Bibliographic record
Abstract
Many fluorescent probes depend on the fluorescence resonance energy transfer (FRET) between fluorescent protein pairs. The efficiency of energy transfer becomes altered by conformational changes of a fused sensory protein in response to a cellular event. A structure-based approach can be taken to design probes better with improved dynamic ranges by computationally modeling conformational changes and predicting FRET efficiency changes of candidate biosensor constructs. FRET biosensors consist of at least three domains fused together: the donor protein, the sensory domain, and the acceptor protein. To more efficiently subclone fusion proteins containing multiple domains, a cassette-based system can be used. Generating a cassette library of commonly used domains facilitates the rapid subcloning of future fusion biosensor proteins. FRET biosensors can then be used with fluorescence microscopy for real-time monitoring of cellular events within live cells by tracking changes in FRET efficiency. Stimulants can be used to trigger a range of cellular events including Ca(2+) signaling, apoptosis, and subcellular translocations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".