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Record W2809472806 · doi:10.7717/peerj.4982

High prevalence of <i>Lynx rufus</i> gammaherpesvirus 1 in wild Vermont bobcats

2018· article· en· W2809472806 on OpenAlexaff
Dagan A. Loisel, Ryan M. Troyer, Sue VandeWoude

Bibliographic record

VenuePeerJ · 2018
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsWestern University
FundersNational Institute of General Medical SciencesCalifornia Department of Fish and WildlifeMorris Animal Foundation
KeywordsGeographyZoologyBiology

Abstract

fetched live from OpenAlex

Gammaherpesviruses (GHVs) are host specific DNA viruses that infect a large range of mammalian species. These viruses preferentially target host lymphocyte cell populations and infection may lead to morbidity or mortality in immunocompromised, co-infected, or non-adapted hosts. In this study, we tested for the presence of Lynx rufus gammaherpesvirus 1 (LruGHV1) in a northeastern United States population of wild bobcats ( L. rufus ). We estimated prevalence of infection and viral load in infected individuals using quantitative real-time PCR analysis of spleen DNA from 64 Vermont bobcats. We observed an overall prevalence of 64% using this methodology. Bobcat age was significantly positively associated with GHV infection status, and we noted a trend for higher viral loads in young animals, but prevalence and viral load were similar in male and female bobcats. A single LruGHV1 variant was identified from the sequencing of the viral glycoprotein B gene of Vermont bobcats. This gene sequence was 100% similar to that reported in Florida bobcats and slightly variant from other isolates identified in the Western USA. Our work suggests broad geographic distribution and high prevalence of LruGHV1 in bobcat populations across the United States with infection attributes that suggest horizontal transmission of the agent. Geographic differences in viral genotype may reflect historical migration and expansion events among bobcat populations.

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 score0.301
Threshold uncertainty score0.717

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)0.0010.000

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 head0.015
GPT teacher head0.282
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 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

Citations6
Published2018
Admission routes1
Has abstractyes

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Same venuePeerJSame topicHerpesvirus Infections and TreatmentsFrench-language works237,207