Heat Shock–Mediated Thermoprotection of Larval Locomotion Compromised by Ubiquitous Overexpression of Hsp70 in<i>Drosophila melanogaster</i>
Bibliographic record
Abstract
Maintaining the competence of locomotor circuitry under stressful conditions can benefit organisms by enabling locomotion to more tolerable microhabitats. We show that prior heat shock protects locomotion and the locomotor central pattern generator of larval Drosophila against subsequent hyperthermic stress. We combined molecular genetic, electrophysiological, and behavioral techniques to investigate heat shock-mediated thermoprotection. Prior heat shock increased the distance traveled by larvae during hyperthermia before failure. The frequency of the rhythm of peristaltic locomotor contractions and the velocity of locomotion were both less thermosensitive after heat shock and were less susceptible to failure at high temperatures. Rhythmic coordinated motor patterns, recorded intracellularly as excitatory junction potentials in body wall muscles of dissected preparations, were centrally generated because patterns could still be generated in the absence of sensory feedback (sensory function disrupted with shibire). Prior heat shock protected central circuit operation during hyperthermic stress by increasing the temperature at which it failed. Overexpression of Hsp70 after a heat shock using transgenic flies (traII) did not enhance thermoprotection, as expected, but had deleterious effects on parameters of 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) | 0.001 | 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".