Impact of Wastewater Treatment Configuration and Seasonal Conditions on Thyroid Hormone Disruption and Stress Effects in Rana catesbeiana Tailfin
Bibliographic record
Abstract
Improved endocrine disrupting compound (EDC) removal is desirable in municipal wastewater treatment plants (MWWTPs) although increased removal does not always translate into reduced biological activity. Suitable methods for determining reduction in biological activity of effluents are needed. In order to determine which MWWTPs are the most effective at removing EDC activities, we operated three configurations of pilot sized biological reactors (conventional activated sludge, CAS; nitrifying activated sludge, NAS; and biological nutrient removal, BNR) receiving the same influent under simulated winter and summer conditions. As frogs are model organisms for the study of thyroid hormone (TH) action, we used the North American species Rana catesbeiana in a cultured tadpole tailfin (C-fin) assay to compare the effluents. TH-responsive (thyroid hormone receptors alpha (thra) and beta (thrb)) and stress-responsive (superoxide dismutase, catalase, and heat shock protein 30) mRNA transcript levels were examined. Effluents infrequently perturbed stress-responsive transcript abundance but thra/thrb levels were significantly altered. In winter conditions, CAS caused frequent TH perturbations while BNR caused none. In summer conditions, however, BNR caused substantial TH perturbations while CAS caused few. Our findings contrast other studies of seasonal variations of EDC removal and accentuate the importance of utilizing appropriate biological readouts for assessing EDC activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".