440 research outputs found

    A hierarchical dynamic model used for investigating feed efficiency and its relationship with hepatic gene expression in APOE*3-Leiden.CETP mice

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    Background: Feed efficiency (FE) is an important trait for livestock and humans. While the livestock industry focuses on increasing FE, in the current obesogenic society it is more of interest to decrease FE. Hence, understanding mechanisms involved in the regulation of FE and particularly how it can be decreased would help tremendously in counteracting the obesity pandemic. However, it is difficult to accurately measure or calculate FE in humans. In this study, we aimed to address this challenge by developing a hierarchical dynamic model based on humanized mouse data. Methods: We analyzed existing experimental data derived from 105 APOE*3-Leiden.CETP (E3L.CETP) mice fed a high-fat high-cholesterol (HFHC) diet for 1 (N = 20), 2 (N = 19), 3 (N = 20), and 6 (N = 46) month. We developed an ordinary differential equation (ODE) based model to estimate the FE based on the longitudinal data of body weight and food intake. Since the liver plays an important role in maintaining metabolic homeostasis, we evaluated associations between FE and hepatic gene expression levels. Depending on the feeding duration, we observed different relationships between FE and hepatic gene expression levels. Results: After 1-month feeding of HFHC diet, we observed that FE was associated with vitamin A metabolism, arachidonic acid metabolism, and the PPAR signaling pathway. After 3- and 6-month feeding of HFHC diet, we observed that FE was associated most strongly with expression levels of Spink1 and H19, genes involved in cell proliferation and glucose metabolism, respectively. Conclusions: In conclusion, our analysis suggests that various biological processes such as vitamin A metabolism, hepatic response to inflammation, and cell proliferation associate with FE at different stages of diet-induced obesity.</p

    A Study Focus on Concrete Replacing LD Slag as Fine Aggregate

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    Concrete is a composite material composed of fine and coarse granular aggregate (which acts as a filler material) embedded in a hard matrix of cement (which acts as binder) that fills the space among the aggregate particles and glue them together. The main constituents being cement, fine aggregate (river sand), coarse aggregate and water. The increase in cement production and its USAge and also its impact on the environment is addressed widely throughout the world in recent years, which gave light to researches to use alternative materials to cement such as fly ash, silica fume, ggbs etc. But now the focus is also on the increase in demand of the other constituent materials of concrete such as fine and coarse aggregate. Following the same lines of research and in a verge to find a new alternative material for river sand which is available in sufficient quantity in India and other countries also as a potential to be use as sand in concrete as resulted in using LD slag ( granulated blast furnace slag) as a fine aggregate in concrete

    Depth of Moist Soil at Planting Affected Grain Sorghum Response to Nitrogen Fertilizer

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    The depth of moist soil before planting is a critical factor for grain crop production in dryland cropping systems. We investigated depth of moist soil at planting and nitrogen (N) fertilizer application effects on continuous grain sorghum yields on a Crete silt loam soil over 32 years in western Kansas. Treatments were four N rates (0, 20, 40, and 60 lb/a) in a randomized complete block design with four replications and depth of moist soil at planting determined with a Paul Brown moisture probe. Grain sorghum yield response to N fertilizer application was -0.10, 14.4, 29.3, and 36.5 lb of grain/a for every lb of N applied in very low yielding (VLY), low yielding (LY), high yielding (HY), and very high yielding (VHY) environments, respectively. Grain yield increased with depth of moist soil at planting for each N rate, with yield increases of 217 to 461 lb/a per inch increase in depth of moist soil at planting for the unfertilized control through 60 lb N/a. Regardless of yield environment, net returns were negative when depth of moist soil at planting was less than 30 inches. These results suggest that continuous grain sorghum should not be planted when depth of moist soil measured with a Paul Brown probe is \u3c 30 inches. Results of this 32-year study showed the depth of moist soil at planting could be used to fine-tune N application rates for sorghum. Despite greater drought tolerance, sorghum N response is dependent on combination of soil water at planting and in-season precipitation. We need to continue this research to identify sorghum hybrids with improved drought tolerance and nitrogen use efficiency to increase probability of dryland sorghum production

    ‘Elevated’ hemidiaphragm due to a pericardial cyst

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    Comparison Of 2D Numerical Schemes For Modelling Supercritical And Transcritical Flows Along Urban Floodplains

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    Urban floodplains usually have irregular geometry due to different obstacles, urban infrastructures and slope conditions. This may change the flow regime from subcritical to supercritical flow conditions, and vice versa. Implementation of the full momentum equation in 2D shallow water equations (SWEs) is not trivial in mixed flow conditions as subcritical and supercritical flows require different boundary conditions and hence different solution algorithms. Some models ignore the convective acceleration term (CAT) to simplify implementation of the momentum equation for mixed flow conditions. This work tried to investigate the effect of neglecting CATs by testing two 2D models which implement - full SWEs and completely reduced CAT. The models\u27 performances were then tested by setting up hypothetical case studies with changing flow regimes. Simulations results were compared to each other by setting the solutions of the method that solve the full equations as a reference. Findings of the numerical tests showed that, in the cases, results of the model which ignore CATs fully were very similar compared to solutions of the model which implement full SWEs. Hence, simplified models which ignore CATs may be used to model urban flood plains without significant loss of accuracy

    Relatório de gestão: Projeto Excelência na Pesquisa Tecnológica (Abipti - Ciclo 2007).

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    Monitoring Climate Impacts on Annual Forage Production across U.S. Semi-Arid Grasslands

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    The ecosystem performance approach, used in a previously published case study focusing on the Nebraska Sandhills, proved to minimize impacts of non-climatic factors (e.g., overgrazing, fire, pests) on the remotely-sensed signal of seasonal vegetation greenness resulting in a better attribution of its changes to climate variability. The current study validates the applicability of this approach for assessment of seasonal and interannual climate impacts on forage production in the western United States semi-arid grasslands. Using a piecewise regression tree model, we developed the Expected Ecosystem Performance (EEP), a proxy for annual forage production that reflects climatic influences while minimizing impacts of management and disturbances. The EEP model establishes relations between seasonal climate, site-specific growth potential, and long-term growth variability to capture changes in the growing season greenness measured via a time-integrated Normalized Difference Vegetation Index (NDVI) observed using a Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting 19 years of EEP were converted to expected biomass (EB, kg ha-1 year-1) using a newly-developed relation with the Soil Survey Geographic Database range production data (R2= 0.7). Results were compared to ground-observed biomass datasets collected by the U.S. Department of Agriculture and University of Nebraska-Lincoln (R2 = 0.67). This study illustrated that this approach is transferable to other semi-arid and arid grasslands and can be used for creating timely, post-season forage production assessments. When combined with seasonal climate predictions, it can provide within-season estimates of annual forage production that can serve as a basis for more informed adaptive decision making by livestock producers and land managers
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