A Multimodal System for Lipid A Structural Analysis from a Single Colony
Bibliographic record
Abstract
Abstract Structural elucidation of Gram-negative bacterial lipid A traditionally requires chemical extraction followed by tandem MS data in the negative ion mode. Previously, we reported FLAT and FLATn as methods to rapidly determine the structure of lipid A without chromatographic techniques. In this work, we extend the capability and effectiveness of these techniques to elucidate the chemical structure in a de novo manner by including the use of positive ion mode (FLAT+ and FLATn+) spectral approaches. Advantages of positive mode analysis of lipid A include the generation of more interpretable and informative fragmentation patterns that include the identification of diagnostic fragments, including selective dissociation of a glycosidic bond between two glucosamine units and the selective dissociation at the secondary acyl chain in 2′-N, allowing for the determination of the composition of fatty acids. As a proof of principle, we present here two previously uncharacterized structures of lipid A from Roseomonas mucosa (R. mucosa) and Moraxella canis (M. canis). In R. mucosa, we determined the lipid A structure with a nonconventional backbone of-β-1,6 linked 2,3-dideoxy-2,3-diamno-d-glucopyranose further modified with galacturonic acid in the place of typical 1-phosphate, and in M. canis, we assigned a single discrete structure using the specific fragmentation patterns of terminal phosphate groups present in lipid A. Therefore, FLATn+, in combination with FLAT and FLATn, provides a multimodal structural platform for rapid structure characterization of unusual and complex lipid A structures from a single colony.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".