Na+ , K+ -ATPase participates in the protective mechanism of rat cerebral ischemia-reperfusion through the interaction with glutamate transporter-1.

Glutamate excitotoxicity in cerebral ischemia/reperfusion is an important cause of neurological damage. The aim of this study was to investigate the mechanism of Na+, K+-ATPase (NKA) involved in l ow concentration of ouabain (Oua, activating NKA)-induced protection of rat cerebral ischemia-reperfusion injury. The 2,3,5-triphenyltetrazolium chloride (TTC) staining and neurological deficit scores (NDS) were performed to evaluate rat cerebral injury degree respectively at 2 h, 6 h, 1 d and 3 d after reperfusion of middle cerebral artery occlusion (MCAO) 2 h in rats. NKA α1/α2 subunits and glutamate transporter-1 (GLT-1) protein expression were investigated by Western blotting. The cerebral infarct volume ratio were evidently decreased in Oua group vs MCAO/R group at 1 d and 3 d after reperfusion of 2 h MCAO in rats (*p ＜ 0.05 ). Moreover, NDS were not significantly different (p ＞ 0.05 ). NKA α1 was decreased at 6 h and 1 d after reperfusion of 2 h MCAO in rats, and was improved in Oua group. However, NKA α1 and α2 were increased at 3 d after reperfusion of 2 h MCAO in rats, and was decreased in Oua group. GLT-1 was decreased at 6 h, 1 d and 3 d after reperfusion of 2 h MCAO in rats, and was improved in Oua group. These data indicated that l ow concentration of Oua could improve MCAO/R injury through probably changing NKA α1/α2 and GLT-1 protein expression, then increasing GLT-1 function and promoting Glu transport and absorption, which could be useful to determine potential therapeutic strategies for patients with stroke. Low concentration of Oua improved rat MCAO/R injury via NKA α1/α2 and GLT-1.

Focus on the Visible Regions: Semantic-Guided Alignment Model for Occluded Person Re-Identification.

The occlusion problem is very common in pedestrian retrieval scenarios. When persons are occluded by various obstacles, the noise caused by the occluded area greatly affects the retrieval results. However, many previous pedestrian re-identification (Re-ID) methods ignore this problem. To solve it, we propose a semantic-guided alignment model that uses image semantic information to separate useful information from occlusion noise. In the image preprocessing phase, we use a human semantic parsing network to generate probability maps. These maps show which regions of images are occluded, and the model automatically crops images to preserve the visible parts. In the construction phase, we fuse the probability maps with the global features of the image, and semantic information guides the model to focus on visible human regions and extract local features. During the matching process, we propose a measurement strategy that only calculates the distance of public areas (visible human areas on both images) between images, thereby suppressing the spatial misalignment caused by non-public areas. Experimental results on a series of public datasets confirm that our method outperforms previous occluded Re-ID methods, and it achieves top performance in the holistic Re-ID problem.

[Short Text Classification of EMR Based on Entities and Dependency Parser].

Nowadays, text classification and text mining of Electronic Medical Record (EMR) have become the basis of the Big Data research in biomedical fields. This paper proposes a method using entity dictionaries and dependency parser as the feature to do the classification of short texts in EMR. It used NLP to preprocess the texts first including sentence segmentation, word segmentation, part of speech and entity extraction. Then several entity dictionaries were built according to the result of NLP. After that the TF-IDF and LSA were deployed to select the vocabulary feature. Then considering the characters of EMR, dependency parser was done to the texts and triple dependency relation features would be used as the expanding feature for text classification. The result of the experiment shows that comparing to the classification which used vocabulary features only, the proposed methods can effectively improve the performance of classifier and the precision and F-value are obviously higher.