Influencing factors and structural characterization of hyperhydricity of in vitro regeneration in <i>Brassica oleracea</i> var. <i>italica</i>
Bibliographic record
Abstract
Yu, U., Zhao, Y.-Q., Zhao, B., Ren, S. and Guo, Y.-D. 2011. Influencing factors and structural characterization of hyperhydricity of in vitro regeneration in Brassica oleracea var. italica. Can. J. Plant Sci. 91: 159–165. This study examines factors that affect the occurrence of hyperhydric tissue in in vitro cultures of Brassica oleracea variety italica. The anatomy of normal and hyperhydric leaves of plantlets regenerated from the hypocotyls was compared using scanning electron microscopy and transmission electron microscopy. In hyperhydric leaves palisade tissue was absent and the spongy mesophyll displayed large, unorganized intercellular spaces. Hyperhydric leaves had abnormal stomata with deformed guard cells. Significant ultrastructural differences were observed between chloroplasts in normal and hyperhydric leaves. The effects of zeatin, indoleacetic acid, silver nitrate and sucrose on the formation of hyperhydric shoots were studied. Zeatin was the most important factor, followed by sucrose concentration, AgNO3 and indoleacetic acid. The process of hyperhydricity was found to be reversed by increasing the agar concentration and eliminating NH4NO3 from the macro-elements in the MS medium. This is the first report of hyperhydricity in Brassica oleracea, and our study gives a better understanding of the factors that influence hyperhydricity during in vitro regeneration in Brassica crops.
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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 | 0.000 |
| 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".