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

Early cell autonomous and niche-mediated alveolar epithelial response to influenza infection in primary lung organoids

Influenza A virus (IAV) infection is a significant cause of morbidity and mortality for patients worldwide. Alveolar type 2 (AT2) cells are the preferential target of IAV as part of the pathogenesis of viral pneumonia and acute respiratory distress syndrome (ARDS). Early IAV infection of alveolar cells has been challenging to model both in vitro and in vivo. To address this challenge, we used a combination of murine and human primary alveolar organoids to define methods for robust IAV infection and evaluated cell-autonomous consequences of IAV using a temporal series of multiome paired single nuclei RNA/ATAC sequencing. Infected AT2 cells undergo conserved changes defined by early loss of surfactant secretion, decreased lipid biogenesis, a rapid burst of antiviral response, and late viral-mediated suppression. Surprisingly, uninfected AT2 cells undergo substantial transcriptional and epigenomic changes in IAV-treated cultures, leading to transition to damage-associated cell states within hours via a process driven by the inflammatory milieu of murine organoids. Together, these data provide new methods for high-fidelity modeling of IAV infection in alveolar cells and define a conserved AT2 cell response signature to IAV with implications for ARDS pathogenesis.

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