EFFECTS OF POTASSIUM PHOSPHITE ON BIOCHEMICAL CONTENTS AND ENZYMATIC ACTIVITIES OF CHINESE POTATOES INOCULATED BY PHYTOPHTHORA INFESTANS
Bibliographic record
Abstract
Potato late blight caused by Phytophthora infestans dominates the entire world where potatoes and other Solanaceae crops are grown.In this study, the effects of potassium phosphite (KPhi) based fungicide on two potato varieties infected by two strains of the pathogen were studied.Tubers coming from foliar spray of potassium phosphite wounded and/or inoculated with pathogens were sampled at 0, 6, 12, 24, and 48 hours.Phytoalexins, phenols, β-1, 3-glucanase (PR-2), chitinase (PR-3), peroxidase (POD), polyphenol oxidase (PPO), superoxidase dismutase (SOD) and catalase (CAT), were analyzed.Results demonstrated that plants applied with KPhi produced tubers with enhanced resistance to the pathogen than their untreated plants.Moreover, tuber slices from KPhi applied plants following infection showed a significant increase in the contents of phytoalexins and phenols.PR-3 activities were induced by KPhi and wounding with the highest level at 48 hours.The activities of PR-2 were not significantly induced by KPhi or wounding, but its content was significantly increased by pathogen infection with the highest in untreated tubers after 48 hours.The KPhi treated tubers produced more enzymatic activities significantly after wounding and pathogen infection than those that were not treated.Our findings suggested that KPhi stimulates a quick and vigorous response in tubers against the pathogen infection via activation of defense responses, such as defense biochemical compounds, pathogenesis-related enzymes and antioxidant enzyme activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".