Tubito, A. | bioRxiv Science

bioRxiv2026

The MosAICo ecosystem: bridging the taxonomic gap in vector surveillance with real-time entomological artificial intelligence

Mosquito-borne diseases represent an escalating global health threat, driven by climate change, urbanization, and the spread of invasive vectors into new territories. Effective surveillance is constrained by a critical taxonomic impediment: the rate of specimen collection far outpaces the capacity of expert entomologists to process and identify trap catches. To address this bottleneck we developed MosAICo, an integrated AI-powered ecosystem for automated mosquito species identification designed for real-world, national-scale entomological surveillance. The system combines a standardized benchtop imaging device with MosAICo-Net, a deep learning pipeline enabling efficient and principled open-set recognition and uncertainty quantification. Trained and evaluated on 12, 499 specimens spanning 15 species collected across Italy, the model identifies seven priority vector species while explicitly rejecting out-of-distribution specimens. On a geographically stratified held-out test set, MosAICo-Net achieved over 90% accuracy on target species, and an AUROC of 0.96 for out-of-distribution detection. Field validation across 20 Italian surveillance sites confirmed these results: 94% micro accuracy on 1, 470 field-collected target specimens and strong agreement with expert manual counts ([Formula] = 0.66). To assess cross-geographic generalizability, the system was further evaluated on 118 Aedes albopictus specimens collected at the fringe of the species invasion front in Ghana: a 97.4% accuracy with only a single specimen escalated to expert review, suggests that MosAICo is well-suited for deployment in distant and epidemiologically critical regions. The system processes up to 82 specimens per image, matching expert throughput at constant speed regardless of taxonomic complexity. By embedding uncertainty-aware AI within a standardized hardware-software pipeline, MosAICo acts as a scalable force multiplier for public health entomology, freeing expert attention for rare, invasive, or ambiguous specimens that require human validation.

ecology↗

bioRxiv2026

Bridging Neurons to Behaviour: A Generative Neural Engine Mechanistically Rejects the Independent Race Model

The brain produces robust, low-dimensional behaviour from variable, high-dimensional activity. The dominant account of action control, the Independent Race Model (IRM), explains stopping as a "Go" and a "Stop" process racing independently, and predicts behaviour accurately. But independence is a behavioural assumption never confronted with neural dynamics, and the same premotor neurons drive both processes. We therefore trained a generative Deep Markov Model on premotor activity from two macaques; trained on neural data alone, it emergently reproduces the full reaction-time distribution, validating it as a proxy for the neural hardware. Used for in silico experiments, this engine reveals systematic violations of both of the IRMs independence axioms, with violations emerging from the geometry of a single shared manifold. Apparent independence was never real: an artifact of observing behaviour without seeing the manifold that produces it.

neuroscience↗