ABSTRACT
Introduction: Spinal cord injury (SCI) often causes continuous neurological damage to clinical patients. Circular RNAs (circRNAs) are related to a lot of diseases, including SCI. We previously found five candidate circRNAs which were likely to regulate the secondary pathophysiological changes in rat model after traumatic SCI. Methods: In this study, we first selected and overexpressed target circRNA in rats. We then explored its functional roles using various functional assays in a rat model after SCI. Results: We found that rno-circRNA-013017-the selected target circRNA-reduced neuron apoptosis, preserved the survival and activity of motor neurons, and regulated apoptosis-related proteins at 3 days post-SCI using western blot, immunofluorescence and polymerase chain reaction. Additionally, we found that rno-circRNA-013017 inhibited descending axonal degeneration and preserved motor neurons and descending axons at 6 weeks post-SCI using immunofluorescence, biotin dextran amine diffusion tensor imaging. Finally, the overexpression of rno-circRNA-013017 promoted the locomotor function of rats after SCI using open-field test and gait analysis. Conclusion: Focusing on the functions of rno-circRNA-013017, this study provides new options for future studies exploring therapeutic targets and molecular mechanisms for SCI.
ABSTRACT
In order to establish volume model,living trees have to be fallen and be divided into many sections, which is a kind of destructive experiment. So hundreds of thousands of trees have been fallen down each year in China. To solve this problem, a new method called living tree volume accurate measurement without falling tree was proposed in the present paper. In the method, new measuring methods and calculation ways are used by using photoelectric theodolite and auxiliary artificial measurement. The diameter at breast height and diameter at ground was measured manually, and diameters at other heights were obtained by photoelectric theodolite. Tree volume and height of each tree was calculated by a special software that was programmed by the authors. Zhonglin aspens No. 107 were selected as experiment object, and 400 data records were obtained. Based on these data, a nonlinear intelligent living tree volume prediction model with Particle Swarm Optimization algorithm based on support vector machines (PSO-SVM) was established. Three hundred data records including tree height and diameter at breast height were randomly selected form a total of 400 data records as input data, tree volume as output data, using PSO-SVM tool box of Matlab7.11, thus a tree volume model was obtained. One hundred data records were used to test the volume model. The results show that the complex correlation coefficient (R2) between predicted and measured values is 0. 91, which is 2% higher than the value calculated by classic Spurr binary volume model, and the mean absolute error rates were reduced by 0.44%. Compared with Spurr binary volume model, PSO-SVM model has self-learning and self-adaption ability,moreover, with the characteristics of high prediction accuracy, fast learning speed,and a small sample size requirement, PSO-SVM model with well prospect is worth popularization and application.
Subject(s)
Support Vector Machine , Trees , Algorithms , China , Nonlinear DynamicsABSTRACT
BACKGROUND: The hematopoietically expressed homeobox (HHEX) gene is an important determinant of mammalian heart development. This study aimed to identify the potential mutations of the gene in Chinese patients with congenital heart disease (CHD). METHODS: We collected 296 CHD patients and 200 controls, and classified the cardiac deformities. Then we conducted sequence analyses of the HHEX gene in those patients. RESULTS: In all the CHD patients, we did not find any causative mutations in the coding region of the HHEX gene. CONCLUSION: To our knowledge, this is the first study to examine the HHEX gene in non-symptomatic CHD cases, and this has expanded our knowledge about its etiology.
