The first complete organellar genomes of an Antarctic red alga,Pyropia endiviifolia:insights into its genome architecture and phylogenetic position within genus Pyropia(Bangiales,Rhodophyta)

Pyropia species grow in the intertidal zone and are cold-water adapted. To date, most of the information about the whole plastid and mitochondrial genomes(ptDNA and mtDNA) of this genus is limited to Northern Hemisphere species. Here, we report the sequencing of the ptDNA and mtDNA of the Antarctic red alga Pyropia endiviifolia using the Illumina platform. The plastid genome(195 784 bp, 33.28% GC content) contains 210 protein-coding genes, 37 tRNA genes and 6 rRNA genes. The mitochondrial genome(34 603 bp, 30.5% GC content) contains 26 protein-coding genes, 25 tRNA genes and 2 rRNA genes. Our results suggest that the organellar genomes of Py. endiviifolia have a compact organization. Although the collinearity of these genomes is conserved compared with other Pyropia species, the genome sizes show significant differences, mainly because of the different copy numbers of rDNA operons in the pt DNA and group II introns in the mtDNA. The other Pyropia species have 2–3 distinct intronic ORFs in their cox 1 genes, but Py. endiviifolia has no introns in its cox 1 gene. This has led to a smaller mtDNA than in other Pyropia species. The phylogenetic relationships within Pyropia were examined using concatenated gene sets from most of the available organellar genomes with both the maximum likelihood and Bayesian methods. The analysis revealed a sister taxa affiliation between the Antarctic species Py. endiviifolia and the North American species Py. kanakaensis.     

无人机遥感在基础测绘快速更新中的应用

无人机遥感具有体积小、造价低、适应性强、操作简便、获取影像速度快、地面分辨率高等一系列优点,成为基础地理信息快速更新的重要手段。探讨了Wingtra无人机系统在基础测绘更新中的控制点布置和影像获取方法,分析了无人机影像的处理方法,研究了基础测绘更新中正射影像制作方法,并利用实地检查点对所制作正射影像的几何精度进行了评价。研究表明,无人机遥感制作的高分辨率正射影像在几何精度上满足基础测绘快速更新的需要,可大大提高基础测绘更新的效率。     

基于空间智能体的交通信号灯模型构建

在我国交通问题日益严峻的背景下,交通信号灯在疏导车流方面的意义愈发重大。采用空间多智能体建模方法,使用Netlogo多智能体可编程建模平台,对交通信号灯模型进行构建。通过车辆智能体与其他车辆智能体、信号灯智能体以及瓦片交互实现对车辆智能体控制。将车辆数量和不同方向的信号灯时长作为模型输入参数,设定三种方案进行动态模拟,监测该模型在一定时长内所有车辆总等待时长。通过调整输入参数以及数据统计,对不同信号灯配时方案进行分析,为交通信号灯配时方案的制定提供一定参考。