Effects of laser polarization on responses of the fluorescent Ca<sup>2+</sup> indicator X-Rhod-1 in neurons and myelin
Bibliographic record
Abstract
Laser-scanning optical microscopes generally do not control the polarization of the exciting laser field. We show that laser polarization and imaging mode (confocal versus two photon) exert a profound influence on the ability to detect [Formula: see text] changes in both cultured neurons and living myelin. With two-photon excitation, increasing ellipticity resulted in a [Formula: see text] reduction in resting X-Rhod-1 fluorescence in homogeneous bulk solution, cell cytoplasm, and myelin. In contrast, varying the angle of a linearly polarized laser field only had appreciable effects on dyes that partitioned into myelin in an ordered manner. During injury-induced [Formula: see text] increases, larger ellipticities resulted in a significantly greater injury-induced signal increase in neurons, and particularly in myelin. Indeed, the traditional method of measuring [Formula: see text] changes using one-photon confocal mode with linearly polarized continuous wave laser illumination produced no appreciable X-Rhod-1 signal increase in ischemic myelin, compared to a robust [Formula: see text] fluorescence increase with two-photon excitation and optimized ellipticity with the identical injury paradigm. This underscores the differences in one- versus two-photon excitation and, in particular, the under-appreciated effects of laser polarization on the behavior of certain [Formula: see text] reporters, which may lead to substantial underestimates of the real [Formula: see text] fluctuations in various cellular compartments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".