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Record W3100001636 · doi:10.1038/s41467-020-19671-3

Dominant subtype switch in avian influenza viruses during 2016–2019 in China

2020· article· en· W3100001636 on OpenAlexaff
Yuhai Bi, Juan Li, Shanqin Li, Guanghua Fu, Tao Jin, Cheng Zhang, Yongchun Yang, Zhenghai Ma, Wen‐xia Tian, Jida Li, Shuqi Xiao, Liqiang Li, Renfu Yin, Yi Zhang, Lixin Wang, Yantao Qin, Zhongzi Yao, Fanyu Meng, Dongfang Hu, Delong Li, Gary Wong, Fei Liu, Na Lv, Liang Wang, Lifeng Fu, Yang Yang, Yun Peng, Jinmin Ma, Kirill Sharshov, А. М. Шестопалов, Marina Gulyaeva, George F. Gao, Jianjun Chen, Yi Shi, William J. Liu, Dong Chu, Yu Huang, Yingxia Liu, Lei Liu, Wenjun Liu, Quanjiao Chen, Weifeng Shi

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversité Laval
FundersEarmarked Fund for Modern Agro-industry Technology Research SystemNational Science and Technology Major ProjectShandong First Medical UniversityChinese Academy of SciencesYouth Innovation Promotion AssociationCenters for Disease Control and PreventionRussian Foundation for Basic ResearchNational Natural Science Foundation of China
KeywordsAvian influenza virusInfluenza A virus subtype H5N1BiologyVirologyChinaInfluenza A virusVirusGeography

Abstract

fetched live from OpenAlex

We have surveyed avian influenza virus (AIV) genomes from live poultry markets within China since 2014. Here we present a total of 16,091 samples that were collected from May 2016 to February 2019 in 23 provinces and municipalities in China. We identify 2048 AIV-positive samples and perform next generation sequencing. AIV-positive rates (12.73%) from samples had decreased substantially since 2016, compared to that during 2014-2016 (26.90%). Additionally, H9N2 has replaced H5N6 and H7N9 as the dominant AIV subtype in both chickens and ducks. Notably, novel reassortants and variants continually emerged and disseminated in avian populations, including H7N3, H9N9, H9N6 and H5N6 variants. Importantly, almost all of the H9 AIVs and many H7N9 and H6N2 strains prefer human-type receptors, posing an increased risk for human infections. In summary, our nation-wide surveillance highlights substantial changes in the circulation of AIVs since 2016, which greatly impacts the prevention and control of AIVs in China and worldwide.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.076
GPT teacher head0.403
Teacher spread0.326 · 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.

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

Citations190
Published2020
Admission routes1
Has abstractyes

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