Design and construction of artificial microbial consortia to enhance lignocellulosic biomass degradation
Bibliographic record
Abstract
Cellulose-rich agricultural residues are promising renewable sources for producing various value-added products such as 2nd generation biofuels. However, the efficiency of the bioconversion process is not always satisfactory due to the slow and incomplete degradation of lignocellulosic biomass. An interesting approach would be using microbial communities with high lignocellulose-degrading ability for environmentally friendly pretreatment. This study focused on characterizing the degradation performance of bacteria, fungal, and yeast strains and designing and constructing different microbial consortia for solid-state treatment of wheat bran and wheat straw. The microbial consortia, namely BFY4 and BFY5, contained different bacteria, fungal, and yeast led to high ratios of sugar accumulation ranging from 3.21 to 3.5 with degradation rates over 33%, owing to more favorable hydrolytic enzyme activities and improved reducing sugar yield during the process. After 72 h, the highest FPase (0.213 IU/gds) and xylanase (7.588 IU/gds) activities were also detected in the wheat straw pretreated by BFY4 and BFY5, respectively, while CMCase activity peaked (0.928 IU/gds) when wheat bran was used as substrate. The amount of released glucose increased during the treatment process when the two substrates were used in the same ratio. Our results indicated that substrate composition also plays an important role in the degradation capacity of mixed cultures. These findings can be instrumental in advancing the primary knowledge required to apply such bioprocesses at the pilot scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".