Nonequilibrium Capillary Electrophoresis of Equilibrium Mixtures (NECEEM): A Novel Method for Biomolecular Screening
Bibliographic record
Abstract
Nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM) is a new separation-based affinity method. It has kinetic capabilities exceeding those of surface plasmon resonance (SPR) and does not require immobilization of molecules on the surface. Another distinctive feature of NECEEM is that--if it is combined with an advanced method for the mixing solutions inside a capillary, termed transverse diffusion of laminar flow profiles (TDLFP)--it requires only nanoliter volumes of solutions. The proven applications of NECEEM to biomolecular screening include 1) measuring kinetic and thermodynamic parameters of protein-ligand interactions, 2) quantitative affinity analyses of proteins and hybridization analyses of DNA and RNA, and 3) selection of binding ligands from combinatorial libraries. NECEEM is easy to automate and parallelize. Because of its simplicity and analytical power, NECEEM has the potential to become a workhorse in studies of biomolecular interactions. The author reviews theoretical bases of NECEEM and its applications to biomolecular screening.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".