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[Display omitted] • TP ameliorated DSS-induced colitis and alleviated intestinal barrier damage. • TP reduced inflammatory cytokines by inhibiting the TLR4/NF-κB signaling pathway. • TP was able to modulate the gut microbiota by changing the abundance of bacteria. • TP derivative peptide GP-9 also ameliorated DSS-induced colitis symptoms. Ulcerative colitis (UC) is a recurrent intestinal disease with an increasing incidence worldwide that seriously affects the life of patients. Turtle peptide (TP) is a bioactive peptide extracted from turtles that has anti-inflammatory, antioxidant and anti-aging properties. However, studies investigating the effect of TP on the progression of UC are lacking. The aim of this study was to investigate effects and underlying mechanisms of TP and its derivative peptide GPAGPIGPV (GP-9) in alleviating UC in mice. The results showed that 500 mg/kg TP treatment significantly ameliorated colitis symptoms and oxidative stress in UC mice. TP alleviated intestinal barrier damage in UC mice by promoting mucosal repair and increasing the expression of tight junction proteins (ZO1, occludin and claudin-1). TP also modulated the composition of the gut microbiota by increasing the abundance of the beneficial bacteria Anaerotignum , Prevotellaceae_UCG-001 , Alistipes , and Lachno-spiraceae_NK4A136_group and decreasing the abundance of the harmful bacteria Prevotella_9 and Parasutterella. Furthermore, we characterized the peptide composition of TP and found that GP-9 ameliorated the symptoms of dextran sodium sulfate (DSS)-induced colitis in mice by inhibiting the TLR4/NF-κB signaling pathway. In conclusion, TP and its derivative peptides ameliorated DSS-induced ulcerative colitis by inhibiting the expression of inflammatory factors and modulating the composition of the intestinal microbiota; this study provides a theoretical basis for the application of TP and its derivative peptides for their anti-inflammatory activity. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposed a self-learning, self-adapting algorithm (ANN-GA-Cascades) for extracting fuzzy rules, which is based on fusion of soft computing. We could use it to attain the fuzzy rules of oiliness in oil exploration: firstly, supervised learning of training sample is performed by using neural networks, with the inputs being the simplest well-logging attribute set which is relevant to the oiliness attributes, and the outputs being the corresponding oiliness partition C k (dry layer, water layer, inferiority layer and oil layer). When the neural network attained precision or the maximum iteration steps, the kth output node of neural network will be the corresponding partition of decision character, with the output function being ψ k = f(x i ,(WG1) ij ,(WG2) jk ), in which (WG1) ij are the connection weights between input layer and hidden layer, (WG2) jk are the connection weights between hidden layer and output layer. Then, the genetic algorithm (GA) was used to randomly assemble the input character and ψ k as the fitness function. In this way, the optimal chromosome will be the fuzzy rule of partition C k . Finally, the empirical study application of this algorithm on oil well oilsk81 and oilsk83 of Jianghan oilfield in China has proved to be satisfactory. [Copyright &y& Elsevier]