Although the role of H3K9 methylation in rice (Oryza sativa) is unclear, in Arabidopsis thaliana the loss of histone H3K9 methylation by mutation of Kryptonite [also known as SU(VAR)3-9 homolog] reduces genome-wide DNA methylation and increases the transcription of transposable elements. Here, we report that rice SDG714 (for SET Domain Group Protein714) encodes a histone H3K9-specific methyltransferase. The C terminus of SDG714 confers enzymatic activity and substrate specificity, whereas the N terminus localizes it in the nucleus. Loss-of-function mutants of SDG714 (SDG714IR transformants) generated by RNA interference display a mostly glabrous phenotype as a result of the lack of macro trichomes in glumes, leaves, and culms compared with control plants. These mutants also show decreased levels of CpG and CNG cytosine methylation as well as H3K9 methylation at the Tos17 locus, a copia-like retrotransposon widely used for the generation of rice mutants. Most interestingly, loss of function of SDG714 can enhance transcription and cause the transposition of Tos17. Together, these results suggest that histone H3K9 methylation mediated by SDG714 is involved in DNA methylation, the transposition of transposable elements, and genome stability in rice.

Abstract:Rolling bearings are key components of rotary machines. To ensure early effective fault diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and an improved kernel extreme learning machine (KELM) is proposed in this paper. A fault signal is decomposed via VMD to obtain the intrinsic mode function (IMF) components, and the approximate entropy (ApEn) of the IMF component containing the main fault information is calculated. An eigenvector is created from the approximate entropy of each component. A bearing diagnosis model is created via a KELM; the KELM parameters are optimized using the particle swarm optimization (PSO) algorithm to obtain a KELM diagnosis model with optimal parameters. Finally, the effectiveness of the diagnosis method proposed in this paper is verified via a fan bearing fault diagnosis test. Under identical conditions, the result is compared with the results obtained using a back propagation (BP) neural network, a conventional extreme learning machine (ELM), and a support vector machine (SVM). The test result shows that the method proposed in this paper is superior to the other three methods in terms of diagnostic accuracy.

No abstract

No abstract