Genetic Variability of Isolates of Ramulispora sorghi From Cáceres-MT, Brazil
Bibliographic record
Abstract
Among the most produced cereals worldwide, sorghum (Sorghum bicolor (L.) Moench), presents low productivity in Brazil, mainly due to the occurrence of diseases, with a prominence of sooty stripe, caused by the fungus Ramulispora sorghi, until then considered a secondary disease, has increased its incidence and severity, especially in conditions of high humidity and temperature. The most efficient means of control is the use of resistant cultivars, in this sense, studies on the genetic variability of the fungus through molecular markers are of great importance for the breeding programs of sorghum. The objective of this work was to evaluate the genetic variability in isolates of Ramulispora sorghi belonging to the mycoteca of the Laboratory of Genetic Resources & Biotechnology of the Universidade do Estado de Mato Grosso, campus of Cáceres, via molecular markers of the ISSR type. The results indicate the existence of genetic variability among the isolates of R. sorghi. The Polymorphic Information Content (PIC) showed that the primers were classified as medium informational with an average value of 0.27. 71 polymorphic fragments were formed considering the 40 isolates of R. sorghi, which presented the value of k = 2, represent the differentiation of the isolates into two distinct clusters. The genetic dissimilarity measures were estimated by the Coefficient of Nei and Li, where the combination between the isolates B107/16 (15) and B103/15 (16) obtained the smallest magnitude (0,12) and the combinations between isolates B111/16 (2) and S114/15 (33), S316/15 (3) and S114/15 (33), B115/16 (4) and S114/15 (33), B118/16 (6) and S114/15 (33) were more dissimilar (1,00). The “UPGMA” method provided a breakdown of the 40 isolates into 4 distinct groups. The Cophenetic Correlaction Coefficient (CCC) presented significant value with r = 0,84. The Tocher’s optimization method allowed the 40 isolates to be distributed in 10 different groups. These results provide relevant information on the genetic variability among the 40 isolates of R. sorghi analyzed. In addition, they indicate that fungus have a wide genetic diversity, and have been recurring in different regions of Brazil and the world, and thus, larger studies become essential for more effective control measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".