贵州省野生凤仙花属植物物种多样性与地理分布

该研究以贵州省野生凤仙花属植物为研究对象,在建立物种名录和地理分布数据库的基础上,对其物种多样性和地理分布格局进行研究。通过查阅文献资料和实地调查,从物种组成、特有性、水平分布、垂直分布和相似性等方面进行分析,最后采用筛除算法确定贵州凤仙花属植物分布的热点地区,为贵州省凤仙花属植物资源的研究和持续利用提供理论参考。结果表明:(1)贵州省凤仙花属植物共记载60种(含种下等级),分布于69个县级行政区,有55个中国特有种和10个贵州特有种,垂直分布以1 000～1 500 m海拔段最为丰富。(2)通过相似性系数分析,发现贵州与湖南的相似程度最高;并筛选得到9个热点县,共代表了86.67%的凤仙花属植物。(3)贵州省气候温暖湿润,具有典型的高原喀斯特地貌,凤仙花属植物的物种多样性和区域特有性明显。     

中国江西龙虎山崖顶植物根中深色有隔内生菌多样性

为探明丹霞地貌区崖顶植物深色有隔内生真菌（dark septate endophyte,DSE）多样性、群落组成以及生态分布规律。本文以江西龙虎山崖顶常见植物刺柏、马尾松、青冈栎、檵木、乌饭树、鸭跖草、苦槠、辣木树和香附子9种植物的根为研究对象,采用经典的组织分离方法分离DSE真菌,结合形态学特征和分子生物学数据研究丹霞地貌区崖顶常见植物DSE真菌多样性。结果表明：从9种植物根部990个组织块中,共分离纯化出404株菌株,经鉴定隶属于45个分类单元。其中,青霉属Penicillium、拟内孢霉属Endomycopsis、曲霉属Aspergillus、头囊霉属Ascocybe为优势属群,属种组成在不同植物中存在差异,且内生真菌属的数目与植物优势度极显著正相关（P<0.01）。9种植物深色有隔内生真菌（DSE）总分离率在0.51%-13.33%之间;各植物DSE Shannon指数在0.0743-1.0400之间,与植物优势度极显著正相关（P<0.01）,差异明显;Simpson指数较高（均值>0.7）且Pielou指数较低（均值<0.4）表明DSE真菌在不同植物分布不均匀;相似性指数普遍不高,在0.071-0.467之间。本研究初步揭示了丹霞地貌区崖顶深色有隔内生真菌的分布及多样性情况,为进一步探讨内生真菌在丹霞地貌等特殊生境的生态功能提供参考。     

External morphology explains the success of biological invasions

Biological invasions have become major players in the current biodiversity crisis, but realistic tools to predict which species will establish successful populations are still unavailable. Here we present a novel approach that requires only a morphometric characterisation of the species. Using fish invasions of the Mediterranean, we show that the abundance of non‐indigenous fishes correlates with the location and relative size of occupied morphological space within the receiving pool of species. Those invaders that established abundant populations tended to be added outside or at the margins of the receiving morphospace, whereas non‐indigenous species morphologically similar to resident ones failed to develop large populations or even to establish themselves, probably because the available ecological niches were already occupied. Accepting that morphology is a proxy for a species' ecological position in a community, our findings are consistent with ideas advanced since Darwin's naturalisation hypothesis and provide a new warning signal to identify invaders and to recognise vulnerable communities.     

An improved alignment-free model for dna sequence similarity metric

Background

DNA Clustering is an important technology to automatically find the inherent relationships on a large scale of DNA sequences. But the DNA clustering quality can still be improved greatly. The DNA sequences similarity metric is one of the key points of clustering. The alignment-free methodology is a very popular way to calculate DNA sequence similarity. It normally converts a sequence into a feature space based on words’ probability distribution rather than directly matches strings. Existing alignment-free models, e.g. k-tuple, merely employ word frequency information and ignore many types of useful information contained in the DNA sequence, such as classifications of nucleotide bases, position and the like. It is believed that the better data mining results can be achieved with compounded information. Therefore, we present a new alignment-free model that employs compounded information to improve the DNA clustering quality.

Results

This paper proposes a Category-Position-Frequency (CPF) model, which utilizes the word frequency, position and classification information of nucleotide bases from DNA sequences. The CPF model converts a DNA sequence into three sequences according to the categories of nucleotide bases, and then yields a 12-dimension feature vector. The feature values are computed by an entropy based model that takes both local word frequency and position information into account. We conduct DNA clustering experiments on several datasets and compare with some mainstream alignment-free models for evaluation, including k-tuple, DMk, TSM, AMI and CV. The experiments show that CPF model is superior to other models in terms of the clustering results and optimal settings.

Conclusions

The following conclusions can be drawn from the experiments. (1) The hybrid information model is better than the model based on word frequency only. (2) For DNA sequences no more than 5000 characters, the preferred size of sliding windows for CPF is two which provides a great advantage to promote system performance. (3) The CPF model is able to obtain an efficient stable performance and broad generalization.     

Meta-Mesh——元基因组数据分析系统

随着元基因组数据的不断增多,建立一个包含高品质的元基因组样本(也称为"微生物群落")数据的集成化的分析平台成为可能,使得微生物群落样本能够被有效分析、比较与搜索,从中发现更加深入的生物学意义。然而,一方面目前大部分元基因组数据库仅仅提供了简单的数据存储,缺乏良好的样本注释或者仅仅提供了很少的分析功能。另一方面,用于计算微生物群落数据相似性的方法所能够接受的样本数据量非常有限。长期以来,科学家们一直在寻找有效的方法计算海量微生物群落之间的相似性,从而研究样本之间的相似度并发现元基因组数据信息的相关性。Meta-Mesh是一个全新的在线元基因组分析系统,它包括元基因组数据库和分析平台,可以对元基因组样本进行系统、有效地分析,并实现样本的群落结构比较和精确搜索。其中,元基因组数据库已经从公共领域和内部实验室收集了超过7 000个高品质、带有有效注释的样本。同时,Meta-Mesh的分析平台提供了多种在线分析工具,可以对元基因组样本进行群落的结构分析与注释,多角度比较,并能通过快速索引策略和群落结构相似性算法在数据库中高效搜索近似的样本。Meta-Mesh通过"人体微生物群落样本的数据库搜索识别"以及"基于相似度矩阵的样本的聚类"等一系列的元基因组研究案例证明了其分析方面的性能。作为一个在线的元基因组数据库和分析系统,Meta-Mesh将服务于元基因组样本的快速分析、识别、比对、搜索等相关领域。     

The potential for indirect effects between co‐flowering plants via shared pollinators depends on resource abundance,accessibility and relatedness

Co‐flowering plant species commonly share flower visitors, and thus have the potential to influence each other's pollination. In this study we analysed 750 quantitative plant–pollinator networks from 28 studies representing diverse biomes worldwide. We show that the potential for one plant species to influence another indirectly via shared pollinators was greater for plants whose resources were more abundant (higher floral unit number and nectar sugar content) and more accessible. The potential indirect influence was also stronger between phylogenetically closer plant species and was independent of plant geographic origin (native vs. non‐native). The positive effect of nectar sugar content and phylogenetic proximity was much more accentuated for bees than for other groups. Consequently, the impact of these factors depends on the pollination mode of plants, e.g. bee or fly pollinated. Our findings may help predict which plant species have the greatest importance in the functioning of plant–pollination networks.     

Functional trait differences influence neighbourhood interactions in a hyperdiverse Amazonian forest

As distinct community assembly processes can produce similar community patterns, assessing the ecological mechanisms promoting coexistence in hyperdiverse rainforests remains a considerable challenge. We use spatially explicit neighbourhood models of tree growth to quantify how functional trait and phylogenetic similarities predict variation in growth and crowding effects for the 315 most abundant tree species in a 25‐ha lowland rainforest plot in Ecuador. We find that functional trait differences reflect variation in (1) species maximum potential growth, (2) the intensity of interspecific interactions for some species, and (3) species sensitivity to neighbours. We find that neighbours influenced tree growth in 28% of the 315 focal tree species. Neighbourhood effects are not detected in the remaining 72%, which may reflect the low statistical power to model rare taxa and/or species insensitivity to neighbours. Our results highlight the spectrum of ways in which functional trait differences can shape community dynamics in highly diverse rainforests.     

多变量置信区间法和模型依赖法在溶出曲线 相似性比较中的应用

根据 FDA 和 CFDA 口服固体制剂溶出度试验技术指导原则的要求,为防止仿制药一致性评价过程中相似因子(f2)法的滥用和 不恰当应用,采用样本数据实例演示的方式说明多变量置信区间法和模型依赖法作为补充手段在溶出曲线相似性比较和 BE 风险预评估中 的重要性。     

Differential Expression and Network Inferences through Functional Data Modeling

Summary .  Time course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this article, we propose a model that allows us to examine differential expression and gene network relationships using time course microarray data. We model each gene-expression profile as a random functional transformation of the scale, amplitude, and phase of a common curve. Inferences about the gene-specific amplitude parameters allow us to examine differential gene expression. Inferences about measures of functional similarity based on estimated time-transformation functions allow us to examine gene networks while accounting for features of the gene-expression profiles. We discuss applications to simulated data as well as to microarray data on prostate cancer progression.