Enzymatic Extraction of Bioactive and Self‐Assembling Wool Keratin for Biomedical Applications

Keratin is widely recognized as a high‐quality renewable protein resource for biomedical applications. Despite their extensive existence, keratin resources such as feathers, wool, and hair exhibit high stability and mechanical properties because of their high disulfide bond content. Consequently, keratin extraction is challenging and its application is greatly hindered. In this work, a biological extraction strategy is proposed for the preparation of bioactive keratin and the fabrication of self‐assembled keratin hydrogels (KHs). Based on moderate and controlled hydrolysis by keratinase, keratin with a high molecular weight of approximately 45 and 28 kDa that retain its intrinsic bioactivities is obtained. The keratin products show excellent ability to promote cell growth and migration and are conferred with significant antioxidant ability because of their intrinsically high cysteine content. In addition, without the presence of any cross‐linking agent, the extracted keratin can self‐assemble into injectable hydrogels. The KHs exhibit a porous network structure and 3D culture ability, showing potential in promoting wound healing. This enzyme‐driven keratin extraction strategy opens up a new approach for the preparation of keratin that can self‐assemble into injectable hydrogels for biomedical engineering.     

A Ternary Supramolecular Self-Assembly Model Based on Cyclopentano Cucurbit[5]uril

Crystallography Reports - This paper reports on the supramolecular self-assembly of cyclopentano cucurbit[5]uril (CyP5Q[5]), L-phenylalanine (L-Phe), and cadmium ions. The crystal structure is...     

Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions

Distance weighted discrimination (DWD) is an appealing classification method that is capable of overcoming data piling problems in high-dimensional settings. Especially when various sparsity structures are assumed in these settings, variable selection in multicategory classification poses great challenges. In this paper, we propose a multicategory generalized DWD (MgDWD) method that maintains intrinsic variable group structures during selection using a sparse group lasso penalty. Theoretically, we derive minimizer uniqueness for the penalized MgDWD loss function and consistency properties for the proposed classifier. We further develop an efficient algorithm based on the proximal operator to solve the optimization problem. The performance of MgDWD is evaluated using finite sample simulations and miRNA data from an HIV study.     

Real-time phase measurement and correction of dynamic multimode beam using a single spatial light modulator

In this Letter,a novel system for adaptively correcting the phase of a dynamic multimode beam is proposed.While using merely one spatial light modulator,the phase measurement of the first-order diffraction pattern and the correction of the zeroth diffraction order are simultaneously realized.The real-time experimental result is obtained at a control rate of 10 Hz.The power-in-the-bucket value is improved from 38.5%to 61.8%,even with fundamental mode content that is consistently below 30%.To the best of our knowledge,this is the first implementation of real-time adaptive correction of the entire multimode beam.     

Effects of Fenton’s reagent and thermal modification on the electrochemical properties of graphite felt for microbial fuel cell

Research on Chemical Intermediates - Effective methods for graphite felt (GF) treatment based on Fenton’s reagent treatment and thermal modification have been used to improve microbial fuel...     

Nonlocal Galerkin Strip Transfer Function Method for Vibration of Double-Layered Graphene Mass Sensor

Double-layered graphene sheets(DLGSs) can be applied to the development of a new generation of nanomechanical sensors due to their unique physical properties. A rectangular DLGS with a nanoparticle randomly located in the upper sheet is modeled as two nonlocal Kirchhoff plates connected by van der Waals forces. The Galerkin strip transfer function method which is a semi-analytical method is developed to compute the natural frequencies of the massplate vibrating system. It can give exact closed-form solutions along the longitudinal direction of the strip. The results obtained from the semi-analytical method are compared with the previous ones, and the differences between the single-layered graphene sheet(SLGS) and the DLGS mass sensors are also investigated. The results demonstrate the similarity of the in-phase mode between the SLGS and DLGS mass sensors. The sensitivity of the DLGS mass sensor can be increased by decreasing the nonlocal parameter, moving the attached nanoparticle closer to the DLGS center and making the DLGS smaller. These conclusions are helpful for the design and application of graphene-sheet-based resonators as nano-mass sensors.     

Isatindolignanoside A,a glucosidic indole-lignan conjugate from an aqueous extract of the Isatis indigotica roots

A glucosidic indole-lignan conjugate with a novel carbon skeleton, named isatindolignanoside A (1), was isolated from an aqueous extract of the Isatis indigotica roots “ban lan gen”. Its structure was determined by comprehensive analysis of spectroscopic data, enzyme hydrolysis, and electronic CD calculations. Compound 1 is active against Coxsackie virus B3 and represents the first example of natural product having a structural feature of conjugation between indole and lignan, and its plausible biosynthetic pathway is postulated.     

Mott-Insulator-Superfluid Phase Transition with Coupled-Cavity Frequency Conversion System

An array of coupled cavities, each of which contains a triangle-down-level atom, is investigated. An effective Hamiltonian can be achieved under the strong classical driving. We can control the transition between Mott-insulator and superfluid states by the classical driving. The frequency conversion in one site has the same effects of nonlinear in BH hamiltonian which can induce insulator-superfluid states.