Bioinspired yeast microcapsules loaded with self-assembled nanotherapies for targeted treatment of cardiovascular disease

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Application potential of bone marrow mesenchymal stem cell (BMSCs) based tissue-engineering for spinal cord defect repair in rat fetuses with spina bifida aperta

Spina bifida aperta are complex congenital malformations resulting from failure of fusion in the spinal neural tube during embryogenesis. Despite surgical repair of the defect, most patients who survive with spina bifida aperta have a multiple system handicap due to neuron deficiency of the defective spinal cord. Tissue engineering has emerged as a novel treatment for replacement of lost tissue. This study evaluated the prenatal surgical approach of transplanting a chitosan–gelatin scaffold seeded with bone marrow mesenchymal stem cells (BMSCs) in the healing the defective spinal cord of rat fetuses with retinoic acid induced spina bifida aperta. Scaffold characterisation revealed the porous structure, organic and amorphous content. This biomaterial promoted the adhesion, spreading and in vitro viability of the BMSCs. After transplantation of the scaffold combined with BMSCs, the defective region of spinal cord in rat fetuses with spina bifida aperta at E20 decreased obviously under stereomicroscopy, and the skin defect almost closed in many fetuses. The transplanted BMSCs in chitosan–gelatin scaffold survived, grew and expressed markers of neural stem cells and neurons in the defective spinal cord. In addition, the biomaterial presented high biocompatibility and slow biodegradation in vivo. In conclusion, prenatal transplantation of the scaffold combined with BMSCs could treat spinal cord defect in fetuses with spina bifida aperta by the regeneration of neurons and repairmen of defective region.     

ITER极向场线圈销轴表面硬化工艺研发

ITER极向场线圈(PF)磁体支撑销轴承载极向场线圈和部分真空室的质量,因使用环境非常恶劣,所以对其表面硬度及耐磨性能有很高的需求。按照ITER极向场线圈销轴的设计要求,研发了两种表面硬化处理方式,包括无界面N离子注入361LN强化处理以及采用在不锈钢表面堆焊一层马氏体不锈钢ER414L的方式分别不同程度地增加基底材料的表面硬度以及耐磨性能,可以满足小尺寸及超大型部件的表面硬化设计需求。     

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.     

Molecular dynamics simulation on flow behavior of nanofluids between flat plates under shear flow condition

Molecular dynamic model of nanofluid between flat plates under shear flow conditions was built. The nanofluid model consisted of 12 spherical copper nanoparticles with each particle diameter of 4 nm and argon atoms as base liquid. The Lennard–Jones (LJ) potential function was adopted to deal with the interactions between atoms. Thus, the motion states of nanoparticles during the process of flowing were obtained and the flow behaviors of nanofluid between flat plates at different moments could be analyzed. The simulation results showed that an absorption layer of argon atoms existed surrounding each nanoparticle and would accompany with the particle to move. The absorption layer contributed little to the flow of nanoparticles but much to the heat transferring in nanofluids. Another phenomenon observed during shear flowing process was that the nanoparticles would vibrate and rotate besides main flowing with liquid argon and these micro-motions could strengthen partial flowing in nanofluids.     

Wireless channel extraction analyzing based on graph theory

Wireless channels comprise various signal characteristics that correspond to different features. This research applies digital signal processing to first excavate and categorize various features found in the channel data. Then, borrowing from graph theory, fast clustering analysis and decision tree modeling are introduced to identify unique “fingerprint” characteristics. Finally, two scenarios were tested using artificial neural networks to identify and verify their applicability in different geographical locations.     

Deriving evidence theoretical functions in multivariate data spaces: a systematic approach.

The mathematical theory of evidence is a generalization of the Bayesian theory of probability. It is one of the primary tools for knowledge representation and uncertainty and probabilistic reasoning and has found many applications. Using this theory to solve a specific problem is critically dependent on the availability of a mass function (or basic belief assignment). In this paper, we consider the important problem of how to systematically derive mass functions from the common multivariate data spaces and also the ensuing problem of how to compute the various forms of belief function efficiently. We also consider how such a systematic approach can be used in practical pattern recognition problems. More specifically, we propose a novel method in which a mass function can be systematically derived from multivariate data and present new methods that exploit the algebraic structure of a multivariate data space to compute various belief functions including the belief, plausibility, and commonality functions in polynomial-time. We further consider the use of commonality as an equality check. We also develop a plausibility-based classifier. Experiments show that the equality checker and the classifier are comparable to state-of-the-art algorithms.