The model peptides A 8 K and A 10 K self-assemble in water into ca. 100 nm long ribbon-like aggregates. These structures can be described as β-sheets laminated into a ribbon structure with a constant elliptical crosssection of 4 by 8 nm, where the longer axis corresponds to a finite number, N ≈ 15, of laminated sheets, and 4 nm corresponds to a stretched peptide length. The ribbon cross-section is strikingly constant and independent of the peptide concentration. High-contrast transmission electron microscopy shows that the ribbons are twisted with a pitch λ ≈ 15 nm. The self-assembly is analyzed within a simple model taking into account the interfacial free energy of the hydrophobic β-sheets and a free energy penalty arising from an increased stretching of hydrogen bonds within the laminated β-sheets, arising from the twist of the ribbons. The model predicts an optimal value N, in agreement with the experimental observations.

The formation of multilamellar vesicles (MLVs) in the lyotropic lamellar phase of the system triethylene glycol mono n-decyl ether (CE)/water is investigated under large amplitude oscillatory shear (LAOS) using spatially resolved rheo-NMR spectroscopy and a combination of rheo-small angle light scattering (rheo-SALS) and conventional rheology. Recent advances in rheo-NMR hardware development facilitated the application of LAOS deformations in high-field NMR magnets. For the range of investigated strain amplitudes (10-50) and frequencies (1 and 2 rad s), MLV formation is observed in all NMR and most SALS experiments. It is found that the MLV size depends on the applied frequency in contrast to previous steady shear experiments where the shear rate is the controlling parameter. The onset of MLV formation, however, is found to vary with the shear amplitude. The LAOS measurements bear no indication of the intermediate structures resembling aligned multilamellar cylinders observed in steady shear experiments. Lissajous curves of stress vs strain reveal a transition from a viscoelastic solid material to a pseudoplastic material.

Purpose Correction of Rician signal bias in magnitude MR images. Methods A model‐based, iterative fitting procedure is used to simultaneously estimate true signal and underlying Gaussian noise with standard deviation σg on a pixel‐by‐pixel basis in magnitude MR images. A precomputed function that relates absolute residuals between measured signals and model fit to σg is used to iteratively estimate σg. The feasibility of the method is evaluated and compared to maximum likelihood estimation (MLE) for diffusion signal decay simulations and diffusion‐weighted images of the prostate considering 21 linearly spaced b‐values from 0 to 3000 s/mm2. A multidirectional analysis was performed with publically available brain data. Results Model simulations show that the Rician bias correction algorithm is fast, with an accuracy and precision that is on par to model‐based MLE and direct fitting in the case of pure Gaussian noise. Increased accuracy in parameter prediction in a low signal‐to‐noise ratio (SNR) scenario is ideally achieved by using a composite of multiple signal decays from neighboring voxels as input for the algorithm. For patient data, good agreement with high SNR reference data of diffusion in prostate is achieved. Conclusions OBSIDIAN is a novel, alternative, simple to implement approach for rapid Rician bias correction applicable in any case where differences between true signal decay and underlying model function can be considered negligible in comparison to noise. The proposed composite fitting approach permits accurate parameter estimation even in typical clinical scenarios with low SNR, which significantly simplifies comparison of complex diffusion parameters among studies.