CaLEA 73 gene from <i>Capsicum annuum</i> L. enhances drought and osmotic tolerance modulating transpiration rate in transgenic <i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
Acosta-García, G., Chapa-Oliver, A. M., Millán-Almaraz, J. R., Guevara-González, R. G., Cortez-Baheza, E., Rangel-Cano, R. M., Ramírez-Pimentel, J. G., Cruz-Hernandez, A., Gueara-Olvera, L., Aguilera-Bibian, J. E., Hernández-Salazar, M. and Torres-Pacheco, I. 2015. CaLEA 73 gene from Capsicum annuum L. enhances drought and osmotic tolerance modulating transpiration rate in transgenic Arabidopsis thaliana. Can. J. Plant Sci. 95: 227–235. Late embryogenesis abundant (LEA) proteins are an important group of proteins related to the protection of several kinds of abiotic stresses in plants. A LEA gene was cloned from Capsicum annuum seeds and named CaLEA73. This gene was expressed in C. annuum plants during several phenological stages as well as in cold stress and exogen ABA applications. The CaLEA73 gene was ectopically expressed in transgenic Arabidopsis thaliana plants in order to analyse its role under drought and salt stress. Our results displayed an increase in tolerance to drought and osmotic, but not under salt stress in the transgenic lines evaluated. Interestingly, proline levels in transgenic lines were not higher than azygous control plants, when the drought stress was evaluated. Transpiration levels in transgenic plants were lower than control, suggesting an improvement in water efficiency use in CaLEA73 transgenic lines. The stomatal density and index were significantly minor in transgenic plants in comparison to azygous control, likely indicating a reason of the minor transpiration in transgenic plants. Our results are discussed in the context of drought stress physiology aspects for crop improvement.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".