Impact of fatty acid on markers of exocrine pancreatic stimulation

Chronic pancreatitis in dogs is typically managed with a low-fat diet. Human research suggests consuming medium-chain triglycerides (MCT) may lower pancreatic enzyme release compared to consuming long chain fatty acids (LCFA). Twelve healthy adult colony dogs were fed a meal of cod and rice with either 3% metabolizable energy (ME) fat (control), high MCT (25% ME MCT oil, 25% ME butter), high saturated LCFA (50% ME butter), or high unsaturated LCFA (50% ME canola oil) in a 4-period by 4-treatment crossover design. Serum concentrations of canine pancreatic lipase immunoreactivity, gastrin, amylase, cholecystokinin (CCK), cholesterol, triglycerides and serum activities of DGGR lipase were measured at times 0 (fasted), 30, 120 and 180 minutes post-prandial. Following a 3-or 4-day wash-out period, each dog was assigned a new diet and the process was repeated for all treatments. Data was analyzed as a repeated-measures mixed model ANOVA. Post-hoc pairwise comparisons were run using Tukey-Kramer adjusted p-values. Shapiro-Wilk tests were used to evaluate residual normality. All statistical assumptions were sufficiently met. Statistical significance was defined as

Influences of Stone–Wales defects on the structure, stability and electronic properties of antimonene: A first principle study

AbstractDefects are inevitably present in materials, and their existence strongly affects the fundamental physical properties of 2D materials. Here, we performed first-principles calculations to study the structural and electronic properties of antimonene with Stone–Wales defects, highlighting the differences in the structure and electronic properties. Our calculations show that the presence of a SW defect in antimonene changes the geometrical symmetry. And the band gap decreases in electronic band structure with the decrease of the SW defect concentration. The formation energy and cohesive energy of a SW defect in antimonene are studied, showing the possibility of its existence and its good stability, respectively. The difference charge density near the SW defect is explored, by which the structural deformations of antimonene are explained. At last, we calculated the STM images for the SW defective antimonene to provide more information and characters for possible experimental observation. These results may provide meaningful references to the development and design of novel nanodevices based on new 2D materials

Improving garment thermal insulation property by combining two non-contact measuring tools

To investigate the effect of air gaps on the heat transfer performance of clothing, the method using the combination of two non-contact measuring tools (infrared thermal camera and 3D body scanner) has been developed considering the quantification of the air gap thickness and clothing surface temperature of different body parts without contacting clothing surface directly. The results show that the air gaps over middle and lower back of upper body have the largest thickness in all body parts, while the front and back shoulders have the smallest air gap thickness. The one-way analysis of variance shows that air gap thickness under shoulder segments has no significant difference in terms of size. Furthermore, clothing surface temperatures of shoulder and chest decrease gradually along with air gap thickness; clothing surface temperatures of front abdomen, front waist, pelvis and hip segments decrease initially but begin to increase when the air gap is above 1.5cm; clothing surface temperatures of middle back and back waist continually increase with the air gap thickness. Based on the comprehensive analyzation of the distributed features of air gap thickness and clothing surface temperature of different body parts, a revised clothing pattern with lower regional temperature and higher thermal insulation is put forward

Forecasting real crude oil prices, their uncertainties and a Bayesian structural method for the world crude oil market

This thesis comprises three essays focusing on real crude oil price forecasting and structural analysis. The first essay (Chapter 2) begins by broadly reproducing Baumeister & Kilian's (2015) main economic findings, where an equal-weight combination of six econometric models outperforms a recursive mean squared predictor error weight based combination in oil-price point forecasting. The six models are an unrestricted global oil market vector autoregression, a commodity-price model, an oil-futures-spread model, a gasoline-spread model, a time-varying parameter product-spread model, and a random-walk model. I use their preferred measures of the real oil price and similar real-time variables. Remaining mindful of the importance of Brent crude oil as a global price benchmark and the divergence in oil price measures since 2010, I extend consideration to the North Sea-based measure and update the evaluation sample to 2016:12, finding that the combined forecasts for Brent crude oil are as accurate as the forecasts for other oil price measures. The extended sample employing the oil price measures adopted by Baumeister & Kilian (2015) yields similar results to those reported in their paper. The second essay (Chapter 3) uses a Bayesian vector autoregression (BVAR) utilising time-varying parameters and stochastic volatility modelling time variation in forecasting real crude oil prices. An unrestricted global oil market vector autoregression and the equal-weight combination in Baumeister & Kilian (2015) are benchmarks. I extend the evaluation for model comparison purposes from standard statistical terms of point and density forecasts to an economic evaluation based xii on which specification would be more profitable in the crude oil futures market, and the forecast likelihood of extreme high and low real crude oil prices. For the same evaluation period as in Chapter 2, 1992:01{2016:12, the empirical results offer strong support for models using stochastic volatility in real crude oil price density forecasts relative to conventional VAR. Restricting time-varying parameters and allowing stochastic volatility can increase the probability of positive excess returns through utilising daily crude oil futures data, and can improve the calibration of the extreme high and low real oil price events forecasting. In conclusion, adding stochastic volatility and using the stochastic model specification search shrinkage prior of Eisenstat et al. (2016) are both important in ensuring reliable forecasts. Finally, in the third essay (Chapter 4) I develop a parallel Metropolis{ Hastings (MH) algorithm to identify and compute Bayesian structural vector autoregressions (SVARs), which I refer to C-BSVARs. The motivation for this is the inefficiency of the traditional computation method for SVARs under certain types of identification. C-BSVARs extend Baumeister & Hamilton's (2015) method from only using sign restrictions to a broader set of identification assumptions and improve the computational efficiency relative to the traditional method for SVARs. Two specifications from the world crude oil market modelling are used to illustrate this. The first employs Kilian & Murphy's (2014) set of identification restrictions, while the second imposes an additional restriction on top of theirs | the uncertainty of a lower-bound on `the short-run oil demand elasticity for use'. C-BSVARs dramatically improve the acceptance rate for models deemed as admissible relative to the method used in Kilian & Murphy (2014), and it can narrow the critical intervals of Kilian & Murphy's (2014) impulse response functions. The additional restriction in the second specification enables precise estimates of oil demand elasticities, which is of importance for deciding the existence of crude oil price endogeneity and the relative weights of oil demand and supply shocks for driving the fluctuation of crude oil prices. To my knowledge, only the C-BSVARs approach in the existing SVARs literature is able to impose the restriction of uncertainties for elasticities, thereby providing a novel way of identifying key structural parameters that are non-linear