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Record W2015677629 · doi:10.1126/science.1250161

Monolignol Ferulate Transferase Introduces Chemically Labile Linkages into the Lignin Backbone

2014· article· en· W2015677629 on OpenAlexaff
Curtis G. Wilkerson, Shawn D. Mansfield, Fachuang Lu, Saunia Withers, J.-Y. Park, Steven D. Karlen, Eliana Gonzales‐Vigil, Dharshana Padmakshan, Faride Unda, Jorge Rencoret, John Ralph

Bibliographic record

VenueScience · 2014
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMonolignolLigninCell wallDepolymerizationChemistryXylemMonomerBiochemistryOrganic chemistryPolymerEnzymeBiosynthesisBotanyBiology

Abstract

fetched live from OpenAlex

Redesigning lignin, the aromatic polymer fortifying plant cell walls, to be more amenable to chemical depolymerization can lower the energy required for industrial processing. We have engineered poplar trees to introduce ester linkages into the lignin polymer backbone by augmenting the monomer pool with monolignol ferulate conjugates. Herein, we describe the isolation of a transferase gene capable of forming these conjugates and its xylem-specific introduction into poplar. Enzyme kinetics, in planta expression, lignin structural analysis, and improved cell wall digestibility after mild alkaline pretreatment demonstrate that these trees produce the monolignol ferulate conjugates, export them to the wall, and use them during lignification. Tailoring plants to use such conjugates during cell wall biosynthesis is a promising way to produce plants that are designed for deconstruction.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.180
Teacher spread0.178 · 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

Citations382
Published2014
Admission routes1
Has abstractyes

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