Non-uniform Velocity of Homogeneous DNA in a Uniform Electric Field: Consequence of Electric-Field-Induced Slow Dissociation of Highly Stable DNA–Counterion Complexes
Bibliographic record
Abstract
Identical molecules move with identical velocities when placed in a uniform electric field within a uniform electrolyte. Here we report that homogeneous DNA does not obey this fundamental rule. While most DNA moves with similar velocities, a fraction of DNA moves with velocities that vary within a multiple-fold range. The size of this irregular fraction increases several orders of magnitude when exogenous counterions are added to DNA. The irregular fraction decreases several orders of magnitude when DNA counterions are removed by dialysis against deionized water in the presence of a strong electric field (0.6 kV/cm). Dialysis without the field is ineffective in decreasing the size of irregular fraction. These results suggest that (i) DNA can form very stable complexes with counterions, (ii) these complexes can be dissociated by an electric field, and (iii) the observed non-uniform velocity of DNA is caused by electric-field-induced slow dissociation of these stable complexes. Our findings help to better understand a fundamental property of DNA: its interaction with counterions. In addition, these findings suggest a practical way of making electromigration of DNA more uniform: removal of strongly bound DNA counterions by electro-dialysis against deionized water.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".