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Record W2121605403 · doi:10.1111/pbi.12453

Modification of starch metabolism in transgenic <i>Arabidopsis thaliana</i> increases plant biomass and triples oilseed production

2015· article· en· W2121605403 on OpenAlexafffund
Fushan Liu, Qianru Zhao, Noel Anthony Mano, Zaheer Ahmed, Felix Nitschké, Yingqi Cai, Kent D. Chapman, Martin Steup, Ian J. Tetlow, Michael J. Emes

Bibliographic record

VenuePlant Biotechnology Journal · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsHospital for Sick ChildrenUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOffice of ScienceU.S. Department of Energy
KeywordsSiliqueEndospermBiologyStarchArabidopsis thalianaBotanyArabidopsisFood scienceBiochemistryMutantGene

Abstract

fetched live from OpenAlex

We have identified a novel means to achieve substantially increased vegetative biomass and oilseed production in the model plant Arabidopsis thaliana. Endogenous isoforms of starch branching enzyme (SBE) were substituted by either one of the endosperm-expressed maize (Zea mays L.) branching isozymes, ZmSBEI or ZmSBEIIb. Transformants were compared with the starch-free background and with the wild-type plants. Each of the maize-derived SBEs restored starch biosynthesis but both morphology and structure of starch particles were altered. Altered starch metabolism in the transformants is associated with enhanced biomass formation and more-than-trebled oilseed production while maintaining seed oil quality. Enhanced oilseed production is primarily due to an increased number of siliques per plant whereas oil content and seed number per silique are essentially unchanged or even modestly decreased. Introduction of cereal starch branching isozymes into oilseed plants represents a potentially useful strategy to increase biomass and oilseed production in related crops and manipulate the structure and properties of leaf starch.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.237
Teacher spread0.212 · 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

Citations25
Published2015
Admission routes2
Has abstractyes

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