Enlighten-anything:When Segment Anything Model Meets Low-light Image Enhancement

Image restoration is a low-level visual task, and most CNN methods are designed as black boxes, lacking transparency and intrinsic aesthetics. Many unsupervised approaches ignore the degradation of visible information in low-light scenes, which will seriously affect the aggregation of complementary information and also make the fusion algorithm unable to produce satisfactory fusion results under extreme conditions. In this paper, we propose Enlighten-anything, which is able to enhance and fuse the semantic intent of SAM segmentation with low-light images to obtain fused images with good visual perception. The generalization ability of unsupervised learning is greatly improved, and experiments on LOL dataset are conducted to show that our method improves 3db in PSNR over baseline and 8 in SSIM. zero-shot learning of SAM introduces a powerful aid for unsupervised low-light enhancement. The source code of Rethink-Diffusion can be obtained from https://github.com/zhangbaijin/enlighten-anythin

Neonatal rhesus monkey is a potential animal model for studying pathogenesis of EV71 infection

AbstractData from limited autopsies of human patients demonstrate that pathological changes in EV71-infected fatal cases are principally characterized by clear inflammatory lesions in different parts of the CNS; nearly identical changes were found in murine, cynomolgus and rhesus monkey studies which provide evidence of using animal models to investigate the mechanisms of EV71 pathogenesis. Our work uses neonatal rhesus monkeys to investigate a possible model of EV71 pathogenesis and concludes that this model could be applied to provide objective indicators which include clinical manifestations, virus dynamic distribution and pathological changes for observation and evaluation in interpreting the complete process of EV71 infection. This induced systemic infection and other collected indicators in neonatal monkeys could be repeated; the transmission appears to involve infecting new monkeys by contact with feces of infected animals. All data presented suggest that the neonatal rhesus monkey model could shed light on EV71 infection process and pathogenesis

Stability and Hydrolyzation of Metal Organic Frameworks with Paddle-Wheel SBUs upon Hydration

Instability of most prototypical metal organic frameworks (MOFs) in the presence of moisture is always a limita- tion for industrial scale development. In this work, we examine the dissociation mechanism of microporous paddle wheel frameworks M(bdc)(ted)0.5 [M=Cu, Zn, Ni, Co; bdc= 1,4-benzenedicarboxylate; ted= triethylenediamine] in controlled humidity environments. Combined in-situ IR spectroscopy, Raman, and Powder x-ray diffraction measurements show that the stability and modification of isostructual M(bdc)(ted)0.5 compounds upon exposure to water vapor critically depend on the central metal ion. A hydrolysis reaction of water molecules with Cu-O-C is observed in the case of Cu(bdc)(ted)0.5. Displacement reactions of ted linkers by water molecules are identified with Zn(bdc)(ted)0.5 and Co(bdc)(ted)0.5. In contrast,. Ni(bdc)(ted)0.5 is less suscept- ible to reaction with water vapors than the other three compounds. In addition, the condensation of water vapors into the framework is necessary to initiate the dissociation reaction. These findings, supported by supported by first principles theoretical van der Waals density functional (vdW-DF) calculations of overall reaction enthalpies, provide the necessary information for de- termining operation conditions of this class of MOFs with paddle wheel secondary building units and guidance for developing more robust units

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance