Long-term live cell cycle imaging of single Cyanidioschyzon merolae cells
Bibliographic record
Abstract
Live cell imaging by fluorescence microscopy is a useful tool for elucidating the localization and function of proteins and organelles in single cells. Especially, time-lapse analysis observing the same field sequentially can be used to observe cells of many organisms and analyze the dynamics of intracellular molecules. By single-cell analysis, it is possible to elucidate the characteristics and fluctuations of individual cells, which cannot be elucidated from the data obtained by averaging the characteristics of an ensemble of cells. The primitive red alga Cyanidioschyzon merolae has a very simple structure and is considered a useful model organism for studying the mechanism of organelle division, since the division is performed synchronously with the cell cycle. However, C. merolae does not have a rigid cell wall, and environmental changes such as low temperature or high pH cause morphological change and disruption easily. Therefore, morphological studies of C. merolae typically use fixed cells. In this study, we constructed a long-term time-lapse observation system to analyze the dynamics of proteins in living C. merolae cells. From the results, we elucidate the cell division process of single living cells, including the function of intracellular components.
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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) | 0.000 | 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".