人工辅助投食对滇金丝猴肠道微生物群落的影响

【背景】肠道菌群与宿主的消化吸收、免疫抵抗和行为等息息相关,并受宿主的饮食、生活环境等因素影响。【目的】人工辅助投食能增加野生动物的营养摄入,但对其肠道菌群影响的研究较少。【方法】以云南白马雪山国家级自然保护区内的野生和人工辅助投食滇金丝猴群的新鲜粪便为材料,通过高通量测序探究人工辅助投食对猴群肠道菌群的影响。【结果】人工辅助投食的猴群肠道菌群丰富度、均匀度及谱系多样性更高,并且个体间群落组成差异更小。通过多级物种差异判别分析(linear discriminant analysis effect size, LEfSe)分析发现,人工辅助投食对20种不同分类水平的细菌相对丰度有影响,包括提升了厚壁菌门(Firmicutes)等8种类群的相对丰度,降低了变形菌门(Proteobacteria)、拟杆菌门(Bacteroidetes)等12种类群的相对丰度。通过构建微生物相关网络发现,野生猴群肠道菌群网络结构更加复杂,鲁棒性更高。京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)功能预测结果表明,人工辅助投食降低了猴群肠...     

Using ecological networks to answer questions in global biogeography and ecology

Ecological networks have classically been studied at site and landscape scales, yet recent efforts have been made to collate these data into global repositories. This offers an opportunity to integrate and upscale knowledge about ecological interactions from local to global scales to gain enhanced insights from the mechanistic information provided by these data. By drawing on existing research investigating patterns in ecological interactions at continental to global scales, we show how data on ecological networks, collected at appropriate scales, can be used to generate an improved understanding of many aspects of ecology and biogeography—for example, species distribution modelling, restoration ecology and conservation. We argue that by understanding the patterns in the structure and function of ecological networks across scales, it is possible to enhance our understanding of the natural world.     

具有节点偏置的高阶神经网络模型

在汪涛文献基础上提出了一个具有节点偏置的高阶神经网络模型、给出了模型的哈密顿量和学习算法,证明了学习算法的收敛性,该模型能对每一神经元自动引入一个节点偏置使得网络能够存储所有学习图样包括相关图样,其存储容量远高于Hebb—rule—like学习算法下的高阶神经网络模型.对由30个神经元组成的二阶神经网络进行了计算机仿真,结果证实了上述结论.此外,对初始突触强度对学习效果的影响和不同存储图样数目下的平均吸引半径进行了仿真计算并分析了所得结果.新模型的特点使其具有良好的应用前景     

采用Reichardt运动检测器和Boltzmann Machine神经网络的运动计算

建立了一个探讨灵长类视皮层从Ｖ１区到ＭＴ区的运动信息加工原理的计算模型,这个过程的突出特征是视觉运动信息经过了从局部检测进步到整体感知。模型的第一层由用于抽提运动模式的局部速度以及结构性质的Ｒｅｉｃｈａｒｄｔ运动检测器组成,进一步的加工是通过Ｂｏｌｔｚｍａｎｎ Ｍａｃｈｉｎｅ神经网络来实现的。这种网络的学习算法具有局部更新的显著性质,在学习阶段,网络不断地修改联结权重以形成对于记录在网络的显单元上     

N-Heterocyclic Carbene-Palladium Polymers Network: Recyclable Pre-catalyst for Effective Suzuki–Miyaura Coupling Reaction in Water

Rational design of high-efficiency N-heterocyclic carbene (NHC) palladium catalyst is of great importance to modern organic synthesis, especially in chemical and pharmaceutical industries. Herein, we fabricate a polymer network containing N-heterocyclic carbene palladium (PNNHC-Pd) catalytic active sites via an immobilization process. The N-heterocyclic carbene palladium can serve as a promising linkage of polymer network as well as an effective catalytic active site owing to its structural preference and strong σ-donating ability with palladium species. The results display that N-heterocyclic carbene palladium disperses homogeneously in polymer network, thus rendering PNNHC-Pd excellent catalytic activity, high stability and superior reusability in palladium-catalyzed Suzuki–Miyaura coupling reaction in aqueous medium. This work provides a new insight into the development of heterogenization of homogeneous catalysts based on polymer network.     

DNA水平上的植物系统学研究方法

本文简要总结了近年来在ＤＮＡ水平上的植物系统学研究方法,着重介绍了限制性长度多态性分析,ＰＣＲ技术在植物系统学上的应用等这一领域最新的进展,并对分子数据的分析方法及系统树的构建进行了详细讨论。     

具有竞争指针的短时记忆神经网络模型

在我们以前提出的短时记忆神经网络模型基础上［３］,我们在新模型中引入突触竞争机制,提出了一个新的短时记忆神经网络模型。模型仍由两个神经网络所组成;其一为与长时记忆共有的信息内容表达网络,另一个为指针神经元环路。由于表达区神经元与指针神经元间的突触权重的竞争,使得模型可以表现出由干扰引起的短时记忆的遗忘。相应于自由回忆序列位置效应和汉字组块两个心理学实验,对模型做了计算机仿真。仿真结果显示模型的行为与两个心理实验定量地符合得很好。由此表明现在的模型更合适于作为短时记忆的模型。