Effect of aluminium and copper on biofilm development of Pseudomonas pseudoalcaligenes KF707 and P. fluorescens as a function of different media compositions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bioremediation efforts worldwide are faced with the problem of metals interfering with the degradation of organic pollutants. There has been little systematic investigation into how the important environmental factors of media composition, buffering agent, and carbon source affect the exertion of metal toxicity on bacteria. This study aimed to systematically separate and investigate the influence of these factors by examining planktonic and biofilm establishment and growth. Two Pseudomonads were chosen, the PCB degrader P. pseudoalcaligenes KF707 and P. fluorescens. The two strains were grown in the presence of Al(3+) and Cu(2+) under different media conditions of carbon source (Lysogeny broth, biphenyl, succinate, aspartic acid, butyric acid, oxaloacetic acid, putrescine and benzoic acid) and under different buffering conditions (high and low phosphate or MOPS). These experiments allowed for the elucidation of an effect of different metabolic conditions and metal speciation on planktonic bacteria growth and biofilm establishment and development under metal stress. Here we show that the nature of bacterial growth (planktonic and biofilm development) is dramatically affected by the interplay between toxic metals, carbon source and media composition. The capacity of a media to bind toxic metals as well as quality of carbon source greatly influences the amount of metal that bacteria can tolerate, depending on both the bacterium and metal. Future studies evaluating metal ion toxicity should consider these effects, as well as their interactions with specific environments into account in order to improve clean-up success.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it