Natural variation in <i>Drosophila</i> larval reward learning and memory due to a cGMP-dependent protein kinase
Bibliographic record
Abstract
Animals must be able to find and evaluate food to ensure survival. The ability to associate a cue with the presence of food is advantageous because it allows an animal to quickly identify a situation associated with a good, bad, or even harmful food. Identifying genes underlying these natural learned responses is essential to understanding this ability. Here, we investigate whether natural variation in the foraging (for) gene in Drosophila melanogaster larvae is important in mediating associations between either an odor or a light stimulus and food reward. We found that for influences olfactory conditioning and that the mushroom bodies play a role in this for-mediated olfactory learning. Genotypes associated with high activity of the product of for, cGMP-dependent protein kinase (PKG), showed greater memory acquisition and retention compared with genotypes associated with low activity of PKG when trained with three conditioning trials. Interestingly, increasing the number of training trials resulted in decreased memory retention only in genotypes associated with high PKG activity. The difference in the dynamics of memory acquisition and retention between variants of for suggests that the ability to learn and retain an association may be linked to the foraging strategies of the two variants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".