Final Stats (flew more than 8min/out of):
– Wild type: 14/17 (82%)
– ElavGal4 x UAS-Ryr 28919: 3/13 (23%)
– ElavGal4 x UAS-Ryr 65885: 13/100 (13%)

Wild Types:

ElavGal4 x UASRyR 65885

Last week (1.-7.Apr 2019) we noted how many flies want to fly more than 8 min and here is the result (only the ones we noted):
– Wild type: 8/9 (88%)
– ElavGal4 x UAS-Ryr 28919: 3/12 (25%)
– ElavGal4 x UAS-Ryr 65885: 6/35 (17%)

Trace (Downsampled 5) Graphs of Recordings

Wild type:

ElavGal4 x UAS-Ryr 28919:

ElavGal4 x UAS-Ryr 65885:

New Findings:

Elav-Gal4 x UAS-SERCA 44581 flies => Lethal before adulthood! (in all 5 vials so far)

And the journey begins…

ElavGal4 x UASRyr65 “the wrong vials”
– > Graphs of the Trace.png (RightTrace – LeftTrace):

Data 1:

Monica:

Anokhi:

This week we worked on perfecting the fly hooking. Some of the data we got is as follows (Data from just one fly).

Confocal image MAX stack of one of the brains at 20x

 

 

and at 40x

 

In this link we have a video of a 3D stainning pattern          zoomed_CC

 

In addition I add here teh boxplots from the final results of the Ping Pong ball setup with these experiments

After performing EMD to 6 fly traces of 20000 data points (that is 1000 sec flight) for each group (tntXwtb; c105;c232>tnt; c105;c232Xwtb). This data size was chosen to reduce computing time of the SMAP procedure. The EMD decomposes the trace into different time scales in nonstationary data. It seems that the nonlinear behavior occurs at the first IMF (the fastest time scale) and a bit in the second IMF. The potential conclusion to this is that the behavior of the the fly is only unpredictable at the fast movements whereas slow movements are very predictable. Nevertheless, to be cautious it could be that this fastest timescale is just noise, and that this noise is nonlinear. I would say that there is no difference at any time scale between groups (pay attention to the different ranges in the Y-axes), so the ring neurons R1, R3, R4d do not have any effect.

As a groundtruth I have used the same analysis pipeline for the traces in the uniform arena from Maye et al. 2007. Here the effect is even more pronounced at the fastest time scales. So I will conclude that this is real fly behavior and not noise that is shared among both setups: the Ping pong ball machine and the torquemeter.In order to gain more insights into the underlying flight structure I took one random flight trace to explain a few observations. The x-axis is the theta (that actually goes from 0-4 in steps of 0.2 and therefore we see the 21 points), in the y-axis is the correlation of the prediction to groundtruth. We see that IMF has a bigger slope, but not only that, also that its prediction correlation is around 0.88, whereas lower timescales prediction is basically perfect. That is, fast time scales are not only more nonlinear but also less unpredictable. This pattern is repeated in every fly measured

 

To have an impression of how these IMF resultant traces look like: IMF1, IMF2 and IMF8

This is for the sake of playing and curiosity. I made out of these two traces a modelling of their flying trace in a 2D world. Direction is right wing amplitude – left wing amplitude and distance flown is dependent on the sum of both (more amplitude of both, more forward thrust). Funny enough, the second one looks kind of fractal, which is characteristic of chaotic behaviors. If there is any comment to add to this new visualization, all ears!

Some traces from this week just so that you have an idea how do they look like. To me they are not the optimal traces I expected. But one can see some signal there. I will start the screen hoping to get enough good traces without too much work.

what do you think is the best quality control for accepting a trace for the analysis or not. I was thinking the 3D mapping gives a good hint but without quantification.

In addition I was writting to Andi Straw to solve the issue of running two cameras in the same computers, but now answer