大黄鱼(Larimichthys crocea)新品种“东海1号”体长相关的DArT标记筛选

大黄鱼(Larimichthys crocea)是我国重要的海产经济鱼类。因多年未加选育的累代养殖,养殖大黄鱼生长缓慢,性早熟、免疫力低下,亟需对养殖大黄鱼进行遗传改良。多样性芯片技术(Diversity arrays technology,DAr T)具有高通量和低成本的显著特点,不需要明确物种的基因组DNA序列信息,因而广泛应用于动植物遗传图谱制作和遗传多样性分析。本研究旨在采用DAr T技术鉴定与"东海1号"大黄鱼体长相关分子标记。首先随机测得"东海1号"大黄鱼199个个体体长,均值为13.45cm,符合正态分布(P0.05),"极端大群体"和"极端小群体"之间差异显著(P0.01)。然后采用7组限制性内切酶组合(Pst I/Alu I、Pst I/Ban II、Pst I/Bsp1286I、Pst I/Bst NI、Pst I/Hae III、Pst I/Rsa I、Pst I/Taq I)分别进行酶切,获得基因组代表性DNA片段并用于文库构建。根据多态性克隆数和多态性率确定Pst I/Rsa I酶切组合为最优降低基因组复杂度方法。之后从Pst I/Rsa I基因组代表性DNA片段文库中扩增获得3360个片段,以此为多样性芯片探针进行芯片点制,杂交筛选获得18个大黄鱼体长相关DAr T候选标记。经过新群体样本再次验证,仍有8个DAr T标记与体长相关。本研究有助于选育生长性状优良的大黄鱼群体。     

<Emphasis Type="Italic">Grateloupia ramosa</Emphasis> Wang &amp; Luan sp. nov. (Halymeniaceae,Rhodophyta), a new species from China based on morphological evidence and comparative <Emphasis Type="Italic">rbc</Emphasis>L sequences

Grateloupia ramosa Wang & Luan sp. nov. (Halymeniaceae, Rhodophyta) is newly described from Hainan Province, southern China. The organism has the following morphological features: (1) purplish red, cartilaginous and lubricous thalli 5–10 cm in height; (2) compressed percurrent axes bearing abundant branches with opposite arrangement; (3) claw-like apices on top, constricted to 2–4 cm at the base; (4) cortex consisting of 3–6 layers of elliptical or anomalous cells and a medulla covered by compact medullary filaments; (5) reproductive structures distributed throughout the thallus, especially centralized at the bottom of the end portion of the branches; and (6) 4-celled Carpogonial branches and 3-celled auxiliary-cell branches, both of the Grateloupia-type. The morphological diff erences were supported by molecular phylogenetics based on ribulose-1, 5-bisphosphate carboxylase/oxygenase (rbcL) gene sequence analysis. There was only a 1 bp divergence between specimens collected from Wenchang and Lingshui of Hainan province. The new species was embedded in the large Grateloupia clade of the Halymeniaceae. The pairwise distances between G. ramosa and other species within Grateloupia ranged from 26 to 105 bp, within pairwise distances of 13–111 bp between species of the large genus Grateloupia in Halymeniaceae. Thus, we propose this new species as G. ramosa Wang & Luan sp. nov.     

基于黑白环扇圆盘的角点自动识别与排序

采用传统棋盘格进行相机标定时,标定模板旋转角度对角点排序带来较大的影响,针对此设计了一种黑白环扇圆盘标定模板,并提出了与该标定模板相应的角点识别和排序算法。新的标定模板设计中心圆为1/4缺口的黑色圆,以此来消除标定模板旋转给角点排序带来的影响。通过模板区域分割算法实现标定模板的提取;创建圆形检测器剔除Harris算法提取的伪角点;利用向量积和角点几何特性完成角点排序。实验结果表明:该标定模板及其角点识别与排序算法可以有效剔除伪角点,角点排序具有良好的旋转不变性,能够实现自动相机标定。     

基于移动GIS技术的户外救助系统的设计与实现——以宁海登山步道导航系统为例

为了让户外救助高效可行,本文设计开发了登山步道救助系统。该系统是一个C/S架构的在线App系统,基于ArcGIS和iOS平台,利用GIS、Web Socket等技术构建,实现了实时通信、实时定位、路径实时规划以及实时导航功能,极大地提高了户外救助的效率。     

基于FY-3C/MWTS-II数据估计西北太平洋热带气旋强度

文章利用FY-3C新型微波温度计(MWTS-Ⅱ)数据估计发生在西北太平洋的热带气旋强度。首先对与热带气旋最强暖核位置相重合的通道5~8进行临边订正,取得了较好的效果,订正偏差均小于各通道的探测灵敏度;之后利用热带气旋微波亮温距平分布分析了热带气旋在对流层中、上层的增暖特征,发现热带气旋中心的增暖强度、形状特征与热带气旋的强度相关,强度较强的气旋中心增温较强,其暖核结构清楚而完整;最后利用通道6和通道7最强亮温距平中的最大值建立了热带气旋强度估计模型,独立样本检验的标准偏差为13.0 hPa,进行扫描角度修正后的标准偏差为12.0 hPa,引入纬度因子后的标准偏差为11.1 hPa,估计精度得到一定程度的改善。     

哈尔滨市城乡结合部不透水面时空变化及驱动力分析

本文选取哈尔滨市城乡结合部为研究区,以1984年、1993年、2002年和2010年的TM影像数据为数据源,利用混合像元分解方法提取了哈尔滨市城乡结合部的不透水面信息。分析了不透水面的时空变化特征。选取坡度、坡向、DEM、距河流距离、距高速公路距离、距铁路距离、距主要路距离和距1984年城区距离8个因子,利用增强回归树法进一步分析了1984-2010年哈尔滨市城乡结合部不透水面扩张的主要影响因素。结果表明：1984年哈尔滨市城乡结合部不透水面所占比例为3.9%、1993年为6.6%、2002年为9.0%、2010年为16.52%。高速公路、铁路、主要路等交通要素带动周边地区的发展,从而导致道路沿线城市扩张速度较快。     

大数据时代的农情监测与预警

农情信息是世界粮农组织、各国政府、粮食贸易企业以及农场管理迫切需要掌握的信息。大数据时代的农情监测与预警正在由模型驱动向数据驱动转变,大数据正逐渐成为监测与预警的核心驱动力。伴随着农情监测与预警大数据的爆炸式增长,大数据与云计算技术的发展为农情监测与预警提供了全新的技术手段。2013年以来,全球农情遥感速报系统(CropWatch)已逐步引入聚类分析、时间序列分析、关联分析、时空变化异常诊断等大数据分析方法,并应用于业务化运行的农情监测与预警中。大数据技术提升了CropWatch的数据挖掘能力,对CropWatch农情监测与预警时空尺度的拓展以及农情监测内容的精细化起到推动作用,促进了面向需求的CropWatch农情信息与预警精准云服务的发展,促成了大数据时代CropWatch农情监测与预警技术体系的升级。未来,大数据时代的农情监测与预警将逐渐向全自动化监测、实时化精准农业管理与智能化信息服务方向发展;通过众源采集技术高效低廉的获取农情观测大数据将成为未来的发展趋势;大数据技术跨领域数据挖掘的能力,使得丰富多元化的跨界信息服务将成为大数据时代农情监测与预警的主流发展方向。大数据时代的CropWatch正在向基于大数据的农情监测与预警系统全速迈进。     

Fusion SST from Infrared and Microwave Measurement of FY-3D MeteorologicalSatellite

Sea surface temperature (SST) is one of the important parameters of global ocean and climate research, whichcan be retrieved by satellite infrared and passive microwave remote sensing instruments. While satellite infrared SST offershigh spatial resolution, it is limited by cloud cover. On the other hand, passive microwave SST provides all-weatherobservation but suffers from poor spatial resolution and susceptibility to environmental factors such as rainfall, coastaleffects, and high wind speeds. To achieve high-precision, comprehensive, and high-resolution SST data, it is essential tofuse infrared and microwave SST measurements. In this study, data from the Fengyun-3D (FY-3D) medium resolutionspectral imager II (MERSI-II) SST and microwave imager (MWRI) SST were fused. Firstly, the accuracy of both MERSI II SST and MWRI SST was verified, and the latter was bilinearly interpolated to match the 5km resolution grid of MERSISST. After pretreatment and quality control of MERSI SST and MWRI SST, a Piece-Wise Regression method wasemployed to correct biases in MWRI SST. Subsequently, SST data were selected based on spatial resolution and accuracywithin a 3-day window of the analysis date. Finally, an optimal interpolation method was applied to fuse the FY-3DMERSI-II SST and MWRI SST. The results demonstrated a significant improvement in spatial coverage compared toMERSI-II SST and MWRI SST. Furthermore, the fusion SST retained true spatial distribution details and exhibited anaccuracy of –0.12±0.74°C compared to OSTIA SST. This study has improved the accuracy of FY satellite fusion SSTproducts in China.