Simple multipulse excitation for enhanced detection of long-lived fluorophores
Bibliographic record
Abstract
Typically the signal-to-background ratio is the limiting aspect of fluorescence-based detecting and imaging. The background signal can be composed of a variety of sources-excitation scattering, contaminants, and autofluorescence from cellular constituents. Most of these sources have a short-lived lifetime (ps to ns range). In order to increase the signal-to-background ratio, fluorophores with high brightness or in large concentrations are typically used along with time-gated detection. This unfortunately sacrifices the probe’s signal unless it has a very long lifetime. Herein we are presenting a simple method to enhance the detection of widely available and well-known mid-range lifetime (~20 ns) fluorophores’ signal against short-lived backgrounds. This requires a repetition rate of ~300 MHz to pump a 20 ns probe sufficiently. Typical laser sources today are not equipped with repetition rates above 80 MHz. However, this multipulse method allows these rates to be attainable for nearly any pulsed laser source. Multiple pulses of excitation are separated by a variable temporal length, which is short compared to the lifetime of the long-lived fluorophore, to increase the excited state population of a long-lived fluorophore, while the short-lived background decays almost completely between pulses. This is accomplished by simply redirecting the pulsed excitation beam through glass and then a delay length any number of times and lengths as desired to control the number of pulses and separation times.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".