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Record W4283589397 · doi:10.1101/2022.06.21.497066

Supplementation in vitamin B3 counteracts the negative effects of tryptophan deficiencies in bumble bees

2022· preprint· en· W4283589397 on OpenAlexaff
Mathilde L. Tissier, Stéphane Kraus, Tamara Gómez‐Moracho, Mathieu Lihoreau

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsBishop's University
FundersCentre National de la Recherche ScientifiqueAssociation Nationale de la Recherche et de la Technologie
KeywordsTryptophanBiologyAmino acidVitaminSerotoninLongevityEssential amino acidFood scienceEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Increasing evidence highlights the importance of diet content in 9 essential amino acids for bee physiological and behavioral performance. However, the tenth essential amino acid, tryptophan, has been overlooked as its experimental measurement requires a specific hydrolysis. Tryptophan is the precursor of serotonin and vitamin B3, which together modulate cognitive and metabolic functions in most animals. Here, we investigated how tryptophan deficiencies influence the longevity and behavior of bumble bees. Tryptophan-deficient diets led to a moderate increase in food intake, aggressiveness and mortality compared to the control diet. Vitamin B3 supplementation in tryptophan-deficient diets tended to buffer these effects on food intake and survival, and significantly reduced workers aggressiveness. Considering that the pollens of major crops and common plants, such as corn and dandelion, are deficient in tryptophan, these effects could have a strong impact on bee populations and their pollination service.

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 score0.002
Threshold uncertainty score0.007

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.211
Teacher spread0.193 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicPlant and animal studies→French-language works237,207→