Density amplification in laser-assisted protein adsorption by photobleaching
Bibliographic record
Abstract
Spatial distributions of proteins are crucial for development, growth and normal life of organisms. Position of cells in a morphogen gradient determines their differentiation in a specific manner. Neutrophils are the initial responders to bacterial infection or other inflammatory stimuli and have the ability to migrate rapidly up shallow gradients of attractants in vivo. Moreover, for the correct wiring of the nervous system, axonal growth cones detect concentration changes of specific proteins called guidance cues to navigate and reach their targets. Guidance cues can either be chemoattractive or chemorepulsive, and the same protein can act successively as both depending on the time point in development or the simultaneous presence of other molecules. A prerequisite to understand chemotaxis in a precise manner is the availability of a method able to reproduce in vitro the spatial distributions of proteins found in vivo. We recently introduced LAPAP (Laser-assisted protein adsoption by photobleaching), an optical method to produce substrate-bound protein patterns with micron resolution. Here, we present how the amount of protein present on the pattern can be increased by one order of magnitude.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".