Dynamic changes in neuronal autophagy and apoptosis in the ischemic penumbra following permanent ischemic stroke

The temporal dynamics of neuronal autophagy and apoptosis in the ischemic penumbra following stroke remains unclear.Therefore,in this study,we investigated the dynamic changes in autophagy and apoptosis in the penumbra to provide insight into potential therapeutic targets for stroke.An adult Sprague-Dawley rat model of permanent ischemic stroke was prepared by middle cerebral artery occlusion.Neuronal autophagy and apoptosis in the penumbra post-ischemia were evaluated by western blot assay and immunofluorescence staining with antibodies against LC3-Ⅱ and cleaved caspase-3,respectively.Levels of both LC3-Ⅱ and cleaved caspase-3 in the penumbra gradually increased within 5 hours post-ischemia.Thereafter,levels of both proteins declined,especially LC3-Ⅱ.The cerebral infarct volume increased slowly 1–4 hours after ischemia,but subsequently increased rapidly until 5 hours after ischemia.The severity of the neurological deficit was positively correlated with infarct volume.LC3-Ⅱ and cleaved caspase-3 levels were high in the penumbra within 5 hours after ischemia,and after that,levels of these proteins decreased at different rates.LC3-Ⅱ levels were reduced to a very low level,but cleaved caspase-3 levels remained high 72 hours after ischemia.These results indicate that there are temporal differences in the activation status of the autophagic and apoptotic pathways.This suggests that therapeutic targeting of these pathways should take into consideration their unique temporal dynamics.

Multi target pigs tracking loss correction algorithm based on Faster R-CNN

In order to solve the problem that target tracking frames are lost during the visual tracking of pigs,this research proposed an algorithm for multi target pigs tracking loss correction based on Faster R-CNN.The video of live pigs was processed by Faster R-CNN to get the object bounding box.Then,the SURF and background difference method were combined to predict whether the target pig will be occluded in the next frame.According to the occlusion condition,the maximum value of the horizontal and vertical coordinate offset of the bounding box in the adjacent two frames of the frame image in continuous N(N is the value of the video frame rate)were calculated.When bounding boxes in a video frame are merged into one bounding box,this maximum value was used to correct the current tracking frame offset in order to achieve the purpose of solving the tracking target loss problem.The experiment results showed that the success rate range of RP Faster-RCNN in the data set was 80%-97% while in term of Faster-RCNN was 40%-85%.And the average center point error of RP Faster-RCNN was 1.46 lower than Faster-RCNN which was about 2.60.The new algorithm was characterized by good robustness and adaptability,which could solve the problem of missing tracking target and accurately track multiple targets when the targets occlude each other.