Rapid high-resolution size distribution analysis for adeno-associated virus using high speed SV-AUC
Bibliographic record
Abstract
Abstract When optimized, sedimentation velocity analytical ultracentrifugation (SV-AUC) provides the most-accurate, broadest-range, and highest-resolution size distribution analysis of any method. Generating simulated data for an adeno-associated virus (AAV) sample consisting of four species differing only in their DNA content and having closely spaced sedimentation coefficients, allows manipulation of the SV-AUC experimental protocol to optimize the size distribution resolution. In developing this high speed SV-AUC (hs-SV-AUC) protocol several experimental challenges must be overcome: 1) the need for rapid data acquisition, 2) avoiding optical artifacts from steep boundaries and 3) overcoming the increased potential for convection. A protocol, hs-SV-AUC, has been developed that uses high rotor speeds, interference detection and low temperatures to overcome these challenges. By confining data analysis to a limited radial-time window and using a very short run time (< 20 min after temperature equilibration), the need to match the sample and reference solvent composition and meniscus positions is relaxed, making interference detection is as simple to employ as absorbance detection. Experimental size distributions from the same AAV sample by hs-SV-AUC at 45K rpm and 10 °C versus low-speed SV-AUC at 15K rpm, and 10 °C illustrates the improved size distribution resolution offered by the hs-SV-AUC protocol.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".