Advantage Of Structural Information Research Articles

Resolved‐detrimental surface crystallization in yttrium lanthanum gallate glasses for optical fiber applications

AbstractGallium‐rich heavy metal oxide glasses have become highly attractive optical materials since they exhibit a wide transparency window spanning from the ultraviolet ∼270 nm up to the mid‐infrared (IR) region ∼6 μm making them promising for a future integration in optical fiber devices. Nonetheless, in most composition, surface crystallization is a key limiting factor for optical fiber drawing using the classical preform‐to‐fiber method. Herein, taking advantage of structural information from vibrational spectroscopies (Raman/IR) and 71Ga Solid‐State Nuclear Magnetic Resonance, we describe the key role of lanthanum and yttrium rare—earth elements on the glass structure and their impact on the capability to draw those new glass compositions into optical fibers. This approach emphasizes that yttrium ions as compared with lanthanum ones favor the glass disorder, increasing significantly the fraction of GaO5 units with respect to GaO4. That, combined with thermal analysis and examination of the crystallization behaviors, highlights that Y2O3 prevents the glass devitrification during the glass shaping. The smaller yttrium radius is believed to be the key physical parameter preventing the precipitation of the BayGa5‐yGey+1La3‐yO14 (y = 0, 1, 2, 3) langasite‐type crystal phase. This study remains particularly relevant and opens up the way for the development of highly robust power scaled fiber devices operating from the visible up to the challenging mid‐IR domain.

Improving Amide Proton Transfer-Weighted MRI Reconstruction Using T2-Weighted Images.

Current protocol of Amide Proton Transfer-weighted (APTw) imaging commonly starts with the acquisition of high-resolution T2-weighted (T2w) images followed by APTw imaging at particular geometry and locations (i.e. slice) determined by the acquired T2w images. Although many advanced MRI reconstruction methods have been proposed to accelerate MRI, existing methods for APTw MRI lacks the capability of taking advantage of structural information in the acquired T2w images for reconstruction. In this paper, we present a novel APTw image reconstruction framework that can accelerate APTw imaging by reconstructing APTw images directly from highly undersampled k-space data and corresponding T2w image at the same location. The proposed framework starts with a novel sparse representation-based slice matching algorithm that aims to find the matched T2w slice given only the undersampled APTw image. A Recurrent Feature Sharing Reconstruction network (RFS-Rec) is designed to utilize intermediate features extracted from the matched T2w image by a Convolutional Recurrent Neural Network (CRNN), so that the missing structural information can be incorporated into the undersampled APT raw image thus effectively improving the image quality of the reconstructed APTw image. We evaluate the proposed method on two real datasets consisting of brain data from rats and humans. Extensive experiments demonstrate that the proposed RFS-Rec approach can outperform the state-of-the-art methods.

Probabilistic reconstruction of ancestral protein sequences.

Using a maximum-likelihood formalism, we have developed a method with which to reconstruct the sequences of ancestral proteins. Our approach allows the calculation of not only the most probable ancestral sequence but also of the probability of any amino acid at any given node in the evolutionary tree. Because we consider evolution on the amino acid level, we are better able to include effects of evolutionary pressure and take advantage of structural information about the protein through the use of mutation matrices that depend on secondary structure and surface accessibility. The computational complexity of this method scales linearly with the number of homologous proteins used to reconstruct the ancestral sequence.