Real-time Biomonitoring of Emerging Contaminants in Water Resources
Bibliographic record
Abstract
Engineered microbes coupled to automated IoT devices offer a potentially effective approach for real-time detection of emerging contaminants (EC) in water, which became a global challenge that poses health risks to humans and ecosystems. Current analytical chemistry-based approaches that are applied for EC screening in environmental waters, such as mass spectrometry remain the golden standard, but due to their high cost, requirement for specialized facilities and user expertise, slow turn-around as well as the growing number of EC’s to screen for, their application remain limited. Our research explores the use of engineered yeast and algae as part of a real-time EC sensor system in which biofilm ‘cell factories’ serve as a constant supply of microbes for inline continuous-flow exposure to environmental contaminants. Saccharomyces cerevisiae was stably transformed with genetic circuitry to report the presence of estrogenic ECs as a fluorescence signal, whereas a mutant Chlamydomonas reinardtii strain, as well as a wildtype strain were tested. The system’s design enables continuous flow exposure without the need for prefiltration or the risk of modified organisms entering the environment where it is installed, with additional advantages such as low cost and deployable requiring minimal skills. A conceptual framework for groundwater EC monitoring and regulation by public and private sector stakeholders, using engineered microbe-driven IoT devices was incorporated to enable effect-based monitoring at remote locations, and to serve as a platform for further refinements such as microbes with improved sensitivity and novel biological pathways to expand the range of health risks that can be detected.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".