Optimization of ultrasound-mediated DNA transfer for bacteria and preservation of frozen competent cells
Bibliographic record
Abstract
ABSTRACT The transformation of DNA into cells is the basis of molecular biology. Commonly employed techniques include heat shock transformation, electro-transformation, conjugation, transduction, and protoplast fusion. Recently, ultrasonic transformation technology has been developed to transfer DNA into competent cells. The transformation conditions, such as temperature and ultrasonic power, were preliminarily studied. However, this technique has not been widely applied because competent cells must be prepared de novo . In this study, various factors, such as ultrasonic frequency and power, were optimized for the ultrasonic transformation of Escherichia coli . The study found that the optimal conditions for ultrasonic transformation with a defined ultrasonic transformation vial were a frequency of 28 kHz and a power of 80 W. Meanwhile, this research demonstrated that combining the 42°C heat shock conditions with ultrasonic transformation is the most efficient method compared to using only heat shock. Furthermore, the cryoprotective agent ratio for ultrasonic competent cells was investigated and optimized. These findings provide new insights into enhancing transformation efficiency and lay a foundation for the broader application of ultrasonic transformation. IMPORTANCE Plasmid transformation is widely applicable in gene expression and modification. As an efficient, non-invasive, and gentle method of transformation, ultrasonic transformation provides a novel approach for strain modification. This research presents new strategies for enhancing transformation efficiency and lays the groundwork for expanding the utilization of ultrasonic transformation.
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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".