The development of a novel biosensor for single species detection using environmental DNA
Bibliographic record
Abstract
Rapid monitoring of aquatic organisms, particularly endangered and invasive species, is essential for preserving the biodiversity in Earth’s water. Management and conservation of fish species, such as Salmo salar, Salmo trutta and Salvelinus alpinus, within these environments requires knowledge of distribution, traditionally gained through visual detection. These methods are expensive, labour intensive and can lead to habitat disruption and harm to the target species. Environmental DNA (eDNA) offers a solution to this, using non-invasive molecular techniques to detect DNA shed into the environment. \nConventional eDNA approaches use PCR-based methodology for single-species detection. In this thesis, a qPCR assay was developed for S. salar detection. However, although sensitive and specific, PCR-based methods pose a logistical challenge for on-site monitoring due to the need for high temperatures and thermal cycling. To circumvent this, we developed an isothermal approach that couples Recombinase Polymerase Amplification to CRISPR-Cas12a detection as a route to a cost-effective biosensor device. This system harnesses the collateral cleavage activity of Cas12a, a ribonuclease guided by a specific CRISPR RNA. We show the applicability of this technique to three salmonid species, with the S. salar assay compared to qPCR as a detection/non-detection assay in samples from Ireland and Canada. \nTo facilitate this RPA-CRISPR-Cas assay as an on-site detection tool, the method was adapted for visualisation on a custom portable fluorometer and via lateral flow. Both systems maintain the specificity and sensitivity of the original assay but enable simplified readouts without complex instrumentation. The assays and visualisation methods developed in this thesis were applied to samples from the Burrishoole Catchment, Co. Mayo, demonstrating their applicability to environmental monitoring. \nIn summary, this thesis provides the first application of CRISPR-Cas diagnostics to eDNA monitoring, and further progresses the field towards field-based applications, by removing the need for complex instrumentation and allowing rapid species detection.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".