Plasticity and invariance of Arabidopsis inflorescence and floral shoot apical meristems in response to mineral nutrients
Bibliographic record
Abstract
Abstract In many species, floral organ production is invariant while flower production rate can be plastic. This allows plants to adapt flower number to their environment whilst maintaining a constant flower structure. The CLAVATA/WUSCHEL feedback loop underpins both inflorescence (IM) and floral meristem (FM) activity, respectively responsible for flower and floral organ production. We explore how plasticity and invariance can differ between IM and FM in response to nutrient availability. FM size is less sensitive to changes in nutrients than IM size, and floral organ production is insensitive to these small size changes. However, clavata3 mutants display larger changes in FM size, approaching those observed in WT IM under nutrient change, with increased floral organ number. This suggests that invariant floral organ production requires that FM size undergoes limited changes in response to nutrients. Compared to the IM, in the FM, levels of cytokinin (CK) signaling are lower and CK signaling and WUSCHEL expression are less impacted by nutrient level. Through genetic perturbations, we show a reduced response of FMs to varying cytokinin levels. Our work shows one way that the balance between plasticity and invariance can be set differently in different contexts.
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 | 0.001 |
| 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".