<i>foraging</i> alters resilience/vulnerability to sleep disruption and starvation in <i>Drosophila</i>
Bibliographic record
Abstract
Recent human studies suggest that genetic polymorphisms allow an individual to maintain optimal cognitive functioning during sleep deprivation. If such polymorphisms were not associated with additional costs, selective pressures would allow these alleles to spread through the population such that an evolutionary alternative to sleep would emerge. To determine whether there are indeed costs associated with resiliency to sleep loss, we challenged natural allelic variants of the foraging gene (for) with either sleep deprivation or starvation. Flies with high levels of Protein Kinase G (PKG) (for(R)) do not display deficits in short-term memory following 12 h of sleep deprivation. However, short-term memory is significantly disrupted when for(R) flies are starved overnight. In contrast, flies with low levels of PKG (for(s), for(s2)) show substantial deficits in short-term memory following sleep deprivation but retain their ability to learn after 12 h of starvation. We found that for(R) phenotypes could be largely recapitulated in for(s) flies by selectively increasing the level of PKG in the α/β lobes of the mushroom bodies, a structure known to regulate both sleep and memory. Together, these data indicate that whereas the expression of for may appear to provide resilience in one environmental context, it may confer an unexpected vulnerability in other situations. Understanding how these tradeoffs confer resilience or vulnerability to specific environmental challenges may provide additional clues as to why an evolutionary alternative to sleep has not emerged.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".