ATP‐induced Changes in Energy Status and Membrane Integrity of Harvested Litchi Fruit and its Relation to Pathogen Resistance
Bibliographic record
Abstract
Abstract Litchi is a subtropical fruit of high commercial value on the international market but the fruit deteriorates rapidly after harvest due to rot development caused by Peronophythora litchii. To investigate the role of energy metabolism during disease development on harvested litchi fruit, fruits were dipped into solutions of either 0 or 1.0 mm adenosine triphosphate (ATP) for 3 min before being inoculated with Peronophythora litchii or not. Fruit were then stored for 6 days at 25°C and 90–100% relative humidity. Significant reductions in pericarp browning and disease severity and significant delays in membrane permeability and malondialdehyde (MDA) content were found in ATP‐treated and P. litchii‐inoculated fruit. Higher ATP concentrations and adenylate energy charge (EC) were observed in ATP‐treated fruit. In addition, lower activities of phospholipase D, acid phosphatase and lipoxygenase enzymes involved in membrane lipid peroxidation and hydrolysis were recorded in ATP‐treated fruit. Thus, treatment with ATP maintained higher energy levels, inhibited activities of the membrane hydrolysis‐related enzymes, reduced membrane lipid peroxidation and helped maintain membrane integrity of the harvested litchi fruit at the early stage of storage, which could account for the inhibition of disease development of P. litchii‐inoculated fruit.
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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 | 0.000 |
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".