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Record W4413941816 · doi:10.1021/acs.jafc.5c06403

Toward Nutrient-Rich Rice: Biofortification through Mineral Accumulation and Low Phytic Acid Content

2025· article· en· W4413941816 on OpenAlexaff
Md Mizanor Rahman, Dong-Yoon Seo, Md Mustafizur Rahman, Chi Huang, Atsushi Ogawa, Thomas Beaurepère, May Sann Aung, Laurent Ouerdane, Won‐Yong Song, Sichul Lee, Gynheung An, Jong‐Seong Jeon

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsNutrasource
FundersNational Research Foundation of KoreaRural Development AdministrationNational Research Foundation
KeywordsBiofortificationPhytic acidNutrientChemistryFood scienceMineralBioavailabilityAgronomyMicronutrientBiology

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Nicotianamine synthases regulate the biosynthesis of two mineral chelators key for rice grain biofortification, nicotianamine (NA) and 2′-deoxymugineic acid (DMA). We produced transgenic rice expressing OsNAS2 under the OsRCc3 promoter ( RcN2 ) and mutated OsLpa1 in these ( lpa1 RcN2 ) and the wild-type ( lpa1 ) to enhance essential mineral accumulation in grains while lowering phytic acid (PA) levels. NA and DMA contents were higher in the brown grains of RcN2, lpa1 RcN2, and lpa1 plants. The grains of these lines accumulated increased levels of essential minerals, with lpa1 RcN2 exhibiting the greatest increases. PA-bound iron and zinc levels were lower in lpa1 and lpa1 RcN2 grains, while NA- and DMA-bound iron and zinc levels were higher in RcN2, lpa1 RcN2, and lpa1 grains. Field-grown RcN2 plants showed no significant growth penalties, unlike lpa1 and lpa1 RcN2 . Furthermore, lpa1 and lpa1 RcN2 grains were chalky, a property that facilitates rice flour production.

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 score0.310
Threshold uncertainty score0.174

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 head0.038
GPT teacher head0.243
Teacher spread0.205 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural and Food ChemistrySame topicPlant Micronutrient Interactions and EffectsFrench-language works237,207