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A double-stranded RNA (dsRNA) mycovirus was isolated from Talaromyces neofusisporus isolate HJ1-6 and named "Talaromyces neofusisporus chrysovirus 1" (TnCV1). It was found to consist of four dsRNA segments (TnCV1-1, TnCV1-2, TnCV1-3, and TnCV1-4) with lengths of 3595 bp, 3063 bp, 3054 bp, and 2876 bp, respectively. Sequence analysis showed that TnCV1-1 contains an open reading frame (ORF) encoding a putative RNA-dependent RNA polymerase (RdRp) of 1136 amino acids (aa), TnCV1-2 contains an ORF encoding a hypothetical protein of 906 aa, TnCV1-3 contains an ORF encoding a putative capsid protein (CP) of 938 aa, and TnCV1-4 contains an ORF encoding a hypothetical protein of 849 aa. The 5' and 3' untranslated regions (UTRs) of TnCV1-1, TnCV1-2, TnCV1-3, and TnCV1-4 showed a high degree of sequence similarity to each other. Phylogenetic analysis based on RdRp sequences suggested that TnCV1 is a new member of the genus Alphachrysovirus in the family Chrysoviridae. This is the first chrysovirus isolated from T. neofusisporus. [ABSTRACT FROM AUTHOR]

Aspergillus niger is an important filamentous phytopathogenic fungus with a broad host range. A novel double-stranded (ds) RNA mycovirus, named Aspergillus niger victorivirus 1 (AnV1), isolated from A. niger strain baiyun3.23-4, was sequenced and analyzed. The AnV1 genome is 5317 nucleotides long with a GC content of 56%. AnV1 contains two open reading frames (ORF1 and 2), overlapping at a tetranucleotide sequence (AUGA). ORF1 encodes a putative capsid protein (CP) of 778 amino acids (aa), while ORF2 potentially encodes a putative RNA-dependent RNA polymerase (RdRp) of 826 aa. Phylogenetic analysis indicated that AnV1 is a new member of the genus Victorivirus in the family Totiviridae. As far as we know, this is the first report of the complete genome sequence of a victorivirus infecting A. niger. [ABSTRACT FROM AUTHOR]

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Maintenance is an important aspect in the lifecycle of communication network devices. Prevalent problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of work orders, which significantly restrict the efficiency of maintenance work. Moreover, most maintenance systems are still based on cloud architectures that slow down data transfer. With a focus on the completion time, quality, and load balancing of maintenance work, we propose in this paper a learning-based virus evolutionary genetic algorithm with multiple quality-ofservice (QoS) constraints to implement intelligent scheduling in an edge network. The algorithm maintains the diversity of the population and improves the speed of convergence using a fitness function and a learning-based population generation mechanism. The test results demonstrate that the algorithm delivers good performance in terms of load balancing and QoS guarantee. We also propose a knowledge push algorithm based on a context model for intelligently pushing relevant knowledge according to the given conditions. The simulation results demonstrate that our scheme can improve the efficiency of on-site maintenance. [ABSTRACT FROM AUTHOR]