Time-Dependent Biosensor Fluorescence as a Measure of Bacterial Arsenic Uptake Kinetics and Its Inhibition by Dissolved Organic Matter
Bibliographic record
Abstract
The speciation and toxicity of arsenic in environments like rice paddy soils and groundwater aquifers are controlled by microbe-mediated reactions. These reactions often require As to be taken up into cells prior to enzymatic reaction, but there is limited understanding of how microbial arsenic uptake is affected by variations in water chemistry. In this study, we explored the effect of dissolved organic matter (DOM) quantity and quality on microbial As uptake, with a focus on the role of thiol functional groups that are well known to form aqueous complexes with arsenic. We developed a quantitative framework for interpreting fluorescence time series from whole-cell biosensors and used this technique to evaluate effects of DOM on the rates of microbial arsenic uptake. We show that thiol-containing compounds significantly decrease rates of As uptake into microbial cells at environmentally relevant DOM/As ratios, revealing the importance of DOM quality in regulating arsenic uptake, and subsequent biotransformation, in the environment.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".