Development of a fluorescence<i>in situ</i>hybridization protocol for the identification of micro-organisms associated with wastewater particles and flocs
Bibliographic record
Abstract
Fluorescence in situ hybridization (FISH) provides a unique tool to study micro-organisms associated with particles and flocs. FISH enables visual examination of micro-organisms while they are structurally intact and associated with particles. However, application of FISH to wastewater and sludge samples presents a specific set of problems. Wastewater samples generate high background fluorescence due to their organic and inorganic content making it difficult to differentiate a probe-conferred signal from naturally fluorescing particles with reasonable certainty. Furthermore, some of the FISH steps involve harsh treatment of samples, and are likely to disrupt the floc structure. This study developed a FISH protocol for studying micro-organisms that are associated with particles and flocs. The results indicate that choice of a proper fluorochrome and labeling technique is a key step in reducing the background fluorescence and non-specific binding, and increasing the intensity of the probe signal. Compared to other fluorochromes tested, CY3 worked very well and enabled the observation of particles and debris in red and probe signal from microbes in yellow. Fixation, hybridization, and washing steps disturbed the floc structure and particle-microbe association. Modifications to these steps were necessary, and were achieved by replacing centrifugation with filtration and employment of nylon filters. Microscope slides generated excellent quality images, but polycarbonate membrane filters performed better in preserving the floc structure.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".