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Record W2146694813 · doi:10.1002/bit.20163

Potato flour viscosity improvement is associated with the expression of a wheat LMW‐glutenin gene

2004· article· en· W2146694813 on OpenAlexaff
Mustapha Benmoussa, Louis‐P. Vézina, Michel Pagé, Pierre Gélinas, Serge Yelle, Serge Laberge

Bibliographic record

VenueBiotechnology and Bioengineering · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsUniversité LavalAgriculture and Agri-Food Canada
Fundersnot available
KeywordsGluteninSolanum tuberosumTransgeneGeneAgrobacteriumAgrobacterium tumefaciensGene expressionChemistryBiologyMolecular biologyFood scienceBotanyBiochemistryProtein subunit

Abstract

fetched live from OpenAlex

It has been previously shown that expression of a high-molecular-weight glutenin (HMW-GS) in transgenic wheat seeds resulted in the improvement of flour functional properties. In this study, potato flour viscosity was improved through a specific expression of a low-molecular-weight glutenin (LMW-GS-MB1) gene in tuber. The resulting construct was introduced into potato leaf explants (Solanum tuberosum cv Kennebec) through Agrobacterium tumefaciens-mediated gene transfer. Southern and Northern analysis of transgenic potato confirmed that the integration of LMW-GS-MB1 in genomic DNA was stable and its mRNA was abundant in transgenic line 16 tubers. Western blot analysis of line 16 extract shows a LMW-GS subunit accumulation in tuber. To demonstrate the capacity of transgenic lines to produce tubers with improved flour functional properties, transgenic lines 9 and 16 exhibiting, respectively, moderate and high expression of LMW-GS-MB1 mRNA and nontransgenic plants were transferred to field plots. The mean viscosity value of flour obtained from the field-grown tubers of transgenic line 16 exhibited a 3-fold increase in viscosity at 23 degrees C when compared to flour from nontransgenic tubers.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.164

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 head0.007
GPT teacher head0.175
Teacher spread0.167 · 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

Citations9
Published2004
Admission routes1
Has abstractyes

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