目的:利用网络上已建立的空间数据资源和G IS应用,构建自己的W eb G IS系统.方法:GML作为数据交换标准和OGC G IS W eb服务实现G IS互操作.结果:利用deegree服务器,建立了一个基于WMS和WFS的W eb G IS系统.结论:基于GML和G ISW eb服务的...目的:利用网络上已建立的空间数据资源和G IS应用,构建自己的W eb G IS系统.方法:GML作为数据交换标准和OGC G IS W eb服务实现G IS互操作.结果:利用deegree服务器,建立了一个基于WMS和WFS的W eb G IS系统.结论:基于GML和G ISW eb服务的W eb G IS实现了空间数据的无缝集成和G IS功能上的互操作.展开更多
Protein nitration and nitrosylation are essential post-translational modifications （PTMs）involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosyla...Protein nitration and nitrosylation are essential post-translational modifications （PTMs）involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases.Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively,demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%à42%improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http：//deepnitro.renlab.org.展开更多
Various sensors connected to the World Wide Web are used to obtain real-time hydrological observations.Thus,real-time management and utilization of such distributed in situ observations in the cyber-physical environme...Various sensors connected to the World Wide Web are used to obtain real-time hydrological observations.Thus,real-time management and utilization of such distributed in situ observations in the cyber-physical environment becomes possible.A Sensor Observation Service(SOS)chaining Web Feature Service(WFS)method is proposed to integrate geographical reference observation data collected by a hydrological Sensor Web into a virtual globe.This method hides the complexity of a series of information and service models in the Sensor Web realm to enable the integration of heterogeneous distributed hydrological data sources into a Spatial Data Infrastructure(SDI).The core components-a dynamic schema transformer and automatic information extractor-were designed and implemented.The SOS schema is matched to WFS schema that uses the schema transformer dynamically.The information extractor extracts and serves features automatically,conforming to standard SOS operations for observation retrieval and insertion.Feasibility experiments conducted on the Jinsha River tested this proposed method.Results show that the proposed approach allows the integration of SOS servers into legacy applications that have a higher degree of availability within many SDIs.However,this is accompanied with the drawback that only a limited part of the SOS functionality is available to clients.展开更多
Schema matching is a critical step in the integration of heterogeneous web service,which contains various types of web services and multi-version services of the same type.Mapping loss or mismatch usually occurs due t...Schema matching is a critical step in the integration of heterogeneous web service,which contains various types of web services and multi-version services of the same type.Mapping loss or mismatch usually occurs due to schema differences in structure and content and the variety in concept definition and organization.Current instance schema matching methods are not mature enough for heterogeneous web service because they cannot deal with the instance data in web service domain and capture all the semantics,especially metadata semantics.The metadata-based and the instance-based matching methods,in the case of being employed individually,are not efficient to determine the concept relationships,which are crucial for finding high-quality matches between schema attributes.In this paper,we propose an improved schema matching method,based on the combination of instance and metadata(CIM)matcher.The main method of our approach is to utilize schema structure,element labels,and the corresponding instance data information.The matching process is divided into two phases.In the first phase,the metadata-based matchers are used to compute the element label similarity of multi-version open geospatial consortium web service schema,and the generated matching results are raw mappings,which will be reused in the next instance matching phase.In the second phase,the designed instance matching algorithms are employed to the instance data of the raw mappings and fine mappings are generated.Finally,the raw mappings and the fine mappings are combined,and the final mappings are obtained.Our experiments are executed on different versions of web coverage service and web feature service instance data deployed in Geoserver.The results indicate that,the CIM method can obtain more accurate matching results and is flexible enough to handle the web service instance data.展开更多
文摘目的:利用网络上已建立的空间数据资源和G IS应用,构建自己的W eb G IS系统.方法:GML作为数据交换标准和OGC G IS W eb服务实现G IS互操作.结果:利用deegree服务器,建立了一个基于WMS和WFS的W eb G IS系统.结论:基于GML和G ISW eb服务的W eb G IS实现了空间数据的无缝集成和G IS功能上的互操作.
基金supported by grants from the National Natural Science Foundation of China (Grant Nos. 91753137, 31471252, 31771462, 81772614, and U1611261)National Key Research and Development Program of China (Grant No. 2017YFA0106700)+2 种基金Guangdong Natural Science Foundation (Grant Nos. 2014TQ01R387 and 2014A030313181)Science and Technology Program of Guangzhou, China (Grant Nos. 201604020003 and 201604046001)China Postdoctoral Science Foundation (Grant No. 2017M622864)
文摘Protein nitration and nitrosylation are essential post-translational modifications （PTMs）involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases.Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively,demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%à42%improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http：//deepnitro.renlab.org.
基金This work has been supported in part by the National Basic Research Program of China(973 Program)under Grant 2011CB707101by the National Natural Science Foundation of China under Grant 41023001,41171315,and 41021061+1 种基金by the program for New Century Excellent Talents in University under Grant NCET-11-0394by National High Technology Research and Development Program of China(863 Program)under Grant 2012AA121401.
文摘Various sensors connected to the World Wide Web are used to obtain real-time hydrological observations.Thus,real-time management and utilization of such distributed in situ observations in the cyber-physical environment becomes possible.A Sensor Observation Service(SOS)chaining Web Feature Service(WFS)method is proposed to integrate geographical reference observation data collected by a hydrological Sensor Web into a virtual globe.This method hides the complexity of a series of information and service models in the Sensor Web realm to enable the integration of heterogeneous distributed hydrological data sources into a Spatial Data Infrastructure(SDI).The core components-a dynamic schema transformer and automatic information extractor-were designed and implemented.The SOS schema is matched to WFS schema that uses the schema transformer dynamically.The information extractor extracts and serves features automatically,conforming to standard SOS operations for observation retrieval and insertion.Feasibility experiments conducted on the Jinsha River tested this proposed method.Results show that the proposed approach allows the integration of SOS servers into legacy applications that have a higher degree of availability within many SDIs.However,this is accompanied with the drawback that only a limited part of the SOS functionality is available to clients.
基金This work was supported by the National Natural Science Foundation of China[grant number 41201393]the Open Research Fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing of Wuhan University[grant number 14I03].
文摘Schema matching is a critical step in the integration of heterogeneous web service,which contains various types of web services and multi-version services of the same type.Mapping loss or mismatch usually occurs due to schema differences in structure and content and the variety in concept definition and organization.Current instance schema matching methods are not mature enough for heterogeneous web service because they cannot deal with the instance data in web service domain and capture all the semantics,especially metadata semantics.The metadata-based and the instance-based matching methods,in the case of being employed individually,are not efficient to determine the concept relationships,which are crucial for finding high-quality matches between schema attributes.In this paper,we propose an improved schema matching method,based on the combination of instance and metadata(CIM)matcher.The main method of our approach is to utilize schema structure,element labels,and the corresponding instance data information.The matching process is divided into two phases.In the first phase,the metadata-based matchers are used to compute the element label similarity of multi-version open geospatial consortium web service schema,and the generated matching results are raw mappings,which will be reused in the next instance matching phase.In the second phase,the designed instance matching algorithms are employed to the instance data of the raw mappings and fine mappings are generated.Finally,the raw mappings and the fine mappings are combined,and the final mappings are obtained.Our experiments are executed on different versions of web coverage service and web feature service instance data deployed in Geoserver.The results indicate that,the CIM method can obtain more accurate matching results and is flexible enough to handle the web service instance data.
