The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-nois...The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.展开更多
Background The proliferation of porcine ovarian granulosa cells(GCs)is essential to follicular development and the ubiquitin–proteasome system is necessary for maintaining cell cycle homeostasis.Previous studies foun...Background The proliferation of porcine ovarian granulosa cells(GCs)is essential to follicular development and the ubiquitin–proteasome system is necessary for maintaining cell cycle homeostasis.Previous studies found that the deubiquitinase ubiquitin carboxyl-terminal hydrolase 1(UCHL1)regulates female reproduction,especially in ovarian development.However,the mechanism by which UCHL1 regulates porcine GC proliferation remains unclear.Results UCHL1 overexpression promoted GC proliferation,and knockdown had the opposite effect.UCHL1 is directly bound to cyclin B1(CCNB1),prolonging the half-life of CCNB1 and inhibiting its degradation,thereby promoting GC proliferation.What's more,a flavonoid compound-isovitexin improved the enzyme activity of UCHL1 and promoted the proliferation of porcine GCs.Conclusions UCHL1 promoted the proliferation of porcine GCs by stabilizing CCNB1,and isovitexin enhanced the enzyme activity of UCHL1.These findings reveal the role of UCHL1 and the potential of isovitexin in regulating proliferation and provide insights into identifying molecular markers and nutrients that affect follicle development.展开更多
Ovarian follicle development is associated with the physiological functions of granulosa cells(GCs),including proliferation and apoptosis.The level of miR-24-3p in ovarian tissue of high-yielding Yorkshire×Landra...Ovarian follicle development is associated with the physiological functions of granulosa cells(GCs),including proliferation and apoptosis.The level of miR-24-3p in ovarian tissue of high-yielding Yorkshire×Landrace sows was significantly higher than that of low-yielding sows.However,the functions of miR-24-3p on GCs are unclear.In this study,using flow cytometry,5-ethynyl-2′-de-oxyuridine(EdU)staining,and cell count,we showed that miR-24-3p promoted the proliferation of GCs increasing the proportion of cells in the S phase and upregulating the expression of cell cycle genes,moreover,miR-24-3p inhibited GC apoptosis.Mechanistically,on-line prediction,bioinformatics analysis,a luciferase reporter assay,RT-qPCR,and Western blot results showed that the target gene of miR-24-3p in proliferation and apoptosis is cyclin-dependent kinase inhibitor 1B(P27/CDKN1B).Furthermore,the effect of miR-24-3p on GC proliferation and apoptosis was attenuated by P27 overexpression.These findings suggest that miR-24-3p regulates the physiological functions of GCs.展开更多
基金financially supported by the National Key R&D Program of China(2018YFA0702504)the National Natural Science Foundation of China(42174152)+1 种基金the Strategic Cooperation Technology Projects of China National Petroleum Corporation(CNPC)and China University of Petroleum-Beijing(CUPB)(ZLZX2020-03)the R&D Department of China National Petroleum Corporation(2022DQ0604-01)。
文摘The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.
基金funded by National Key R&D Program of China(NO.2022YFD1300303)National Natural Science Foundation of China(32272849)。
文摘Background The proliferation of porcine ovarian granulosa cells(GCs)is essential to follicular development and the ubiquitin–proteasome system is necessary for maintaining cell cycle homeostasis.Previous studies found that the deubiquitinase ubiquitin carboxyl-terminal hydrolase 1(UCHL1)regulates female reproduction,especially in ovarian development.However,the mechanism by which UCHL1 regulates porcine GC proliferation remains unclear.Results UCHL1 overexpression promoted GC proliferation,and knockdown had the opposite effect.UCHL1 is directly bound to cyclin B1(CCNB1),prolonging the half-life of CCNB1 and inhibiting its degradation,thereby promoting GC proliferation.What's more,a flavonoid compound-isovitexin improved the enzyme activity of UCHL1 and promoted the proliferation of porcine GCs.Conclusions UCHL1 promoted the proliferation of porcine GCs by stabilizing CCNB1,and isovitexin enhanced the enzyme activity of UCHL1.These findings reveal the role of UCHL1 and the potential of isovitexin in regulating proliferation and provide insights into identifying molecular markers and nutrients that affect follicle development.
基金supported by the National Natural Science Foundation of China(32272849)the National Key R&D Program of China(2021YFF1000602)the earmarked fund for CARS-35-PIG。
文摘Ovarian follicle development is associated with the physiological functions of granulosa cells(GCs),including proliferation and apoptosis.The level of miR-24-3p in ovarian tissue of high-yielding Yorkshire×Landrace sows was significantly higher than that of low-yielding sows.However,the functions of miR-24-3p on GCs are unclear.In this study,using flow cytometry,5-ethynyl-2′-de-oxyuridine(EdU)staining,and cell count,we showed that miR-24-3p promoted the proliferation of GCs increasing the proportion of cells in the S phase and upregulating the expression of cell cycle genes,moreover,miR-24-3p inhibited GC apoptosis.Mechanistically,on-line prediction,bioinformatics analysis,a luciferase reporter assay,RT-qPCR,and Western blot results showed that the target gene of miR-24-3p in proliferation and apoptosis is cyclin-dependent kinase inhibitor 1B(P27/CDKN1B).Furthermore,the effect of miR-24-3p on GC proliferation and apoptosis was attenuated by P27 overexpression.These findings suggest that miR-24-3p regulates the physiological functions of GCs.
