Measurement of cellular adhesions and adhesion protein dynamics using tracking paired with spatio-temporal image correlation spectroscopy
Bibliographic record
Abstract
Proteins are ubiquitous in biological systems and while much is known about protein structure, less is known about the movement of these proteins.The phenomenon of movement also occurs at the cellular level through cell migration and, in particular, cells depend upon the movement of proteins to enable cell motility.More precisely, cell migration is dependent upon cytoskeletal structures, including focal adhesions, complexes which include multiple proteins and enable cells to exert forces upon the underlying substrate and migrate.The basic structure and components of the cytoskeleton are fairly well known, yet much less is known about their dynamic assembly and disassembly.Motile cells are known to be involved in numerous biological processes and thus studying the flow of proteins involved in cell migration has the potential to clarify their roles and lead to a more advanced understanding of cell migration.Major protein components that play a role in the formation of focal adhesions have been identified.Using genetically engineered fluorescent variants of these proteins, we can image cells expressing fluorescently-tagged proteins via fluorescence microscopy, and thereby obtain quantitative results on the location and movement of key proteins of interest in migrating cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".