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This study aimed to analyze the diffusion of electrical stimulation signals in human tissue and provide a theoretical basis for multi-electrode combined stimulation. The standard single-layer human head model based on electromagnetic simulation was taken as the geometric structure model. The model filler was assumed to be muscle tissue, and a finite element model with muscle characteristics was established. A 20-mA DC electrical signal was input, and the propagation mechanism of the signal in the simplified brain model was calculated and analyzed through multi-physical field simulation software. The signal was mainly concentrated around the electrode; when multi-electrode combined stimulation was used, signal superposition existed at the geometric center of the model, and the signal was enhanced. Slice interception analysis demonstrated that the signal attenuation intensity was approximately 8 dB/cm in homogeneous muscle tissue. To compare the performance of the single-layer model and multi-layer model, a semi-refined digital brain model was established, and simulated signal diffusion of the two models was analyzed. Comparative analysis found that due to the uneven distribution of tissues and the high shielding property of bone, the signal was highly scattered at the bone contact, but the superposition of signals in the brain center still existed.

Regulation of transcription is controlled by sets of transcription factors binding specific sites in the regulatory regions of genes. It is therefore believed that regulatory regions driving similar expression profiles share some common structural features. We here introduce a computational approach for finding a small set of rules describing the presence and positioning of motifs in a set of promoter sequences. This rule set is subsequently used for finding promoters that drive similar expression profiles from a genomic set of sequences. We applied our approach on muscle-expressed genes in Caenorhabditis elegans. We obtained a high average performance, and in the best case we found that almost 50% of true positive test genes scored higher than 90% of the true negative test genes. High scoring non-training sequences were enriched for muscle-expressed genes, and predicted motifs fitting the rules showed a significant tendency to be present in experimentally verified regulatory regions. Our model is more general than existing cis-regulatory module models, as rules selected by our model contain a variety of information, including not only proximal but also distal positioning of pairs of motifs, positioning with regard to the translation start site, and simply presences of motifs. We believe our model can help to increase our understanding about transcription factor cooperation and transcription initiation.