Open Research Oklahoma (Oklahoma State Univ.)
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Implementation, costs and benefits of patch-burn grazing
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
Impact of the first year of medical school on anthropometric measurements
No full textBackground: First-year medical students are placed under a great amount of stress upon starting their medical education. Chronic stress has negative impacts on the mental and physical condition of individuals. Persistent stress can lead to lead increases in various physiological measurements including blood pressure, BMI, resting heart rate, respiration rate, and pulse oximetry. In addition, medical students also have stressors outside the walls of medical school that cause increases in these measurements. The purpose of this study was to track first-year medical students' BMI, blood pressure, heart rate, respiratory rate, and pulse oximetry over the first semester of medical school.Methods: Medical students were solicited to participate. At the beginning of the semester data collection, participants were asked to complete an online survey for demographic information, and then their blood pressure, heart rate, pulse oximetry, respiratory rate, height in cm, and weight in kg were collected. At the end of the semester, participants returned to have their anthropometric measurements retaken. BMI was calculated using height and weight. Data were uploaded into SPSS for analysis. Means, standard deviations, and frequencies were calculated for variables. Paired samples t-tests were conducted to determine differences over the course of the semester. T-tests were also used to determine differences in variables at each individual data collection point for parametric data and Mann-Whitney analyses were run on non-parametric data.Results: A convenience sample of 28 medical students (male = 16, female = 12, age = 24.86 + 4.16) participated in the study. The paired samples t-test demonstrated statistical significance for BMI (t=- 2.362, p<0.05) and weight (t=-2.364, p<0.05) with students demonstrating an increase in both. T-tests analyzing differences in variables at the initial data collection demonstrated differences in sex assigned at birth and systolic blood pressure (t=2.39, P=0.02), gender identity and systolic blood pressure (t=2.39, p=0.02), and children in the house and BMI (t= -2.10, p<0.05). This demonstrated that males (both sex assigned at birth and gender identity) had higher levels of systolic blood pressure while those individuals who had children living in the home had a higher BMI. Additionally, within the first data collection, a statistical difference was determined by a Mann Whitney between Being Affiliated with a Native Tribe and diastolic blood pressure (Z= -2.26, p=0.02), with non-natives having higher diastolic blood pressure. T-tests for the second data collection demonstrated differences between children in the house and BMI (t=-2.12, P=0.04) and children in the house systolic blood pressure (t= -2.41, p=0.02), demonstrating those with children had higher values.Conclusions: Attending medical school, although a privilege, ultimately can have negative impacts on students. This study was able to show that the overall rigor and lifestyle of a medical student along with outside factors can negatively impact one's health in terms of weight, BMI, and blood pressure. With that, schools should be encouraged to offer health and wellness programs that students can utilize to improve their overall health. These resources can include but are not limited to, on sight gym, fitness classes, counseling, nutrition classes, mental health resources, and support for habits outside of medical school. You need a statement here on what medical schools can do to mitigate the impact of stress on these measures
Cultivating conversation: Exploring mentorship perspectives and experiences of agricultural communications alumnae
No full textThe growth of the agricultural communications field, combined with the predicted increase in young professionals pursuing careers within agriculture, creates a need to further explore how to support these individuals at all stages in their careers. Although the benefits and importance of mentorship continue to be a research topic and additional mentoring programs and resources are being developed, there is currently a lack of literature examining mentorship in relation to female professionals and alumnae of agricultural communications programs nationwide.This study sought to explore and describe mentorship perspectives and experiences of alumnae from Oklahoma State University’s agricultural communications program. It was framed by social capital theory and data collection consisted of two phases. First, a prescreening Qualtrics form was distributed via Facebook and respondents (n = 85) provided demographic data and answered questions related to their involvement and experiences in agricultural communications as well as the extent to which they received mentorship during their time at OSU and within their professional environments. Most respondents were females between the ages of 21 to 30 who earned a bachelor’s degree in agricultural communications from OSU. A non-probabilistic, purposive sampling approach was then used to select participants eligible to participate in a follow-up interview (n = 71). Ten females were interviewed to examine their pursuit of mentorship, the nature of their mentoring relationships and experiences, the outcomes they received from being mentored, and how they see mentorship being discussed and facilitated within the profession of agricultural communications and the agricultural industry.Findings imply agricultural communications alumnae are being mentored primarily through informal and natural relationships. The structure of their company, organization, or entity either positively or negatively affected their ability to receive mentorship. Agricultural communications alumnae are more likely to seek mentors they perceive as similar and who possess certain qualities. To spark further discussion and ensure more professionals within their field and the agricultural industry have access to mentorship in the future, agricultural communications alumnae recognized a need to continue showcasing the range of opportunities available
Impacts of physiological and socioeconomic parameters on the likelihood of heart disease using a statistical model
No full textBackground: Heart disease has many predisposing factors. Genetics, lifestyle, socio-economic status have all been shown to play a role. The National Health and Nutrition Examination Survey (NHANES) combines data from interviews and physical examinations from approximately 5000 people each year in the United States. It is an excellent source for acquiring nationally representative data on known cardiovascular risk factors. By its nature, survey data, such as from NHANES, frequently has missing entries. Multiple imputation provides a statistically robust way to handle missingness. Rather than discarding partially complete entries in a listwise manner, multiple imputation uses a Bayesian model to produce multiple datasets that include uncertainty on the missing data. The datasets are then recombined to provide a complete dataset with more accurate standard errors than would be obtained by other imputation methods.Methods: We used the R statistical programming language to download and process anonymized NHANES data from the 2017-2018 data acquisition cycle. Several parameters known to have a bearing on cardiac health were analyzed. Multiple imputation was used to handle missingness in the data. Survey weighting was also used to account for under/over-represented demographic groups in the data. Logistic regression was carried out the parameters using the presence of heart disease as the dependent variable.Results: Preliminary results in this study show that the strongest predictors for heart disease were having a first-degree relative suffering from a myocardial infarction before the age of 50, followed by higher Hgb A1c values. The greatest “protectors” against heart disease were having a greater number of family members in the house, followed by more weekend nightly sleep hours.Conclusion: It is no surprise that family history of early myocardial infarction and high A1c values are strong risk factors to acquiring heart disease. However, it may be less obvious that sleep acquired during the weekend and household family size would have much of a bearing. It could be the case that weekend sleep compensates for any sleep deficit acquired during the workweek and thereby reduces physiologic stress from sleep deprivation. Regarding household family size, perhaps having a greater number of dependents fosters more responsible lifestyle behaviors
Maternal AFB1 and AFB5 positively regulates seed dormancy in Arabidopsis
No full textPlant Biology, Ecology and Evolutio
Constructing a values-based foundation for metadata justice work
No full textIn this group activity, participants will have the chance to explore the values and principles that might inform their approach to metadata justice work, cross-pollinate ideas with others hoping to engage in this work, as well as work through key components of a guiding document. Participants will walk away with a framework for building their own values-based foundation for metadata justice work, based on an example of a thoroughly constructed guiding document. Whether you're interested in pursuing this work as an individual, within a group, or more broadly at your institution, this session will help ensure your future work is approached in a values-based and sustainable way.
This session will encourage and empower others from across the information professions to not only engage in the work of metadata justice, but to do so in a sustainable way that aligns with their personal, group, and/or institutional values.Librar
Evaluating scenario generation techniques for stochastic UC: A comparative study of stochastic models and variational autoencoders
No full textThis thesis presents a comprehensive study on developing and applying sophisticated scenario generation techniques for Integrated Energy Systems (IES), focusing on the challenges posed by uncertainties such as renewable energy intermittency, load forecasting ambiguities, and complex multi-energy flow system interactions. Traditional deterministic methods fail to address these uncertainties, highlighting the necessity for advanced stochastic programming and robust scenario generation models. Our research emphasizes evaluating and enhancing ARIMA (Autoregressive Integrated Moving Average) and Variational Autoencoder (VAE) models in scenario generation, aiming to optimize power system operations and facilitate a shift towards a more renewable-centric electricity grid.We assess the effectiveness of the ARIMA model in capturing spatiotemporal demand variability and propose the VAE model as a superior alternative for improving scenario accuracy and reliability. This study also explores the limitations of ARIMA models, including the need for stationarity and the complexities of handling non-Gaussian distributions and spatiotemporal correlations. In contrast, the VAE model shows enhanced capability in generating compact scenarios that more closely align with the statistical properties of actual data. The methodology’s efficacy is demonstrated through a comparative analysis of 100 generated scenarios for multiple bus locations, highlighting the VAE’s superiority in replicating the cross-correlation and spatial relationships inherent in stochastic processes. Our findings suggest that VAE models offer a significant improvement over conventional CRA-ARIMA methods in scenario generation, particularly in addressing the stochastic nature of energy demand and the challenges of spatial correlation. This research contributes to the field of energy systems planning by providing insights into the use of advanced generative machine learning techniques for robust scenario generation, ultimately aiming to reduce operational costs and improve the market integration of renewable energies. The work presented underscores the importance of addressing uncertainties in energy systems and paves the way for future research integrating AI and ML approaches in dynamic stochastic optimization for sustainable and efficient energy systems
Second-generation Hmong Americans' self-confidence and self-perceived competency communicating in English in a variety of settings
No full textAfter resettling to the United States in the late 1970s, Hmong refugees have integrated into American society by learning a second language, English. In recent years, many researchers have focused their studies on the declination of the Hmong language and the impact this decline has on the Hmong American community language (Thao 2020; Xiong-Lor, 2015; Yang Xiong, 2019). On the other hand, there has been less research on the acquisition of English among Hmong Americans and its broader impact on the Hmong community. This research project seeks to explore the experiences of second-generation Hmong Americans after learning English and whether being English language learners has affected their self-confidence and self-perceived competency in communicating in English. Data was collected using a mixed-method that consisted of a language background questionnaire and a 20-minute semi-structured Zoom interview. Questions for the interview portion included a mix of questions from the Bilingual Language Profile, the Language Experience and Proficiency Questionnaire (LEAP-Q), and the Quantifying Bilingual EXperience (Q-BEx) questionnaire. Data collected from 15 participants found that higher self-confidence and self-perceived competency reflected participants' willingness to communicate in different settings. Participants reported higher self-competency and confidence in English compared to lower self-competency and confidence in Hmong, and higher willingness to communicate in English in formal and informal settings compared to lower willingness to communicate in Hmong in formal and informal settings. Thematic analysis for the interviews also found that second-generation Hmong Americans have a positive outlook as the Hmong community shifts from being a Hmong-dominated speaking community to becoming a bilingual Hmong and English-dominated speaking community
Colorimetry coupled with a surface plasmon microarray chip: Ultra-low analyte detection in biofluids
No full textColorimetric sensors are widely used for visually detecting target analytes in solutions and biofluids, but achieving ultra-low detection capabilities in food analysis, environmental samples, and molecular diagnostics is challenging. One way to improve the detection limit of colorimetric assays is to combine them with sensitive signal-amplifying techniques. In this study, we coupled a purpald-based formaldehyde color sensor with a surface plasmon microarray chip to achieve ultra-low formaldehyde detection in biofluids. Our results demonstrate that the coupled surface plasmon detection method is capable of detecting concentrations as low as 86 parts per trillion in solution, which is 140-fold lower than the detection limit of the spectrophotometric method at 12 parts per billion. We achieved a spiked HCHO recovery rate of 92% from a diluted serum, which was comparable to the results obtained from the same concentration of formaldehyde spiked in a buffer solution. However, the recovery of spiked HCHO in diluted urine was only 67%, indicating that the type of clinical sample matrix significantly affects the selective detection and quantification of formaldehyde in complex biological samples. Our approach of coupling a sensitive surface transduction principle with less sensitive colorimetric reactions can be extended to other analytes of interest, such as proteins, DNA, RNA, and small molecules, for broader applications in various fields, including medical diagnostics, food safety, and environmental monitoring
Trends in public concern surrounding 2022 infant formula shortage
No full textBackground: The beginning of 2022 was marked by Food and Drug Administration’s investigation of Abbott Nutrition, a producer of approximately one-third of the United States’ baby formula (Cunningham, n.d.). After report of a third and fourth Cronobacter case of death potentially associated with their products, the FDA recommended that Abbott Nutrition voluntarily recall their product, leading to a voluntary cease in production. Russia invasion of Ukraine additionally contributed to supply chain uncertainty as Ukraine served as a major exporter of infant formula ingredients including sunflower oil (Timeline of Infant Formula Related Activities, n.d.). In order to better understand public concern, we examined Google Trends data surrounding the 2022 United States infant formula shortage. The results of this study have important indications concerning infant health and existing disparities for low-income families.Methods: Our team began by researching the dates, news articles, and AAP/FDA recommendations and findings surrounding the infant formula shortage. Using Google Trends to collect data, we entered various key words found from our research that included “baby formula near me”, “baby formula shortage”, “homemade formula”, and “baby formula recipe” into Google Trends. The parameters we set were to show searches done in the United States and occurring over the previous 12 months from January 2022 to January 2023. After each trend was found, we compiled key word searches onto one line graph to compare the timelines of the trend data. We then determined the times of peak interest and compared them to the AAP/FDA recommendations and findings.Results: According to the Google Trends data, all searches showed a peak interest from May 8th to May 21st. “Baby formula shortage” had the highest search interest. “Homemade formula”, “baby formula recipe”, and “baby formula near me” at their peak interest had 12%, 11%, and 7% of the searches that “baby formula shortage” had, respectively. For all four searches, the interest dropped drastically from May 21st to June 4th. “Baby formula shortage” searches decreased to 12% of its peak interest going into June and was at 4% of its peak interest going into July.Conclusions: Our results suggest that the highest search volumes corresponded to the dates when the infant formula supply shortage was at its lowest, spiking to over 74% nationwide at the end of May 2022 (Laura Stilwell and Lisa A. Gennetian, 2022)