27 research outputs found

    Odour avoidance learning in the larva of Drosophila melanogaster

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    Drosophila larvae can be trained to avoid odours associated with electric shock. We describe here, an improved method of aversive conditioning and a procedure for decomposing learning retention curve that enables us to do a quantitative analysis of memory phases, short term (STM), middle term (MTM) and long term (LTM) as a function of training cycles. The same method of analysis when applied to learning mutants dunce, amnesiac, rutabaga and radish reveals memory deficits characteristic of the mutant strains

    Image Enhancement for Tracking the Translucent Larvae of Drosophila melanogaster

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    Drosophila melanogaster larvae are model systems for studies of development, synaptic transmission, sensory physiology, locomotion, drug discovery, and learning and memory. A detailed behavioral understanding of larvae can advance all these fields of neuroscience. Automated tracking can expand fine-grained behavioral analysis, yet its full potential remains to be implemented for the larvae. All published methods are unable to track the larvae near high contrast objects, including the petri-dish edges encountered in many behavioral paradigms. To alleviate these issues, we enhanced the larval contrast to obtain complete tracks. Our method employed a dual approach of optical-contrast boosting and post-hoc image processing for contrast enhancement. We reared larvae on black food media to enhance their optical contrast through darkening of their digestive tracts. For image processing we performed Frame Averaging followed by Subtraction then Thresholding (FAST). This algorithm can remove all static objects from the movie, including petri-dish edges prior to processing by the image-tracking module. This dual approach for contrast enhancement also succeeded in overcoming fluctuations in illumination caused by the alternating current power source. Our tracking method yields complete tracks, including at the edges of the behavioral arena and is computationally fast, hence suitable for high-throughput fine-grained behavioral measurements

    A Low Concentration of Ethanol Impairs Learning but Not Motor and Sensory Behavior in Drosophila Larvae

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    Drosophila melanogaster has proven to be a useful model system for the genetic analysis of ethanol-associated behaviors. However, past studies have focused on the response of the adult fly to large, and often sedating, doses of ethanol. The pharmacological effects of low and moderate quantities of ethanol have remained understudied. In this study, we tested the acute effects of low doses of ethanol (∼7 mM internal concentration) on Drosophila larvae. While ethanol did not affect locomotion or the response to an odorant, we observed that ethanol impaired associative olfactory learning when the heat shock unconditioned stimulus (US) intensity was low but not when the heat shock US intensity was high. We determined that the reduction in learning at low US intensity was not a result of ethanol anesthesia since ethanol-treated larvae responded to the heat shock in the same manner as untreated animals. Instead, low doses of ethanol likely impair the neuronal plasticity that underlies olfactory associative learning. This impairment in learning was reversible indicating that exposure to low doses of ethanol does not leave any long lasting behavioral or physiological effects

    A Bayesian measure of association that utilizes the underlying distributions of noise and information.

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    We propose a new approach, Bayesian Probability of Association (BPA) which takes into account the probability distributions of information and noise in the variables and uses Bayesian statistics to predict associations better than existing approaches. Our approach overcomes the limitations of linearity of the relationship and normality of the data, assumed by the Pearson correlation coefficient. It is different from the current measures of association because considering information separately from noise helps identify the association in information more accurately, makes the approach less sensitive to noise and also helps identify causal directions. We tested the approach on 15 datasets with no underlying association and on 75 datasets with known causal relationships and compared the results with other measures of association. No false associations were detected and true associations were predicted in more than 90% cases whereas the Pearson correlation coefficient and mutual information content predicted associations for less than half of the datasets

    FAST allowed increased sensitivity for tracking.

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    <p>A. FAST was able to obtain the complete track of the larva. 1. With the binary threshold method, applying a high threshold (0.09) resulted in incomplete tracks as well as noise. 2. On the other hand, applying a low threshold (0.073) resulted in noise levels too high to reliably generate complete larval tracks. 3. In contrast, using FAST with a very low threshold (0.028) produced little noise and allowed for reliable generation of complete tracks. Top row: magnified view of a single movie frame showing pixels above threshold for each method. For visualization these pixels are represented as black pixels on white background. FAST was able to isolate the larva while eliminating other noise. Arrows indicate the larva. Middle row: tracks generated using each method. Each track segment of the larva is represented by a different color. Bottom row: magnified view of middle row. B. FAST was at least 3 times faster per movie than using binary threshold method with high threshold and 20 times faster than using low threshold (n = 5).</p

    Feeding larvae black dyed food enhanced contrast.

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    <p>A. Dyed and control larvae in 15 cm plastic petri-dish under recording camera. Top: Dyed and control larvae under low exposure. Bottom: Same larvae under high exposure, where the petri-dish edge is barely visible. Arrows indicate dyed larva. B. Dye fed larvae were visible under high exposure for up to 3 hours after extraction.</p

    Larvae in the videos were tracked using the Frame Averaging followed by Subtraction then Thresholding method (FAST).

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    <p>We improved video tracking by subtracting individual frames from the average of all frames. The tracking algorithm is as follows: A. For each video, calculate an average of all the frame values. B. Obtain each frame in the video. C. Calculate difference between each frame and the average frame value. D. The result was then analyzed using a binary threshold process. For better visualization the larvae are represented as dark pixels on light background for C and D.</p

    Dye-fed larvae and FAST are both necessary for reliable larval tracking.

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    <p>A. Without dye and FAST, the larva could not be followed once it encounters the petri-dish edge. B. FAST without dye also cannot reliably follow the larva when it encounters the edge. C. Similarly, using dyed larva without FAST results in a failure to follow the larva near the edge. D. Only when both dyed larva and FAST were used in conjunction can the larvae be reliably tracked near the edge. The same movie was used between panels A., B. and C., D. Red dashed lines indicate the untracked portion of the larval track. For better illustration C and D are show as dark tracks on white background. We concluded that both the dye-fed larvae and FAST are necessary for reliable larval tracking (E).</p
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