Sentinels at the wall: cell wall receptors and sensors
Bibliographic record
Abstract
Summary The emerging view of the plant cell wall is of a dynamic and responsive structure that exists as part of a continuum with the plasma membrane and cytoskeleton. This continuum must be responsive and adaptable to normal processes of growth as well as to stresses such as wounding, attack from pathogens and mechanical stimuli. Cell expansion involving wall loosening, deposition of new materials, and subsequent rigidification must be tightly regulated to allow the maintenance of cell wall integrity and co‐ordination of development. Similarly, sensing and feedback are necessary for the plant to respond to mechanical stress or pathogen attack. Currently, understanding of the sensing and feedback mechanisms utilized by plants to regulate these processes is limited, although we can learn from yeast, where the signalling pathways have been more clearly defined. Plant cell walls possess a unique and complicated structure, but it is the protein components of the wall that are likely to play a crucial role at the forefront of perception, and these are likely to include a variety of sensor and receptor systems. Recent plant research has yielded a number of interesting candidates for cell wall sensors and receptors, and we are beginning to understand the role that they may play in this crucial aspect of plant biology. Contents Summary 7 I. Introduction 8 II. Cell expansion and plant growth 8 III. Cell wall responses to stress 11 IV. Pathogen attack and mechanical stimuli 11 V. Lessons from yeast 12 VI. Candidate sensors and receptors in plants 14 VII. Conclusions 17 Acknowledgements 17 References 17
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".