<title>Live cell studies of adhesion receptors by two-photon image correlation spectroscopy and image cross-correlation spectroscopy</title>
Bibliographic record
Abstract
Our ability to study the complex interactions between macromolecules within living cells has been greatly enhanced by the development of biophysical techniques such as fluorescence correlation spectroscopy (FCS) and multiphoton microscopy. One area of great interest to cell biologists is the molecular mechanism that governs cellular adhesion. Direct physical and chemical measurements on intact living cells will be important for obtaining a better understanding of how cells control their adhesive properties at the molecular level in order to control tissue development, maintain tissue integrity, and regulate cellular migration. Cells dynamically regulate the formation and disassembly of macromolecules in focal adhesions within the basal membrane so it would be advantageous to be able to measure such phenomena in situ. By combining two-photon microscopy imaging of living cells expressing fusion proteins of adhesion molecules and mutants of the green fluorescent protein, and image correlation spectroscopy (ICS) and image cross-correlation spectroscopy (ICCS) analysis, we have been able to perform direct studies of the molecular transport and clustering. We report on the characterization of flow, diffusion, aggregation, and co-localization of adhesion macromolecules/fluorescent protein constructs in living cells by two-photon ICS and ICCS experiments at 37 degree(s)C.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".