Less is More: Longer Exposure Times with Low Light Intensity is Less Photo-Toxic
Bibliographic record
Abstract
Fluorescence microscopy has been prevalent for more than a century [1] and the introduction of fluorescent probes has allowed for highly selective targets to be labeled and imaged with high sensitivity. The major advantage of fluorescence microscopy has been the ability to visualize these selective targets against a dark background, which has revolutionized the field of cell biology [2]. As such, it is not surprising that modern fluorescence microscopy is a foundational tool in most research laboratories. The discovery of green fluorescent protein (GFP) [3], the use of GFP as a genetic beacon [4], and the development of fluorescent proteins with an array of colors [5–8] have allowed researchers to use microscopy to observe fluorescently tagged proteins functioning in situ in living systems [9–19]. During the fluorescence process, secondary photo-physical processes can lead to the generation of reactive oxygen species (ROS), which can cause fluorophores to photo-bleach and can be photo-toxic to living systems. The basic fluorescence process itself is inefficient in that a considerable dose of light must be imposed on the system to achieve a significant emission of fluorescence light from the fluorophores [20]. In many cases the light dose delivered to living samples can be quite damaging and can create abnormalities in biological processes and cellular physiology [20]. Additionally, a cell can be more susceptible to photo-toxicity if that cell is already exposed to stress factors such as transfections, knockdowns of key proteins, or even changes in the pH of the cell culture medium [20].
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.016 |
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".