Scholarworks

ScholarWorks is an open access repository for the capture of the intellectual work of Montana State University (MSU) in support of its teaching, research and service missions. MSU ScholarWorks is a central point of discovery for accessing, collecting, sharing, preserving, and distributing knowledge to the Montana State University community and the world.

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Dietary Intervention with Cottonseed and Olive Oil Differentially Affect the Circulating Lipidome and Immunoregulatory Compounds—A Randomized Clinical Trial
(MDPI AG, 2025) Cooper, Gwendolyn; Bhattarai, Prabina; Sather, Brett; Bailey, Marguerite; Chamberlin, Morgan; Miles, Mary; Bothner, Brian
Background/Objectives: Cottonseed oil (CSO) is a dietary oil especially high in the n-6 polyunsaturated fatty acid (PUFA), linoleic acid (FA 18:2), which is a precursor for many pro-inflammatory eicosanoids. Curiously, diets rich in CSO have not been shown to cause increases in inflammatory markers or other negative health outcomes in humans. To rigorously test this, we have compared the health impact of a diet rich in CSO to olive oil (OO), which is generally considered to be a healthy oil. Methods: Specifically, this study examines circulating metabolite and lipid profiles during a 4-week dietary intervention with CSO or OO on 47 healthy adults. Untargeted metabolomics, targeted bulk lipidomics, and targeted lipid mediator analyses were conducted on fasting plasma samples taken pre- and post-dietary intervention. Results: A high degree of similarity was observed in the global metabolomic profiles of CSO and OO participants, indicating that CSO may elicit metabolic responses comparable to those of OO, potentially supporting similar effects on metabolic health markers. Targeted bulk lipidomics revealed changes in acyl chain composition reflective of the dominant fatty acid consumed—either 18:2 in CSO or 18:1 in OO. Immunoregulatory lipids 15-deoxy-PGJ2 and prostaglandin F2 alpha (PGF2a) were both higher in abundance in high-CSO diets, demonstrating differential effects of CSO and OO on immunoregulatory compounds. A correlative network analysis revealed two clusters arising from the dietary intervention as drivers of the dietary and immune responses. Conclusions: This study shows that CSO and OO differentially impact the circulating lipidome and immunoregulatory compounds in healthy adults.
Single-event upset simulation and detection in configuration memory
(Frontiers Media SA, 2025-07) Austin, Hezekiah; Major, Chris; Barney, Colter; Williams, Justin; Becker, Zachary; Smith, Michael C.; LaMeres, Brock J.
Single-event upsets (SEUs) from radiation strikes in configuration memory are potentially catastrophic due to their widespread effects. For field-programmable gate arrays (FPGAs), faults in configuration memory propagate into the implemented logic design at the hardware interconnection level, leading to unpredictable results. Two payloads consisting of a pair of quad modular redundant (QMR) FPGA-based processor were deployed to the International Space Station (ISS) for 13 months. During operation, these payloads experienced a number of faults from radiation, including one payload that experienced a rare multi-core fault. Investigation suggested that the multi-core fault was the result of a single-event effect (SEE), either directly in a voter on the logic design or as an SEE in the FPGA configuration memory changing the implemented logic. An injection procedure for the FPGA’s configuration memory was developed to simulate radiation strikes and test fault detection. The injection procedure was paired with the QMR processor. This provided a full configuration memory testing environment, where the implemented logic design was capable of detecting faults propagating from the FPGA’s configuration memory. Injection throughout the configuration memory was used to create a map of particularly vulnerable locations in configuration memory and the implemented logic design. Testing with injected faults produced similar results to the multi-core fault observed in orbit on the payload. The testing procedure provides a comprehensive testing strategy, which pairs systematic injection in configuration memory with a logic design capable of detecting the induced errors to localize the propagating fault in the design.
A Multi-State Evaluation of Agricultural Safety Learning through Secondary Students' Supervised Agricultural Experience Journal Entries
(American Society of Agricultural and Biological Engineers, 2025-01) Smalley, Scott; Perry, Dustin K.; Lawver, Rebecca G.; Pate, Michael; Hanagriff, Roger D.; Ewell, Clay
The Supervised Agricultural Experience Safety Award program was launched with Montana, Utah, and South Dakota agriculture teachers. A combination of video conferencing and in-person training workshops were offered to school-based agriculture teachers in Montana, Utah, and South Dakota. Zoom webinar workshops were held with teachers during the COVID-19 pandemic. The five annual training topics included: Year 1) Tractor/Equipment Roll-over Hazards, Year 2) ATV/UTV Operation Hazards, Year 3) Tractor/Equipment Operation Hazards, Year 4) PTO/Entanglement Hazards, and Year 5) Agricultural Machinery Transport Hazards Associated with use on Public Roadways. To assess the influence of agricultural machinery safety training, students‘ journal reflections were collected through the Agricultural Experience Tracker. Students‘ production-based agricultural experiences were coded by USDA National Agricultural Statistics Service (NASS) Commodity Codes, describing students‘ safety reporting using Supervised Agricultural Experience (SAE) journal entries, and quantifying teachers‘ workshop participation. A total of 2,257 journal entries were reviewed from Montana, Utah, and South Dakota. A total of 760 unique student journal entries were associated with a teacher participating in the training program. Most student journal entries focused on machinery operations. A total of 49 journal entries specifically reported safety. A total of 203 journal entries recorded the use of tractors. A total of 160 agricultural production work entries (38.8%, n = 412) noted crop production as the agricultural production work experience. The results provide recommendations for developing an application model for translation using an FFA award structure.
A roadmap for equitable reuse of public microbiome data
(Springer Science and Business Media LLC, 2025-09) Hug, Laura A.; Hatzenpichler, Roland; Moraru, Cristina; Soares, André R.; Meyer, Folker; Heyder, Anke; The Data Reuse Consortium; Probst, Alexander J.
Science benefits from rapid open data sharing, but current guidelines for data reuse were established two decades ago, when databases were several million times smaller than they are today. These guidelines are largely unfamiliar to the scientific community, and, owing to the rapid increase in biological data generated in the past decade, they are also outdated. As a result, there is a lack of community standards suited to the current landscape and inconsistent implementation of data sharing policies across institutions. Here we discuss current sequence data sharing policies and their benefits and drawbacks, and present a roadmap to establish guidelines for equitable sequence data reuse, developed in consultation with a data consortium of 167 microbiome scientists. We propose the use of a Data Reuse Information (DRI) tag for public sequence data, which will be associated with at least one Open Researcher and Contributor ID (ORCID) account. The machine-readable DRI tag indicates that the data creators prefer to be contacted before data reuse, and simultaneously provides data consumers with a mechanism to get in touch with the data creators. The DRI aims to facilitate and foster collaborations, and serve as a guideline that can be expanded to other data types.
Improving genomic prediction for plant disease using environmental covariates
(Springer Science and Business Media LLC, 2025-08) Brault, Charlotte; Conley, Emily; Read, Andrew C.; Glover, Karl D.; Cook, Jason P.; Gill, Harsimardeep S.; Fiedler, Jason D.; Anderson, James A.
Background. Fusarium Head Blight (FHB) is a destructive fungal disease affecting wheat and barley, leading to significant yield losses and reduced grain quality. Susceptibility to FHB is influenced by genetic factors, environmental conditions, and genotype-by-environment interactions (GxE), making it challenging to predict disease resistance across diverse environments. This study investigates GxE in a long-term spring wheat multi-environment uniform nursery trial focusing on the evaluation of resistant lines in northern US breeding programs. Results. Traditionally, GxE has been analyzed as a reaction norm over an environment index. Here, we computed the environment index as a linear combination of environmental covariables specific to each environment, and we derived an environment relationship matrix. Three methods were compared, all aimed at predicting untested genotypes in untested environments: the widely used Finlay-Wilkinson regression (FW), the joint-genomic regression analysis (JGRA) method, and mixed models incorporating an environmental covariates matrix. These were benchmarked against a baseline genomic selection model (GS) without environmental covariates. Predictive abilities were assessed within and across environments. The results revealed that the JGRA marker effect method was more accurate than GS in within- and across-environment predictions, although the differences were small. The predictive ability slightly decreased when the target environment was less related to the training environments. Mixed models performed similarly to JGRA within-environment, but JGRA outperformed the other methods for across-environment predictions. Additionally, JGRA identified significant genetic markers associated with baseline FHB resistance and environmental sensitivity. Furthermore, location-specific genomic estimated breeding values were predicted, providing insights into genotype stability across varying locations. Conclusion. These findings highlight the value of incorporating environmental covariates to increase predictive ability and improve the selection of resistant genotypes for diverse, untested environments. By leveraging this approach, breeders can effectively exploit GxE interactions to improve disease management at no additional cost.