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Recent research has elucidated mechanochemical pathways of single cell polarization, but much less is known about collective motility initiation in adhesive cell groups. We used galvanotactic assays of zebrafish keratocyte cell groups, pharmacological perturbations, electric field switches, particle imaging velocimetry, and cell tracking to show that large cell groups initiate motility in minutes toward the cathode. Interestingly, while PI3K-inhibited single cells are biased toward the anode, inhibiting PI3K does not affect the cathode-directed cell group migration. We observed that control groups had the fastest cathode-migrating cell at the front, while the front cells in PI3K-inhibited groups were the slowest. Both control and PI3K-inhibited groups rapidly repolarized when the electric field direction was reversed, and the group migration continued after the electric field was switched off. Inhibiting myosin disrupted the cohesiveness of keratocyte groups and abolished the collective directionality and ability to switch direction when the electric field is reversed. Our data are consistent with a model according to which cells in the group sense the electric field individually and mechanical integration of the cells results in coherent group motility. 

New depth sensors, like the Microsoft Kinect, produce streams of human pose data. These discrete pose streams can be viewed as noisy samples of an underlying continuous ideal curve that describes a trajectory through high-dimensional pose space. This paper introduces a technique for generalized curvature analysis (GCA) that determines features along the trajectory which can be used to characterize change and segment motion. Tools are developed for approximating generalized curvatures at mean points along a curve in terms of the singular values of local mean-centered data balls. The features of the GCA algorithm are illustrated on both synthetic and real examples, including data collected from a Kinect II sensor. We also applied GCA to the Carnegie Mellon University Motion Capture (MoCaP) database. Given that GCA scales linearly with the length of the time series we are able to analyze large data sets without down sampling. It is demonstrated that the generalized curvature approximations can be used to segment pose streams into motions and transitions between motions. The GCA algorithm can identify 94.2% of the transitions between motions without knowing the set of possible motions in advance, even though the subjects do not stop or pause between motions.