A PCR-based assay to facilitate early detection of Diplosoma listerianum in Atlantic Canada
Bibliographic record
Abstract
The recent detection of the invasive colonial tunicate Diplosoma listerianum in Havre- Aubert, Magdalen Islands (Quebec, Canada) in 2008, prompted the development of a molecular assay as a method to detect and monitor for the potential invasion of this species in Prince Edward Island. The aim of this study was to design a highly sensitive, species-specific Polymerase Chain Reaction (PCR) assay capable of detecting D. listerianum with a high efficacy in local water samples. To accomplish this, oligonucleotide primer sets were designed from the 18S rDNA gene of D. listerianum. Primer sets were evaluated for specificity using the GenBank database, followed by a series of spiked water sample trials involving various tunicate species. Assay efficacy was tested and then evaluated by conducting spiked water sample trials using D. listerianum samples from two different geographic locations (Japan and Canada). Primer sets that were shown to be species specific were then tested for their analytical sensitivity and environmental efficacy by spiking local water samples with various amounts of D. listerianum tissue. The primer set DlistF1/DlistR1 was found to be species specific and yielded no false positive results when tested with tissue from the four invasive tunicate species currently present on Prince Edward Island (PEI) (Styela clava, Botryllus schlosseri, Botrylloides violaceus, and Ciona intestinalis). This assay was also capable of detecting D. listerianum DNA from two different populations, demonstrating its potential for use in other geographic locations, which may possess different haplotypes of the species. As the results of this study demonstrate, the DlistF1/DlistR1 assay has a high analytical sensitivity, detecting DNA from as little as 1 zooid in a water sample, and was not inhibited when tested with water samples collected from various bays across both PEI and the Magdalen Islands. The DlistF1/DlistR1 molecular assay provides a monitoring tool for shellfish aquaculture regions and can be used to facilitate early detection of this species. This level of early detection is beneficial to facilitate the implementation of mitigation programs in time to prevent D. listerianum from reaching nuisance levels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".