With the development of national-scale forest biomass monitoring work, accurate estimation of forest biomass on a large scale is becoming an important research topic in forestry. In this study, the stem wood, branches, stem bark, needles, roots and total biomass models for larch were developed at the regional level, using a general allometric equation, a dummy variable model, a mixed effects model, and a Bayesian hierarchical model, to select the most effective method for predicting large-scale forest biomass. Results showed total biomass of trees with the same diameter gradually decreased from southern to northern regions in China, except in the Hebei province. We found that the stem wood, branch, stem bark, needle, root, and total biomass model relationships were statistically significant (p-values < 0.01) for the general allometric equation, linear mixed model, dummy variable model, and Bayesian hierarchical model, but the linear mixed, dummy variable, and Bayesian hierarchical models showed better performance than the general allometric equation. An F-test also showed significant differences between the models. The R² average values of the linear mixed model, dummy variable model, and Bayesian hierarchical model were higher than those of the general allometric equation by 0.007, 0.018, 0.015, 0.004, 0.09, and 0.117 for the total tree, root, stem wood, stem bark, branch, and needle models respectively. However, there were no significant differences between the linear mixed model, dummy variable model, and Bayesian hierarchical model. When the number of categories was increased, the linear mixed model and Bayesian hierarchical model were more flexible and applicable than the dummy variable model for the construction of regional biomass models. Full article

Accurate biomass estimations are important for assessing and monitoring forest carbon storage. Bayesian theory has been widely applied to tree biomass models. Recently, a hierarchical Bayesian approach has received increasing attention for improving biomass models. In this study, tree biomass data were obtained by sampling 310 trees from 209 permanent sample plots from larch plantations in six regions across China. Non-hierarchical and hierarchical Bayesian approaches were used to model allometric biomass equations. We found that the total, root, stem wood, stem bark, branch and foliage biomass model relationships were statistically significant (p-values < 0.001) for both the non-hierarchical and hierarchical Bayesian approaches, but the hierarchical Bayesian approach increased the goodness-of-fit statistics over the non-hierarchical Bayesian approach. The R² values of the hierarchical approach were higher than those of the non-hierarchical approach by 0.008, 0.018, 0.020, 0.003, 0.088 and 0.116 for the total tree, root, stem wood, stem bark, branch and foliage models, respectively. The hierarchical Bayesian approach significantly improved the accuracy of the biomass model (except for the stem bark) and can reflect regional differences by using random parameters to improve the regional scale model accuracy. Full article

Endotoxemia is a common event in alcoholic liver disease. Elevated intestinalpermeability is the major factor involved in the mechanism of alcoholic endotoxemia andthe pathogenesis of alcoholic liver disease. This study examined the effect ofepigallocatechin-3-gallate (EGCG) on alcohol-induced gut leakiness, and explored therelated mechanisms involved in its protection against alcohol-induced liver injury in rats.Four groups of female Sprague-Dawley rats were studied. Alcohol and alcohol/EGCGgroups rats received fish oil along with alcohol daily via gastrogavage for 6 weeks, anddextrose and dextrose/EGCG groups rats were given fish oil along with isocaloric dextroseinstead of alcohol. The dextrose/EGCG and alcohol/EGCG groups received additionaltreatment of EGCG (100mg.kg^-1 body weight) daily intragastrically by gavage. Intestinalpermeability was assessed by urinary excretion of lactulose and mannitol (L/M ratio). Liverinjury was evaluated histologically and by serum alanine aminotransferase (ALT). Plasmaendotoxin and serum tumor necrosis factor-α (TNF-α) levels were assayed; livermalondialdehyde (MDA) contents determined. CD14 and inflammatory factors, such asTNF-α, cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) mRNAs inthe liver were analyzed by reverse transcriptase-polymerase chain reaction (RT-PCR). Ratsgiven fish oil plus alcohol had gut leakiness (L/M ratio was increased), which wasassociated with both endotoxemia and liver injury. The above responses were accompaniedby increased CD14, TNF-α, COX-2 and iNOS mRNA expressions in the liver. EGCGsupplementation partly blocked the gut leakiness, reduced endotoxemia and lipidperoxidation, and blunted the elevated expressions of CD14, TNF-α, COX-2 and iNOS, allof which were associated with improved liver injury. These results show that EGCG can block alcohol-induced gut leakiness, reduce endotoxemia, and inhibit inflammatory factors expressions in the liver, thereby ameliorates alcohol-induced liver injury. Full article