Bibliographic record
Abstract
Development, differentiation and maintenance of the differentiated state of glial cells require a cell type-specific gene regulatory network that is both definitive enough to secure cell identity and at the same time flexible enough to allow lineage progression and adequate responses to external stimuli.This requires cooperative and cross-regulatory activities of cell type-specific and stage-specific transcription factors, their interactions with histone modifying and chromatin remodeling machineries as well as with components of the basic transcription machinery, and multiple functional interactions with non-coding regulatory RNAs.The HMG-domain transcription factor Sox10 is the only known lineage determining transcription factor in both myelinating Schwann cells and oligodendrocytes.It does not only provide an excellent tool to analyze important gene regulatory events in myelinating glia but also allows to compare networks between Schwann cells and oligodendrocytes.I will present examples of the relation and interplay of Sox10 with other transcription factors, chromatin modifying complexes and microRNAs to illustrate our current understanding of network activity and function in myelinating glia, but also point to limitations and future directions in this important field of study.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.693 | 0.449 |
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".