Comprehensive evaluation of UV inactivation of E. coli using multiple gene targets and real-time quantitative PCR
Bibliographic record
Abstract
UV disinfection is extensively used for wastewater disinfection and disinfection efficiency is commonly monitored using culture-based enumeration of E. coli . While culture-independent real-time quantitative polymerase chain reaction (qPCR) based methods are attractive due to faster turnaround and easier application, previous attempts with qPCR to monitor disinfection have been unsuccessful. In this study, the effect of UV irradiation on a pure E. coli culture was examined in collimated beam (CB) experiments and monitored using both a culturing technique and DNA damage quantified using both short amplicon (SA; <∼200 bp) qPCR and longer amplicon (LA; ∼500-bp) qPCR. The results, covering a UV dose range of 0 - 20 mJ/cm 2 commonly used for wastewater disinfection, indicate a correlation between DNA gene damage quantified by both SA- and LA-qPCR and the decline in E. coli observed through culture-based methods. This demonstrates the potential of qPCR to serve as rapid alternative for monitoring wastewater disinfection efficacy. Furthermore, LA-qPCR was observed to be more sensitive than SA-qPCR. The results using LA-qPCR also revealed that UV exposure caused widespread and indiscriminate damage to E. coli ’s genome, which is considered critical for its function and survival. The combined effect of UV on E. coli ’s ability to function, grow or repair damage is suggested as the reason for the decline in culturability observed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".