Improved 2D 1H–13C NMR permits in vivo analysis of Daphnia magna metabolism without isotopic enrichment
Bibliographic record
Abstract
Daphnia magna are small crustaceans, used commonly in aquatic toxicity due to their sensitivity to pollutants. In vivo NMR provides unique insights into real-time metabolic responses, information needed to understand “why” at the biochemical level, contaminants are toxic. Due to overlap caused by the magnetic susceptibility distortions in 1D 1 H NMR, 2D is required for metabolic fingerprinting in vivo . In particular, 2D 1 H– 13 C NMR offers excellent spectral dispersion but is time-consuming and has intrinsic low sensitivity. As such, to date nearly all studies have used 13 C isotopically enriched D. magna , but this limits studies to lab-raised organisms. Here, in order to assess the feasibility of studying Daphnia at natural abundance, symmetric ASAP HSQC in combination with time-resolved non-uniform sampling (TR-NUS) is explored. The combined approach (TR-NUS ASAP HSQC) improved Signal-to-Noise Ratios (SNRs) up to 3 times versus standard HSQC, while reconstruction of TR-NUS data provided information on a 4 min time scale. In turn, this allowed anoxia (and recovery from anoxia) to be studied for the first time on unlabelled Daphnia using HSQC. TR-NUS ASAP 1 H– 13 C HSQC is a key step towards to investigate environmental adaptability and exposure in living organisms in close to real-time. As the approach does not require isotopic enrichment it affords future possibilities to sample organisms directly from the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".