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Chitosan and PEG-based self-healable in situ hydrogel developed as a long-term MRI reporter.

Point cloud classification is quite challenging due to the influence of noise, occlusion, and the variety of types and sizes of objects. Currently, most methods mainly focus on subjectively designing and extracting features. However, the features rely on prior knowledge, and it is also difficult to accurately characterize the complex objects of point clouds. In this paper, we propose a concise multi-scale convolutional network (MSNet) for adaptive and robust point cloud classification. Both the local feature and global context are incorporated for this purpose. First, around each point, the spatial contexts of different sizes are partitioned as voxels of different scales. A voxel-based MSNet is then simultaneously applied at multiple scales to adaptively learn the discriminative local features. The class probability of a point cloud is predicted by fusing the features together across multiple scales. Finally, the predicted class probabilities of MSNet are optimized globally using the conditional random field (CRF) with a spatial consistency constraint. The proposed method was tested with data sets of mobile laser scanning (MLS), terrestrial laser scanning (TLS), and airborne laser scanning (ALS) point clouds. The experimental results show that the proposed method was able to achieve appreciable classification accuracies of 83.18%, 98.24%, and 97.02% on the MLS, TLS, and ALS data sets, respectively. The results also demonstrate that the proposed network has a strong generalization capability for classifying different kinds of point clouds under complex urban environments.

Novel 2,3-bis(1H-pyrrol-2-yl)quinoxaline-functionalized Schiff bases were prepared and characterized as new fluorescent sensors for mercury(II) ion. The X-ray crystal structures of compounds 4, 5, 4a and 5a were determined. The binding properties of 4 and 5 for cations were examined by UV-vis and fluorescence spectroscopy. The UV-vis and fluorescence data indicate that a 1 : 1 stoichiometric complex is formed between compound 4 (or 5) and mercury(II) ion, and the association constant is (3.81 +/- 0.7) x 10(5) M(-1) for 4 and (3.43 +/- 0.53) x 10(5) M(-1) for 5. The recognition mechanism between compound 4 (or 5) and metal ion was discussed based on their chemical construction and the fluorescence quenching effect when they interact with each other. Competition experiments revealed that compound 4 (or 5) has a highly selective response to mercury(II) ion in aqueous solution.

PURPOSE:To investigate the effect of metformin on renal tubular epithelial cell apoptosis and inflammation after kidney ischemia/ reperfusion in rats. METHODS:Eighteen SD rats were randomly divided into three groups: Sham (S), Ischemia/reperfusion (I/R), and Metformin (E).Before establishing the I/R model, group E was administered metformin for three days, while groups S and I/R were administered equal volumes of saline. After three days, a right nephrectomy was performed on all groups, after which the left kidneys of groups E and I/R rats were subjected to 45 min renal ischemia. Renal function, histology, and cell apoptosis were assessed. AMPK, pAMPK, COX-2, and Caspase 3 were also detected. RESULTS:Compared to I/R group, Caspase 3 and COX-2 levels were decreased in group E. COX-2, Caspase3 and pAMPK levels were higher in groups E and I/R than in group S. The pAMPK level of group E was higher than that of I/R group, while COX-2 and caspase 3 were lower in group E than they were in the other groups. There was no significant difference between E and I/R groups in AMPK levels. CONCLUSION:Metformin preconditioning attenuated the inflammation caused by ischemia/reperfusion and inhibited the apoptosis of renal tubular epithelial cells.

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