Efficient Genomic DNA Extraction from Low Target Concentration Bacterial Cultures Using SCODA DNA Extraction Technology
Bibliographic record
Abstract
Methods for the extraction of nucleic acids are straightforward in instances where there is ample nucleic acid mass in the sample and contamination is minimal. However, applications in areas such as metagenomics, life science research, clinical research, and forensics, that are limited by smaller amounts of starting materials or more dilute samples, require sample preparation methods that are more efficient at extracting nucleic acids. Synchronous coefficient of drag alteration (SCODA) is a novel electrophoretic nucleic acid purification technology that has been tested successfully with both highly contaminated and dilute samples and is a promising candidate for new sample preparation challenges. In this article, as an example of SCODA's performance with limited sample material, we outline a genomic DNA (gDNA) extraction protocol from low abundance cultures of Escherichia coli DH10B. This method is equally well suited to high biomass 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".