Biodegradation of Endocrine Disruptors in Solid-Liquid Two-Phase Partitioning Systems by Enrichment Cultures
Bibliographic record
Abstract
Naturally occurring and synthetic estrogens and other molecules from industrial sources strongly contribute to the endocrine disruption of urban wastewater. Because of the presence of these molecules in low but effective concentrations in wastewaters, these endocrine disruptors (EDs) are only partially removed after most wastewater treatments, reflecting the presence of these molecules in rivers in urban areas. The development of a two-phase partitioning bioreactor (TPPB) might be an effective strategy for the removal of EDs from wastewater plant effluents. Here, we describe the establishment of three ED-degrading microbial enrichment cultures adapted to a solid-liquid two-phase partitioning system using Hytrel as the immiscible water phase and loaded with estrone, estradiol, estriol, ethynylestradiol, nonylphenol, and bisphenol A. All molecules except ethynylestradiol were degraded in the enrichment cultures. The bacterial composition of the three enrichment cultures was determined using 16S rRNA gene sequencing and showed sequences affiliated with bacteria associated with the degradation of these compounds, such as Sphingomonadales. One Rhodococcus isolate capable of degrading estrone, estradiol, and estriol was isolated from one enrichment culture. These results highlight the great potential for the development of TPPB for the degradation of highly diluted EDs in water effluents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".