陡坡无土排岩场植被生态修复技术

针对无土陡坡排岩场特殊的立地条件,以城市污泥、粉煤灰等固体废弃物配制的人工土壤进行基质改良的基础上,以坑植式种植刺槐(Robina pseudoacacia)及棉槐(Amor-pha fruticosa),同时在种植坑中撒播牧草以培肥土壤,促进乔木生长固坡。结果表明,在人工土壤Ⅱ(粉煤灰∶城市污泥=1∶1)上种植的刺槐有较好的成活率(70%)和生长势,说明该修复技术在实现以废治废,变废为宝的同时,既解决了矿山废弃地修复中珍贵的土源问题,又改善了矿区生态环境。     

Data-Mining Discovery of Pattern and Process in Ecological Systems

ABSTRACT Most ecologists use statistical methods as their main analytical tools when analyzing data to identify relationships between a response and a set of predictors; thus, they treat all analyses as hypothesis tests or exercises in parameter estimation. However, little or no prior knowledge about a system can lead to creation of a statistical model or models that do not accurately describe major sources of variation in the response variable. We suggest that under such circumstances data mining is more appropriate for analysis. In this paper we 1) present the distinctions between data-mining (usually exploratory) analyses and parametric statistical (confirmatory) analyses, 2) illustrate 3 strengths of data-mining tools for generating hypotheses from data, and 3) suggest useful ways in which data mining and statistical analyses can be integrated into a thorough analysis of data to facilitate rapid creation of accurate models and to guide further research.     

Identification of 10 882 porcine microsatellite sequences and virtual mapping of 4528 of these sequences

A total of 10 882 porcine microsatellite repeats were identified in genomic shotgun sequences from the Sino-Danish Pig Genome Sequencing Consortium (http://www.piggenome.dk). Of these, 4528 microsatellites were placed on a pig-human comparative map by blast analysis of porcine sequences against the human genome (blast cut-off threshold =1 x 10(-5)). All microsatellite sequences placed on the comparative map are accessible at http://www.animalgenome.org/QTLdb/pig.html. These sequences increase the number of identified microsatellites in the porcine genome by several orders of magnitude. They are a new resource of microsatellite sequences for generating markers to be used in linkage studies and in fine mapping and positional cloning of quantitative trait loci.     

复杂疾病靶基因识别与网络重建的计算系统生物研究

复杂疾病相关靶基因的识别、构建疾病驱使相关基因网络及进行疾病机制研究,是功能基因组学研究中非常重要的科学问题。文章以计算系统生物学的观点和三维的角度,综述了基于生物谱（SNP遗传谱、芯片表达谱和2D-PAGE蛋白质谱等）的复杂疾病靶基因识别、多水平（SNPs虚拟网络、基因调控网络、蛋白质互作网络等）遗传网络逆向重构方法,及不同水平的网络之间在生物学和拓扑学上的纵向映射关系,并给出复杂疾病靶基因识别与网络关系的计算系统生物方法研究的未来展望。     

Functional gene clustering via gene annotation sentences, MeSH and GO keywords from biomedical literature

Gene function annotation remains a key challenge in modern biology. This is especially true for high-throughput techniques such as gene expression experiments. Vital information about genes is available electronically from biomedical literature in the form of full texts and abstracts. In addition, various publicly available databases (such as GenBank, Gene Ontology and Entrez) provide access to gene-related information at different levels of biological organization, granularity and data format. This information is being used to assess and interpret the results from high-throughput experiments. To improve keyword extraction for annotational clustering and other types of analyses, we have developed a novel text mining approach, which is based on keywords identified at the level of gene annotation sentences (in particular sentences characterizing biological function) instead of entire abstracts. Further, to improve the expressiveness and usefulness of gene annotation terms, we investigated the combination of sentence-level keywords with terms from the Medical Subject Headings (MeSH) and Gene Ontology (GO) resources. We find that sentence-level keywords combined with MeSH terms outperforms the typical 'baseline' set-up (term frequencies at the level of abstracts) by a significant margin, whereas the addition of GO terms improves matters only marginally. We validated our approach on the basis of a manually annotated corpus of 200 abstracts generated on the basis of 2 cancer categories and 10 genes per category. We applied the method in the context of three sets of differentially expressed genes obtained from pediatric brain tumor samples. This analysis suggests novel interpretations of discovered gene expression patterns.     

Plant communities on ironstone outcrops: a diverse and endangered Brazilian ecosystem

Mountain areas are recognized centres of endemism and diversity on account of their isolation and altitudinal diversity. In tropical regions, mountain tops usually stand as islands of xeric vegetation among mesophytic assemblages. Unlike the vegetation growing on other rock outcrops lithologies, such as inselbergs (granite/gneiss) or campos rupestres (quartz/arenite), ironstone outcrop plant communities still lack systematic studies in Brazil. These outcrops (locally known as canga) share most of the characteristics of other rock outcrops, such as isolation and edapho-climatic harshness, but differ in that they are the object of opencast mining, and thus subjected to irrecoverable degradation. In addition, they are expected to harbour metal-tolerant and hyperaccumulator plant species. A botanical survey of two ironstone outcrop locations in the most important mining region of southeastern Brazil, the Iron Quadrangle, revealed a high within-site (138 and 160 species per site), and between-site diversity (only 27% of common species), totaling 64 families and 234 species among basal families and eudicots (154 species), monocots (68 species), and ferns (12 species). Canga crusts are rich in dicots, several of which play an important role in community structuring, together with the more usual monocot aggregations. Distinct plant communities are found associated to different microhabitats within the iron crust, depending primarily on the amount of soil and moisture retention in the different microtopographies. The environmental uniqueness, high diversity, lack of studies and rapid destruction of these ecosystems pose an immediate challenge for their conservation.     

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。     

Strategic mining of cyanobacterial patents from the USPTO patent database and analysis of their scope and implications

Patent analysis with the help of the strategic mining of patents from databases is important and useful within the framework of application-oriented research and its commercialization. In the analysis reported here, we have mined cyanobacterial patents from the patent database of the United States Patent and Trademark Office (USPTO). In order to make an assessment of the commercial potentials of cyanobacteria, we conducted the patent search (from 1976 to April 2006) using certain generic terms and the 84 genera of cyanobacteria as keywords. The search was performed in two major ways – searching the abstracts and claims of the patents cumulatively and searching the entire patent documents by the mode of ‘all fields’ in USPTO. In the abstract- and claims-based search, 234 patents were obtained after the removal of overlapping patents among the keywords. An additional 31 patents were added following the ‘all fields’ search; these patents were not covered in the search that was based on abstracts and claims. The entire package of 265 patents, of which 244 were related to cyanobacteria, was then analyzed. Information derived from these patents identified five major areas of cyanobacterial utilization. Cyanobacteria have been patented as a source of a wide spectrum of products, for medical, agriculture and environmental applications, for gene-based products, for methods of cultivation and for methods of control. The chronological development in granting cyanobacterial patents was also traced. This study demonstrates that such strategic mining and analysis of patent data can be used as an index for future development.     

耐低温酵母Curvibasidium rogersii菌株在松萝样品中的首次分离鉴定及基于基因组分析的生物特性探究

【目的】对西藏松萝地衣来源的两株非常规酵母进行分离鉴定,并通过基因组序列分析探究其生物学特性和应用潜力。【方法】从西藏来源的松萝地衣样品内部分离得到2株耐低温酵母菌株,通过26S D1/D2和ITS序列比对分析以及生理生化实验进行菌种鉴定;通过全基因组序列分析和验证探究菌株的生物特性。【结果】两株酵母菌株经鉴定均为Curvibasidium rogersii,可以在10℃低温良好生长,在20℃生长最佳,25℃及以上温度生长缓慢或不生长。对其进行基因组测序和基因组挖掘,测序结果发现,其基因组注释出功能的部分与产油脂的低温酵母白冬孢酵母Leucosporidium creatinivorum具有最高相似性,尼罗红染色发现两株酵母都能够生产油脂,另外在基因组序列中还发现了可能参与木糖代谢的相关蛋白编码基因,实验证明两个酵母菌株可以利用木糖生长。【结论】首次分离鉴定了来自西藏松萝的酵母C. rogersii,为充分开发利用松萝和其他地衣来源微生物,以及利用可代谢木糖的新资源酵母生产微生物油脂提供了基础。     

放线菌来源的羊毛硫肽研究进展

羊毛硫肽(lanthipeptide)是一类由核糖体合成并经翻译后修饰的含羊毛硫氨酸或β-甲基羊毛硫氨酸的多肽。近年来,放线菌来源的羊毛硫肽因其突出的抗菌活性和罕见的生物活性而备受关注。本文重点对放线菌来源的不同类型的羊毛硫肽的结构特征及其特性进行了综述,讨论了生物或化学方法修饰天然羊毛硫肽和基因组挖掘发现结构新颖的羊毛硫肽在开发符合实际应用需求的放线菌来源的羊毛硫肽中的应用,并对放线菌来源的羊毛硫肽的应用潜力进行了总结和展望。