0 INTRODUCTION.The global availability of digital elevation model(DEM)data,such as 90-m Shuttle Radar Topography Mission(SRTM)DEM and 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital ...0 INTRODUCTION.The global availability of digital elevation model(DEM)data,such as 90-m Shuttle Radar Topography Mission(SRTM)DEM and 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM),has been extensively utilized in morphotectonic analyses(e.g.,Wang et al.,2024;Cheng et al.,2018;Pérez-Pe?a et al.,2010;El Hamdouni et al.,2008).展开更多
Building,as an integral aspect of human life,is vital in the domains of urban management and urban analysis.To facilitate large-scale urban planning applications,the acquisition of complete and reliable building data ...Building,as an integral aspect of human life,is vital in the domains of urban management and urban analysis.To facilitate large-scale urban planning applications,the acquisition of complete and reliable building data becomes imperative.There are a few publicly available products that provide a lot of building data,such as Microsoft and Open Street Map.However,in East Asia,due to the more complex distribution of buildings and the scarcity of auxiliary data,there is a lack of building data in these regions,hindering the large-scale application in East Asia.Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression.However,the reliance on inaccurate buildings data introduces cumulative errors,rendering this simulation data highly unreliable,leading to limitations in achieving precise research in East Asian region.Therefore,we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia,and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings.The evaluation shows the validity of our building product,with an average overall accuracy of 89.63%and an F1 score of 82.55%.In addition,a comparison with existing products further shows the high quality and completeness of our building data.Finally,we conduct spatial analysis of our building data,revealing its value in supporting urban-related research.The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931.展开更多
Since the first genome-wide association study(GWAS)was published in 2005[1],enormous progress has been made in elucidating the genetic underpinnings of common complex diseases or traits over the past two decades.While...Since the first genome-wide association study(GWAS)was published in 2005[1],enormous progress has been made in elucidating the genetic underpinnings of common complex diseases or traits over the past two decades.While earlier GWAS studies mainly use cross-sectional case-control designs to identify disease-associated single nucleotide variants,the emerging large cohorts with available genomic data,such as population-based biobanks(e.g.,UK Biobank[2],iPSYCH[3],and deCODE[4]),have opened new avenues for exploring complex quantitative traits among deeply phenotyped participants.The specific advantages of these resources are shaped by the design,depth,and diversity of the cohort.Longitudinal cohorts with repeated measures over time,in particular,provide unique opportunities to investigate phenotypes that require temporal resolution,such as disease trajectories[5],preclinical biomarkers[6],and gene-environment interactions[7].Furthermore,those datasets enable a wide range of downstream applications,including the development of polygenic risk scores[8],causal inference using Mendelian randomization[9],the identification of early intervention windows[10],pharmacogenomic discovery[11],and the validation of potential therapeutic targets[12-14],thereby offering a powerful foundation for precision medicine and translational research.展开更多
基金supported by the National Key Research and Development Project of China(No.2023YFC3007303)the Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing(No.KLIGIP-2019B08)。
文摘0 INTRODUCTION.The global availability of digital elevation model(DEM)data,such as 90-m Shuttle Radar Topography Mission(SRTM)DEM and 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM),has been extensively utilized in morphotectonic analyses(e.g.,Wang et al.,2024;Cheng et al.,2018;Pérez-Pe?a et al.,2010;El Hamdouni et al.,2008).
基金supported in part by the National Key R&D Program of China under Grant 2022YFB3903402National Natural Science Foundation of China under Grant 42222106+1 种基金National Natural Science Foun-dation of China under Grant 61976234Fundamental Research Funds for the Central Universities,Sun Yat-sen University under Grant 22lgqb12.
文摘Building,as an integral aspect of human life,is vital in the domains of urban management and urban analysis.To facilitate large-scale urban planning applications,the acquisition of complete and reliable building data becomes imperative.There are a few publicly available products that provide a lot of building data,such as Microsoft and Open Street Map.However,in East Asia,due to the more complex distribution of buildings and the scarcity of auxiliary data,there is a lack of building data in these regions,hindering the large-scale application in East Asia.Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression.However,the reliance on inaccurate buildings data introduces cumulative errors,rendering this simulation data highly unreliable,leading to limitations in achieving precise research in East Asian region.Therefore,we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia,and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings.The evaluation shows the validity of our building product,with an average overall accuracy of 89.63%and an F1 score of 82.55%.In addition,a comparison with existing products further shows the high quality and completeness of our building data.Finally,we conduct spatial analysis of our building data,revealing its value in supporting urban-related research.The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931.
基金supported by the National Natural Science Foundation of China(Grant Nos.82471535 to HS and 82404350 to JS)the Sichuan Science and Technology Program(Grant No.2024NSFSC1637 to JS),China.
文摘Since the first genome-wide association study(GWAS)was published in 2005[1],enormous progress has been made in elucidating the genetic underpinnings of common complex diseases or traits over the past two decades.While earlier GWAS studies mainly use cross-sectional case-control designs to identify disease-associated single nucleotide variants,the emerging large cohorts with available genomic data,such as population-based biobanks(e.g.,UK Biobank[2],iPSYCH[3],and deCODE[4]),have opened new avenues for exploring complex quantitative traits among deeply phenotyped participants.The specific advantages of these resources are shaped by the design,depth,and diversity of the cohort.Longitudinal cohorts with repeated measures over time,in particular,provide unique opportunities to investigate phenotypes that require temporal resolution,such as disease trajectories[5],preclinical biomarkers[6],and gene-environment interactions[7].Furthermore,those datasets enable a wide range of downstream applications,including the development of polygenic risk scores[8],causal inference using Mendelian randomization[9],the identification of early intervention windows[10],pharmacogenomic discovery[11],and the validation of potential therapeutic targets[12-14],thereby offering a powerful foundation for precision medicine and translational research.
