Potential for mcl-PHA production from nonanoic and azelaic acids
Bibliographic record
Abstract
Greater than 65% of canola and high-oleic soy oil fatty acids is oleic acid, which is readily converted to nonanoic (NA) and azelaic (AzA) acids by ozonolysis. NA is an excellent substrate for medium-chain-length polyhydroxyalkanoate (mcl-PHA) production but AzA has few uses. Pseudomonas citronellolis DSM 50332 and Pseudomonas fluorescens ATCC 17400, both able to produce mcl-PHA from fatty acids and to grow on AzA as the sole source of carbon and energy, were assessed for the accumulation of mcl-PHA from AzA and NA. In N-limited shake flasks using NA, P. citronellolis produced 32% of its dry biomass as mcl-PHA containing 78% 3-hydroxynonanoate with 22% 3-hydroxyheptanoate. Pseudomonas fluorescens produced only 2% PHA. N-limited P. citronellolis on AzA produced 20% dry weight PHA containing 75% 3-hydroxydecanoate and 25% 3-hydroxyoctanoate, indicative of de novo synthesis. Although selective pressure, including β-oxidation inhibition, under well-controlled (chemostat) conditions was applied to P. citronellolis, no side-chain carboxyl groups were detected. It was concluded that one or more of FabG and PhaJ or the PHA synthase cannot catalyze reactions involving ω-carboxy substrates. However, a process based on oleic acid could be established if Pseudomonas putida was engineered to grow on AzA.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".