Genetic improvement of native xylose-fermenting yeasts for ethanol production
Bibliographic record
Abstract
Lignocellulosic substrates are the largest source of fermentable sugars for bioconversion to fuel ethanol and other valuable compounds. To improve the economics of biomass conversion, it is essential that all sugars in potential hydrolysates be converted efficiently into the desired product(s). While hexoses are fermented into ethanol and some high-value chemicals, the bioconversion of pentoses in hydrolysates remains inefficient. This remains one of the key challenges in lignocellulosic biomass conversion. Native pentose-fermenting yeasts can ferment both glucose and xylose in lignocellulosic biomass to ethanol. However, they perform poorly in the presence of hydrolysate inhibitors, exhibit low ethanol tolerance and glucose repression, and ferment pentoses less efficiently than the main hexoses glucose and mannose. This paper reviews classical and molecular strain improvement strategies applied to native pentose-fermenting yeasts for improved ethanol production from xylose and lignocellulosic substrates. We focus on Pachysolen tannophilus, Scheffersomyces (Candida) shehatae, Scheffersomyces (Pichia) stipitis, and Spathaspora passalidarum which are good ethanol producers among the native xylose-fermenting yeasts. Strains obtained thus far are not robust enough for efficient ethanol production from lignocellulosic hydrolysates and can benefit from further improvements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".