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Enregistrement W2937926691 · doi:10.1242/jeb.204214

Muscles brake and bend joints to shape wings

2019· article· en· W2937926691 sur OpenAlexaboutno aff
Kathryn Knight

Notice bibliographique

RevueJournal of Experimental Biology · 2019
Typearticle
Langueen
DomaineEngineering
ThématiqueMechanics and Biomechanics Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBird flightAeronauticsFlappingWingBrakeAnatomyEngineeringMechanical engineeringMedicineAerospace engineering

Résumé

récupéré en direct d'OpenAlex

The courage of the earliest human flight pioneers is genuinely inspiring. Fortunately, many of these innovators emerged relatively unscathed from the wreckage of their unsuccessful attempts, with the least successful models – usually based on flapping – never leaving the ground. Yet birds rarely endure the undignified collisions experienced by the first human aviators. ‘Birds are capable of diverse flight behaviours and manoeuvres’, says Jolan Thériault from the University of British Columbia (UBC), Canada, explaining that most of our current understanding of the mechanisms that allow birds to remain aloft is based on studies of the pectoral muscles, which power flight. However, much of bird's agility depends on the subtle ways in which they adjust the shape of their wings as they weave and dart through the air. ‘The contribution of the wing muscles has received relatively little attention’, says Thériault, who decided, with colleagues Joseph Bahlman (California State University, Sacramento) and Doug Altshuler and Bob Shadwick, also from UBC, to investigate how the humerotriceps muscle, which sits behind the humerus and extends the wing elbow joint, functions when flapping pigeons fly.Muscles can absorb energy to function as brakes, in addition to consuming energy when contracting to bend limbs, so the team decided to measure the amount of energy generated or absorbed by the humerotriceps muscle during different muscle activation cycles that occur at different stages of wing beats. As nerve signals trigger muscles to either consume energy and contract, or absorb energy when lengthening and behaving like brakes, Thériault was able to take advantage of measurements of muscle length changes in response to nerve signals in flying pigeons, which had been previously recorded by Angie Berg Robertson and Andy Biewener. She used these values to simulate how the muscle performs during flight activation cycles in the lab, measuring the forces produced as the muscle contracted and as they absorbed energy. Next, Thériault plotted the force and muscle length values for each activation cycle on graphs to calculate the power generated, or absorbed, to find out how the humerotriceps muscle was contributing to shaping the wing during each wing beat.Comparing the muscle's performances, it was clear that it contributed to extending the wing, generating force to spread the wing wide. However, the team could see the muscle absorbing energy, like a brake, during other activation cycles, which they suggest could hold the joint steady as the pigeon folds the wing during the upstroke of the wingbeat. And when they analysed the shape of some of the graphs where they had plotted force against muscle length, it looked as if the muscle could also store energy while stabilising the joint, ready for later use, much like a spring. Thériault also realised that, more impressively, the muscle could switch between exerting force to extend the joint and functioning as a brake within a single activation cycle and she suggests, ‘birds could adjust wing shape by changing activation and/or length-change patterns, effectively helping them produce different flight behaviours’.So pigeons are capable of fine-tuning how they use the muscles that control wing shape during flight and the team is eager to find out how these animals put their muscle versatility into practice to control their legendary manoeuvrability.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,294

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,247
Écart entre enseignants0,233 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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