Graded Egfr activity patterns the<i>Drosophila</i>eggshell independently of autocrine feedback
Bibliographic record
Abstract
The pattern of the Drosophila eggshell is determined by the establishment of a complex and stereotyped pattern of cell fates in the follicular epithelium of the ovary. Localized activation of the Epidermal growth factor receptor (Egfr) is essential for this patterning. Modulation of Egfr pathway activity in time and space determines distinct fates at their appropriate locations, but the details of how Egfr signaling is regulated and how the profile of Egfr activity corresponds to cell fate remain unclear. Here we analyze the effect of loss of various Egfr regulators and targets on follicle cell patterning, using a marker for follicle cell fate, and on the mature eggshell phenotype, using a novel eggshell marker. We show, contrary to current patterning models, that feedback regulation of Egfr activity by the autocrine ligand Spitz and the inhibitor Argos is not necessary for patterning. Given the cell-autonomous nature of the mutant phenotypes we observed, we propose instead that the pattern of cell fates is generated by spatial information derived directly from the germline ligand Gurken, without a requirement for subsequent patterning by diffusible Egfr regulators in the follicular epithelium.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".