A second generation human haplotype map of over 3.1 million SNPs

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.     

Genome-wide detection and characterization of positive selection in human populations

With the advent of dense maps of human genetic variation, it is now possible to detect positive natural selection across the human genome. Here we report an analysis of over 3 million polymorphisms from the International HapMap Project Phase 2 (HapMap2). We used 'long-range haplotype' methods, which were developed to identify alleles segregating in a population that have undergone recent selection, and we also developed new methods that are based on cross-population comparisons to discover alleles that have swept to near-fixation within a population. The analysis reveals more than 300 strong candidate regions. Focusing on the strongest 22 regions, we develop a heuristic for scrutinizing these regions to identify candidate targets of selection. In a complementary analysis, we identify 26 non-synonymous, coding, single nucleotide polymorphisms showing regional evidence of positive selection. Examination of these candidates highlights three cases in which two genes in a common biological process have apparently undergone positive selection in the same population:LARGE and DMD, both related to infection by the Lassa virus, in West Africa;SLC24A5 and SLC45A2, both involved in skin pigmentation, in Europe; and EDAR and EDA2R, both involved in development of hair follicles, in Asia.     

The medaka draft genome and insights into vertebrate genome evolution

Teleosts comprise more than half of all vertebrate species and have adapted to a variety of marine and freshwater habitats. Their genome evolution and diversification are important subjects for the understanding of vertebrate evolution. Although draft genome sequences of two pufferfishes have been published, analysis of more fish genomes is desirable. Here we report a high-quality draft genome sequence of a small egg-laying freshwater teleost, medaka (Oryzias latipes). Medaka is native to East Asia and an excellent model system for a wide range of biology, including ecotoxicology, carcinogenesis, sex determination and developmental genetics. In the assembled medaka genome (700 megabases), which is less than half of the zebrafish genome, we predicted 20,141 genes, including approximately 2,900 new genes, using 5'-end serial analysis of gene expression tag information. We found single nucleotide polymorphisms (SNPs) at an average rate of 3.42% between the two inbred strains derived from two regional populations; this is the highest SNP rate seen in any vertebrate species. Analyses based on the dense SNP information show a strict genetic separation of 4 million years (Myr) between the two populations, and suggest that differential selective pressures acted on specific gene categories. Four-way comparisons with the human, pufferfish (Tetraodon), zebrafish and medaka genomes revealed that eight major interchromosomal rearrangements took place in a remarkably short period of approximately 50 Myr after the whole-genome duplication event in the teleost ancestor and afterwards, intriguingly, the medaka genome preserved its ancestral karyotype for more than 300 Myr.     

区域城乡协调发展的综合评价——以宁波为例

城乡之间的协调表现在很多方面.要全面、客观地评价一个地区的城乡协调发展情况,必须建立科学合理的评价指标体系,选择恰当的评价方法.本文结合宁波市的实际情况,讨论了城乡协调发展的内涵和目标,提出了城乡协调发展评价指标体系和方法,揭示了宁波市近年城乡协调发展方面存在的问题和未来可能的变化态势.     

微束X射线荧光分析谱仪及其对松针中元素的分布分析

介绍了一种小型的微束X射线荧光分析谱仪,它是由导管X光透镜和能量色散X射线荧光分析谱仪组成.谱仪具有较高的空间分辨率和能量分辨率.使用本谱仪对松树针叶进行了微区分析,得出各种元素沿松针长度方向和横切面径向的分布规律.结果表明使用X光透镜的微束X射线荧光分析方法可以实现植物样品的微区分析,有助于进一步了解植物的生长与元素迁移的关系.     

混沌高效遗传算法在水库含沙量预报中的应用

利用混沌映射的遍历性和实编码遗传算法的全局优化性,通过在遗传进化过程中加入混沌变异操作,在变量的定义域内投放大量的混沌初始群体,在实编码遗传算法进化过程中加入单纯形法学习算子,建立了一种新的混沌高效遗传算法(chaos higher efficient genetic algorithm, CHEGA).应用该法对3个非线性、高维、多峰值测试函数进行了仿真,在收敛速度和全局优化方面好于现有的简单遗传算法和改进的遗传算法.建立了水库含沙量预报模型.并将CHEGA用于求解上述模型的参数优化问题,与实数编码加速遗传算法(RAGA)、二进制加速遗传算法和随机优化算法等方法相比,CHEGA可以遍历到整个区域,较好的保持了种群的多样性,并且精度高、收敛速度快.CHEGA对求解实际水库计算模型的参数优化问题非常有效.     

旅游流规模结构的Zipf特征与差异度对比研究——以四川省为例

以1996年至2004年入境旅游统计数据为样本,借用齐夫(Zipf)参数和差异度、均衡度指标,对四川省入境旅游流规模结构的齐夫(Zipf)特征和差异度进行了对比研究.结果发现齐夫(Zipf)参数可对旅游流规模结构进行分段处理,并以此分析各标度区内旅游流规模结构的空间分布形态;对不满足Zipf法则的无效标度区内部结构只能借助差异度和均衡度指标予以完成,但由于该指标既不能很好说明旅游流规模结构的标度分段特征,又无法反映旅游流结构的空间分布形态,故只能在分析旅游流规模结构时对齐夫(Zipf)参数起补充作用;用Zipf参数和差异度、均衡度指标定量研究旅游流规模结构所得到的结果与客观实际相符合,从而证明了Zipf定律、差异度、均衡度指标均可作为旅游流规模结构分析的有效手段或工具,这对丰富旅游流的分析方法具有重要意义.     

都江堰灌区金马河河流健康空间形态分析

以都江堰灌区金马河为研究对象,从河流健康的基础层次──河流形态景观特征的角度出发,应用遥感技术采集河流空间信息,依据生态需水理论和景观生态学方法对金马河河流空间形态特征进行分析,得到金马河河流健康情况下基本空间形态特征参数:水域面积占河道面积的比例不低于40%,平均斑块面积(MPS)大于1.97 hm2且斑块数量(NP)大于1948个,最大斑块指数(LPI)在20%～45%之间,修正的Simpson多样性指数(MSIDI)大于0.6,修正后的Simpson均匀度(MSIEI)高于0.55.同时,通过研究证明遥感技术在河流健康空间形态特征分析中具有很好的应用前景.     

对象迁移自动机在TDT中的聚类研究

对象迁移自动机(OMA)是一种能够较好地解决话题识别与跟踪(TDT)中聚类问题的方法,但是,传统OMA模型由于聚类速度慢等缺点,难以满足TDT实时和增量聚类的要求.针对这一问题,本文一方面改进传统的OMA模型中自动机的动作设计,同时提出文档选择策略,加快了OMA的聚类速度.改进的方法在中等文档集上进行了实验,实验结果表明,该方法具有较好的聚类效果.