Acid Catalyzed Stereocontrolled Ferrier-Type Glycosylation Assisted by Perfluorinated Solvent.

Described herein is the first application of perfluorinated solvent in the stereoselective formation of O-/S-glycosidic linkages that occurs via a Ferrier rearrangement of acetylated glycals. In this system, the weak interactions between perfluoro-n-hexane and substrates could augment the reactivity and stereocontrol. The initiation of transformation requires only an extremely low loading of resin-H+ and the mild conditions enable the accommodation of a broad spectrum of glycal donors and acceptors. The 'green' feature of this chemistry is demonstrated by low toxicity and easy recovery of the medium, as well as operational simplicity in product isolation.

Palladium-Catalyzed O-Glycosylation through π-π Interactions.

A stereodivergent synthesis of ß- and α-O-glycosides using 3-O-quinaldoyl glucals was developed by palladium catalysis at 60 and 110 °C respectively. Various alcohols, monosaccharides, and amino acid were glycosylated to form ß- and α- products in good yields with high stereoselectivity. Mechanistic studies indicated no classic Pd-N (quinoline) coordination, but π-π stacking interactions promoted the anomeric stereodiversity. The practicality was demonstrated by glycosylating natural products/drugs and synthesizing a complex tetrasaccharide.

Multi-Patch Collaborative Point Cloud Denoising via Low-Rank Recovery with Graph Constraint.

Point cloud is the primary source from 3D scanners and depth cameras. It usually contains more raw geometric features, as well as higher levels of noise than the reconstructed mesh. Although many mesh denoising methods have proven to be effective in noise removal, they hardly work well on noisy point clouds. We propose a new multi-patch collaborative method for point cloud denoising, which is solved as a low-rank matrix recovery problem. Unlike the traditional single-patch based denoising approaches, our approach is inspired by the geometric statistics which indicate that a number of surface patches sharing approximate geometric properties always exist within a 3D model. Based on this observation, we define a rotation-invariant height-map patch (HMP) for each point by robust Bi-PCA encoding bilaterally filtered normal information, and group its non-local similar patches together. Within each group, all patches are geometrically similar, while suffering from noise. We pack the height maps of each group into an HMP matrix, whose initial rank is high, but can be significantly reduced. We design an improved low-rank recovery model, by imposing a graph constraint to filter noise. Experiments on synthetic and raw datasets demonstrate that our method outperforms state-of-the-art methods in both noise removal and feature preservation.