Polymerase Chain Reaction-Mediated Mutagenesis in Sequences Resistant to Homogeneous Amplification
Bibliographic record
Abstract
For many biologists, the polymerase chain reaction (PCR) has become an indispensable tool serving many diverse applications, from simple DNA amplification to complex diagnostics. In basic research, a very important application has been the use of PCR in the introduction of site-specific mutations into target DNA ( 1 , 2 ). A simple and commonly used example of this approach is the two-step “megaprimer” method Fig. 1 ), in which the mutant oligomer is first incorporated into DNA in one direction, then this DNA itself is used as a megaprimer to complete the mutant sequence in the other direction (3). Over the years, such an approach has been used in many laboratories with considerable success. In the authors’ experiments, this approach has been used for over a decade to introduce many changes into the ribosomal genes of yeast cells ( 4 – 6 ). Although some modifications were made some modifications in the amplification conditions ( 7 ), to improve the efficiency of the megaprimer method, the basic strategy, as originally described, has proven to be effective and reliable in most instances. Overview of the two-step megaprimer method for PCR-mediated mutagenesis. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".