Metal ions limit or enhance environmental <scp>DNA</scp> detectability in marine sediments
Bibliographic record
Abstract
Abstract Natural matrices affect environmental DNA (eDNA) detections. Effects of matrix‐eluted compounds on the polymerase chain reaction (PCR) step have been the focus of most inhibition studies. Factors affecting eDNA detections in a typical laboratory workflow, i.e., DNA extraction and PCR steps, are mostly unknown. Here, we assessed the effect of four metal ions (Ca 2+ , Fe 3+ , Mn 2+ , Cu 2+ ) present in marine sediments on DNA detectability for both the extraction and the PCR detection steps. A single metal ion and exogenous DNA were added to marine sediments treated chemically to remove inhibitors. Our results showed that natural concentrations of calcium, iron, and manganese ions in surface marine sediments can impede completely DNA detections. Alternatively, copper ions added to the matrix increased DNA detectability by 7.7%. We also observed bimodal inhibitory effects of calcium and iron ions on DNA detectability, suggesting that the extraction and the PCR steps are both affected. Our findings highlight new limitations of eDNA detections. Avenues to optimize eDNA detection protocols applicable to multiple matrices are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".