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Klebsiella pneumoniae is an opportunistic pathogen that causes a wide range of infections. The emergence and spread of hypervirulent K. pneumoniae (hvKp), which appears to be different from the classical K. pneumoniae (cKp) in several microbiological aspects, is an urgent global threat. However, the virulence characteristics of hvKp and its differences from cKp are poorly understood. This work aimed to investigate the correlation between the expression characteristics of proteins and hypervirulence, using proteomics. Our results revealed that 185 proteins were upregulated while 266 proteins were downregulated in hvKp isolates when compared with cKp isolates. The differentially expressed proteins were functionally categorized according to the Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. These proteins were predominantly metabolism associated, which indicates that changes in the metabolic pathways in hvKp isolates might in part contribute to hypervirulence.

The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data.

Type II topoisomerases, including DNA gyrase (GyrA) and topoisomerase IV (ParC), contribute to fluoroquinolone resistance in Enterococcus spp. This study investigated the mutational status of the quinolone resistance-determining regions (QRDRs) of GyrA and ParC in the clinical isolates of enterococci from a hospital in Baotou, China. We analyzed 110 enterococcal isolates, including 57 Enterococcus faecalis and 53 Enterococcus faecalis faecium. The resistance rates of E. faecalis and E. faecium to ciprofloxacin were 63.16% and 84.91%, respectively. We found that 32 samples of E. faecalis and 42 of E. faecium had single or combined mutations in gyrA and/or parC, which were all resistant to ciprofloxacin. Only two ciprofloxacin-resistant E. faecalis isolates had no mutation. No mutations in gyrA and parC genes in all ciprofloxacinsusceptible isolates were found. Ciprofloxacin minimal inhibitory concentrations (MICs) in the mutation group were significantly higher than those of the non-mutation group, indicating that mutations in the QRDRs of gyrA and parC were correlated with MIC elevation. Two novel substitutions (GyrA Ser83Phe and ParC Ser80Leu) of E. faecalis were identified herein. Three-dimensional modeling revealed that these novel amino acid substitutions could disrupt the water/metal-ion bridge and decrease the interaction between the enzymes and ciprofloxacin. The data showed a diversity of mutation types in QRDRs of type II topoisomerases whose association with fluoroquinolone resistance in clinical isolates of enterococci warrants further investigation.

Colorectal cancer (CRC) is one of the most common gastrointestinal cancers in the world, and its incidence is increasing all over the world including China. In recent years, research data show that some miRNAs are differentially expressed in cancer tissues, and their expression is closely contributed with the prognosis of CRC. Microarray technology was used, and 179 miRNAs were screened out with significantly altered expression in CRC tissues compared with adjacent tissues. The expression of mir-145-5p in tumor tissues was 3.48 times lower than that in normal tissues. Using bioinformatics technology and network resource prediction, we found that mir-145-5p had a potential target gene relationship with msln gene. Then, qRT-PCR was used to validate the expression level of mir-145-5p and msln mRNA in CRC and paracancerous tissues. The results showed that msln mRNA was higher than in normal tissues, while mir-145-5p was lower, with statistically significant difference (P

In this paper, we consider a class of delay coupled Lotka-Volterra ring systems. Based on the symmetric bifurcation theory of delay differential equations and representation theory of standard dihedral groups, properties of phase locked periodic solutions are given. Moreover, the direction and the stability of the Hopf bifurcation periodic orbits are obtained by using normal form and center manifold theory. Finally, the research results are verified by numerical simulation.

Discrete point cloud data from unmanned aerial vehicle laser scanning (UAV-LS) can provide information on the three-dimensional structure of a forest, the leaf area index (LAI) at the landscape or sample plot scales, the distribution of the vertical forest structure at a fine resolution, and other information. The retrieved parameters, however, may be affected in a non-negligible way by the inclusion of scan angle information. In this study, we introduced a relational model that encompasses the angular effect, predicted the mechanism of this effect, and extracted the vegetation structure indices that the angular effect might influence. Second, we quantified the direct and indirect effects, particularly the magnitude of the angular effect in broadleaf forests, and used mediated effects to investigate the components and processes that influence the angular effect. The findings demonstrate that some of the differences between the LAIe extracted by UAV-LS and the Decagon LAIe considering the angular effect of UAV-LS can be explained by adjusting physical LiDAR parameters (aerial height, laser divergence fraction, and scanning angle) and vertical forest structure variables. Along continuous and closed forest vertical gradients, the indirect angle impact is negative for the upper canopy and positive for the understory. Three-dimensional vegetation measurements were created using multiangle LiDAR data. In conclusion, this article (1) addresses the angular effect in UAV-LS; and (2) discusses how the angular effect affects 3D vegetation parameters such as LAIe, demonstrates the nonlinear trend of the angular effect, and demonstrates how multiangle LiDAR data can be used to obtain 3D vegetation parameters. This study serves as a reference for reducing the uncertainty in simulations of the angular effect and vegetation light transmission, in addition to the uncertainty in analyses of the vegetation characteristics determined by UAV-LS (e.g., the uncertainty of LAIe).