Returning to normal? Assessing transcriptome recovery over time in male rainbow darter (<i>Etheostoma caeruleum</i>) liver in response to wastewater-treatment plant upgrades
Bibliographic record
Abstract
The present study measured hepatic transcriptome responses in male rainbow darter (Etheostoma caeruleum) exposed to 2 municipal wastewater-treatment plants (MWWTPs; Kitchener and Waterloo) over 4 fall seasons (2011-2014) in the Grand River (Ontario, Canada). The overall goal was to determine if upgrades at the Kitchener MWWTP (in 2012) resulted in transcriptome responses indicative of improved effluent quality. The number of differentially expressed probes in fish downstream of the Kitchener outfall (904-1223) remained comparable to that downstream of Waterloo (767-3867). Noteworthy was that year and the interaction of year and site explained variability in more than twice the number of transcripts than site alone, suggesting that year and the interaction of year and site had a greater effect on the transcriptome than site alone. Gene set enrichment analysis revealed a gradual reduction in the number of gene ontologies over time at exposure sites, which corresponded with lower contaminant load. Subnetwork enrichment analysis revealed that there were noticeable shifts in the cell pathways differently expressed in the liver preupgrade and postupgrade. The dominant pathways altered preupgrade were related to genetic modifications and cell division, whereas postupgrade they were associated with the immune system, reproduction, and biochemical responses. Molecular pathways were dynamic over time, and following the upgrades, there was little evidence that gene expression profiles in fish collected from high-impact sites postupgrade were more similar to those in fish collected from reference site. Environ Toxicol Chem 2017;36:2108-2122. © 2017 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".