Open Research Oklahoma (ORO) serves as the home for Oklahoma State University's open-access intellectual output. It includes digital dissertations, faculty publications, OSU Extension publications, undergraduate research, open educational resources, and more. Email openresearch@okstate.edu to see how your Oklahoma-based institution can join.
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Publication Open Access Effect of supplemental protein degradability on nitrogen metabolism and performance of beef steers grazing dormant tallgrass prairie(2025-07)Protein is often the first nutrient that is limited in diets of growing calves on low quality forage diets. Therefore, nitrogen utilization is vital to optimize nutrition of beef calves grazing dormant native range. The objective of this study was to determine the effects of supplements containing low or high proportions of rumen degradable protein (RDP) on nitrogen utilization while cattle were grazing dormant tallgrass prairie. Twenty-three Angus steers (328 ± 27.8 kg) were allowed unlimited access to a prairie grass pasture (7.7 ha)for 85 days. Steers were randomly assigned to either a high RDP (71.5 % RDP), or low RDP (41.1% RDP) supplement at 6.36 kg/week prorated for feeding three times weekly. Supplements consisted of mainly soybean meal for the high RDP supplement and wheat middlings, and corn gluten meal for the low RDP supplements. Steers were gathered at 0800 each Tuesday, Thursday, and Saturday, placed into individual feeding stations and provided their respective supplement before returning to pasture. Blood and fecal samples were collected on days 0, 42, 63, and 84. Fecal samples were serially collected on day 46 to 50 and day 67 to 71 in 4-hour increments to measure passage rate and fecal output using titanium dioxide as a pulse dosed external marker. Forage masticate samples were collected on d 50 and day 71 following rumen evacuation of a ruminally cannulated steer grazing with steers on trial. Body weights (BW) were collected full with no previous shrink on days 0, 1, 29, 42, 63, 84, and 85 for performance measurements. Data were analyzed as a completely random design experiment using the mixed models procedure of SAS (SAS Inst. Inc., Cary NC) with treatment as the fixed effect and steer the experimental unit. The low RDP treatment group was 27 kg heavier (P = 0.03) than the high RDP treatment group steers at the conclusion of the study. Steers fed a low RDP supplement while grazing dormant range recycled adequate protein to meet supplementation requirements and improve performance compared with the high RDP supplement. While showing no differences in nitrogen excretion between the two treatment groupsPublication Open Access Age-heterogenous marriages and cognitive aging(2025-07)Cognitive health is often explored within social relationships, where spousal relationships and the bond between the partners have been widely documented in previous literature. However, there has not been enough literature that explores whether age-heterogeneous marriages influence individuals' cognitive health differently for both men and women. Comparing age heterogeneous marriages with age homogeneous marriages, this study explores if men and women have different cognitive health outcomes. Data has been used from the National Social Life, Health, and Aging Project (NSHAP), a longitudinal, population-based study of older adults in the United States, conducted in 2010-2011 (wave 2). The results from the interaction models show a gendered pattern. For older men in age heterogenous marriages, a significant negative association between spousal age gap and cognitive functioning was found as they aged compared to men from age homogenous marriages. However, for younger women in age-heterogeneous marriages, the association is steady across their age compared to women in same age marriages. These findings highlight the importance of exploring the influence of spousal age gap on cognitive health for future longitudinal studies in order to further our understanding of marriage, age gap, gender, and cognitive health.Publication Open Access Derivation and characterization of hybrid bovine x bison embryonic stem cell lines(2025-07)Embryonic stem cells (ESCs) are pluripotent cells derived from the inner cell mass (ICM) of blastocysts, capable of differentiating into all three germ layers and maintained indefinitely in culture under proper conditions. ESCs are widely used in developmental biology and regenerative medicine due to their self-renewal and differentiation potential. In livestock, they offer powerful applications in genetic improvement and reproductive technologies. Hybrid ESCs provide a unique in vitro platform to investigate parent-of-origin effects, imprinting regulation, and gene expression dynamics across species, advancing our understanding of how genetic and epigenetic mechanisms operate in interspecies systems. This project aims to establish and validate ESC lines derived from bovine x bison hybrid embryos, which represents a novel and valuable model for exploring both beefalo research and the interplay of genetic and epigenetic inheritance. Validation of ESC lines involves multiple complementary approaches to confirm their pluripotency and genomic integrity. In this study, ESC lines were assessed through immunofluorescence staining for key pluripotency markers (OCT4 and SOX2), embryoid body (EB) formation to evaluate in vitro differentiation capacity and karyotyping to ensure chromosomal stability. Additionally, a teratoma assay will be conducted in collaboration with Dr. Amanda Patterson (University of Missouri) as part of future in vivo validation. These assays are crucial to demonstrate the quality and utility of derived cell lines for future applications. To our knowledge, this study reports the first successful derivation and preliminary validation of ESC lines from bovine x bison hybrid embryos. Establishing culture conditions and validation protocols for these cells will not only advance hybrid embryo research but also support long-term goals in genetic preservation, reproductive biology, and the study of epigenetic mechanisms in livestock species.Publication Open Access Assessment of environmental impacts from septic tank irrigation systems in Oklahoma(2025-08)Septic tank irrigation systems are widely used for decentralized wastewater management. While effective in treatment, their environmental impacts, particularly on soil microbial processes and resulting greenhouse gas (GHG) emissions are not well understood. This study examined soil microbial contributions to GHG fluxes from irrigated and non-irrigated plots using soil flux measurements in the field and microbial enzyme assays in the lab. Results showed negative fluxes of methane (CH₄) and nitrous oxide (N₂O) in control plots, indicating net uptake, while carbon dioxide (CO₂) exhibited positive fluxes. Statistically significant differences in CH₄ and N₂O fluxes were observed between irrigated and non-irrigated zones, with cumulative mean fluxes higher for all three GHG in irrigated soils. Soil and water quality assessments revealed elevated pH, hardness, and nutrient loads, particularly ammonium and orthophosphate, exceeding EPA-recommended thresholds. In-vitro targeted enzymatic assays for mcrA and nosZ indicated elevated potential for CH₄ and N₂O production in the treated zone. Amplicon sequencing of microbial communities showed distinct differences in taxonomic composition and functional gene diversity, including a heterogeneous distribution of mcrA in irrigated soils. Quantitative PCR revealed significantly higher abundance of nosZ operational taxonomic unit 1, which encodes nitrous oxide reductase, in the irrigated plot. Overall, the study highlights that septic tank irrigation systems can alter microbial activity and increase trace gas emissions, suggesting potential adverse effects on ecosystem function and the need for revised management strategies.Publication Open Access Artificial intelligence approaches for multi-object pavement condition and safety evaluation(2025-07)Ensuring the safety and durability of roadway infrastructure relies heavily on accurate pavement condition assessment and effective crash prediction. Conventional techniques for detecting pavement distress—such as manual inspections and contact-based friction tests—are resource-intensive, time-consuming, and susceptible to subjectivity. Additionally, traditional Safety Performance Functions (SPFs), which are generally derived from basic roadway and traffic characteristics, often fail to account for the influence of surface conditions on crash likelihood. Recent advancements in computer vision and deep learning have enabled the automation of pavement assessment through image-based analysis. Notably, the integration of multimodal data sources, including 2D grayscale images and 3D depth data, has shown promise in enhancing the detection of subtle surface irregularities under various environmental conditions. Deep learning (DL) architectures, including convolutional neural networks (CNNs) and mixed transformers (MiT), provide robust capabilities for pixel-level segmentation and feature extraction from complex pavement surfaces. Two different deep learning models have been developed in this study for multimodal multi-object pavement feature detection, including a U-Net-based model and a SegFormer-B5 model with MiT backbones. Both models demonstrate effective capabilities for identifying various pavement features, achieving overall accuracy of 97.10% and 98.24% for the U-Net-based model and the SegFormer-B5 model, respectively. Simultaneously, crash modeling can benefit from incorporating detailed pavement surface condition indicators, especially texture parameters that affect tire–road interaction and vehicle control. This study introduces a comprehensive framework that leverages high-resolution 3D imaging and DL to automate pavement condition assessment and safety evaluation at highway speeds. A mobile sensing platform equipped with a 0.1-mm resolution 3D laser safety sensor and a 1-mm resolution 3D condition survey system was deployed to collect both high-resolution data at low speed and lower-resolution data during highway-speed operation. A super-resolution model, PT-SRGAN based on a recursive generative adversarial network (GANs), was employed to reconstruct 0.1-mm texture surface data from 1-mm highway-speed imagery, enabling both macro- and micro-texture features to be captured for safety evaluation. Subsequently, texture metrics at both levels and seven 3D parameters (provide details) were calculated. Data from 27 representative pavement sections, along with historical crash data, were used to develop texture enhanced SPFs based on the reconstructed texture datasets. This study combines deep learning and high-resolution texture analysis to improve pavement evaluation and crash prediction. The U-Net and SegFormer-B5 models precisely identify surface features, while the PT-SRGAN model reconstructs detailed textures from highway-speed data. These metrics were then incorporated into the Safety Performance Functions, providing a scalable, data-driven framework for enhanced roadway safety management.
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