Design and Modeling of a Bio-Inspired <i>Aequorin</i> -Based Light-Emitting Unit: Applications in Optogenetics and Beyond
Bibliographic record
Abstract
Biophotonics includes a wide range of applications that use light-based technologies to investigate and manipulate biological systems. Traditionally, bioluminescence has been extensively used as a reporting agent in various biophotonics applications. However, its potential as a light source has not been explored. In this study, we propose the use of wireless Aequorin-based illumination as a bioinspired light-emitting source within biological tissue. This approach can have applications in a range of technologies; from optogenetics to bio-optical communications to human-brain interfaces. Drawing inspiration from the natural bioluminescent properties found in marine organisms, we designed a wireless Aequorin-based bioluminescence unit and developed an equivalent circuit model to describe the biological processes involved in illumination. Our model predicts the behavior of the bioluminescent units under various physical conditions, offering a framework for understanding how variations in physical parameters influence luminescence characteristics. In the absence of experimental studies focusing on Aequorin-based bioluminescence as a light source, our findings provide valuable guidance for researchers. These insights can help in understanding the system's behavior, designing more complex bioluminescence systems composed of multiple illumination units, and selecting parameters for future experimental research.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".