Imp and Syp mediated temporal patterning of neural stem cells in the developing <i>Drosophila</i> CNS
Bibliographic record
Abstract
The assembly of complex neural circuits requires that stem cells generate diverse types of neurons in the correct temporal order. Pioneering work in the Drosophila embryonic ventral nerve cord has shown that neural stem cells are temporally patterned by the sequential expression of rapidly changing transcription factors to generate diversity in their progeny. In recent years, a second temporal patterning mechanism, driven by the opposing gradients of the Imp and Syp RNA-binding proteins, has emerged as a powerful way to generate neural diversity. This long-range temporal patterning mechanism is utilized in the extended neural stem cell lineages of the postembryonic fly brain. Here, we review the role played by Imp and Syp gradients in several neural stem cell lineages, focusing on how they specify sequential neural fates through the post-transcriptional regulation of target genes, including the Chinmo and Mamo transcription factors. We further discuss how upstream inputs, including hormonal signals, modify the output of these gradients to couple neurogenesis with the development of the organism. Finally, we review the roles that the Imp and Syp gradients play beyond the generation of diversity, including the regulation of stem cell proliferation, the timing of neural stem cell lineage termination, and the coupling of neuronal birth order to circuit assembly.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".