Changes in Bacterial Community Structure after Exposure to Silver Nanoparticles in Natural Waters
Bibliographic record
Abstract
Silver nanoparticles (AgNPs) are widely used in commercial products as antibacterial agents, but AgNPs might be hazardous to the environment and natural aquatic bacterial communities. Our recent research demonstrated that AgNPs rapidly but temporarily inhibited natural bacterioplankton production. The current study investigates the mechanism for the observed bacterial reaction to AgNPs by examining how AgNPs impact bacterial abundance, metabolic activity (5-cyano-2,3-ditolyl tetrazolium chloride (CTC+) cells), and 16S rRNA community composition. Natural bacterioplankton communities were dosed with carboxy-functionalized AgNPs at four concentrations (0.01-1 mg-Ag/L), incubated in triplicate, and monitored over 5 days. Ionic silver (AgNO(3)) and Milli-Q water treatments were used as a positive and negative control, respectively. Four general AgNP exposure responses, relative to the negative control, were observed: (1) intolerant, (2) impacted but recovering, (3) tolerant, and (4) stimulated phylotypes. Relationships between cell activity indicators and bacterial phylotypes, suggested that tolerant and recovering bacteria contributed the most to the community's productivity and rare bacteria phylotypes stimulated by AgNPs did not appear to contribute much to cell activity. Overall, natural bacterial communities tolerated single, low level AgNP doses and had similar activity levels to the negative control within five days of exposure, but bacterial community composition was different from that of the control.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".