Assessment of physiological state of microorganisms in activated sludge with flow cytometry: application for monitoring sludge production minimization
Bibliographic record
Abstract
Many sludge reduction processes have been studied for the minimization of sludge production in biological wastewater treatment. The investigations on most of these processes have monitored the increase of the soluble chemical oxygen demand, the sludge mass reduction, or the decrease of the floc size, but little information has been obtained on cell lysis and the change of the biological cell activity. However, employing any strategy for reducing sludge production may have an impact of microbial community in biological wastewater treatment process. This impact may influence the sludge characteristics and the quality of effluent. The objective of this study concerns the determination of the physiological state of activated sludge microorganisms during a sludge minimization process. A thermal treatment at 80 degrees C for 5, 20, 40 and 60 min was chosen in this study. Staining bacteria with CTC and SYTOX green was used to evaluate biological cell activity and viability of cell types contained in activated sludge, respectively. The monitoring of cell activity and viability was performed using flow cytometry (FCM) analysis before and after thermal treatment of activated sludge. Results indicated an increase in the number of permeabilized cells and a decrease in the number of active cells, subsequent to the thermal treatment. The study also confirms the potential of FCM to successfully evaluate the physiological heterogeneity of an activated sludge bacterial population. Moreover, the experimentally observed correlations between the FCM results and the organic matter solubilization in activated sludge samples during thermal treatment revealed that the increase in the soluble organic matter concentration was predominantly due to an intracellular material release. Identifying the increase in activated sludge hydrolysis requires a precise knowledge of the involved mechanisms, and this study indicated that the FCM, used in conjunction with specific probes, could be a useful tool.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".