Effect of Immune Inducers on Nosema ceranae Multiplication and Their Impact on Honey Bee (Apis mellifera L.) Survivorship and Behaviors
Bibliographic record
Abstract
Nosema disease is a major disease of honey bees caused by two species of microsporidia, Nosema apis and N. ceranae. Current control involves using antibiotics, which is undesirable because of possible antibiotic resistance and contamination. In this study, flagellin, zymosan, chitosan, and peptidoglycan were investigated as alternatives for controlling N. ceranae infections and for their effect on bee survivorship and behaviors. Chitosan and peptidoglycan significantly reduced the infection, and significantly increased survivorship of infected bees, with chitosan being more effective. However, neither compound altered the bees’ hygienic behavior, which was also not affected by the infection. Chitosan significantly increased pollen foraging and both compounds significantly increased non-pollen foraging compared to healthy and infected bees. Memory retention, evaluated with the proboscis extension reflex assay, was temporarily impaired by chitosan but was not affected by peptidoglycan, nor was it affected by N. ceranae infection compared to the non-infected bees. This study indicates that chitosan and peptidoglycan provide benefits by partially reducing N. ceranae spore numbers while increasing survivorship compared to N. ceranae infected bees. Also, chitosan and peptidoglycan improved aspects of foraging behavior even more than in healthy bees, showing that they may act as stimulators of important honey bee behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".