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Record W2158676115 · doi:10.1073/pnas.0608224104

Queen pheromone modulates brain dopamine function in worker honey bees

2007· article· en· W2158676115 on OpenAlexaff
Kyle T. Beggs, Kelly A. Glendining, Nicola M. Marechal, Vanina Vergoz, Ikumi Nakamura, Keith N. Slessor, Alison R. Mercer

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsSimon Fraser University
FundersMarsden FundRoyal Society Te Apārangi
KeywordsPheromoneDopamineSex pheromoneBiologyNeuroscienceOffspringHoney beeBiogenic amineZoologyNeurotransmitterEcologyGeneticsCentral nervous systemPregnancy

Abstract

fetched live from OpenAlex

Honey bee queens produce a sophisticated array of chemical signals (pheromones) that influence both the behavior and physiology of their nest mates. Most striking are the effects of queen mandibular pheromone (QMP), a chemical blend that induces young workers to feed and groom the queen and primes bees to perform colony-related tasks. But how does this pheromone operate at the cellular level? This study reveals that QMP has profound effects on dopamine pathways in the brain, pathways that play a central role in behavioral regulation and motor control. In young worker bees, dopamine levels, levels of dopamine receptor gene expression, and cellular responses to this amine are all affected by QMP. We identify homovanillyl alcohol as a key contributor to these effects and provide evidence linking QMP-induced changes in the brain to changes at a behavioral level. This study offers exciting insights into the mechanisms through which QMP operates and a deeper understanding of the queen's ability to regulate the behavior of her offspring.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.285
Teacher spread0.267 · 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 designBench or experimental
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

Citations181
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicInsect and Arachnid Ecology and BehaviorFrench-language works237,207