MétaCan
Menu
Back to cohort
Record W3136329889 · doi:10.3390/cryst11030318

Microtubules as One-Dimensional Crystals: Is Crystal-Like Structure the Key to the Information Processing of Living Systems?

2021· article· en· W3136329889 on OpenAlexaff
Noemi Sánchez-Castro, M. Palomino‐Ovando, Pushpendra Singh, Satyajit Sahu, M. Toledo-Solano, Jocelyn Faubert, J. E. Lugo, Anirban Bandyopadhyay, Kanad Ray

Bibliographic record

VenueCrystals · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and Biological Electrophysiology Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMicrotubuleCrystal (programming language)TubulinSIGNAL (programming language)DimerTranslational symmetryPhysicsKey (lock)Biological systemBiophysicsChemical physicsMaterials scienceCondensed matter physicsComputer scienceBiologyCell biologyNuclear magnetic resonance

Abstract

fetched live from OpenAlex

Each tubulin protein molecule on the cylindrical surface of a microtubule, a fundamental element of the cytoskeleton, acts as a unit cell of a crystal sensor. Electromagnetic sensing enables the 2D surface of microtubule to act as a crystal or a collective electromagnetic signal processing system. We propose a model in which each tubulin dimer acts as the period of a one-dimensional crystal with effective electrical impedance related to its molecular structure. Based on the mathematical crystal theory with one-dimensional translational symmetry, we simulated the electrical transport properties of the signal across the microtubule length and compared it to our single microtubule experimental results. The agreement between theory and experiment suggests that one of the most essential components of any Eukaryotic cell acts as a one-dimensional crystal.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.212
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCrystalsSame topicPlant and Biological Electrophysiology StudiesFrench-language works237,207