Automated control of optical polarization for nonlinear microscopy
Bibliographic record
Abstract
Laser-scanning non-linear optical techniques such as multi-photon fluorescence excitation microscopy (MPM), Second/ Third Harmonic Generation (SHG/THG), and Coherent Anti-Stokes Raman Scattering (CARS) are being utilized in research laboratories worldwide. The efficiencies of these non-linear effects are dependent on the polarization state of the excitation light relative to the orientation of the sample being imaged. In highly ordered anisotropic biological samples this effect can become pronounced and the excitation polarization can have a dramatic impact on imaging experiments. Therefore, controlling the polarization state of the exciting light is important; however this is challenging when the excitation light passes through a complex optical system. In a typical laser-scanning microscope, components such as the dichroic filters, lenses, and even mirrors can alter the polarization state of a laser beam before it reaches the sample. We present an opto-mechanical solution to compensate for the polarization effects of an optical path, and to precisely program the polarization state of the exciting laser light. The device and accompanying procedures allow the delivery of precise laser polarization states at constant average power levels to a sample during an imaging experiment.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".