Bibliographic record
Abstract
Often in biology, rare individuals within a population dominate the population’s overall behavior, and we wish to extract those individuals for further analysis. We design a sorting instrument as a flexible platform for the development of novel microfluidic sorting techniques. We demonstrate a microfluidic cell sorter, which screens cells at rates approaching those of commercially-available fluorescence-activated cell sorters. This device incorporates a three-dimensional flow-focusing nozzle with a slanted ceiling groove to enhance the capabilities of a surface acoustic wave (SAW) transducer by harnessing the component of the SAW oriented normal to the plane of the substrate. The device achieves sorting at a rate of 9000 events/s with 54% purity and yields 89% purity, while operating at 1000 events/s; this level of performance approaches that of a FACS operating in its high-purity mode. We also present a rare event sorting technique, which can successfully extract desired droplets from a sample containing nearly a billion droplets. The technique yields pure samples after two rounds. The preliminary round is fast, capable of screening 10 ml of droplets at 100 ml/h, but each sort also captures many other droplets together with the droplet of interest. The second round of sorting enriches the sample to nearly 100% purity, using known designs for high purity drop-by-drop sorting. Thus, we devise a method, which can sort droplets rapidly and achieve high purity of few droplets from samples containing large numbers of droplets. The two disparate approaches to microfluidic sorting use a common platform to create new methods for sorting with biological applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".