PKHD1 mutations are generally considered to cause autosomal recessive polycystic kidney disease(ARPKD).ARPKD is a rare disorder and one o f the most severe conditions leading to end-stage renal disease in childhood.Wi...PKHD1 mutations are generally considered to cause autosomal recessive polycystic kidney disease(ARPKD).ARPKD is a rare disorder and one o f the most severe conditions leading to end-stage renal disease in childhood.With the biallelic deletion mutation,patients have difficulty in surviving the perinatal period,resulting in perinatal or neonatal death.This study retrospectively analyzed patient characteristics,imaging characteristics,laboratory examinations and family surveys from 7 Chinese children with different PKHD1 gene mutations diagnosed by high-throughput sequencing from January 2014 to February 2018.O f the 7 children,there were 3 males and 4 females.Eight missense mutations,two frameshift mutations,two deletion mutations,and two intronic slicing mutations were identified.Six of the mutations have not previously been identified.In the literature search,we identified a total of 29 Chinese children with PKHD1 mutations.The missense mutation c.2507T>C in exon 24 was found in one patient in our study,and five patients with liver fibrosis but normal renal function were reported in the literature.The missense mutation c.5935G>A in exon 37 was found in two patients in our study and three cases in the literature.Four patients had renal failure at an age as young as 1 year of those five patients with the missense mutation c.5935G>A in exon 37.It was concluded that:(1)Kidney length more than 2-3 SDs above the mean and early-onset hypertension might be associated with PKHDI-associated ARPICD;(2)The more enlarged the kidney size is,the lower the renal function is likely to be;(3)c.5935G>A may be a hot spot that leads to early renal failure in Chinese children with PKHD1 mutations;(4)c.2507T>C may be a hot-spot mutation associated with hepatic lesions in Chinese children with PKHD1.展开更多
Video colorization aims to add color to grayscale or monochrome videos.Although existing methods have achieved substantial and noteworthy results in the field of image colorization,video colorization presents more for...Video colorization aims to add color to grayscale or monochrome videos.Although existing methods have achieved substantial and noteworthy results in the field of image colorization,video colorization presents more formidable obstacles due to the additional necessity for temporal consistency.Moreover,there is rarely a systematic review of video colorization methods.In this paper,we aim to review existing state-of-the-art video colorization methods.In addition,maintaining spatial-temporal consistency is pivotal to the process of video colorization.To gain deeper insight into the evolution of existing methods in terms of spatial-temporal consistency,we further review video colorization methods from a novel perspective.Video colorization methods can be categorized into four main categories:optical-flow based methods,scribble-based methods,exemplar-based methods,and fully automatic methods.However,optical-flow based methods rely heavily on accurate optical-flow estimation,scribble-based methods require extensive user interaction and modifications,exemplar-based methods face challenges in obtaining suitable reference images,and fully automatic methods often struggle to meet specific colorization requirements.We also discuss the existing challenges and highlight several future research opportunities worth exploring.展开更多
Video captioning is the task of assigning complex high-level semantic descriptions （e.g., sentences or paragraphs） to video data. Different from previous video analysis techniques such as video annotation, video eve...Video captioning is the task of assigning complex high-level semantic descriptions （e.g., sentences or paragraphs） to video data. Different from previous video analysis techniques such as video annotation, video event detection and action recognition, video captioning is much closer to human cognition with smaller semantic gap. However, the scarcity of captioned video data severely limits the development of video captioning. In this paper, we propose a novel video captioning approach to describe videos by leveraging freely-available image corpus with abundant literal knowledge. There are two key aspects of our approach： 1） effective integration strategy bridging videos and images, and 2） high efficiency in handling ever-increasing training data. To achieve these goals, we adopt sophisticated visual hashing techniques to efficiently index and search large-scale images for relevant captions, which is of high extensibility to evolving data and the corresponding semantics. Extensive experimental results on various real-world visual datasets show the effectiveness of our approach with different hashing techniques, e.g., LSH （locality-sensitive hashing）, PCA-ITQ （principle component analysis iterative quantization） and supervised discrete hashing, as compared with the state-of-the-art methods. It is worth noting that the empirical computational cost of our approach is much lower than that of an existing method, i.e., it takes 1/256 of the memory requirement and 1/64 of the time cost of the method of Devlin et al.展开更多
基金This project was supported by the National Natural Science Foundation of China(No.81873596).
文摘PKHD1 mutations are generally considered to cause autosomal recessive polycystic kidney disease(ARPKD).ARPKD is a rare disorder and one o f the most severe conditions leading to end-stage renal disease in childhood.With the biallelic deletion mutation,patients have difficulty in surviving the perinatal period,resulting in perinatal or neonatal death.This study retrospectively analyzed patient characteristics,imaging characteristics,laboratory examinations and family surveys from 7 Chinese children with different PKHD1 gene mutations diagnosed by high-throughput sequencing from January 2014 to February 2018.O f the 7 children,there were 3 males and 4 females.Eight missense mutations,two frameshift mutations,two deletion mutations,and two intronic slicing mutations were identified.Six of the mutations have not previously been identified.In the literature search,we identified a total of 29 Chinese children with PKHD1 mutations.The missense mutation c.2507T>C in exon 24 was found in one patient in our study,and five patients with liver fibrosis but normal renal function were reported in the literature.The missense mutation c.5935G>A in exon 37 was found in two patients in our study and three cases in the literature.Four patients had renal failure at an age as young as 1 year of those five patients with the missense mutation c.5935G>A in exon 37.It was concluded that:(1)Kidney length more than 2-3 SDs above the mean and early-onset hypertension might be associated with PKHDI-associated ARPICD;(2)The more enlarged the kidney size is,the lower the renal function is likely to be;(3)c.5935G>A may be a hot spot that leads to early renal failure in Chinese children with PKHD1 mutations;(4)c.2507T>C may be a hot-spot mutation associated with hepatic lesions in Chinese children with PKHD1.
基金supported by the National Natural Science Foundation of China under Grant Nos.U22B2049 and 62332010.
文摘Video colorization aims to add color to grayscale or monochrome videos.Although existing methods have achieved substantial and noteworthy results in the field of image colorization,video colorization presents more formidable obstacles due to the additional necessity for temporal consistency.Moreover,there is rarely a systematic review of video colorization methods.In this paper,we aim to review existing state-of-the-art video colorization methods.In addition,maintaining spatial-temporal consistency is pivotal to the process of video colorization.To gain deeper insight into the evolution of existing methods in terms of spatial-temporal consistency,we further review video colorization methods from a novel perspective.Video colorization methods can be categorized into four main categories:optical-flow based methods,scribble-based methods,exemplar-based methods,and fully automatic methods.However,optical-flow based methods rely heavily on accurate optical-flow estimation,scribble-based methods require extensive user interaction and modifications,exemplar-based methods face challenges in obtaining suitable reference images,and fully automatic methods often struggle to meet specific colorization requirements.We also discuss the existing challenges and highlight several future research opportunities worth exploring.
基金This work was partially supported by the National Basic Research 973 Program of China under Grant No. 2014CB347600, the National Natural Science Foundation of China under Grant Nos. 61522203, 61572108, 61632007, and 61502081, tile National Ten-Thousand Talents Program of China （Young Top-Notch Talent）, the National Thousand Young Talents Program of China, the Fundamental Research Funds for the Central Universities of China under Grant Nos. ZYGX2014Z007 and ZYGX2015J055, and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140058.
文摘Video captioning is the task of assigning complex high-level semantic descriptions （e.g., sentences or paragraphs） to video data. Different from previous video analysis techniques such as video annotation, video event detection and action recognition, video captioning is much closer to human cognition with smaller semantic gap. However, the scarcity of captioned video data severely limits the development of video captioning. In this paper, we propose a novel video captioning approach to describe videos by leveraging freely-available image corpus with abundant literal knowledge. There are two key aspects of our approach： 1） effective integration strategy bridging videos and images, and 2） high efficiency in handling ever-increasing training data. To achieve these goals, we adopt sophisticated visual hashing techniques to efficiently index and search large-scale images for relevant captions, which is of high extensibility to evolving data and the corresponding semantics. Extensive experimental results on various real-world visual datasets show the effectiveness of our approach with different hashing techniques, e.g., LSH （locality-sensitive hashing）, PCA-ITQ （principle component analysis iterative quantization） and supervised discrete hashing, as compared with the state-of-the-art methods. It is worth noting that the empirical computational cost of our approach is much lower than that of an existing method, i.e., it takes 1/256 of the memory requirement and 1/64 of the time cost of the method of Devlin et al.
