Faculty Opinions recommendation of Optogenetic manipulation of stomatal kinetics improves carbon assimilation, water use, and growth.
Bibliographic record
Abstract
Stomata serve dual and often conflicting roles, facilitating carbon dioxide influx into the plant leaf for photosynthesis and restricting water efflux via transpiration. Strategies for reducing transpiration without incurring a cost for photosynthesis must circumvent this inherent coupling of carbon dioxide and water vapor diffusion. We expressed the synthetic, light-gated K+ channel BLINK1 in guard cells surrounding stomatal pores in Arabidopsis to enhance the solute fluxes that drive stomatal aperture. BLINK1 introduced a K+ conductance and accelerated both stomatal opening under light exposure and closing after irradiation. Integrated over the growth period, BLINK1 drove a 2.2-fold increase in biomass in fluctuating light without cost in water use by the plant. Thus, we demonstrate the potential of enhancing stomatal kinetics to improve water use efficiency without penalty in carbon fixation.Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. PMID: 30923223 Funding information This work was supported by: Biotechnology and Biological Sciences Research Council, United Kingdom Grant ID: BB/F001673/1 Biotechnology and Biological Sciences Research Council, United Kingdom Grant ID: BB/F001630/1 Biotechnology and Biological Sciences Research Council, United Kingdom Grant ID: BB/H009817/1 Biotechnology and Biological Sciences Research Council, United Kingdom Grant ID: P12750 Biotechnology and Biological Sciences Research Council, United Kingdom Grant ID: BB/C500595/1 Biotechnology and Biological Sciences Research Council, United Kingdom Grant ID: BB/D001528/1 More Less keyboard_arrow_down
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.501 | 0.246 |
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".