Quantitative Characterization of Stringency in SELEX
Bibliographic record
Abstract
High stringency (e.g., low target concentration) is key for obtaining high-affinity aptamers in SELEX; however, excessive stringency greatly increases the probability of SELEX failure. The control of stringency in SELEX remains a technical art based solely on intuition. A major reason for this is the lack of a measure of stringency. Here we introduce the Binder-to-Nonbinder Ratio at the output of partitioning (BNR), a parameter that characterizes stringency quantitatively: increasing stringency leads to decreasing BNR. BNR is determined experimentally by simply measuring by qPCR the quantities of oligonucleotides after partitioning in the presence and absence of target. The theory suggests, and our SELEX experiments with two targets confirm, that BNR must be kept statistically significantly greater than zero to avoid SELEX failure due to excessive stringency. Using BNR will help experimenters to rationalize the choice of conditions which the stringency depends on, e.g., target concentration and time of partitioning.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".