2D HSQC-derived “dark forest” image with enhanced local resolution via first derivative processing–logarithmic cosine transformation (FDP–LCT): Demonstration on per- <i>O</i> -ethylated kappa- and iota-carrageenans
Bibliographic record
Abstract
Abstract Solution-state two-dimensional (2D) 1 H– 13 C HSQC NMR is a powerful tool for polysaccharide structure elucidation but often suffers from limited sensitivity and broad peaks due to the low natural abundance of 13 C and poor digital resolution of the indirect dimension, respectively, as well as the typically low concentration and high viscosity of polysaccharide solutions. It is therefore pivotal to improve the resolution of 2D 1 H– 13 C HSQC spectra for accurate peak picking and assignment, particularly in the indirect 13 C dimension. In this study, we developed an algorithm that combines first derivative processing with a novel logarithmic cosine transformation (FDP–LCT) to convert 2D 1 H– 13 C HSQC spectra into local-resolution-enhanced images resembling a dark forest of straight, densely standing trees. These images revealed sharpened spectral features and enabled extraction of precise 1 H and 13 C chemical shifts, as demonstrated using per- O -ethylated kappa-and iota-carrageenans, two sulfated galactans differing only by a single substitution at the O -2 position of anhydrogalactose. In conclusion, this approach provides an effective post-acquisition strategy for enhancing digital resolution in 2D HSQC spectra and improving the structural analysis of closely related complex polysaccharides. Graphic abstract Highlights - An algorithm combining first derivative processing and logarithmic cosine transformation was developed for 2D HSQC. - The algorithm was used to boost the local resolution of 2D HSQC spectra of the per- O -ethylated carrageenans. - Structures were distinguished by complete interpretation of 2D NMR spectra. - A post-processing workflow was developed to facilitate chemical shift extraction from sharpened HSQC.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".