Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modelling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91 253, and the maximum number of beds required for hospitalised patients would be 62 794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resource allocation and planning in the health sector while providing a theoretical basis for governments to respond to future global health crises or epidemics.
Thionins are antimicrobial peptides that are involved in plant defence. Here, we present an in-depth analysis of the role of rice thionin genes in defence responses against two root pathogens: the root-knot nematode Meloidogyne graminicola and the oomycete Pythium graminicola. The expression of rice thionin genes was observed to be differentially regulated by defence-related hormones, whereas all analysed genes were consistently down-regulated in M. graminicola-induced galls, at least until 7 days post-inoculation (dpi). Transgenic lines of Oryza sativa cv. Nipponbare overproducing OsTHI7 revealed decreased susceptibility to M. graminicola infection and P. graminicola colonization. Taken together, these results demonstrate the role of rice thionin genes in defence against two of the most damaging root pathogens attacking rice.
Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modeling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91,253, and the maximum number of beds required for hospitalized patients would be 62,794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resourceallocation and planning in the health sector while providing a theoretical basis forgovernments to respond to future global health crises or epidemics.
Although the protein translation inhibition activity of ribosome inactivating proteins (RIPs) is well documented, little is known about the contribution of the lectin chain to the biological activity of these proteins. In this study, we compared the in vitro and intracellular activity of several S. nigra (elderberry) RIPs and non-RIP lectins. Our data demonstrate that RIPs from elderberry are much more toxic to HeLa cells than to primary fibroblasts. Differences in the cytotoxicity between the elderberry proteins correlated with differences in glycan specificity of their lectin domain, cellular uptake efficiency and intracellular destination. Despite the fact that the bulk of the RIPs accumulated in the lysosomes and partly in the Golgi apparatus, we could demonstrate effective inhibition of protein synthesis in cellula. As we also observed cytotoxicity for non-RIP lectins, it is clear that the lectin chain triggers additional pathways heralding cell death. Our data suggest that one of these pathways involves the induction of autophagy.
In the past three decades a lot of research has been done on the extended family of carbohydrate-binding proteins from Sambucus nigra, including several so-called type 2 RIPs as well as hololectins. Although all these proteins have been studied for their carbohydrate-binding properties using hapten inhibition assays, detailed carbohydrate specificity studies have only been performed for a few Sambucus proteins. In particular SNA-I, has been studied extensively. Because of its unique binding characteristics this lectin was developed as an important tool in glycoconjugate research to detect sialic acid containing glycoconjugates. At present much less information is available with respect to the detailed carbohydrate binding specificity of other S. nigra lectins and RIPs, and as a consequence their applications remain limited. In this paper we report a comparative analysis of several lectins from S. nigra using the glycan microarray technology. Ultimately a better understanding of the ligands for each lectin can contribute to new/more applications for these lectins in glycoconjugate research. Furthermore, the data from glycan microarray analyses combined with the previously obtained sequence information can help to explain how evolution within a single lectin family eventually yielded a set of carbohydrate-binding proteins with a very broad specificity range.
The structure of sedimentary bacterial communities in mangroves depends on environmental factors such as pH, salinity, organic matter content, and metal pollution. To investigate the effect of heavy metal pollution on such communities, core samples of sediments from four sites in three distinct mangrove reserves (Golden Bay Mangrove Reserve in Beihai, Guangxi province (GXJHW), Shankou Mangrove Reserve in Hepu, Guangxi province (GXSK), and MaiPo mangrove in Hong Kong (MPCT and MPFQ)) in South China were analyzed for physicochemical properties, multiple chemical forms of metals, and vertical bacterial diversity. Sedimentary bacterial communities varied greatly among the different sampling sites, with biodiversity decreasing in the order of GXSK, GXJHW, MPFQ, and MPCT. Proteobacteria was the dominant phylum, followed by Chloroflexi, across all four sampling sites. Multivariate statistical analysis of the effect of environmental factors on the sedimentary bacterial communities found that total carbon was the only physicochemical factor with a significant influence at all four sites. The correlations between environmental factors and bacterial structure were weak for the two sites in Guangxi province, but strong at MPCT in Hong Kong where environmental factors were almost all significantly negatively correlated with bacterial diversity. Variance partitioning analysis revealed that physicochemical properties and chemical forms of metals could explain most of the changes in bacterial diversity. Overall, we observed that heavy metal forms were more important than total metal content in influencing the sedimentary bacterial diversity in mangroves, consistent with the more bioavailable metal species having the greatest effect.
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