Turning behavior in <i>Drosophila</i> larvae: a role for the small <i>scribbler</i> transcript
Bibliographic record
Abstract
The Drosophila larva is extensively used for studies of neural development and function, yet the mechanisms underlying the appropriate development of its stereotypic motor behaviors remain largely unknown. We have previously shown that mutations in scribbler (sbb), a gene encoding two transcripts widely expressed in the nervous system, cause abnormally frequent episodes of turning in the third instar larva. Here we report that hypomorphic sbb mutant larvae display aberrant turning from the second instar stage onwards. We focus on the smaller of the two sbb transcripts and show that its pan-neural expression during early larval life, but not in later larval life, restores wild type turning behavior. To identify the classes of neurons in which this sbb transcript is involved, we carried out transgenic rescue experiments. Targeted expression of the small sbb transcript using the cha-GAL4 driver was sufficient to restore wild type turning behavior. In contrast, expression of this sbb transcript in motoneurons, sensory neurons or large numbers of unidentified interneurons was not sufficient. Our data suggest that the expression of the smaller sbb transcript may be needed in a subset of neurons for the maintenance of normal turning behavior in Drosophila larvae.
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 | 0.000 | 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) | 0.001 | 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".