Lipid and fatty acid compositions of cod (<Emphasis Type="Italic">Gadus morhua</Emphasis>), haddock (<Emphasis Type="Italic">Melanogrammus aeglefinus</Emphasis>) and halibut (<Emphasis Type="Italic">Hippoglossus hippoglossus</Emphasis>)     

Duan Zeng  Kangsen Mai  Qinghui Ai  Joyce E. Milley  Santosh P. Lall 《中国海洋大学学报(英文版)》2010,9(4):381-388

This study was conducted to compare lipid and fatty acid composition of cod, haddock and halibut. Three groups of cod (276 g ± 61 g), haddock (538 g ± 83 g) and halibut (3704 g ± 221 g) were maintained with commercial feeds mainly based on fish meal and marine fish oil for 12 weeks prior to sampling. The fatty acid compositions of muscle and liver were determined by GC/FID after derivatization of extracted lipids into fatty acid methyl esters (FAME). Lipids were also fractionated into neutral and polar lipids using Waters silica Sep-Pak?. The phospholipid fraction was further separated by high-performance thin-layer chromatography (HPTLC) and the FAME profile was obtained. Results of the present study showed that cod and haddock were lean fish and their total muscle lipid contents were 0.8% and 0.7%, respectively, with phospholipid constituting 83.6% and 87.5% of the total muscle lipid, respectively. Halibut was a medium-fat fish and its muscle lipid content was 8%, with 84% of the total muscle lipid being neutral lipid. Total liver lipid contents of cod, haddock and halibut were 36.9%, 67.2% and 30.7%, respectively, of which the neutral lipids accounted for the major fraction (88.1%–97.1%). Polyunsaturated fatty acids were the most abundant in cod and haddock muscle neutral lipid. Monounsaturated fatty acid level was the highest in halibut muscle neutral lipid. Fatty acid compositions of phospholipid were relatively constant. In summary, the liver of cod and haddock as lean fish was the main lipid reserve organ, and structural phospholipid is the major lipid form in flesh. However, as a medium-fat fish, halibut stored lipid in both their liver and muscle.  相似文献   

Event Stratigraphy, Biostratigraphy and Sequence Stratigraphy of Neoproterozoic in North China   总被引：3，自引：0，他引：3  

Qiao Xiufu Gao Linzhi Gao Mai Institute of Geology  Chinese Academy of Geological Sciences  Beijing 《中国地质大学学报(英文版)》1999,10(2):40

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南海东北部及台湾海峡附近环流的一个耦合模式Ⅱ.模式的试验结果     

蔡树群  黄企洲  麦波强 《热带海洋学报》1998,(4)

利用一个耦合的数值模式对南海东北部及台湾海峡附近的环流进行数值模拟,以研究多种可能的动力机制对该海区的影响。结果表明：当以风应力、黑潮作为驱动机制时,该模式的计算结果大致与正压模式的计算结果相同;风应力促使研究海区流场的结构和强度发生变化,而黑潮才可能是该海区动力机制的主要因子。黑潮人流、入射角及海区层结程度等因素对该海区流态变化的影响较大;文章同时指出摩擦效应对于流场结构产生影响的重要性。  相似文献   

民用太阳能热水系统雷电灾害分析和防雷技术   总被引：2，自引：1，他引：1  

聂长春  郭麦  陈青山 《气象科技》2008,36(6):830-833

通过对太阳能热水系统雷电灾害事例的理论分析及对现行各类防雷技术标准的相关研究,从集热器部分直击雷的防护、电气与自动控制系统的LEMP防护以及人身安全保护3个方面提出太阳能热水系统防雷技术要点.建议根据安装太阳能热水系统的建筑物高度以及所在地年平均雷暴日数等因素进行雷击风险评估,以决定是否需要在集热器附近安装接闪杆.对电气与自动控制系统的LEMP防护应考虑一次性投入、维修维护费用与设备的价值之比决定是否采取屏蔽、等电位连接、防闪络措施(合理布线)及加装浪涌保护器(SPD)等措施.  相似文献   

云南哀牢山地区化探异常分布规律及找矿意义     

戚东平  李麦兑 《黄金地质》2000,6(2):66-69

云南哀牢山地区地质条件复杂,矿产丰富。１：５万化探分散流圈定的Ａｕ异常,因地质体的不同,异常特征各异。以哀牢山地区已知金矿地质地球化学特征为依据,划分了３类不同地质体控制的Ａｕ异常,在此基础上提出了地质找矿远景区。  相似文献   

对筒柱藻属33株总脂含量及脂肪酸组成的比较研究     

梁英  麦康森  孙世春  詹冬梅 《中国海洋大学学报(自然科学版)》1999,(3)

在温度为２５±１℃,盐度为２８的条件下,用Ｆ／２培养基对青岛海洋大学微藻种质库保存的３３株筒柱藻进行培养。在指数生长期末期进行收获。测定了３３株筒柱藻的脂肪酸组成,总脂含量,收获时的细胞密度,生物量。其主要脂肪酸为１４∶０,１６∶０,１６∶１（ｎ－７）和２０∶５（ｎ－３）,其中Ｂ２００的２０∶５（ｎ－３）含量最高,占总脂肪酸的１５．９％。  相似文献   

30株海洋绿藻的总脂含量和脂肪酸组成   总被引：9，自引：2，他引：7  

曹春晖  孙世春  麦康森  梁英 《中国海洋大学学报(自然科学版)》2000,30(3):428-434

对 11属 (小球藻属、绿囊藻属、微绿球藻属、海绿球藻属、卵胞藻属、原球藻属、咸胞藻属、杜氏藻属、裂丝藻属、塔胞藻属和衣藻属 )的 30株海洋绿藻进行特定条件下的一次性培养 ,在指数生长末期收获 ,进行了总脂含量和脂肪酸组成的分析。 2 1株海洋绿藻的总脂含量超过干重的10 % ,达 11.6 1%～ 34.4 9% ,其它 9株在 4 .2 5%～ 9.4 8%之间 ,绿藻的 16碳和 18碳脂肪酸最为丰富 ,有着含量较高的 16∶ 0、16∶ (n- 3)、18∶ 2 (n- 6 )和 18∶ 3(n- 3)脂肪酸。两株小球藻 (C95,C97)的2 0∶ 5(n- 3)脂肪酸含量较高 ,分别为 2 0 .8%和 2 6 .1%。另一株小球藻 (C10 2 )和两株裂丝藻 (C19和C2 0 ) EPA含量居中 ,分别为 8.0 % ,6 .0 %和 8.1%。其它藻株一般只含有少量的 2 0∶ 5(n- 3)和 2 2∶ 6 (n- 3)或不含 2 2∶ 6 (n- 3)  相似文献   

Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting     

Ling Cai  Krzysztof Janowicz  Gengchen Mai  Bo Yan  Rui Zhu 《Transactions in GIS》2020,24(3):736-755

Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐temporal dependencies at different scales. Recently, several hybrid deep learning models have been developed to capture such dependencies. These approaches typically utilize convolutional neural networks or graph neural networks (GNNs) to model spatial dependency and leverage recurrent neural networks (RNNs) to learn temporal dependency. However, RNNs are only able to capture sequential information in the time series, while being incapable of modeling their periodicity (e.g., weekly patterns). Moreover, RNNs are difficult to parallelize, making training and prediction less efficient. In this work we propose a novel deep learning architecture called Traffic Transformer to capture the continuity and periodicity of time series and to model spatial dependency. Our work takes inspiration from Google’s Transformer framework for machine translation. We conduct extensive experiments on two real‐world traffic data sets, and the results demonstrate that our model outperforms baseline models by a substantial margin.  相似文献   

Deeply integrating Linked Data with Geographic Information Systems   总被引：1，自引：0，他引：1  

Gengchen Mai  Krzysztof Janowicz  Bo Yan  Simon Scheider 《Transactions in GIS》2019,23(3):579-600

The realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web‐scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains, including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which Geographic Information Systems (GIS) are still used today and the open‐ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri's ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within GIS. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on the fly, how to make use of reasoning, how to derive data that are ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step toward a guided geographic question answering system over geographic knowledge graphs.  相似文献   

Making direction a first‐class citizen of Tobler's first law of geography     

Rui Zhu  Krzysztof Janowicz  Gengchen Mai 《Transactions in GIS》2019,23(3):398-416

Waldo Tobler frequently reminded us that the law named after him was nothing more than calling for exceptions. This article discusses one of these exceptions. Spatial relations between points are frequently modeled as vectors in which both distance and direction are of equal prominence. However, in Tobler's first law of geography, such a relation is described only from the perspective of distance by relating the decreasing similarity of observations in some attribute space to their increasing distance in geographic space. Although anisotropic versions of many geographic analysis techniques, such as directional semivariograms, anisotropy clustering, and anisotropic point pattern analysis, have been developed over the years, direction remains on the level of an afterthought. We argue that, compared to distance, directional information is still under‐explored and anisotropic techniques are substantially less frequently applied in everyday GIS analysis. Commonly, when classical spatial autocorrelation indicators, such as Moran's I, are used to understand a spatial pattern, the weight matrix is only built from distance, without direction being considered. Similarly, GIS operations, such as buffering, do not take direction into account either, with distance in all directions being treated equally. In reality, meanwhile, particularly in urban structures and when processes are driven by the underlying physical geography, direction plays an essential role. In this article we ask whether the development of early GIS, data (sample) sparsity, and Tobler's law lead to a theory‐induced blindness for the role of direction. If so, is it possible to envision direction becoming a first‐class citizen of equal importance to distance instead of being an afterthought only considered when the deviation from a perfect circle becomes too obvious to be ignored?  相似文献