Advancing High-Resolution Imaging of Virus Assemblies in Liquid and Ice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interest in liquid-electron microscopy (liquid-EM) has skyrocketed in recent years as scientists can now observe real-time processes at the nanoscale. It is extremely desirable to pair high-resolution cryo-EM information with dynamic observations as many events occur at rapid timescales - in the millisecond range or faster. Improved knowledge of flexible structures can also assist in the design of novel reagents to combat emerging pathogens, such as SARS-CoV-2. More importantly, viewing biological materials in a fluid environment provides a unique glimpse of their performance in the human body. Presented here are newly developed methods to investigate the nanoscale properties of virus assemblies in liquid and vitreous ice. To accomplish this goal, well-defined samples were used as model systems. Side-by-side comparisons of sample preparation methods and representative structural information are presented. Sub-nanometer features are shown for structures resolved in the range of ~3.5-Å-10 Å. Other recent results that support this complementary framework include dynamic insights of vaccine candidates and antibody-based therapies imaged in liquid. Overall, these correlative applications advance our ability to visualize molecular dynamics, providing a unique context for their use in human health and disease.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it