Multilocus Sequence Typing (MLST) and Random Polymorphic DNA (RAPD) Comparisons of Geographic Isolates of Neoparamoeba perurans, the Causative Agent of Amoebic Gill Disease
Bibliographic record
Abstract
Neoparamoba perurans, is the aetiological agent of amoebic gill disease (AGD), a disease that affects farmed Atlantic salmon worldwide. Multilocus sequence typing (MLST) and Random Amplified Polymorphic DNA (RAPD) are PCR-based typing methods that allow for the highly reproducible genetic analysis of population structure within microbial species. To the best of our knowledge, this study represents the first use of these typing methods applied to N. perurans with the objective of distinguishing geographical isolates. These analyses were applied to a total of 16 isolates from Australia, Canada, Ireland, Scotland, Norway, and the USA. All the samples from Australia came from farm sites on the island state of Tasmania. Genetic polymorphism among isolates was more evident from the RAPD analysis compared to the MLST that used conserved housekeeping genes. Both techniques consistently identified that isolates of N. perurans from Tasmania, Australia were more similar to each other than to the isolates from other countries. While genetic differences were identified between geographical isolates, a BURST analysis provided no evidence of a founder genotype. This suggests that emerging outbreaks of AGD are not due to rapid translocation of this important salmonid pathogen from the same area.
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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) | 0.000 | 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".