The insular cortex (IC) integrates diverse sensory and interoceptive signals to support emotional and cognitive functions, exhibiting topologically functional distinctions. Although long-range pathways of the IC have been extensively mapped, the contribution of intra-IC architecture to network-level information processing remains poorly understood. Here we reconstructed single-cell projectomes of 2,267 IC neurons, generating a comprehensive cellular-resolution map of intra-IC connectivity. Graph-theoretic analyses revealed a hierarchical, topographically organized modular network with a hub-and-spoke organization and selective motif enrichment. Embedding these anatomical priors into recurrent neural network models demonstrated that intrinsic IC topology confers a distinct computational advantage: across multiple cognitive tasks, IC-initialized networks learned faster and exhibited better robustness to perturbations than those initialized with shuffled, randomized, or neighboring somatosensory cortical architectures. Together, these findings identify the IC as a structurally specialized cortical hub whose internal wiring supports efficient learning and provides design principles for brain-inspired networks.
Quantitative polymerase chain reaction (qPCR) is limited in measuring absolute nucleic acid copy numbers due to the inherent variability of calibrators. Here, we introduce the Quantal PCR (quPCR), a novel method that eliminates the need for calibrators by defining an intrinsic quantal unit derived from the thermodynamic and kinetic properties of the replication system. This approach first determines amplification efficiency at high template concentrations, which is then used as the replication probability to construct quantification cycle (Cq) distribution profiles. ### Competing Interest Statement Author X.T. is the inventor on patents related to the methodology described in this paper. This potential conflict of interest has been reviewed and managed by the author's company. Chinese Academy of Sciences, https://ror.org/034t30j35, XDB1010101 Department of Human Resources and Social Security of Jiangsu Province, https://ror.org/02kv0wv09 Jiawang District People's Government, Xuzhou City, Jiangsu Province，China Chinese Academy of Science, JCYJ-SHFY-2022-010
