Bibliographic record
Abstract
Localization of messenger RNA (mRNA) is a process used by eukaryotes to control the spatio-temporal expression of proteins involved in cellular motility, asymmetric cell division, or polarized cell growth. A better understanding of this process relies on methods to detect specifically the position of an mRNA in fixed or living cells. This chapter presents methods to visualize mRNA in both fixed and living yeast Saccharomyces cerevisiae. In fixed cells, position of mRNAs can be assessed by using Fluorescent In Situ Hybridization (FISH) that consists of the hybridization of fluorescent probes that target a specific transcript in situ. In living cells, dynamics of mRNAs can be monitored using a bipartite system composed of MS2 stem-loops inserted in the mRNA of interest. These stem-loops are recognized specifically by the MS2 RNA-binding protein, fused to a fluorescent protein. In vivo association between the reporter (fluorescent MS2 protein) and the MS2-tagged mRNA reconstitutes active fluorescent ribonucleoparticles that can be followed by live cell imaging. Detailed protocols for the realization of these methods are provided and several technical considerations are discussed. Together, these methods provide very robust tools to determine the intracellular position and dynamics of your mRNA of interest in yeast.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".