Phase-gradient contrast in thick tissue with a scanning microscope
Bibliographic record
Abstract
It is well known that the principle of reciprocity is valid for light traveling even through scattering or absorptive media. This principle has been used to establish an equivalence between conventional widefield microscopes and scanning microscopes. We make use of this principle to introduce a scanning version of oblique back-illumination microscopy, or sOBM. This technique provides sub-surface phase-gradient and amplitude images from unlabeled tissue, in an epi-detection geometry. That is, it may be applied to arbitrarily thick tissue. sOBM may be implemented as a simple, cost-effective add-on with any scanning microscope, requiring only the availability of an extra input channel in the microscope electronics. We demonstrate here its implementation in combination with two-photon excited fluorescence (TPEF) microscopy and with coherent anti-Stokes Raman scattering (CARS) microscopy, applied to brain or spinal cord tissue imaging. In both cases, sOBM provides information on tissue morphology complementary to TPEF or CARS contrast. This information is obtained simultaneously and is automatically co-registered. Finally, we show that sOBM can be operated at video rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".