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https://www.selleckchem.com/products/ebselen.html Additionally, bacteriophages have been deployed in developing rapid, sensitive, and specific biosensors for various bacterial targets. Intriguingly, their bioengineering capabilities show great promise in improving their adaptability and effectiveness as biocontrol and detection tools. This review aims to provide a balanced perspective on bacteriophages by outlining advantages, challenges, and future steps needed in order to boost their therapeutic and biocontrol potential, while also providing insight on their potential role in contributing to bacterial evolution and survival.At present, in the field of video-based human action recognition, deep neural networks are mainly divided into two branches the 2D convolutional neural network (CNN) and 3D CNN. However, 2D CNN's temporal and spatial feature extraction processes are independent of each other, which means that it is easy to ignore the internal connection, affecting the performance of recognition. Although 3D CNN can extract the temporal and spatial features of the video sequence at the same time, the parameters of the 3D model increase exponentially, resulting in the model being difficult to train and transfer. To solve this problem, this article is based on 3D CNN combined with a residual structure and attention mechanism to improve the existing 3D CNN model, and we propose two types of human action recognition models (the Residual 3D Network (R3D) and Attention Residual 3D Network (AR3D)). Firstly, in this article, we propose a shallow feature extraction module and improve the ordinary 3D residual structure, which reduces the parameters and strengthens the extraction of temporal features. Secondly, we explore the application of the attention mechanism in human action recognition and design a 3D spatio-temporal attention mechanism module to strengthen the extraction of global features of human action. Finally, in order to make full use of the residual structure

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