2SQ-03 Mechanosensitive nucleotide release in the lung(2SQ Developing Mechanobiology:from fundamentals to medical applications,The 49th Annual Meeting of the Biophysical Society of Japan)
Bibliographic record
Abstract
TheBiophysicalSociety of Japan General IncorporatedAssociation been clarified, From a mathematically rigorous viewpoint, however, the situation in which noise has an active rolc is not common except bet-hedging phenemenon where unpredictable risk is compensated by the phenotypic variability of cells due to intracellulur noise.Rather, stochasticity and noise in cellular systems may be attributed to our lack of knowledge to extract the relevant infonnatien that apparently noisy signal has.If relevant information is tactically embcdded in such noisy signal, a whelc ccllular sysLem may be able to operate robustly.To unvei] such a hidden information, information and statistiea] theory play a pivotal ro]e.In this work, by using theory for statistica] inferencc, we will clarify that intracellular reactions with specific structures can imp]ement a statjstically optimal dynamics to extract (decode} the relevant information in apparently noisy signal.Then, we wi]1 show that the decoding dynmnics can have a potential to generate noise-induced symmetry-breaking, which indicates that decoding of infonnation is tightly linked to spontaneous signa] amplification by noise.Finally, by employing the concept of information theory.we suggest that apparently noisier signal may have higher performance to transmit information.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".