Wei Guo

Post‐transcriptional tRNA modifications contribute to the decoding efficiency of tRNAs by supporting codon recognition and tRNA stability. Recent work shows that the molecular and cellular functions of tRNA modifications and tRNA‐modifying‐enzymes are linked to brain development and neurological disorders. Lack of these modifications affects codon recognition and decoding rate, promoting protein aggregation and translational stress response pathways with toxic consequences to the cell. In this review, we discuss the peculiarity of local translation in neurons, suggesting a role for fine‐tuning of translation performed by tRNA modifications. We provide several examples of tRNA modifications involved in physiology and pathology of the nervous system, highlighting their effects on protein translation and discussing underlying mechanisms, like the unfolded protein response (UPR), ribosome quality control (RQC), and no‐go mRNA decay (NGD), which could affect neuronal functions. We aim to deepen the understanding of the roles of tRNA modifications and the coordination of these modifications with the protein translation machinery in the nervous system.

In "Literary Variation of Indian Buddhist Stories in Chinese 志怪 Novels," Wei Guo discusses Buddhist Sutra scriptures which have been a reservoir of inspiration for Zhiguai novels since their first introduction in Chinese literature. Buddhist texts were less relevant for the "documentary" tradition of Chinese literature owing to their rough structure, vague context, and lack of a sense of history and reality, since they were originally intended as texts of didacticism. Hence, in order to integrate these exotic literary materials with local aesthetic concepts, Chinese writers explored creative adaptations including the addition of adding detail, linguistic embellishments, and the endowment of each story with specific narrative scenes in terms of character, place, and time. Guo argues that Indian Buddhist stories have been remolded in Zhiguai novels and transformed from imaginary religious literature into figurative and documentary literature whereby the convergence of the source texts with the target texts shows the processes of formation of world literature.

The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time-consuming. In order to address this problem, an infrared image generation method, infrared generative adversarial networks, based on conditional generative adversarial networks architecture is proposed. In I-GANs, visible images instead of random noise are used as the inputs, and the D-LinkNet network is also utilized to build the generative model, enabling improved learning of rich image textures and identification of dependencies between images. Moreover, the PatchGAN architecture is employed to build a discriminant model to process the high-frequency components of the images effectively and reduce the amount of calculation required. In addition, batch normalization is used to optimize the training process, and thereby, the instability and mode collapse of the generated adversarial network training can be alleviated. Finally, experimental verification is conducted on the produced infrared/visible light dataset. The experimental results reveal that high-quality and reliable infrared data are generated by the proposed I-GANs.