As the medium of human-computer interaction, it is crucial to correctly and quickly interpret the motion information of surface electromyography. Deep learning can recognize a variety of sEMG actions by end-to-end training. However, most of the existing deep learning approaches have complex structures and numerous parameters, which make the network optimization problem difficult to realize. In this paper, a novel PSO-based optimized lightweight convolution neural network is designed to improve the accuracy and optimize the model with applications in sEMG signal (...) movement recognition. With the purpose of reducing the structural complexity of the deep neural network, the designed convolution neural network model is mainly composed of three convolution layers and two full connection layers. Meanwhile, the particle swarm optimization is used to optimize hyperparameters and improve the autoadaptive ability of the designed sEMG pattern recognition model. To further indicate the potential application, three experiments are designed according to the progressive process of body movements with respect to the Ninapro standard data set. Experiment results demonstrate that the proposed PLCNN recognition method is superior to the four other popular classification methods. (shrink)

Innovative loss‐of‐function techniques developed in recent years have made it much easier to target specific genomic loci at transcriptional levels. CRISPR interference (CRISPRi) has been proven to be the most effective and specific tool to knock down any gene of interest in mammalian cells. The catalytically deactivated Cas9 (dCas9) can be fused with transcription repressors to downregulate gene expression specified by sgRNA complementary to target genomic sequence. Although CRISPRi has huge potential for gene knockdown, there is still a lack of (...) systematic guidelines for efficient and widespread use. Here we describe the working mechanism and development of CRISPRi, designing principles of sgRNA, delivery methods and applications in mammalian cells in detail. Finally, we propose possible solutions and future directions with regard to current challenges. (shrink)

In user cluster analysis, users with the same or similar behavior characteristics are divided into the same group by iterative update clustering, and the core and larger user groups are detected. In this paper, we present the formulation and data mining of the correlation rules based on the clustering algorithm through the definition and procedure of the algorithm. In addition, based on the idea of the K-mode clustering algorithm, this paper proposes a clustering method combining related rules with multivalued discrete (...) features. In this paper, we construct a method to calculate the similarity between users using Jaccard distance and combine correlation rules with Jaccard distances to improve the similarity between users. Next, we propose a clustering method suitable for MDF. Finally, the basic K-mode algorithm is improved by the similarity measure method combining the correlation rule with the Jaccard distance and the cluster center update method which is the ARMDKM algorithm proposed in this paper. This method solves the problem that the MDF cannot be effectively processed in the traditional model and demonstrates its theoretical correctness. This experiment verifies the correctness of the new method by clustering purity, entropy, contour, and other indicators. (shrink)