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A Knowledge-Based Image Segmentation System and Its Hardware Support System
Jing, Shen Xuan,Ji, Qian Qing 대한전자공학회 1992 HICEC:Harbin International Conference on Electroni Vol.1 No.1
A major problem in machine vision is the segmentation of images. In this paper, we present a new approach to solve the image segmentation problem that is based on the design of a knowledge-based image segmentation system(KBISS). The KBISS is an expert system which is based on production system, and according to two type knowledge, i.e region analysis rules and line analysis rules, performs the image segmentation. In general, a knowledge-based system realized on the conventional computer is low efficient. For this reason, we developed a KBISS hardware support system which is an electro-optical hybrid system. The experiment results show the KBISS and its hardware support system is valid and reasonable.
Fractional order viscoelasticity in characterization for atrial tissue
Shen, Jing Jin,Li, Cheng Gang,Wu, Hong Tao,Kalantari, Masoud 한국유변학회 2013 Korea-Australia rheology journal Vol.25 No.2
Atrial tissue due to its solid-like and fluid-like constituents shows highly viscoelastic properties. Up to now, the distribution pattern of muscle fiber in heart is not well established, and it is hard to establish the constitutive model for atrial tissue completely based on the microstructure level. Consider the equivalence between the fractional viscoelasticity and the fractal spring-dashpot model, a generalized fractional order Maxwell model is proposed to model the porcine atrial tissue in the phenomenological sense. This model has a simple expression and intuitively physical meanings. The constitutive parameters in the model are estimated in the complex domain by a genetic algorithm. Final results illustrate the proposed model gets a well agreement with the experimental data.
Jing Jin Shen,Jia Ming Zhou,Shan Lu,Yue Yang Hou,Rong Qing Xu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.12
Instrumented indentation is a versatile method of extracting hyper-elastic material parameters, particularly useful for applications where stress-strain data are difficult to be insitu measured. Because the analytical force-displacement relation is still unavailable for the indentation of hyper-elastic materials, identifying hyper-elastic parameters often requires an iterative optimization strategy that fits finite element simulations with experimental data. However, the optimization strategy is burdened by heavy computation and its prediction accuracy is greatly influenced by the choice of optimization algorithm. To address these challenges in this study, a bidirectional long short-term memory (BLSTM) neural network is presented that directly predicts hyper-elastic material parameters from indentation load-displacement data, focusing on Mooney-Rivlin hyper-elasticity as an example. To improve the predication accuracy, the condition numbers for the inverse identification of the hyper-elastic parameters are investigated. And, a normalization procedure is proposed to treat the input data, which can guarantee the BLSTM network is well-conditioned. During evaluation, the trained BLSTM network significantly outperforms the iterative optimization strategy using a genetic algorithm. Furthermore, the effect of the normalization procedure is demonstrated.
Shen Xuefang,Chen Xiangyuan,Wang Jing,Liu Jing,Wang Zhiyao,Hua Qing,Wu Qichao,Su Yanguang,He Huanzhong,Hu Yuqin,Meng Zhipeng,Xiong Wanxia,Zhu Minmin 생화학분자생물학회 2020 Experimental and molecular medicine Vol.52 No.-
Hyperglycemia-mediated endothelial inflammation participates in the pathogenesis of cardiovascular complications in subjects with diabetes. Previous studies reported that phosphatase and tensin homolog deleted on chromosome ten (PTEN) and SET8 participate in high glucose-mediated endothelial inflammation. In this study, we hypothesize that SET8 regulates PTEN expression, thus contributing to high glucose-mediated vascular endothelial inflammation. Our data indicated that plasma soluble intercellular adhesion molecule-1 (sICAM-1) and endothelial selectin (e-selectin) were increased in patients with diabetes and diabetic rats. PTEN expression was augmented in the peripheral blood mononuclear cells of patients with diabetes and in the aortic tissues of diabetic rats. Our in vitro study indicated that high glucose increased monocyte/endothelial adhesion, endothelial adhesion molecule expression and p65 phosphorylation in human umbilical vein endothelial cells (HUVECs). Moreover, high glucose led to endothelial inflammation via upregulation of PTEN. Furthermore, high glucose inhibited SET8 expression and histone H4 lysine 20 methylation (H4K20me1), a downstream target of SET8. SET8 overexpression reversed the effects of high-glucose treatment. shSET8-mediated endothelial inflammation was counteracted by siPTEN. Furthermore, SET8 was found to interact with FOXO1. siFOXO1 attenuated high glucose-mediated endothelial inflammation. FOXO1 overexpression-mediated endothelial inflammation was counteracted by siPTEN. H4K20me1 and FOXO1 were enriched in the PTEN promoter region. shSET8 increased PTEN promoter activity and augmented the positive effect of FOXO1 overexpression on PTEN promoter activity. Our in vivo study indicated that SET8 was downregulated and FOXO1 was upregulated in the peripheral blood mononuclear cells of patients with diabetes and the aortic tissues of diabetic rats. In conclusion, SET8 interacted with FOXO1 to modulate PTEN expression in vascular endothelial cells, thus contributing to hyperglycemia-mediated endothelial inflammation.
Shen, Xing-Rong,Feng, Rui,Chai, Jing,Cheng, Jing,Wang, De-Bin Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.22
Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.
Shen, Xing-Rong,Chai, Jing,Feng, Rui,Liu, Tong-Zhu,Tong, Gui-Xian,Cheng, Jing,Li, Kai-Chun,Xie, Shao-Yu,Shi, Yong,Wang, De-Bin Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.24
The big gap between efficacy of population level prevention and expectations due to heterogeneity and complexity of cancer etiologic factors calls for selective yet personalized interventions based on effective risk assessment. This paper documents our research protocol aimed at refining and validating a two-stage and web-based cancer risk assessment tool, from a tentative one in use by an ongoing project, capable of identifying individuals at elevated risk for one or more types of the 80% leading cancers in rural China with adequate sensitivity and specificity and featuring low cost, easy application and cultural and technical sensitivity for farmers and village doctors. The protocol adopted a modified population-based case control design using 72, 000 non-patients as controls, 2, 200 cancer patients as cases, and another 600 patients as cases for external validation. Factors taken into account comprised 8 domains including diet and nutrition, risk behaviors, family history, precancerous diseases, related medical procedures, exposure to environment hazards, mood and feelings, physical activities and anthropologic and biologic factors. Modeling stresses explored various methodologies like empirical analysis, logistic regression, neuro-network analysis, decision theory and both internal and external validation using concordance statistics, predictive values, etc..