Immediate‐early gene <i>Homer1a</i> intranuclear transcription focus intensity as a measure of relative neural activation
Bibliographic record
Abstract
Abstract Immediate‐early genes (IEGs) exhibit a rapid, transient transcription response to neuronal activation. Fluorescently labeled mRNA transcripts appear as bright intranuclear transcription foci (INF), which have been used as an all‐or‐nothing indicator of recent neuronal activity; however, it would be useful to know whether INF fluorescence can be used effectively to assess relative activations within a neural population. We quantified the Homer1a (H1a) response of hippocampal neurons to systematically varied numbers of exposures to the same places by inducing male Long‐Evans rats to run laps around a track. Previous studies reveal relatively stable firing rates across laps on a familiar track. A strong linear trend (r2 > 0.9) in INF intensity was observed between 1 and 25 laps, after which INF intensity declined as a consequence of dispersion related to the greater elapsed time. When the integrated fluorescence of the entire nucleus was considered instead, the linear relationship extended to 50 laps. However, there was only an approximate doubling of H1a detected for this 50‐fold variation in total spiking. Thus, the intranuclear H1a RNA fluorescent signal does provide a relative measure of how many times a set of neurons was activated over a ~10 min period, but the dynamic range and hence signal‐to‐noise ratios are poor. This low dynamic range may reflect previously reported reductions in the IEG response during repeated episodes of behavior over longer time scales. It remains to be determined how well the H1a signal reflects relative firing rates within a population of neurons in response to a single, discrete behavioral event.
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 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 | 0.001 | 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 | 0.000 |
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 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".