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Record W4225435160 · doi:10.3390/plants11020209

Transcriptome and Metabolite Insights into Domestication Process of Cultivated Barley in China

2022· article· en· W4225435160 on OpenAlexaff
Yu Zhou, Guang Lu, Genlou Sun, Daokun Sun, Xifeng Ren

Bibliographic record

VenuePlants · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsDomesticationBiologyTranscriptomeMetaboliteBotanyGeneGeneticsBiochemistry

Abstract

fetched live from OpenAlex

The domestication process of cultivated barley in China remains under debate because of the controversial origins of barley. Here, we analyzed transcriptomic and non-targeted metabolic data from 29 accessions together with public resequencing data from 124 accessions to explore the domestication process of cultivated barley in China (Cb-C). These analyses revealed that both Cb-C and Tibetan wild barley (Wb-T) were the descendants of wild barley from the Near East Fertile Crescent (Wb-NE), yielding little support for a local origin of Wb-T. Wb-T was more likely an intermediate in the domestication process from Wb-NE to Cb-C. Wb-T contributed more genetically to Cb-C than Wb-NE, and was domesticated into Cb-C about 3300 years ago. These results together seem to support that Wb-T may be a feralized or hybrid form of cultivated barley from the Near East Fertile Crescent or central Asia. Additionally, the metabolite analysis revealed divergent metabolites of alkaloids and phenylpropanoids and these metabolites were specifically targeted for selection in the evolutionary stages from Wb-NE to Wb-T and from Wb-T to Cb-C. The key missense SNPs in the genes HORVU6Hr1G027650 and HORVU4Hr1G072150 might be responsible for the divergence of metabolites of alkaloids and phenylpropanoids during domestication. Our findings allow for a better understanding of the domestication process of cultivated barley in China.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.090

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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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