A unified framework for hydromechanical signaling: Do plant signals go with the flow?
Bibliographic record
Abstract
Abstract Local wounding in plants triggers signals that travel locally within the wounded leaf or systemically through the vasculature to distal leaves. The transmission mechanisms of this ubiquitous class of signals remain poorly understood. Here, we develop a unifying framework based on poroelastic dynamics to study two coupled biophysical processes – propagation of pressure changes and transmission of chemical elicitors via mass flows driven by these pressure changes – as potential mechanisms for the initiation and propagation of wound-induced signals. We show that rapid pressure changes in the xylem can transmit mechanical information across the plant, while their coupling with neighboring non-vascular tissue drives swelling and mass flow that can transport chemical elicitors to distal leaves. We confront our model predictions with signaling dynamics measurements in several species, and show that the poroelastic model captures observed mechanical, biochemical, and electrophysiological signals. This framework provides a valuable foundation for assessing mechanisms of signal transmission and for designing future experiments to elucidate factors involved in signal initiation, propagation, and target elicitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".