Isoflavonoids from Astragalus membranaceus Hairy Roots

Chemistry of Natural Compounds -     

Flower-like titanium dioxide as novel co-reaction accelerator for ultrasensitive “off–on” electrochemiluminescence aptasensor construction based on 2D g-C3N4 layer for thrombin detection

Journal of Solid State Electrochemistry - An ultrasensitive “signal-off–on” electrochemiluminescence (ECL) biosensor is constructed based on f1-TiO2/g-C3N4/PDA for thrombin...     

A microfluidics-dispensing-printing strategy for Janus photonic crystal microspheres towards smart patterned displays

From the implementation point of view, the printable magnetic Janus colloidal photonic crystals (CPCs) microspheres are highly desirable. Herein, we developed a dispensing-printing strategy for magnetic Janus CPCs display via a microfluidics-automatic printing system. Monodisperse core/shell colloidal particles and magnetic Fe₃O₄ nanoparticles precursor serve as inks. Based on the equilibrium of three-phase interfacial tensions, Janus structure is successfully formed, followed by UV irradiation and self-assembly of colloid particle to generate magnetic Janus CPCs microspheres. Notably, this method shows distinct superiority with highly uniform Janus CPCs structure, where the TMPTA/Fe₃O₄ hemisphere is in the bottom side while CPCs hemisphere is in the top side. Thus, by using Janus CPCs microspheres with two different structural colors as pixel points, a pattern with red flower and green leaf is achieved. Moreover, 1D linear Janus CPCs pattern encapsulated by hydrogel is also fabricated. Both the color and the shape can be changed under the traction of magnets, showing great potentials in flexible smart displays. We believe this work not only offers a new feasible pathway to construct magnetic Janus CPCs patterns by a dispensing-printable fashion, but also provides new opportunities for flexible and smart displays.     

Identification of phytocompounds from Houttuynia cordata Thunb. as potential inhibitors for SARS-CoV-2 replication proteins through GC–MS/LC–MS characterization,molecular docking and molecular dynamics simulation

The COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a massive viral disease outbreak of international concerns. The present study is mainly intended to identify the bioactive phytocompounds from traditional antiviral herb Houttuynia cordata Thunb. as potential inhibitors for three main replication proteins of SARS-CoV-2, namely Main protease (Mpro), Papain-Like protease (PLpro) and ADP ribose phosphatase (ADRP) which control the replication process. A total of 177 phytocompounds were characterized from H. cordata using GC–MS/LC–MS and they were docked against three SARS-CoV-2 proteins (receptors), namely Mpro, PLpro and ADRP using Epic, LigPrep and Glide module of Schrödinger suite 2020-3. During docking studies, phytocompounds (ligand) 6-Hydroxyondansetron (A104) have demonstrated strong binding affinity toward receptors Mpro (PDB ID 6LU7) and PLpro (PDB ID 7JRN) with G-score of???7.274 and???5.672, respectively, while Quercitrin (A166) also showed strong binding affinity toward ADRP (PDB ID 6W02) with G-score -6.788. Molecular Dynamics Simulation (MDS) performed using Desmond module of Schrödinger suite 2020–3 has demonstrated better stability in the ligand–receptor complexes A104-6LU7 and A166-6W02 within 100 ns than the A104-7JRN complex. The ADME-Tox study performed using SwissADMEserver for pharmacokinetics of the selected phytocompounds 6-Hydroxyondansetron (A104) and Quercitrin (A166) demonstrated that 6-Hydroxyondansetron passes all the required drug discovery rules which can potentially inhibit Mpro and PLpro of SARS-CoV-2 without causing toxicity while Quercitrin demonstrated less drug-like properties but also demonstrated as potential inhibitor for ADRP. Present findings confer opportunities for 6-Hydroxyondansetron and Quercitrin to be developed as new therapeutic drug against COVID-19.

Graphic abstract

     

基于快速神经网络架构搜索的鲁棒图像水印网络算法

为解决深度学习在图像水印算法中计算量大且模型冗余的问题，提高图像水印算法在抵抗噪声、旋转和剪裁等攻击时的鲁棒性，提出基于快速神经网络架构搜索（neural architecture search，NAS）的鲁棒图像水印网络算法。通过多项式分布学习快速神经网络架构搜索算法，在预设的搜索空间中搜索最优网络结构，进行图像水印的高效嵌入与鲁棒提取。首先，将子网络中线性连接的全卷积层设置为独立的神经单元结构，并参数化表示结构单元内节点的连接，预先设定结构单元内每个神经元操作的搜索空间；其次，在完成一个批次的数据集训练后，依据神经元操作中的被采样次数和平均损失函数值动态更新概率；最后，重新训练搜索完成的网络。水印网络模型的参数量较原始网络模型缩减了92%以上，大大缩短了模型训练时间。由于搜索得到的网络结构更为紧凑，本文算法具有较高的时间性能和较好的实验效果，在隐藏图像时，对空域信息的依赖比原始网络更少。对改进前后的2个网络进行了大量鲁棒性实验，对比发现，本文算法在CIFAR-10数据集上对抵抗椒盐噪声和旋转、移除像素行（列）等攻击优势显著；在ImageNet数据集上对抵抗椒盐高斯噪声、旋转、中值滤波、高斯滤波、JPEG压缩、裁剪等攻击优势显著，特别是对随机移除行（列）和椒盐噪声有较强的鲁棒性。     

基于MOF (Cu)@biochar气凝胶的制备及其在缓释氮肥上的应用

以羧甲基纤维素(CMC)、明胶和MOF(Cu)@biochar为原料,采用简单有效的冷冻干燥方法制备了(CMC/Gelatin/MOF(Cu)@biochar)杂化气凝胶,并用傅里叶变换红外光谱(FT-IR)、热重分析(TG)、X射线粉末衍射(XRD)、扫描电镜(SEM)和X射线光电子能谱(XPS)技术对其进行了表征;研究了MOF(Cu)@biochar含量、pH和不同的盐水溶液对杂化气凝胶溶胀行为的影响;以该气凝胶负载氯化铵,制备了一种新型缓释肥料(SRF),并研究了含2%(wt)SRF的沙性土壤的保水能力;SRF在土壤中第30天的累积释放率为79.4%;肥料在土壤中释放符合非Fickian扩散和阳离子交换的协同作用机理。