Background： The community-based Ontology of Biological and Clinical Statistics （OBCS） represents and standardizes biological and clinical data and statistical methods. Methods： Both OBCS and the Vaccine Ontology ...Background： The community-based Ontology of Biological and Clinical Statistics （OBCS） represents and standardizes biological and clinical data and statistical methods. Methods： Both OBCS and the Vaccine Ontology （VO） were used to ontologically model various components and relations in a typical host response to vaccination study. Such a model was then applied to represent and compare three microarray studies of host responses to the yellow fever vaccine YF-17D. A literature meta-analysis was then conducted to survey yellow fever vaccine response papers and summarize statistical methods, using OBCS. Results： A general ontological model was developed to identify major components in a typical host response to vaccination. Our ontology modeling of three similar studies identified common and different components which may contribute to varying conclusions. Although these three studies all used the same vaccine, human blood samples, similar sample collection time post vaccination, and microarray assays, statistically differentially expressed genes and associated gene functions differed, likely due to the differences in specific variables （e.g., microarray type and human variations）. Our manual annotation of 95 papers in human responses to yellow fever vaccines identified 38 data analysis methods. These statistical methods were consistently represented and classified with OBCS. Eight statistical methods not available in existing ontologies were added to OBCS. Conclusions： The study represents the first single use case of applying OBCS ontology to standardize, integrate, and use biomedical data and statistical methods. Our ontology-based meta-analysis showed that different experimental results might be due to different experimental assays and conditions, sample variations, and data analysis methods.展开更多
By summing geophone and hydrophone data with opposite polarity responses to water layer reverberation,the ocean bottom cable dual-sensor acquisition technique can effectively eliminate reverberation,broaden the freque...By summing geophone and hydrophone data with opposite polarity responses to water layer reverberation,the ocean bottom cable dual-sensor acquisition technique can effectively eliminate reverberation,broaden the frequency bandwidth,and improve both the resolution and fidelity of the seismic data.It is thus widely used in industry.However,it is difficult to ensure good coupling of the geophones with the seabed because of the impact of ocean flow,seafloor topography,and field operations;therefore,geophone data are seriously affected by the transfer function of the geophone-seabed coupling system.As a result,geophone data frequently have low signal-to-noise ratios（S/N）,which causes large differences in amplitude,frequency,and phases between geophone and hydrophone data that severely affect dual-sensor summation.In contrast,the hydrophone detects changes in brine pressure and has no coupling issues with the seabed;thus,hydrophone data always have good S/N.First,in this paper,the mathematical expression of the transfer function between geophone and seabed is presented.Second,the transfer function of the geophone-seabed is estimated using hydrophone data as reference traces,and finally,the coupling correction based on the estimated transfer function is implemented.Using this processing,the amplitude and phase differences between geophone and hydrophone data are removed,and the S/N of the geophone data are improved.Synthetic and real data examples then show that our method is feasible and practical.展开更多
文摘Background： The community-based Ontology of Biological and Clinical Statistics （OBCS） represents and standardizes biological and clinical data and statistical methods. Methods： Both OBCS and the Vaccine Ontology （VO） were used to ontologically model various components and relations in a typical host response to vaccination study. Such a model was then applied to represent and compare three microarray studies of host responses to the yellow fever vaccine YF-17D. A literature meta-analysis was then conducted to survey yellow fever vaccine response papers and summarize statistical methods, using OBCS. Results： A general ontological model was developed to identify major components in a typical host response to vaccination. Our ontology modeling of three similar studies identified common and different components which may contribute to varying conclusions. Although these three studies all used the same vaccine, human blood samples, similar sample collection time post vaccination, and microarray assays, statistically differentially expressed genes and associated gene functions differed, likely due to the differences in specific variables （e.g., microarray type and human variations）. Our manual annotation of 95 papers in human responses to yellow fever vaccines identified 38 data analysis methods. These statistical methods were consistently represented and classified with OBCS. Eight statistical methods not available in existing ontologies were added to OBCS. Conclusions： The study represents the first single use case of applying OBCS ontology to standardize, integrate, and use biomedical data and statistical methods. Our ontology-based meta-analysis showed that different experimental results might be due to different experimental assays and conditions, sample variations, and data analysis methods.
文摘By summing geophone and hydrophone data with opposite polarity responses to water layer reverberation,the ocean bottom cable dual-sensor acquisition technique can effectively eliminate reverberation,broaden the frequency bandwidth,and improve both the resolution and fidelity of the seismic data.It is thus widely used in industry.However,it is difficult to ensure good coupling of the geophones with the seabed because of the impact of ocean flow,seafloor topography,and field operations;therefore,geophone data are seriously affected by the transfer function of the geophone-seabed coupling system.As a result,geophone data frequently have low signal-to-noise ratios（S/N）,which causes large differences in amplitude,frequency,and phases between geophone and hydrophone data that severely affect dual-sensor summation.In contrast,the hydrophone detects changes in brine pressure and has no coupling issues with the seabed;thus,hydrophone data always have good S/N.First,in this paper,the mathematical expression of the transfer function between geophone and seabed is presented.Second,the transfer function of the geophone-seabed is estimated using hydrophone data as reference traces,and finally,the coupling correction based on the estimated transfer function is implemented.Using this processing,the amplitude and phase differences between geophone and hydrophone data are removed,and the S/N of the geophone data are improved.Synthetic and real data examples then show that our method is feasible and practical.
