Utilizing novel <i>Escherichia coli</i>‐specific conserved signature proteins for enhanced monitoring of recreational water quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Escherichia coli serves as a proxy indicator of fecal contamination in aquatic ecosystems. However, its identification using traditional culturing methods can take up to 24 h. The application of DNA markers, such as conserved signature proteins (CSPs) genes (unique to all species/strains of a specific taxon), can form the foundation for novel polymerase chain reaction (PCR) tests that unambiguously identify and detect targeted bacterial taxa of interest. This paper reports the identification of three new highly-conserved CSPs (genes), namely YahL, YdjO, and YjfZ, which are exclusive to E. coli/Shigella. Using PCR primers based on highly conserved regions within these CSPs, we have developed quantitative PCR (qPCR) assays for the evaluation of E. coli/Shigella species in water ecosystems. Both in-silico and experimental PCR testing confirmed the absence of sequence match when tested against other bacteria, thereby confirming 100% specificity of the tested CSPs for E. coli/Shigella. The qPCR assays for each of the three CSPs provided reliable quantification for all tested enterohaemorrhagic and environmental E. coli strains, a requirement for water testing. For recreational water samples, CSP-based quantification showed a high correlation (r > 7, p < 0.01) with conventional viable E. coli enumeration. This indicates that novel CSP-based qPCR assays for E. coli can serve as robust tools for monitoring water ecosystems and other critical areas, including food monitoring.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it