With the rapid advancement of sequencing technologies and the growing volume of omics data in plants, there is much anticipation in digging out the treasure from such big data and accordingly refining the current agri...With the rapid advancement of sequencing technologies and the growing volume of omics data in plants, there is much anticipation in digging out the treasure from such big data and accordingly refining the current agricultural practice to be applied in the near future. Toward this end, database resources that deliver web services for plant omics data submission, archiving, and integration are urgently needed. As a part of Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences (CAS), BIG Data Center (http://bigd.big.ac.cn) provides open access to a suite of database resources (Table 1), with the aim of supporting plant research activities for domestic and international users in both academia and industry to translate big data into big discoveries (BIG Data Center Members, 2017;BIG Data Center Members, 2018;BIG Data Center Members, 2019). Here, we give a brief introduction of plant-related database resources in BIG Data Center and appeal to plant research com丒 munities to make full use of these resources for plant data submission, archiving, and integration.展开更多
Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable ...Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's body.Although the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security challenges.In this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors.We highlight the fundamental features of security and privacy for wearable device applications.Then,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors.We also present a case study on privacy-preserving machine learning techniques.Herein,we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance.We explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption algorithm.Finally,we explore the obstacles and gaps in the deployment of practical real-world applications.Following a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.展开更多
An Auto-Exposure(AE)algorithm based on image big data and information entropy is proposed.On the basis of the traditional algorithm for automatic exposure adjustment based on image brightness,image big data analysis i...An Auto-Exposure(AE)algorithm based on image big data and information entropy is proposed.On the basis of the traditional algorithm for automatic exposure adjustment based on image brightness,image big data analysis is introduced for the first time.Through the combination of ambient luminance evaluation and image information entropy,the dimension of information acquisition of the automatic exposure system is improved,thus improving the image effect and scene adaptability of the camera.Especially in high dynamic range scenes,compared with the traditional algorithm,the effect is significantly improved.展开更多
River surface change detection is a vital technology for watershed monitoring,enabling real-time identification of dynamic hydrological variations through remote sensing image analysis.This technology facilitates the ...River surface change detection is a vital technology for watershed monitoring,enabling real-time identification of dynamic hydrological variations through remote sensing image analysis.This technology facilitates the precise assessment of water resource utilization and ecological environmental changes,which are essential for sustainable water management.However,accurately identifying river surfaces remains a challenge,as it requires simultaneously considering both local and global information within the river area.Recently,we developed a Graph Generative Structure-aware Transformer(GraphGST)for hyperspectral image classification.Specifically,we employ the GraphGST as a component of the new approach,leveraging it to capture local-global correlations by feature representation,thereby facilitating river surface change detection in both multispectral and hyperspectral images.This approach is referred to as GraphGST-river.This paper adopts three hyperspectral and multispectral image datasets from GF-5 and Jilin-I GF-02B satellites to validate the effectiveness of the new GraphGST-river.In these confirmatory experiments,our method achieved average accuracies of 99.81%,99.91%,and 99.72%,surpassing existing state-of-the-art approaches.These results demonstrate the superiority of our approach in refining water body contour recognition and enhancing overall change detection performance.展开更多
Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs...Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.展开更多
Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed ...Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.展开更多
Background:Stroke is the second leading cause of death and third leading cause of disability worldwide and is the leading cause of death and disability among adults in China,with its incidence rate continuing to rise....Background:Stroke is the second leading cause of death and third leading cause of disability worldwide and is the leading cause of death and disability among adults in China,with its incidence rate continuing to rise.In China,the average age of firsttime stroke patients is 66.4 years,and the intravenous thrombolysis rate using recombinant tissue plasminogen activator within 3 h of onset is only 16%.Given this fact,there is a pressing need for real‐time predictive tools,particularly for elderly individuals at home,that can provide early warnings for potential strokes.Methods:We collected continuous monitoring data from nonintrusive smart beds and multimodal temporal data from electronic medical records at the National Center for Neurological Disorders.The data included smart bed monitoring indicators,laboratory tests,nurse observations,and static data as potential predictors,with stroke as the outcome.We applied feature representation and feature selection techniques and then input the predictors into machine learning models.Additionally,deep learning models were used after preprocessing the irregular temporal data.Finally,we evaluated the performance of the stroke prediction models and assessed the importance of the features.We used continuously updated vital signs and clinical data during hospitalization to generate timely stroke risk alerts during the same period of admission.Results:A total of 37,041 samples were analyzed,of which 7020 patients were diagnosed with stroke.When only the smart bed features were used for prediction,the model achieved an area under the receiver operating characteristic curve(AUROC)of 0.59−0.63,with an accuracy ranging from 60%−65%.Among the four artificial intelligence algorithms,the random forest model demonstrated the best performance.After all the available features were incorporated,the AUROC increased to 0.94,and the accuracy improved to 92%.Conclusions:In this study,the occurrence of stroke was successfully identified by integrating multimodal temporal data from electronic medical records.Noncontact monitoring of respiration and heart rate offers a promising approach for daily stroke surveillance in home‐based populations,particularly for elderly individuals living alone.展开更多
“双碳”背景下,以煤电为主的大型城市能源系统如何低成本低碳转型成为重要研究课题。当前研究较少详细定量考虑未来高比例可再生能源情景下的高波动性风光出力带来的运营安全挑战,特别是长时储能、短时储能的部署及其成本效益。针对中...“双碳”背景下,以煤电为主的大型城市能源系统如何低成本低碳转型成为重要研究课题。当前研究较少详细定量考虑未来高比例可再生能源情景下的高波动性风光出力带来的运营安全挑战,特别是长时储能、短时储能的部署及其成本效益。针对中国北方某大型城市,构建2021—2060长期能源系统规划模型(long-range energy alternatives planning system,LEAP),基于下一代能源优化模块(next energy modeling system for optimization,NEMO)考虑每年8760 h的需求负荷和可再生能源出力,实现40年长期能源规划与小时级电力运营优化相统一。构建基准情景、电化学储能情景和氢储能情景,探究不同情景下能源系统的运营调度、成本和碳排放量。结果表明电化学储能和氢储能的成本下降和加速部署有助于支撑更高比例的可再生能源装机和发电,减少对化石燃料和调入电力的依赖,降低城市能源系统碳排放。短期内电化学储能和氢储能的部署会提高约15%电力系统成本,而在中长期会降低约39%的电力系统成本和约28%的总成本。展开更多
基金Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19050302 to Z.Z.XDA08020102 to Z.Z.)+2 种基金National Natural Science Foundation of China (31871328 to Z.Z.)K.C.Wong Education Foundation (to Z.Z.)The Youth Innovation Promotion Association of Chinese Academy of Sciences (2017141 to S.S.).
文摘With the rapid advancement of sequencing technologies and the growing volume of omics data in plants, there is much anticipation in digging out the treasure from such big data and accordingly refining the current agricultural practice to be applied in the near future. Toward this end, database resources that deliver web services for plant omics data submission, archiving, and integration are urgently needed. As a part of Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences (CAS), BIG Data Center (http://bigd.big.ac.cn) provides open access to a suite of database resources (Table 1), with the aim of supporting plant research activities for domestic and international users in both academia and industry to translate big data into big discoveries (BIG Data Center Members, 2017;BIG Data Center Members, 2018;BIG Data Center Members, 2019). Here, we give a brief introduction of plant-related database resources in BIG Data Center and appeal to plant research com丒 munities to make full use of these resources for plant data submission, archiving, and integration.
文摘Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's body.Although the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security challenges.In this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors.We highlight the fundamental features of security and privacy for wearable device applications.Then,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors.We also present a case study on privacy-preserving machine learning techniques.Herein,we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance.We explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption algorithm.Finally,we explore the obstacles and gaps in the deployment of practical real-world applications.Following a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.
文摘An Auto-Exposure(AE)algorithm based on image big data and information entropy is proposed.On the basis of the traditional algorithm for automatic exposure adjustment based on image brightness,image big data analysis is introduced for the first time.Through the combination of ambient luminance evaluation and image information entropy,the dimension of information acquisition of the automatic exposure system is improved,thus improving the image effect and scene adaptability of the camera.Especially in high dynamic range scenes,compared with the traditional algorithm,the effect is significantly improved.
基金National Natural Science Foundation of China(No.42571463)Macao Young Scholars Program(No.AM2023033)+2 种基金Shaanxi Province Youth Science and Technology Star Program(No.2024ZC-KJXX-115)Natural Science Foundation Research Program of Shaanxi Province(No.2025JC-YBMS-257)Open Research Fund of Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing(No.KF20250302)。
文摘River surface change detection is a vital technology for watershed monitoring,enabling real-time identification of dynamic hydrological variations through remote sensing image analysis.This technology facilitates the precise assessment of water resource utilization and ecological environmental changes,which are essential for sustainable water management.However,accurately identifying river surfaces remains a challenge,as it requires simultaneously considering both local and global information within the river area.Recently,we developed a Graph Generative Structure-aware Transformer(GraphGST)for hyperspectral image classification.Specifically,we employ the GraphGST as a component of the new approach,leveraging it to capture local-global correlations by feature representation,thereby facilitating river surface change detection in both multispectral and hyperspectral images.This approach is referred to as GraphGST-river.This paper adopts three hyperspectral and multispectral image datasets from GF-5 and Jilin-I GF-02B satellites to validate the effectiveness of the new GraphGST-river.In these confirmatory experiments,our method achieved average accuracies of 99.81%,99.91%,and 99.72%,surpassing existing state-of-the-art approaches.These results demonstrate the superiority of our approach in refining water body contour recognition and enhancing overall change detection performance.
文摘Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82204396,82304491,and 82400511).
文摘Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.
基金supported by the National Natural Science Foundation of China(72204169,82425101,82271516,81801187)Noncommunicable Chronic Diseases‐National Science and Technology Major Project(2023ZD0504800,2023ZD0504801,2023ZD0504802,2023ZD0504803,2023ZD0504804)+2 种基金Beijing Municipal Science&Technology Commission(Z231100004823036)Capital's Funds for Health Improvement and Research(2022‐2‐2045)National Key R&D Program of China(2024YFC3044800,2022YFF1501500,2022YFF1501501,2022YFF1501502,2022YFF1501503,2022YFF1501504,2022YFF1501505).
文摘Background:Stroke is the second leading cause of death and third leading cause of disability worldwide and is the leading cause of death and disability among adults in China,with its incidence rate continuing to rise.In China,the average age of firsttime stroke patients is 66.4 years,and the intravenous thrombolysis rate using recombinant tissue plasminogen activator within 3 h of onset is only 16%.Given this fact,there is a pressing need for real‐time predictive tools,particularly for elderly individuals at home,that can provide early warnings for potential strokes.Methods:We collected continuous monitoring data from nonintrusive smart beds and multimodal temporal data from electronic medical records at the National Center for Neurological Disorders.The data included smart bed monitoring indicators,laboratory tests,nurse observations,and static data as potential predictors,with stroke as the outcome.We applied feature representation and feature selection techniques and then input the predictors into machine learning models.Additionally,deep learning models were used after preprocessing the irregular temporal data.Finally,we evaluated the performance of the stroke prediction models and assessed the importance of the features.We used continuously updated vital signs and clinical data during hospitalization to generate timely stroke risk alerts during the same period of admission.Results:A total of 37,041 samples were analyzed,of which 7020 patients were diagnosed with stroke.When only the smart bed features were used for prediction,the model achieved an area under the receiver operating characteristic curve(AUROC)of 0.59−0.63,with an accuracy ranging from 60%−65%.Among the four artificial intelligence algorithms,the random forest model demonstrated the best performance.After all the available features were incorporated,the AUROC increased to 0.94,and the accuracy improved to 92%.Conclusions:In this study,the occurrence of stroke was successfully identified by integrating multimodal temporal data from electronic medical records.Noncontact monitoring of respiration and heart rate offers a promising approach for daily stroke surveillance in home‐based populations,particularly for elderly individuals living alone.
文摘“双碳”背景下,以煤电为主的大型城市能源系统如何低成本低碳转型成为重要研究课题。当前研究较少详细定量考虑未来高比例可再生能源情景下的高波动性风光出力带来的运营安全挑战,特别是长时储能、短时储能的部署及其成本效益。针对中国北方某大型城市,构建2021—2060长期能源系统规划模型(long-range energy alternatives planning system,LEAP),基于下一代能源优化模块(next energy modeling system for optimization,NEMO)考虑每年8760 h的需求负荷和可再生能源出力,实现40年长期能源规划与小时级电力运营优化相统一。构建基准情景、电化学储能情景和氢储能情景,探究不同情景下能源系统的运营调度、成本和碳排放量。结果表明电化学储能和氢储能的成本下降和加速部署有助于支撑更高比例的可再生能源装机和发电,减少对化石燃料和调入电力的依赖,降低城市能源系统碳排放。短期内电化学储能和氢储能的部署会提高约15%电力系统成本,而在中长期会降低约39%的电力系统成本和约28%的总成本。
