Towards Multiplexed Nuclear Magnetic Resonance for Environmental Analysis
Bibliographic record
Abstract
Nuclear Magnetic Resonance (NMR) is one of the most powerful analytical tools in modern research. Within the scope of environmental analysis, NMR can identify unknown pollutants, examine non-covalent binding in soils and, uniquely, study biochemical changes in living organisms. Here, novel approaches are explored to address current challenges and further improve the utility of environmental NMR. The ability to study organisms in vivo (meaning while they are alive) was applied to track metabolic changes in the critical species Daphnia magna. Specifically, a novel pulse sequence that allows the selection of chosen compounds out of a complex mixture was employed, addressing the key challenge of spectral overlap. This powerful technique was used to follow the biochemical impacts of external stressors by tracking changes in key metabolites (e.g., glucose). Another novel “slice-selective” pulse sequence was developed and used to non-destructively examine specific parts of a larger sample. The uptake of 13C enriched food in an earthworm (Eisenia fetida) was studied, and there were considerable biochemical differences in uptake across different body parts, which would have been missed with standard approaches. Following this, the focus shifted to specialized NMR hardware called microcoils. These devices greatly improve sensitivity for very small samples such as D. magna eggs (<400 μm in diameter) which are critical to aquatic ecosystem health. Cutting-edge complementary metal-oxide-semiconductor (CMOS) technology was used to create microcoils tailored to studying these eggs. The CMOS microcoils showed considerable potential including excellent sensitivity, the ability to analyze many different nuclei, and facile expansion to multiple coil arrays. The latter refers to multiple coils for the study of multiple samples at the same time, which addresses the low throughput of single microcoil approaches. A three coil CMOS array was used to study three D. magna eggs simultaneously for the first time, laying the groundwork for improved throughput and allowing concurrent analysis of control and exposed organisms, reducing variability in toxicological studies. In combination, the approaches detailed here improve the applicability of NMR to environmental research, paving the way for future efficient study of microscopic samples, whether they be aquatic eggs, cells, or even air particles.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".