Optimización de la producción de xilanasa bacteriana a partir de Bacillus sp. K1 utilizando residuos lignocelulósicos en el Departamento de Biología de la Universidad de Lakehead, Canadá.
Bibliographic record
Abstract
El objetivo de presente trabajo de titulacion fue verificar y optimizar la produccion de xilanasa por parte de la bacteria Bacillus sp. K1 en el Departamento de Biologia de la Universidad de Lakehead, Canada. Las variables investigadas para la optimizacion fueron fisicas tales como pH, temperatura y volumen de inoculo; y de composicion del medio de fermentacion tales como fuente de carbono, fuente de nitrogeno organico e inorganico y relacion porcentual entre dichos componentes. Adicionalmente, se determino el efecto de iones metalicos (sodio, potasio, magnesio, calcio, ferroso, niqueloso, cuprico, cobaltoso y manganoso) y surfactantes (Polisorbato-20, dodecil sulfato de sodio y Triton X-100) en la actividad enzimatica de la xilanasa producida. La mayor produccion de xilanasa fue tras 36h de fermentacion y lascondiciones fisicas optimas asociadas a la produccion de xilanasa fueron pH 6, temperatura 35oC y volumen de inoculo 1%. La composicion optima del medio de fermentacion determinada esta constituida por: Afrecho de trigo 4%, Glucosa 0.5%, NH4NO3 0.5%, K2HPO4 0.1%, KCl 0.1%, MgSO4 0.05%, peptona 0.5%, SDS 0.1%. La actividad enzimatica generada es de 264.96±7.89 IU/L, con un 248,79% de incremento con respecto al inicial. Los inhibidores mas importantes fueron los iones cuprico y manganoso con 32% y 49,16% de inhibicion comparado con el experimento de control. Las condiciones fisicas de fermentacion y la presencia de celulasa favorecen una futura aplicacion en la industria del bioetanol de segunda generacion.
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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".