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Single strain or mixed strains of Lactobacillus plantarum FPS 2520 and Bacillus subtilis N1 were used to ferment soybean meal (SBM), and the antiobesity activity of the fermented SBM product was investigated in rats fed with high-fat diet (HFD). After fermentation, free amino nitrogen, total peptide, and isoflavone contents were markedly raised, and genistein and daidzein were the major isoflavones in the fermented SBM. After fed with HFD for 10 weeks, obese Sprague-Dawley rats were orally treated with various fermented products for 6 weeks. The body weight gains, as well as weights of abdominal fat and epididymis fat, of rats fed with fermented SBM products were significantly downregulated. The treatment with the mixed-strains fermented SBM product significantly decreased plasma levels of triglyceride (TG), total cholesterol (TC), and low-density lipoprotein-cholesterol, but increased the level of high-density lipoprotein-cholesterol. Moreover, the levels of TG, TC, fatty acid synthase, and acetyl-CoA carboxylase (ACC) in liver were diminished, and the activities of hormone-sensitive lipase and lipoprotein lipase in adipose tissue were augmented. Taken together, these data demonstrated the antiobesity activity of fermented SBM products, among which the product fermented by the mixed strains being the most effective one. Therefore, these fermented SBM products are potential to be developed as functional foods or additives for treatment of obesity and prevention against obesity-induced complications.

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This paper proposes a hybrid estimation of distribution algorithm (EDA) with ant colony system (ACS) for the minimization of makespan in permutation flow shop scheduling problems. The core idea of EDA is that in each iteration, a probability model is estimated based on selected members in the iteration along with a sampling method applied to generate members from the probability model for the next iteration. The proposed algorithm, in each iteration, applies a new filter strategy and a local search method to update the local best solution and, based on the local best solution, generates pheromone trails (a probability model) using a new pheromone-generating rule and applies a solution construction method of ACS to generate members for the next iteration. In addition, a new jump strategy is developed to help the search escape if the search becomes trapped at a local optimum. Computational experiments on Taillard's benchmark data sets demonstrate that the proposed algorithm generated highquality solutions by comparing with the existing populationbased search algorithms, such as genetic algorithms, ant colony optimization, and particle swarm optimization.