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.

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Peanut (Arachis hypogaea L.) is one of the most important oil crops in the world. However, biotechnological based improvement of peanut is far behind many other crops. It is critical and urgent to establish the biotechnological platform for peanut germplasm innovation. In this study, a peanut seed cDNA library was constructed to establish the biotechnological platform for peanut germplasm innovation. About 17,000 expressed sequence tags (ESTs) were sequenced and used for further investigation. Among which, 12.5% were annotated as metabolic related and 4.6% encoded transcription or post-transcription factors. ESTs encoding storage protein and enzymes related to protein degradation accounted for 28.8% and formed the largest group of the annotated ESTs. ESTs that encoded stress responsive proteins and pathogen-related proteins accounted for 5.6%. ESTs that encoded unknown proteins or showed no hit in the GenBank nr database accounted for 20.1% and 13.9%, respectively. A total number of 5066 EST sequences were selected to make a cDNA microarray. Expression analysis revealed that these sequences showed diverse expression patterns in peanut seeds, leaves, stems, roots, flowers, and gynophores. We also analyzed the gene expression pattern during seed development. Genes that were upregulated (≥twofold) at 15, 25, 35, and 45 days after pegging (DAP) were found and compared with 70 DAP. The potential value of these genes and their promoters in the peanut gene engineering study is discussed.

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In this paper, a fault diagnosis method that is based on the deep structure and the sparse least squares support vector machine (SLSSVM) is proposed. This method constructs the structure of a multi-layer support vector machine (SVM). First, the SVM on the first layer is trained by using the training samples, and it learns the shallow features of the data. Then, the ''feature extraction formula'' is used to generate a new expression of the sample, which is used as input of the next layer. The new layer of the SVM trains on the new sample, and it extracts and learns the deep features of the signal layer by layer; eventually, after multiple feature mapping, it outputs the diagnostic results on the last layer. Because of the deep structure, the algorithm complexity and operation time increase. Therefore, in this paper, the least squares support vector machine (LSSVM) is combined with the sparse theory. By constructing the approximate maximal linearly independent vector set in the feature space, we conduct the sparse expression of samples and obtain the discriminant function for classification, which effectively solves the problem of sparsity deficiency for the LSSVM. Last, the method is used to diagnose centrifugal pump faults and rolling bearing faults and compares with the several methods of the SVM, the SLSSVM, deep SVM, and convolutional neural networks. The diagnostic results indicate that the method in this paper has good performance. INDEX TERMS Fault diagnosis, deep structure, support vector machine, sparsity.

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