Design and Implementation of a Custom Built Optical Projection Tomography System
Bibliographic record
Abstract
Optical projection tomography (OPT) is an imaging modality that has, in the last decade, answered numerous biological questions owing to its ability to view gene expression in 3 dimensions (3D) at high resolution for samples up to several cm(3). This has increased demand for a cabinet OPT system, especially for mouse embryo phenotyping, for which OPT was primarily designed for. The Medical Research Council (MRC) Technology group (UK) released a commercial OPT system, constructed by Skyscan, called the Bioptonics OPT 3001 scanner that was installed in a limited number of locations. The Bioptonics system has been discontinued and currently there is no commercial OPT system available. Therefore, a few research institutions have built their own OPT system, choosing parts and a design specific to their biological applications. Some of these custom built OPT systems are preferred over the commercial Bioptonics system, as they provide improved performance based on stable translation and rotation stages and up to date CCD cameras coupled with objective lenses of high numerical aperture, increasing the resolution of the images. Here, we present a detailed description of a custom built OPT system that is robust and easy to build and install. Included is a hardware parts list, instructions for assembly, a description of the acquisition software and a free download site, and methods for calibration. The described OPT system can acquire a full 3D data set in 10 minutes at 6.7 micron isotropic resolution. The presented guide will hopefully increase adoption of OPT throughout the research community, for the OPT system described can be implemented by personnel with minimal expertise in optics or engineering who have access to a machine shop.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".