A Model of Differential Growth-Guided Apical Hook Formation in Plants
Bibliographic record
Abstract
Differential cell growth enables flexible organ bending in the presence of environmental signals such as light or gravity. A prominent example of the developmental processes based on differential cell growth is the formation of the apical hook that protects the fragile shoot apical meristem when it breaks through the soil during germination. Here, we combined in silico and in vivo approaches to identify a minimal mechanism producing auxin gradient-guided differential growth during the establishment of the apical hook in the model plant Arabidopsis thaliana Computer simulation models based on experimental data demonstrate that asymmetric expression of the PIN-FORMED auxin efflux carrier at the concave (inner) versus convex (outer) side of the hook suffices to establish an auxin maximum in the epidermis at the concave side of the apical hook. Furthermore, we propose a mechanism that translates this maximum into differential growth, and thus curvature, of the apical hook. Through a combination of experimental and in silico computational approaches, we have identified the individual contributions of differential cell elongation and proliferation to defining the apical hook and reveal the role of auxin-ethylene crosstalk in balancing these two 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".