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A Generative Statistical Algorithm for Automatic Detection of Complex Postures

Fig 7

(A) Left: an overlay of postures before, during, and after coiling of a wild-type animal. Arrows depict the direction of locomotion and the scale bar represents 100 μm. Middle: the probabilities of forward locomotion, reversals, and non-directional dwelling before and after a detected period of anterior coiling. Right: locomotion probabilities before and after a period of posterior coiling. In the case of wild-type animals, most coiling events occur during Ω–turns. The horizontal time axis depicts the time leading to and immediately following a continuous period of coiling, i.e., the entry into and exit from a coiling event. The gaps signify that locomotion during the (variable) time of the coiling events themselves is not plotted. (B)-(E) The same as (A) for mutants exhibiting coiling phenotypes. In all panels, 9–12 L4 larvae of each genotype were imaged at 10 frames per second for 2–4 hours. Thin lines depict animal-to-animal variation (mean ± s.e.m).

Fig 7

doi: https://doi.org/10.1371/journal.pcbi.1004517.g007