on Monday, December 17th, 2018 1:32 | by Anders Eriksson
on Monday, October 1st, 2018 2:58 | by Anders Eriksson
on Monday, September 10th, 2018 1:03 | by Anders Eriksson
The experiments in the flight simulator. Self-learning performance indices in a two-minute test with the heat switched off after 4 and 8 minutes of training, indicated impairment of 17d-TNT flies.
The flies also showed clear impairments in their flight performance. To quantify this I assessed both possible alterations in their motor coordination (using climbing assay) as well as flight performance. The climbing assay relies on walking rather than flying. Both experiments show reduced ability of motor coordination and flight performance.
To confirm the specificity of the 17d-Gal4 fly I used the trans-tango flies.
The trans-tango is notorious for having a low expression in adult flies, which was also observed by me. The image is taking without any GFP-antibody.
on Tuesday, August 7th, 2018 2:49 | by Anders Eriksson
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.
on Monday, July 30th, 2018 2:08 | by Amanda Torres
on | by Anders Eriksson
on Friday, July 27th, 2018 3:54 | by Gaia Bianchini
Positive Control: Gr28bd-G4, TrpA1-G4
Parameters: Light: intensity (500 Lux side, 1000 Lux bottom); frequency = 20Hz; Delay = 1 ms; Duration = 9.9 ms; volts = 6.4
Red lines: completed
mb025b: not selected against tubby
on Monday, July 23rd, 2018 6:40 | by Christian Rohrsen
This is to see if the flies have less degrees of freedom at any segment by measuring the standard deviation at each segment. There does not seem to be any effect. Although this might be mixed with the wiggle scores. I think measuring entropy is a better measure.
All the same plots as above but for TH-D’, the interesting line from the screen.
on | by Christian Rohrsen
lm: linear model
+ int: taking double interaccions into consideration
b lm: bayesian linear model with bayesglm function
b lm MCMC: bayesian linear model with MCMCglm function
nlm: nonlinear model with lm function with splines fitted
b nlm: splines fitted to each cluster and MCMCglm function
GAM: general additive model with gam function
Adding double interactions seems to produce better models, nonlinearities also make models better and frequentist also. To me it seems like this data might be noise and therefore adding interactions, nonlinearities and frequentist methods is just fitting the noise better (overfitting) and that is why I get better scores with them. In addition, care needs to be taken since I use different functions that calculate the model performance scores differently (although the formulas are theoretically the same for all!)
on Friday, July 20th, 2018 3:46 | by Christian Rohrsen
I would say blue 1 is a line that was negative for all the tests I have so far seen. So this might be an interesting line. What to do next?
I would unblind the blue1, which is TH-D’. It was shown to be required for classical conditionning in shock and temperature learning (Galili et al. 2014). Another interesting observation is that th-g4+th-g80 seems to have like zero PI scores in all of the experiments (Naman and Gaia in the Tmaze, Joystick and Y-mazes). So could it be that all of these neurons have indeed a meaning, but is depending every time in the context?? Maybe Vanessa Ruta´s work might be interesting for that.