Low-cost multimodal light sheet microscopy for optically cleared tissues and living specimens
Bibliographic record
Abstract
Light sheet microscopy techniques have expanded with designs to address many new applications. Due to rapid advancements in computing power, camera/detector technologies, and tissue clearing techniques, light sheet methods are becoming increasingly popular for biomedical imaging applications at the cellular and tissue levels. Light sheet imaging modalities couple rapid imaging rates, low-levels of phototoxicity, and excellent signal to noise ratios, contributing to their popularity for experimental biology. However, the current major limitation of light sheet microscopy arises from optical aberrations, with the main drawback being the defocusing introduced by refractive index variations that accompany clearing techniques. Here, we propose an inexpensive and easy to build light sheet based instrumentation to overcome this limitation by optomechanically decoupling the sample scanning movement from the detection step. Our solution is relatively simple to implement and also provides increased modularity by using a swappable excitation arm. This expands the range of samples we can image on a single system, from high resolution for single cells at ? m spatial resolution, to tissues with mm spatial resolution. We demonstrate our approach, using the system to image iDISCO cleared embryos and sciatic nerves, and provide the full three-dimensional reconstruction of these objects in minutes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".