Phototactic T-maze Behavioral Assay for Comparing the Functionality of Color-sensitive Photoreceptor Subtypes in the Drosophila Visual System
Bibliographic record
Abstract
The Drosophila retina contains light-sensitive photoreceptors (R cells) with distinct spectral sensitivities that allow them to distinguish light by its spectral composition.R7 and R8 photoreceptors are important for color vision, and can be further classified into pale (p) or yellow (y) subtypes depending on the rhodopsin expressed.While both R7y and R7p are sensitive to UV light, R8y and R8p detect light in the green and blue spectrum, respectively.The ability of R7 and R8 photoreceptors to distinguish different spectral sensitivities and the natural preference for Drosophila towards light sources (phototaxis), allow for the development of a phototactic T-maze assay that compares the functionality of different R7 and R8 subtypes.A "UV vs. blue" choice can compare the functionalities of R7p and R8p photoreceptors, while a "UV vs. green" choice can compare the functionalities of R7y and R8y photoreceptors.Additionally, a "blue vs. green" choice could be used to compare R8p and R8y photoreceptors, while a "dark vs. light" choice could be used to determine overall vision functionality.Although electrophysiological recordings and calcium imaging have been used to examine functionality of R7 and R8 photoreceptors, these approaches require expensive equipment and are technically challenging.The phototactic T-maze assay we present here is a robust, straight-forward and an inexpensive method to study genetic and developmental factors that contribute to the individual functionality of R7 and R8 photoreceptors, and is especially useful when performing large-scale genetic screens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".