Cross-Facility Reliable Deep Learning Based Beef Marbling Assessment Via Unsupervised Domain Adaptation Regression

Beef is one of the most important sources of animal protein and plays a crucial role in daily diets to support muscle growth, energy production and overall health. Historically, each beef carcass was evaluated by 3-5 skilled United States Department of Agriculture (USDA) meat graders, which are still considered as the gold standard for grading. Since 2009, electronic imaging grading systems have been approved by the USDA and have been gradually adopted in the grading facilities. Accurate and consistent beef grading plays a critical role in maintaining the quality, market value, and consumer trust in U.S. beef products. The USDA beef grading system, primarily based on fat marbling characteristics, has long served as the industry standard for classifying beef quality. However, traditional grading methods relying on human visual inspection have been prone to variability among graders and inconsistencies across processing plants. To address these challenges, this study developed a deep learning based solution to predict USDA beef grades by analyzing fat marbling patterns in carcass images collected from multiple processing facilities. The dataset provided by the USDA, which includes expert-graded images of beef carcasses from seven processing plants, provides numerical scores on a range of 0-1500, which was used to train convolutional neural networks (CNNs) such as ResNet, VGG, and Inception. Additionally, this study proposes a unified unsupervised domain adaptation regression method, which was employed to enhance the models’ ability to generalize across different environments. The dataset reflected real-world variability, such as differences in lighting, equipment, and operational practices, ensuring the models could adapt to diverse plant conditions. By correlating image-based features with USDA grades, the models successfully identified intricate marbling patterns and reduced variability compared to human graders. Statistical analyses showed that the deep learning based approach provided more uniform results across plants, addressing the inconsistencies of traditional methods. In comparison with commonly utilized generic CNN deep learning models, the newly proposed unsupervised domain adaptation model achieves a significantly better marbling score prediction performance in different processing facilities. This study not only automated the beef grading process but also introduced a scalable and efficient solution for the industry. The integration of domain adaptation ensured robust performance in varying plant environments, making the system practical for widespread adoption. Beyond reducing labor costs and improving grading accuracy, this research contributed to a fairer system for producers by further standardizing grading criteria across facilities. By bridging advanced technology with traditional practices, this study provides a framework for policy makers to regulate and approve deep learning based beef grading protocol into commonplace beef grading practices.https://scholarworks.uark.edu/hnrcsturpc25/1041/thumbnail.jp

Unbridled Healing: Identifying the Barriers of Equine-Assisted Therapy

Despite the documented benefits of equine-assisted therapy (EAT) for mental health conditions, this complementary approach remains underutilized. This qualitative study investigated barriers preventing wider EAT implementation by examining challenges faced by clients, facilities, and volunteers. Data collected through open-ended surveys from 33 participants and the researcher\u27s autoethnographic observations revealed eight common barriers: Accessibility (48 mentions), Money (43), Time (30), Awareness (23), Physical Factors (13), Relationships (13), Risk (13), and Weather (7). Clients identified financial constraints as their primary barrier, particularly insurance limitations. Facilitators struggled most with accessibility challenges, including lack of resources, trained staff, suitable horses, and appropriate facilities. Volunteers faced significant time constraints and communication difficulties. Novel findings included communication issues for volunteers, relationship barriers affecting retention, physical limitations to participation, and weather-related constraints affecting all stakeholder groups. The study\u27s findings support advocacy efforts to address these barriers and potentially expand EAT access to more diverse populations.https://scholarworks.uark.edu/hnrcsturpc25/1046/thumbnail.jp

Who’s in control? Perceived stress and emotional regulation partially mediate the impact of Adverse Childhood Experiences (ACEs) on mental health and life satisfaction.

Adverse childhood experiences (ACEs) are potentially traumatic events that occur during the first 18 years of life and have a strong potential to negatively impact health and well-being in adulthood. The exploration of this connection is important because health and lifestyle behaviors have been linked to the leading causes of death in the United States. ACEs have been found to alter brain development and have been associated with mental health issues and lower life satisfaction, but information about the pathway between these connections is scarce. The present study aimed to investigate if inadequate emotional regulation and higher levels of perceived stress, stemming from ACEs, may explain the relationship between ACEs and mental health issues as well as the relationship between ACEs and lower life satisfaction. Evidence of partial mediation was found in that emotional regulation and perceived stress partially mediate the relationship between ACEs and mental health, and ACEs and life satisfaction

Comparative Analysis of the Community Reinvestment Act and the B Impact Assessment

This thesis presents a comparative analysis of the Community Reinvestment (CRA) and the B Corp Certification through the B-Impact Assessment (BIA). The CRA was enacted in 1977 and is a mandatory regulatory measure aimed at addressing discriminatory lending practices and encouraging equitable credit distribution in underserved communities. In contrast, the BIA is a voluntary assessment developed by the nonprofit B Lab to evaluate a company’s social, environmental and governance practices, which lead to B Corp Certification. This analysis explores the historical background, mission, methodology, success metrics and financial costs associated with each framework. Ultimately, this thesis highlights how both models, despite their differences in origin and application, contribute meaningfully to fostering responsible corporate behavior and community development

Gene Mutation and Chemotherapy Response in Azoxymethane Induced Colorectal Tumors

Colorectal cancer research is a field of increasing importance as disease prevalence continues to increase over time. In looking at the origin of the disease, a driver gene known as p53 is of particular interest for its role in the stimulation of the mutated Wnt pathway. Developing as a missense-type mutation, this gene that ordinarily functions as a tumor suppressor is investigated as a gene of interest for tumor development. In this study, a mouse model was used to examine the development of colorectal cancer with a total of n=34 samples, including n=9 tumors and n=25 colons. The concentration of the expressed p53 gene was investigated for its relationship to mouse tissue types and its response to the chemotherapies 5-fluorouracil, cisplatin, and saline. In order to investigate these relationships, an enzyme-linked immunosorbent assay (ELISA) was performed on mouse colon and tumor tissue and measured for absorbance. An ANOVA and t-test evaluation for statistical significance showed no significance in mouse tissue type between tumors and colons in the expression of p53. The ANOVA performed for the tumor samples, however, found a statistically significant difference between chemotherapy treatment types and the expression of p53 in the tissue. In comparison the control group of saline chemotherapy, cisplatin was revealed to show the highest amount of p53 expression in the tumor tissue. These relationships should be further investigated in the future with larger treatment groups and similar laboratory conditions. Other potential driver genes can also be investigated for their connection to colorectal cancer development

Can Spatiotemporal Gait Parameters Predict State Anxiety?

Background: Anxiety is one of the most experienced mental health disorders. Recent research has suggested a relationship between gait parameters and anxiety, however the disparity in measurement methodologies of both anxiety and gait makes it difficult to draw broad conclusions. Better understanding the relationship between anxiety and gait mechanics creates opportunity for a more holistic understanding of the interaction between mental health, functional capacity, and quality of life. Purpose: The purpose of this study is to evaluate if spatiotemporal gait parameters measured on a pressure-sensing walkway are related to anxiety.https://scholarworks.uark.edu/coesym25/1011/thumbnail.jp

Widening the Net: How CogAT and ACT Aspire Compare in Gifted Identification

Previous research indicates that many academically accomplished students from disadvantaged backgrounds are not identified for gifted and talented (G/T) programs. This study examines a large sample of students (N = 10,508), many of whom took both the Cognitive Abilities Test (CogAT) and the ACT Aspire test. We examined test similarities and differences with an eye to widen the net for G/T identification in practice. This study demonstrates that the ACT Aspire and CogAT have a significant correlation in our sample of r = .59. However, the correlation varies across cohorts noticeably from r = .72 to r = .49. This variation in correlations and inconsistency in the predictive nature of diversity of both tests across cohorts suggest greater caution in the interchangeable use of ACT Aspire and CogAT as indicators. Instead, this suggests the use of more than one test as part of the package for identification

Do Perceptions Affect Outcomes in Patients with Metabolic-Associated Steatotic Liver Disease? A Clinical Inquiry.

Abstract BACKGROUND & AIMS: Metabolic-associated steatotic liver disease is a chronic condition that often coexists with metabolic disorders and impacts a significant portion of the global population. The literature indicates that patients’ perceptions are linked to outcomes; however, there is a gap in the literature examining the influence of these perceptions on the outcomes of metabolic-associated steatotic liver disease. This clinical inquiry investigated this relationship. METHODS: Data collection occurred between January and February 2025 in a primary care setting, utilizing a 5-point Likert scale of patients’ perceptions of care, disease severity, and self-efficacy. Patients were identified through a retrospective chart review and were approached for the perceptions survey. The participating Federally Qualified Health Center has participated in several clinical trials addressing metabolic-associated steatotic liver disease. Participants were separated depending on whether they had previously participated in one of the clinical trials or not. Perception scores were compared to hemoglobin A1C, aspartate transferase, alanine transaminase, and body mass index. Descriptive statistics were conducted to conclude how patients’ perceptions influence outcomes. RESULTS: Seven participants completed the survey. Mean perception scores for those previously enrolled in a clinical trial compared to those not enrolled include the following: perceptions of care (5.0 vs. 4.5), perceptions of disease severity (2.3 vs. 3.8), and perceptions of self-efficacy (5.0 vs. 4.25). A positive correlation was found in hemoglobin A1C (Durbin-Watson = 1) with no correlation in body mass index (Durbin-Watson = 2.23) for those enrolled in a clinical trial. A negative correlation was found in hemoglobin A1C (Durbin-Watson = 2.781) and body mass index (Durbin-Watson = 2.95) for those not enrolled in a clinical trial. CONCLUSION: Positive patient perceptions may improve outcomes in those at risk for or diagnosed with metabolic dysfunction-associated steatotic liver disease. Strategies should focus on enhancing patient self-motivation and coping skills to mitigate health risks associated with this disease