Traditional BP model of neural network is easy to get a local minimum rather than the global optimal solution. As the training times increases, the learning efficiency is falling low, so as the convergence rate. Improvement on the traditional model of BP neural network algorithm improves the convergence rate of the neural network, and reduces the training times, so that the output of the neural network can not only determine the type of the train failure occurred, to improve the accuracy of diagnostic results, but also to diagnose within a certain range even the fault does not appear, to make the fault of train intelligent and simple. The simulation results show that the improved algorithm is effective.
Objectives
Pearl millet (Pennisetum glaucum) is a cereal crop that is tolerant to a high temperature, a drought and a nutrient-poor condition. Characterizing pearl millet proteins can help to improve productivity of pearl millet and other crops. Transcription factors in general are proteins that regulate transcription of their target genes and thereby regulate diverse processes. Some transcription factor families in pearl millet were characterized in previous studies, but most of them are not. The objective of the data presented was to characterize amino acid sequences for most transcription factors in pearl millet.
Data description
Sequences of 2395 pearl millet proteins that have transcription factor-associated domains were extracted. Subcellular and suborganellar localization of these proteins was predicted by MULocDeep. Conserved domains in these sequences were confirmed by CD-Search. These proteins were classified into 85 families on the basis of those conserved domains. A phylogenetic tree including pearl millet proteins and their counterparts in Arabidopsis thaliana and rice was constructed for each of these families. Sequence motifs were identified by MEME for each of these families.
For the shortcomings of an unstable clustering effect and low accuracy caused by the manual setting of the two parameters Eps and MinPts of the DBSCAN (density-based spatial clustering of applications with noise) algorithm, this paper proposes an adaptive determination method for DBSCAN algorithm parameters based on the K-dist graph, noted as X-DBSCAN. The algorithm uses the least squares polynomial curve fitting method to fit the curve in the K-dist graph to generate a list of candidate Eps parameters and uses the mathematical expectation method and noise reduction threshold to generate the corresponding MinPts parameter list. According to the clustering results of each group of parameters in the Eps and MinPts parameter lists, a stable range of cluster number changes is found, and the MinPts and Eps corresponding to the maximum K value in the stable range are selected as the optimal algorithm parameters. The optimality of this parameter was verified using silhouette coefficients. A variety of experiments were designed from multiple angles on the artificial dataset and the UCI real dataset. The experimental results show that the clustering accuracy of X-DBSCAN was 21.83% and 15.52% higher than that of DBSCAN on the artificial and real datasets, respectively. The X-DBSCAN algorithm was also superior to other algorithms through comprehensive evaluation and analysis of various clustering indicators. In addition, experiments on four synthetic Gaussian datasets of different dimensions showed that the average clustering indices of the proposed algorithm were above 0.999. The X-DBSCAN algorithm can select parameters adaptively in combination with the characteristics of the dataset; the clustering effect is better, and clustering process automation is realized.
Pearl millet (Pennisetum glaucum (L.) R. Br.) is a cereal crop that is tolerant to drought, high-temperature and poor-nutrient stresses throughout its life cycle. The DOF (DNA binding with One Finger) family genes encode plant-specific zinc finger transcription factors. These transcription factors have a variety of functions and can be involved in regulating the stress tolerance of pearl millet. In this study, we identified 12 DOF family genes (PgDOFs) in pearl millet. Ten of them were distributed on four chromosomes and the other two were on scaffolds (i.e., non-chromosomal sequences). Protein sequence analysis showed that PgDOFs have the DOF domain in their N-terminal regions and that other conserved motifs are also present in them. PgDOFs and DOF proteins from five other plant species were divided into seven groups, and the 12 PgDOFs could be classified into six of those seven groups. In gene expression analysis, most PgDOFs were upregulated by cold, heat, dehydration and salinity stress. These data can be useful for further characterization of the PgDOFs.
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