Characterization of the complete chloroplast genome of Juniperus recurva (Cupressaceae), the Dropping Juniper from the Himalaya

The complete chloroplast genome of the Drooping Juniper, Juniperus recurva, was characterized. The cp genome was 127,602 bp in length, which contained 119 genes, including 82 protein-coding genes, 33 tRNA, and four ribosomal RNA genes. Eight genes (atpF, ndhA, ndhB, rpoC1, petD, petB, rpl16, and rpl2) had a single intron, whereas two genes (rps12 and ycf3) contained two introns. The GC content of this circle genome is 35.0%. Phylogenomic analysis based on 78 common protein sequences strongly supported the close relationship between J. recurva and J. tibetica

Reinforcement Learning-Based Approach for Minimizing Energy Loss of Driving Platoon Decisions

Reinforcement learning (RL) methods for energy saving and greening have recently appeared in the field of autonomous driving. In inter-vehicle communication (IVC), a feasible and increasingly popular research direction of RL is to obtain the optimal action decision of agents in a special environment. This paper presents the application of reinforcement learning in the vehicle communication simulation framework (Veins). In this research, we explore the application of reinforcement learning algorithms in a green cooperative adaptive cruise control (CACC) platoon. Our aim is to train member vehicles to react appropriately in the event of a severe collision involving the leading vehicle. We seek to reduce collision damage and optimize energy consumption by encouraging behavior that conforms to the platoon’s environmentally friendly aim. Our study provides insight into the potential benefits of using reinforcement learning algorithms to improve the safety and efficiency of CACC platoons while promoting sustainable transportation. The policy gradient algorithm used in this paper has good convergence in the calculation of the minimum energy consumption problem and the optimal solution of vehicle behavior. In terms of energy consumption metrics, the policy gradient algorithm is used first in the IVC field for training the proposed platoon problem. It is a feasible training decision-planning algorithm for solving the minimization of energy consumption caused by decision making in platoon avoidance behavior

SARS-CoV-2 impairs interferon production via NSP2-induced repression of mRNA translation

Viruses evade the innate immune response by suppressing the production or activity of cytokines such as type I interferons (IFNs). Here we report the discovery of a mechanism by which the SARS-CoV-2 virus coopts an intrinsic cellular machinery to suppress the production of the key immunostimulatory cytokine IFN-β. We reveal that the SARS-CoV-2 encoded nonstructural protein 2 (NSP2) directly interacts with the cellular GIGYF2 protein. This interaction enhances the binding of GIGYF2 to the mRNA cap-binding protein 4EHP, thereby repressing the translation of the Ifnb1 mRNA. Depletion of GIGYF2 or 4EHP significantly enhances IFN-β production, which inhibits SARS-CoV-2 replication. Our findings reveal a target for rescuing the antiviral innate immune response to SARS-CoV-2 and other RNA viruses