Fluorescence and SEM correlative microscopy for nanomanipulation of subcellular structures
Bibliographic record
Abstract
Nanomanipulation under scanning electron microscopy (SEM) enables direct interactions of a tool with a sample. We recently developed a nanomanipulation technique for the extraction and identification of DNA contained within sub-nuclear locations of a single cell nucleus. In nanomanipulation of sub-cellular structures, a key step is to identify targets of interest through correlating fluorescence and SEM images. The DNA extraction task must be conducted with low accelerating voltages resulting in low imaging resolutions. This is imposed by the necessity of preserving the biochemical integrity of the sample. Such poor imaging conditions make the identification of nanometer-sized fiducial marks difficult. This paper presents an affine scale-invariant feature transform (ASIFT) based method for correlating SEM images and fluorescence microscopy images. The performance of the image correlation approach under different noise levels and imaging magnifications was quantitatively evaluated. The optimal mean absolute error (MAE) of correlation results is 68±34 nm under standard conditions. Compared with manual correlation by skilled operators, the automated correlation approach demonstrates a speed that is higher by an order of magnitude. With the SEM-fluorescence image correlation approach, targeted DNA was successfully extracted via nanomanipulation under SEM conditions. A fast method for correlating scanning electron microscopy (SEM) and fluorescence images when handling subcellular objects has been acheived. Yu Sun of the University of Toronto, Canada, and co-workers in China and Canada developed the image correlation technique to improve the implementation of a method for extracting DNA from cells. Such correlation is essential, because SEM images lack sufficient contrast to enable structures of interest to be identified and because of the need to use low-energy (and hence low-resolution) SEM imaging to avoid damaging DNA. Their correlation technique employs image processing algorithms to automatically correlate the two microscopy techniques and is an order of magnitude faster than manual image correlation by skilled operators. The researchers have demonstrated the method's usefulness by using it to extract DNA from cells under SEM imaging.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".