We studied the short-latency (SL) effects of postural perturbations produced by impulses applied over the spine of the C7 vertebra or the sternum ("axial impulses") in 12 healthy subjects. EMG recordings were made bilaterally from the triceps brachii, biceps brachii, soleus, and tibialis anterior muscles, and unilaterally from the deltoid, forearm flexors, forearm extensors, and first dorsal interosseous (FDI) muscles. Sternal impulses evoked short-latency responses in the biceps when subjects leaned posteriorly to support approximately 12% of their body weight with the arms, but these responses were only modestly larger than for isometric contraction of the arms (26.3 vs. 14.7%). In contrast, clear excitatory responses could be evoked in the deltoid, triceps, forearm muscles, and FDI when leaning anteriorly to support similar amounts of body weight. These responses were significantly larger than during isometric contraction. The deltoid (42.5%) and triceps (44.7%) had the largest responses in supported anterior lean and onset latencies increased distally in this condition (mean 31.8 ms in deltoid to 53.7 ms in FDI). There was a disproportionate delay between the forearm muscles and FDI. For both directions of lean, postural reflex responses normally present in the legs were severely attenuated. SL upper limb excitatory responses were bigger in proximal muscles as well as larger and more widespread for anterior axial perturbations compared to posterior axial perturbations when using the arms to support body weight. Our findings also provide further evidence of a role for reticulospinal pathways in mediating these rapid postural responses to accelerations of the trunk.
The features that characterize the onset of Huntington disease (HD) are poorly understood yet have significant implications for research and clinical practice. Motivated by the need to address this issue, and the fact that there may be inaccuracies in clinical HD data, we apply robust optimization and duality techniques to study support vector machine (SVM) classifiers in the face of uncertainty in feature data. We present readily numerically solvable semi-definite program reformulations via conic duality for a broad class of robust SVM classification problems under a general spectrahedron uncertainty set that covers the most commonly used uncertainty sets of robust optimization models, such as boxes, balls, and ellipsoids. In the case of the box-uncertainty model, we also provide a new simple quadratic program reformulation, via Lagrangian duality, leading to a very efficient iterative scheme for robust classifiers. Computational results on a range of datasets indicate that these robust classification methods allow for greater classification accuracies than conventional support vector machines in addition to selecting groups of highly correlated features. The conic duality-based robust SVMs were also successfully applied to a new, large HD dataset, achieving classification accuracies of over 95% and providing important information about the features that characterize HD onset.
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