Improved 2D 1H-13C NMR permits in vivo analysis of Daphnia magna metabolism without isotopic enrichment
Bibliographic record
Abstract
This dataset contains data from a study that assessed the feasibility of using nuclear magnetic resonance (NMR) to measure the real-time in vivo metabolic responses of the small freshwater crustacean, *Daphnia magna*, in samples derived from the wild (rather than raised in a laboratory) to investigate aquatic contaminant toxicity. To date, nearly all previous studies have used 13C isotopically enriched D.magna, but this limits studies to lab-raised organisms. These data were taken from an experimental approach that did not require isotopic enrichment, allowing samples to be taken directly from the environment. Due to overlap caused by the magnetic susceptibility distortions in 1D 1H NMR, 2D is required for metabolic fingerprinting in vivo. In particular, 2D 1H-13C NMR offers excellent spectral dispersion but is time-consuming and has intrinsic low sensitivity. To study D. magna in natural abundance, symmetric ASAP HSQC in combination with time-resolved non-uniform sampling (TR-NUS) was used. This combined approach (TR-NUS ASAP HSQC) improved sensitivity up to 3 times versus standard HSQC, while reconstruction of TR-NUS data provided information on a 4 minute time scale. In turn, this allowed anoxia (and recovery from anoxia) to be studied for the first time on unlabelled Daphnia using HSQC. This method could, therefore, become key for investigating environmental adaptability and exposure in living organisms in conditions that are closer to real-time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".