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Record W3016830892 · doi:10.1139/gen-2019-0161

Genetic and molecular features for hepadnavirus and plague infections in the Himalayan marmot

2020· article· en· W3016830892 on OpenAlexvenueno aff
Baoning Liu, Liang Bai, Yu Fu, Sihai Zhao, Liu Haiqing, Rong Wang, Weirong Wang, Yandong Li, Yuanqing Tao, Zhongdong Wang, Jianglin Fan, Enqi Liu

Bibliographic record

VenueGenome · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyMarmotVirologyGenePlague (disease)Major histocompatibility complexImmune systemGenetics

Abstract

fetched live from OpenAlex

The Himalayan marmot (Marmota himalayana), a natural host and transmitter of plague, is also susceptible to the hepadnavirus infection. To reveal the genetic basis of the hepadnavirus susceptibility and the immune response to plague, we systematically characterized the features of immune genes in Himalayan marmot with those of human and mouse. We found that the entire major histocompatibility complex region and the hepatitis B virus pathway genes of the Himalayan marmot were conserved with those of humans. A Trim (tripartite motif) gene cluster involved in immune response and antiviral activity displays dynamic evolution, which is reflected by the duplication of Trim5 and the absence of Trim22 and Trim34. Three key regions of Ntcp, which is critical for hepatitis B virus entry, had high identity among seven species of Marmota. Moreover, we observed a severe alveolar hemorrhage, inflammatory infiltrate in the infected lungs and livers from Himalayan marmots after infection of EV76, a live attenuated Yersinia pestis strain. Lots of immune genes were remarkably up-regulated, which several hub genes Il2rγ, Tra29, and Nlrp7 are placed at the center of the gene network. These findings suggest that Himalayan marmot is a potential animal model for study on the hepadnavirus and plague infection.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.389

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 head0.267
Teacher spread0.253 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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