Deconvolution of Calcium Fluorescent Indicator Signal from AFM Cantilever Reflection
Bibliographic record
Abstract
Atomic force microscopy (AFM) can be combined with fluorescence microscopy to measure the changes in intracellular calcium levels (indicated by fluorescence of Ca²⁺ sensitive dye fluo-4) in response to mechanical stimulation performed by AFM. Mechanical stimulation using AFM is associated with cantilever movement, which may interfere with the fluorescence signal. The motion of the AFM cantilever with respect to the sample resulted in changes of the reflection of light back to the sample and a subsequent variation in the fluorescence intensity, which was not related to changes in intracellular Ca²⁺ levels. When global Ca²⁺ responses to a single stimulation were assessed, the interference of reflected light with the fluorescent signal was minimal. However, in experiments where local repetitive stimulations were performed, reflection artifacts, correlated with cantilever motion, represented a significant component of the fluorescent signal. We developed a protocol to correct the fluorescence traces for reflection artifacts, as well as photobleaching. An added benefit of our method is that the cantilever reflection in the fluorescence recordings can be used for precise temporal correlation of the AFM and fluorescence measurements.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".