Bibliographic record
Abstract
The nucleation, polymerization, and depolymerization of actin filaments is spatially and temporally controlled in order to regulate cell motility, cell morphology, and protein organization within the plasma membrane. By limiting receptor diffusion, the submembrane actin cytoskeleton modulates the signaling output of receptors such as the B-cell antigen (Ag) receptor (BCR) that are activated by clustering. Restricting BCR mobility limits BCR-BCR collisions and the resultant ‘tonic’ signaling. Conversely, more dynamic actin filaments or F-actin clearance promotes BCR-BCR collisions and leads to a ‘primed’ state where the threshold for Ag-induced activation is reduced. In chapter 2, I show a mechanism of receptor cross-talk where microbial danger signals (TLR ligands) prime B cells for Ag-induced activation by enhancing actin dynamics. TLR signaling reduced BCR confinement, promoted BCR-BCR collisions and potentiated responses to low densities of membrane-associated Ags. The interaction of B-cells with antigen-presenting cells displaying membrane-Ags results in initial BCR signaling that promotes cell spreading and increases the probability of BCRs encountering Ag. This is coupled with increased BCR mobility and the formation of BCR microclusters that recruit and activate signaling enzymes. Cell spreading and BCR microcluster mobility require severing of cortical submembrane actin, a precursor to F-actin branching that drives cell spreading. In chapter 3, I show that BCR signaling increases actin dynamics and BCR microcluster formation by activating the actin-severing protein cofilin via a signaling pathway involving Rap GTPases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".