In vivo Kinetic Biodistribution of Nano‐Sized Outer Membrane Vesicles Derived from Bacteria

Evaluation of kinetic distribution and behaviors of nanoparticles in vivo provides crucial clues into their roles in living organisms. Extracellular vesicles are evolutionary conserved nanoparticles, known to play important biological functions in intercellular, inter‐species, and inter‐kingdom communication. In this study, the first kinetic analysis of the biodistribution of outer membrane vesicles (OMVs)—bacterial extracellular vesicles—with immune‐modulatory functions is performed. OMVs, injected intraperitoneally, spread to the whole mouse body and accumulate in the liver, lung, spleen, and kidney within 3 h of administration. As an early systemic inflammation response, increased levels of TNF‐α and IL‐6 are observed in serum and bronchoalveolar lavage fluid. In addition, the number of leukocytes and platelets in the blood is decreased. OMVs and cytokine concentrations, as well as body temperature are gradually decreased 6 h after OMV injection, in concomitance with the formation of eye exudates, and of an increase in ICAM‐1 levels in the lung. Following OMV elimination, most of the inflammatory signs are reverted, 12 h post‐injection. However, leukocytes in bronchoalveolar lavage fluid are increased as a late reaction. Taken together, these results suggest that OMVs are effective mediators of long distance communication in vivo.     

Revisiting the Parallel Strategy for DOACROSS Loops

Journal of Computer Science and Technology - DOACROSS loops are significant parts in many important scientific and engineering applications, which are generally exploited pipeline/wave-front...     

Traffic identification method based on multiple probabilistic neural network model

Neural Computing and Applications - Traffic identification is currently an important challenge for network management and security. In this paper, we propose a novel application identification...     

裂缝性孔隙介质频变AVAZ反演方法研究进展

由于尺度的限制,利用地震方法进行裂缝参数定量表征一直是地球科学的难题。基于裂缝性孔隙介质动态等效理论的频变AVAZ反演方法给出了研究思路。首先介绍了裂缝性孔隙介质的等效介质理论研究进展,分析比较了各种裂缝性储层岩石静态等效介质模型和动态等效介质模型;然后给出了基于Chapman模型的裂缝参数和渗透率定量表征的正问题数学模型以及反演问题求解的方法和策略,以及基于模型数据的数值计算结果;最后讨论了频变AVAZ反演方法目前存在的问题和进一步研究方向。     

Wearing prediction of stellite alloys based on opposite degree algorithm

In order to predict the wearing of stellite alloys,the related methods of rare metals data processing were discussed. The method of opposite degree(OD) algorithm was put forward to predict the wearing of stellite alloys.OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation(BP) and radial basis function(RBF) neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.