Bibliographic record
Abstract
The transport of messenger RNAs (mRNAs) in neurons serves many purposes. During development, trafficking of mRNAs to both axonal and dendritic growth cones regulates neuronal growth. After synapse formation, mRNAs continue to be transported to dendrites both as a mechanism for the localization of proteins to specific compartments and as a substrate for local translational regulation of synaptic plasticity. Finally, activity-dependent mRNAs are transported quickly to dendrites after transcription. Determining how mRNAs are transported and specifically translated in these different paradigms is a major unanswered question. Addressing this question is also complicated by the presence of many other RNA processing and storage centers that may not be involved in transport but share components with the transport structures. In the present review, we will discuss several recent studies addressing mechanisms of mRNA transport in neurons, as well as proteomic characterization of mRNA transporting structures in neurons. We define two types of RNA transport structures in neurons, transport particles and RNA granules and distinguish them by the presence or absence of ribosomes. We will present a number of different molecular models for how mRNAs are repressed during transport, and how these may affect the regulation of local translation in neurons.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".