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Long-duration animal tracking in difficult lighting conditions.

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Date
2015-07-01
Authors
Stern, Ulrich
Zhu, Edward Y
He, Ruo
Yang, Chung-Hui
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Abstract
High-throughput analysis of animal behavior requires software to analyze videos. Such software typically depends on the experiments' being performed in good lighting conditions, but this ideal is difficult or impossible to achieve for certain classes of experiments. Here, we describe techniques that allow long-duration positional tracking in difficult lighting conditions with strong shadows or recurring "on"/"off" changes in lighting. The latter condition will likely become increasingly common, e.g., for Drosophila due to the advent of red-shifted channel rhodopsins. The techniques enabled tracking with good accuracy in three types of experiments with difficult lighting conditions in our lab. Our technique handling shadows relies on single-animal tracking and on shadows' and flies' being accurately distinguishable by distance to the center of the arena (or a similar geometric rule); the other techniques should be broadly applicable. We implemented the techniques as extensions of the widely-used tracking software Ctrax; however, they are relatively simple, not specific to Drosophila, and could be added to other trackers as well.
Type
Journal article
Subject
Animals
Behavior, Animal
Drosophila melanogaster
Lighting
Ovum
Time Factors
Ultraviolet Rays
Permalink
https://hdl.handle.net/10161/10574
Published Version (Please cite this version)
10.1038/srep10432
Publication Info
Stern, Ulrich; Zhu, Edward Y; He, Ruo; & Yang, Chung-Hui (2015). Long-duration animal tracking in difficult lighting conditions. Sci Rep, 5. pp. 10432. 10.1038/srep10432. Retrieved from https://hdl.handle.net/10161/10574.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Yang

Rebecca Chung-Hui Yang

Associate Professor of Neurobiology
Our lab is interested in understanding the neural basis of simple decision-making processes.  We use Drosophila egg-laying site selection as our model system.  To understand how the Drosophila brain assesses and ranks the values of egg-laying options, we use a combined approach that includes high-throughput optogenetics-based behavioral screen, automated (machine vision) behavioral tracking of single animals, molecular genetic tools to identify critical circuit compone
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