Total RNA analysis of the active microbiome on moving bed biofilm reactor carriers under incrementally increasing micropollutant concentrations
Bibliographic record
Abstract
Micropollutants are increasingly prevalent in the aquatic environment. A major part of these originates from wastewater treatment plants since traditional treatment technologies do not remove micropollutants sufficiently. Moving bed biofilm reactors (MBBRs), however, have been shown to aid in micropollutant removal when applied to conventional wastewater treatment as a polishing step. Here, we used Total RNA sequencing to investigate both the active microbial community and functional dynamics of MBBR biofilms when these were exposed to increasing micropollutant concentrations over time. Concurrently, we conducted batch culture experiments using biofilm carriers from the MBBRs to assess micropollutant degradation potential. Our study showed that biofilm eukaryotes, in particular protozoa, were negatively influenced by micropollutant exposure, in contrast to prokaryotes that increased in relative abundance. Further, we found several functional genes that were differentially expressed between the MBBR with added micropollutants and the control. These include genes involved in aromatic and xenobiotic compound degradation. Moreover, the biofilm carrier batch experiment showed vastly different alterations in benzotriazole and diclofenac degradation following the increased micropollutant concentrations in the MBBR. Ultimately, this study provides essential insights into the microbial community and functional dynamics of MBBRs and how an increased load of micropollutants influences these dynamics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".