Temperature effects on development and lifelong behavior in zebrafish
Bibliographic record
Abstract
In recent decades, global warming has intensified temperature changes, placing substantial pressure on organism survival. Understanding how temperature variations impact development and behavior is crucial for conservation strategies. This study examined how temperature affects zebrafish embryo development and behavior, focusing on mRNA expression changes under thermal challenges. Zebrafish embryos were reared at 27 °C (control), 22 °C, and 30 °C, monitored from 24 to 120 hpf for structural development, and tested for optomotor responses at 7 dpf. Juvenile (30 dpf) and adult (90 dpf) fish reared at 27 °C were subjected to acute temperature shifts (22 °C and 30 °C for 2 h), followed by behavioral assessments and brain sampling for hsp90a and hspb1 mRNA expression analysis. Survival rates were significantly lower at 22 °C, with higher hatching rates at 30 °C but decreased at 22 °C. Developmental abnormalities varied: head malformations were more common at 30 °C, pericardial and yolk sac edema at 22 °C, and tail malformations at both extremes. Optomotor responses were impaired in fish from 22 °C. Social and aggressive behaviors were mostly unaffected, but fish from extreme temperatures showed increased risk-taking and reduced response to alarm substances. hsp90a mRNA expression was elevated in fish raised at 30 °C and those exposed to the 30 °C challenge, while hspb1 mRNA expression remained stable across temperatures. Cooling environments detrimentally affected embryo growth and survival, while warmer conditions induced pronounced growth defects. Elevated temperatures posed greater risks, triggering heightened hsp90a expression crucial for stress adaptation. Understanding thermal variation impacts on embryo development is crucial for mitigating climate change effects on species' viability and reproduction.
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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) | 0.001 | 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".