Fluorophore‐binding <scp>RNA</scp> aptamers and their applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Why image RNA? Of all the biological molecules, RNA exhibits the most diverse range of functions. Evidence suggests that transcription produces a wide range of noncoding RNAs (ncRNAs), both short (e.g., siRNAs, miRNAs) and long (e.g., telomeric RNAs) that regulate many aspects of gene expression, including the epigenetic processes that underlie cell fate determination, polarization, and morphogenesis. All these functions are realized through the exquisite temporal and spatial control of RNA expression levels and the stability of specific RNAs within well-defined sub-cellular compartments. Given the central importance of RNA in dictating cell behavior via gene-related functions, there is a great demand for RNA imaging methods so as to determine the composition of the cellular 'transcriptome' and to acquire a complete spatial-temporal profile of RNA localization. Recent advances in fluorophore-binding RNA aptamers promise to provide exactly this knowledge, which can ultimately advance our understanding of cell function and behavior in conditions of health and disease, and in response to external stimuli. WIREs RNA 2016, 7:843-851. doi: 10.1002/wrna.1383 For further resources related to this article, please visit the WIREs website.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 it