The H9N2 subtype avian influenza virus(AIV)continues to propagate and undergo evolution within China,thereby posing a significant threat to the poultry industry.This study encompassed the collection of 436 samples and...The H9N2 subtype avian influenza virus(AIV)continues to propagate and undergo evolution within China,thereby posing a significant threat to the poultry industry.This study encompassed the collection of 436 samples and swabs in East China over the period spanning 2018 to 2019,from which 31 strains of the H9N2 subtype viruses were isolated and purified.We revealed that the HA and NA genes of the 31 isolates categorized within the Y280 branch,while the PB2 and M genes were associated with the G1 branch,and the remaining genes aligned with the F/98 branch.Despite this alignment,antigenic mapping demonstrated differences between the 2018 and 2019 strains,with the early vaccine strains displaying low serological reactivity toward these isolates.Notably,the CK/SH/49/19 isolate exhibited lethality in mice,characterized by a PB2 E627V mutation and a HA deletion at amino acid position 217.Mechanistically,in vitro studies showed that the influenza virus CK/SH/49/19 carrying PB2627V and HA 217M mutations displayed enhanced replication capacity,attributed to the heightened activity of the polymerase with PB2627V.Moreover,the absence of the amino acid at the HA 217 site obstructed viral adsorption and internalization,resulted in lower activation pH,and impeded the virus budding process.Critically,in vivo experiments revealed that CK/SH/49/19(PB2627V,HA 217Δ)triggered a robust activation of interferon response and interferon-stimulated genes.This study furnished a theoretical foundation for the scientific prevention and control strategies against H9N2 subtype avian influenza.展开更多
To address the challenges of ill-defined optimization objectives,difficult constraint coordination,and lack of quantitative basis for interconnection splicing and switch placement in current distribution network topol...To address the challenges of ill-defined optimization objectives,difficult constraint coordination,and lack of quantitative basis for interconnection splicing and switch placement in current distribution network topology optimization,this paper proposes a data-driven intelligent optimization method for panoramic construction of distribution network topology based on the Common Information Model(CIM).This method integrates multi-source heterogeneous data relationships-including equipment,terminals,and connection nodes-through joint analysis of multi-line CIM and hierarchical topology extraction.It automatically identifies feeder trunk paths and branch structures,incorporates inter-connection switch splicing and intelligent path optimization strategies,and performs topology opti-mization and switch placement based on the principle of minimizing outage impact.This constructs a complete,robust main-branch topology graph model.The algorithm employs depth-first search(DFS)for supply path modeling,complemented by semantic analysis of equipment attributes and hierarchical node classification to refine topology simplification.Batch testing on a dataset of 6880 medium-voltage feeders in a Central China city achieved a 98.30%successful modeling rate for complete interconnection information,with an average processing time of approximately 4.57 s per feeder.Further validation using representative overhead,cable,and hybrid lines demonstrated high consistency between the automatically generated topology and the original system diagram in node identification,path con-struction,and information annotation,confirming the algorithm's structural adaptability and engi-neering practicality.These findings provide dynamically interactive topology model support for multiple distribution network scenarios-including planning,operation,and maintenance-offering significant application and promotion value.展开更多
基金supported by National Natural Science Foundation of China[Grant number:32272992(JP),31772775(JP)]National Key Research and Development Program of China[Grant number:2021YFD1800205(JP)].
文摘The H9N2 subtype avian influenza virus(AIV)continues to propagate and undergo evolution within China,thereby posing a significant threat to the poultry industry.This study encompassed the collection of 436 samples and swabs in East China over the period spanning 2018 to 2019,from which 31 strains of the H9N2 subtype viruses were isolated and purified.We revealed that the HA and NA genes of the 31 isolates categorized within the Y280 branch,while the PB2 and M genes were associated with the G1 branch,and the remaining genes aligned with the F/98 branch.Despite this alignment,antigenic mapping demonstrated differences between the 2018 and 2019 strains,with the early vaccine strains displaying low serological reactivity toward these isolates.Notably,the CK/SH/49/19 isolate exhibited lethality in mice,characterized by a PB2 E627V mutation and a HA deletion at amino acid position 217.Mechanistically,in vitro studies showed that the influenza virus CK/SH/49/19 carrying PB2627V and HA 217M mutations displayed enhanced replication capacity,attributed to the heightened activity of the polymerase with PB2627V.Moreover,the absence of the amino acid at the HA 217 site obstructed viral adsorption and internalization,resulted in lower activation pH,and impeded the virus budding process.Critically,in vivo experiments revealed that CK/SH/49/19(PB2627V,HA 217Δ)triggered a robust activation of interferon response and interferon-stimulated genes.This study furnished a theoretical foundation for the scientific prevention and control strategies against H9N2 subtype avian influenza.
基金supported by the State Grid Corporation of China science and technology project funding(5400-202322560A-3-2-ZN).
文摘To address the challenges of ill-defined optimization objectives,difficult constraint coordination,and lack of quantitative basis for interconnection splicing and switch placement in current distribution network topology optimization,this paper proposes a data-driven intelligent optimization method for panoramic construction of distribution network topology based on the Common Information Model(CIM).This method integrates multi-source heterogeneous data relationships-including equipment,terminals,and connection nodes-through joint analysis of multi-line CIM and hierarchical topology extraction.It automatically identifies feeder trunk paths and branch structures,incorporates inter-connection switch splicing and intelligent path optimization strategies,and performs topology opti-mization and switch placement based on the principle of minimizing outage impact.This constructs a complete,robust main-branch topology graph model.The algorithm employs depth-first search(DFS)for supply path modeling,complemented by semantic analysis of equipment attributes and hierarchical node classification to refine topology simplification.Batch testing on a dataset of 6880 medium-voltage feeders in a Central China city achieved a 98.30%successful modeling rate for complete interconnection information,with an average processing time of approximately 4.57 s per feeder.Further validation using representative overhead,cable,and hybrid lines demonstrated high consistency between the automatically generated topology and the original system diagram in node identification,path con-struction,and information annotation,confirming the algorithm's structural adaptability and engi-neering practicality.These findings provide dynamically interactive topology model support for multiple distribution network scenarios-including planning,operation,and maintenance-offering significant application and promotion value.
