Editorial: Transcription Regulation—Brain Development and Homeostasis—A Finely Tuned and Orchestrated Scenario in Physiology and Pathology
Bibliographic record
Abstract
A finely tuned regulation of gene expression is essential for shaping the nervous system and for maintaining its homeostasis throughout life. Disruptions in gene regulation can impact brain development and physiology in ways that contribute to diverse pathologies. Classic and state-of-the art experimental models and technologies have advanced our knowledge of transcriptional regulators and the ways they interact in the healthy and diseased brain. Further in-depth characterization of the mechanisms of transcriptional regulation is needed to better understand how each element, from genes to cells, defines and maintains identities and functionalities in the nervous system. This Research Topic focuses on transcriptional regulation within the nervous system, with an emphasis on developmental and homeostatic processes, their dysregulation, and their association with neurodevelopmental disorders and neurodegenerative diseases. Eleven peer-reviewed manuscripts including six original articles, three reviews, one mini review, and one brief research report, encompass this special volume. Fifty-nine authors from research laboratories located in 10 countries: Argentina, Canada, China, Germany, Israel, Russia, Serbia, United Kingdom, United States, and Vietnam, took part in this initiative.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".