Scale‐dependent effects of neighborhood biodiversity on individual tree productivity in a coniferous and broad‐leaved mixed forest in China

The relationship between biodiversity and productivity has stimulated an increasing body of research over the past decades, and this topic still occupies a central place in ecology. While most studies have focused on biomass production in quadrats or plots, few have investigated the scale‐dependent relationship from an individual plant perspective. We present an analysis of the effects of biodiversity (species diversity and functional diversity) on individual tree growth with a data set of 16,060 growth records from a 30‐ha temperate forest plot using spatially explicit individual tree‐based methods. A significant relationship between species diversity and tree growth was found at the individual tree level in our study. The magnitude and direction of biodiversity effects varies with the spatial scale. We found positive effects of species diversity on tree growth at scales exceeding 9 m. Individual tree growth rates increased when there was a greater diversity of species in the neighborhood of the focal tree, which provides evidence of a niche complementarity effect. At small scales (3–5 m), species diversity had negative effects on tree growth, suggesting that competition is more prevalent than complementarity or facilitation in these close neighborhoods. The results also revealed many confounding factors which influence tree growth, such as elevation and available sun light. We conclude that the use of individual tree‐based methods may lead to a better understanding of the biodiversity‐productivity relationship in forest communities.     

Differential responses of litter decomposition in the air and on the soil surface to shrub encroachment in a graminoid-dominated temperate wetland

Plant and Soil - Graminoid-dominated wetlands have been subjected to widespread shrub encroachment, yet the effect of this shift in species composition on litter decomposition remains unclear,...     

Plant litter quality regulates soil eco-enzymatic stoichiometry and microbial nutrient limitation in a citrus orchard

Plant and Soil - The impacts associated with litter decomposition in intensive cropping on eco-enzymatic stoichiometry and microbial nutrient limitation are still under debate. To evaluate the...     

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.     

楝酰胺类化合物的抗肿瘤活性探究进展

楝酰胺类化合物存在于楝属植物中,因其独特的化学结构而具有杀虫、抗炎及抗癌的活性.目前研究发现,楝酰胺类化合物对多种癌症如肺癌、肾癌、胰腺癌、恶性外周神经鞘瘤等都具有独特的细胞凋亡作用,而对正常细胞无毒害作用.楝酰胺类化合物抗肿瘤的机制主要有:抑制癌细胞翻译起始、调控细胞周期、诱导肿瘤细胞凋亡、抑制细胞增殖、降低药物细胞毒性等.因此,楝酰胺及其衍生物作为潜在抗癌药物也有极大的应用前景,成为近年来的研究热点.本综述总结了楝属植物中的次生代谢产物-楝酰胺类化合物的发现过程、结构特征和抗癌活性,重点阐述了其在癌症中的作用机制.以期为楝酰胺类化合物的进一步研究和开发利用提供理论依据和研究思路,并对楝酰胺类化合物的应用前景进行了展望.