Objective:This study aimed to evaluate the effects of mitochondrial pyruvate carrier(MPC)blockade on the sensitivity of detection and radiotherapy of prostate cancer(PCa).Methods:We investigated glycolysis reprogrammi...Objective:This study aimed to evaluate the effects of mitochondrial pyruvate carrier(MPC)blockade on the sensitivity of detection and radiotherapy of prostate cancer(PCa).Methods:We investigated glycolysis reprogramming and MPC changes in patients with PCa by using metabolic profiling,RNASeq,and tissue microarrays.Transient blockade of pyruvate influx into mitochondria was observed in cellular studies to detect its different effects on prostate carcinoma cells and benign prostate cells.Xenograft mouse models were injected with an MPC inhibitor to evaluate the sensitivity of 18F-fluorodeoxyglucose positron emission tomography with computed tomography and radiotherapy of PCa.Furthermore,the molecular mechanism of this different effect of transient blockage towards benign prostate cells and prostate cancer cells was studied in vitro.Results:MPC was elevated in PCa tissue compared with benign prostate tissue,but decreased during cancer progression.The transient blockade increased PCa cell proliferation while decreasing benign prostate cell proliferation,thus increasing the sensitivity of PCa cells to 18F-PET/CT(SUVavg,P=0.016;SUVmax,P=0.03)and radiotherapy(P<0.01).This differential effect of MPC on PCa and benign prostate cells was dependent on regulation by a VDAC1-MPC-mitochondrial homeostasis-glycolysis pathway.Conclusions:Blockade of pyruvate influx into mitochondria increased glycolysis levels in PCa but not in non-carcinoma prostate tissue.This transient blockage sensitized PCa to both detection and radiotherapy,thus indicating that glycolytic potential is a novel mechanism underlying PCa progression.The change in the mitochondrial pyruvate influx caused by transient MPC blockade provides a critical target for PCa diagnosis and treatment.展开更多
User portrait has been a booming concept in big data industry in recent years which is a direct way to restore users’information.When it talks about user portrait,it will be connected with precise marketing and opera...User portrait has been a booming concept in big data industry in recent years which is a direct way to restore users’information.When it talks about user portrait,it will be connected with precise marketing and operating.However,there are more ways which can reflect the good use of user portrait.Commercial use is the most acceptable use but it also can be used in different industries widely.The goal of this paper is forecasting gender by user portrait and making it useful in transportation safety.It can extract the information from people who violated traffic principle to know the features of them then forecast the gender of these people.Finally,it will analyze the prediction based on characteristics correlation and forecasting results from models which can verify if gender can have an obvious influence on the traffic violation.Also we hope give some advice to drivers and traffic department by doing this research.展开更多
The improvement of the accuracy of simulated cloud-related variables,such as the cloud fraction,in global climate models(GCMs)is still a challenging problem in climate modeling.In this study,the influence of cloud mic...The improvement of the accuracy of simulated cloud-related variables,such as the cloud fraction,in global climate models(GCMs)is still a challenging problem in climate modeling.In this study,the influence of cloud microphysics schemes(one-moment versus two-moment schemes)and cloud overlap methods(observation-based versus a fixed vertical decorrelation length)on the simulated cloud fraction was assessed in the BCC_AGCM2.0_CUACE/Aero.Compared with the fixed decorrelation length method,the observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions.The utilization of a two-moment cloud microphysics scheme,on the other hand,notably improved the simulated cloud fraction compared with the one-moment scheme;specifically,the relative bias in the global mean total cloud fraction decreased by 42.9%–84.8%.Furthermore,the total cloud fraction bias decreased by 6.6%in the boreal winter(DJF)and 1.64%in the boreal summer(JJA).Cloud radiative forcing globally and in the four regions improved by 0.3%−1.2% and 0.2%−2.0%,respectively.Thus,our results showed that the interaction between clouds and climate through microphysical and radiation processes is a key contributor to simulation uncertainty.展开更多
With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bri...With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).展开更多
Dear Editor,This letter presents an inspection method for process monitoring of underwater oil transportation via multiple autonomous underwater vehicles(AUV).To improve the adaptability of our method in practice,we i...Dear Editor,This letter presents an inspection method for process monitoring of underwater oil transportation via multiple autonomous underwater vehicles(AUV).To improve the adaptability of our method in practice,we introduce the dynamic complex ocean current data to the previously mentioned case by using regional ocean modeling system(ROMS)for the first time.展开更多
In this study,the decomposed fast and slow responses of clouds to an abruptly quadrupled CO_(2)concentration(approximately 1139 ppmv)in East Asia(EA)are obtained quantitatively by using a general circulation model,BCC...In this study,the decomposed fast and slow responses of clouds to an abruptly quadrupled CO_(2)concentration(approximately 1139 ppmv)in East Asia(EA)are obtained quantitatively by using a general circulation model,BCC–AGCM2.0.Our results show that in the total response,the total cloud cover(TCC),low cloud cover(LCC),and high cloud cover(HCC)all increased north of 40°N and decreased south of 40°N except in the Tibetan Plateau(TP).The mean changes of the TCC,LCC,and HCC in EA were–0.74%,0.38%,and–0.38%in the total response,respectively;1.05%,–0.03%,and 1.63%in the fast response,respectively;and–1.79%,0.41%,and–2.01%in the slow response,respectively.By comparison,we found that changes in cloud cover were dominated by the slow response in most areas in EA due to the changes in atmospheric temperature,circulation,and water vapor supply together.Overall,the changes in the cloud forcing over EA related to the fast and slow responses were opposite to each other,and the final cloud forcing was dominated by the slow response.The mean net cloud forcing(NCF)in the total response over EA was–1.80 W m^(–2),indicating a cooling effect which partially offset the warming effect caused by the quadrupled CO_(2).The total responses of NCF in the TP,south China(SC),and northeast China(NE)were–6.74 W m^(–2),6.11 W m^(–2),and–7.49 W m^(–2),respectively.Thus,the local effects of offsetting or amplifying warming were particularly obvious.展开更多
The influence of non-Independent Identically Distribution(non-IID)data on Federated Learning(FL)has been a serious concern.Clustered Federated Learning(CFL)is an emerging approach for reducing the impact of non-IID da...The influence of non-Independent Identically Distribution(non-IID)data on Federated Learning(FL)has been a serious concern.Clustered Federated Learning(CFL)is an emerging approach for reducing the impact of non-IID data,which employs the client similarity calculated by relevant metrics for clustering.Unfortunately,the existing CFL methods only pursue a single accuracy improvement,but ignore the convergence rate.Additionlly,the designed client selection strategy will affect the clustering results.Finally,traditional semi-supervised learning changes the distribution of data on clients,resulting in higher local costs and undesirable performance.In this paper,we propose a novel CFL method named ASCFL,which selects clients to participate in training and can dynamically adjust the balance between accuracy and convergence speed with datasets consisting of labeled and unlabeled data.To deal with unlabeled data,the prediction labels strategy predicts labels by encoders.The client selection strategy is to improve accuracy and reduce overhead by selecting clients with higher losses participating in the current round.What is more,the similarity-based clustering strategy uses a new indicator to measure the similarity between clients.Experimental results show that ASCFL has certain advantages in model accuracy and convergence speed over the three state-of-the-art methods with two popular datasets.展开更多
The undersea volcano,located in the South Pacific island nation of Tonga,violently erupted from 14 to 15 January 2022.The Tonga volcano eruption has aroused extensive discussion in the climate change field.Some climat...The undersea volcano,located in the South Pacific island nation of Tonga,violently erupted from 14 to 15 January 2022.The Tonga volcano eruption has aroused extensive discussion in the climate change field.Some climatologists believe that this event will cause little effect on global climate change while others insist that it will trigger“the year without a summer”as the Tambora eruption did in 1815.How will the Tonga volcano eruption affect global climate change?Based on the indices of past volcanic eruptions and the eruption data of El Chichón volcano in 1982,we use a simplified radiation equilibrium model to quantify the stratospheric aerosol radiative forcing and the change in global mean surface air temperature(Ts)caused by the Tonga volcano eruption.The results show that the global average Ts will decrease by about 0.0315-0.1118℃in the next 1-2 years.The Tonga eruption will slightly slow down the global warming in a short period of time,but it will not change the global warming trend in the long term.In addition,we propose a generalized approach for estimating the impact of future volcanic eruption on global mean T_(s).展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.81902616 to F.W.)Science and Technology Support Project in the field of biomedicine of Shanghai Science and Technology Action Plan(Grant No.19441909200,F.W.)+6 种基金Clinical Research Project of Shanghai Municipal Commission of Health and Family Planning(Grant No.20184Y0130,F.W.)Precision Medicine Program of Second Military Medical University(Grant No.2017JZ35,F.W.)Youth Startup Program of the Second Military Medical University(Grant No.2016QN12,F.W.)Jiangsu Provincial Medical Youth Talent(Grant No.QNRC2016739,X.W.)Shanghai Sailing Program(Grant No.21YF1423300,H.X.)Natural Science Foundation of Shanghai(Grant No.21ZR1437800,H.X.)Cross-disciplinary Research Fund of Shanghai Ninth People’s Hospital,Shanghai Jiaotong University School of Medicine(Grant No.YG2021QN75,H.X.).
文摘Objective:This study aimed to evaluate the effects of mitochondrial pyruvate carrier(MPC)blockade on the sensitivity of detection and radiotherapy of prostate cancer(PCa).Methods:We investigated glycolysis reprogramming and MPC changes in patients with PCa by using metabolic profiling,RNASeq,and tissue microarrays.Transient blockade of pyruvate influx into mitochondria was observed in cellular studies to detect its different effects on prostate carcinoma cells and benign prostate cells.Xenograft mouse models were injected with an MPC inhibitor to evaluate the sensitivity of 18F-fluorodeoxyglucose positron emission tomography with computed tomography and radiotherapy of PCa.Furthermore,the molecular mechanism of this different effect of transient blockage towards benign prostate cells and prostate cancer cells was studied in vitro.Results:MPC was elevated in PCa tissue compared with benign prostate tissue,but decreased during cancer progression.The transient blockade increased PCa cell proliferation while decreasing benign prostate cell proliferation,thus increasing the sensitivity of PCa cells to 18F-PET/CT(SUVavg,P=0.016;SUVmax,P=0.03)and radiotherapy(P<0.01).This differential effect of MPC on PCa and benign prostate cells was dependent on regulation by a VDAC1-MPC-mitochondrial homeostasis-glycolysis pathway.Conclusions:Blockade of pyruvate influx into mitochondria increased glycolysis levels in PCa but not in non-carcinoma prostate tissue.This transient blockage sensitized PCa to both detection and radiotherapy,thus indicating that glycolytic potential is a novel mechanism underlying PCa progression.The change in the mitochondrial pyruvate influx caused by transient MPC blockade provides a critical target for PCa diagnosis and treatment.
基金This research work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan ProvinceHunan Provincial Key Laboratory of Big Data Science and Technology,Finance and Economics+3 种基金Key Laboratory of Information Technology and Security,Hunan Provincial Higher Education.This research is funded by the Open Foundation for the University Innovation Platform in the Hunan Province(Grant No.18K103)Open Project(Grant Nos.20181901CRP03,20181901CRP04,20181901CRP05)Hunan Provincial Education Science 13th Five Year Plan(Grant No.XJK016BXX001)Social Science Foundation of Hunan Province(Grant No.17YBA049).
文摘User portrait has been a booming concept in big data industry in recent years which is a direct way to restore users’information.When it talks about user portrait,it will be connected with precise marketing and operating.However,there are more ways which can reflect the good use of user portrait.Commercial use is the most acceptable use but it also can be used in different industries widely.The goal of this paper is forecasting gender by user portrait and making it useful in transportation safety.It can extract the information from people who violated traffic principle to know the features of them then forecast the gender of these people.Finally,it will analyze the prediction based on characteristics correlation and forecasting results from models which can verify if gender can have an obvious influence on the traffic violation.Also we hope give some advice to drivers and traffic department by doing this research.
基金supported by the National Key R&D Program of China(2017YFA0603502)(Key)National Natural Science Foundation of China(91644211)S&T Development Fund of CAMS(2021KJ004).
文摘The improvement of the accuracy of simulated cloud-related variables,such as the cloud fraction,in global climate models(GCMs)is still a challenging problem in climate modeling.In this study,the influence of cloud microphysics schemes(one-moment versus two-moment schemes)and cloud overlap methods(observation-based versus a fixed vertical decorrelation length)on the simulated cloud fraction was assessed in the BCC_AGCM2.0_CUACE/Aero.Compared with the fixed decorrelation length method,the observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions.The utilization of a two-moment cloud microphysics scheme,on the other hand,notably improved the simulated cloud fraction compared with the one-moment scheme;specifically,the relative bias in the global mean total cloud fraction decreased by 42.9%–84.8%.Furthermore,the total cloud fraction bias decreased by 6.6%in the boreal winter(DJF)and 1.64%in the boreal summer(JJA).Cloud radiative forcing globally and in the four regions improved by 0.3%−1.2% and 0.2%−2.0%,respectively.Thus,our results showed that the interaction between clouds and climate through microphysical and radiation processes is a key contributor to simulation uncertainty.
基金supported by the Technology Project of State Grid Tianjin Electric Power Company(KJ22-1-47).
文摘With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).
基金supported by the National Natural Science Foundation of China(61871283)。
文摘Dear Editor,This letter presents an inspection method for process monitoring of underwater oil transportation via multiple autonomous underwater vehicles(AUV).To improve the adaptability of our method in practice,we introduce the dynamic complex ocean current data to the previously mentioned case by using regional ocean modeling system(ROMS)for the first time.
基金supported by the National Key R&D Program of China(2017YFA0603502)the National Natural Science Foundation of China(Grant No.41905081)S&T Development Fund of CAMS(2021KJ004&2022KJ019).
文摘In this study,the decomposed fast and slow responses of clouds to an abruptly quadrupled CO_(2)concentration(approximately 1139 ppmv)in East Asia(EA)are obtained quantitatively by using a general circulation model,BCC–AGCM2.0.Our results show that in the total response,the total cloud cover(TCC),low cloud cover(LCC),and high cloud cover(HCC)all increased north of 40°N and decreased south of 40°N except in the Tibetan Plateau(TP).The mean changes of the TCC,LCC,and HCC in EA were–0.74%,0.38%,and–0.38%in the total response,respectively;1.05%,–0.03%,and 1.63%in the fast response,respectively;and–1.79%,0.41%,and–2.01%in the slow response,respectively.By comparison,we found that changes in cloud cover were dominated by the slow response in most areas in EA due to the changes in atmospheric temperature,circulation,and water vapor supply together.Overall,the changes in the cloud forcing over EA related to the fast and slow responses were opposite to each other,and the final cloud forcing was dominated by the slow response.The mean net cloud forcing(NCF)in the total response over EA was–1.80 W m^(–2),indicating a cooling effect which partially offset the warming effect caused by the quadrupled CO_(2).The total responses of NCF in the TP,south China(SC),and northeast China(NE)were–6.74 W m^(–2),6.11 W m^(–2),and–7.49 W m^(–2),respectively.Thus,the local effects of offsetting or amplifying warming were particularly obvious.
基金supported by the National Key Research and Development Program of China(No.2019YFC1520904)the National Natural Science Foundation of China(No.61973250).
文摘The influence of non-Independent Identically Distribution(non-IID)data on Federated Learning(FL)has been a serious concern.Clustered Federated Learning(CFL)is an emerging approach for reducing the impact of non-IID data,which employs the client similarity calculated by relevant metrics for clustering.Unfortunately,the existing CFL methods only pursue a single accuracy improvement,but ignore the convergence rate.Additionlly,the designed client selection strategy will affect the clustering results.Finally,traditional semi-supervised learning changes the distribution of data on clients,resulting in higher local costs and undesirable performance.In this paper,we propose a novel CFL method named ASCFL,which selects clients to participate in training and can dynamically adjust the balance between accuracy and convergence speed with datasets consisting of labeled and unlabeled data.To deal with unlabeled data,the prediction labels strategy predicts labels by encoders.The client selection strategy is to improve accuracy and reduce overhead by selecting clients with higher losses participating in the current round.What is more,the similarity-based clustering strategy uses a new indicator to measure the similarity between clients.Experimental results show that ASCFL has certain advantages in model accuracy and convergence speed over the three state-of-the-art methods with two popular datasets.
基金Supported by the National Key Research and Development Program of China(2017YFA0603502)。
文摘The undersea volcano,located in the South Pacific island nation of Tonga,violently erupted from 14 to 15 January 2022.The Tonga volcano eruption has aroused extensive discussion in the climate change field.Some climatologists believe that this event will cause little effect on global climate change while others insist that it will trigger“the year without a summer”as the Tambora eruption did in 1815.How will the Tonga volcano eruption affect global climate change?Based on the indices of past volcanic eruptions and the eruption data of El Chichón volcano in 1982,we use a simplified radiation equilibrium model to quantify the stratospheric aerosol radiative forcing and the change in global mean surface air temperature(Ts)caused by the Tonga volcano eruption.The results show that the global average Ts will decrease by about 0.0315-0.1118℃in the next 1-2 years.The Tonga eruption will slightly slow down the global warming in a short period of time,but it will not change the global warming trend in the long term.In addition,we propose a generalized approach for estimating the impact of future volcanic eruption on global mean T_(s).