Rapid characterization of aquatic hyphomycetes by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry
Bibliographic record
Abstract
Protein fingerprinting using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI--TOF MS) is a rapid, reliable, and economical method to characterize isolates of terrestrial fungi and other microorganisms. The objective of our study was to evaluate the suitability of MALDI-TOF MS for the identification of aquatic hyphomycetes, a polyphyletic group of fungi that play crucial roles in stream ecosystems. To this end, we used 34 isolates of 21 aquatic hyphomycete species whose identity was confirmed by spore morphology and internal transcribed spacer (ITS1-5.8S-ITS2 = ITS) nuc rDNA sequencing. We tested the efficiency of three protein extraction methods, including chemical and mechanical treatments using 13 different protocols, with the objective of producing high-quality MALDI-TOF mass spectra. In addition to extraction protocols, mycelium age was identified as a key parameter affecting protein extraction efficiency. The dendrogram based on mass-spectrum similarity indicated good and relevant taxonomic discrimination; the tree structure was comparable to that of the phylogram based on ITS sequences. Consequently, MALDI-TOF MS could reliably identify the isolates studied and provided greater taxonomic accuracy than classical morphological methods. MALDI-TOF MS seems suited for rapid characterization and identification of aquatic hyphomycete species.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".