Integrated Approaches for the Identification of Bioactive Environmental Contaminants
Bibliographic record
Abstract
Environmental contaminants, including ~350,000 anthropogenic chemicals and their transformation products, pose significant risks to both ecosystems and health. These contaminants can affect organisms through various exposure routes, causing subsequent adverse effects. High trophic level species, such as marine mammals, serve as bioindicators of environmental health due to their ability to accumulate contaminants, while lower trophic level organisms like fish and microbes also experience harmful impacts, such as mortality and antibiotic resistance, respectively. This thesis integrates nontargeted analysis and bioanalytical tools to report the occurrence of a myriad of contaminants (i.e., persistent organic pollutants, emerging pollutants, natural products, and metabolites) in the environment and their potential toxicological effects on organisms. This is done through three case studies focusing on marine mammals (i.e., belugas), bacteria (i.e., E. coli), and fish (i.e., rainbow trout). Chapters 2 through 4 present a comprehensive analysis of the occurrence and bioactivity of various contaminants in St. Lawrence Estuary (SLE) belugas. In Chapter 2, temporal trends of per- and polyfluoroalkyl substances (PFASs) in SLE belugas reveal a decline in legacy compounds and an increase in certain short-chain PFASs, highlighting the need to expand the suite of PFASs monitored in the environment. Chapter 3 investigates aryl hydrocarbon receptor (AhR) activity in SLE belugas, highlighting a natural product as an agonist contributing to AhR activity, as well as the potential for many polar emerging contaminants to contribute to the activity. In Chapter 4, the nuclear receptor (NR) activity profiles of SLE beluga tissue were measured, revealing significant receptor activation, particularly in orphan receptors like PXR and CAR. Further, Tox21 nontargeted screening identified 156 emerging contaminants as potential NR agonists. Chapter 5 uses non-targeted screening and a battery of chemical biology tools to identify triclosan as the primary antibacterial compound in Ontario wastewater treatment plants, emphasizing its role in promoting antibiotic resistance. Finally, Chapter 6 examines the toxicity mechanism of N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine-quinone (6PPD-Q) in salmonids, confirming C4-alkyl-OH-6PPD-Q as the primary detoxification product, thus informing future research on safer antioxidant alternatives. By integrating bioanalytical tools and nontargeted chemical analysis, this research enhances our understanding of the impacts of environmental contaminants on organisms and offers valuable insights for improving ecotoxicological assessments and toxicity predictions
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".