1. Three datasets in Worm Tracking: Spline, Tail, Centroid Below are images of spline, tail and centroid tracking. Most of these models are shown for single, isolated worms or a pair of worms. http://www.youtube.com/ watch?v=qDvSYxNGSNg Body is modeled as 8 points and they connect to form a spline at a given time t Centroid is plotted (in red) and tail position is plotted (in blue)

2. Tracking Program should handle collisions efficiently Some cases shown below where the green and red worms are identified and kept distinct from one another Check out the pseudo code in this paper

3. New Concepts in our Tracking Program 1)To make it versatile to work for tracking worms on agar plates or microfluidic chambers & for swimming or crawling worms. 2)To be able to detect mutants that pause for a long time, mutants that move very slow, and mutants that appear physically different from wild-type (either longer or shorter in length). 3)If possible, we should be able to track worms even in environments that have slowly moving objects (e.g. air bubbles). 4)We would be able to ask the user to select individual worms that he wants to track over the length of video. The default assumption would be to track all worms in the video. 5)We would be able to recognize body postures of multiple worms (see later slides for the 4 states) from spline shapes.

4. List of RAW Parameters extracted from the Tracking Program Extract Body centroid (x, y) coordinates of a single worm Extract Tail coordinates (x, y) of a single worm Extract Spline coordinates (up to 20) of a single worm Save each extracted dataset in excel sheets  Repeat for multiple (or all) worms in a single video  Repeat for multiple videos in a single folder GUI asks the user to upload videos and reads parameters from the user (e.g. worm length, device under test)

5. List of Parameters Derived from the RAW data (extracted from the Tracking Program) Extract centroid velocity (dx/dt and dy/dt) of multiple worms Extract Tail velocity (dx/dt and dy/dt) of multiple worms From Spline coordinates of each worm, guess if the worm is in which of the four states: (a) forward motion, (b) backward motion, (c) paused, or (d) omega turn. Plot the above derived parameters over the video’s time period GUI then calculates derived parameters from the three data sets (Raw)

6. Use of Derived Parameters to Biologists Forward Motion Backward MotionPause Omega Turn The time duration a worm spends in each of the four states tells a lot about the worm under test. Crudely, a lot of pauses and backward motion tells the worm does not like the environment. Lots of forward motion tells the worms likes the environment. In real-world, some mutants may not be easy to into a certain category. This is why, human visual observations can fail sometimes. Using a software for tracking and analyzing worm parameters is virtually free of bias. In most experiments, we have two groups of worms: one is wild-type and other is the extreme mutant. Visually, you can different the wild-type from extreme mutant easily. But, if a third mutant is created, we want a faster way of saying “is it closer to wild- type or closer to the extreme mutant?” This process is called screening mutants.

7. Use of Derived Parameters to Biologists Forward Motion Backward MotionPause Omega Turn  One Possible graph from the GUI to be given to biologists who gave us five different sets of worms (mut1, mut2, mut3, mut4, mut5): Time of video (minutes) Different worm mutants color codes Usually moves forward Usually pauses a lot More omega turns