<i>In vivo</i>hyperspectral CARS and FWM microscopy of carotenoid accumulation in<i>H. Pluvialis</i>
Bibliographic record
Abstract
Coherent anti-Stokes Raman scattering (CARS) and four-wave-mixing (FWM) microscopy are a related pair of powerful nonlinear optical characterization tools. These techniques often yield strong signals from concentrated samples, but because of their quadratic dependence on concentration, they are not typically employed for imaging or identifying dilute cellular constituents. We report here that, depending on the excitation wavelengths employed, both CARS and degenerate-FWM signals from carotenoid accumulations in alga cysts can be exceptionally large, allowing for low-power imaging of astaxanthin (AXN) deposits in Haematococcus pluvialis microalga. By use of a broadband laser pulse scheme for CARS and FWM, we are able to simultaneously collect strong intrinsic two-photon-excitation fluorescence signals from cellular chlorophyll in vivo. We show that CARS signals from astaxanthin (AXN) samples in vitro strictly follow the expected quadratic dependence on concentration, and we demonstrate the collection of wellresolved CARS spectra in the fingerprint region with sensitivity below 2mM. We suggest that multimodal nonlinear optical microscopy is sufficiently sensitive to AXN and chlorophyll concentrations that it will allow for non-invasive monitoring of carotenogenesis in live H. pluvialis microalgae.
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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) | 0.000 | 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) | 0.001 | 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".