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Record W2727913367 · doi:10.1242/jeb.147470

Beauty is in the brain size of the beholder

2017· article· en· W2727913367 on OpenAlexaff
Sarah L. Alderman

Bibliographic record

VenueJournal of Experimental Biology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBeautyMate choiceTest (biology)Fish <Actinopterygii>PsychologyQuality (philosophy)Sexual selectionRecallSocial psychologyBiologyCognitive psychologyEcologyMatingAestheticsFisheryEpistemology

Abstract

fetched live from OpenAlex

Poop is all the rage these days. It isn't some new weird trend championed by hipster youth, but rather the enthusiasm of scientists who study our microbiomes, the bacteria that live on and within us. These bacteria, which are often best characterized by studying our poop (hence the rage), provide endless benefits to our health and well-being. They influence metabolism, development, immunity, behavior and much else. But when these beneficial microbes are eliminated by antibiotics, which are crucial life-savers against bacterial pathogens, things can go horribly awry. Most notably, treatment can cause dysbiosis, a microbial imbalance in our guts that increases our susceptibility to opportunistic bacteria like Clostridium difficile. But dysbiosis isn't just a human phenomenon. As elegantly shown in a new paper in PLoS Biology by Kasie Raymann and her colleagues from the University of Texas, what's true for humans is also true for bees.Bees are in global decline and one of the reasons for their troubles is a disease called American foulbrood, caused by the bacterial pathogen Paenibacillus larvae. Bee keepers can treat foulbrood by feeding bees or spraying hives with the broad-spectrum antibiotic oxytetracycline, which is effective against P. larvae. However, Raymann and her team wondered whether these drugs could also have off-target effects on the normal microbiota of bees and, if so, would the resulting dysbiosis harm bees just like it does humans?To test this, the team fed bees with oxytetracycline and compared their microbiomes with those of bees fed the same diet without the drug. As expected, antibiotic treatment had a dramatic effect on the core bacterial species of the bee microbiome. Bacterial abundance in oxytetracycline-treated bee guts declined nearly 5-fold as did overall bacterial diversity. However, it wasn't only core bacteria that declined; so too did bee health. Roughly two-thirds of the treated bees died; around twice the mortality of control bees. But why did the treated bees die?The cause, it turns out, is highly reminiscent of the factors leading to opportunistic C. difficile infections in humans: dysbiosis. So, oxytetracycline caused the ‘good’ bacteria of the bee microbiome to decline and in so doing created a vacuum that allowed other species to thrive, including a well-known insect pathogen called Serratia. And when the team fed antibiotic-treated larvae with Serratia, these bees died too, providing unambiguous evidence that antibiotic-induced dysbiosis increased bee susceptibility to off-target opportunistic pathogens. More importantly, the results confirmed that the core microbiome is a barrier to disease under normal conditions.Sometimes in medicine, the treatment is worse than the disease itself. This is unlikely to be the case here, as the benefits of foulbrood eradication almost certainly exceed the costs of Serratia-induced mortality. However, this study nicely demonstrates the unanticipated dangers of perturbing the microbiome. In addition, it perhaps suggests a solution to bee dysbiosis. One of the most promising treatments for C. difficile infections is to repopulate the human gut with a so-called fecal transplant. This provides an apparent barrier to C. difficile disease and works markedly better than antibiotics. Although I don't envy the bee proctologist administering microbiome enemas to larval bees, perhaps this type of fecal replacement (‘bee-cal’ transplants) is just what the hive ordered? Such an approach would avoid the dysbiosis that increases the risks of Serratia and other pathogens, while maintaining the treatment benefits against foulbrood. Novel solutions are needed to arrest global bee declines. Microbiome manipulation seems a worthwhile place to look.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.005
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.048
GPT teacher head0.316
Teacher spread0.268 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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