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Enregistrement W2087860434 · doi:10.1242/jeb.000810

EVOLUTION OF ENDURANCE ATHLETES

2007· article· en· W2087860434 sur OpenAlexaff
Graham R. Scott

Notice bibliographique

RevueJournal of Experimental Biology · 2007
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenetics and Physical Performance
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésAthletesEndurance trainingNatural selectionSelection (genetic algorithm)Physical medicine and rehabilitationBiologyMedicineComputer sciencePhysical therapyArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

What's the difference between a couch potato and a marathon runner? It's partly that the marathoner actually gets off the couch and runs, training him or herself to become a better runner. However, physiologists know that being a good athlete also depends on genes. Genetics can contribute up to half of the variation seen in human exercise capacity, and some other species have evolved the ability to perform amazing feats of athleticism, often with very little training. It is because exercise capacity has a genetic basis that it can evolve, and can confer a strong advantage to animals in the wild. To investigate further, Norberto Gonzalez from the University of Kansas and his colleagues focus in a recent paper on the evolution of running endurance in rats.Transporting oxygen to mitochondria in exercising muscles is extremely important for making energy in the form of ATP. Animals with greater exercise capacity are generally better at transporting O2 along the pathway from environment to mitochondria, and this pathway has many steps:O2 is brought into the lungs with each breath, where it diffuses into the blood and is delivered throughout the body, then finally diffuses to mitochondria in the tissues. It was unclear how each step of the O2transport pathway evolves in athletic species, which led Gonzalez and his team to find out more by artificially selecting rats for running endurance.Artificial selection is a way of mimicking natural selection in the lab. Every generation, individual rats with the best running endurance were selected and bred together, generating high endurance populations that could run much further than rats with low running endurance. To understand the basis for these differences, Gonzalez and colleagues first measured the maximal rate of oxygen consumption(V̇O2max) during heavy exercise in rats after 15 generations of selective breeding. They found that the high endurance runners had a higher V̇O2max than rats in earlier generations, so they knew that the oxygen transport pathway was evolving. To find out what was causing the changes in V̇O2max, the authors analyzed each individual step in the O2 pathway. Both the rate of O2 delivery to the tissues by the blood and rate of O2 diffusion into the tissues from the blood were enhanced in high endurance runners, which explained their higher V̇O2max. All other steps in the O2 pathway were the same in high and low endurance runners.Gonzalez and colleagues made a remarkable finding when they compared these results to experiments in the same lines of rats after only seven generations of artificial selection: at this early stage of evolution, only the rates of O2 diffusion into the tissues were enhanced in high capacity runners. This discovery has important implications for how physiological systems evolve. It implies that when selection is applied to the O2transport pathway as a whole, different components of that pathway each evolve at a different pace. The authors conclude that the changes observed in tissue O2 diffusion at generation seven promoted changes in O2delivery at generation 15. In more general terms, evolution of the first physiological trait increased the selective advantage of the second trait,which later evolved as well. Gonzalez and colleagues have therefore shown us that the evolution of endurance capacity involves multiple physiological changes, and that there are many interesting differences between couch potatoes and marathon runners!

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0020,001
Science ouverte0,0010,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 Opus0,008
Tête enseignante GPT0,279
Écart entre enseignants0,271 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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
Publié2007
Routes d'admission1
Résumé présentoui

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