Copper nanoparticles prompt the activity of estuarine denitrifying bacterial communities at relevant environmental concentrations
Bibliographic record
Abstract
Abstract Effects of metallic nanoparticles (NPs) to the estuarine biota have mostly been shown for concentrations higher than those actually measured or predicted in these environments. To address this gap, a range of concentrations expected to occur in estuarine environments (from 0.01 to 1 μg g-1) was employed in microcosms studies to assess the impact of Cu NPs in the denitrification pathway. That was achieved by quantifying gene expression and the potential denitrification rate in estuarine sediments exposed to Cu NPs for up to six days. Expression of nitrite ( nirS ) and nitrous oxide ( nosZ ) reductase genes was enhanced in a timewise manner. For the highest Cu NPs (1 μg g -1 ) an increase in gene expression could be seen immediately after 1 h of exposure, and continuing to be enhanced up until 7 h of exposure. For the lowest Cu NPs (0.01 μg g -1 ) an increase in gene expression could only be seen after 4 h or 7 h of exposure; however it continued to rise up until 24 h of exposure. In any case, after 48 h the expression levels were no longer different from the non-exposed control. Concomitantly to increased gene expression the potential denitrification rate was increased by 30 %. Our results suggest that deposition and adsorption of Cu NPs to estuarine sediments promotes the immediate and transient expression of key genes of the denitrification pathway. The long term impact of continuous inputs of Cu NPs into estuaries deserves renewed analysis to account for their effects, not just on the biota, but especially on ecosystems services. Environmental significance Interactions of metallic nanoparticles with microbial communities of estuarine sediments are poorly characterized and its impact towards ecosystem services even less. By assessing the effect of copper nanoparticles on the expression of key genes of the denitrification pathway, an essential step for nitrogen (N) removal, we were able to show that denitrifying communities are immediately activated after exposure, increasing the denitrification rates in estuaries. The importance of denitrification lies in its release of dinitrogen (N 2 ) to the atmosphere but also in the emissions of N 2 O (a potent greenhouse gas). The results obtained in this study gather data that contribute information on the denitrification dynamics in estuaries, invaluable for a timely response to the expected upcoming changes in coastal areas. Table of contents In estuaries the deposition upon the sediments of copper nanoparticles can contribute to change metal availability and promote the activity of denitrifying bacteria
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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".