Coordination of differentiation rate and local patterning in compound‐leaf development
Bibliographic record
Abstract
The variability in leaf form in nature is immense. Leaf patterning occurs by differential growth, taking place during a limited window of morphogenetic activity at the leaf marginal meristem. While many regulators have been implicated in the designation of the morphogenetic window and in leaf patterning, how these effectors interact to generate a particular form is still not well understood. We investigated the interaction among different effectors of tomato (Solanum lycopersicum) compound-leaf development, using genetic and molecular analyses. Mutations in the tomato auxin response factor SlARF5/SlMP, which normally promotes leaflet formation, suppressed the increased leaf complexity of mutants with extended morphogenetic window. Impaired activity of the NAC/CUC transcription factor GOBLET (GOB), which specifies leaflet boundaries, also reduced leaf complexity in these backgrounds. Analysis of genetic interactions showed that the patterning factors SlMP, GOB and the MYB transcription factor LYRATE (LYR) coordinately regulate leaf patterning by modulating in parallel different aspects of leaflet formation and shaping. This work places an array of developmental regulators in a morphogenetic context. It reveals how organ-level differentiation rate and local growth are coordinated to sculpture an organ. These concepts are applicable to the coordination of pattering and differentiation in other species and developmental processes.
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 | 0.000 |
| 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".