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Record W2161296028 · doi:10.1186/1741-7007-10-104

Sex-specific effects of a parasite evolving in a female-biased host population

2012· article· en· W2161296028 on OpenAlexaff
David Duneau, Pepijn Luijckx, Ludwig Ruder, Dieter Ebert

Bibliographic record

VenueBMC Biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of Toronto
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsBiologyParasite hostingHost (biology)ZoologySexual dimorphismPopulationDaphniaLongevityAdaptation (eye)EcologyGeneticsDemography

Abstract

fetched live from OpenAlex

BACKGROUND: Males and females differ in many ways and might present different opportunities and challenges to their parasites. In the same way that parasites adapt to the most common host type, they may adapt to the characteristics of the host sex they encounter most often. To explore this hypothesis, we characterized host sex-specific effects of the parasite Pasteuria ramosa, a bacterium evolving in naturally, strongly, female-biased populations of its host Daphnia magna. RESULTS: We show that the parasite proliferates more successfully in female hosts than in male hosts, even though males and females are genetically identical. In addition, when exposure occurred when hosts expressed a sexual dimorphism, females were more infected. In both host sexes, the parasite causes a similar reduction in longevity and leads to some level of castration. However, only in females does parasite-induced castration result in the gigantism that increases the carrying capacity for the proliferating parasite. CONCLUSIONS: We show that mature male and female Daphnia represent different environments and reveal one parasite-induced symptom (host castration), which leads to increased carrying capacity for parasite proliferation in female but not male hosts. We propose that parasite induced host castration is a property of parasites that evolved as an adaptation to specifically exploit female hosts.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.413

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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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 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

Citations83
Published2012
Admission routes1
Has abstractyes

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