Monitoring for propagules of Ciona intestinalis in marine water samples: the development of temporal gene expression markers for viability and life stage specific assays
Bibliographic record
Abstract
Due to the challenges of managing invasive species, new technologies to assist in monitoring for the early detection of early life stages of invasive species is critical for many regions of the world. Invasive tunicates have been causing challenges to the shellfish aquaculture industry in Atlantic Canada, with Ciona intestinalis causing the most significant challenges to date. While existing early detection systems for this species depend on morphological screening of preserved water samples or DNA based assay screening of fresh/preserved water samples, these methods are limited as they do not have the ability to evaluate the viability of propagules present, and they are also not able to distinguish between different life stages of C. intestinalis that may be present in water samples. These factors are important since non-viable propagules are not a risk to shellfish regions and unfertilized eggs have a different risk factor than viable free swimming larvae. It is most important for shellfish aquaculture growers to know when free swimming larvae life stages are present in the water so that they can appropriately time their mitigation treatments. This study successfully used RT-qPCR to screen temporally expressed genes for exclusively expressed mRNA transcripts at target life stages of C. intestinalis. In addition, this study identified mRNA transcripts that are quickly degraded post-mortem which makes them excellent candidate markers for larval viability assays. Based on the results of this study, HOX2 or TPM1 markers can be used in RT-qPCR assays to determine whether the C. intestinalis material present in a water samples is an egg or a larvae. Finally the TPM2 marker can be used to evaluate whether the larvae are viable or non-viable. Such an assay would be beneficial in screening water samples around mussel processing plant effluent outflows as well as water samples from ballast water, thereby reducing the potential further spread of the invasive species.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".