We studied the geochemical characteristics of the fluid inclusions in the Ordovician carbonates and the Oligocene Shahejie Formation sandstones from 15 wells in the Gangxi Fault Belt, Huanghua Depression. The fluid in...We studied the geochemical characteristics of the fluid inclusions in the Ordovician carbonates and the Oligocene Shahejie Formation sandstones from 15 wells in the Gangxi Fault Belt, Huanghua Depression. The fluid inclusions are all sec- ondary with gas/liquid ratio of 5%~10%. Base on Raman they are mainly composed of H2O, CO2 and CH4. The homogenization temperatures, combined with burial and geothermal history of the host rock, indicate that the fluid flows in the Shahejie Formation and the Ordovician carbonates were trapped in Neocene. Using a VG5400 mass spectrometer, the helium isotopic compositions were analyzed. Interpretation of results suggested a significant amount of mantle-derived helium mainly accumulating in the intersections of the NWW trending Xuzhuangzi and NE trending Gangxi faults. The maturity of hydrocarbon decreases from the intersection to the outside pointing out that the fluid related to the NWW trending Xuzhuangzi and NE trending Gangxi faults. These factors implied the fluid inclusions have a close relationship to the local tectonic setting. Gangxi Fault Belt experienced intensive Neo-tectonic activities in Cenozoic. Widespread faulted-depressions and strong volcanic eruptions manifested its tec- tonic status of extensional stress field. Mantle uplift caused the movement of magma that carried mantle-derived gases and deep heat flows, the deep-rooted tension faults provided the passages for the gases and heat flows to shallow crust levels.展开更多
MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requ...MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requires considerable time and money,a growing number of researchers are working on developing computational methods to predict MDAs.High accuracy is critical for prediction.To date,many algorithms have been proposed to infer novel MDAs.However,they may still have some drawbacks.In this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known MDAs.In this method,three networks(i.e.,a miRNA functional similarity network,a disease semantic similarity network and a known MDA network)are constructed first.In the process of building the miRNA network and the disease network,Gaussian interaction profile(GIP)kernel is computed to increase the kernel similarities,and the logistic function is used to extract valuable information and protect known MDAs.Next,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false negatives.Then,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network.Finally,the predictive ability of the LWBRW method is confirmed by the average AUC of 0.9393(0.0061)in 5-fold cross-validation(CV)and the AUC value of 0.9763 in leave-one-out cross-validation(LOOCV).In addition,case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.展开更多
基金Project supported by the Key Laboratory of Marginal Sea Geology,South China Sea Institute of Oceanology, Chinese Academy of Sci-ences (No. MSGL0609)the Chinese Academy of Sciences (No.KZCX2-209)
文摘We studied the geochemical characteristics of the fluid inclusions in the Ordovician carbonates and the Oligocene Shahejie Formation sandstones from 15 wells in the Gangxi Fault Belt, Huanghua Depression. The fluid inclusions are all sec- ondary with gas/liquid ratio of 5%~10%. Base on Raman they are mainly composed of H2O, CO2 and CH4. The homogenization temperatures, combined with burial and geothermal history of the host rock, indicate that the fluid flows in the Shahejie Formation and the Ordovician carbonates were trapped in Neocene. Using a VG5400 mass spectrometer, the helium isotopic compositions were analyzed. Interpretation of results suggested a significant amount of mantle-derived helium mainly accumulating in the intersections of the NWW trending Xuzhuangzi and NE trending Gangxi faults. The maturity of hydrocarbon decreases from the intersection to the outside pointing out that the fluid related to the NWW trending Xuzhuangzi and NE trending Gangxi faults. These factors implied the fluid inclusions have a close relationship to the local tectonic setting. Gangxi Fault Belt experienced intensive Neo-tectonic activities in Cenozoic. Widespread faulted-depressions and strong volcanic eruptions manifested its tec- tonic status of extensional stress field. Mantle uplift caused the movement of magma that carried mantle-derived gases and deep heat flows, the deep-rooted tension faults provided the passages for the gases and heat flows to shallow crust levels.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.61902215,61872220 and 61701279.
文摘MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requires considerable time and money,a growing number of researchers are working on developing computational methods to predict MDAs.High accuracy is critical for prediction.To date,many algorithms have been proposed to infer novel MDAs.However,they may still have some drawbacks.In this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known MDAs.In this method,three networks(i.e.,a miRNA functional similarity network,a disease semantic similarity network and a known MDA network)are constructed first.In the process of building the miRNA network and the disease network,Gaussian interaction profile(GIP)kernel is computed to increase the kernel similarities,and the logistic function is used to extract valuable information and protect known MDAs.Next,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false negatives.Then,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network.Finally,the predictive ability of the LWBRW method is confirmed by the average AUC of 0.9393(0.0061)in 5-fold cross-validation(CV)and the AUC value of 0.9763 in leave-one-out cross-validation(LOOCV).In addition,case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.
