Proteomes & Juveniles: A synthesis of ideas on the ontogeny of hypoxia tolerance
Bibliographic record
Abstract
Surviving in low oxygen environments requires complex and integrated physiological responses, many of which center on economizing energy budgets. At the cellular level, significant reductions in protein synthesis and even translational arrest (in anoxia) can save as much as 90% of routine ATP demand. This suggests strategic investment in the protein complement of essential tissues, like the heart, that must maintain some level of function for the animal to survive through and recover from a hypoxic bout. The use of quantitative proteomics in animal models of hypoxia tolerance has unveiled new ideas about how the cardiac proteome is influenced by hypoxia exposure, as well as the ontogenic constraints on this aspect of cardiac plasticity. For example, developmental hypoxia exposure of American alligators (Alligator mississippiensis) induces persistent shifts in the cardiac proteome that, paradoxically, reflect an increased capacity for translation, proteolysis, and metabolic capacity. Concomitant with these changes was an equitable down-regulation of seemingly random cardiac proteins, which may offset the cellular energy budget. In Western painted turtles (Chrysemys picta bellii), the cold acclimation required to prepare tissues for chronic anoxia during winter dormancy induced general down-regulation of electron transport system proteins in adult but not hatchling turtles. Interestingly, both studies revealed ‘age’ as a key explanatory variable in the cardiac proteome. This suggests a robust developmental program in the ectotherm heart that is resilient even to potent environmental stressors like oxygen and temperature, and warrants further investigations into the developmental thresholds of hypoxia tolerance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".