Mutant kri1l causes abnormal retinal development via cell cycle arrest and apoptosis induction.

Damage to the ribosome or an imbalance in protein biosynthesis can lead to some human diseases, such as diabetic retinopathy (DR) and other eye diseases. Here, we reported that the kri1l gene was responsible for retinal development. The kri1l gene encodes an essential component of the rRNA small subunit processome. The retinal structure was disrupted in kri1l mutants, which resulted in small eyes. The boundaries of each layer of cells in the retina were blurred, and each layer of cells was narrowed and decreased. The photoreceptor cells and Müller glia cells almost disappeared in kri1l mutants. The lack of photoreceptor cells caused a fear of light response. The development of the retina started without abnormalities, and the abnormalities began two days after fertilization. In the kri1l mutant, retinal cell differentiation was defective, resulting in the disappearance of cone cells and Müller cells. The proliferation of retinal cells was increased, while apoptosis was also enhanced in kri1l mutants. γ-H2AX upregulation indicated the accumulation of DNA damage, which resulted in cell cycle arrest and apoptosis. The kri1l mutation reduced the expression of some opsin genes and key retinal genes, which are also essential for retinal development.

A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD.

In modern industry, due to the poor working environment and the complex working conditions of mechanical equipment, the characteristics of the impact signals caused by faults are often submerged in strong background signals and noises. Therefore, it is difficult to effectivelyextract the fault features. In this paper, a fault feature extraction method based on improved VMD multi-scale dispersion entropy and TVD-CYCBD is proposed. First, the marine predator algorithm (MPA) is used to optimize the modal components and penalty factors in VMD. Second, the optimized VMD is used to model and decompose the fault signal, and then the optimal signal components are filtered according to the combined weight index criteria. Third, TVD is used to denoise the optimal signal components. Finally, CYCBD filters the de-noised signal and then envelope demodulation analysis is carried out. Through the simulation signal experiment and the actual fault signal experiment, the results verified that multiple frequency doubling peaks can be seen from the envelope spectrum, and there is little interference near the peak, which shows the good performance of the method.

Bearing Fault Diagnosis Method Based on RCMFDE-SPLR and Ocean Predator Algorithm Optimizing Support Vector Machine.

For the problem that rolling bearing fault characteristics are difficult to extract accurately and the fault diagnosis accuracy is not high, an unsupervised characteristic selection method of refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) combined with self-paced learning and low-redundant regularization (SPLR) is proposed, for which the fault diagnosis is carried out by support vector machine (SVM) optimized by the marine predator algorithm (MPA). First, we extract the entropy characteristics of the bearings under different fault states by RCMFDE and the introduction of the fine composite multiscale coarse-grained method and fluctuation strategy improves the stability and estimation accuracy of the bearing characteristics; then, a novel dimensionality-reduction method, SPLR, is used to select better entropy characteristics, and the local flow structure of the fault characteristics is preserved and the redundancy is constrained by two regularization terms; finally, using the MPA-optimized SVM classifier by combining Levy motion and Eddy motion strategies, the preferred RCMFDE is fed into the MPA-SVM model for fault diagnosis, for which the obtained bearing fault diagnosis accuracy is 97.67%. The results show that the RCMFDE can effectively improve the stability and accuracy of the bearing characteristics, the SPLR-based low-dimensional characteristics can suppress the redundancy characteristics and improve the effectiveness of the characteristics, and the MPA-based adaptive SVM model solves the parameter randomness problem and, therefore, the proposed method has outstanding superiority.

Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA.

In order to separate the sub-signals and extract the feature frequency in the signal accurately, we proposed a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) feature extraction method based on the improved grasshopper optimization algorithm (IGOA). The method not only improved the local optimal problem of GOA, but could also determine the bandwidth threshold and B-spline order of TVF-EMD adaptively. Firstly, a nonlinear decreasing strategy was introduced in this paper to adjust the decreasing coefficient of GOA dynamically. Then, energy entropy mutual information (EEMI) was introduced to comprehensively consider the energy distribution of the modes and the dependence between the modes and the original signal, and the EEMI was used as the objective function. In addition, TVF-EMD was optimized by IGOA and the optimal parameters matching the input signal were obtained. Finally, the feature frequency of the signal was extracted by analyzing the sensitive mode with larger kurtosis. The optimization experiments of 23 sets of benchmark functions showed that IGOA not only enhanced the balance between exploration and development, but also improved the global and local search ability and stability of the algorithm. The analysis of the simulation signal and bearing signal shows that the parameter-adaptive TVF-EMD method can separate the modes with specific physical meanings accurately. Compared with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), TVF-EMD with fixed parameters and GOA-TVF-EMD, the decomposition performance of the proposed method is better. The proposed method not only improved the under-decomposition, over-decomposition and modal aliasing problems of TVF-EMD, but could also accurately separate the frequency components of the signal and extract the included feature information, so it has practical significance in mechanical fault diagnosis.

Research on Twin Extreme Learning Fault Diagnosis Method Based on Multi-Scale Weighted Permutation Entropy.

Due to the complicated engineering operation of the check valve in a high-pressure diaphragm pump, its vibration signal tends to show non-stationary and non-linear characteristics. These leads to difficulty extracting fault features and, hence, a low accuracy for fault diagnosis. It is difficult to extract fault features accurately and reliably using the traditional MPE method, and the ELM model has a low accuracy rate in fault classification. Multi-scale weighted permutation entropy (MWPE) is based on extracting multi-scale fault features and arrangement pattern features, and due to the combination of extracting a sequence of amplitude features, fault features are significantly enhanced, which overcomes the deficiency of the single-scale permutation entropy characterizing the complexity of vibration signals. It establishes the check valve fault diagnosis model from the twin extreme learning machine (TELM). The TELM fault diagnosis model established, based on MWPE, aims to find a pair of non-parallel classification hyperplanes in the equipment state space to improve the model's applicability. Experiments show that the proposed method effectively extracts the characteristics of the vibration signal, and the fault diagnosis model effectively identifies the fault state of the check valve with an accuracy rate of 97.222%.

Genetic polymorphism of both 29 Y-STRs and 213 Y-SNPs in Han populations from Shandong Province, China.

Genetic markers on the Y chromosome, including short tandem repeats (Y-STRs) and single nucleotide polymorphisms (Y-SNPs), are used widely in forensic genetics. Both Y-STR-based haplotypes and Y-SNP-based haplogroups provide information on a population's genetic structure, which is useful for the identification of individuals. However, there are few studies on these two types of genetic markers in the various Chinese populations. In this study, 284 Han individuals from four prefecture-level cities in Shandong Province (Binzhou, Dezhou, Heze, and Weihai) were genotyped by 29 Y-STRs (from our previous study) and 213 Y-SNPs (self-designed for the Haplogroup O2 Y-SNP panel). Haplogroup O was the most predominant among the four cities. The highest haplogroup diversity (0.9745) was observed in the Heze population, with a discrimination capacity (DC) value of 0.5625. The haplotype diversity and DC values of the Binzhou and Heze populations were 1.0000. Furthermore, genetic differences were observed between the coastal and inland cities; the results of their statistical analysis are presented herein.

An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform.

The Empirical Wavelet Transform (EWT), which has a reliable mathematical derivation process and can adaptively decompose signals, has been widely used in mechanical applications, EEG, seismic detection and other fields. However, the EWT still faces the problem of how to optimally divide the Fourier spectrum during the application process. When there is noise interference in the analyzed signal, the parameterless scale-space histogram method will divide the spectrum into a variety of narrow bands, which will weaken or even fail to extract the fault modulation information. To accurately determine the optimal resonant demodulation frequency band, this paper proposes a method for applying Adaptive Average Spectral Negentropy (AASN) to EWT analysis (AEWT): Firstly, the spectrum is segmented by the parameterless clustering scale-space histogram method to obtain the corresponding empirical mode. Then, by comprehensively considering the Average Spectral Negentropy (ASN) index and correlation coefficient index on each mode, the correlation coefficient is used to adjust the ASN value of each mode, and the IMF with the highest value is used as the center frequency band of the fault information. Finally, a new resonant frequency band is reconstructed for the envelope demodulation analysis. The experimental results of different background noise intensities show that the proposed method can effectively detect the repetitive transients in the signal.

Polymorphisms in mucin genes in the development of gastric cancer.

Gastric cancer (GC) is the third leading cause of cancer-related death worldwide. In areas of high prevalence, such as Japan, South Korea and China, most cases of GC are related to Helicobacter pylori (H. pylori), which involves well-characterized sequential stages, including infection, atrophic gastritis, intestinal metaplasia, dysplasia, and GC. Mucins are the most abundant high-molecular-weight glycoproteins in mucus, which is the first line of defense and plays a major role in blocking pathogenic factors. Normal gastric mucosa shows expression of MUC1, MUC5AC and MUC6 that is specific to cell type. However, the specific pattern of MUC1, MUC5AC and MUC6 expression is changed in gastric carcinogenesis, accompanied by de novo expression of secreted MUC2. Recent studies have provided evidence that variations in these mucin genes affect many steps of GC development, such as H. pylori infection, and gastric precancerous lesions. In this review, we focus on studies of the association between polymorphisms in mucin genes and development of GC. This information should be helpful for the early detection, surveillance, and treatment of GC.

Effects of two novel amino acid substitutions on the penicillin binding properties of the PBP5 C­terminal from Enterococcus faecium.

The low­affinity penicillin­binding protein (PBP)5 is responsible for resistance to ß­lactam antibiotics in Enterococcus faecium. (E. faecium). In order to evaluate more fully the potential of this species for the development of resistance to ß-lactam antibiotics, the present study aimed to examine the extent of penicillin-binding protein (PBP) variations in a collection of clinical E. faecium isolates. In the present study, the C­terminal domain of PBP5 (PBP5­CD) of 13 penicillin­resistant clinical isolates of E. faecium were sequenced and the correlation between penicillin resistance and particular amino acid changes were analyzed. The present study identified for the first time, to the best of our knowledge, two novel substitutions (Tyr460Phe and Ala462Thr or Val462Thr) of E. faecium PBP5­CD. The covalent interaction between penicillin and PBP5­CD was also investigated using homology modeling and molecular docking methods. The theoretical calculation revealed that Phe460 and Thr462 were involved in penicillin binding, suggesting that substitutions at these positions exert effects on the affinity for penicillin, and this increased affinity translates into lower resistance in vitro.

Association analysis of common genetic variations in MUC5AC gene with the risk of non-cardia gastric cancer in a Chinese population.

Several lines of evidence suggest that genetic variation in MUC5AC gene might contribute to the risk of gastric cancer. We conducted a case-control study to evaluate the relationship between common genetic variations in MUC5AC gene and non-cardia gastric cancer using an LD-based tagSNP approach in Baotou, north-western China. We genotyped 12 tagSNPs by TaqMan method among 288 cases with non-cardia gastric cancer and 281 normal controls. Unconditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for non-cardia gastric cancer risk in association with alleles, genotypes and haplotypes. We observed that the frequencies of rs3793964 C allele and rs11040869 A allele were significantly lower in cases than in controls. Meanwhile, minor allele homozygotes of rs3793964 and rs11040869 were significantly associated with a decreased risk of non-cardia gastric cancer when compared with their major allele homozygotes. Furthermore, a statistically significantly protective effect of rs885454 genotypes on non-cardia gastric cancer was also observed (for CT vs. CC: OR=0.581, 95%CI=0.408-0.829; for CT/TT vs. CC: OR=0.623, 95%CI=0.451-0.884). Our results indicated that some common genetic variations in the MUC5AC gene might have effects on the risk of non-cardia gastric cancer in our studied population.

Common genetic variations in the MUC5AC gene are not related to helicobacter pylori serologic status.

Several lines of evidence suggest that MUC5AC genetic polymorphisms might confer susceptibility to H. pylori infection and therefore gastric cancer risk. We here assessed the association of common polymorphisms in the MUC5AC gene with H. pylori seroprevalence using an LD-based tagSNP approach in a north-western Chinese Han population. A total of 12 tagSNPs were successfully genotyped among 281 unrelated ethnic Han Chinese who had no cancer history, and no identifiable gastric disease or genetic disease. No significant association between any alleles, genotypes or haplotypes and H. pylori seroprevalence was observed. Our results suggest that common genetic variations in MUC5AC gene might not make a major contribution to the risk of H. pylori infection.

Lack of association of common polymorphisms in MUC1 gene with H. pylori infection and non-cardia gastric cancer risk in a Chinese population.

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.