Live Cell Imaging and Measurements of Molecular Dynamics
Bibliographic record
Abstract
w3-2 Live cell microscopy is becoming widespread across all fields of the life sciences, as well as, many areas of the physical sciences. In order to accurately obtain live cell microscopy data, the live specimens must be properly maintained on the imaging platform. In addition, the fluorescence light path must be optimized for efficient light transmission in order to reduce the intensity of excitation light impacting the living sample. With low incident light intensities the processes under study should not be altered due to phototoxic effects from the light allowing for the long term visualization of viable living samples. Aspects for maintaining a suitable environment for the living sample, minimizing incident light and maximizing detection efficiency will be presented for various fluorescence based live cell instruments. Raster Image Correlation Spectroscopy (RICS) is a technique that uses the intensity fluctuations within laser scanning confocal images, as well as the well characterized scanning dynamics of the laser beam, to extract the dynamics, concentrations and clustering of fluorescent molecules within the cell. In addition, two color cross-correlation RICS can be used to determine protein-protein interactions in living cells without the many technical difficulties encountered in FRET based measurements. RICS is an ideal live cell technique for measuring cellular dynamics because the potentially damaging high intensity laser bursts required for photobleaching recovery measurements are not required, rather low laser powers, suitable for imaging, can be used. The RICS theory will be presented along with examples of live cell applications.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".