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Record W2773004246 · doi:10.1080/07060661.2017.1417915

Molecular cloning and functional analysis of two novel polygalacturonase genes in <i>Rhizoctonia solani</i>

2017· article· en· W2773004246 on OpenAlexvenueno aff
Xijun Chen, Lili Li, Zhen He, Jiahao Zhang, Benli Huang, Zongxiang Chen, Shimin Zuo, XU Jing-you

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsRhizoctonia solaniPectinaseGeneCloning (programming)Molecular massBiologyVirulenceMolecular cloningRecombinant DNASequence analysisMicrobiologyPhylogenetic treePeptide sequenceBiochemistryEnzymeBotany

Abstract

fetched live from OpenAlex

Two novel polygalacturonase (PG) genes, RsPG3 and RsPG4, were cloned from Rhizoctonia solani isolate YN-7, which is a member of anastomosis group (AG) subgroup 1A and causes rice sheath blight. The predicted protein product of RsPG3 contained 239 amino acid (aa) residues, with a molecular mass of 25.0 kD, whereas RsPG4 encoded a deduced protein of 345 aa residues, with a mass of 37.5 kD. Sequence alignment, phylogenetic analysis and gene ontology indicated that RsPG3 and RsPG4 had endo-PG and exo-PG activity, respectively. Recombinant RsPG3 and RsPG4 both exhibited PG activity that led to the destruction of rice sheaths and release of reducing sugars. Pathogenicity assays showed that the two PGs induced tissue necrosis in rice sheaths, indicating that both RsPG3 and RsPG4 are important virulence factors in the R. solani–rice interaction. Further studies are underway to more clearly define the role of these enzymes in rice cell wall degradation and their interaction with PG inhibitor proteins.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
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 head0.018
GPT teacher head0.216
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2017
Admission routes1
Has abstractyes

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