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Record W2802511761 · doi:10.1139/gen-2018-0019

Identification of chromosomes in <i>Thinopyrum intermedium</i> and wheat <i>Th. intermedium</i> amphiploids based on multiplex oligonucleotide probes

2018· article· en· W2802511761 on OpenAlexvenueno aff
Yu Cui, Yanping Zhang, Juan Qi, Honggang Wang, Richard R.‐C. Wang, Yinguang Bao, Xingfeng Li

Bibliographic record

VenueGenome · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyMultiplexFluorescence in situ hybridizationGeneticsOligonucleotideChromosomeMultiplex polymerase chain reactionGenomeFish <Actinopterygii>Chromosomal translocationMolecular biologyGenePolymerase chain reaction

Abstract

fetched live from OpenAlex

Synthesized oligonucleotides (oligos) can be used as effective probes similar to plasmid clones for chromosome identification in fluorescence in situ hybridization (FISH) analysis, making oligo FISH a simpler and more efficient molecular cytogenetic technique for studying plants. In this study, multiplex oligonucleotide probes, including pSc119.2-1, pAs1-4, (GAA) 10 , (AAC) 6 , and pTa71, were combined and used in FISH to identify chromosomes in common wheat, Thinopyrum intermedium, and a wheat – Th. intermedium amphiploid TE256-1. In comparison with general FISH probes, signals generated by the multiplex probes were more abundant, colorful, and characteristic. Combining the results of genomic in situ hybridization (GISH) with FISH, Th. intermedium chromosomes and alien chromosomes in TE256-1 could be classified and identified more precisely, especially the J- and J s -genome chromosomes. Moreover, based on the FISH results using multiplex probes, more structural variations in wheat chromosomes of TE256-1 were detected. The results indicated that multiplex oligo probes would have a wide range of application prospects in the creation and identification of wheat – Th. intermedium germplasms.

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 score0.866
Threshold uncertainty score0.293

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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