From the environment to NMR: water suppression for whole samples in their native state
Bibliographic record
Abstract
Environmental context Environmental samples are best analysed in their native state, with minimal sample preparation, to fully understand the complex interactions and processes occurring in environmental systems. Nuclear magnetic resonance spectroscopy is a powerful tool used to study environmental samples but sample pre-treatment is often required to remove water and improve analysis. We introduce an experimental approach to remove water signals from environmental samples in their natural state, which opens the door to intact sample analysis and more environmentally relevant science. Abstract Studying environmental samples in their natural state is critical as drying, fractionating or extractions can alter the composition, structure, conformation and biological activity, as well as perturb essential interfaces and domains. Nuclear magnetic resonance (NMR) spectroscopy is a powerful and versatile tool that provides unprecedented levels of information regarding structure and interactions. Both high-resolution magic-angle-spinning and comprehensive-multiphase NMR probes facilitate the study of natural multiphase samples. 1H NMR spectroscopy is the most sensitive and provides unique information on swollen components and interfaces. However, samples such as plants, organisms and soil have a high aqueous content and a range of free, exchanging and bound water, leading to a broad and intense water signal that can span the entire 1H spectral region masking information from other components. In this manuscript, a water suppression approach termed Tailored Water suppression for Inhomogeneous Natural Samples (TWINS) is developed out of a practical need to study samples in their native state. TWINS builds upon the most effective approach to date (SPR-W5-WATERGATE) for natural samples with the addition of various elements to make the approach effective in the most challenging systems. TWINS was demonstrated on a range of environmental samples in both 1-D and 2-D NMR experiments. A lock capillary was developed to separate the lock solvent from the sample, further reducing sample alteration. In summary the more challenging the sample, the more TWINS outperformed conventional approaches. In turn this increases the range and diversity of samples that can be studied in their natural state critical for a wide variety of fields and applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".