Pathogenic Bacteria in Sewage Treatment Plants as Revealed by 454 Pyrosequencing
Bibliographic record
Abstract
This study applied 454 high-throughput pyrosequencing to analyze potentially pathogenic bacteria in activated sludge from 14 municipal wastewater treatment plants (WWTPs) across four countries (China, U.S., Canada, and Singapore), plus the influent and effluent of one of the 14 WWTPs. A total of 370,870 16S rRNA gene sequences with average length of 207 bps were obtained and all of them were assigned to corresponding taxonomic ranks by using RDP classifier and MEGAN. It was found that the most abundant potentially pathogenic bacteria in the WWTPs were affiliated with the genera of Aeromonas and Clostridium. Aeromonas veronii, Aeromonas hydrophila, and Clostridium perfringens were species most similar to the potentially pathogenic bacteria found in this study. Some sequences highly similar (>99%) to Corynebacterium diphtheriae were found in the influent and activated sludge samples from a saline WWTP. Overall, the percentage of the sequences closely related (>99%) to known pathogenic bacteria sequences was about 0.16% of the total sequences. Additionally, a platform-independent Java application (BAND) was developed for graphical visualization of the data of microbial abundance generated by high-throughput pyrosequencing. The approach demonstrated in this study could examine most of the potentially pathogenic bacteria simultaneously instead of one-by-one detection by other methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".