T cell adhesion mechanisms revealed by receptor lateral mobility
Bibliographic record
Abstract
Abstract Cell surface receptors mediate the exchange of information between cells and their environment. In the case of adhesion receptors, the spatial distribution and molecular associations of the receptors are critical to their function. Therefore, understanding the mechanisms regulating the distribution and binding associations of these molecules is necessary to understand their functional regulation. Experiments characterizing the lateral mobility of adhesion receptors have revealed a set of common mechanisms that control receptor function and thus cellular behavior. The T cell provides one of the most dynamic examples of cellular adhesion. An individual T cell makes innumerable intercellular contacts with antigen presenting cells, the vascular endothelium, and many other cell types. We review here the mechanisms that regulate T cell adhesion receptor lateral mobility as a window into the molecular regulation of these systems, and we present a general framework for understanding the principles and mechanisms that are likely to be common among these and other cellular adhesion systems. We suggest that receptor lateral mobility is regulated via four major mechanisms—reorganization, recruitment, dispersion, and anchoring—and we review specific examples of T cell adhesion receptor systems that utilize one or more of these mechanisms. © 2007 Wiley Periodicals, Inc. Biopolymers 89: 409–419, 2008. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".