Background: Basic helix-loop-helix (bHLH) is a superfamily of transcription factors that is widely found in plants and animals, and is the second largest transcription factor family in eukaryotes after MYB. They have been shown to be important regulatory components in tissue development and many different biological processes. However, no systemic analysis of the bHLH transcription factor family has yet been reported in Sorghum bicolor. Results: We conducted the first genome-wide analysis of the bHLH transcription factor family of Sorghum bicolor and identified 174 SbbHLH genes. Phylogenetic analysis of SbbHLH proteins and 158 Arabidopsis thaliana bHLH proteins was performed to determine their homology. In addition, conserved motifs, gene structure, chromosomal spread, and gene duplication of SbbHLH genes were studied in depth. To further infer the phylogenetic mechanisms in the SbbHLH family, we constructed six comparative syntenic maps of S. bicolor associated with six representative species. Finally, we analyzed the gene-expression response and tissue-development characteristics of 12 typical SbbHLH genes in plants subjected to six different abiotic stresses. Gene expression during flower and fruit development was also examined. Conclusions: This study is of great significance for functional identification and confirmation of the S. bicolor bHLH superfamily and for our understanding of the bHLH superfamily in higher plants.

In the traditional digital finite impulse response (FIR) filter design, there exist some limitations. For instance, unwanted signals cannot be filtered out by using these FIR filters. Therefore, to break these limitations, this paper proposes an optimization method for designing digital FIR filters based on the back propagation neural network (BPNN) algorithm. Firstly, an amplitude response model has been established based on the linear properties of the digital FIR filters. Then, the BPNN algorithm has been used to minimize the estimation error between the ideal and the actual amplitude response such that the optimal coefficients of the digital FIR filter can be obtained. Finally, several design examples are used to verify the performance of our proposed optimal design based on BPNN. The simulation results show that, compared with the optimal designs based on the sequential and rectangular window, our proposed optimal design based on BPNN can achieve better filtering effectiveness but at the cost of larger computational complexity.

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