Targeted Compound Selection with Increased Sensitivity in <sup>13</sup>C-Enriched Biological and Environmental Samples Using <sup>13</sup>C-DREAMTIME in Both High-Field and Low-Field NMR
Bibliographic record
Abstract
Chemical characterization of complex mixtures by Nuclear Magnetic Resonance (NMR) spectroscopy is challenging due to a high degree of spectral overlap and inherently low sensitivity. Therefore, NMR experiments that reduce overlap and increase signal intensity hold immense potential for the analysis of mixtures such as biological and environmental media. Here, we introduce a 13 C version of DREAMTIME (Designed Refocused Excitation And Mixing for Targets In Vivo and Mixture Elucidation) NMR, which, when analyzing 13 C-enriched materials, allows the user to selectively detect only the compound(s) of interest and remove all other peaks in a 13 C spectrum. Selected peaks can additionally be “focused” into sharp “spikes” to increase sensitivity. 13 C-DREAMTIME is first demonstrated at high field strength (500 MHz) with simultaneous selection of eight amino acids in a 13 C-enriched cell free amino acid mixture and of six metabolites in an extract of 13 C-enriched green algae and demonstrated at low field strength (80 MHz) with a standard solution of 13 C- d -glucose and 13 C- l -phenylalanine. 13 C-DREAMTIME is then applied at high-field to analyze metabolic changes in 13 C-enriched Daphnia magna after exposure to polystyrene “microplastics,” as well as at low-field to track fermentation of 13 C- d -glucose using wine yeast. Ultimately, 13 C-DREAMTIME reduces spectral overlap as only selected compounds are recorded, resulting in the detection of analyte peaks that may otherwise not have been discernable. In combination with focusing, up to a 6-fold increase in signal intensity can be obtained for a given peak. 13 C-DREAMTIME is a promising experiment type for future reaction monitoring and for tracking metabolic processes with 13 C-enriched compounds.
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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) | 0.001 | 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 | 0.000 |
| 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".