Polymerase Chain Reaction-Based Signature-Tagged Mutagenesis
Bibliographic record
Abstract
The study of bacterial pathogenicity in vitro has identified many signals, at the molecular and cellular levels that affect expression of virulence and other factors in causing disease. Because these pathways are not necessarily reproduced in vitro, their implication and their regulation in pathogenesis in vivo remains circumstantial. Genomics-based technologies can now be used to study pathogenesis in vivo ( 1 ). Signature-tagged mutagenesis (STM) ( 2 ) is an elegant method, based on negative selection, to identify mutations in a gene, which is essential during the infection process. In STM, transposon (Tn) mutants are generated, and each unique cell clone is tagged with a specific DNA sequence ( 2 ). Compared to traditional pathogenicity assays, STM minimizes the number of animals to be utilized, and eliminates false-positive and false-negative results. The strategy of STM depends on tagged Tn mutants, defective in virulence, which cannot be maintained in vivo. Attenuated mutants are selected and retested to confirm attenuation; disrupted genes are cloned via the Tn marker, and the inactivated gene confirmed by DNA sequencing. Modifications of STM, allowing rapid and easy identification of attenuated mutants, have recently been described and called “polymerase chain reaction (PCR)based STM” ( 3 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".