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Record W4319333546 · doi:10.1111/ppl.13871

<scp>CRISPR‐Cas9</scp>‐mediated editing of <i>BADH2</i> gene triggered fragrance revolution in rice

2023· review· en· W4319333546 on OpenAlex
Muhammad Imran, Sarfraz Shafiq, Xiangru Tang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysiologia Plantarum · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsWestern University
FundersGuangzhou Science and Technology Program key projectsNational Natural Science Foundation of China
KeywordsAromaCRISPRAromatic riceGermplasmGenome editingBiologyBiotechnologyGeneOryza sativaBotanyBiochemistryFood science

Abstract

fetched live from OpenAlex

Fragrance is one of the most important quality traits for breeding in rice. The natural aroma substance 2-acetyl-1-pyrroline (2-AP) is a key fragrance compound among over 200 volatiles identified in fragrant rice. In addition to rice, there are other plant species that contain a germplasm that naturally produces a fragrant aroma. These other plant species all have lower activity levels of the enzyme BETAINE ALDEHYDE DEHYDROGENASE 2 (BADH2). Therefore, improving fragrance efficiency has been a focus of intensive research. Recent studies have engineered BADH2 gene, which is responsible for fragrance trait in non-fragrant cultivars of rice, using CRISPR-Cas9. Although engineering rice BADH2 can be useful for upregulating 2-AP, there are still a lot of restrictions on how it can be applied in practice. In this review article, we discuss the recent developments in BADH2 editing and propose potential future strategies to effectively target BADH2 for transcriptional regulation, with the goal of producing a better fragrance.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.874
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.076
GPT teacher head0.308
Teacher spread0.232 · 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