Adiposis is reputed as a twin disease of type 2 diabetes and
greatly harmful to human health. In order to understand the
molecular mechanisms of adiposis, the changes of physiological,
pathological, epigenetic and correlative gene expression were
investigated during the adiposis development of C57BL/6J mice
induced by long time (9 months) high-fat and high-sucrose diet
(HFSD) sustainably. The results showed that mRNA transcription
level of the Leptin, Glut4 and Glut2 genes have been obviously
changed, which exhibit a negative correlation with methylation
on their promoter DNA. The results also revealed that HFSD
induced higher level of DNA methyltransferase 1 (DNMT1) in fat
tissue might play important role in regulating the changes of
methylation pattern on Glut4 and Leptin genes, and which might
be one of the molecular mechanisms for the adiposis
development.
This paper presents a hybrid probabilistic neural network (PNN) and particle swarm optimization (PSO) techniques to predict the soil liquefaction. The PSO algorithm is employed in selecting the optimal smoothing parameter of the PNN to improve the forecasting accuracy. Seven parameters such as earthquake magnitude, normalized peak horizontal acceleration at ground surface, standard penetration number, penetration resistance, relative compaction, mean grain diameter and groundwater table are selected as the evaluating indices. The predictions from the PSO-PNN model were compared with those from two models: backpropagation neural network (BPNN) model and support vector machine (SVM) model. The study concluded that the proposed PSO-PNN model can be used as a reliable approach for predicting soil liquefaction.