Phylogenetic identification of fungi isolated from strawberry and papaya fruits and their susceptibility to fatty acids
Bibliographic record
Abstract
Strawberry (Fragaria × ananassa) and papaya (Carica papaya) are susceptible to fungal plant pathogens such as Botrytis cinerea and Colletotrichum spp., which are controlled mainly by chemical fungicides. However, their use causes adverse health effects. Therefore, the search for ecological alternatives is essential to reduce the impact of pesticides. This study aimed to identify Botrytis spp. and Colletotrichum spp. isolated from strawberries and papaya fruits with symptoms of grey mould and anthracnose disease and to evaluate the in vitro antifungal activity of different fatty acids on their mycelial growth. Phylogenetic analysis allowed the identification of Botrytis cinerea and Colletotrichum nymphaeae isolated from strawberry and C. siamense from papaya, which have been reported as pathogens of both crops. The in vitro antifungal index (AI) of the fatty acids was determined at concentrations of 100, 1000, and 2000 µM. Inhibition assays showed AIs of 100% for B. cinerea, C. nymphaeae, and C. siamense using decanoic acid (1000 and 2000 µM). Sodium octanoate (1000 and 2000 µM) and hexanoic acid (2000 µM) also inhibited C. nymphaeae with an AI of 100%. This is the first report on the antifungal effects of decanoic acid on three isolated fungi, as well as sodium octanoate and hexanoic acid, on C. nymphaeae. These results suggest that these molecules could potentially control diseases caused by these fungi in strawberry and papaya fruits.
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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 | 0.001 | 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".