Competitive, Inhibitory and Mutually Existing Interaction Mapping of Secondary Metabolites in Filamentous Fungi
Bibliographic record
Abstract
Natural products are compounds that are produced by living organisms, such as bacteria and fungi. These compounds are essential for advancements in the medical field, such as the discovery of new antibiotics. When living organisms like bacteria and fungi interact with one another, they can be forced to compete for resources to survive (antagonistic) or cooperate (synergistic). It is through these relationships that new metabolites can be produced and subsequently observed. The current research on co-culturing between bacterial and fungal species is limited, thus there are many possible undiscovered metabolites. Therefore, co-culturing of this nature can be analyzed using mass spectrometry for new natural product discovery. While typical methods for microorganism culturing can be applied to co-culturing, alternatively forcing species to interact in an artificially constructed growth chamber can offer interesting insights and changes to the metabolite profile. Herein, a 3D-printed artificial chamber was created to observe the growth and interaction of each species in diverse locations, thus allowing a spatiotemporal map of natural product growth to be generated through spectral analysis. In this work, spectral analysis is conducted using the Liquid Micro-Junction Surface Sampling Probe (LMJ-SSP), which allows for non-destructive sampling of the microorganism interactions. As a result, sampling can be conducted repeatedly at different times in growth development, as well as at diverse locations throughout the chamber. Ultimately, the further exploitation of both the competitive and synergistic relationship of microorganism growth within this chamber will offer novel insights into natural product discovery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".