Specific and sensitive detection of the conifer pathogen Gremmeniella abietina by nested PCR
Bibliographic record
Abstract
BACKGROUND: Gremmeniella abietina (Lagerb.) Morelet is an ascomycete fungus that causes stem canker and shoot dieback in many conifer species. The fungus is widespread and causes severe damage to forest plantations in Europe, North America and Asia. To facilitate early diagnosis and improve measures to control the spread of the disease, rapid, specific and sensitive detection methods for G. abietina in conifer hosts are needed. RESULTS: We designed two pairs of specific primers for G. abietina based on the 18S rDNA sequence variation pattern. These primers were validated against a wide range of fungi and 14 potential conifer hosts. Based on these specific primers, two nested PCR systems were developed. The first system employed universal fungal primers to enrich the fungal DNA targets in the first round, followed by a second round selective amplification of the pathogen. The other system employed G. abietina-specific primers in both PCR steps. Both approaches can detect the presence of G. abietina in composite samples with high sensitivity, as little as 7.5 fg G. abietina DNA in the host genomic background. CONCLUSION: The methods described here are rapid and can be applied directly to a wide range of conifer species, without the need for fungal isolation and cultivation. Therefore, it represents a promising alternative to disease inspection in forest nurseries, plantations and quarantine control facilities.
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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 | 0.002 |
| 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 | 0.001 |
| 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 | 0.002 |
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".