Side scatter light for micro-size differentiation and cellular analysis
Bibliographic record
Abstract
High resolution 2D side scatter patterns from polystyrene beads were obtained by using an integrated microfluidic waveguide cytometer. A He-Ne laser beam was prism-coupled into a microfluidic chip, and waveguide modes were excited to illuminate a single scatterer. While immobilizing a single scatterer on chip in the observation window area, high resolution 2D scatter patterns were obtained by using a CCD array located beneath the microchip. This cytometer is sensitive to variations in both the refractive index and the size of a single scatterer. Fourier transforms of Mie simulation results from a single scatterer show that forward scattered light at large angles is optimal for micro-size differentiation. While side scatter light was reported to contain rich information about organelles in a single cell, we show here that side scatter light can be used to perform fast micro-size differentiation and cellular analysis. A cross section scan of the experimental scatter pattern gives an oscillation distribution of the scattered intensity. This oscillation has a frequency that is typical for a given micro-size scatterer. A Fourier method for quick micro-size differentiation is reported, based on the comparisons between the Mie simulations and the experimental results. Finite-difference time-domain (FDTD) simulations of single white blood cells in the waveguide cytometer are studied, which allows extraction of microstructural and nano-structural information from single cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".