Metabolomic responses to sublethal contaminant exposure in neonate and adult<i>Daphnia magna</i>
Bibliographic record
Abstract
The use of consumer products and pharmaceuticals that act as contaminants entering waterways through runoff and wastewater effluents alters aquatic ecosystem health. Traditional toxicological endpoints may underestimate the toxicity of contaminants, as lethal concentrations are often orders of magnitude higher than those found within freshwater ecosystems. While newer techniques examine the metabolic responses of sublethal contaminant exposure, there has been no direct comparison with ontogeny in Daphnia. It was hypothesized that Daphnia magna would have distinct metabolic changes after 3 different sublethal contaminant exposures, because of differences in the toxic mode of action and ontogeny. To test this hypothesis, the proton nuclear magnetic resonance metabolomic profiles were measured in D. magna aged day 0 and 18 after exposure to 28% of the lethal concentration of 50% of organisms tested (LC50) of atrazine, propranolol, and perfluorooctanesulfonic acid (PFOS) for 48 h. Principal component analysis revealed significant separation of contaminants from the control daphnids in both neonates and adults exposed to propranolol and PFOS. In contrast, atrazine exposure caused separation from the controls in only the adult D. magna. Minimal ontogenetic changes in the targeted metabolites were seen after exposure to propranolol. For both atrazine and PFOS exposures ontogeny exhibited unique changes in the targeted metabolites. These results indicate that, depending on the contaminant studied, neonates and adults respond uniquely to sublethal contaminant exposure. Environ Toxicol Chem 2017;36:938-946. © 2016 SETAC.
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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 | 0.000 |
| 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".