目的探讨原发性肾病综合征(PNS)患儿肾组织中瞬时受体电位阳离子通道蛋白6(TRPC6)的表达与足细胞损伤的关系及其临床意义。方法获取18例PNS患儿的肾组织,常规切片染色光镜观察肾脏组织病理改变,电镜观察足细胞的结构变化,分别用q PCR和...目的探讨原发性肾病综合征(PNS)患儿肾组织中瞬时受体电位阳离子通道蛋白6(TRPC6)的表达与足细胞损伤的关系及其临床意义。方法获取18例PNS患儿的肾组织,常规切片染色光镜观察肾脏组织病理改变,电镜观察足细胞的结构变化,分别用q PCR和免疫组化测定组织中TRPC6 m RNA和蛋白的表达,并将TRPC6 m RNA表达分别与血清白蛋白(Alb)、肌酐(Cr)、三酰甘油(TG)、胆固醇(Tch)、补体C3水平及24 h尿蛋白定量和评估的肾小球滤过率(e GFR)进行相关性分析。结果 PNS患儿肾组织中TRPC6蛋白表达较对照组上调,差异有统计学意义(P<0.05)。PNS患儿肾组织中TRPC6 m RNA的相对表达量与肾组织TRPC6蛋白表达呈显著正相关(r=0.508,P<0.05),与血清Alb、Cr、TG、Tch、补体C3、e GFR水平以及24 h尿蛋白定量无相关性(P>0.05)。结论 PNS患儿的病理类型仍以足细胞病变为主,其肾小球足细胞中TRPC6蛋白表达升高。TRPC6检测可能有助于足细胞病变诊断。展开更多
Rice planting decreased total iron but increased active iron.Iron activation varied greatly among different paddy soils but not in woodland soils.Paddy soil iron was mainly affected by pH,SOC and particle composition....Rice planting decreased total iron but increased active iron.Iron activation varied greatly among different paddy soils but not in woodland soils.Paddy soil iron was mainly affected by pH,SOC and particle composition.The decrease of soil Fe was mainly in the form of Fec and was closely related to SOC.展开更多
Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes...Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF data.Given this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this area.We summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep learning.We analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed requirements.In addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.展开更多
Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentati...Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF rendering.However,there is a contradiction between spatial and angular resolution during the LF image acquisition period.To overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian models.Deep learning-based methods are more popular than conventional methods because they have better performance and more robust generalization capabilities.In this paper,the present approach can mainly divided into conventional methods and deep learning-based methods.We discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),respectively.Subsequently,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these datasets.Finally,we discuss the potential innovations of the LFSR to propose the progress of our research field.展开更多
Light field(LF)cameras record multiple perspectives by a sparse sampling of real scenes,and these perspectives provide complementary information.This information is beneficial to LF super-resolution(LFSR).Compared wit...Light field(LF)cameras record multiple perspectives by a sparse sampling of real scenes,and these perspectives provide complementary information.This information is beneficial to LF super-resolution(LFSR).Compared with traditional single-image super-resolution,LF can exploit parallax structure and perspective correlation among different LF views.Furthermore,the performance of existing methods are limited as they fail to deeply explore the complementary information across LF views.In this paper,we propose a novel network,called the light field complementary-view feature attention network(LF-CFANet),to improve LFSR by dynamically learning the complementary information in LF views.Specifically,we design a residual complementary-view spatial and channel attention module(RCSCAM)to effectively interact with complementary information between complementary views.Moreover,RCSCAM captures the relationships between different channels,and it is able to generate informative features for reconstructing LF images while ignoring redundant information.Then,a maximum-difference information supplementary branch(MDISB)is used to supplement information from the maximum-difference angular positions based on the geometric structure of LF images.This branch also can guide the process of reconstruction.Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method.The proposed LF-CFANet has a more advanced reconstruction performance that displays faithful details with higher SR accuracy than state-of-the-art methods.展开更多
Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph analysis.The k-truss is one of the most commonly studied co...Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph analysis.The k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a k-truss.Existing works mostly focus on truss computation in static graphs by sequential models.However,the graphs are constantly changing dynamically in the real world.We study distributed truss computation in dynamic graphs in this paper.In particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed model.Iteratively decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition algorithm.Moreover,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the computation.Extensive experiments have been conducted to show the scalability and efficiency of the proposed algorithm.展开更多
文摘目的探讨原发性肾病综合征(PNS)患儿肾组织中瞬时受体电位阳离子通道蛋白6(TRPC6)的表达与足细胞损伤的关系及其临床意义。方法获取18例PNS患儿的肾组织,常规切片染色光镜观察肾脏组织病理改变,电镜观察足细胞的结构变化,分别用q PCR和免疫组化测定组织中TRPC6 m RNA和蛋白的表达,并将TRPC6 m RNA表达分别与血清白蛋白(Alb)、肌酐(Cr)、三酰甘油(TG)、胆固醇(Tch)、补体C3水平及24 h尿蛋白定量和评估的肾小球滤过率(e GFR)进行相关性分析。结果 PNS患儿肾组织中TRPC6蛋白表达较对照组上调,差异有统计学意义(P<0.05)。PNS患儿肾组织中TRPC6 m RNA的相对表达量与肾组织TRPC6蛋白表达呈显著正相关(r=0.508,P<0.05),与血清Alb、Cr、TG、Tch、补体C3、e GFR水平以及24 h尿蛋白定量无相关性(P>0.05)。结论 PNS患儿的病理类型仍以足细胞病变为主,其肾小球足细胞中TRPC6蛋白表达升高。TRPC6检测可能有助于足细胞病变诊断。
基金financially supported by Scientific Research Fund of Hunan Provincial Education Department(Grant No.20A234)San’an Nie thanks the National Natural Science Foundation of China(Grant No.42177288)as well as the National Natural Science Foundation of Hunan Province,China(Grant No.2023JJ30307).
文摘Rice planting decreased total iron but increased active iron.Iron activation varied greatly among different paddy soils but not in woodland soils.Paddy soil iron was mainly affected by pH,SOC and particle composition.The decrease of soil Fe was mainly in the form of Fec and was closely related to SOC.
基金supported by the National Key R&D Program of China(2022YFC3803600)the National Natural Science Foundation of China(62372023)the Open Fund of the State Key Laboratory of Software Development Environment,China(SKLSDE-2023ZX-11).
文摘Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF data.Given this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this area.We summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep learning.We analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed requirements.In addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.
基金supported by the National Key R&D Program of China(2022YFC3803600)the National Natural Science Foundation of China(62372023)the Open Fund of the State Key Laboratory of Software Development Environment,PR China(SKLSDE-2023ZX-11)。
文摘Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF rendering.However,there is a contradiction between spatial and angular resolution during the LF image acquisition period.To overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian models.Deep learning-based methods are more popular than conventional methods because they have better performance and more robust generalization capabilities.In this paper,the present approach can mainly divided into conventional methods and deep learning-based methods.We discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),respectively.Subsequently,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these datasets.Finally,we discuss the potential innovations of the LFSR to propose the progress of our research field.
基金supported by the National Key R&D Program of China(2018YFB2100500)the National Natural Science Foundation of China(61872025)+1 种基金the Science and Technology Development Fund,Macao SAR(0001/2018/AFJ)the Open Fund of the State Key Laboratory of Software Development Environment(SKLSDE-2021ZX-03).
文摘Light field(LF)cameras record multiple perspectives by a sparse sampling of real scenes,and these perspectives provide complementary information.This information is beneficial to LF super-resolution(LFSR).Compared with traditional single-image super-resolution,LF can exploit parallax structure and perspective correlation among different LF views.Furthermore,the performance of existing methods are limited as they fail to deeply explore the complementary information across LF views.In this paper,we propose a novel network,called the light field complementary-view feature attention network(LF-CFANet),to improve LFSR by dynamically learning the complementary information in LF views.Specifically,we design a residual complementary-view spatial and channel attention module(RCSCAM)to effectively interact with complementary information between complementary views.Moreover,RCSCAM captures the relationships between different channels,and it is able to generate informative features for reconstructing LF images while ignoring redundant information.Then,a maximum-difference information supplementary branch(MDISB)is used to supplement information from the maximum-difference angular positions based on the geometric structure of LF images.This branch also can guide the process of reconstruction.Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method.The proposed LF-CFANet has a more advanced reconstruction performance that displays faithful details with higher SR accuracy than state-of-the-art methods.
基金supported in part by the National Key Research and Development Program of China(No.2020YFB1005900)in part by National Natural Science Foundation of China(No.62122042)in part by Shandong University Multidisciplinary Research and Innovation Team of Young Scholars(No.2020QNQT017)。
文摘Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph analysis.The k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a k-truss.Existing works mostly focus on truss computation in static graphs by sequential models.However,the graphs are constantly changing dynamically in the real world.We study distributed truss computation in dynamic graphs in this paper.In particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed model.Iteratively decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition algorithm.Moreover,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the computation.Extensive experiments have been conducted to show the scalability and efficiency of the proposed algorithm.
