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Record W6995454956

Oleaginous Yeast as a Cell Factory for the Sustainable Biofuel Feedstock Production from the Canadian Forest Residues

2022· other· en· W6995454956 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2022
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsnot available
Fundersnot available
KeywordsXyloseFurfuralHydrolysateBiofuelRaw materialSugarOleic acidYeast
DOInot available

Abstract

fetched live from OpenAlex

With an ever-growing population, global energy demand increases, thereby contributing to the depletion of fossil resources and their limited reserves. Thereby, to lessen the environmental damage caused by fossil fuels, there has been a surge of interest in developing and producing biofuels from renewable feedstocks, such as microbial lipids. Typically, they are derived via a biochemical process using liquid hydrolysates obtained from forestry residues as a substrate. However, microbial lipid production using hydrolysates presents numerous challenges, including the need for a strain that can accumulate high lipid titers, consume five-carbon sugars (C5), and tolerate inhibitory compounds (e.g., furans, phenols, and organic acids), among others. Out of several microorganisms, Rhodosporidium toruloides, an oleaginous yeast, could be a potential alternative to produce lipids. It is known to accumulate lipids up to 70% of its dry cell weight, use different carbon sources, and tolerate several inhibitory compounds. In this sense, the current thesis explores the ability of Rhodosporidium toruloides as a bio-factory to produce microbial lipids using C5 and C6 wood hydrolysates as a culture media. Different R. toruloides strains were screened, and R. toruloides-1588 was determined to have the highest lipid accumulation of 35%. Following the culture media, carbon to nitrogen ratio, use of lipid inducers, and sugar concentration optimization, the lipid accumulation increased from 35% to 57.14%, with 95% and 80% of glucose and xylose utilization in hydrolysates, respectively. Likewise, palmitic, stearic, and oleic fatty acids were the most prominently on the produced lipids. Finally, R. toruloides-1588 demonstrates the capacity to grow, accumulate lipids, and transform furfural into furfuryl alcohol and 2-furoic acid. The strain was also assessed for its ability to tolerate inhibitory compounds, such as 5-hydroxymethyl furfural, vanillin, syringaldehyde, levulinic acid, ferulic acid, acetic acid, vanillic acid, and aminobenzoic acid. With all these findings, this dissertation concludes that R. toruloides-1588 is a suitable microorganism to produce microbial lipids, which can serve as a feedstock to manufacture biodiesel or advanced biofuels using undetoxified wood hydrolysates as a renewable and sustainable culture media.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.170
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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