MétaCan
Menu
Back to cohort

Biotechnology in Future Food Lipids: Opportunities and Challenges

2023· review· en· W4318003419 on OpenAlexaff
Qingqing Xu, Qingyun Tang, Yang Xu, Junjun Wu, Xiangzhao Mao, Fuli Li, Shi‐An Wang, Yonghua Wang

Bibliographic record

VenueAnnual Review of Food Science and Technology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMetabolic engineeringBiotechnologyBiologyFood sciencePopulationMicroorganismEssential nutrientNutrientBiochemistryBacteriaEcologyEnzyme

Abstract

fetched live from OpenAlex

Lipids are a large group of essential nutrients in daily diets that provide energy and maintain various physiological functions. As the global population is rapidly expanding, there is an urgent need to enhance the production and quality of food lipids. The development of modern biotechnology allows the manipulation of oil production in plants and microorganisms and the improvement of the nutritional value of food lipids. Various metabolic engineering strategies have been exploited to increase oil production and produce value-added oils in traditional oil crops and other novel lipid sources (e.g., plant vegetative tissues, microalgae, and oleaginous microorganisms). Furthermore, natural lipid structures can be modified by lipases to prepare functional lipids, e.g., diacylglycerols, medium-long-medium-type structured triacylglycerols, human milk-fat substitutes, and structuralphospholipids, for specific nutritional demands. In this review, we focus on the recent advances in metabolic engineering of lipid production in plants and microorganisms, and the preparation of functional lipids via biocatalysis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.061
GPT teacher head0.308
Teacher spread0.247 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAnnual Review of Food Science and TechnologySame topicLipid metabolism and biosynthesisFrench-language works237,207