Assessing microbial growth monitoring methods for challenging strains and cultures
Bibliographic record
Abstract
Abstract This is a paper focusing on the comparison of growth curves using field relevant testing methods and moving away from colony counts. Challenges exist to explore antimicrobial growth of fastidious strains, poorly culturable bacterial and bacterial communities of environmental interest. Thus, various approaches have been explored to follow bacteria growth that can be an efficient surrogate for classical optical density or colony forming unit measurements. Here we tested optical density, ATP assays, DNA concentrations and 16S rRNA qPCR as means to monitor pure culture growth of six different species including Acetobacterium woodii, Bacillus subtilis, Desulfovibrio vulgaris, Geoalkalibacter subterraneus, Pseudomonas putida and Thauera aromatica . Optical density is and excellent, rapid monitoring method of pure culture planktonic cells but cannot be applied to environmental or complex samples. ATP assays provide rapid results but conversions to cell counts may be misleading for different species. DNA concentration is a very reliable technique which can be used for any sample type and provides genetic materials for downstream applications. qPCR of the 16S rRNA gene is a widely applicable technique for monitoring microbial cell concentrations but is susceptible to variation between replicates. DNA concentrations were found to correlate the best with the other three assays and provides the advantages of rapid extraction, consistency between replicates and potential for downstream analysis, DNA concentrations is determined to be the best universal monitoring method for complex environmental samples.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".