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Record W1787139186 · doi:10.1139/gen-2014-0082

Genome-wide analysis of esterase-like genes in the striped rice stem borer, <i>Chilo suppressalis</i>

2015· article· en· W1787139186 on OpenAlexvenueno aff
Baoju Wang, Ying Wang, Yang Zhang, Ping Han, Fei Li, Zhaojun Han

Bibliographic record

VenueGenome · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsnot available
FundersGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsChilo suppressalisBiologyEsteraseGeneGeneticsLepidoptera genitaliaPhylogenetic treeComplementary DNABotanyBiochemistryEnzyme

Abstract

fetched live from OpenAlex

The striped rice stem borer, Chilo suppressalis, a destructive pest of rice, has developed high levels of resistance to certain insecticides. Esterases are reported to be involved in insecticide resistance in several insects. Therefore, this study systematically analyzed esterase-like genes in C. suppressalis. Fifty-one esterase-like genes were identified in the draft genomic sequences of the species, and 20 cDNA sequences were derived which encoded full- or nearly full-length proteins. The putative esterase proteins derived from these full-length genes are overall highly diversified. However, key residues that are functionally important including the serine residue in the active site are conserved in 18 out of the 20 proteins. Phylogenetic analysis revealed that most of these genes have homologues in other lepidoptera insects. Genes CsuEst6, CsuEst10, CsuEst11, and CsuEst51 were induced by the insecticide triazophos, and genes CsuEst9, CsuEst11, CsuEst14, and CsuEst51 were induced by the insecticide chlorantraniliprole. Our results provide a foundation for future studies of insecticide resistance in C. suppressalis and for comparative research with esterase genes from other insect species.

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 score0.840
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.238
Teacher spread0.223 · 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
Published2015
Admission routes1
Has abstractyes

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