Here we report a chirality transfer of cysteine, which at first was to the plasmonic resonance region of gold nanobipyramids and then to that of Ag nanoshells with increasing growth of Ag layers, owing to the important role of the free Cys embedded within the Ag nanoshell. Our finding is helpful for developing new chiral plasmonic nanomaterials with the best designed asymmetric properties.While Nature harnesses RNA and DNA to store, read and write genetic information, the inherent programmability, synthetic accessibility and wide functionality of these nucleic acids make them attractive tools for use in a vast array of applications. In medicine, antisense oligonucleotides (ASOs), siRNAs, and therapeutic aptamers are explored as potent targeted treatment and diagnostic modalities, while in the technological field oligonucleotides have found use in new materials, catalysis, and data storage. The use of natural oligonucleotides limits the possible chemical functionality of resulting technologies while inherent shortcomings, such as susceptibility to nuclease degradation, provide obstacles to their application. Modified oligonucleotides, at the level of the nucleobase, sugar and/or phosphate backbone, are widely used to overcome these limitations. https://www.selleckchem.com/products/msc2530818.html This review provides the reader with an overview of non-native modifications and the challenges faced in the design, synthesis, application and outlook of novel modified oligonucleotides.Cell membranes interact with a myriad of curvature-active proteins that control membrane morphology and are responsible for mechanosensation and mechanotransduction. Some of these proteins, such as those containing BAR domains, are curved and elongated, and hence may adopt different states of orientational order, from isotropic to maximize entropy to nematic as a result of crowding or to adapt to the curvature of the underlying membrane. Here, extending the classical work of Onsager for ordering in hard particle systems and that of [E. S. Nascimento et al., Phys. Rev. E, 2017, 96, 022704], we develop a mean-field density functional theory to predict the orientational order and evaluate the free energy of ensembles of elongated and curved objects on curved membranes. This theory depends on the microscopic properties of the particles and explains how a density-dependent isotropic-to-nematic transition is modified by anisotropic curvature. We also examine the coexistence of isotropic and nematic phases. This theory predicts how ordering depends on geometry but we assume here that the geometry is fixed. It also lays the ground to understand the interplay between membrane reshaping by BAR proteins and molecular order, examined by [Le Roux et al., submitted, 2020].Fiber-based fabrics have great potential in impacting protection. Here, we propose a novel nanostructure, wherein single-walled CNTs (SWCNTs) were employed to weave plain 2D films. The in-plane mechanical properties and impacting properties of SWCNT woven films (SWFs) were investigated via fully atomic molecular dynamics (MD) simulation. It was found that their fracture strength and Young's modulus present obvious anisotropy, depending on the loading direction. When the loading is along the CNT axis, the mechanical performances are the best. From the impacting test, we found that this SWF synchronously possesses high impacting strength and a percentage of absorbed energy. This is mainly a result of high intrinsic strength, excellent flexibility and radial deformation capability of CNTs. In addition, it was observed that the high-speed impact of projectile can lead to the intricate entanglements of CNTs, which also could dissipate some energy by friction between the CNTs. This study provides an in-depth understanding on the mechanical properties of SWFs and broadens the applications of CNT-based nanomaterials.Fetal Magnetic Resonance Imaging (MRI) is challenged by the fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion commonly occurs in between slices acquisitions. Motion correction for each slice is thus very important for reconstruction of 3D fetal brain MRI, but is highly operator-dependent and time-consuming. Approaches based on convolutional neural networks (CNNs) have achieved encouraging performance on prediction of 3D motion parameters of arbitrarily oriented 2D slices, which, however, does not capitalize on important brain structural information. To address this problem, we propose a new multi-task learning framework to jointly learn the transformation parameters and tissue segmentation map of each slice, for providing brain anatomical information to guide the mapping from 2D slices to 3D volumetric space in a coarse to fine manner. In the coarse stage, the first network learns the features shared for both regression and segmentation tasks. In the refinement stage, to fully utilize the anatomical information, distance maps constructed based on the coarse segmentation are introduced to the second network. Finally, incorporation of the signed distance maps to guide the regression and segmentation together improves the performance in both tasks. Experimental results indicate that the proposed method achieves superior performance in reducing the motion prediction error and obtaining satisfactory tissue segmentation results simultaneously, compared with state-of-the-art methods.Using large-scale, public data sources, this editorial provides a high-level description of educational technology trends leading up to and encompassing the year 2020. Data sources included (a) 17.9 million Facebook page posts by K-12 educational institutions in the U.S., (b) 131,760 tweets to the #EdTech hashtag on Twitter, and (c) 29,636 educational technology articles in the Scopus database. We provide a variety of descriptive results in the form of participation frequency charts, keyword matches, URL domain link counts, co-occurring hashtags, tweet text word trees, and common word and bigram frequencies. Results from the analysis of Facebook posts indicated that (a) schools increasingly used the platform over time, (b) the pandemic increased frequency (but not the nature) of use, (c) schools are progressively sharing more media, information, and tools, and (d) some of these tools align with trends identified by Weller (2020) while others do not. Analysis of tweets indicated that (a) discussions in 2020 revolved around "remote learning" and related topics, (b) this emphasis shifted or morphed into "elearning" and "online learning" as the year progressed, (c) shared posts were primarily informational or media-based, and (d) the space was heavily directed by a relatively small group of Superusers.