Induction and resistance against <i>Fusarium</i> wilt disease of tomato by using sweet basil (<i>Ocimum basilicum</i> L) extract
Bibliographic record
Abstract
Akladious, S. A., Isaac, G. S. and Abu-Tahon, M. A. 2015. Induction and resistance against Fusarium wilt disease of tomato by using sweet basil (Ocimum basilicum L) extract. Can. J. Plant Sci. 95: 689–701. The antifungal activity of Ocimum basilicum (sweet basil) extract against Fusarium oxysporum f. sp. lycopersici race 3 the causal agent of tomato wilt and its ability in inducing disease resistance were studied in vivo using seed-soaking treatment before sowing. Plants were harvested at 45 and 105 d (vegetative and flowering stages) after sowing. Treatment with O. basilicum extract decreased the disease incidence from 94.70 to 18.00%. Results revealed that growth parameters and photosynthetic pigments were markedly inhibited in tomato plants in response to Fusarium wilt disease, whereas the contents of non-enzymatic and enzymatic antioxidants were increased as compared with healthy control plants. Moreover, presoaking in basil extract enhanced all the mentioned parameters in both healthy and infected plants. SDS-PAGE analysis of tomato leaves revealed that seed treated with basil extract resulted in an induction of novel protein bands during the vegetative stage. These new proteins were not detected in untreated healthy or infected control plants. Electrophoretic studies of polyphenol oxidase, esterase and malate dehydrogenase isoenzymes showed wide variations in their intensities and densities among all treatments. It seems that O. basilicum extract was able to enhance the biological control of Fusarium wilt disease of tomato.
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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 | 0.000 | 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".