Single molecule localization deep within thick cells; a novel super‐resolution microscope
Why this work is in the frame
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Bibliographic record
Abstract
A novel 3D imaging system based on single‐molecule localization microscopy is presented to allow high‐accuracy drift‐free (<0.7 nm lateral; 2.5 nm axial) imaging many microns deep into a cell. When imaging deep within the cell, distortions of the point‐spread function result in an inaccurate and very compressed Z distribution. For the system to accurately represent the position of each blink, a series of depth‐dependent calibrations are required. The system and its allied methodology are applied to image the ryanodine receptor in the cardiac myocyte. Using the depth‐dependent calibration, the receptors deep within the cell are spread over a Z range that is many hundreds of nanometers greater than implied by conventional analysis. We implemented a time domain filter to detect overlapping blinks that were not filtered by a stringent goodness of fit criterion. This filter enabled us to resolve the structure of the individual (30 nm square) receptors giving a result similar to that obtained with electron tomography. High‐accuracy deep imaging of the ryanodine receptor in the cardiac myocyte, using single‐molecule localization microscopy. Depth‐dependent calibrations are performed for accurate depth localization. The optical design featuring two independent and variable focal planes allows real‐time feedback for drift‐free deep imaging. magnified image High‐accuracy deep imaging of the ryanodine receptor in the cardiac myocyte, using single‐molecule localization microscopy. Depth‐dependent calibrations are performed for accurate depth localization. The optical design featuring two independent and variable focal planes allows real‐time feedback for drift‐free deep imaging.
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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