Real-time membrane fusion of giant polymer vesicles

Membrane fusion is very important for the formation of many complex organs in metazoans throughout evolution, such as muscles, bones, and placentae. Lipid vesicles (liposomes) are frequently used as model membranes to study the fusion process. This work demonstrates for the first time the real-time membrane fusion of giant polymer vesicles by directly displaying a series of high-resolution and real-time transformation images of individual vesicles. The fusion process includes the sequential steps of membrane contact, forming the center wall, symmetric expansion of fusion pore and complete fusion, undergoing the intermediates of "8" shape with a protruding rim at the contact site, peanut (pear) shape, and oblate sphere. The vesicle swells during fusion, and the fusing vesicle only deforms in the neck domain around the fusion pore in the lateral direction, which verifies the importance of the lateral tension on the fusion pore at the vesicle deformation level. The successful fusion of the synthetic and protein-free polymer vesicles reported here also supports that vesicle proximity combined with membrane perturbation suffices to induce membrane fusion, and that the protein is not necessary for the fusion process.     

Electrostatic layer-by-layer assembled carbon nanotube multilayer film and its electrocatalytic activity for O2 reduction

Multilayer films of shortened multiwalled carbon nanotubes (MWNTs) are homogeneously and stably assembled on glassy carbon electrodes with the layer-by-layer (LBL) method, based on electrostatic interaction of positively charged poly(diallyldimethylammonium chloride) and negatively charged and shortened MWNTs. The film assembly and electrochemical property as well as the electrocatalytic activity toward O2 reduction of the MWNT multilayer film are studied. Scanning electron microscopy, the quartz crystal microbalance technique, ultraviolet-visible-near-infrared spectroscopy, and cyclic voltammetry are used for characterization of film assembly. Experimental results revealed that film growth is uniform, almost with the same coverage of the MWNTs in each layer, and that the assembled MWNTs are mainly in the form of small bundles or single tubes on the electrodes. Electrochemical studies indicate that the LBL assembled MWNT films possess a remarkable electrocatalytic activity toward O2 reduction in alkaline media. This property, combined with the well-dispersed, porous and conductive features of the MWNT film illustrated with the LBL method, suggests the potential application of the MWNT film for constructing an efficient alkaline air electrode for energy conversions.     

对全入射角负折射二维光子晶体结构和电磁参量的研究

利用二维光子晶体的等频线分析原理和平面波展开方法,得到了使二维光子晶体产生全入射角负折射(All-Angle Negative Refraction,AANR)现象时,入射电磁波的频率取值范围.同时,分析了AANR频率范围随着结构参量(晶格类型、介质棒半径与晶格周期的比值)和电磁参量(介质柱介电常量、本底介电常量、入射电磁波偏振方向)变化的行为.结果表明:固定组成光子晶体的一种介质的介电常量,另一介质的介电常量只有达到一定阈值,才有可能使光子晶体出现AANR现象.在给定两种介质介电常量的条件下,存在使AANR频率范围最大化的结构参量和电磁参量.     

Butterfly and Nest-Shaped Tp*-W-Cu-S Cluster Monomers and Dimers with Hexamethylenetetramine as Ligand: Anion-Dependent Structures and Nonlinear Optical Properties

The assembly of[Et4N][Tp*WS3](1) with CuX (Tp* =hydridotris(3,5-dimethylpyrazol-1-yl)borate;X =Cl,Br,and I) or[Cu(MeCN)4][PF6]in the presence of tetratopic liga...     

基于激光诱导击穿光谱(LIBS)的煤灰熔点快速检测

炉内结渣是影响火电机组和气化工艺可靠运行的关键因素之一,准确预测灰熔点可以提前调整炉膛出口温度以避免结渣。本论文采用激光诱导击穿光谱（LIBS）采集煤灰样中金属元素的光谱,分别建立煤灰中的金属元素的谱线强度与煤灰熔点的随机森林模型、支持向量机回归模型和线性回归模型,直接预测煤灰熔点温度。采用基于马氏距离(MD)的异常数据剔除算法和基于稀疏矩阵的基线估计与降噪算法（BEADS）,对粉煤灰样的全光谱数据进行了预处理。随机森林模型对粉煤灰熔点的预测平均相对误差（MRE）为54.74%,支持向量机回归模型的预测平均相对误差为60.08%,而线性回归模型的预测平均相对误差达到了9.78%。研究结果表明,线性回归模型对煤灰熔点的预测结果更准确。     

X射线荧光光谱(XRF)法测定绿泥石中镁、铝、硅、磷、钾、钙、钛、铁元素含量

建立了X射线荧光光谱法测定绿泥石中镁、铝、硅、磷、钾、钙、钛、铁元素含量的快速分析方法。以标准物质及标准物质与基准试剂氧化镁、氧化钙人工混合配制标样的方法建立标准工作曲线,重点讨论了熔剂和脱模剂的选取。最佳熔样条件：采用8.0g四硼酸锂和偏硼酸锂混合熔剂(质量比67: 33)+0.8g样品并添加溴化锂作为脱模剂,熔样温度1100℃,熔样时间10min。该方法相对标准偏差(n=12)均小于5.57%,绿泥石样品测定结果与化学法一致,硅酸盐标准物质测定结果均满足不确定度要求。     

Speckle characteristics of simulated deep Fresnel region under shadowing effect

After the three-dimensional self-affine fractal random surface simulation, we use the optical scattering theory to calculate the deep Fresnel region speckle(DFRS) under consideration of the more strict shadowing effect. The evolution of DFRS with the scattering distance and the intensity probability distribution are studied. It is found that the morphology of the scatterer has an antisymmetric relationship with the intensity distribution of DFRS, and the effect of micro-lenses on the scattering surface causes the intensity probability distribution of DFRS to deviate from the Gaussian speckle in the high light intensity area.     

A general electron donor–acceptor complex for photoactivation of arenes via thianthrenation

General photoactivation of electron donor–acceptor (EDA) complexes between arylsulfonium salts and 1,4-diazabicyclo[2.2.2]octane with visible light or natural sunlight was discovered. This practical and efficient mode enables the production of aryl radicals under mild conditions, providing an unrealized opportunity for two-step para-selective C–H functionalization of complex arenes. The novel mode for generating aryl radicals via an EDA complex was well supported by UV-vis absorbance measurements, nuclear magnetic resonance titration experiments, and density functional theory (DFT) calculations. The method was applied to the regio- and stereo-selective arylation of various N-heterocycles under mild conditions, yielding an assembly of challengingly linked heteroaryl–(hetero)aryl products. Remarkably, the meaningful couplings of bioactive molecules with structurally complex drugs or agricultural pharmaceuticals were achieved to display favorable in vitro antitumor activities, which will be of great value in academia or industry.

General photoactivation of EDA complexes between arylsulfonium salts and 1,4-diazabicyclo[2.2.2]octane was discovered. This practical mode enables the generation of aryl radicals for C–H functionalization of arenes.     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.