A Noncoding A-to-U Kozak Site Change Related to the High Transmissibility of Alpha, Delta, and Omicron VOCs.

Three prevalent SARS-CoV-2 variants of concern (VOCs) emerged and caused epidemic waves. It is essential to uncover advantageous mutations that cause the high transmissibility of VOCs. However, viral mutations are tightly linked, so traditional population genetic methods, including machine learning-based methods, cannot reliably detect mutations conferring a fitness advantage. In this study, we developed an approach based on the sequential occurrence order of mutations and the accelerated furcation rate in the pandemic-scale phylogenomic tree. We analyzed 3,777,753 high-quality SARS-CoV-2 genomic sequences and the epidemiology metadata using the Coronavirus GenBrowser. We found that two noncoding mutations at the same position (g.a28271-/u) may be crucial to the high transmissibility of Alpha, Delta, and Omicron VOCs although the noncoding mutations alone cannot increase viral transmissibility. Both mutations cause an A-to-U change at the core position -3 of the Kozak sequence of the N gene and significantly reduce the protein expression ratio of ORF9b to N. Using a convergent evolutionary analysis, we found that g.a28271-/u, S:p.P681H/R, and N:p.R203K/M occur independently on three VOC lineages, suggesting that coordinated changes of S, N, and ORF9b proteins are crucial to high viral transmissibility. Our results provide new insights into high viral transmissibility co-modulated by advantageous noncoding and nonsynonymous changes.

Application of Internet of Things Artificial Intelligence and Knowledge Innovation System in Table Tennis Teaching and Training.

The Internet of Things is an important link in the future development of the Internet and will have a significant impact on development over the next five years or so. The purpose of this article is to learn how to use the Internet of Things to learn and practice table tennis and find new ways to integrate table tennis and the internet. This article proposes to build a detection system for IoT sensors and collect information about the ball's movement state and the athlete's movement state during table tennis. Through data analysis, we better guide athletes to develop table tennis training. At the same time, it also uses knowledge innovation to integrate the Internet of Things with the reality of life and better derive the development of the Internet of Things artificial intelligence. Based on this, this paper designs the construction of IoT sensors to collect the sports status of table tennis enthusiasts and verify the sports experience of table tennis enthusiasts by comparing factors such as physical training, basic skills and mobile training, interest, and confidence. The experimental results of this article show that the Internet of Things artificial intelligence has great potential, and it can innovatively promote industrial change. It better realizes the teaching and training of table tennis players and improves the teaching and training ability of table tennis by 20%.