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Record W4401118489 · doi:10.3390/insects15080578

1H NMR Profiling of Honey Bee Brains across Varying Ages and Seasons

2024· article· en· W4401118489 on OpenAlexaff
Nuria Morfín, Paul H. Goodwin, Ernesto Guzmán‐Novoa, Nicole Legge, James G. Longstaffe

Bibliographic record

VenueInsects · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBiologyHoney beeHoney BeesProfiling (computer programming)Swarming (honey bee)ZoologyBotanyEcology

Abstract

fetched live from OpenAlex

) provide a useful model for studying aging because of the differences in longevity between the relatively short-lived summer and long-lived winter bees, as well as bees lacking signs of cognitive senescence as they age. Bee brains were dissected from newly emerged, 14-day-, and 28-day- old bees in mid- and late summer, as well as brood nest bees in fall, winter, and spring, before, during, and after overwintering, respectively. Brains were examined with nuclear magnetic resonance (NMR) spectroscopy to analyze their metabolome. Nine variable importance in projection (VIP) variables were identified, primarily amino acids and choline derivatives. Differences in metabolite concentrations were found with different ages of summer bees, mostly between newly emerged and 14-day- old bees, such as a decrease in phenylalanine and an increase in β-alanine, but there were also changes in older adults, such as o-phosphocholine that declined in 28-day- old bees. Differences in brood nest bees were observed, including a decline in tryptophan and an increase in β-alanine. These may provide distinct metabolomic signatures with age and season. Such research holds promise for a better understanding of the complex interplays between bee physiology, development, and aging, which has implications for improving bee health and management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.111

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.055
GPT teacher head0.256
Teacher spread0.201 · 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 teacher head, 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

Citations3
Published2024
Admission routes1
Has abstractyes

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