Lew Wentz FoundationSociolog