Permeability Changes in Model and Phytoplankton Membranes in the Presence of Aquatic Humic Substances
Bibliographic record
Abstract
Aquatic humic and fulvic acids can increase the permeability of biological membranes to lipophilic solutes. In in vivo experiments, passive diffusion of fluorescein diacetate (FDA) into the green alga Selenastrum capricornutum increased in the presence of Suwannee River humic and fulvic acids at pH 5 (humic > fulvic) but not at pH 7. The observation of enhanced diffusion at the lower pH is consistent with adsorption measurements, which showed that the association of humic and fulvic acids with the algal surface was greater at pH 5 than at pH 7. Permeability experiments were also performed on model membranes to investigate the interaction of these humic substances with membrane lipids. In these in vitro experiments, we followed leakage of the fluorescent probe sulforhodamine-B (SRB) that had been encapsulated within 1-palmitoyl-2-oleoyl- sn -glycero-3-phosphatidylcholine (POPC) vesicles; this model phospholipid is representative of those found in the plasmalemma of green algae. Release of SRB from the vesicles was markedly accelerated in the presence of Suwannee River humic and fulvic acids (humic > fulvic); for the humic acid, lowering the pH from 7.6 to 5.7 enhanced this surfactant-like effect. The demonstration that humic substances can alter the permeability of phytoplankton and model membranes at natural concentrations and pH values has potential implications for the uptake and regulation of toxic and essential solutes by the phytoplankton community.
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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) | 0.000 | 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".