Cloning and phylogenetic analysis of a fatty acid elongase gene from Nannochloropsis oculata CS179

Nannochloropsis oculata CS179, a unicellular marine microalga, is rich in long-chain polyunsaturated fatty acids (LCPUFAs). Elongase and desaturase play a key role in the biosynthesis of PUFAs. A new elongase gene, which encodes 322 amino acids, was identified via RT-PCR and 5′ and 3′ RACE. The sequence of the elongase gene was blast-searched in the NCBI GenBank and showed a similarity to those of the cryptosporidium. But the NJ-tree revealed that the N. oculata CS179 elongase clustered with those of the microalgae Phaeodactylum tricornutum, Ostreococcus tauri and Thalassiosira pseudonana.     

日照市海水入侵现状与治理方案

通过遥感解译对日照市海水入侵现状进行分析,对海水入侵程度进行评价,发现养殖池和河滩面积在大幅增加,盐田、滩涂、沙滩等均在减少。在日照沿海地区,地下水的储备与人类的用水需求之间存在矛盾,人类过量开采地下淡水是产生海水入侵的主导因素。建议采取兴建地下水库、减少地下水开采和人工回灌等措施防治海水入侵。     

三维城市模型的研究与实践(英文)

The way we interact with spatial data has been changed from 2D map to 3D Virtual Geographic Environment (VGE). Three-dimensional representations of geographic information on a computer are known as VGE, and in particular 3D city models provide an efficient way to integrate massive, heterogenous geospatial information and georeferenced information in urban areas. 3D city modeling (3DCM) is an active research and practice topic in distinct application areas. This paper introduces different modeling paradigms employed in 3D GIS, virtual environment, and AEC/FM. Up-to-date 3DCM technologies are evolving into a data integration and collaborative approach to represent the full spatial coverage of a city, to model both aboveground and underground, outdoor and indoor environments including man-made objects and natural features with 3D geometry, appearance, topology and semantics. Supported by the National Natural Science Foundation of China ( No. 40871212, No. 40671158), the Leading Academic Discipline Project of Shanghai Educational Committee( No.J50104).     

基于主成分分析的ENVISAT ASAR数据在福建省漳州市香蕉园地提取中的应用(英文)

Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multi-temporal ENVISAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis (PCA) was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the VV and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st component, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier. Supported by the Program for New Century Excellent Talents in University (NCET-05-0573), Fujian Science and Technology Project (No.2006I0018), the Science Project of the Education Department of Fujian Province(No. 2006F5022).     

Mesoscale physical processes and zooplankton transport-retention in the northern Norwegian shelf region

Northern Norwegian shelf regions are highly productive, supporting fisheries rich in commercially important species such as cod, herring and capelin. It has been long recognized that the mesoscale jets, meanders and eddies associated with interactions between the North Atlantic Current, Norwegian Coastal Current and regional bottom topographic features such as troughs, banks and shelfbreaks play important roles in transporting and retaining zooplankton. To investigate zooplankton distributions and their correspondence with the physical fields, three large-scale surveys with mesoscale resolutions on physical and biological fields were conducted in northern Norwegian shelf regions between latitudes 68°15′N and 70°15′N in springs of 2000–2002. Survey results provide insights into the relationships between zooplankton distributions and the physical features such as fronts, the Norwegian Coastal Current and eddies related to topographic features. The physical and biological data are integrated and analyzed focusing on water types, estimation of geostrophic currents from direct current measurements, along-shelf transport of zooplankton, and retention of zooplankton by the mesoscale meander–eddy over a typical bank area on the shelf. The estimated mean transport in the upper 100 m on the shelf in the survey region is approximately 6.4×10³ tonnes wet weight day⁻¹ northward. High zooplankton abundances were found over both Malangsgrunnen and Sveinsgrunnen banks. The specific accumulation rate from northward–southward transport in the upper 100 m over Malangsgrunnen was approximately 0.08 day⁻¹, while variable currents with an offshore gradient of zooplankton abundance over Sveinsgrunnen implies an offshore dispersion of coastal-originated zooplankton cohort.     

耿达短水准异常与汶川8.0级地震

结合龙门山断裂带其他跨断层资料,对耿达短水准在汶川8.0级地震前后的观测资料进行了重新分析,发现耿达短水准在汶川地震前后的异常为当地居民修建生活小区所致,不应视为地震前兆异常。     

GRACE与SLR J_2项时间序列的小波分析

利用小波功率谱分析和离散小波变换,对CNES/GRGS、CSR、GFZ等研究机构发布的GRACE时变重力位模型的J2项时间序列进行了周期分析和特征提取,并与利用SLR跟踪数据获得的J2项时间序列的分析结果进行了比较,结果显示:SLR跟踪数据获得的J2项相较GRACEJ2项具有更明显的季节性变化特征.GRACE 时变重力位模型的J2项中混淆进了161天的周期信号,是由海洋潮汐模型中存在的太阳半日分潮S2项的误差引起的.     

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浅层气藏流体相控属性参数建模及应用

对钻井资料较少的莺歌海盆地某气田采用流体相控属性建模,利用叠前纵横波速度比反演预测储层含气性和储层测井综合解释成果,用随机模拟方法建立储层的流体相模型;用流体相模型进行间接相控,建立了用波阻抗约束的随机模拟孔隙度模型,解决了浅层气藏由于受含气效应影响导致模拟的气层孔隙度偏高而难以正确反映储层物性分布的问题。在此基础上求得连通因子平面分布,勾出孔隙度分隔带,解释了研究区压力和气体类型分区特征。     

BoxCox multi-output linear regression for 10.7 cm solar radio flux prediction

We consider the problem of predicting the mid-term daily 10.7 cm solar radio flux(F10.7),a widely-used solar activity index.A novel approach is proposed for this task,in which BoxCox transformation with a proper parameter is first applied to make the data satisfy the property of homoscedasticity that is a basic assumption of regression models,and then a multi-output linear regression model is used to predict future F10.7 values.The experiment shows that the BoxCox transformation significantly improves the predictive performance and our new approach works substantially better than the prediction from the US Airforce and other alternative methods like Auto-regressive Model,Multi-layer Perceptron,and Support Vector Regression.