Physical Forces Modulate Oxidative Status and Stress Defense Meditated Metabolic Adaptation of Yeast Colonies: Spaceflight and Microgravity Simulations
Bibliographic record
Abstract
) has broad genetic homology to human cells. Although typically grown as 1-2mm diameter colonies under certain conditions yeast can form very large (10 + mm in diameter) or 'giant' colonies on agar. Giant yeast colonies have been used to study diverse biomedical processes such as cell survival, aging, and the response to cancer pharmacogenomics. Such colonies evolve dynamically into complex stratified structures that respond differentially to environmental cues. Ammonia production, gravity driven ammonia convection, and shear defense responses are key differentiation signals for cell death and reactive oxygen system pathways in these colonies. The response to these signals can be modulated by experimental interventions such as agar composition, gene deletion and application of pharmaceuticals. In this study we used physical factors including colony rotation and microgravity to modify ammonia convection and shear stress as environmental cues and observed differences in the responses of both ammonia dependent and stress response dependent pathways We found that the effects of random positioning are distinct from rotation. Furthermore, both true and simulated microgravity exacerbated both cellular redox responses and apoptosis. These changes were largely shear-response dependent but each model had a unique response signature as measured by shear stress genes and the promoter set which regulates them These physical techniques permitted a graded manipulation of both convection and ammonia signaling and are primed to substantially contribute to our understanding of the mechanisms of drug action, cell aging, and colony differentiation.
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".