Trinity College Dublin

Isolated focal dystonia has fewer than five known causal genes, and small families with incomplete penetrance make linkage analysis hard. A 3D convolutional network, DystoniaGeneNet, judged familial status from T1-weighted brain MRI and correctly classified 18 of 21 independent familial cases (85.7%), then guided exome variant prioritization and segregation analysis in 214 individuals. Rare variants segregating with disease emerged in RAP1GAP, TNN and ITSN2, all converging on perisynaptic extracellular matrix–synapse signalling.

Isolated focal dystonia has fewer than five known causal genes, and small families with incomplete penetrance make linkage analysis hard. A 3D convolutional network, DystoniaGeneNet, judged familial status from T1-weighted brain MRI and correctly classified 18 of 21 independent familial cases (85.7%), then guided exome variant prioritization and segregation analysis in 214 individuals. Rare variants segregating with disease emerged in RAP1GAP, TNN and ITSN2, all converging on perisynaptic extracellular matrix–synapse signalling.

Short-horizon model predictive control runs fast but cannot see an opponent beyond its horizon, so it needs a learned, context-dependent cost correction; when passing is possible on either side, both corrections are good and deterministic regression averages them into the opponent. ZipDrift instead trains a one-step drifting generator over cost corrections, tilted by closed-loop cost and learned without differentiating through the optimiser, then generates a few candidates ranked by a learned critic. On a straight-track overtaking benchmark with an eight-dimensional stagewise correction, all 600 test episodes were collision-free, against a 4.0% crash rate for an idealised ZipMPC-style regressor.

Short-horizon model predictive control runs fast but cannot see an opponent beyond its horizon, so it needs a learned, context-dependent cost correction; when passing is possible on either side, both corrections are good and deterministic regression averages them into the opponent. ZipDrift instead trains a one-step drifting generator over cost corrections, tilted by closed-loop cost and learned without differentiating through the optimiser, then generates a few candidates ranked by a learned critic. On a straight-track overtaking benchmark with an eight-dimensional stagewise correction, all 600 test episodes were collision-free, against a 4.0% crash rate for an idealised ZipMPC-style regressor.