Recent Patents on Genetic Modification of Plants and Microbes for Biomass Conversion to Biofuels
Bibliographic record
Abstract
Development of sustainable energy systems based on renewable biomass feedstocks is now a global effort. Lignocellulosic biomass contains polymers of cellulose, hemicellulose, and lignin, bound together in a complex structure. Liquid biofuels, such as ethanol, can be made from biomass via fermentation of sugars derived from the cellulose and hemicellulose within lignocellulosic materials, but pre-treatment of the biomass to release sugars for microbial conversion is a significant barrier to commercial success of lignocellulosic biofuel production. Strategies to reduce the energy and cost inputs required for biomass pre-treatment include genetic modification of plant materials to reduce lignin content. Significant efforts are also underway to create recombinant microorganisms capable of converting sugars derived from lignocellulosic biomass to a variety of biofuels. An alternative strategy to reduce the costs of cellulosic biofuel production is the use of cellulolytic microorganisms capable of direct microbial conversion of ligno-cellulosic biomass to fuels. This paper reviews recent patents on genetic modification of plants and microbes for biomass conversion to biofuels. Keywords: Biomass, Biofuels, Genetic modification, Lignocellulose, Patents, Plants, Pretreatment, Microorganisms, Hemicellulose, PRE-TREATED BIOMASS, cellulolytic enzymes, altered morphology, Genetic Engineering of Lignin, Flccellulase-transgenic plants, Zymomonas Species, recombinant hosts, quorum sensing, Gene knockout mesophilic, pentose sugars, CONSOLIDATED BIOPROCESSING
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".