A PCR-Based Assay to Detect <i>Rhynchosporium secalis</i> in Barley Seed
Bibliographic record
Abstract
A polymerase chain reaction (PCR)-based diagnostic assay was developed to detect Rhynchosporium secalis, the barley scald fungus, in barley seed. Species-specific primers were designed based on sequence data of a region consisting of the 5.8S RNA gene and internal transcribed spacers 1 and 2 of R. secalis. The sequenced regions showed 100% homology between the two R. secalis isolates and 93% homology between R. secalis and R. orthosporum. Five sets of synthesized oligonucleotide primers were tested for their specificity using 29 isolates of R. secalis of diverse geographic origins and from different barley cultivars. In addition, DNA extracts from 22 species of microbes either taxonomically related to or from the same niche as R. secalis were tested as negative controls. Among five sets of primers, a primer set, RS8 and RS9, was selected for use in detecting R. secalis because it amplified a 264-bp fragment from the DNA of all R. secalis isolates but not the DNA from other species used for validation of the specificity of this primer set. This primer set was also used to detect R. secalis in barley seed and successfully amplified the predicted size of the DNA fragment in the infected material. PCR detection of as little as 1 to 10 pg of R. secalis DNA was possible. The method described here requires 1 day for completion, compared to 10 days required for the cultural method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".