Explore the vast range of titles available.

X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy. To directly reconstruct the distribution of emission quantum yield for x-ray Cherenkov-luminescence tomography, we proposed a three-component deep learning algorithm that includes a Swin transformer, convolution neural network, and locality module model. A data-to-image model x-ray Cherenkov-luminescence tomography is developed based on a Swin transformer, which is used to extract pixel-level prior information from the sinogram domain. Meanwhile, a convolutional neural network structure is deployed to transform the extracted pixel information from the sinogram domain to the image domain. Finally, a locality module is designed between the encoder and decoder connection structures for delivering features. Its performance was validated with simulation, physical phantom, and experiments. This approach can better deal with the limits to data than conventional FBP methods. The method was validated with numerical and physical phantom experiments, with results showing that it improved the reconstruction performance mean square error ( ), peak signal-to-noise ratio ( ), and Pearson correlation ( ) compared with the FBP algorithm. The Swin-CNN also achieved a 32.1% improvement in PSNR over the deep learning method AUTOMAP. This study shows that the three-component deep learning algorithm provides an effective reconstruction method for x-ray Cherenkov-luminescence tomography.

Sugar transporters play important roles in controlling carbohydrate transport and are responsible for mediating the movement of sugars into cells in numerous organisms. In insects, sugar transporters not only play a role in sugar transport but may also act as receptors for virus entry and the accumulation of plant defense compounds. The brown planthopper, Nilaparvata lugens, inflicts damage on rice plants by feeding on their phloem sap, which is rich in sugars. In the present study, we identified 34 sugar transporters in N. lugens, which were classified into three subfamilies based on phylogenetic analysis. The motif numbers varied from seven to eleven, and motifs 2, 3, and 4 were identified in the functional domains of all 34 NlST proteins. Chromosome 1 was found to possess the highest number of NlST genes, harboring 15. The gut, salivary glands, fat body, and ovary were the different tissues enriched with NlST gene expression. The expression levels of NlST2, 3, 4, 7, 20, 27, 28, and 31 were higher in the gut than in the other tissues. When expressed in a Saccharomyces cerevisiae hexose transporter deletion mutant (strain EBY.VW4000), only ApST4 (previously characterized) and NlST4, 28, and 31 were found to transport glucose and fructose, resulting in functional rescue of the yeast mutant. These results provide valuable data for further studies on sugar transporters in N. lugens and lay a foundation for finding potential targets to control N. lugens.

Based on the material flow analysis method, the green development index system of industrial park was constructed. By using the PSR model, 13 indexes were divided into pressure layer, state layer and response layer, the indexes were standardized, weight distribution and synthesis. The green development level of industrial park was divided into 5 grades combined with the pressure state response factors. The results show that the energy consumption per unit industrial output of a stainless-steel industrial park is 0.7116 tons of standard coal per million yuan, the water consumption per unit industrial output is 6.1466 cubic meters per million yuan, the wastewater discharge per unit industrial added value is 3.12 tons per million yuan, the reduction rate of carbon emissions per unit industrial added value is 19.53%, and the green development index is 0.65, which belongs to the third level of green development. The problems are reflected in particulate emission, resource recycling, energy efficiency, industrial structure, and park greening. In the future, the stainless-steel industrial park should focus on industrial restructuring and transformation, environmental protection infrastructure construction, new policy guidance, and ecological environment construction of the park.

Dysregulated microRNAs are closely related to the malignant progression of colorectal cancer (CRC). Although abnormal let-7i-3p expression has been reported in various human cancers, its biological role and potential mechanism in CRC remain unclear. Therefore, the purpose of this study was to investigate the expression and regulation of let-7i-3p in CRC. Here, we demonstrated that let-7i-3p expression was significantly downregulated in three CRC cell lines while CyclinD1 (CCND1) was upregulated compared with the normal colon epithelial FHC cells. Moreover, bioinformatics and luciferase reporter assays revealed that CCND1 was a direct functional target of let-7i-3p. In addition, let-7i-3p overexpression or CCND1 silencing inhibited cell cycle, proliferation, invasion, and migration and diminished the activation of p-ERK in HCT116 cells. However, exogenously expressing CCND1 alleviated these effects. Taken together, our findings may provide new insight into the pathogenesis of CRC and let-7i-3p/CCND1 might function as new therapeutic targets for CRC.