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Predicting species abundance is one of the most fundamental pursuits of ecology. Combining the information encoded in functional traits and metacommunities provides a new perspective to predict the abundance of species in communities. We applied a community assembly via trait selection model to predict quadrat-scale species abundances using functional trait variation on ontogenetic stages and metacommunity information for over 490 plant species in a subtropical forest and a lowland tropical forest in Yunnan, China. The relative importance of trait-based selection, mass effects, and stochasticity in shaping local species abundances is evaluated using different null models. We found both mass effects and trait selection contribute to local abundance patterns. Trait selection was detectable at all studied spatial scales (0.04–1 ha), with its strength stronger at larger scales and in the subtropical forest. In contrast, the importance of stochasticity decreased with spatial scale. A significant mass effect of the metacommunity was observed at small spatial scales. Our results indicate that tree community assembly is primarily driven by ontogenetic traits and metacommunity effects. Our findings also demonstrate that including ontogenetic trait variation into predictive frameworks allows ecologists to infer ecological mechanisms operating in community assembly at the individual level.

Kai-Xin-San (KXS) is a Chinese medicine formulation that is commonly used to treat depression caused by dual deficiencies in the heart and spleen. Recent studies indicated that miRNAs were involved in the pathophysiology of depression. However, there have been few studies on the mechanism underlying the miRNAs directly mediating antidepressant at clinical level, especially in nature drugs and TCM compound. In this study, we identified circulating miRNAs defferentially expressed among the depression patients (DPs), DPs who underwent 8weeks of KXS treatment and health controls (HCs). A total of 45 miRNAs (17 were up-regulated and 28 were down-regulated) were significantly differentially expressed among three groups. Subsequently, qRT-PCR was used to verify 10 differentially expressed candidate miRNAs in more serum samples, and the results showed that 6 miRNAs (miR-1281, miR-365a-3p, miR-2861, miR-16-5p, miR-1202 and miR-451a) were consistent with the results of microarray. Among them, miR-1281, was the novel dynamically altered and appeared to be specifically related to depression and antidepressant effects of KXS. MicroRNA-gene-pathway-net analysis showed that miR-1281-regulated genes are mostly key nodes in the classical signaling pathway related to depression. Additionally, our data suggest that ADCY1 and DVL1 were the targets of miR-1281. Thus, based on the discovery of miRNA expression profiles in vivo, our findings suggest a new role for miR-1281 related to depression and demonstrated in vitro that KXS may activate cAMP/PKA/ERK/CREB and Wnt/β-catenin signal transduction pathways by down-regulating miR-1281 that targets ADCY1 and DVL1 to achieve its role in neuronal cell protection.

Rice has an important position in China as well as in the world. With the wide application of rice hybridization technology, the problem of mixing between individual varieties has become more and more prominent, so the variety identification of rice is important for the agricultural production, the phenotype collection, and the scientific breeding. Traditional identification methods are highly subjective and time-consuming. To address this issue, we propose a novel locality preserved selective projection learning (LPSPL) method for non-destructive rice variety identification based on leaf hyperspectral characteristics. The proposed LPSPL method can select the most discriminative spectral features from the leaf hyperspectral characteristics of rice, which is helpful to distinguish different rice varieties. In the experiments, support vector machine (SVM) is adopted to conduct the rice variety identification based on the selected spectral features. The experimental results show that the proposed method here achieves higher identification rates, 96% for the early rice and 98% for the late rice, respectively, which are superior to some state-of-the-art methods.