Construction of Porphyra yezoensis Pure Line from Protoplasts and Its 18S rDNA Sequence Determination

1　Introduction　PorphyraisthemainobjectforalgafarmingandplaysaveryimportantroleinChinesemarineindus tries.Recently ,therehasbeenagreatlossinPor phyracultivationduetothedegenerationoftheculti var,sothereisanincreasingdemandforgoodPor phyracultivars .Theprerequisiteofthetraditionalbreed ingandbioengineeringresearchofPorphyraistheconstructionofpurelines .Traditionally ,theclassifi cationofPorphyrawasaccordingtotheirmorphologi calcharacteristics .However ,mostmorphologicalfea turesofPorphyraar…     

Construction of Porphyra yezoensis Pure Line from Protoplasts and Its 18S rDNA Sequence Determination

The wild Porphyra yezoensis collected from the Qingdao coast was used to prepare protoplasts by enzyme digestion. The pure line was constructed by cultivating the protoplasts. The 18S rDNA of the P. yezoensis pure line was cloned and sequenced. Sequence analysis was executed for this sequence and other 22 sequences retrieved from GenBank. A phylogenetic tree was constructed using the neighbor-joining method. The results revealed a high diversity of 18S rDNA sequences in genus Porphyra and the considerable variation of 18S rDNA sequences in different strains of the same species P. yezoensis and P. tenera. Significant difference of 18S rDNA sequence was observed between P. yezoensis from Qingdao, China, and the two strains of P. yezoensis from Japan, but the three strains of P. yezoensis formed a stable clade in the phylogenetic tree. These results indicate the possibility of interspecies and intraspecies discrimination of Porphyra using the 18S rDNA sequences.     

星系相互作用的一个结果:环星系形成的数值模拟

通过数值模拟相互作用星系（包括靶星系和入侵星系）的碰撞，研究环星系的形成．由于采用旋涡结构作为靶盘试验星分布函数，不同于Toomre采用一系列同心圆作为试验星的分布函数，模拟得到环的结构比Toomre的模拟更接近于实际环星系．     

判定树归纳分类法在数据挖掘中的应用

介绍了判定树归纳分类法的概念及基于ID3算法的基本策略,给出通过最高信息增益构 造判定树的数据挖掘算法进行分类的预测模型,对判定树归纳分类法的具体应用给出了实例并作 分析。     

川东北地区水稻镉积累与生态健康风险评价

【研究目的】重金属镉主要通过“土壤-植物”系统进入食物链进而影响人体健康,开展农作物镉积累研究对保障人类健康安全具有重要意义。【研究方法】系统采集了四川省东北部南充市、巴中市、广安市等地的土壤及水稻样品,采用ICP-MS方法检测了土壤和水稻中的镉含量,并运用CART决策树和相关系数法进行了数据分析。【研究结果】结果显示,川东北地区表层土壤镉含量相对较低,为0.071~0.92 mg/kg,平均0.254 mg/kg,几乎所有（99.9%）样品镉含量都低于标准限值;水稻（糙米）镉含量差异性较大,为0.002~0.803 mg/kg,平均0.076 mg/kg,超标率达14.0%;水稻镉超标区成人每日通过稻谷摄入的镉达90.4 μg/d,已超过允许摄入量标准60 μg/d。【结论】土壤镉不超标而农作物镉超标的现象可能与土壤的低pH、低CaO、高SiO₂等特点有关,这一认识对于指导区域内粮食安全生产具有重要指导意义。创新点：土壤和水稻综合调查发现土壤镉不超标但农作物超标的现象,CART和相关系数法分析了其原因。     

Automated extraction of 3D vector topographic feature line from terrain point cloud

This paper presents an automated topographic feature lines detection method that directly extracts 3D vector topographic feature lines from terrain point cloud. First, signed surface variation (SSV) is introduced to extract the potential feature points. Secondly, the potential feature points are segmented to different clusters by combining region growing segmentation and conditional Euclidean clustering. In order to extract feature points, the potential feature points in each cluster are iteratively thinned using a HC-Laplacian smoothing method with SSV weighted taken into account. Besides, SSV-based and elevation-based simple rules are added for accelerating this thinning process. Finally, the feature lines are obtained by constructing the minimum spanning tree of the extracted feature points. By comparing with manually digitized reference lines, the correctness and the completeness of extracted results are about 80% or even higher, which are much higher than those extracted by D8 algorithm.     

Quantifying shoreline change in Funafuti Atoll,Tuvalu using a time series of Quickbird,Worldview and Landsat data

Funafuti Atoll, Tuvalu is located in the southwestern Pacific Ocean, which has experienced some of the highest rates of global sea-level rise over the past 60 years. Atoll islands are low-lying accumulations of reef-derived sediment that provide the only habitable land in Tuvalu, and are considered vulnerable to the myriad possible impacts of climate change, especially sea-level rise. This study examines the shoreline change of twenty-eight islands in Funafuti Atoll between 2005 and 2015 using 0.65 m QuickBird, 0.46 m WorldView-2, and 0.31 m WorldView-3 imagery using an image segmentation and decision tree classification. Shoreline change estimates are compared to previous study that used a visual interpretation approach. The feasibility of estimating island area with Landsat-8 Operational Land Imager (OLI) data is explored using CLASlite software. Results indicate a 0.13% (0.35 ha) decrease in net island area over the study time period, with 13 islands decreasing in area and 15 islands increasing in area. Substantial decreases in island area occurred on the islands of Fuagea, Tefala and Vasafua, which coincides with the timing of Cyclone Pam in March, 2015. Comparison between the WorldView-2 shoreline maps and those created from Landstat-8 indicate that the estimates tend to be in higher agreement for islands that have an area > 0.5 ha, a compact shape, and no built structures. Ten islands had > 90% agreement, with percent disagreements ranging from 2.78 to 100%. The methods and results of this study speak to the potential of automated EoV shoreline monitoring through segmentation and classification tree approach, which would reduce down data processing and analysis time. With the growing constellation of high and medium spatial resolution satellite-based sensors and the development of semi or fully automated image processing technology, it is now possible to remotely assess the short and medium-term shoreline dynamics on dynamic atolls. Landsat estimates were reasonably matched to those derived from fine resolution imagery, with some caveats about island size and shape.     

Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains,California

Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling. Airborne Laser Scanning (ALS) can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains. This study proposed an ALS-based framework to quantify tree growth and competition. Bi-temporal ALS data were used to quantify tree growth in height (ΔH), crown area (ΔA), crown volume (ΔV), and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests. We analyzed the correlations between tree growth attributes and controlling factors (i.e. tree sizes, competition, forest structure, and topographic parameters) at multiple levels. At the individual tree level, ΔH had no consistent correlations with controlling factors, ΔA and ΔV were positively related to original tree sizes (R?>?0.3) and negatively related to competition indices (R?R|?>?0.7), ΔV was positively related to original tree sizes (|R|?>?0.8). Multivariate regression models were simulated at individual tree level for ΔH, ΔA, and ΔV with the R² ranged from 0.1 to 0.43. The ALS-based tree height estimation and growth analysis results were consistent with field measurements.     

决策树分类模型的住宅建筑物图斑识别

针对大范围内住宅建筑图斑自动识别较为困难的问题,该文提出基于决策树模型的分类方法,利用基础测绘中高精度的建筑数据、地理省情监测数据、POI数据、DSM、DEM、腾讯大数据等,以建筑物本身特征和建筑物不同邻域内建筑类型相关因素的空间特征作为决策变量,分别采用QUEST、CHAID、C&RT和C5.0算法构建用于住宅分类识别的决策树模型并进行优选。以济南市(不含原莱芜市)为研究区域进行了实验,结果显示4个模型均未出现过拟合现象,可以用于住宅建筑图斑识别。     

深度学习的街景行道树自动识别提取研究

针对城市行道树调查中,街景影像背景环境复杂多变、行道树个体差异大,依靠目视判读费时费力的问题,该文基于车载移动测量系统采集的全景影像数据,利用深度学习算法,在快速区域卷积神经网络的目标检测方法基础上,建立适用于街景行道树检测的深度神经网络模型。模型采用基于共有显著性区域及冗余策略的行道树多示例目标候选区域选择方法,使用车载图像的几何约束进一步筛选合适的候选区域,从而实现行道树目标候选区域的统一选择,提升行道树目标的检测效果。实验结果表明,该文提出的方法能够实现多种行道树的准确自动识别与提取,进而大大降低行道树绿化调查的成本。