Ultraviolet Rate Constants of Pathogenic Bacteria: A Database of Genomic Modeling Predictions
Bibliographic record
Abstract
Abstract A database of bacterial ultraviolet (UV) susceptibilities is developed from an empirical model that correlates genomic parameters with UV rate constants. Software is used to count and evaluate potential ultraviolet photodimers and identifying hot spots in bacterial genomes. The method counts dimers that potentially form between adjacent bases that occur at specific genomic motifs such as TT, TC, CT, & CC. Hot spots are identified where clusters of three or more consecutive pyrimidines can enhance absorption of UV photons. The model incorporates nine genomic parameters into a single variable for each species that represents its relative dimerization potential. The bacteria model is based on a curve fit of the dimerization potential to the ultraviolet rate constant data for 92 bacteria species represented by 216 data sets from published studies. There were 4 outliers excluded from the model resulting in a 98% Confidence Interval. The curve fit resulted in a Pearson correlation coefficient of 80%. All identifiable bacteria important to human health, including zoonotic bacteria, were included in the database and predictions of ultraviolet rate constants were made based on their specific genomes. This database is provided to assist healthcare personnel and researchers in the event of outbreaks of bacteria for which the ultraviolet susceptibility is untested and where it may be hazardous to assess due to virulence. Rapid sequencing of the complete genome of any emerging pathogen will now allow its ultraviolet susceptibility to be estimated with equal rapidity. Researchers are invited to challenge these predictions. Importance This research demonstrates the feasibility of using the complete genomes of bacteria to determine their susceptibility to ultraviolet light. Ultraviolet rate constants can now be estimated in advance of any laboratory test. The genomic methods developed herein allow for the assembly of a complete database of ultraviolet susceptibilities of pathogenic bacteria without resorting to laboratory tests. This UV rate constant information can be used to size effective ultraviolet disinfection systems for any specific bacterial pathogen when it becomes a problem.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".