Molecular and Aggressiveness Characterization of Isolates of Fusarium solani and Fusarium oxysporum f.sp. passiflorae Associated to Passion Fruit Wilting
Bibliographic record
Abstract
This work was carried out with the objective of performing a molecular and aggressiveness characterization of F. solani and F. oxysporum f.sp. passiflorae collected in the Pantanal, Cerrado and Amazon biomes. We selected the most aggressive isolates for use in breeding programs aiming resistance to Collar Rot and Fusariosis. For inoculation of the isolates of F. oxysporum f.sp. passiflorae the washed root method was used. The molecular characterization of the isolates was carried out by partial sequencing of the Transcribed Internal Spacer of the rDNA region. The isolates of F. solani formed two distinct groups in relation to aggressiveness. Among all isolates, FSUNEMAT 40 and FSUNEMAT 46 were the most aggressive. The model with K=2 was taken as the best model to explain the genetic structure of the F. solani populations, with clear combinations of genes from both gene pools. There were three groups with respect to the aggressiveness of the isolates of F. oxysporum f.sp. passiflorae, with the isolated FOUNEMAT 22 being the most aggressive. In view of these results, the isolates of F. solani collected in P. edulis in the state of Mato Grosso presented a high molecular variability independent from the biome of origin, and this was also observed in the tests of aggressiveness. The results indicate the need to consider the molecular variability and the aggressiveness of the pathogens in the evaluation of genotypes of sour passion fruit in programs of selection of resistant cultivars.
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 | 0.001 | 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) | 0.000 | 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".