Real-time 4D tracking of airborne virus-laden droplets and aerosols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract There is currently no real-time airborne virus tracking method, hindering the understanding of rapid virus changes and associated health impacts. Nano-digital in-line holographic microscopy (Nano-DIHM) is a lensless technology that can directly obtain the interference patterns of objects by recording the scattered light information originating from the objects. Here, we provide evidence for real-time physicochemical tracking of virus-laden droplets and aerosols in the air using desktop label-free Nano-DIHM. The virus interference patterns, as single and ensemble particles, were imaged by the Nano-DIHM with 32.5 ms resolution. The next-generation Stingray and Octopus software was used to automate object detection, characterization and classification from the recorded holograms. The detection system was demonstrated to detect active MS2 bacteriophages, inactivated SARS-CoV-2 and RNA fragments, and an MS2 mixture with metallic and organic compounds. This work demonstrates the feasibility of using Nano-DIHM to provide rapid virus detection to improve transmission management in real time.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it