Fussl was crossed with either Stinger (ctrl) or a UAS-TNT line to block the synaptric transmission of the Fussl positive neurons. A third construct was used but did not yield any data due to difficulties with their flight performance. The Fussl-Stinger along with Fussl-TNT flies do also show difficulties in flying. These differences will be assessed.

The experiment was done as a pilot experiment before doing a larger scale.

The data is a bit inconsistent but shows a positive and reassuring numerical difference. The control is a bit lower than expected, compared to WTB flies (showing usually a PI 0f 0.6). The flies have a slightly different background than wtb flies and have pale orange eyes (still no apparent impairments in vision). Further experiments will be conducted before proceeding with a larger sample size of the flies.

FoxP3955 flies were raised and compared to normal WTB flies. Reportedly, the Foxp mutants have a reduced flight performance as their total flight duration is decreased. This was also something I experienced. The problem seemed to be greater due to the heat in the flight simulator room, initial temperature was 27°C but increased to close to 30°C. I had troubles getting a large sample size enough (same number of Foxp and wtb were loaded into the flight simulator), heat-shock proteins and other stress-related behavior might be an issue. The genotype of the flies were known during the hooking of the flies but was later on concelead and flies were randomly distributed.  

PKG 2_descr_anal

wtb-rut-colorlearning

Replicated wild type berlin flies in the flight simulator

The results from wiltype Berlin flies in the flight simulator.

This is a picture of the supplemental figure from Maye et al. 2007

Below the results from the sathish scripts running on the data from Maye et al. 2007. It matches, so Sathish scripts in my hands work fine

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

The first figure shows each of the central complex ring neurons types (Martín Pena et al., 2014). The c105-G4 targets the R1 neurons and the c232-G4 targets the R2 and the R4d neurons

This is the c105-G4 stainning from Martín Pena et al., 2014

232-G4 stainning from Kahsai et al., 2012

Axel stainning from c232-G4 alone

Axel stainning from c105-G4 alone

Axel stainning from both drivers together. I would say it really contains both driver lines.

This are both driver lines together as well from Axel. To me it seems that only c105 is present

This are my stainnings at the fluorescence microscope (no confocal). This is to show that in all of the 10-12 brains I have looked at, they all had the c232 pattern present

In addition, they had many more neurons outside from the central complex which I believe belong to the c105-G4 line. This is my only proof to show that c105 is also present, since the R1 neurons seem to be hidden when R2 neurons are stained.

I was also looking to the youtube video you have online, Björn. To me it seems I can only see the R1 ring neuron from the c105

Trace segment from Maye et al. 2007 in the uniform arena

Trace after filtering by selecting the first 8 IMFs (intrinsic mode functions) from EMD (empirical mode decomposition). Since the signal should be quite clean I do not take out the first IMFs. The last IMFs, however, are too slow and change the baseline to much

Some examples of the IMFs obtained from EMD:

Since this is separating behavior adapting to the data intrinsic time scales I am now thinking of analysing with the SMAP algorithm to see if the behavior is more or less nonlinear at certain time scales.

In addition, I have thought of using ICA (Independent component analysis), that is an algorithm famous for the blind source separation problem by extracting the most independent signals from the input signals (in this case the IMFs which are different time scales of the behavior). So the ICs should consist of mixes of different time scales that are correlated together and thus belong (but not necessarily) to the same action/movement module/… Here a few ICs (from 10). My idea is that muscles might coordinate independently between ICs but coordinated within ICs. However to prove that is not that easy I guess

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