Genetic biosensors to measure the activity of toxigenic cyanobacteria: towards a new standardized method to forecast harmful algal blooms
Bibliographic record
Abstract
Efficient and proactive management of public health risk associated with cyanobacterial harmful blooms requires appropriate tools that can generate rapid and informative data on the proliferation of toxigenic cyanobacteria in freshwater environments. The present pilot study aimed at assessing the suitability of a new biosensor to follow the population dynamics of toxigenic genera Microcystis, Planktothrix, Aphanizomenon and Dolichospermum to rapidly assess the toxic risk associated with their occurrence. The genetic biosensor is adapted into a simple ELISA-type colorimetric format that has been designed to recognize and quantify ribosomal RNA to rapidly detect a population entering a growing phase. Five different reference lakes in Canada, Switzerland and Luxembourg were selected to conduct temporal series in addition to depth profiles and spatial investigations. Biosensor measurements were compared with (in situ) algal pigment screening, taxonomic analyses as well as microcystin quantification using both standard LC-MS workflows and rapid in situ strip tests. Preliminary results demonstrated a high sensitivity of the biosensor to detect the onset of blooms. The proof-of-concept will provide insights for the use of the biosensor to track toxigenic cyanobacteria and establish risk categories. A conceptual model is presented to implement this new tool into future monitoring programs and early warning systems as a complement to conventional microscopy and toxin analyses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".