Abstract
The biteOscope enables the high-resolution monitoring and video recording of blood-feeding mosquitoes. Mosquito biting is induced by combining host cues, an artificial bloodmeal, a membrane, and a transparent heater in a transparent behavioral arena. Machine vision techniques enable the tracking and pose estimation of individual mosquitoes to discern behavior and resolve individual feeding events. The workflow allows multiple replicates and large amounts of imaging data to be generated rapidly. These data are suitable for downstream analysis using machine learning tools for behavioral analysis, allowing subtle behavioral effects to be characterized.
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