Fungal Composition and Diversity of the Tobacco Leaf Phyllosphere during Curing of Leaves
Bibliographic record
Abstract
Abstract Background: Rhizopus oryzae causes tobacco pole rot in China during tobacco flue-curing. Flue-curing is a post-harvest process done to prepare tobacco leaves and involves three different stages: the yellowing stage has the lowest temperatures and highest humidity, then the color-fixing stage has higher temperatures and medium humidity, and finally the stem-drying stage has the highest temperatures and lowest humidity.Results: In this study, fungal culturing and IonS5TMXL high-throughput sequencing techniques were used to reveal the fungal community of the petioles and lamina of tobacco leaves infected with pole rot during flue-curing. A total of 108 fungal isolates belonging to 5 genera and 10 species were isolated on media. The most common fungi isolated was R. oryzae, most often found equally on petioles and laminas in the color-fixing stage, followed by saprotrophs, mostly Aspergillus fumigatus and Aspergillus flavus. High-throughput sequencing revealed saprotrophs, Alternaria being the most abundant genus, followed by Phoma, Cercospora and Aspergillus, whereas Rhizopus was the tenth most abundant genus, which was mostly found on petioles at the yellowing stage. Both culturable fungal diversity and fungal sequence diversity was higher at stem-drying stage than the yellowing and color-fixing stages, and diversity was higher with leaf lamina than petioles revealing that the changes in fungal composition and diversity during the curing process were similar with both methods.Conclusions: A better understanding of the saprotrophic fungi during curing of leaves with pole rot could result in the identification of highly competitive saproptrophs that may act as potential biocontrol agents of tobacco pole rot.
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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 | 0.000 |
| 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".