目的针对目前数据库在提供组织、存储和展示文献来源的蛋白质相互作用知识和数据支撑方面的不足,设计并构建了蛋白质相互作用信息数据库系统(protein-protein interaction information database,dbPPII)。方法根据文献来源的蛋白质相互...目的针对目前数据库在提供组织、存储和展示文献来源的蛋白质相互作用知识和数据支撑方面的不足,设计并构建了蛋白质相互作用信息数据库系统(protein-protein interaction information database,dbPPII)。方法根据文献来源的蛋白质相互作用数据的特点,使用MySQL设计包含蛋白质相互作用、文献及本体三方面信息的数据库结构,引入本体工具展示数据,并使用JSP等技术开发实现。结果数据库系统实现了基于本体的信息组织和展示,提供多种数据查询方式及丰富的文献信息,并具有数据下载功能。目前,dbPPII系统已经应用于组织、存储和展示人及小鼠肝脏相关文献挖掘得到的蛋白质相互作用信息。结论 dbPPII系统具有存储和检索文献来源的蛋白质相互作用信息的多种优势,并有效地利用了蛋白质相互作用本体信息框架组织和展示蛋白质相互作用数据。dbPPII访问主页:http://ppii.hupo.org.cn。展开更多
As the largest pool of terrestrial organic carbon, soils interact strongly with atmosphere composition, climate, and land change. Soil organic carbon dynamics in ecosystem plays a great role in global carbon cycle and...As the largest pool of terrestrial organic carbon, soils interact strongly with atmosphere composition, climate, and land change. Soil organic carbon dynamics in ecosystem plays a great role in global carbon cycle and global change. With development of mathematical models that simulate changes in soil organic carbon, there have been considerable advances in understanding soil organic carbon dynamics. This paper mainly reviewed the composition of soil organic matter and its influenced factors, and recommended some soil organic matter models worldwide. Based on the analyses of the developed results at home and abroad, it is suggested that future soil organic matter models should be developed toward based-process models, and not always empirical ones. The models are able to reveal their interaction between soil carbon systems, climate and land cover by technique and methods of GIS (Geographical Information System) and RS (Remote Sensing). These models should be developed at a global scale, in dynamically describing the spatial and temporal changes of soil organic matter cycle. Meanwhile, the further researches on models should be strengthen for providing theory basis and foundation in making policy of green house gas emission in China.展开更多
In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones...In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones by thevenn diagrams.Finally,a nice example shows that the entropies of sequential effect algebra depend extremely on theorder of its sequential product.展开更多
Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However, in many practical situations, this assumption is violated. One examp...Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However, in many practical situations, this assumption is violated. One example is the practical action videos with complex background and the universal human action databases of Kangliga Tekniska Hogskolan （KTH）. When training data are very scarce, supervised learning is difficult. However, it will cost lots of human and material resources to establish a labeled video set which includes a large amount of videos with complex backgrounds. In this paper, we propose an action recognition framework which uses transfer boosting learning algorithm. By using this algorithm, we can train an action recognition model fitting for most practical situations just relaying on the universal action video dataset and a tiny set of action videos with complex background. And the experiment results show that the performance is improved.展开更多
In this paper, a new approach of muhi-modality image registration is represented with not only image intensity, but also features describing image structure. There are two novelties in the proposed method. Firstly, in...In this paper, a new approach of muhi-modality image registration is represented with not only image intensity, but also features describing image structure. There are two novelties in the proposed method. Firstly, instead of standard mutual information （ MI ） based on joint intensity histogram, regional mutual information （ RMI ） is employed, which allows neighborhood information to be taken into account. Secondly, a new feature images obtained by means of phase congruency are invariants to brightness or contrast changes. By incorporating these features and intensity into RMI, we can combine the aspects of both structural and neighborhood information together, which offers a more robust and a high level of registration accuracy.展开更多
文摘目的针对目前数据库在提供组织、存储和展示文献来源的蛋白质相互作用知识和数据支撑方面的不足,设计并构建了蛋白质相互作用信息数据库系统(protein-protein interaction information database,dbPPII)。方法根据文献来源的蛋白质相互作用数据的特点,使用MySQL设计包含蛋白质相互作用、文献及本体三方面信息的数据库结构,引入本体工具展示数据,并使用JSP等技术开发实现。结果数据库系统实现了基于本体的信息组织和展示,提供多种数据查询方式及丰富的文献信息,并具有数据下载功能。目前,dbPPII系统已经应用于组织、存储和展示人及小鼠肝脏相关文献挖掘得到的蛋白质相互作用信息。结论 dbPPII系统具有存储和检索文献来源的蛋白质相互作用信息的多种优势,并有效地利用了蛋白质相互作用本体信息框架组织和展示蛋白质相互作用数据。dbPPII访问主页:http://ppii.hupo.org.cn。
基金The research is funded by National Natural Science Foundation (40231016) and Canadian International Development Agency (CIDA).
文摘As the largest pool of terrestrial organic carbon, soils interact strongly with atmosphere composition, climate, and land change. Soil organic carbon dynamics in ecosystem plays a great role in global carbon cycle and global change. With development of mathematical models that simulate changes in soil organic carbon, there have been considerable advances in understanding soil organic carbon dynamics. This paper mainly reviewed the composition of soil organic matter and its influenced factors, and recommended some soil organic matter models worldwide. Based on the analyses of the developed results at home and abroad, it is suggested that future soil organic matter models should be developed toward based-process models, and not always empirical ones. The models are able to reveal their interaction between soil carbon systems, climate and land cover by technique and methods of GIS (Geographical Information System) and RS (Remote Sensing). These models should be developed at a global scale, in dynamically describing the spatial and temporal changes of soil organic matter cycle. Meanwhile, the further researches on models should be strengthen for providing theory basis and foundation in making policy of green house gas emission in China.
基金Supported by Research Foundation of Kumoh National Institute of Technology
文摘In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones by thevenn diagrams.Finally,a nice example shows that the entropies of sequential effect algebra depend extremely on theorder of its sequential product.
基金National Natural Science Foundation of China ( No.60873179)Shenzhen Municipal Science and Technology Planning Program for Basic Research, China ( No. JC200903180630A)Research Fund for the Doctoral Program of Higher Education of China (No.20090121110032)
文摘Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However, in many practical situations, this assumption is violated. One example is the practical action videos with complex background and the universal human action databases of Kangliga Tekniska Hogskolan （KTH）. When training data are very scarce, supervised learning is difficult. However, it will cost lots of human and material resources to establish a labeled video set which includes a large amount of videos with complex backgrounds. In this paper, we propose an action recognition framework which uses transfer boosting learning algorithm. By using this algorithm, we can train an action recognition model fitting for most practical situations just relaying on the universal action video dataset and a tiny set of action videos with complex background. And the experiment results show that the performance is improved.
文摘In this paper, a new approach of muhi-modality image registration is represented with not only image intensity, but also features describing image structure. There are two novelties in the proposed method. Firstly, instead of standard mutual information （ MI ） based on joint intensity histogram, regional mutual information （ RMI ） is employed, which allows neighborhood information to be taken into account. Secondly, a new feature images obtained by means of phase congruency are invariants to brightness or contrast changes. By incorporating these features and intensity into RMI, we can combine the aspects of both structural and neighborhood information together, which offers a more robust and a high level of registration accuracy.
