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( Jiebo Zhu ),( Min Joung Lee ),( Hee Jin Chang ),( Xianshu Ju ),( Jianchen Cui ),( Yu Lim Lee ),( Dahyun Go ),( Woosuk Chung ),( Eungseok Oh ),( Jun Young Heo ) 생화학분자생물학회 2022 BMB Reports Vol.55 No.4
Ventriculomegaly induced by the abnormal accumulation of cerebrospinal fluid (CSF) leads to hydrocephalus, which is accompanied by neuroinflammation and mitochondrial oxidative stress. The mitochondrial stress activates mitochondrial unfolded protein response (UPRmt), which is essential for mitochondrial protein homeostasis. However, the association of inflammatory response and UPRmt in the pathogenesis of hydrocephalus is still unclear. To assess their relevance in the pathogenesis of hydrocephalus, we established a kaolin-induced hydrocephalus model in 8-week-old male C57BL/6J mice and evaluated it over time. We found that kaolin-injected mice showed prominent ventricular dilation, motor behavior defects at the 3-day, followed by the activation of microglia and UPRmt in the motor cortex at the 5-day. In addition, PARP-1/NF-κB signaling and apoptotic cell death appeared at the 5-day. Taken together, our findings demonstrate that activation of microglia and UPRmt occurs after hydrocephalic ventricular expansion and behavioral abnormalities which could be lead to apoptotic neuronal cell death, providing a new perspective on the pathogenic mechanism of hydrocephalus. [BMB Reports 2022; 55(4): 181-186]
Jointly Image Topic and Emotion Detection using Multi-Modal Hierarchical Latent Dirichlet Allocation
Ding, Wanying,Zhu, Junhuan,Guo, Lifan,Hu, Xiaohua,Luo, Jiebo,Wang, Haohong Korea Multimedia Society 2014 The journal of multimedia information system Vol.1 No.1
Image topic and emotion analysis is an important component of online image retrieval, which nowadays has become very popular in the widely growing social media community. However, due to the gaps between images and texts, there is very limited work in literature to detect one image's Topics and Emotions in a unified framework, although topics and emotions are two levels of semantics that often work together to comprehensively describe one image. In this work, a unified model, Joint Topic/Emotion Multi-Modal Hierarchical Latent Dirichlet Allocation (JTE-MMHLDA) model, which extends previous LDA, mmLDA, and JST model to capture topic and emotion information at the same time from heterogeneous data, is proposed. Specifically, a two level graphical structured model is built to realize sharing topics and emotions among the whole document collection. The experimental results on a Flickr dataset indicate that the proposed model efficiently discovers images' topics and emotions, and significantly outperform the text-only system by 4.4%, vision-only system by 18.1% in topic detection, and outperforms the text-only system by 7.1%, vision-only system by 39.7% in emotion detection.