A simulation study on ultrasound backscattering by cell aggregates with poly-disperse cells
Bibliographic record
Abstract
A Monte Carlo simulation study on ultrasound backscattering by cell aggregates with poly-disperse cells is discussed. The nuclei in a cell aggregate were assumed as scatterers and the Anderson model was used to obtain backscattering amplitude for each nucleus. The resultant backscatter echo from many particles was determined by using linear superposition of backscatter signals emitted by the nuclei. The random sequential adsorption (RSA) method was employed to generate spatial organizations of nuclei. The frequency dependent backscattering coefficient (BSC) and signal envelope statistics were obtained from tissue samples with different size distributions. For each poly-disperse sample the nuclear populations followed a Gaussian distribution with the nuclear packing fraction fixed at 50.36%. It was found that integrated backscattering coefficient (IBSC) computed between 10-30 MHz increased about 7 dB for the highest poly-disperse sample considered compared to that of a mono-disperse sample. A Gaussian input pulse was employed to investigate signal envelope statistics. It was found that envelope histograms followed the Rayleigh distribution. The Rayleigh fit parameter (σ) increased as dispersity increased. For example, for the highest poly-disperse sample, σ increased about 105% and 157% compared to mono-disperse sample for input pulses with 5 and 25 MHz as the center frequencies and 80% bandwidths. The present work shows that poly-dispersity contributes to ultrasound backscatter but the shapes of histograms did not vary with the size distribution of scatterers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".