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The evaluation of various biomarkers of acute kidney injury in cats using a toxin-induced model
Get PDFSeveral feline-specific models, such as ischemia-reperfusion injury (IRI), remnant kidney (RK), and toxin-induced injury (TI), have been developed to study feline kidney disease. Each model has distinct advantages and limitations, making the careful selection of appropriate models critical for progressing research in feline nephrology. Using a toxin-induced model employing meloxicam, we aimed to compare serum concentrations of symmetric dimethylarginine (SDMA) and creatinine and evaluate the concentration vs time course profiles of urinary tissue inhibitor of metalloproteinase-2 (TIMP-2), insulin-like growth factor binding protein-7 (IGFBP-7), and kidney injury molecule–1 (KIM-1) in cats before, during, and after induction of renal injury. Twelve healthy adult cats were obtained from a commercial breeder. Cats were randomly allocated to control and treatment groups. Cats in the treatment group received meloxicam 0.3 mg/kg subcutaneous sly (SC) every 24 hours for 31 days. Cats in the control group received saline (0.1 mL SC). Renal injury was defined as the presence of tubular damage, basement membrane damage, and/or interstitial inflammation in histological sections of kidney tissue. Serum creatinine and SDMA and urinary TIMP-2, IGFBP-7, KIM-1, and creatinine concentrations were measured every 4-6 days. In the control group, no cats developed renal azotemia. In the treatment group, four out of six cats developed elevated serum creatinine and histopathological evidence of renal injury. Three of these cats developed an elevation in serum SDMA. The time to the development of renal azotemia using serum creatinine or SDMA was not significantly different (p>0.05). The urinary biomarker concentrations between control cats and the four treatment group cats that developed elevated serum creatinine and histopathological evidence of renal injury were compared by calculating the area under the curve (AUC) for each biomarker normalized for urine creatinine (UC) concentration vs time course profile, and reported as mean ±SE. The AUC for urinary IGFBP-7/UC was higher (p = 0.0152) in the treatment group (0.042 ±0.0062) compared to the control group (0.02676 ±0.0018). The AUC for urinary KIM-1/UC was higher (p = 0.0083) in the treatment group (0.034 ±0.0055) compared to the control group (0.019 ±0.0022). An increase in urinary TIMP-2 was detected in only 50% of the treatment group. In this small pilot study, there was no evidence that serum SDMA was superior to serum creatinine at detecting impaired renal function, and urinary IGFBP-7 and KIM-1 increased in cats that developed AKI after repeated meloxicam administration
Upgrading biocrude oil into sustainable aviation fuel using zeolite-supported iron-molybdenum carbide nanocatalysts
Get PDFFood waste is an underdeveloped source for production of sustainable aviation fuel (SAF). Now, there is no certi-fed conversion process of food waste for SAF by American Society for Testing and Materials (ASTM). We report the use of zeolite-supported molybdenum carbide nanocatalysts in upgrading biocrudes, produced from food wastes through HTL, into SAF precursors. Our data show a complete removal of oxygen from the biocrude through hydro-deoxygenation and a higher heating value of 46.5 MJ/kg, which is comparable to that of Jet A (46.1 MJ/kg). The prescreening tests (tier alpha and beta) show the average carbon number of the distillation cut (150° to 230°C) of upgraded fuel is 10.6, close to the value of 11.4 for average conventional jet fuel, and the specifcations of properties including surface tension, viscosity, heating value, fash point, and freezing point were found to meet the standards of SAF. The metal carbide nanocatalysts were reusable in upgrading tests, and the activity of deoxyge
Frequencies of Grandparent Caregivers in the 2023 American Community Survey
No full textThese data from the 2023 American Community Survey show the frequency of grandparents who are 55 years and older, living with their own grandchildren, and are caring for one or more grandchildren. The data focuses on American Indian, Alaska Native, Native Hawaiian, Pacific Islander, and White grandparents
Implementing genomic selection in beef herds
Get PDFThis publication will discuss genomic selection technologies and tools available to beef producers and how they may be used or applied. Beef producers, veterinarians, and consultants will also find information regarding the benefits of genomic selection and how genomic tools can improve the beef industry
COMMUNITY-ENGAGED AND EMPIRICALLY GROUNDED APPROACHES TO ISOTOPIC VARIABILITY AND AGRICULTURAL TRANSFORMATION
Get PDFClimate warming and variability are reshaping precipitation patterns, with topography influencing regional climate responses and affecting water resources, agriculture, and ecosystems. Stable isotopes of hydrogen (δ2H) and oxygen (δ18O) in precipitation offer insights into water cycle dynamics, but linking isotopic variability to meteorological processes remains challenging. At the same time, climate-driven agricultural challenges—such as aridification, drought vulnerability, and yield instability—reinforce reliance on industrial practices that degrade ecosystems. Regenerative agriculture (RA) is emerging as a regional alternative to address environmental degradation and improve grower economic stability. However, the transition to RA requires understanding the systemic barriers and drivers of change. This dissertation combines citizen science and community engagement to investigate (1) the meteorological processes driving daily precipitation isotope variability and (2) the transformation toward RA, including its challenges, opportunities, and support needs.We collaborated with a citizen-science precipitation network to collect daily samples at 18 locations in the Pacific Northwest, USA (n=2371). Using air mass trajectory data combined with meteorological and topographic variables, we applied Random Forest and Multiple Linear Regression to identify key drivers of isotopic variability, build a predictive model for daily precipitation isotopes, and generate spatiotemporal isoscapes to analyze patterns related to seasonal moisture transport pathways. The model explains 63% of daily isotopic variability, with key drivers differing between the windward and leeward sides of the Cascades: the windward side is influenced by the Pacific Ocean, while the leeward side experiences a rainshadow effect and a continental climate. Upwind rainout, parameterized through our novel methodology using air mass trajectories and temperature (surface and cloud top), play a dominant role in windward isotopic variability. Surface temperature and precipitation amount have stronger effects on δ²H in the leeward region, supporting sub-cloud evaporation as the primary driver. The model also provides a regionally dispersed δ2H lapse rate estimate of -33.5 0/00/km (-3.4 0/00/km δ18O), and trajectory cluster analysis reveals seasonal isotope patterns driven by northwesterly storm trajectories (30-50% of storms), which enhance Pacific moisture transport and draw colder air from the Canadian Rockies in winter. Additionally, isotopic indicators suggest that terrestrial moisture recycling plays a significant role in the leeward region. These findings create a novel isotopic dataset for the Pacific Northwest, providing high-resolution (4 km) daily precipitation isoscapes. Tools that enhance hydrologic modeling, improve tracer-aided model calibration, and refine assumptions about isotopic variability in a rainshadow setting.Community engagement shaped the development of research questions and a conceptual systems model to understand the agricultural-food system. We used a systems approach, starting with a model of the Intermountain West, USA, and refining it through regional models in three workshops held in Spokane, WA, Farmington, NM, and Alamosa, CO. Agricultural-food system leaders in these workshops assessed the current and future states of RA. Using a guided transformation framework, we examined stressors driving growers toward RA, as well as barriers and opportunities for transformation. In the Intermountain West, RA transformation is driven by small, transdisciplinary teams. Grower peer-to-peer networks are central to RA transformation and require investment and collaboration with USDA researchers and land-grant universities. Structured participatory processes are key to fostering learning and building community capacity. County and University Extension staff, with their long-standing relationships in local communities, can become trained facilitators, supporting sustained transformations. Key barriers to scalability include a lack of medium-scale infrastructure for processing and distribution, while increasing consumer interest in soil health and food quality highlights the need for peer-reviewed research to identify practices that optimize product quality. Our findings demonstrate how a community-engaged, systems approach can identify gaps and practical steps for RA transformation, emphasizing the importance of training facilitators and formalizing their roles within regional peer-learning networks to catalyze RA transformation and overcome barriers
INTEGRACIÓN DE ENFOQUES EXPERIMENTALES Y DE MODELADO MOLECULAR PARA LA PRODUCCIÓN OPTIMIZADA DE BIOCHAR: INVESTIGACIÓN DE DISTRIBUCIONES DE HAP, REACCIONES DE PIROLISIS SECUNDARIA, DESARROLLO DE BASES DE DATOS ESPECTRALES Y CARBONIZACIÓN SELECTIVA MEDIANTE PRETRATAMIENTOS CON ÁCIDO Y PRESIÓN
Get PDFThis thesis explored atomistic modeling, spectroscopic prediction, optimized carbonization methods, and secondary reaction mechanisms of cellulose to deepen our understanding of biochar's structural transformations and functionality. The study began by evaluating the effectiveness of large-scale atomistic models of lignocellulosic and carbonaceous materials, aiming to unravel the connections between production parameters, biochar behavior, molecular transformations, and pyrolysis kinetics, beyond conventional levoglucosan-based methodologies.A computational framework was developed to generate realistic, large-scale (>10,000 atoms) atomistic representations of biochar. These models integrated experimental data and chemical information derived from computational methods such as density functional theory (DFT) and reactive molecular dynamics (MD) simulations. The resulting models accurately replicated structural and chemical characteristics, facilitating predictive biochar macroscopic properties and behavior simulations.Further enhancing biochar characterization, the research combined DFT calculations with machine learning techniques to predict spectroscopic signatures, creating a comprehensive spectral database encompassing X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, infrared (IR), and nuclear magnetic resonance (NMR). The theoretical spectra obtained exhibited remarkable agreement with experimental data, significantly enhancing the reliability of molecular-level interpretations and spectroscopic predictions.In addition, optimized carbonization strategies were investigated by assessing the effects of pressure and acid pretreatments on carbon yield and chemical properties. Both experimental and computational analyses revealed pathways to precisely control biochar composition, porosity, and chemical functionality, highlighting biochar’s potential for carbon storage and environmental remediation applications. These selective carbonization methods enhanced carbon retention by up to 78%.Lastly, the thesis examined secondary reaction mechanisms underlying cellulose fast pyrolysis through reactive MD simulations. By identifying critical intermediates and reaction networks, this study provided novel insights into biochar formation, corroborated by experimental mass spectrometry and molecular characterization. Integrating computational and experimental approaches throughout the research provided a comprehensive understanding of biochar science, enabling rational design and enhanced performance in sustainable applications