Biocontrol of wood-rotting fungi with<i>Streptomyces violaceusniger</i>XL-2
Bibliographic record
Abstract
During the previous decade, chitinases have received increased attention because of their wide range of applications. Chito-oligomers produced by enzymatic hydrolysis of chitin have been of interest in recent years because of their broad applications in medical, agricultural, and industrial applications, such as antibacterial, antifungal, hypo cholesterolemic, and antihypertensive activity, and as food quality enhancer. Fungal cell walls being rich in chitin also enable the use of chitinases in biocontrol of fungal pathogens, as bio-fungicides. An actinomycete was isolated from the bark of trees of Dehradun in India and was later identified as Streptomyces violaceusniger. This strain exhibits strong antagonism towards various wood-rotting fungi, such as Phanerochaete chrysosporium, Postia placenta, Coriolus versicolor, and Gloeophyllum trabeum. Further, studies showed an extracellular bioactive compound was responsible for the antagonism. The conditions for the production of this biocontrol agent were optimized, and the effects of various stress factors (like nitrogen-deficient media, carbon-deficient media, etc.) were studied. The presence of chitin in the growth media was found to be an essential factor for the active production of the biocontrol agent. The pH and temperature optima for the biocontrol agent were determined. Purification and characterization of this specific biocontrol agent was performed through anion exchange chromatography using a DEAE-cellulose column, and a single protein band was obtained on a 10% sodium dodecyl sulfate-polyacrylamide gel. The protein was later identified as a 28 kDa endo chitinase by MALDI-TOF (matrix-assisted laser desorption ionization-time of flight) and by a chitobiose activity assay.
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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 | 0.000 | 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".