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Simple Summary: Smell is an important sense for insects. The olfactory sense of insects is involved in their feeding, mating, egg-laying, predation avoidance, and communication behaviors. The use of insect-specific odorants to control insect behavior is an important control method. Here, we built iORandLigandDB, a platform for the batch prediction of insect-specific odorants based on artificial intelligence technology. The 3D structure of existing ORs in insects and the docking data with relevant odorants can be retrieved from the database. The use of insect-specific odorants to control the behavior of insects has always been a hot spot in research on "green" control strategies of insects. However, it is generally time-consuming and laborious to explore insect-specific odorants with traditional reverse chemical ecology methods. Here, an insect odorant receptor (OR) and ligand database website (iORandLigandDB) was developed for the specific exploration of insect-specific odorants by using deep learning algorithms. The website provides a range of specific odorants before molecular biology experiments as well as the properties of ORs in closely related insects. At present, the existing three-dimensional structures of ORs in insects and the docking data with related odorants can be retrieved from the database and further analyzed. [ABSTRACT FROM AUTHOR]

Image denoising is an important low-level computer vision task, which aims to reconstruct a noise-free and high-quality image from a noisy image. With the development of deep learning, convolutional neural network (CNN) has been gradually applied and achieved great success in image denoising, image compression, image enhancement, etc. Recently, Transformer has been a hot technique, which is widely used to tackle computer vision tasks. However, few Transformer-based methods have been proposed for low-level vision tasks. In this paper, we proposed an image denoising network structure based on Transformer, which is named DenSformer. DenSformer consists of three modules, including a preprocessing module, a local-global feature extraction module, and a reconstruction module. Specifically, the local-global feature extraction module consists of several Sformer groups, each of which has several ETransformer layers and a convolution layer, together with a residual connection. These Sformer groups are densely skip-connected to fuse the feature of different layers, and they jointly capture the local and global information from the given noisy images. We conduct our model on comprehensive experiments. In synthetic noise removal, DenSformer outperforms other state-of-the-art methods by up to 0.06–0.28 dB in gray-scale images and 0.57–1.19 dB in color images. In real noise removal, DenSformer can achieve comparable performance, while the number of parameters can be reduced by up to 40%. Experimental results prove that our DenSformer achieves improvement compared to some state-of-the-art methods, both for the synthetic noise data and real noise data, in the objective and subjective evaluations. [ABSTRACT FROM AUTHOR]