Bibliographic record
Abstract
Epi-fluorescence microscopy is available in most life sciences research laboratories, and when optimized can be a central laboratory tool. In this chapter, the epi-fluorescence light path is introduced and the various components are discussed in detail. Recommendations are made for incident lamp light sources, excitation and emission filters, dichroic mirrors, objective lenses, and charge-coupled device (CCD) cameras in order to obtain the most sensitive epi-fluorescence microscope. The even illumination of metal-halide lamps combined with new "hard" coated filters and mirrors, a high resolution monochrome CCD camera, and a high NA objective lens are all recommended for high resolution and high sensitivity fluorescence imaging. Recommendations are also made for multicolor imaging with the use of monochrome cameras, motorized filter turrets, individual filter cubes, and corresponding dyes being the best choice for sensitive, high resolution multicolor imaging. Images should be collected using Nyquist sampling and images should be corrected for background intensity contributions and nonuniform illumination across the field of view. Photostable fluorescent probes and proteins that absorb a lot of light (i.e., high extinction co-efficients) and generate a lot of fluorescence signal (i.e., high quantum yields) are optimal. A neuronal immune-fluorescence labeling protocol is also presented. Finally, in order to maximize the utility of sensitive wide-field microscopes and generate the highest resolution images with high signal-to-noise, advice for combining wide-field epi-fluorescence imaging with restorative image deconvolution is presented.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.024 |
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".