Green to red photoconversion of GFP for protein tracking in vivo
Bibliographic record
Abstract
A variety of fluorescent proteins have been identified that undergo shifts in spectral emission properties over time or once they are irradiated by ultraviolet or blue light. Such proteins are finding application in following the dynamics of particular proteins or labelled organelles within the cell. However, before genes encoding these fluorescent proteins were available, many proteins have already been labelled with GFP in transgenic cells; a number of model organisms feature collections of GFP-tagged lines and organisms. Here we describe a fast, localized and non-invasive method for GFP photoconversion from green to red. We demonstrate its use in transgenic plant, Drosophila and mammalian cells in vivo. While genes encoding fluorescent proteins specifically designed for photoconversion will usually be advantageous when creating new transgenic lines, our method for photoconversion of GFP allows the use of existing GFP-tagged transgenic lines for studies of dynamic processes in living cells.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".