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Enregistrement W2053416609 · doi:10.1242/jeb.077594

ATTACK OF THE EXPLODING TERMITES

2012· article· en· W2053416609 sur OpenAlexaff
Katie E. Marshall

Notice bibliographique

RevueJournal of Experimental Biology · 2012
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueInsect and Arachnid Ecology and Behavior
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésThorax (insect anatomy)AbdomenWhite (mutation)HymenopteraBlue lightBiologyAnatomyZoologyPhysicsOptics

Résumé

récupéré en direct d'OpenAlex

Aging is not always the most graceful of processes in animals. Hair greys, eyesight dims and teeth lose their edge. But perhaps no animal has it as tough as termites. A paper recently published in Science by J. Šobotník and an international team of colleagues shows that as Neocapritermes taracua workers age they develop a backpack stuffed with toxic chemicals that explodes when they are attacked.The researchers had noticed that workers of N. taracua come in two varieties: a regular-looking ‘white’ worker, and one that has two long dark blue markings at the space where the thorax and abdomen join (‘blue’ workers). And when either blue or white workers were attacked by other termite species, they burst, emitting a small amount of fluid. Upon closer inspection, the researchers found that the blue markings appeared to be caused by small groups of dark blue crystals held in pouches between the abdomen and an outgrowth of the thorax.To figure out what was going on, the researchers went to French Guiana and collected several colonies of N. taracua. Based on previous studies, they hypothesized that, as older workers were less useful to the colony – because their mandibles were worn and not as effective at chewing – they would be more likely to have blue crystals, more likely to burst when attacked and, when they burst, produce more toxic fluid. They first compared the mass of blue crystals to the sharpness of the mandibles in N. taracua workers and found that older workers (with duller mandibles) had greater amounts of blue crystal. Then they set up fighting matches where they paired N. taracua workers with other termite species to see how long blue and white workers held out before bursting. They found that blue workers were more aggressive to other species, and burst more quickly than white workers.Šobotník and the other researchers then applied fluid from burst white and blue workers to termites of other species to compare the toxicity of the fluids. In addition, they gently removed the crystals from the blue workers, to see whether they were solely responsible for any differences in toxicity. They found that the fluid emitted by blue workers (complete with blue crystals) was extremely effective at killing other termites, whereas the fluid emitted by white workers was much less potent. However, without their blue crystals the blue workers' fluid was far less toxic. Similarly, if the team added blue crystals to the fluid from a white worker, the toxicity of the mixture was increased (although it was never as toxic as the unaltered mixture from a blue worker).This pointed to the blue crystals being an important component of the toxicity. Using several chemical techniques, the researchers found that the blue crystals contained a 76 kDa protein, likely an oxygen-binding copper protein from the hemocyanin/phenoloxidase family, containing a large amount of copper (which gave the crystals their blue colour). The also found that salivary gland extract (which mixes with the blue crystals during the explosion) from the blue and white workers appeared to differ in composition. This led the researchers to conclude that blue N. taracua workers have a two-component toxin: the crystal protein and salivary gland components mix to produce the toxin.Aging is not exactly a peaceful process in this termite species. Rather than settling down and picking up a hobby as they age, workers of N. taracua develop a hash of toxic chemicals that they carry around in a backpack on their cuticle, ready to burst at any moment in a Kamikaze mission against their enemies.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
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 enseignants0,007
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,029
Tête enseignante GPT0,322
Écart entre enseignants0,293 · 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
Publié2012
Routes d'admission1
Résumé présentoui

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