Species turnover is fundamental for understanding the mechanisms that influence large-scale species richness patterns. However, few studies have described and interpreted large-scale spatial variation in plant species turnover, and the causes of this variation remain elusive. In addition, the determinants of species turnover depend on the dispersal ability of growth forms. In this study, we explored the large-scale patterns of woody species turnover across the latitude gradient based on eight large stem-mapping plots (covering 184 ha forest) in East Asia. The patterns of woody species turnover increased significantly with increasing latitude differences in East Asia. For overall woody species, environment explained 36.30, 37.20, and 48.48% of the total variance in Jaccard’s (βj), Sorenson’s, (βs), and Simpson’s dissimilarity (βsim). Spatial factors explained 47.92, 48.39, and 41.38% of the total variance in βj, βs, and βsim, respectively. The effects of pure spatial and spatially structured environments were stronger than pure environmental effects for overall woody species. Our results support the hypothesis that the effect of neutral processes on woody species turnover is more important than the effect of the environment. Neutral processes explained more variation for turnover of tree species, and environmental factors explained more variation for the turnover of shrub species on a large scale. Therefore, trees and shrubs should be subjected to different protection strategies in future biodiversity conservation efforts.
Elucidating the major drivers of bryophyte distribution is the first step to protecting bryophyte diversity. Topography, forest, substrates (ground, tree trunks, roots, rocks, and rotten wood), and spatial factor, which factors are the major drivers of bryophyte distribution? In this study, 53 plots were set in 400 m2 along the elevation gradient in Xiaoqinling, China. All bryophytes in the plots were collected and identified. Regression analysis was used to examine the relationship between bryophyte and substrate diversity. We compared the patterns of overall bryophyte diversity and diversity of bryophytes found on the ground, tree, and rock along elevational gradients. Canonical correspondence analysis was applied to relate species composition to selected environmental variables. The importance of topography, forest, substrates, and spatial factors was determined by variance partitioning. A total of 1378 bryophyte specimens were collected, and 240 species were identified. Bryophyte diversity was closely related to substrate diversity. The overall bryophyte diversity significantly increased with elevation; however, the response varied among ground, tree, and rock bryophytes. Tree diversity and herb layer were considered important environmental factors in determining bryophyte distribution. Species abundance was best explained by stand structure (17%), and species diversity was best explained by stand structure (35%) and substrate (40%). Results directly indicated that substrate diversity can improve bryophyte species diversity. The effects of micro-habitat formed by stand structure and substrate diversity were higher than those of spatial processes and topography factors on bryophyte distribution. This study proved that the determinant factors influencing bryophyte diversity reflect the trends in recent forest management, providing a real opportunity to improve forest biodiversity conservation.
The improvement of accuracy of short-term passenger flow prediction plays a key role in the efficient and sustainable development of metro operation. The primary objective of this study is to explore the factors that influence prediction accuracy from time granularity and station class. An important aim of the study was also in presenting the proposition of change in a forecasting method. Passenger flow data from 87 Metro stations in Xi’an was collected and analyzed. A framework of short-term passenger flow based on the Empirical Mode Decomposition-Support Vector Regression (EMD-SVR) was proposed to predict passenger flow for different types of stations. Also, the relationship between the generation of passenger flow prediction error and passenger flow data was investigated. First, the metro network was classified into four categories by using eight clustering factors based on the characteristics of inbound passenger flow. Second, Pearson correlation coefficient was utilized to explore the time interval and time granularity for short-term passenger flow prediction. Third, the EMD-SVR was used to predict the passenger flow in the optimal time interval for each station. Results showed that the proposed approach has a significant improvement compared to the traditional passenger flow forecast approach. Lookback Volatility (LVB) was applied to reflect the fluctuation difference of passenger flow data, and the linear fitting of prediction error was conducted. The goodness-of-fit (R2) was found to be 0.768, indicating a good fitting of the data. Furthermore, it revealed that there are obvious differences in the prediction error of the four kinds of stations.
The hybrid coupling of biocatalysts and chemical catalysts plays a vital role in the fields of catalysis, sensing, and medical treatment due to the integrated advantages in the high activity of natural enzymes and the excellent stability of nanozymes. Herein, a new nanozyme/natural enzyme hybrid biosensor was established for ultrasensitive glutamate detection. The MIL-88B(Fe)-NH 2 material with remarkable peroxidase mimic activity and stability was used as a nanozyme and carrier for immobilizing glutamate oxidase (GLOX) through Schiff base reaction to construct a chem-enzyme cascade detector (MIL-88B(Fe)-NH 2 @GLOX). The resultant MIL-88B(Fe)-NH 2 @GLOX exhibited a wide linear range (1−100 μM), with a low detection limit of 2.5 μM for glutamate detection. Furthermore, the MIL-88B(Fe)-NH 2 @GLOX displayed excellent reusability and storage stability. After repeated seven cycles, MIL-88B(Fe)-NH 2 -GLOX (GLOX was adsorbed on MIL-88B(Fe)-NH 2 ) lost most of its activity, whereas MIL-88B(Fe)-NH 2 @GLOX still retained 69% of its initial activity. Meanwhile, MIL-88B(Fe)-NH 2 @GLOX maintained 60% of its initial activity after storage for 90 days, while free GLOX only retained 30% of its initial activity. This strategy of integrating MOF mimics and natural enzymes for cascade catalysis makes it possible to design an efficient and stable chemo-enzyme composite catalysts, which are promising for applications in biosensing and biomimetic catalysis.
BackgroundFamilial nonmedullary thyroid cancer (FNMTC) accounts for approximately 3%–9% of all thyroid cancers; however, the mechanisms underlying FNMTC remain unclear. Environmental and genetic (especially genetic mutation) factors may play important roles in FNMTC etiology, development, and pathogenesis.MethodsThree affected members, including two first‐degree relatives, and three healthy members of a family with FNMTC were studied. We performed whole‐exome and targeted gene sequencing to identify gene mutations that may be associated with FNMTC pathogenesis. The results were analyzed using Exome Aggregation Consortium data and the Genome Aggregation Database and further validated using Sanger sequencing.ResultsOf 28 pivotal genes with rare nonsynonymous mutations found, 7 were identified as novel candidate FNMTC pathogenic genes (ANO7, CAV2, KANK1, PIK3CB, PKD1L1, PTPRF, and RHBDD2). Among them, three genes (PIK3CB, CAV2, and KANK1) are reportedly involved in tumorigenesis through the PI3K/Akt signaling pathway.ConclusionWe identified seven pathogenic genes in affected members of a family with FNMTC. The PI3K/Akt signaling pathway is thought to be closely related to the development of FNMTC, and three of the susceptibility genes identified herein are associated with this pathway. These findings expand our understanding of FNMTC pathogenesis and underscore PI3K/Akt pathology as a potential therapy target.
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