文峪河上游河岸林群落类型及其生态适应性

以文峪河上游河岸林为研究对象,采用TWINSPAN法对研究地区河岸林进行群落分类,对各群落类型特征进行分析.在提出河岸林群落生态功能适应性指标的基础上,对研究地区河岸林群落进行生态功能适应性分组.通过研究,文峪河上游河岸林可划分为阔叶混交林、华北落叶松阔叶混交林、云杉落叶松混交林、云杉阔叶混交林、阔针混交林、油松阔叶混交林、青杨林、沙棘灌丛和柳树灌丛等9个群落类型,但群落类型之间的分异性总体表现不高,且表现出突出的多样性特征;群落乔木层和灌木层的物种组成复杂,草本层多为一些耐干扰种和耐水湿种,总体上越远离河岸,高地群落中的物种比例越高,表现出高地森林与河流之间生态过渡带的典型特点和河岸带生态环境的高度异质性;根据本文构建的群落的生态功能适应性指标,研究地区9个河岸林群落类型可以划分为强入侵性功能组、中等入侵性功能组、弱入侵性功能组和高逃避性功能组等4个生态适应性功能组,不同生态适应性功能组的群落中,乔木层和灌木层的主要物种具有明显不同的生态对策,而草本层物种的差异不明显.     

亚热带峰丛洼地恢复演替植物群落的数量分类和排序——以广西马山弄拉峰丛洼地为例

采用双向指示种分析法(TWINSPAN)和除趋势对应分析(DCA)对弄拉峰丛洼地恢复演替植物群落进行数量分类和排序。TWINSPAN将20个样方分为10个组,根据植被分类的原则划分为10个群丛,论述了各群丛的群落学特征。20个样方的DCA排序结果反映了恢复演替群落与环境梯度的关系,表明影响群丛分布格局的主导生态因子为恢复时间、人为干扰及露石率。DCA排序将53个种分为4个种组,影响峰丛洼地恢复演替群落及其物种分布格局的主导生态因子为恢复时间、岩性和水分等。各物种在排序轴上的位置反映了种组成员的生态适应性及其在群落中的重要性和更新生态位。     

European map of alien plant invasions based on the quantitative assessment across habitats

Aim Recent studies using vegetation plots have demonstrated that habitat type is a good predictor of the level of plant invasion, expressed as the proportion of alien to all species. At local scale, habitat types explain the level of invasion much better than alien propagule pressure. Moreover, it has been shown that patterns of habitat invasion are consistent among European regions with contrasting climates, biogeography, history and socioeconomic background. Here we use these findings as a basis for mapping the level of plant invasion in Europe. Location European Union and some adjacent countries. Methods We used 52,480 vegetation plots from Catalonia (NE Spain), Czech Republic and Great Britain to quantify the levels of invasion by neophytes (alien plant species introduced after ad 1500) in 33 habitat types. Then we estimated the proportion of each of these habitat types in CORINE land‐cover classes and calculated the level of invasion for each class. We projected the levels of invasion on the CORINE land‐cover map of Europe, extrapolating Catalonian data to the Mediterranean bioregion, Czech data to the Continental bioregion, British data to the British Isles and combined Czech–British data to the Atlantic and Boreal bioregions. Results The highest levels of invasion were predicted for agricultural, urban and industrial land‐cover classes, low levels for natural and semi‐natural grasslands and most woodlands, and the lowest levels for sclerophyllous vegetation, heathlands and peatlands. The resulting map of the level of invasion reflected the distribution of these land‐cover classes across Europe. Main conclusions High level of invasion is predicted in lowland areas of the temperate zone of western and central Europe and low level in the boreal zone and mountain regions across the continent. Low level of invasion is also predicted in the Mediterranean region except its coastline, river corridors and areas with irrigated agricultural land.     

Phylogenetic position of the parasitoid nanoflagellate Pirsonia inferred from nuclear-encoded small subunit ribosomal DNA and a description of Pseudopirsonia n. gen. and Pseudopirsonia mucosa (Drebes) comb. nov

Sequences of the nuclear encoded small subunit (SSU) rRNA were determined for Pirsonia diadema, P. guinardiae, P. punctigerae, P. verrucosa, P. mucosa and three newly isolated strains 99-1, 99-2, 99-S. Based on phylogenetic analysis all Pirsonia strains, except P. mucosa, clustered together in one clade, most closely related to Hyphochytrium catenoides within the group of stramenopiles. However, P. mucosa was most closely related to Cercomonas sp. SIC 7235 and Heteromita globosa and belongs to the heterogenic group of Cercozoa. In addition to the SSU rDNA sequences, P. mucosa differs from the stramenopile Pirsonia species in some characteristics and was therefore redescribed in this paper as Pseudopirsonia mucosa. The three newly isolated strains 99-1, 99-2, and 99-S differed by 28 bp in their SSU rDNA sequences from their closest neighbour P. diadema and only 1 to 3 bp among themselves. These base differences and a host range similar to P. formosa were sufficient to assign them as new strains of P. formosa.     

Marine Glutathione <Emphasis Type="Italic">S</Emphasis>-Transferases

The aquatic environment is generally affected by the presence of environmental xenobiotic compounds. One of the major xenobiotic detoxifying enzymes is glutathione S-transferase (GST), which belongs to a family of multifunctional enzymes involved in catalyzing nucleophilic attack of the sulfur atom of glutathione (γ-glutamyl-cysteinylglycine) to an electrophilic group on metabolic products or xenobiotic compounds. Because of the unique nature of the aquatic environment and the possible pollution therein, the biochemical evolution in terms of the nature of GSTs could by uniquely expressed. The full complement of GSTs has not been studied in marine organisms, as very few aquatic GSTs have been fully characterized. The focus of this article is to present an overview of the GST superfamily and their critical role in the survival of organisms in the marine environment, emphasizing the critical roles of GSTs in the detoxification of marine organisms and the unique characteristics of their GSTs compared to those from non-marine organisms.     

基于改进型组织特异纹理滤波器的乳腺肿瘤超声图像处理

基于乳腺超声图像的多参数纹理分类实验,改进了Gjenna Sfippel等的自适应纹理滤波器,通过引入模糊函数、增加重叠区域和迭代次数的措施,在减少图像噪声的同时,增强肿瘤与周围正常组织的视觉差别。量化比较乳腺超声图像经该滤波算法和几种常用滤波算法处理前后的的统计特征参量和肿瘤边缘检测的精确率,验证了该算法的有效性和优越性。     

基因芯片数据分析方法研究进展

基因芯片技术的出现改变了生物医学研究的前景,其产生的海量数据是限制其发展的瓶颈问题。为提取其中所隐含的有价值的信息,在基因芯片数据分析的复杂计算工具和方法方面近年来有很多尝试。本文对近5年来基因芯片表达数据的分类分析方法进行综述,既分类比较了以聚类分析为基础的分类方法,也吸收了当前应用数据挖掘、信息融合等系统生物学思路的研究技术,并对数据的分析结果进行评价。     

SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data

Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom .