The aperture-opening process resulting from dissociative linker exchange in zirconium-based metal-organic framework (MOF) UiO-66 was used to encapsulate the ruthenium complex (PNP)Ru(CO)HCl in the framework (PNP = 2,6-bis((di- tert-butyl-phosphino)methyl)pyridine). The resulting encapsulated complex, [Ru]@UiO-66, was a very active catalyst for the hydrogenation of CO to formate. Unlike the analogous homogeneous catalyst, [Ru]@UiO-66 could be recycled five times, showed no evidence for bimolecular catalyst decomposition, and was less prone to catalyst poisoning. These results demonstrated for the first time how the aperture-opening process in MOFs can be used to synthesize host-guest materials useful for chemical catalysis.
Under linker exchange conditions, large guests with molecular diameters 3-4 times the framework aperture size have been encapsulated into preformed nanocrystals of the metal-organic framework ZIF-8. Guest encapsulation is facilitated by the formation of short-lived "open" states of the pores upon linker dissociation. Kinetic studies suggested that linker exchange reactions in ZIF-8 proceed via a competition between dissociative and associative exchange mechanisms, and guest encapsulation was enhanced under conditions where the dissociative pathway predominates.
Deep learning based remote sensing image scene classification methods are the current mainstream, and enough labeled samples are very important for their performance.Considering the fact that manual labeling of samples requires high labor and time cost, lots of methods have been proposed to automatically generate pseudo samples from real samples, however, existing methods can not directly sift the pseudo samples from the perspective of model training. To address this problem, a generating and sifting pseudo labeled samples scheme is proposed in this paper. First of all, the existing SinGAN is used to generate multiple groups of pseudo samples from the real samples. Afterwards, the proposed quantitative sifting measure which can evaluate both the authenticity and diversity from the perspective of model training is employed to select the best pseudo samples from the multiple generated pseudo samples. Finally, the selected pseudo samples and real samples are used to pretrain and finetune the deep scene classification network (DSCN) respectively. Moreover, the focal loss which is originally proposed for object detection is adopted to replace the traditional cross entropy loss in this paper. A designed quantitative evaluation shows that the value of proposed quantitative sifting measure is proportional to the overall accuracy, which validates the effectiveness of proposed quantitative sifting measure. The comprehensive quantitative comparisons on AID and NWPU-RESISC45 datasets in terms of overall accuracy and confusion matrices demonstrate that incorporating the pseudo samples selected by proposed sifting measure and the focal loss can improve the performance of DSCN.
Orphan nuclear receptor Nur77 has been reported to be implicated in a diverse range of metabolic processes, including carbohydrate metabolism and lipid metabolism. However, the detailed mechanism of Nur77 in the regulation of metabolic pathway still needs to be further investigated. In this study, we created a global nur77 knockout zebrafish model by CRISPR/Cas9 technique, and then performed whole-organism RNA sequencing analysis in wildtype and nur77-deficient zebrafish to dissect the genetic changes in metabolic-related pathways. We found that many genes involved in amino acid, lipid, and carbohydrate metabolism changed by more than twofold. Furthermore, we revealed that nur77−/− mutant displayed increased total cholesterol (TC) and triglyceride (TG), alteration in total amino acids, as well as elevated glucose. We also demonstrated that the elevated glucose was not due to the change of glucose uptake but was likely caused by the disorder of glycolysis/gluconeogenesis and the impaired β-cell function, including downregulated insb expression, reduced β-cell mass, and suppressed insulin secretion. Importantly, we also verified that targeted expression of Nur77 in the β cells is sufficient to rescue the β-cell defects in global nur77−/− larvae zebrafish. These results provide new information about the global metabolic network that Nur77 signaling regulates, as well as the role of Nur77 in β-cell function.
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