在家蚕品种C1(H)中发现了一种新的卵色突变体。该突变体卵色为淡红色,成虫复眼为红色,遗传分析表明该突变性状由1个隐性基因控制,可能与红卵突变基因re互为等位基因,命名为淡红卵突变体(pale red egg,re^p-1)。红卵突变体(re)被认为是MF...在家蚕品种C1(H)中发现了一种新的卵色突变体。该突变体卵色为淡红色,成虫复眼为红色,遗传分析表明该突变性状由1个隐性基因控制,可能与红卵突变基因re互为等位基因,命名为淡红卵突变体(pale red egg,re^p-1)。红卵突变体(re)被认为是MFS(major facilitator superfamily)基因的序列发生了改变,故对re^p-1突变体MFS基因的结构及表达进行分析。与C1(H)卵色正常型品系的MFS基因比较,re^p-1突变体的MFS基因在第6外显子中的一段长59 bp片段被长14 bp的片段所替换,但编码蛋白质的跨膜结构未发生根本改变,而re突变体中MFS基因的结构变化对表达产物的功能有显著影响。此外,利用qRT-PCR检测MFS基因在C1(H)的卵色正常型品系、re^p-1突变体和re突变体3个家蚕品系5龄幼虫各个组织中均有表达,但表达水平存在差异:相较于C1(H)卵色正常型品系和re^p-1突变体,re突变体所有组织中MFS基因的表达量普遍偏低;相较于C1(H)卵色正常型品系,re^p-1突变体的后部丝腺和精巢中MFS基因具有较高的表达量,在其他组织中的表达量无明显差异。上述结果将有助于对re^p-1突变基因定位分析和卵色突变形成机制的研究。展开更多
Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose...Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.展开更多
基金supported in part by National Basic Research Program of China (973 Program) under Grant No. 2011CB302203the National Natural Science Foundation of China under Grant No. 61273285
文摘Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.
