Author

K. Nair

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Open access Jul 2026

RfxCas13d mediates robust cross-strain suppression of highly pathogenic avian influenza viruses

Highly pathogenic avian influenza viruses (HPAIVs) continue to cause substantial disease in birds and mammals, with repeated H5N1 spillovers highlighting the need for broadly protective antiviral strategies. Here, we develop a programmable RNA-targeting antiviral platform based on RfxCas13d and evaluate its activity in avian cells. Screening of five Cas13 orthologs in chicken DF1 fibroblasts revealed RfxCas13d as the most potent and well tolerated effector. Virus-specific CRISPR RNAs (crRNAs) targeting conserved regions of positive- and negative-sense influenza RNA were tested against A/WSN/033 (H1N1) and multiple HPAIV isolates, including a member of clade 2.3.4.4b H5N1. Targeting positive-sense RNA conferred superior influenza inhibitory activity and was further enhanced by multiplexed crRNA expression. These findings establish RfxCas13d as a versatile RNA-guided antiviral platform and provide a foundation for cross-strain influenza control through conserved RNA targeting.

Sudip Dhakal, Aaron J. Smith, Emiliana Weiss et al. · npj Antimicrobials and Resis... · 0 citations

Preprint Aug 2026

SAGE: Surrogate-gradient Adaptation via Attention-Guided Entropy for Spiking Transformers

Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep neural networks by exploiting sparse event-driven computation, but their training remains challenging because the non-differentiable spike function requires surrogate gradients whose fixed shape may be suboptimal across layers and training stages. In this work, we introduce SAGE, an uncertainty-modulated surrogate-gradient mechanism for Transformer-based SNNs. SAGE estimates block-level uncertainty from normalized self-attention entropy and uses this signal to adapt the surrogate-gradient slope during training while leaving the inference model unchanged. By modulating only the training-time surrogate parameter, the proposed method preserves the original architecture and deployment cost while improving optimization flexibility. Experiments on CIFAR-10/100 demonstrate that SAGE achieves improved accuracy over fixed-surrogate baselines, with results up to 1-2\% consistent gains across multiple simulation time steps. These results highlight the potential of attention-derived uncertainty as a lightweight training signal for adaptive surrogate-gradient learning in transformer-based SNNs.

K. Nair, Rodrigue Rizk, K. Santosh · 0 citations