Low-invasive 5D visualization of mitotic progression by two-photon excitation spinning-disk confocal microscopy
Bibliographic record
Abstract
Non-linear microscopy, such as multi-photon excitation microscopy, offers spatial localities of excitations, thereby achieving 3D cross-sectional imaging with low phototoxicity even in thick biological specimens. We had developed a multi-point scanning two-photon excitation microscopy system using a spinning-disk confocal scanning unit. However, its severe color cross-talk has precluded multi-color simultaneous imaging. Therefore, in this study, we introduced a mechanical switching system to select either of two NIR laser light pulses and an image-splitting detection system for 3- or 4-color imaging. As a proof of concept, we performed multi-color fluorescent imaging of actively dividing human HeLa cells and tobacco BY-2 cells. We found that the proposed microscopy system enabled time-lapse multi-color 3D imaging of cell divisions while avoiding photodamage. Moreover, the application of a linear unmixing method to the 5D dataset enabled the precise separation of individual intracellular components in multi-color images. We thus demonstrated the versatility of our new microscopy system in capturing the dynamic processes of cellular components that could have multitudes of application.
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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".