Context The key gut microbial biomarkers for PCOS and how dysbiosis causes insulin resistance and PCOS remain unclear. Objective To assess the characteristics of intestinal flora in PCOS and explore whether abnormal intestinal flora can affect insulin resistance and promote PCOS and whether chenodeoxycholic acid (CDCA) can activate intestinal farnesoid X receptor (FXR), improving glucose metabolism in PCOS. Setting and design The intestinal flora of treatment-naïve PCOS patients and hormonally healthy controls was analysed. Phenotype analysis, intestinal flora analysis and global metabolomic profiling of caecal contents were performed on a letrozole (LET)-induced PCOS mouse model; similar analyses were conducted after 35 days of antibiotic treatment on the PCOS mouse model, and glucose tolerance testing (GTT) was performed on the PCOS mouse model after a 35-day CDCA-treatment. Mice receiving faecal microbiota transplants from PCOS patients or healthy controls were evaluated after 10 weeks. Results Bacteroides was significantly enriched in treatment-naïve PCOS patients. The enrichment in Bacteroides was reproduced in the PCOS mouse model. Gut microbiota removal ameliorated the PCOS phenotype, insulin resistance and increased relative FXR mRNA levels in the ileum and serum fibroblast growth factor 15 (FGF15) levels. PCOS stool-transplanted mice exhibited insulin resistance at ten weeks but not PCOS. Treating the PCOS mouse model with CDCA improved glucose metabolism. Conclusions Bacteroides is a key microbial biomarker in PCOS and shows diagnostic value. Gut dysbiosis can cause insulin resistance. FXR activation might play a beneficial rather than detrimental role in glucose metabolism in PCOS.

Due to the complexity of ART network, the setting of parameters is rather difficult. Based on analyzing the architecture and the membrane equation of layer 2 (L2) in ART1 network, this paper describes the oscillation possibility of the activities of neurons in L2 layer and studies the influence of parameters setting on the behavior of L2 Layer, such as the number of neurons (S 2 ), the biases + b andb, the initial value of L1-L2 connection matrix W 1:2 , through a simulation case whose transfer function is faster-than-linear. It also suggests a few basic principles of the parameters setting.

The slip mechanism on the surface of silicon wafers under laser irradiation was studied by numerical simulations and experiments. Firstly, the slip was explained by an analysis of the generalized stacking fault energy and the associated restoring forces. Activation of unexpected {110} slip planes was predicted to be a surface phenomenon. Experimentally, {110} slip planes were activated by changing doping concentrations of wafers and laser parameters respectively. Slip planes were {110} when slipping started within several atomic layers under the surface and turned into {111} with deeper slip. The scale effect was shown to be an intrinsic property of silicon.