Nanoscopy – Pushing the Limits of Light Microscopy.
Bibliographic record
Abstract
Nanoscopy has been a rapidly expanding field over the last 5 years. Different modalities have been developed based on optical, computational and combined optical/computational techniques that can be used to circumvent the diffraction barrier of visible light. Stimulated emission depletion (STED) microscopy is a purely optical technique that was originally developed by Stephane Hell and has been recently commercialized by Leica Microsystems. This technique uses two superimposed laser beams. One beam excites a point of fluorescence while the second beam – a donut shape – depletes the fluorescence emission around the spot through stimulated emission. The result is a point source of excitation which can be scanned across a fluorescence sample giving resolutions on the order of 20–40 nm. Single molecule localization microscopy is a purely computation technique that comes under many flavors (PALM, F-PALM, STORM, dSTORM). Each technique relies on the repetitive excitation and localization of a small subset of fluorescent molecules within a sample. Single molecules are imaged as Gaussian spots and with sufficient signal-to-noise can each be fit with a precision approaching 20 nm. Finally, structured illumination uses a combination of optics and image processing. Grid patterns are super-imposed on fluorescent samples with 15–25 different angles and z-axis positions. The result is a translation of high frequency sample information into low frequency Moire patterns. The 15–25 images are post processed based on the imposed patterns to de-convolve out fine sample details with at least twice the resolution limit of a conventional light microscope. This overview talk will introduce these various nanoscopy techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".