Remediating Office Environments of Spore-Forming Bacteria
Bibliographic record
Abstract
This study examines decontamination processes that were developed on an emergency basis to eliminate Bacillus anthracis spores from deliberately contaminated buildings. The recommended steps include a survey with sampling, the removal of sensitive items, and HEPA vacuuming of all readily available surfaces, followed by biocide treatment and subsequent analyses for viable cells. There are several analytical challenges posed by this approach. These include the ability to discriminate the added strain from naturally occurring resident microbes, determining detection limits for anthrax spores in settled dusts, and detecting viable but nonculturable spores. There are also logistical issues relating to the various skill sets required from investigation to reconstruction. In the present study, a model office was constructed, and a strain of Bacillus pumilus was isolated from the carpet and reintroduced to the office in excess. The abundance of the B. pumilus strain was monitored in settled dust using a strain-specific, quantitative polymerase chain reaction (QPCR)-based detection method following repeated HEPA vacuum cleanings. The QPCR method had a limit of detection corresponding to < or = 10(2) colony forming units per gram of settled dust. QPCR results were compared with measures of dust recoveries and fungal glucan and endotoxin levels in the dust samples. The largest fraction (ca. 81%) of added spores was recovered during the first HEPA cleaning. Subsequent cleanings resulted in incrementally lower recoveries, with removal of 93% of the initial inoculum by the third HEPA vacuuming. HEPA vacuuming prior to removal of items such as office contents and furnishings would result in much less resuspension of dust and limiting the extent of contamination. This approach also ensures that residual contaminants are as low as can be reasonably achieved.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".