University of Tennessee Institute of Agriculture
University of Tennessee, Knoxville: TraceNot a member yet
47048 research outputs found
Sort by
Use of Plant Growing Promoting Bacteria as an alternative fertilizer for Bermudagrass
Get PDFGreater demands for beef has been observed over the years; however, concerns related to its production efficiency and sustainability have been raised. To achieve desired production levels with less harm to the environment, newer technologies have been studied and adopted around the world. For the southeastern United States, bermudagrass (Cynodon dactylon (L.) Pers) is an important and predominant warm-season forage, highly responsive to nitrogen (N) fertilization. Nevertheless, higher N use has raised concerns regarding N leaching and greenhouse gas emissions, contaminating groundwater and damaging the atmosphere. Alternative strategies to enhance plant growth have been developed, such as various plant growth-promoting bacteria (PGPB), which have been adopted due to their beneficial characteristics associated with forage plants. Among PGPBs, Azospirillum spp., Bacillus spp., and Paenibacillus spp., have demonstrated beneficial interaction modulating phytohormones, fixing N, and solubilizing nutrients in the soil with the Poaceae family. Thus, this study aimed to evaluate the inclusion of Azospirillum Ab-V5 and Ab-V6, Bacillus subtilis, Paenibacillus riograndensis, and Methylobacterium symbioticum either in field or greenhouse conditions with or without varying N management strategies. Findings from this research may contribute to climate-smart agricultural practices, improving forage systems’ sustainability while mitigating environmental impacts. The integration of PGPB in bermudagrass forage systems could serve as an alternative approach to increasing the system’s efficiency, reducing inputs, and supporting long-term productivity in forage-based livestock systems
Heme Acquisition in Campylobacter jejuni using the Chu Heme Transport System and its Role in Colonization and Pathogenesis.
Get PDFThis paper investigates the Campylobacter jejuni Chu heme transport system and its impact on pathogenesis. Campylobacter jejuni is one of the most common causes of bacterial gastroenteritis. The ability of C. jejuni to take up iron is an important factor in survival, particularly under low-iron conditions. For this purpose, the chu operon encoding chuA, chuB, chuC, and chuD was investigated by generating specific knockout mutants. Growth experiments tested these mutant strains’ capabilities in vitro under iron-restricted conditions with heme supplementation. Furthermore, the C57BL6 IL-10-/- knockout mice were employed to assess the role of these genes in vivo. The ChuA heme receptor was identified as essential for growth under iron-limited conditions, while other genes in the operon demonstrated compensatory mechanisms. In vivo, all chu knockout strains exhibited significantly reduced fitness compared to wild-type C. jejuni, indicating that disrupted heme uptake adversely affects microbial survival. Furthermore, organ-specific differences in chu gene expression suggest that host factors, including diet, microbiome composition, and immune interactions, play critical roles in the fitness of chu knockout mutant strains
Thermal conditions in residential tents in Knoxville, Tennessee, during hot and cold nights
No full textUnhoused individuals are exposed to hazardous weather due to their lack of shelter and resources. Extreme temperatures are one hazard experienced frequently by those sleeping outside, including those who sleep in tents. Given the potential impacts of hot and cold nights on human health, and the scarcity of research on overnight temperature exposure of the unhoused population, the study aimed to fill gaps and contribute to our understanding of how unhoused individuals are exposed to heat and cold over the course of a day in harsh conditions. This study uses data gathered during two periods in Knoxville, Tennessee, including once in the summer and once in the winter, with 10 study participants in each sample period. Participants who were living in a tent throughout the study period, which was one week long, were invited to participate. iButton Hygrochons were placed inside tents to observe temperature and humidity levels. After the data were collected, they were analyzed to discern patterns of temperature exposure within residential tents. The findings include elevated in-tent temperatures during summer nights, potentially hindering the body’s ability to recover from daytime heat exposure. On the other hand, colder nighttime temperatures during the winter led to dangerous cold exposure, though tents provided some warmth. The findings demonstrated how tents may not aid, and may in fact worsen, exposure to dangerous thermal conditions, highlighting the vulnerability of unhoused individuals using them as overnight shelters
Promoting Well-Being Among Intercollegiate Student-Athletes: The Impact of Collegiate Sport Business Practices
No full textThe purpose of this experimental study was to identify if the implementation of an educational gratitude workshop could increase intercollegiate student-athletes’ self-reported perceptions of well-being and reduce stress, considering sport administrators must address rising mental health concerns among athletes. Participants of various sport teams were randomly assigned to an experimental or control group to assess differences among self-reported measures of state gratitude, psychological distress, life satisfaction, athlete burnout, and perceived available support in sport. Statistical significance was found in the data analysis which demonstrated that the workshop was meaningful, and it significantly influenced the experimental group’s perceived available support in sport, emotionally. The data suggests that student-athletes perceive there is not enough emotional support available to them in their sport, and workshops could help. Implementing educational workshops could help collegiate sport administrators meet the needs of student-athletes’ well-being as a cost-effective solution
Distracted Driving Detection through the Analysis of Real-time Driver, Vehicle, and Roadway Volatilities
No full textCommon examples of distracted driving are secondary activities, such as cell phone use, eating, talking with passengers, adjusting vehicle infotainment controls, and looking at roadside elements or advertisement boards. Distracted driving usually gives rise to driving instability which leads to increased crash risk and higher crash frequency. Early detection of driver distraction is critical to prevent traffic crashes by providing feedback and warning messages to drivers and the surrounding vehicles. This study harnesses real-time multidimensional data collected through sensors that examine the variations in driver biometrics, vehicle kinematics, and roadway surroundings in different driving scenarios conducted on a Multimodal Virtual Reality Simulator. The driving behaviors of the study participants were examined under various visual detection response tasks of increasing complexity. The study classifies driving behaviors as normal and distracted on a 5-level ordinal scale by estimating a Panel Ordered Logit Model, Random Forest, and Artificial Neural Network, using real-time volatilities in driver biometric signals, vehicle speed and acceleration, and roadway surroundings. The study results reveal that the driver gaze and the coefficients of variation in vehicle speed, driver eye movements, vehicular distances from the lane centreline, and the following vehicle significantly impact distracted driving. The study’s findings align with the principles of the safe systems approach by emphasizing the development of proactive safety measures in the form of feedback and warning the driver and surrounding vehicles of a potential distracted driving event, helping to foster safer user behavior and vehicles
Novel Regulators of Adipogenesis
No full textIn adipose tissue research, adipogenesis is commonly used to refer to the process of adipocyte formation, spanning from stem cell commitment to the development of mature, functional adipocytes. Although, this term should encompass a wide range of processes beyond commitment and differentiation, to also include other stages of adipose tissue development such as hypertrophy, hyperplasia, angiogenesis, macrophage infiltration, polarization, etc... collectively, referred to herein as the adipogenic cycle. The term “differentiation”, conversely, should only be used to refer to the process by which committed stem cells progress through distinct phases of subsequent differentiation. Recognizing this distinction is essential for accurately interpreting research findings on the mechanisms and stages of adipose tissue development and function. Furthermore, adipogenesis is regulated by a wide range of both positive and negative factors, two of which–PKM2 and IRX3–have been recently characterized by the Bettaieb lab. Briefly, our results demonstrated that the deficiency of PKM2, a key rate-limiting glycolytic enzyme, enhances the development and maintenance of brown adipose tissue (BAT), as evidenced by increased markers of brown fat differentiation and function. In our second investigation, IRX3 overexpression in white adipocyte precursor cells led to the increased expression of various differentiation markers as well as brown-fat specific proteins. The latter suggests that IRX3 plays a critical role in regulating adipocyte fate and ultimately, function. Collectively, our findings indicate that both PKM2 and IRX3 play integral roles in regulating the complex process of adipogenesis
Integration of Slurm and Kubernetes for University Research Computing Workloads
No full textSlurm is the de facto standard for resource and workload management for a variety of academic and research applications on campus clusters in many university environments. Kubernetes, originally designed and developed by Google, is a cloud-native open-source software system for resource and workload management of containerized application workloads and is considered the de facto standard for Artificial Intelligence applications and orchestrated workflows. Conventional and accelerator compute resources and storage are widely available in university campus high-performance computing clusters, along with Slurm to manage and schedule the jobs on those campus clusters. With increasing interest among university researchers in developing Artificial Intelligence applications using campus cluster resources and the prevalence of Artificial Intelligence and Machine Learning applications being developed in Docker containers and deployed on the widely adopted Kubernetes platform, there is a need to investigate solutions for the integration of Slurm and Kubernetes onto university clusters to facilitate Artificial Intelligence workloads. An optimal solution would prevent the need for bifurcating campus cluster resources into separately managed Slurm and Kubernetes clusters or for needing to purchase and create entirely new clusters just for Kubernetes operation. This thesis describes the researcher\u27s Artificial Intelligence computational needs, the differences between compute clusters managed by Slurm and Kubernetes, surveys the available Slurm and Kubernetes integration solutions, and describes the experiences of implementation and use of one of the integration solutions at the University of Tennessee, Knoxville, on the university cluster
AMINO ACID-REGULATED ELECTROLYTES FOR RECHARGEABLE ZINC BATTERIES
No full textAqueous zinc batteries (AZBs) are promising candidates for energy storage batteries due to their safety, low cost, and environmentally friendly nature. However, obstacles such as dendritic zinc growth, hydrogen evolution reaction (HER), electrochemical irreversibility at lower temperature, etc. have partially limited their application. In this study, a perchlorate-based aqueous electrolyte (2.0 molar (M) Zinc Perchlorate Zn(ClO₄)₂) was investigated systematically, where it was modified using bio-inspired amino acid additives, L-histidine (0.02 M) and L-glutamic acid (0.05 M). Through electrochemical techniques, including cyclic voltammetry (CV), linear sweep voltammetry (LSV), Tafel analysis and plating/stripping tests, it was found that the L-amino acids additives modulate Zn²⁺ solvation, stabilize HER and allow the possibility for highly reversible plating/stripping with Coulombic efficiencies near 98%. Full-cell Zn∥α-MnO₂ tests also revealed that histidine helped long-term capacity retention (\u3e80 mAh g⁻¹ after 100 cycles) while glutamic acid stabilized the kinetics. Both amino acid additives displayed near 100% Coulombic efficiency at sub-zero temperatures (−20 °C). The combined results reveal that coupling chaotropic perchlorate electrolytes with amino acid additives delivers high reversibility, cycle stability, and reliable subzero operation, underscoring their potential for grid-scale storage and cold-climate applications
NOVEL NUTRITIONAL STRATEGIES TO AMELIORATE EFFECTS OF HEAT STRESS IN LACTATING DAIRY COWS
No full textEffects of heat stress on dairy production have been a subject of investigation for decades and yet an economically feasible and sustainable solution to mitigate these effects remains elusive. The collective goal of this dissertation was to test novel dietary strategies to ameliorate the effects of heat stress on dairy production. To achieve this goal, two animal experiments with heat stressed-lactating dairy cows were conducted. Experiment 1 was designed to evaluate the effects of dietary supplementation with resveratrol (0.5 g/cow/d), on productivity and welfare of heat stressed lactating dairy cows. The study utilized 48 lactating Holstein cows in first, second and third or more lactations, under natural summer heat stress conditions. Results revealed that resveratrol reduced noon rectal temperature and increased respiratory rate in cows. Resveratrol supplementation improved milk yield by 0.7 kg, lactose yield by 60 g and protein yield by 40 g in heat stressed lactating cows. With a significant treatment × parity interaction, resveratrol improved body weight in first lactation cows. Analysis of antioxidant markers in circulation revealed that resveratrol improved total antioxidant capacity in heat stressed parity 2 cows. To conclude, resveratrol seems to be an effective compound in reducing heat induced productivity loss and welfare issues in dairy cows. Experiment 2 was designed to test the dietary modification of changing molasses inclusion (10.7% DM), on heat stress associated inflammation and productivity loss in dairy cows. Study utilized 24 multiparous Holstein cows in a 2×2 factorial arrangement of environment (thermoneutral (TN) or heat stress (HS)) and diet (corn grain or molasses). Study was conducted in three periods: P1: adaptation to diet for 15 d, P2: adaptation to facility for 6 d, P3: treatment period for 6 d (induced heat stress in climate controlled facility for HS cows and pair feeding for TN cows). Our results revealed that dietary modification with molasses did not mitigate production loss in heat stressed dairy cows. Molasses maintained lactose content and reduced somatic cell count in heat stressed cows. Heat stress reduced basophil and monocyte concentrations and increased eosinophil percentage in cows. Interestingly, molasses maintained hematological profile with no change in basophil and monocyte concentrations, eosinophil percentage. Taken together, supplementation of resveratrol and molasses are effective in reducing heat stress effects on lactating dairy cows
Successive Refinement Algorithm for Solving Decision-Dependent Stochastic Programs and Driver Assignment in Shared Truckload Transportation
No full textThis dissertation develops models and algorithms for decision-making under uncertainty, with applications ranging from infrastructure protection to freight transportation. A central focus is stochastic optimization problems in which the probability distributions of uncertain outcomes depend on decision variables, a class of problems that presents significant modeling and computational challenges.
The first two papers advance solution methods for decision-dependent uncertainty. The first paper examines a class of two-stage stochastic programs where uncertain component capacities are directly influenced by first-stage decisions. Standard scenario enumeration produces intractable deterministic equivalents with high-degree nonlinearities. To address this challenge, structural properties of the problem are established, and a successive refinement algorithm is introduced that progressively tightens bounds within a branch-and-cut framework. Computational experiments demonstrate that this method significantly outperforms benchmark approaches, with optimal solutions identified before state space growth becomes prohibitive.
The second paper extends these ideas to defender–attacker models. Traditional tri-level formulations assume perfect defense and interdiction, assumptions that oversimplify real-world systems. An imperfect defender–attacker model is introduced in which defense and attack resources only partially influence component reliability, resulting in a stochastic optimization problem with decision-dependent probabilities. To overcome the computational intractability of the deterministic equivalent, a successive refinement algorithm is developed that dynamically refines scenario supports. Results on stochastic maximum flow problems show that the method solves more instances and achieves speedups of up to 66 times, thereby enabling the analysis of imperfect defense and interdiction in complex networks.
The third paper addresses driver assignment in shared truckload (STL) freight transportation, where uncertainty in load forecasts complicates planning. A deterministic optimization model is first formulated to assign drivers to STL bundles, and a heuristic algorithm is developed to improve its computational scalability. Building on this foundation, a two-stage stochastic optimization model is proposed to incorporate both immediate and forecasted future costs. Computational results reveal the rapid growth in complexity with the number of drivers and bundles, highlighting the importance of scalable algorithms. The analysis further shows that the value of accurate forecasts increases with problem size, emphasizing the role of predictive analytics in STL planning