Several lines of evidence support the notion that MUC1 is often aberrantly expressed in gastric cancer, and it is a ligand for Helicobacter pylori. Genetic variation in MUC1 gene may confer susceptibility to H. pylori infection and gastric cancer. We assessed the association of common polymorphisms in MUC1 gene with H. pylori infection and non-cardia gastric cancer using an LD-based tag SNP approach in north-western Chinese Han population. A total of four SNPs were successfully genotyped among 288 patients with non-cardia gastric cancer and 281 age-and sex-matched controls. None of the tested SNPs was associated with H. pylori infection. SNP rs9426886 was associated with a decreased risk of non-cardia gastric cancer, but lost significance after adjustment for multiple testing. Overall, our data indicated that common genetic variations in MUC1 gene might not make a major contribution to the risk of H. pylori infection and non-cardia gastric cancer in our studied population.
The nonstationary components and noises contained in the bearing vibration signal make it particularly difficult to extract fault features, and minimum entropy deconvolution (MED), maximum correlated kurtosis deconvolution (MCKD), and fast spectral kurtosis (FSK) cannot achieve satisfactory results. However, the filter size and period range of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) need to be set in advance, so it is difficult to achieve satisfactory filtering results. Aiming at these problems, a parameter adaptive MOMEDA feature extraction method based on grasshopper optimization algorithm (GOA) is proposed. Firstly, the multipoint kurtosis (MKurt) of MOMEDA filtered signal is used as the optimization objective, and the optimal filter size and periodic initial value which matched with the vibration signal can be determined adaptively through multiple iterations of GOA. Secondly, the periodic impact contained in the vibration signal is extracted by the optimized MOMEDA, and the fault features in the impact signal are extracted by Hilbert envelope demodulation. Finally, the simulation signal and bearing signal are processed by the proposed approach. The results show that the introduction of GOA not only solves the problem of parameter selection in MOMEDA, but also achieves better performance compared with other optimization methods. Meanwhile, the feasibility and superiority of the approach are fully proved by comparing it with the three methods MED, MCKD, and FSK after parameter optimization. Therefore, this approach provides a novel way and solution for fault diagnosis of the rolling bearing, gear, and shaft.
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