6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

A20 Enhances the Expression of the Proto-Oncogene C-Myc by Downregulating TRAF6 Ubiquitination after ALV-A Infection

Hens infected with avian leukosis virus subgroup A (ALV-A) experience stunted growth, immunosuppression, and potentially, lymphoma development. According to past research, A20 can both promote and inhibit tumor growth. In this study, DF-1 cells were infected with ALV-A rHB2015012, and Gp85 expression was measured at various time points. A recombinant plasmid encoding the chicken A20 gene and short hairpin RNA targeting chicken A20 (A20-shRNA) was constructed and transfected into DF-1 cells to determine the effect on ALV-A replication. The potential signaling pathways of A20 were explored using bioinformatics prediction, co-immunoprecipitation, and other techniques. The results demonstrate that A20 and ALV-A promoted each other after ALV-A infection of DF-1 cells, upregulated A20, inhibited TRAF6 ubiquitination, and promoted STAT3 phosphorylation. The phosphorylated-STAT3 (p-STAT3) promoted the expression of proto-oncogene c-myc, which may lead to tumorigenesis. This study will help to further understand the tumorigenic process of ALV-A and provide a reference for preventing and controlling ALV

Preparation of a novel monoclonal antibody against Avian leukosis virus subgroup J Gp85 protein and identification of its epitope

ABSTRACT: Avian leukosis virus subgroup J (ALV-J) is an avian oncogenic retrovirus that has caused huge economic losses in the poultry industry due to its great pathogenicity and transmission ability. However, the continuous emergence of new strains would bring challenges to diagnosis and control of ALV-J. .This study focuses on preparing the monoclonal antibody (MAb) against ALV-J Gp85 and identifying its epitope. The truncated ALV-J gp85 gene fragment was amplified and then cloned into expression vectors. Purified GST-Gp85 was used to immune mice and His-Gp85 was used to screen MAb. Finally, a hybridoma cell line named J16 that produced specific MAb against the ALV-J. Immunofluorescence assay showed that MAb J16 specifically recognized ALV-J rather than ALV-A or ALV-K infected DF-1 cells. To identify the epitope recognized by MAb J16, fourteen partially overlapping ALV-J Gp85 fragments were prepared and tested by Western blot. The results indicated that peptide 150-LIRPYVNQ-157 was the minimal epitope of ALV-J Gp85 recognized by MAb J16. Alignment analysis of Gp85 from different ALV subgroups showed that the epitope keep high conservation among 36 ALV-J strains, but significant different from that of ALV subgroup A, B, C, D, E and K. Overall, we prepared a MAb specific against ALV-J and identified peptide 150-LIRPYVNQ-157 as a novel specific epitope of ALV-J Gp85, which may assist in laying the foundation for specific ALV-J detection methods

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions