Strong Static Magnetic Fields Increase the Gel Signal in Partially Hydrated DPPC/DMPC Membranes
Bibliographic record
Abstract
NIt was recently reported that static magnetic fields increase lipid order in the hydrophobic membrane core of dehydrated native plant plasma membranes [Poinapen, Soft Matter 9:6804-6813, 2013]. As plasma membranes are multicomponent, highly complex structures, in order to elucidate the origin of this effect, we prepared model membranes consisting of a lipid species with low and high melting temperature. By controlling the temperature, bilayers coexisting of small gel and fluid domains were prepared as a basic model for the plasma membrane core. We studied molecular order in mixed lipid membranes made of dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) using neutron diffraction in the presence of strong static magnetic fields up to 3.5 T. The contribution of the hydrophobic membrane core was highlighted through deuterium labeling the lipid acyl chains. There was no observable effect on lipid organization in fluid or gel domains at high hydration of the membranes. However, lipid order was found to be enhanced at a reduced relative humidity of 43%: a magnetic field of 3.5 T led to an increase of the gel signal in the diffraction patterns of 5%. While all biological materials have weak diamagnetic properties, the corresponding energy is too small to compete against thermal disorder or viscous effects in the case of lipid molecules. We tentatively propose that the interaction between the fatty acid chains' electric moment and the external magnetic field is driving the lipid tails in the hydrophobic membrane core into a better ordered state.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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; 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".