Cell homogenization and subcellular fractionation in two phytoplanktonic algae: implications for the assessment of metal subcellular distributions
Bibliographic record
Abstract
Metal subcellular distribution in phytoplankton is of interest from both ecotoxicologic and trophic transfer perspectives. Differential centrifugation preceded by cell disruption is frequently used to separate metals in different intracellular compartments. Homogenization efficiency varies widely among species, however, and a quantitative assessment of this parameter is necessary. Moreover, fractions isolated by differential centrifugation remain operationally defined, and confirmation of the nature of these fractions is thus needed. In the present study, homogenization efficiencies for two chlorophytes were evaluated for different methods (sonicator, beadbeater, rotor‐stator homogenizer). For the most promising approach (sonication), homogenization efficiency was optimized, using a particle counter, 14C uptake, and growth experiments. The separation efficiency of a subcellular fractionation protocol was also optimized and applied to algae that had been exposed to environmentally relevant concentrations of Cd (0.7 nM Cd2+). The homogenization efficiency could be reliably estimated with the particle counter. Contrasting homogenization efficiencies were obtained for the two test species; virtually all C. reinhardtii cells were easily broken, whereas a large proportion of P. subcapitata cells remained intact (73.2 ± 2.8%). Failure to consider the specific homogenization efficiency for P. subcapitata cells would lead to a greater than threefold underestimate of Cd quotas in the organelle and cytosol fractions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".