Treatment with low-energy shock wave alleviates pain in an animal model of uroplakin 3A-induced autoimmune interstitial cystitis/painful bladder syndrome

Huixi Li,Zhichao Zhang,Jing Peng,Zhongcheng Xin,Meng Li,Bicheng Yang,Dong Fang,Yuan Tang,Yinglu Guo 대한비뇨의학회 2019 Investigative and Clinical Urology Vol.60 No.5

Purpose: To investigate whether treatment with low-energy shock wave (LESW) alleviates pain and bladder dysfunction in a mouse model of uroplakin 3A (UPK3A)-induced interstitial cystitis/painful bladder syndrome (IC/PBS). Materials and Methods: Forty female BALB/c mice were divided into four groups (n=10/group): Sham, Sham+LESW, UPK3A, and UPK3A+LESW. At 6 weeks of age, mice were injected with an emulsion containing water and complete Freund's adjuvant with (UPK3A and UPK3A+LESW groups) or without (Sham and Sham+LESW groups) 200 µg of UPK3A. At 10 weeks, mice received a second dose of Freund's adjuvant to booster immunization. At 12 weeks, mice underwent pain assessment and a frequency volume chart (FVC) test as the pretreatment assessment. LESW treatment and pain assessment were conducted from 13 to 15 weeks. One week after the final treatment, pain assessment and the FVC were conducted again as the post-treatment assessment. Mice were euthanized and sacrificed at 17 weeks. Results: The presence of tactile allodynia and bladder dysfunction was significant in the UPK3A-injected mice. LESW raised the pain threshold and improved bladder function with decreased urinary frequency and increased mean urine output. Expression and secretion of local and systemic inflammatory markers, including tumor necrosis factor-α (TNF-α) and nerve growth factor (NGF), increased after UPK3A immunization. These markers were significantly decreased after LESW treatment (p<0.05). Conclusions: LESW treatment attenuated pain and bladder dysfunction in a UPK3A-induced model of IC/PBS. Local and systemic inflammation was partially controlled, with a reduced number of infiltrated inflammatory cells and reduced levels of TNF-α and NGF.

Object Classification based on Weakly Supervised E2LSH and Saliency map Weighting

( Yongwei Zhao ),( Bicheng Li ),( Xin Liu ),( Shengcai Ke ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.1

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

Xi, Yaoyi,Li, Bicheng,Liu, Yang Korean Institute of Information Scientists and Eng 2015 Journal of Computing Science and Engineering Vol.9 No.2

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

Yaoyi Xi,Bicheng Li,Yang Liu 한국정보과학회 2015 Journal of Computing Science and Engineering Vol.9 No.2

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

Classifying Articles in Chinese Wikipedia with Fine-Grained Named Entity Types

Jie Zhou,Bicheng Li,Yongwang Tang 한국정보과학회 2014 Journal of Computing Science and Engineering Vol.8 No.3

Named entity classification of Wikipedia articles is a fundamental research area that can be used to automatically build large-scale corpora of named entity recognition or to support other entity processing, such as entity linking, as auxiliary tasks. This paper describes a method of classifying named entities in Chinese Wikipedia with fine-grained types. We considered multi-faceted information in Chinese Wikipedia to construct four feature sets, designed different feature selection methods for each feature, and fused different features with a vector space using different strategies. Experimental results show that the explored feature sets and their combination can effectively improve the performance of named entity classification.

Classifying Articles in Chinese Wikipedia with Fine-Grained Named Entity Types

Zhou, Jie,Li, Bicheng,Tang, Yongwang Korean Institute of Information Scientists and Eng 2014 Journal of Computing Science and Engineering Vol.8 No.3

Named entity classification of Wikipedia articles is a fundamental research area that can be used to automatically build large-scale corpora of named entity recognition or to support other entity processing, such as entity linking, as auxiliary tasks. This paper describes a method of classifying named entities in Chinese Wikipedia with fine-grained types. We considered multi-faceted information in Chinese Wikipedia to construct four feature sets, designed different feature selection methods for each feature, and fused different features with a vector space using different strategies. Experimental results show that the explored feature sets and their combination can effectively improve the performance of named entity classification.

Context-Based Value Tracking

Yaoyi Xi,Bicheng Li,Yuan Gao,Yongwang Tang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8

Value tracking aims to capture the changes of attribute values along with the evolution of topic. Existing researches on value tracking only extracted the attribute values chronologically, and took no use of the context information to verify the correctness of the values. This paper proposes a context-based value tracking method. First, extract the candidate attribute values according to the patterns generated by the regular expressions; Second, recognize the temporal expressions in the source sentences of the candidate values based on conditional random fields; Finally, identify the real attribute values according to the temporal features and location features in the context. Experiments on TREC 2013 Knowledge Base Acceleration (KBA) stream corpus and human-build Chinese corpus demonstrate that the proposed method can track the changes of the attribute values effectively.

Bag of Visual Words Method based on PLSA and Chi-Square Model for Object Category

( Yongwei Zhao ),( Tianqiang Peng ),( Bicheng Li ),( Shengcai Ke ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.7

The problem of visual words` synonymy and ambiguity always exist in the conventional bag of visual words (BoVW) model based object category methods. Besides, the noisy visual words, so-called “visual stop-words” will degrade the semantic resolution of visual dictionary. In view of this, a novel bag of visual words method based on PLSA and chi-square model for object category is proposed. Firstly, Probabilistic Latent Semantic Analysis (PLSA) is used to analyze the semantic co-occurrence probability of visual words, infer the latent semantic topics in images, and get the latent topic distributions induced by the words. Secondly, the KL divergence is adopt to measure the semantic distance between visual words, which can get semantically related homoionym. Then, adaptive soft-assignment strategy is combined to realize the soft mapping between SIFT features and some homoionym. Finally, the chi-square model is introduced to eliminate the “visual stop-words” and reconstruct the visual vocabulary histograms. Moreover, SVM (Support Vector Machine) is applied to accomplish object classification. Experimental results indicated that the synonymy and ambiguity problems of visual words can be overcome effectively. The distinguish ability of visual semantic resolution as well as the object classification performance are substantially boosted compared with the traditional methods.