Neurogenetic and plastic components of food-related behaviors due to the foraging gene in Drosophila melanogaster
Bibliographic record
Abstract
Successful foraging is necessary for procurement of nutritional resources necessary for an animal's survival. Foraging behavior is driven by a physiological response to the absence of food, and is identified by a number of behaviors including finding, evaluating, acquiring, and consuming food. In recent years, significant advances have been made in the underlying genetic basis of this complex behavior. In Drosophila melanogaster, natural genetic variation for food-specific locomotion has been localized to a single major gene, the foraging (for) gene. This provides a unique opportunity to investigate the cellular, genetic, and selective mechanisms that mediate food-related behavior. This thesis describes for's influence on food acquisition, the homeostatic mechanisms underlying energy balance, and learning and memory. These findings show that for affects food intake, glucose absorption and allocation, and glycogen storage. In addition to a role in regulating energy acquisition and storage, for also affects the response to both extended periods of depleted food, and brief periods of food absence. Changes in for expression can induce corrective changes in food intake and blood glucose levels in response to food deprivation. Moreover, for inhibits neuropeptide Y-like receptor cells to induce changes in food acquisition and locomotion on food, thus introducing a novel mechanism through which for mediates foraging behavior. This thesis also introduces a role for for in the ability to associate an odor with a taste reward, thus confirming a relationship between foraging behavior, learning and memory. The role of for in several aspects of foraging behavior described here suggests that it influences higher-order integrative brain function. Furthermore, for's role in energy homeostasis, learning, and memory suggest it influences the phenotypic response to environmental change, and thereby affects plasticity.
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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".