Living without Oxygen: Anoxia-Responsive Gene Expression and Regulation
Bibliographic record
Abstract
Many species of marine mollusks demonstrate exceptional capacities for long term survival without oxygen. Analysis of gene expression under anoxic conditions, including the subsequent translational responses, allows examination of the functional mechanisms that support and regulate natural anaerobiosis and permit noninjurious transitions between aerobic and anoxic states. Identification of stress-specific gene expression can provide important insights into the metabolic adaptations that are needed for anoxia tolerance, with potential applications to anoxia-intolerant systems. Various methods are available to do this, including high throughput microarray screening and construction and screening of cDNA libraries. Anoxia-responsive genes have been identified in mollusks; some have known functions in other organisms but were not previously linked with anoxia survival. In other cases, completely novel anoxia-responsive genes have been discovered, some that show known motifs or domains that hint at function. Selected genes are expressed at different times over an anoxia-recovery time course with their transcription and translation being actively regulated to ensure protein expression at the optimal time. An examination of transcript status over the course of anoxia exposure and subsequent aerobic recovery identifies genes, and the proteins that they encode, that enhance cell survival under oxygen-limited conditions. Analysis of data generated from non-mainstream model systems allows for insight into the response by cells to anoxia stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".