Application of novel analytical ultracentrifuge analysis to solutions of fungal mannans
Bibliographic record
Abstract
Polysaccharides, the most abundant biopolymers, are required for a host of activities in lower organisms, animals, and plants. Their solution characterization is challenging due to their complex shape, heterogeneity, and size. Here, recently developed data analysis approaches were applied for traditional sedimentation equilibrium and velocity methods in order to investigate the molar mass distribution(s) of a subtype of polysaccharide, namely, mannans from four Candida spp. The molecular weight distributions of these mannans were studied using two recently developed equilibrium approaches: SEDFIT-MSTAR and MULTISIG, resulting in corroboratory distribution profiles. Additionally, sedimentation velocity data for all four mannans, analyzed using ls-g*(s) and Extended Fujita approaches, suggest that two of the fungal mannans (FM-1 and FM-3) have a unimodal distribution of molecular species whereas two others (FM-2 and FM-4) displayed bi-modal and broad distributions, respectively: this demonstrates considerable molecular heterogeneity in these polysaccharides, consistent with previous observations of mannans and polysaccharides in general. These methods not only have applications for the characterization of mannans but for other biopolymers such as polysaccharides, DNA, and proteins (including intrinsically disordered proteins).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".