The Multifaceted Role of Epoxide Hydrolases in Human Health and Disease

Epoxide hydrolases (EHs) are key enzymes involved in the detoxification of xenobiotics and biotransformation of endogenous epoxides. They catalyze the hydrolysis of highly reactive epoxides to less reactive diols. EHs thereby orchestrate crucial signaling pathways for cell homeostasis. The EH family comprises 5 proteins and 2 candidate members, for which the corresponding genes are not yet identified. Although the first EHs were identified more than 30 years ago, the full spectrum of their substrates and associated biological functions remain partly unknown. The two best-known EHs are EPHX1 and EPHX2. Their wide expression pattern and multiple functions led to the development of specific inhibitors. This review summarizes the most important points regarding the current knowledge on this protein family and highlights the particularities of each EH. These different enzymes can be distinguished by their expression pattern, spectrum of associated substrates, sub-cellular localization, and enzymatic characteristics. We also reevaluated the pathogenicity of previously reported variants in genes that encode EHs and are involved in multiple disorders, in light of large datasets that were made available due to the broad development of next generation sequencing. Although association studies underline the pleiotropic and crucial role of EHs, no data on high-effect variants are confirmed to date.

Fault Diagnosis of Rolling-Element Bearing Using Multiscale Pattern Gradient Spectrum Entropy Coupled with Laplacian Score

Feature extraction is recognized as a critical stage in bearing fault diagnosis. Pattern spectrum (PS) and pattern spectrum entropy (PSE) in recent years have been smoothly applied in feature extraction, whereas they easily ignore the partial impulse signatures hidden in bearing vibration data. In this paper, the pattern gradient spectrum (PGS) and pattern gradient spectrum entropy (PGSE) are firstly presented to improve the performance of fault feature extraction of two approaches (PS and PSE). Nonetheless, PSE and PGSE are only able to evaluate dynamic behavior of the time series on a single scale, which implies there is no consideration of feature information at other scales. To address this problem, a novel approach entitled multiscale pattern gradient spectrum entropy (MPGSE) is further implemented to extract fault features across multiple scales, where its key parameters are determined adaptively by grey wolf optimization (GWO). Meanwhile, a Laplacian score- (LS-) based feature selection strategy is employed to choose the sensitive features and establish a new feature set. Finally, the selected new feature set is imported into extreme learning machine (ELM) to identify different health conditions of rolling bearing. Performance of our designed algorithm is tested on two experimental cases. Results confirm the availability of our proposed algorithm in feature extraction and show that our method can recognize effectively different bearing fault categories and severities. More importantly, the designed approach can achieve higher recognition accuracies and provide better stability by comparing with other entropy-based methods involved in this paper.

A Fault Diagnosis Method Based on Improved Pattern Spectrum and FOA-SVM

A fault diagnosis method using improved pattern spectrum (IP S) and F OA−SV M is proposed. Improved pattern spectrum is introduced for feature extraction by employing morphological erosion operator, and this feature is able to present fault information for roller bearing on different scales. Simulation analysis is processed and shows that, the value of IP S has a steady distinction among different fault types and the calculating amount is less than traditional method. After feature extraction, SV M with F OA, which can help with seeking optimal parameters, is employed for pattern recognition. Experiments were conducted, and the proposed method is verified by roller bearing vibration data including different fault types. The classification accuracy of the proposed approach on training is 87.5% ( 21 24 ) and reaches 91.7% ( 44 48 ) in a testing data set. The analysis shows that the method has a good diagnosis effect and an acceptable recognition result.

Fault diagnosis using improved pattern spectrum and fruit fly optimization algorithm–support vector machine

A fault diagnosis method using improved pattern spectrum and fruit fly optimization algorithm–support vector machine is proposed. Improved pattern spectrum is introduced for feature extraction by employing morphological erosion operator. Simulation analysis is processed, and the improved pattern spectrum curves present a steady distinction feature and smaller calculating amount than pattern spectrum method. Support vector machine with fruit fly optimization algorithm which can help seeking optimal parameters is employed for pattern recognition. Experiments were conducted, and the proposed method is verified by roller bearing vibration data including different fault types. The classification accuracy of the proposed approach reaches 87.5% (21/24) in training and 91.7% (44/48) in testing, showing an acceptable diagnosis effect.