Micro RNA-124（mi R-124） is abundantly expressed in neurons in the mammalian central nervous system, and plays critical roles in the regulation of gene expression during embryonic neurogenesis and postnatal neural di...Micro RNA-124（mi R-124） is abundantly expressed in neurons in the mammalian central nervous system, and plays critical roles in the regulation of gene expression during embryonic neurogenesis and postnatal neural differentiation. However, the expression profile of mi R-124 after spinal cord injury and the underlying regulatory mechanisms are not well understood. In the present study, we examined the expression of mi R-124 in mouse brain and spinal cord after spinal cord injury using in situ hybridization. Furthermore, the expression of mi R-124 was examined with quantitative RT-PCR at 1, 3 and 7 days after spinal cord injury. The mi R-124 expression in neurons at the site of injury was evaluated by in situ hybridization combined with Neu N immunohistochemical staining. The mi R-124 was mainly expressed in neurons throughout the brain and spinal cord. The expression of mi R-124 in neurons significantly decreased within 7 days after spinal cord injury. Some of the neurons in the peri-lesion area were Neu N＋/mi R-124-. Moreover, the neurons distal to the peri-lesion site were Neu N＋/mi R-124＋. These findings indicate that mi R-124 expression in neurons is reduced after spinal cord injury, and may reflect the severity of spinal cord injury.展开更多
Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature ...Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.展开更多
基金supported by the National Natural Science Foundation of China,No.81371364
文摘Micro RNA-124（mi R-124） is abundantly expressed in neurons in the mammalian central nervous system, and plays critical roles in the regulation of gene expression during embryonic neurogenesis and postnatal neural differentiation. However, the expression profile of mi R-124 after spinal cord injury and the underlying regulatory mechanisms are not well understood. In the present study, we examined the expression of mi R-124 in mouse brain and spinal cord after spinal cord injury using in situ hybridization. Furthermore, the expression of mi R-124 was examined with quantitative RT-PCR at 1, 3 and 7 days after spinal cord injury. The mi R-124 expression in neurons at the site of injury was evaluated by in situ hybridization combined with Neu N immunohistochemical staining. The mi R-124 was mainly expressed in neurons throughout the brain and spinal cord. The expression of mi R-124 in neurons significantly decreased within 7 days after spinal cord injury. Some of the neurons in the peri-lesion area were Neu N＋/mi R-124-. Moreover, the neurons distal to the peri-lesion site were Neu N＋/mi R-124＋. These findings indicate that mi R-124 expression in neurons is reduced after spinal cord injury, and may reflect the severity of spinal cord injury.
基金supported by the Science and Technology Plan Projects of Sichuan Province of China under Grant No.2008GZ0003the Key Technologies R & D Program of Sichuan Province of China under Grant No.2008SZ0100
文摘Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.
