Monitoring by laser-flow-cytometry of the polycyclic aromatic hydrocarbon-degrading<i>Sphingomonas</i>sp. strain 107 during biotreatment of a contaminated soil
Bibliographic record
Abstract
A flow cytometric method (FCM) was used to detect and accurately enumerate a polycyclic aromatic hydrocarbon-degrading bacterial strain, Sphingomonas sp. 107, inoculated into a soil sample artificially contaminated with pyrene. To compare the FCM method with colony forming unit (CFU) assays, a rifampicin-resistant Sphingomonas sp. 107 was obtained which could be distinguished from the indigenous microflora, since there was no organism resistant to rifampicin in the soil that could transform indole to indigo (naphthalene dioxygenase activity). By combining light-scattering profiles (i.e., morphological properties), ethidium bromide influx (i.e., cell wall permeability), and fluorescence in situ hybridization against the 16S rRNA (i.e., detection specificity), we could enumerate the bacterial population of interest from the indigenous microflora and soil debris during the biotreatment. The FCM technique revealed that the number of inoculated Sphingomonas cells decreased gradually for 15 days of incubation before reaching a steady level of 7 to 12 x 10(5) cells.g-1 of soil. Similar values were obtained with the CFU assay. During this period, pyrene concentration decreased from 632 to 26 mg.kg-1 of dry soil. The FCM detection was improved by adding blocking reagent to the hybridization buffer to minimize the non-specific attachment of the fluorescent probe to soil particles. Combined with the improvements in probe technology, FCM detection was shown to be a good alternative to the conventional culture methods for the analysis of bacterial populations in environmental samples. This technique could be potentially useful for the detection of microorganisms that grow poorly in culture.
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.
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".