FeNN-DMA: A RISC-V SoC for SNN acceleration

arXiv:2511.00732v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to

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Characterization of genetically novel chimeric plasmids from Salmonella Heidelberg conferring multi-drug resistance and increased pathogenicity

Salmonella enterica serotype Heidelberg (S. Heidelberg) is a significant cause of human salmonellosis, with resistance to extended-spectrum cephalosporins posing challenges for clinical management. In this study, we genetically and functionally characterized six plasmids isolated from distinct PFGE-types of S. Heidelberg previously reported in The Netherlands, revealing a chimeric IncC-I1a plasmid that shares similarities with the epidemic pESI plasmid identified in emergent S. Infantis isolates. Our analysis showed the accumulation of genetic determinants conferring resistance to antibiotics (blaCMY-2, tetA and sul2) and heavy metals (mer operon), as well as enhanced pathogenicity (Yersinia high-pathogenicity island). All these elements are located on a stable non-conjugative but likely mobilizable plasmid backbone that imposes only a marginal fitness cost on its bacterial host. This convergence of multidrug resistance and pathogenicity likely enhances bacterial adaptability and virulence, undermining control strategies based solely on reducing selective pressure. The emergence and dissemination of such hybrid plasmids represent an increasing threat to both public and animal health, analogous to that posed by pESI-like plasmids, and underscore the urgent need for integrated genomic surveillance and risk assessment, as their continued expansion could complicate antimicrobial therapy and containment efforts during Salmonella outbreaks.

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