Yeasts isolated from a lotic continental environment in Brazil show potential to produce amylase, cellulase and protease
Bibliographic record
Abstract
Yeasts have wide applicability in the industrial field, as in the production of enzymes used in biocatalysts. Biocatalysts are more efficient when compared to chemical catalysts, with emphasis on hydrolytic enzymes, such as amylase, cellulase and protease. Here we focused on prospecting yeasts, with a high capacity to synthesize hydrolytic enzymes, from a continental lotic ecosystem environment in Brazil. 75 yeasts were grown in Yeast Extract-Peptone-Dextrose (YPD) medium supplemented with antibacterial and their capacity for enzymatic production was tested in specific media. Accordingly, 64 yeasts showed enzyme production capacity. From those, six showed good enzyme indexes, 3 for amylase, 2 for cellulase and 1 for protease. All showed at least one hydrolytic enzyme activity for the tested enzymes (amylase, cellulase and protease), which suggested that the yeasts are metabolically active. By sequencing the 26S gene, we identified Naganishia diffluens and Apiotrichum mycotoxinivorans as the species with highest enzyme production activities. Those species showed potential for application as biological catalysts in the biotechnological scope, collaborating in a sustainable way for the development of industrial products.
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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 | 0.001 | 0.001 |
| 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".