BackgroundObesity is a health hazard that is associated with a number of diseases and metabolic abnormalities, such as type-2 diabetes, hypertension, dyslipidemia, and coronary heart disease. In the current study, we investigated the effects of Citrus aurantium flavonoids (CAF) on the inhibition of adipogenesis and adipocyte differentiation in 3T3-L1 cells.MethodsDuring adipocyte differentiation, 3T3-L1 cells were treated with 0, 10, and 50 μg/ml CAF, and then the mRNA and protein expression of adipogenesis-related genes was assayed. We examined the effect of CAF on level of phosphorylated Akt in 3T3-L1 cells treated with CAF at various concentrations during adipocyte differentiation.ResultsThe insulin-induced expression of C/EBPβ and PPARγ mRNA and protein were significantly down-regulated in a dose-dependent manner following CAF treatment. CAF also dramatically decreased the expression of C/EBPα, which is essential for the acquisition of insulin sensitivity by adipocytes. Moreover, the expression of the aP2 and FAS genes, which are involved in lipid metabolism, decreased dramatically upon treatment with CAF. Interestingly, CAF diminished the insulin-stimulated serine phosphorylation of Akt (Ser473) and GSK3β (Ser9), which may reduce glucose uptake in response to insulin and lipid accumulation. Furthermore, CAF not only inhibited triglyceride accumulation during adipogenesis but also contributed to the lipolysis of adipocytes.ConclusionsIn the present study, we demonstrate that CAF suppressed adipogenesis in 3T3-L1 adipocytes. Our results indicated that CAF down-regulates the expression of C/EBPβ and subsequently inhibits the activation of PPARγ and C/EBPα. The anti-adipogenic activity of CAF was mediated by the inhibition of Akt activation and GSK3β phosphorylation, which induced the down-regulation of lipid accumulation and lipid metabolizing genes, ultimately inhibiting adipocyte differentiation.

We present an interactive tree modeling and deformation system that supports an efficient collision detection and avoidance using a bounding volume hierarchy of sweep surfaces. Starting with conventional tree models (given as meshes), we convert them into sweep surfaces and deform their branches interactively while detecting and avoiding collisions with many other branches. Multiple tree models (sharing the same topology) can be generated with great ease using this sweep-based approach, and they can serve as a basis for the generation of a multiparameter family of trees. We demonstrate the effectiveness of our approach in an automatic generation of similar trees, the colonization of trees to form a forest, and the tree growth, aging, and withering simulations.