Effect of strong homonuclear proton coupling on localized <sup>13</sup>C detection using PRESS
Bibliographic record
Abstract
The effect of strong homonuclear proton coupling on (13)C incorporation measurements by either indirect or direct means was investigated (and illustrated with glutamate) both numerically and experimentally at 3.0 T. In particular, two sequences were considered, each using a proton PRESS sequence for localization. The indirect (13)C detection method incorporated the POCE (proton observe carbon edited) technique onto PRESS, and for direct (13)C detection a DEPT (distortionless enhancement by polarization transfer) sequence was appended to the PRESS localization. Both analysis and experiment demonstrate that when strong homonuclear coupling of protons is additional to heteronuclear coupling with (13)C spins, the (13)C measures derived from either the indirect PRESS-POCE sequence or the direct-but-enhanced PRESS-DEPT sequence are significantly modified. Specifically, the MR lineshapes of both (13)C-bonded and nonbonded protons are changed during (13)C incorporation, giving rise, for example, to a potential cross-contamination of < or =30% between glutamate (13)C(3) and (13)C(4) measures from the PRESS-POCE indirect method. During direct-but-enhanced detection, the DEPT enhancement is reduced for glutamate (13)C(2), (13)C(3), and (13)C(4) but not equally, and the reduction is further exacerbated by proton PRESS localization, which gives rise to enhancements that are strong functions of PRESS TE(1) and TE(2).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".