The <i>foraging</i> gene of <i>Drosophila melanogaster</i>: Spatial‐expression analysis and sucrose responsiveness
Bibliographic record
Abstract
The ability to identify and respond to food is essential for survival, yet little is known about the neural substrates that regulate natural variation in food-related traits. The foraging (for) gene in Drosophila melanogaster encodes a cGMP-dependent protein kinase (PKG) and has been shown to function in food-related traits. To investigate the tissue distribution of FOR protein, we generated an antibody against a common region of the FOR isoforms. In the adult brain we localized FOR to neuronal clusters and projections including neurons that project to the central complex, a cluster within the dorsoposterior region of the brain hemispheres, a separate cluster medial to optic lobes and lateral to brain hemispheres, a broadly distributed frontal-brain cluster, axon bundles of the antennal nerve and of certain subesophageal-ganglion nerves, and the medulla optic lobe. These newly described tissue distribution patterns of FOR protein provide candidate neural clusters and brain regions for investigation of neural networks that govern foraging-related traits. To determine whether FOR has a behavioral function in neurons we expressed UAS-for in neurons using an elav-gal4 driver and measured the effect on adult sucrose responsiveness (SR), known to be higher in rovers than sitters, the two natural variants of foraging. We found that pan-neuronal expression of for caused an increase in the SR of sitters, demonstrating a neural function for PKG in this food-related behavior.
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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) | 0.002 | 0.001 |
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".