Biosynthesis of artificial starch and microbial protein from agricultural residue
Bibliographic record
Abstract
Growing populations and climate change pose great challenges to food security. Humankind is confronting a serious question: how will we feed the world in the near future? This study presents an out-of-the-box solution involving the highly efficient biosynthesis of artificial starch and microbial proteins from available and abundant agricultural residue as new feed and food sources. A one-pot biotransformation using an in vitro coenzyme-free synthetic enzymatic pathway and baker's yeast can simultaneously convert dilute sulfuric acid-pretreated corn stover to artificial starch and microbial protein under aerobic conditions. The β-glucosidase-free commercial cellulase mixture plus an ex vivo two-enzyme complex containing cellobiose phosphorylase and potato α-glucan phosphorylase displayed on the surface of Saccharomyces cerevisiae, showed better cellulose hydrolysis rates than a commercial β-glucosidase-rich cellulase mixture. This is because the channeling of the hydrolytic product from the solid cellulosic feedstock to the yeast mitigated the inhibition of the cellulase cocktail. Animal tests have shown that the digestion of artificial amylose results in slow and relatively small changes in blood sugar levels, suggesting that it could be a new health food component that prevents obesity and diabetes. A combination of the utilization of available agricultural residue and the biosynthesis of starch and microbial protein from non-food biomass could address the looming food crisis in the food-energy-water nexus.
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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) | 0.000 | 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".