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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".