A multiplex PCR assay for the detection of six foliar fungal pathogens of faba bean
Bibliographic record
Abstract
Faba bean is susceptible to several foliar fungal diseases: ascochyta blight (Ascochyta fabae), alternaria leaf spot (Alternaria sp.), chocolate spot (Botrytis cinerea and Botrytis fabae), anthracnose (Colletotrichum lentis) and stemphylium blight (Stemphylium spp.). Under conducive conditions these diseases can cause significant economic losses. Visual diagnosis based on the appearance of lesions on faba beans is problematic, especially as they progress. The purpose of this study was to develop a multiplex polymerase chain reaction assay to detect the causal organisms of these diseases. Five pairs of primers, AFF7/AFR7 for A. fabae, BCF1/BCR1 for B. cinerea, BFF2/BFR2 for B. fabae, ClF2/ClR2 for C. lentis and Stem_F_S/Stem_R_S for Stemphylium spp. were developed. The previously developed primer pair aagpf1/aagpr1 was used to detect Alternaria sp. The primers were tested for specificity to their target pathogen and primer pairs AFF6/AFR6, aagpf1/aagpr1, BCF1/BCR1, BFF2/BFR2 and ClF2/ClR2 identified their target pathogens. The primer pair Stem_F_S/Stem_R_S identified S. beticola, S. botryosum, S. eturmiunum and S. vesicarium. Identification of pathogens with primers was evaluated using DNA from mycelia and from infected faba bean leaves generated through artificial inoculations under controlled conditions and collected from the field. A multiplex PCR assay with six primer pairs allowed for detection of the target pathogens to the species level for five pathogens, and to the genus level for species causing stemphylium blight. This assay enables quick diagnosis of leaf spots on faba bean, and minimizing time and effort needed to identify the primary cause of the infection through traditional isolation procedures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".