The separation of actinides has a vital place in nuclear fuel reprocessing, recovery of radionuclides and remediation of environmental contamination. Here we propose a new paradigm of nanocluster-based actinide separation, namely nano-extraction, that can achieve efficient sequestration of uranium in an unprecedented form of giant coordination nanocages using a cone-shaped macrocyclic pyrogallol [4]arene as the extractant. The U 24 -based hexameric pyrogallol[4]arene nanocages with distinctive [U 2 PG 2 ] binuclear units (PG = pyrogallol), that rapidly assembled in situ in monophasic solvent, were identified by single-crystal XRD, MALDI-TOF-MS, NMR, and SAXS/SANS. Comprehensive biphasic extraction studies show that this novel separation strategy has enticing advantages such as fast kinetics, high efficiency, and good selectivity over lanthanides, and thus demonstrate its potential for efficient separation of actinide ions.

Tick-borne encephalitis virus (TBEV) is one of the flaviviruses that targets the CNS and causes encephalitis in humans. The mechanism of TBEV that causes CNS destruction remains unclear. It has been reported that RANTES-mediated migration of human blood monocytes and T lymphocytes is specifically induced in the brain of mice infected with TBEV, which causes ensuing neuroinflammation and may contribute to brain destruction. However, the viral components responsible for RANTES induction and the underlying mechanisms remain to be fully addressed. In this study, we demonstrate that the NS5, but not other viral proteins of TBEV, induces RANTES production in human glioblastoma cell lines and primary astrocytes. TBEV NS5 appears to activate the IFN regulatory factor 3 (IRF-3) signaling pathway in a manner dependent on RIG-I/MDA5, which leads to the nuclear translocation of IRF-3 to bind with RANTES promoter. Further studies reveal that the activity of RNA-dependent RNA polymerase (RdRP) but not the RNA cap methyltransferase is critical for TBEV NS5-induced RANTES expression, and this is likely due to RdRP-mediated synthesis of dsRNA. Additional data indicate that the residues at K359, D361, and D664 of TBEV NS5 are critical for RdRP activity and RANTES induction. Of note, NS5s from other flaviviruses, including Japanese encephalitis virus, West Nile virus, Zika virus, and dengue virus, can also induce RANTES expression, suggesting the significance of NS5-induced RANTES expression in flavivirus pathogenesis. Our findings provide a foundation for further understanding how flaviviruses cause neuroinflammation and a potential viral target for intervention.

Effective extraction of disaster information of buildings from remote sensing images is of great importance to supporting disaster relief and casualty reduction. In high-resolution remote sensing images, object-oriented methods present problems such as unsatisfactory image segmentation and difficult feature selection, which makes it difficult to quickly assess the damage sustained by groups of buildings. In this context, this paper proposed an improved Convolution Neural Network (CNN) Inception V3 architecture combining remote sensing images and block vector data to evaluate the damage degree of groups of buildings in post-earthquake remote sensing images. By using CNN, the best features can be automatically selected, solving the problem of difficult feature selection. Moreover, block boundaries can form a meaningful boundary for groups of buildings, which can effectively replace image segmentation and avoid its fragmentary and unsatisfactory results. By adding Separate and Combination layers, our method improves the Inception V3 network for easier processing of large remote sensing images. The method was tested by the classification of damaged groups of buildings in 0.5 m-resolution aerial imagery after the earthquake of Yushu. The test accuracy was 90.07% with a Kappa Coefficient of 0.81, and, compared with the traditional multi-feature machine learning classifier constructed by artificial feature extraction, this represented an improvement of 18% in accuracy. Our results showed that this improved method could effectively extract the damage degree of groups of buildings in each block in post-earthquake remote sensing images.