Identification of <i>Monilinia fructigena, M. fructicola, M. laxa</i>, and <i>Monilia polystroma</i> on Inoculated and Naturally Infected Fruit Using Multiplex PCR
Bibliographic record
Abstract
Monilinia fructigena, M. fructicola, M. laxa, and Monilia polystroma each have a different regulatory status. To monitor imported and exported fruit for the presence of quarantined Monilinia or Monilia species, a timely identification method is required. Random amplified polymorphic DNA analysis was used to generate an M. fructigena-specific band that was characterized by sequencing. Using the sequence obtained, primers were designed to amplify bands in the same genomic region of M. fructicola and M. laxa. These bands were also characterized by sequencing. From all three sequences, a multiplex polymerase chain reaction (PCR) method based on a common reverse primer (MO368-5) and three species-specific forward primers (MO368-8R, MO368-10R, and Laxa-R2) was established for the differentiation of the three Monilinia species. The multiplex PCR was tested with additional isolates and consistently produced a 402-bp PCR product for M. fructigena, a 535-bp product for M. fructicola, and a 351-bp product for M. laxa. The method was also used with isolates of the recently characterized Monilia polystroma, and all isolates amplified a 425-bp PCR product. The identification method was shown to amplify a PCR product directly from inoculated apples, and the PCR band produced was specific to the inoculated Monilinia or Monilia species. Furthermore, the multiplex PCR was used to identify Monilinia species on naturally infected stone fruits. The method correctly identified infections by both M. laxa and M. fructicola by successful amplification of corresponding PCR products for each species.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".