Biomedical applications of mRNA nanomedicine

As an attractive alternative to plasmid DNA, messenger RNA (mRNA) has recently emerged as a promising class of nucleic acid therapeutics for biomedical applications. Advances in addressing the inherent shortcomings of mRNA and in the development of nanoparticle-based delivery systems have prompted the development and clinical translation of mRNA-based medicines. In this review, we discuss the chemical modification strategies of mRNA to improve its stability, minimize immune responses, and enhance translational efficacy. We also highlight recent progress in nanoparticle-based mRNA delivery. Considerable attention is given to the increasingly widespread applications of mRNA nanomedicine in the biomedical fields of vaccination, protein-replacement therapy, gene editing, and cellular reprogramming and engineering.     

Computational Parametric Analysis of Cellular Solids with the Miura-Ori Metamaterial Geometry under Quasistatic Compressive Loads

Origami-based metamaterials have widespread application prospects in various industries including aerospace, automotive, flexible electronics, and civil engineering structures. Among the wide range of origami patterns, the fourfold tessellation known as Miura-ori is of particular attraction to engineers and designers. More specifically, researchers have proposed different 3D structures and metamaterials based on the geometric characteristics of this classic origami pattern. Herein, a computational modeling approach for the design and evaluation of 3D cellular solids with the Miura-ori metamaterial geometry which can be of zero or nonzero thicknesses is presented. To this end, first, a range of design alternatives generated based on a numerical parametric model is designed. Next, their mechanical properties and failure behavior under quasistatic axial compressive loads along three perpendicular directions are analyzed. Then, the effects of various geometric parameters on their energy absorption behavior under compression in the most appropriate direction are investigated. The findings of this study provide a basis for future experimental investigations and the potential application of such cellular solids for energy-absorbing purposes.     

Abrasion-Resistant and Enhanced Super-Slippery Flush Toilets Fabricated by a Selective Laser Sintering 3D Printing Technology

Flush toilets waste a significant amount of water every day due to the unavoidable adhesions between human waste and the toilet surfaces. Super-slippery surfaces can repel complex fluids and various viscoelastic solids, however, are easily broken by mechanical abrasions. Herein, the fabrication of an abrasion-resistant super-slippery flush toilet (ARSFT) is reported using a selective laser sintering 3D printing technology. Unlike traditional super-slippery surfaces with limited thicknesses which can be easily worn away, the powder-sintered strategy endows the ARSFT not only with a self-supporting 3D complex shape but also with a porous structure that can accommodate considerable lubricants for an abrasion-resistant super-slippery property. As a result, the as-prepared ARSFT remains clean after contacting with various liquids such as milk, yogurt, highly sticky honey, and starch gel mixed congee, demonstrating excellent repellence to complex fluids. Besides liquids, the ARSFT exhibits a high resistance to sticky synthetic feces. Notably, even after being abraded to 1,000 cycles of abrasion using sandpaper, the ARSFT maintains its record-breaking super-slippery capability. The concept of the 3D-printed object with a superior abrasion-resistant slippery ability will improve the development of super-slippery materials and further save water consumption in the human society.     

Preparation of Porous Silver Nanowires-Melamine Formaldehyde Hybrid Composite Materials and Their Electromagnetic Shielding and Flame-Retardant Properties

Along with the booming development of communication technology and electronic equipment, higher requirements of flame-retardant and EMI shielding performances for electromagnetic interference (EMI) shielding materials are put forward. Herein, the ultralight and porous silver nanowires (AgNWs)-melamine formaldehyde (MF) hybrid composite with unique micro-/nanostructure is developed by a facile dip-coating method, which uses the AgNWs as 1D conductive coating and MF foam (MF foam) as 3D skeleton template. Benefiting from the unique porous micro-/nanostructure, the resultant hybrid composite displays low density, excellent EMI shielding performances, and superior flame-retardant property. The EMI shielding effectiveness (SE) and specific EMI SE (SSEt) of the hybrid composite in X-band (8.2–12.4 GHz) can be up to 77 dB and 26971.4 dB cm⁻² g⁻¹, respectively. At the same time, the hybrid composite also passes the vertical burning test and shows an increased LOI value of 40.6%. The combination of flame-retardant and EMI shielding performances for EMI shielding materials makes the AgNWs-MF hybrid composite great application potential in civil and military fields. This work provides a new guide for the design of multifunctional high-performance EMI shielding materials.     

汽车铸件生产工时数理统计计算方法

采用多元非线性回归分析方法 ,对我国汽车铸造生产管理数据进行微机辅助分析 ,得出了铸件生产工时计算公式 ,编制了计算机辅助管理程序。     

基于多目视觉的变形钢板三维测量方法

提出了一种使用4个相机测量变形钢板三维形状的方法;在没有纹理的钢板上画一些点,使用4个相机同时拍摄钢板;利用边缘检测算法找出钢板的边缘,利用特征点提取算法提取出4个图像中钢板上的特征点,并找到4个图像上精确的特征点匹配关系,然后测量出这些特征点的三维点;最后,以这些三维点为种子点向外区域增长,从而得到钢板的三维形状;实验对变形钢板进行三维测量,得到了钢板的精确的三维形状,结果验证了该方法的有效性和可行性。     

OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems

We provide an overview of the key architectural features of recent microprocessor designs and describe the programming model and abstractions provided by OpenCL, a new parallel programming standard targeting these architectures.     

Several multi-criteria programming methods for classification

In this paper, we propose a general optimization-based model for classification. Then we show that some well-known optimization-based methods for classification, which were developed by Shi et al. [Data mining in credit card portfolio management: a multiple criteria decision making approac. In: Koksalan M, Zionts S, editors. Multiple criteria decision making in the new millennium. Berlin: Springer; 2001. p. 427–36] and Freed and Glover [A linear programming approach to the discriminant problem. Decision Sciences 1981; 12: 68–79; Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 1981; 7: 44–60], are special cases of our model. Moreover, three new models, MCQP (multi-criteria indefinite quadratic programming), MCCQP (multi-criteria concave quadratic programming) and MCVQP (multi-criteria convex programming), are developed based on the general model. We also propose algorithms for MCQP and MCCQP, respectively. Then we apply these models to three real-life problems: credit card accounts, VIP mail-box and social endowment insurance classification. Extensive experiments are done to compare the efficiency of these methods.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.