Tolerance and adaptation of ethanologenic yeasts to lignocellulosic inhibitory compounds
Bibliographic record
Abstract
Synthetic mixtures of predominant lignocellulosic hexose sugars were supplemented with separate aliquots of three inhibitory compounds (furfural, hydroxymethylfurfural (HMF), and acetic acid) in a series of concentrations and fermented by the spent sulfite liquor (SSL)-adapted yeast strain Tembec T1 and the natural isolate Saccharomyces cerevisiae (S. cerevisiae) Y-1528 to compare tolerance and assess fermentative efficacy. The performance of Y-1528 exceeded that of Tembec T1 by a significant margin, with faster hexose sugar consumption, higher ethanol productivity, and in the case of furfural and HMF, faster inhibitor consumption. Nevertheless, furfural had a dose-proportionate effect on sugar consumption rate and ethanol productivity in both strains, but did not substantially affect ethanol yield. HMF had a similar effect on sugar consumption rate and ethanol productivity, and also lowered ethanol yield. Surprisingly, acetic acid had the least impact on sugar consumption rate and ethanol productivity, and stimulated ethanol yield at moderate concentrations. Sequential iterations of softwood (SW) and hardwood (HW) SSL were subsequently inoculated with the two yeast strains in order to compare adaptation to, and performance in lignocellulosic substrates in a cell recycle batch fermentation (CRBF) regime. Both strains were severely affected by the HW SSL, which was attributed to specific syringyl lignin-derived degradation products and synergistic interactions between inhibitors. Though ethanologenic capacity was preserved, a net loss of performance was evident from both strains, indicating the absence of adaptation to the substrates, regardless of the sequence in which the SSL types were employed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".