Development of a New Method for Bioassay of Allelopathy Using Protoplasts of a Leguminous Plant Mucuna pruriens With a High Content of the Allelochemical L-DOPA
Bibliographic record
Abstract
To study allelopathy at the cellular level, we established a new bioassay method using protoplasts from etiolated leaves of a leguminous plant, Mucuna pruriens, which contains a large amount of L-3,4-dihydroxyphenylalanine (L-DOPA) and protoplasts prepared from cotyledons, leaves or roots of Lactuca sativa (lettuce) grown in an axenic condition. The bioassay was performed using 96 multi-well culture plates and required only a small volume of medium, 50 ?L, per well. For the mixed culture of Mucuna and recipient lettuce protoplast, the optimum condition was Murashige and Skoog’s basal medium, containing 1 ?M of 2,4-dichlorophenoxyacetic acid, 5 ?M of benzyladenine and 0.4 M mannitol solution. The inhibitory effect of M. pruriens protoplasts was found to vary with the density of the recipient L. sativa protoplasts. The M. pruriens protoplasts had no effect on the division of protoplasts from suspension cells of Oryza sativa. These results were in agreement with those obtained by the conventional plant box method. Our study offered a novel in vitro assay method, which will be widely applicable to study allelopathy of various plants on lettuce and also for quantitative studies between plant species under different culture conditions in order to simulate the possible future environmental risk.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".