Diacetatobis[1,3-bis­(benzimidazol-2-yl)benzene]zinc(II) dihydrate

In the title complex, [Zn(CH3COO)2(C20H14N4)2]·2H2O, the ZnII atom, which lies on a crystallographic twofold axis, is coordinated by two O atoms of two acetate ligands and two N atoms from two 1,3-bis­(benzimidazol-2-yl)benzene ligands in a distorted tetra­hedral geometry. The complex mol­ecules and solvent water mol­ecules are connected via O—H⋯N, O—H⋯O and N—H⋯O hydrogen bonds, forming a three-dimensional network

Monodispersed Bioactive Glass Nanoclusters with Ultralarge Pores and Intrinsic Exceptionally High miRNA Loading for Efficiently Enhancing Bone Regeneration

Bioactive glass nanoparticles (BGNs) have attracted much attention in drug delivery and bone tissue regeneration, due to the advantages including biodegradation, high bone‐bonding bioactivity, and facile large‐scale fabrication. However, the wide biomedical applications of BGNs such as efficient gene delivery are limited due to their poor pore structure and easy aggregation. Herein, for the first time, this study reports novel monodispersed bioactive glass nanoclusters (BGNCs) with ultralarge mesopores (10–30 nm) and excellent miRNA delivery for accelerating critical‐sized bone regeneration. BGNCs with different size (100–500 nm) are fabricated by using a branched polyethylenimine as the structure director and catalyst. BGNCs show an excellent apatite‐forming ability and high biocompatibility. Importantly, BGNCs demonstrate an almost 19 times higher miRNA loading than those of conventional BGNs. Additionally, BGNCs–miRNA nanocomplexes exhibit a significantly high antienzymolysis, enhance cellular uptake and miRNA transfection efficiency, overpassing BGNs and commercial Lipofectamine 3000. BGNCs‐mediated miRNA delivery significantly improves the osteogenic differentiation of bone marrow stromal stem cells in vitro and efficiently enhances bone formation in vivo. BGNCs can be a highly efficient nonviral vector for various gene therapy applications. The study may provide a novel strategy to develop highly gene‐activated bioactive nanomaterials for simultaneous tissue regeneration and disease therapy.Monodispersed bioactive glass nanoclusters (BGNCs) with ultra‐large mesopores (10–30 nm) are developed for miRNA delivery to enhance bone regeneration. BGNCs demonstrated an ultrahigh miRNA loading and transfection efficiency, overpassing commercial lipofectamine. BGNCs‐mediated miRNA delivery significantly improved osteogenic differentiation of bone marrow stromal stem cells in vitro and enhanced bone formation in vivo.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139128/1/adhm201700630-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139128/2/adhm201700630.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139128/3/adhm201700630_am.pd

Progress on Optical Fiber Biochemical Sensors Based on Graphene

Graphene, a novel form of the hexagonal honeycomb two-dimensional carbon-based structural material with a zero-band gap and ultra-high specific surface area, has unique optoelectronic capabilities, promising a suitable basis for its application in the field of optical fiber sensing. Graphene optical fiber sensing has also been a hotspot in cross-research in biology, materials, medicine, and micro-nano devices in recent years, owing to prospective benefits, such as high sensitivity, small size, and strong anti-electromagnetic interference capability and so on. Here, the progress of optical fiber biochemical sensors based on graphene is reviewed. The fabrication of graphene materials and the sensing mechanism of the graphene-based optical fiber sensor are described. The typical research works of graphene-based optical fiber biochemical sensor, such as long-period fiber grating, Bragg fiber grating, no-core fiber and photonic crystal fiber are introduced, respectively. Finally, prospects for graphene-based optical fiber biochemical sensing technology will also be covered, which will provide an important reference for the development of graphene-based optical fiber biochemical sensors

Fuzzy Student’s T-Distribution Model Based on Richer Spatial Combination

Fuzzy c-means (FCM) algorithms with spatial information have been widely applied in the field of image segmentation. However, most of them suffer from two challenges. One is that introduction of fixed or adaptive single neighboring information with narrow receptive field limits contextual constraints leading to clutter segmentations. The other is that incorporation of superpixels with wide receptive field enlarges spatial coherency leading to block effects. To address these challenges, we propose fuzzy Students t-distribution model based on richer spatial combination (FRSC) for image segmentation. In this Paper, we make two significant contributions. The first is that both narrow and wide receptive fields are integrated into the objective function of FRSC, which is convenient to mine image features and distinguish local difference. The second is that the rich spatial combination under Students t-distribution ensures that spatial information is introduced into the updated parameters of FRSC,which is helpful in finding a balance between the noise-immunity and detail-preservation. Experimental results on synthetic and publicly available images, further demonstrate that the proposed FRSC addresses successfully the limitations of FCM algorithms with spatial information and provides better segmentation results than state-of-the-art clustering algorithms.National Natural Science Foundation of China (Grant Number: Grant 61871259, Grant 61861024); Natural Science Basic Research Program of Shaanxi (Grant Number: 2021JC-47); Key Research and Development Program of Shaanxi (Grant Number: 2021ZDLGY08-07)

Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering

Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms

Determination of 4 Kinds of β-Agonists Residues in Braised Meat by Ultra Performance Liquid Chromatography-Tandem Mass Spectrometry

An ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS) method was developed for the determination of four β-agonists (terbutaline, clenbuterol, ractopamine, salbutamol) in braised meat. Samples were hydrolyzed by β-glucuronidase and cleaned up by an SLS solid phase extraction column. The separation was performed on a Thermo Hypersil Gold C18 column with a gradient elution of 0.1% formic acid water and acetonitrile as mobile phases, ESI+ was used for multiple response monitoring (MRM) and quantitative analysis by internal standard method. The linear relationship of the four β-agonists was good in the concentration range of 0.5 μg/L to 9.5 μg/L, and the correlation coefficient (r) was greater than 0.9988. The limit of detection (LOD) was 0.1 μg/kg, and the limit of quantitation (LOQ) was 0.3 μg/kg. The recoveries were 87.9%~113.7% and RSDs were 1.48%~9.32% at three spiked levels (1, 5 and 9 μg/kg). In a total of 162 batches of braised meat samples, one sample of braised pig’s trotter was found to contain 1.51 μg/kg of clenbuterol and 3.65 μg/kg of ractopamine. Additionally, another sample of braised lamb was found to contain 11.5 μg/kg of clenbuterol. The method is rapid and accurate, and can be used for qualitative and quantitative determination of four β-agonists (terbutaline, clenbuterol, ractopamine, salbutamol) in braised meat

An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies

Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions