Nowadays,Web browsers have become an important carrier of 3D model visualization because of their convenience and portability.During the process of large-scale 3D model visualization based on Web scenes with the probl...Nowadays,Web browsers have become an important carrier of 3D model visualization because of their convenience and portability.During the process of large-scale 3D model visualization based on Web scenes with the problems of slow rendering speed and low FPS(Frames Per Second),occlusion culling,as an important method for rendering optimization,can remove most of the occluded objects and improve rendering efficiency.The traditional occlusion culling algorithm(TOCA)is calculated by traversing all objects in the scene,which involves a large amount of repeated calculation and time consumption.To advance the rendering process and enhance rendering efficiency,this paper proposes an occlusion culling with three different optimization methods based on the WebGPU Computing Pipeline.Firstly,for the problem of large amounts of repeated calculation processes in TOCA,these units are moved from the CPU to the GPU for parallel computing,thereby accelerating the calculation of the Potential Visible Sets(PVS);Then,for the huge overhead of creating pipeline caused by too many 3D models in a certain scene,the Breaking Occlusion Culling Algorithm(BOCA)is introduced,which removes some nodes according to building a Hierarchical Bounding Volume(BVH)scene tree to reduce the overhead of creating pipelines;After that,the structure of the scene tree is transmitted to the GPU in the order of depth-first traversal and finally,the PVS is obtained by parallel computing.In the experiments,3D geological models with five different scales from 1:5,000 to 1:500,000 are used for testing.The results show that the proposed methods can reduce the time overhead of repeated calculation caused by the computing pipeline creation and scene tree recursive traversal in the occlusion culling algorithm effectively,with 97%rendering efficiency improvement compared with BOCA,thereby accelerating the rendering process on Web browsers.展开更多
Long noncoding RNAs(lncRNAs)have been confirmed to play a crucial role in various biological processes across several species.Though many efforts have been devoted to the expansion of the lncRNAs landscape,much about ...Long noncoding RNAs(lncRNAs)have been confirmed to play a crucial role in various biological processes across several species.Though many efforts have been devoted to the expansion of the lncRNAs landscape,much about lncRNAs is still unknown due to their great complexity.The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases.In this review,we introduced genome-wide research of lncRNAs in three parts:(i)novel lncRNA identification by high-throughput sequencing and computational pipelines;(ii)functional characterization of lncRNAs by expression atlas profiling,genome-scale screening,and the research of cancer-related lncRNAs;(iii)mechanism research by large-scale experimental technologies and computational analysis.Besides,primary experimental methods and bioinformatic pipelines related to these three parts are summarized.This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.展开更多
基金supported by the National Natural Science Foundation of China (42172333,41902304,U1711267)the fund of the State Key Laboratory of Biogeology and Environmental Geology (2021)+1 种基金Science and Technology Strategic Prospecting Project of Guizhou Province ( [2022]ZD003)the Knowledge Innovation Program of Wuhan-Shuguang Project (2022010801020206).
文摘Nowadays,Web browsers have become an important carrier of 3D model visualization because of their convenience and portability.During the process of large-scale 3D model visualization based on Web scenes with the problems of slow rendering speed and low FPS(Frames Per Second),occlusion culling,as an important method for rendering optimization,can remove most of the occluded objects and improve rendering efficiency.The traditional occlusion culling algorithm(TOCA)is calculated by traversing all objects in the scene,which involves a large amount of repeated calculation and time consumption.To advance the rendering process and enhance rendering efficiency,this paper proposes an occlusion culling with three different optimization methods based on the WebGPU Computing Pipeline.Firstly,for the problem of large amounts of repeated calculation processes in TOCA,these units are moved from the CPU to the GPU for parallel computing,thereby accelerating the calculation of the Potential Visible Sets(PVS);Then,for the huge overhead of creating pipeline caused by too many 3D models in a certain scene,the Breaking Occlusion Culling Algorithm(BOCA)is introduced,which removes some nodes according to building a Hierarchical Bounding Volume(BVH)scene tree to reduce the overhead of creating pipelines;After that,the structure of the scene tree is transmitted to the GPU in the order of depth-first traversal and finally,the PVS is obtained by parallel computing.In the experiments,3D geological models with five different scales from 1:5,000 to 1:500,000 are used for testing.The results show that the proposed methods can reduce the time overhead of repeated calculation caused by the computing pipeline creation and scene tree recursive traversal in the occlusion culling algorithm effectively,with 97%rendering efficiency improvement compared with BOCA,thereby accelerating the rendering process on Web browsers.
基金supported by grants from the National Natural Science Foundation of China(No.82273158,81974436,81900559,and 31701116)the Natural Science Foundation of Guangdong Province,China(No.2022A1515010704,2022A1515011413,and 2021A1515010477)+2 种基金the Program for Guangdong Introducing Innovative and Entrepreneurial Teams(China)(No.2019ZT08Y485)Guangzhou Science and Technology Plan Projects(China)(No.202102020104)the Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation(China)(No.2020B1212060018).
文摘Long noncoding RNAs(lncRNAs)have been confirmed to play a crucial role in various biological processes across several species.Though many efforts have been devoted to the expansion of the lncRNAs landscape,much about lncRNAs is still unknown due to their great complexity.The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases.In this review,we introduced genome-wide research of lncRNAs in three parts:(i)novel lncRNA identification by high-throughput sequencing and computational pipelines;(ii)functional characterization of lncRNAs by expression atlas profiling,genome-scale screening,and the research of cancer-related lncRNAs;(iii)mechanism research by large-scale experimental technologies and computational analysis.Besides,primary experimental methods and bioinformatic pipelines related to these three parts are summarized.This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.
