YB-1 downregulation attenuates UQCRC1 protein expression level in H9C2 cells and decreases the mitochondrial membrane potential

UQCRC1 is one of the 10 mitochondrial complex III subunits,this protein has a role in energy metabolism,myocardial protection,and neurological diseases.The upstream mechanism of the UQCRC1 protective effect on cardiomyocytes is currently unavailable.In order to explore the upstream molecules of UQCRC1 and elucidate the protective mechanism of UQCRC1 on cardiomyocytes in more detail,we focused on the nuclease-sensitive elementbinding protein 1(YB-1).We hypothesized YB-1 acts as an upstream regulatory molecule of UQCRC1.This study found that YB-1 RNAi significantly reduces the expression of the UQCRC1 protein level(p<0.05)and obviously decreases the mitochondrial membrane potential(p<0.05),and that YB-1 interacts with UQCRC1 protein in vivo,but YB-1 RNAi has little effect on the UQCRC1 gene transcription.

中国乡村旅游地的空间分布特征及影响因素

以中国农业农村部、文化和旅游部公布的乡村旅游地为样本,运用最邻近指数、核密度和地理探测器等方法探究乡村旅游地的空间分布规律和影响因素。结果表明(:1)中国乡村旅游地呈现“东多西少,南多北少”集聚特征,其东西差异比南北更为显著;东部、中部和西部集聚性逐渐增强,除新疆和青海外,其他省市最邻近指数值为0.7~1.2,趋于均衡分布。(2)中国乡村旅游地形成“两极-两轴-多核”的空间分布形态;五类乡村旅游地呈“一高四低、分异显著”类型特征,休闲观光类占比最高为33.06%,文化民俗类占比最低为8.28%,五类乡村旅游地空间分异的驱动因素差异较大,与村落资源禀赋和区位特征具有密切关系。(3)乡村旅游地空间分异是村域自然环境、社会发展水平、文化资源潜力、区域设施水平、旅游投资水平和旅游市场规模等因素共同作用的结果,其中,社会发展水平和旅游市场规模为主导因素,解释力为0.64、0.62;海拔、水文条件与交通区位等因素对乡村旅游地空间分布具有显著的负相关性。

Feature extraction for classification of different weather conditions

Classification of different weather conditions provides a first step support for outdoor scene modeling,which is a core component in many different applications of outdoor video analysis and computer vision.Features derived from intrinsic properties of the visual effects of different weather conditions contribute to successful classification.In this paper,features representing both the autocorrelation of pixel-wise intensities over time and the max directional length of rain streaks or snowflakes are proposed.Based on the autocorrelation of each pixel’s intensities over time,two temporal features are used for coarse classification of weather conditions according to their visual effects.On the other hand,features are extracted for fine classification of video clips with rain and snow.The classification results on 249 video clips associated with different weather conditions indicate the effectiveness of the extracted features,by using C-SVM as the classifier.

A primary-secondary background model with sliding window PCA algorithm

Rain and snow seriously degrade outdoor video quality.In this work,a primary-secondary background model for removal of rain and snow is built.First,we analyze video noise and use a sliding window sequence principal component analysis de-nosing algorithm to reduce white noise in the video.Next,we apply the Gaussian mixture model(GMM)to model the video and segment all foreground objects primarily.After that,we calculate von Mises distribution of the velocity vectors and ratio of the overlapped region with referring to the result of the primary segmentation and extract the interesting object.Finally,rain and snow streaks are inpainted using the background to improve the quality of the video data.Experiments show that the proposed method can effectively suppress noise and extract interesting targets.