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51. A short review on block issues in hydrogen recirculation loop in Proton Exchange Membrane Fuel Cell system and counter measures

Author

Feikun Zhou, Wenwen Guo, Minghui Xia, Shu Yuan, Junliang Yu, and Dongwei Tian

Subjects
0209 industrial biotechnology, Materials science, Hydrogen, business.industry, Nuclear engineering, Proton exchange membrane fuel cell, chemistry.chemical_element, 02 engineering and technology, Purge, Anode, Chemical energy, 020901 industrial engineering & automation, chemistry, 0202 electrical engineering, electronic engineering, information engineering, Fuel efficiency, 020201 artificial intelligence & image processing, Electricity, business, Zero emission
Abstract

The proton exchange membrane fuel cell (PEMFC) is one of promising zero emission tools which are applied in the automobile. The PEMFC system is the integration which convert the chemical energy of hydrogen to the electricity and provide the driving force to the vehicle. The hydrogen recirculation loop is commonly used in the vehicle fuel cell system to get a higher fuel consumption efficiency. However, the block issues can happen in the hydrogen recirculation loop and the performance of fuel cell system decreases. The failure mode and mechanisms caused by block issues in hydrogen recirculation loop are shortly reviewed including the water management and nitrogen blanketing. These problems may cause the fuel starvation, carbon corrosion and fuel cell system performance decreasing. Regard to the response option, some counter measures are adapted including the hydrogen purge and hydrogen oscillation.

Published
2020

52. Enhancing Social Recommendation with Adversarial Graph Convolutional Networks

Author

Hongzhi Yin, Lizhen Cui, Junliang Yu, Min Gao, Zi Huang, and Jundong Li

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, business.industry, Computer science, Computer Science - Social and Information Networks, 02 engineering and technology, Recommender system, Machine learning, computer.software_genre, Autoencoder, Social relation, Computer Science Applications, Computer Science - Information Retrieval, Adversarial system, Computational Theory and Mathematics, Social profile, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), Artificial intelligence, business, computer, Information Retrieval (cs.IR), Information Systems
Abstract

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent reports from industry show that social recommender systems consistently fail in practice. According to the negative findings, the failure is attributed to: (1) A majority of users only have a very limited number of neighbors in social networks and can hardly benefit from social relations; (2) Social relations are noisy but they are indiscriminately used; (3) Social relations are assumed to be universally applicable to multiple scenarios while they are actually multi-faceted and show heterogeneous strengths in different scenarios. Most existing social recommendation models only consider the homophily in social networks and neglect these drawbacks. In this paper we propose a deep adversarial framework based on graph convolutional networks (GCN) to address these problems. Concretely, for (1) and (2), a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the social profile to guarantee the validity of the identified neighborhood. After obtaining enough purified social relations for each user, a GCN-based attentive social recommendation module is designed to address (3) by capturing the heterogeneous strengths of social relations. Finally, we adopt adversarial training to unify all the components by playing a Minimax game and ensure a coordinated effort to enhance recommendation performance. Extensive experiments on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component., Accepted by TKDE

Published
2020

53. Liquid water breakthrough location distances on a gas diffusion layer of polymer electrolyte membrane fuel cells

Author

Uwe Reimer, Junliang Yu, Werner Lehnert, and Dieter Froning

Subjects
chemistry.chemical_classification, Materials science, Field (physics), Renewable Energy, Sustainability and the Environment, Lattice Boltzmann methods, Energy Engineering and Power Technology, Lilliefors test, 02 engineering and technology, Polymer, Electrolyte, Mechanics, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, 0104 chemical sciences, Normal distribution, Membrane, chemistry, Electrical and Electronic Engineering, Physical and Theoretical Chemistry, 0210 nano-technology, Stochastic geometry
Abstract

The lattice Boltzmann method is adopted to simulate the three dimensional dynamic process of liquid water breaking through the gas diffusion layer (GDL) in the polymer electrolyte membrane fuel cell. 22 micro-structures of Toray GDL are built based on a stochastic geometry model. It is found that more than one breakthrough locations are formed randomly on the GDL surface. Breakthrough location distance (BLD) are analyzed statistically in two ways. The distribution is evaluated statistically by the Lilliefors test. It is concluded that the BLD can be described by the normal distribution with certain statistic characteristics. Information of the shortest neighbor breakthrough location distance can be the input modeling setups on the cell-scale simulations in the field of fuel cell simulation.

Published
2018

54. Stochastic Analysis of the Gas Flow at the Gas Diffusion Layer/Electrode Interface of a High-Temperature Polymer Electrolyte Fuel Cell

Author

Junliang Yu, Werner Lehnert, Dieter Froning, and Uwe Reimer

Subjects
chemistry.chemical_classification, Materials science, Hydrogeology, Stochastic process, 020209 energy, General Chemical Engineering, 02 engineering and technology, Polymer, Mechanics, Real structure, Electrolyte, 021001 nanoscience & nanotechnology, Microstructure, Catalysis, chemistry, Electrode, 0202 electrical engineering, electronic engineering, information engineering, 0210 nano-technology, Porosity
Abstract

In polymer electrolyte fuel cells of the types PEFC, DMFC and HT-PEFC, the gas diffusion layer (GDL) connects the electrodes with the feeding channels of the bipolar plate. The GDL is typically composed of materials based on carbon fibers, e.g., paper, woven or non-woven textiles. Efficient fuel cell operation requires that the electrodes are sufficiently supplied by gaseous fluids from the channels. Also, reaction products must be transported away from the electrodes. The GDL also has to provide electronic contact to the bipolar plates, but its major task is the mass transport of fluids. The gas transport in through-plane direction is simulated in the porous structure of the GDL, represented by stochastic geometries equivalent to the real structure. In order to support multi-scale simulation, effective properties can be calculated from these mesoscale simulation results to provide model parameters for continuum approaches in cell-scale simulations. In this paper, the resulting gas flow is analyzed with statistical methods with the focus on the interface between GDL and electrode. This approach provides the opportunity to detect quantitative relationships between functionality and microstructure and to design virtual GDL materials with improved transport properties. The evaluation of the interface with stochastic methods provides substantiated properties suitable for connecting regions representing fuel cell components of different spatial scales.

Published
2018

55. Apparent contact angles of liquid water droplet breaking through a gas diffusion layer of polymer electrolyte membrane fuel cell

Author

Dieter Froning, Uwe Reimer, Junliang Yu, and Werner Lehnert

Subjects
chemistry.chemical_classification, Materials science, Field (physics), Renewable Energy, Sustainability and the Environment, 05 social sciences, Lattice Boltzmann methods, Energy Engineering and Power Technology, 02 engineering and technology, Polymer, Mechanics, Electrolyte, 021001 nanoscience & nanotechnology, Condensed Matter Physics, Contact angle, Gas diffusion layer, Fuel Technology, Membrane, chemistry, 0502 economics and business, Fuel cells, 050207 economics, 0210 nano-technology
Abstract

The lattice Boltzmann method is used to simulate the three-dimensional dynamic process of liquid water breaking through the gas diffusion layer (GDL) in the polymer electrolyte membrane fuel cell. An accurate method is introduced to analyze asymmetric droplet shape. Ten micro-structures of Toray GDL were built based on a stochastic geometry model. It was found that asymmetric droplets are produced on the GDL surfaces. Their local apparent contact angles vary with different view angles and geometries. They are different to the idealized contact angles by symmetric simplification. It was concluded that the apparent contact angles are influenced by GDL structures and view angles. This information can help to bridge the gap between mesoscale and cell-scale simulations in the field of fuel cell simulation.

Published
2018

56. Review on resistance based water content monitoring in vehicle fuel cell stack

Author

Qiang Ren, Junliang Yu, Feikun Zhou, Yuemei Zhang, Xu Yongliang, and Shu Yuan

Subjects
business.industry, In vehicle, Environmental science, Proton exchange membrane fuel cell, Fuel cells, Process engineering, business, Water content, On resistance
Abstract

The vehicle fuel cell works with complex operating conditions. It is important to monitor the water content of the fuel cell to prevent the fuel cell from dry-out and flooding. However, few water content measurement techniques are suitable to be integrated in vehicles. Because the electrical test methods for resistance can be operated without modifying the fuel cell stack and water content in proton exchange membrane (PEM) has certain correlation with its resistance, the resistance-based water content measurement techniques have received extensive attentions. This article explained the basic concepts and resistance based online water content monitoring techniques in the fuel cell vehicle applications.

Published
2019

57. Nonlinear Transformation for Multiple Auxiliary Information in Music Recommendation

Author

Junliang Yu, Yuqi Song, Qingyu Xiong, Junwei Zhang, Min Gao, and Xinyi Wang

Subjects
Information retrieval, Artificial neural network, Computer science, Feature vector, 0102 computer and information sciences, 02 engineering and technology, Recommender system, Space (commercial competition), 01 natural sciences, Transformation (music), 010201 computation theory & mathematics, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Representation (mathematics), Digital audio
Abstract

Online music recommender systems are becoming increasingly prevalent because of the popularity of digital music and music recommendation generally caters to users by discovering songs that match their preferences. However, these systems have to face a challenge: how to recommend new songs in a situation where prior knowledge is scarce. Some researches take auxiliary information into consideration in new recommendation approaches to deal with this problem. Nevertheless, they rarely pay attention to complex relationships among different feature spaces when they map those information to a latent space. To this end, this paper proposes an approach that uses non-linear transformation to integrate different auxiliary information into the songs latent representations. Unlike other studies which directly map auxiliary information to the feature space, the proposed music recommendation model (NeuTrans) maps different information features to low-dimensional vector representation by non-linear neural networks. Specifically, the NeuTrans separately employs matrix factorization and attribute network embedding to extract auxiliary information (historical interaction, network structure and attributes of songs). The feature space of different information is obtained by nonlinearly mapping the feature space of the songs. Experimental analysis on two real-world datasets shows that our framework outperforms the state-of-the- art approaches on Top-N music recommendation.

Published
2019

58. A Minimax Game for Generative and Discriminative Sample Models for Recommendation

Author

Qingyu Xiong, Zongwei Wang, Xinyi Wang, Min Gao, Junliang Yu, and Junhao Wen

Subjects
Computer science, business.industry, Sample (statistics), 02 engineering and technology, Recommender system, Machine learning, computer.software_genre, Minimax, Missing data, MovieLens, Generative model, Discriminative model, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Performance improvement, business, computer
Abstract

Recommendation systems often fail to live up to expectations in real situations because of the lack of user feedback, known as the data sparsity problem. A large number of existing recommendation methods resort to side information to gain a performance improvement. However, these methods are either too complicated to follow or time-consuming. To alleviate the data sparsity problem, in this paper we propose UGAN, which is a general adversarial framework for recommendation tasks and consists of a generative model and a discriminative model. In UGAN, the generative model, acts as an attacker to cheat the discriminative model to capture the pattern of the original data input and generate similar user profiles, while the counterpart, the discriminative model aims to distinguish the forged samples from the real data. By competing with each other, two model are alternatively updated like playing a minimax game until the generative model has learned the original data distribution. The experimental results on two real-world datasets, Movielens and Douban, show that the user profiles forged by UGAN can be easily integrated into a wide range of recommendation methods and significantly improve their performance, which provides a promising way to mitigate the adverse impact of missing data.

Published
2019

59. Long‐term safety and efficacy of stenting on correcting internal jugular vein and cerebral venous sinus stenosis

Author

Chaobo Bai, Zhiying Chen, Yuchuan Ding, Xunming Ji, Junliang Yuan, and Ran Meng

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Objectives To compare the long‐term safety and efficacy of stenting in correcting cerebral venous sinus stenosis (CVSS) and internal jugular venous stenosis (IJVS). Methods Patients confirmed with CVSS or IJVS by imaging were enrolled in this real‐world study from 2014 through 2021. Clinical characteristics and long‐term outcomes of these two diseases entities post‐stenting were followed up and compared. Results Three hundred and nineteen patients were enrolled in this study, with a mean age of 48.83 years and a BMI of 25.08 on average. In which, 144 patients underwent stenting, the stenotic segments were corrected and the venous blood flow was restored immediately post‐stenting. At 6.15 ± 1.67 days follow‐up, significant improvement was observed in headache, tinnitus, insomnia, ICP, and mean pressure gradient in both groups (all p

Published
2023
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60. Impact of compression on gas transport in non-woven gas diffusion layers of high temperature polymer electrolyte fuel cells

Author

Volker Schmidt, Uwe Reimer, Junliang Yu, Werner Lehnert, Dieter Froning, Ingo Manke, and Gerd Gaiselmann

Subjects
chemistry.chemical_classification, Renewable Energy, Sustainability and the Environment, Chemistry, Stochastic modelling, 020209 energy, Lattice Boltzmann methods, Analytical chemistry, Energy Engineering and Power Technology, 02 engineering and technology, Polymer, Electrolyte, 021001 nanoscience & nanotechnology, Synchrotron, law.invention, law, Homogeneity (physics), 0202 electrical engineering, electronic engineering, information engineering, Gaseous diffusion, Electrical and Electronic Engineering, Physical and Theoretical Chemistry, Composite material, 0210 nano-technology, Material properties
Abstract

Gas transport in non-woven gas diffusion layers of a high-temperature polymer electrolyte fuel cell was calculated with the Lattice Boltzmann method. The underlying micro structure was taken from two sources. A real micro structure was analyzed in the synchrotron under the impact of a compression mask mimicking the channel/rib structure of a flow field. Furthermore a stochastic geometry model based on synchrotron X-ray tomography studies was applied. The effect of compression is included in the stochastic model. Gas transport in these micro structures was simulated and the impact of compression was analyzed. Fiber bundles overlaying the micro structure were identified which affect the homogeneity of the gas flow. There are significant deviations between the impact of compression on effective material properties for this type of gas diffusion layers and the Kozeny-Carman equation.

Published
2016

61. Social Recommendation Based on Implicit Friends Discovering Via Meta-Path

Author

Junliang Yu, Yuqi Song, Min Gao, and Qingyu Xiong

Subjects
Information retrieval, Social network, Computer science, business.industry, 02 engineering and technology, Recommender system, Popularity, Social relation, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, business, Feature learning, Heterogeneous network
Abstract

With the growing popularity of online social platforms, it has been universally recognized that incorporating social relations into recommender systems can usually alleviate the problem of data sparsity. However, social recommender systems based on explicit relations are not as successful as expected due to the noise and the social cold issue of explicit social links. The intuition of utilizing explicit relations is that users share similar preferences if they are friends in the social network. In fact, quite a lot of users who are distant from each other in the social network also have similar tastes. The user item network and the user social network can provide useful information that can complement each other, so that exploring the implicit friends using the heterogeneous network they formed would be more helpful. In this paper, we propose an approach IFSR to discover implicit friends over the heterogeneous network to improve the performance of social recommendation. To find out reliable implicit ties, we first model the system as a heterogeneous network upon which both the preferences and social information are coupled. Over the HIN, similarities between each pair of users can be quantified through network embedding based representation learning. To reduce the computational cost while preserving the information embedded in the original networks and uncover the latent information hiding in the HIN, several meaningful meta-paths over the HIN are designed to guide the process of random walks. Finally, the Top-K implicit friends are incorporated into a social bayesian ranking model to enhance the performance of item ranking. Experimental results on three datasets demonstrate IFSR outperforms the state-of-the-art methods and illustrate why the implicit friends are advantageous for social recommendation.

Published
2018

62. Detection of Shilling Attack Based on Bayesian Model and User Embedding

Author

Min Gao, Fan Yang, Yuqi Song, Junliang Yu, and Xinyi Wang

Subjects
Computer science, business.industry, Feature extraction, 02 engineering and technology, Recommender system, Bayesian inference, Machine learning, computer.software_genre, MovieLens, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, Unsupervised learning, Embedding, 020201 artificial intelligence & image processing, Artificial intelligence, business, Feature learning, computer
Abstract

The recommendation systems have been widely employed due to the effectiveness on mitigating the information overload issue. At present, the recommendation systems have made great progress, but they are under the threat of shilling attack because of their open nature. Shilling attack is the way by which the attackers can manipulate the recommendation results and cause great harm to recommendation systems. Existing shilling attack detection models are mainly based on statistical measures to extract features like the rating deviation, which are generally susceptible to attack strategies. Once the attacker changes attack strategy, the detection model which is based on the statistical method may fail. Some researchers have identified that implicit features hidden in user-user interactions and user-item interactions can be utilized to solve the problem. Their research aims to learn potential relationship between users to update features. However, the research ignores the significance of learning features by employing label information. To solve this problem, in this paper, we propose a novel detection model, named BayesDetector, which takes not only the user-user and user-item interactions but also the label information into consideration in the process of learning user implicit features. Furthermore, to take full advantage of user labels, the Bayesian model is added to the feature learning. Experiments on two datasets, Amazon and Movielens, show that BayesDetector significantly outperforms the state-of-the-art methods.

Published
2018

63. Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation

Author

Min Gao, Huan Liu, Hongzhi Yin, Junliang Yu, and Jundong Li

Subjects
Information retrieval, Social network, business.industry, Computer science, 02 engineering and technology, Recommender system, Network topology, Social relation, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Embedding, 020201 artificial intelligence & image processing, business, Feature learning, Heterogeneous network
Abstract

The explicitly observed social relations from online social platforms have been widely incorporated into recommender systems to mitigate the data sparsity issue. However, the direct usage of explicit social relations may lead to an inferior performance due to the unreliability (e.g., noises) of observed links. To this end, the discovery of reliable relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to adaptively identify implicit friends toward discovering more credible user relations. Particularly, implicit friends are those who share similar tastes but could be distant from each other on the network topology of social relations. Methodologically, to find the implicit friends for each user, we first model the whole system as a heterogeneous information network, and then capture the similarity of users through the meta-path based embedding representation learning. Finally, based on the intuition that social relations have varying degrees of impact on different users, our approach adaptively incorporates different numbers of similar users as implicit friends for each user to alleviate the adverse impact of unreliable social relations for a more effective recommendation. Experimental analysis on three real-world datasets demonstrates the superiority of our method and explain why implicit friends are helpful in improving social recommendation.

Published
2018

64. Integrating User Embedding and Collaborative Filtering for Social Recommendations

Author

Qingyu Xiong, Wenge Rong, Min Gao, Junliang Yu, Qianqi Fang, and Yuqi Song

Subjects
Computer science, business.industry, media_common.quotation_subject, 02 engineering and technology, Recommender system, Machine learning, computer.software_genre, Social relation, Matrix decomposition, Side effect (computer science), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, Embedding, 020201 artificial intelligence & image processing, Word2vec, Quality (business), Artificial intelligence, business, computer, media_common
Abstract

Social recommendation has attracted increasing attention over the years due to the potential value of social relations, which can be harnessed to mitigate the dilemma of data sparsity in traditional recommender systems. However, recent studies show that social recommenders fail in the practical use in industry for the reason that some problems in social relations, such as the noise, lead to a degradation in recommendation quality. To solve the problem, in this paper, a social recommender, SocialEM, which integrates the neural user embedding and collaborative filtering is proposed. Enlightened by the factorization of the word co-occurrence matrix which is equivalent to the skip-gram model in word2vec, SocialEM will jointly decomposes the user-item rating matrix and the user-user co-occurrence matrix with shared user latent factors. For each pair of users, the co-occurrence matrix encodes the number of being trusted together by other users in the social relation network. Experiments conducted on the real-world datasets have shown that the side effect of social relations can be diminished by tuning parameters for SocialEM. And compared with previous studies, our method significantly improves the quality of recommendations.

Published
2018

65. PUED: A Social Spammer Detection Method Based on PU Learning and Ensemble Learning

Author

Yuqi Song, Min Gao, Lulan Yu, Junliang Yu, Xinyu Xiao, and Wentao Li

Subjects
Computer science, business.industry, media_common.quotation_subject, 020206 networking & telecommunications, 02 engineering and technology, Machine learning, computer.software_genre, Ensemble learning, Imbalanced data, Spamming, Random forest, Dilemma, ComputingMethodologies_PATTERNRECOGNITION, 020204 information systems, Voting, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, business, computer, PU learning, Classifier (UML), media_common
Abstract

© 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. In social network, people generally tend to share information with others, thus, those who have frequent access to the social network are more likely to be affected by the interest and opinions of other people. This characteristic is exploited by spammers, who spread spam information in network to disturb normal users for interest motives seriously. Numerous notable studies have been done to detect social spammers, and these methods can be categorized into three types: unsupervised, supervised and semi-supervised methods. While the performance of supervised and semi-supervised methods is superior in terms of detection accuracy, these methods usually suffer from the dilemma of imbalanced data since the number of unlabeled normal users is far more than spammers’ in real situations. To address the problem, we propose a novel method only relying on normal users to detect spammers exactly. We present two steps: one picks out reliable spammers from unlabeled samples which is imposed on a voting classifier; while the other trains a random forest detector from the normal users and reliable spammers. We conduct experiments on two real-world social datasets and show that our method outperforms other supervised methods.

Published
2018

66. Impact of the Important Users on Social Recommendation System

Author

Junliang Yu, Zehua Zhao, Min Zhang, Min Gao, Xinyi Wang, and Yuqi Song

Subjects
Social network, Computer science, business.industry, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 050301 education, 020201 artificial intelligence & image processing, 02 engineering and technology, Recommender system, business, 0503 education, Data science, Information overload
Abstract

Recommendation methods have attracted extensive attention recently because they intent to alleviate the information overload problem. Among them, the social recommendation methods have become one of the popular research fields because they are benefit to solve the cold start problem. In social recommendation systems, some users are of great significance, because they usually have decisive impacts on the recommendation results. However, it is still lack of research on how the important users make influence to recommendation methods. This paper presents three types of important users and utilizes three social frequently-used recommendation methods to analyze the influence from multiple perspectives. The experiments demonstrate that all the recommendation methods achieve better performance with important users, and the important neighbor users have the greatest impact on the recommendation methods.

Published
2018

67. Collaborative Shilling Detection Bridging Factorization and User Embedding

Author

Yuqi Song, Qingyu Xiong, Tong Dou, Min Gao, Qianqi Fang, and Junliang Yu

Subjects
Word embedding, Computer science, business.industry, Generalization, 02 engineering and technology, Recommender system, Machine learning, computer.software_genre, Matrix decomposition, Bridging (programming), Factorization, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, Embedding, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract

The recommender system based on collaborative filtering is vulnerable to shilling attacks due to its open nature. With the wide employment of recommender systems, an increasing number of attackers are disordering the system in order to benefit from the manipulated recommendation results. Therefore, how to effectively detect shilling attacks now becomes more and more crucial. Most existing detection models recognize attackers in statistics-based manners. However, they failed in capturing the fine-grained interactions between users and items, leading to a degradation in detection accuracy. In this paper, inspired by the success of word embedding models, we propose a collaborative shilling detection model, CoDetector, which jointly decomposes the user-item interaction matrix and the user-user co-occurrence matrix with shared user latent factors. Then, the learned user latent factors containing network embedding information are used as features to detect attackers. Experiments conducted on simulated and real-world datasets show that CoDetector has a good performance and generalization capacity and outperforms state-of-the-art methods.

Published
2018

68. Meta-path Based Heterogeneous Graph Embedding for Music Recommendation

Author

Junhao Wen, Junliang Yu, Ling Liu, and Qianqi Fang

Subjects
Information retrieval, Graph embedding, Computer science, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, Embedding, Graph (abstract data type), 020201 artificial intelligence & image processing, Active listening, 02 engineering and technology, Feature learning
Abstract

The prosperous online music streaming industry makes personalized music recommendation a topic worthy of extensive study. Traditional music recommendation techniques which are based on conventional collaborative filtering or acoustic content features usually sufffer from data sparsity or time-consuming computation problems, respectively. In fact, online music services not only generate listening history for each user but also accumulate a large amount of heterogeneous data including performers, tags, ownerships and so on. Capturing underlying user preference from the heterogeneous data to enhance music recommendation is transparently promising, because on one hand these data can mitigate the sparsity of listening history while incorporating them into recommendation model is computationally affordable. To this end, in this paper we propose a novel music recommendation approach. It first models the music system as a heterogeneous music graph. Then, to make full use of the heterogeneous data, carefully designed meta-paths are used to dig up the information lying in the graph. Finally, we learn user preferences through a combination of Bayesian Personalized Ranking model and heterogeneous embedding representation learning. Extensive experimental analysis on real-world public dataset validates that the proposed approach outperforms the baselines, especially on cold start users.

Published
2018

69. Hybrid attacks on model-based social recommender systems

Author

Wentao Li, Junliang Yu, Min Gao, Junhao Wen, Wenge Rong, and Qingyu Xiong

Subjects
Statistics and Probability, Social network, Relation (database), Computer science, business.industry, Fluids & Plasmas, 020206 networking & telecommunications, 02 engineering and technology, Recommender system, Condensed Matter Physics, Computer security, computer.software_genre, Popularity, Term (time), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, business, computer
Abstract

© 2017 Elsevier B.V. With the growing popularity of the online social platform, the social network based approaches to recommendation emerged. However, because of the open nature of rating systems and social networks, the social recommender systems are susceptible to malicious attacks. In this paper, we present a certain novel attack, which inherits characteristics of the rating attack and the relation attack, and term it hybrid attack. Furtherly, we explore the impact of the hybrid attack on model-based social recommender systems in multiple aspects. The experimental results show that, the hybrid attack is more destructive than the rating attack in most cases. In addition, users and items with fewer ratings will be influenced more when attacked. Last but not the least, the findings suggest that spammers do not depend on the feedback links from normal users to become more powerful, the unilateral links can make the hybrid attack effective enough. Since unilateral links are much cheaper, the hybrid attack will be a great threat to model-based social recommender systems.

Published
2017

70. Connecting Factorization and Distance Metric Learning for Social Recommendations

Author

Min Gao, Qingyu Xiong, Wenge Rong, Junliang Yu, Zehua Zhao, and Yuqi Song

Subjects
Computer science, business.industry, 02 engineering and technology, Recommender system, Machine learning, computer.software_genre, Social relation, Matrix decomposition, Cold start, Factorization, 020204 information systems, Metric (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Interpretability
Abstract

Social relations can help to relieve the dilemmas called cold start and data sparsity in traditional recommender systems. Most of existing social recommendation methods are based on matrix factorization, which has been proven effective. In this paper, we introduce a novel social recommender based on the idea that distance reflects likability. It aims to make users in recommender systems be spatially close to their friends and items they like, and be far away from items they dislike by connecting factorization model and distance metric learning. In our method, the positions of users and items are decided by the ratings and social relations jointly, which can help to find appropriate locations for users who have few ratings. Finally, the learnt metric and locations are used to generate understandable and reliable recommendations. The experiments conducted on the real-world dataset have shown that, compared with methods only based on factorization, our method has advantages on both interpretability and accuracy.

Published
2017

71. PUD: Social Spammer Detection Based on PU Learning

Author

Qingyu Xiong, Wentao Li, Yuqi Song, Min Gao, Junliang Yu, and Junhao Wen

Subjects
Social network, Computer science, business.industry, 020206 networking & telecommunications, 02 engineering and technology, Machine learning, computer.software_genre, Spamming, ComputingMethodologies_PATTERNRECOGNITION, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Artificial Intelligence & Image Processing, Artificial intelligence, business, PU learning, Classifier (UML), computer
Abstract

© Springer International Publishing AG 2017. Social networks act as the communication channels for people to share various information online. However, spammers who generate spam information reduce the satisfaction of common users. Numerous notable studies have been done to detect social spammers, and these methods can be categorized into three types: unsupervised, supervised and semi-supervised methods. While the performance of supervised and semi-supervised methods is superior in terms of detection accuracy, these methods usually suffer from the dilemma of imbalanced data since the labeled normal users are far more than spammers in real situations. To address the problem, we propose a novel method only relying on normal users to detect spammers. Firstly, a classifier is built from a part of normal and unlabeled samples to pick out reliable spammers from unlabeled samples. Secondly, our well-trained detector, which is based on the given normal users and predicted spammers, can distinguish between normal users and spammers. Experiments conducted on real-world datasets show that the proposed method is competitive with supervised methods.

Published
2017

72. Make Users and Preferred Items Closer: Recommendation via Distance Metric Learning

Author

Qingyu Xiong, Min Gao, Yuqi Song, Wenge Rong, Qianqi Fang, and Junliang Yu

Subjects
Alternative methods, Information retrieval, business.industry, 02 engineering and technology, Recommender system, Machine learning, computer.software_genre, Information overload, Matrix decomposition, Dilemma, Factorization, 020204 information systems, Metric (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Mathematics
Abstract

Recommender systems can help to relieve the dilemma called information overload. Collaborative filtering is a primary approach based on collective historical ratings to recommend items to users. One of the most competitive collaborative filtering algorithm is matrix factorization. In this paper, we proposed an alternative method. It aims to make users be spatially close to items they like and be far away from items they dislike, by connecting matrix factorization and distance metric learning. The metric and latent factors are trained simultaneously and then used to generate reliable recommendations. The experiments conducted on the real-world datasets have shown that, compared with methods only based on factorization, our method has advantage in terms of accuracy.

Published
2017

73. Pattern and associated factors of cognitive failures in the general chinese population during the early stages of the COVID-19 pandemic: A cross-sectional study

Author

Wei Yan, Shuyu Ni, Chang Liu, Junliang Yuan, Yimiao Zhao, Na Zeng, Huan Mei, Shuilin Wu, Jianyu Que, Kai Yuan, Le Shi, Siwei Sun, Shiqiu Meng, Maosheng Ran, Yue Leng, Arun V Ravindran, Jie Shi, Yanping Bao, and Lin Lu

Subjects
cognitive failure, coronavirus disease-2019, general population, risk factors, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background: The coronavirus disease-2019 (COVID-19) pandemic worldwide has caused a high burden of mental problems, which may be associated with subjective cognitive impairment in the general population. Objectives: This cross-sectional survey aimed to investigate the level and pattern of cognitive failure (CF) and its negative changes during the COVID-19 pandemic among the general population in China. Methods: The participants completed an online questionnaire between April 18 and May 4, 2020, and those aged between 18 and 70 were included in this study. CF was measured using the 14-item CF Questionnaire (CFQ-14). Factors associated with CF and negative changes in CF were evaluated using multiple linear and logistic regression models. A total of 30,879 eligible participants were recruited; most were female (59.10%) and aged 31–45 (61.51%). Results: The mean CFQ-14 score was 15.62 (standard deviation = 11.55), and 4,619 (14.96%) participants reported negative changes in CF during the pandemic. Multiple regression analyses showed that participants with female gender, history of physical and mental disease, the self-perceived influence of COVID-19, altered appetite and taste preference, worse interpersonal relationships, long sleep duration, poor sleep quality, depressive, anxiety and posttraumatic stress disorder symptoms had a higher level of CF and negative CF changes, while regular exercise was associated with a lower risk of both outcomes. Conclusions: This study indicates that CF symptoms should be monitored in the general population during pandemics. A healthy lifestyle and reduction in psychological stress could help promote normal cognitive function during pandemics.

Published
2023
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74. Polytetrafluorethylene effects on liquid water flowing through the gas diffusion layer of polymer electrolyte membrane fuel cells

Author

Uwe Reimer, Junliang Yu, Dieter Froning, and Werner Lehnert

Subjects
chemistry.chemical_classification, Chemical substance, Materials science, Polytetrafluoroethylene, Physics::Instrumentation and Detectors, Renewable Energy, Sustainability and the Environment, Lattice Boltzmann methods, Energy Engineering and Power Technology, 02 engineering and technology, Polymer, Electrolyte, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, 0104 chemical sciences, chemistry.chemical_compound, Membrane, chemistry, Two-phase flow, Electrical and Electronic Engineering, Physical and Theoretical Chemistry, Composite material, 0210 nano-technology, Saturation (chemistry)
Abstract

A three-dimensional (3D) lattice Boltzmann model is applied to simulations of the dynamic process of liquid water moving through the gas diffusion layer, processed by covering the fibers with polytetrafluorethylene (PTFE), in polymer electrolyte membrane fuel cells. The PTFE content and two through-plane distribution styles were analyzed regarding their impact on the liquid water's flow behavior. The total saturation curves show that the water's breakthrough times are affected by these two factors. Different characteristic shapes of local saturation curves are observed with different types of PTFE distribution. The dynamic elevation plots of water fronts show that certain PTFE distribution styles can be of advantage to fuel cell efficiency.

Published
2019

75. Sex differences in clinical cognitive impairment with Lewy bodies: a Chinese multicenter study

Author

Jinghuan Gan, Zhichao Chen, Zhihong Shi, Xudong Li, Shuai Liu, Yiming Liu, Hongcan Zhu, Lu Shen, Guili Zhang, Yong You, Qihao Guo, Nan Zhang, Yang Lv, Baozhi Gang, Junliang Yuan, and Yong Ji

Subjects
Gender, Sex ratio, Lewy body disease, Mild cognitive impairment, Parkinson’s disease, Medicine, Physiology, QP1-981
Abstract

Highlights There are significant sex differences in Chinese population with cognitive impairment in Lewy body disease. Women were more common in dementia with Lewy bodies and mild cognitive impairment with Lewy bodies cases, had more frequent and severe neuropsychiatric symptoms, and poorer cognition than men. Men predominant in Parkinson’s disease with mild cognitive impairment and Parkinson’s disease with dementia cases, and performed more frequent RBD and parkinsonism than women. Dementia with Lewy bodies vs. Parkinson’s disease with dementia, and mild cognitive impairment with Lewy bodies vs. Parkinson’s disease with mild cognitive impairment are distinct disease forms and should not be confused.

Published
2022
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76. The performance of patients with cerebral microbleeds in different cognitive tests: A cross-sectional study

Author

Xuanting Li, Shuna Yang, Yue Li, Wei Qin, Lei Yang, Junliang Yuan, and Wenli Hu

Subjects
cerebral microbleed, cerebral small vessel disease, cognitive impairment, cognitive test, neuroimaging, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

BackgroundThe clinical features and pathological process of cerebral microbleed (CMB)-related cognitive impairment are hot topics of cerebral small vessel disease (CSVD). However, how to choose a more suitable cognitive assessment battery for CMB patients is still an urgent issue to be solved. This study aimed to analyze the performance of CMB patients on different cognitive tests.MethodsThis study was designed as a cross-sectional study. The five main markers of CSVD (including the CMB, white matter hyperintensities, perivascular spaces, lacunes and brain atrophy) were assessed according to magnetic resonance imaging. The burden of CMB was categorized into four grades based on the total number of lesions. Cognitive function was assessed by Mini-Mental State Examination (MMSE), Trail-Making Test (TMT, Part A and Part B), Stroop color-word test (Stroop test, Part A, B and C), Verbal Fluency Test (VF, animal), Digit-Symbol Substitution Test (DSST), Digit Cancellation Test (DCT) and Maze. Multiple linear regression analysis was conducted to analyze the association between CMB and cognitive findings.ResultsA total of 563 participants (median age of 69 years) were enrolled in this study, including 218 (38.7%) CMB patients. CMB patients showed worse performance than non-CMB subjects in each cognitive test. Correlation analysis indicated the total number of CMB lesions had positive correlations with the time of TMT, Maze and Stroop test, and negative correlations with the performance of MMSE, VF, DSST, and DCT. After the adjustment for all the potential confounders by linear regression, the CMB burden grade was correlated with the performance of VF, Stroop test C, Maze and DCT.ConclusionThe presence of CMB lesions was associated with much worse cognitive performances. In VF, Stroop test C, Maze and DCT, the correlations between CMB severity and assessment results were more significant. Our study further confirmed that the attention/executive function domain was the most commonly evaluated in CMB, which provided a picture of the most utilized tools to analyze the prognostic and diagnostic value in CMB.

Published
2023
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77. Progress and prospect of CNOOC's oil and gas well drilling and completion technologies

Author

Zhong Li, Renjun Xie, Yi Wu, and Junliang Yuan

Subjects
CNOOC, 13th five-year plan, Deep-water oil and gas well drilling, Deep-water oil and gas field development, “Deepsea 1” giant gas field, High temperature and high pressure, Gas industry, TP751-762
Abstract

Offshore oil and gas production has become an important growth pole to ensure national energy security. However, China's offshore oil and gas production is lack of key core technologies and weak in tool and equipment foundation and can hardly support the optimized fast development of important fields. To solve these technological difficulties, China National Offshore Oil Corporation (CNOOC) insisted on independent technological innovation and overcame a series of key core technologies through theoretical research and key technology research and test during the 13th Five-Year Plan. And the following research results are obtained. First, several key technologies are broken through, including efficient drilling and completion in the middle and deep layers of the Bohai Sea, offshore large-scale heavy oil thermal recovery, deep-water oil and gas field development, and high temperature and high pressure well drilling and completion in the South China Sea, unconventional oil and gas stimulation, and offshore emergency rescue. Domestic first independently operated ultra deep water giant gas field, namely “Deepsea 1” is successfully put into production, so that the leap from 300 m to 1500 m of water depth and from exploration to development is realized. Second, key tools and equipment are developed, such as logging while drilling and rotary steering drilling system, deep-water drilling surface conductor, underwater emergency killing device, and underwater wellhead Christmas tree, which promote the high-quality development of China's offshore oil industry. Finally, some suggestions are proposed as follows. In the future, CNOOC shall strengthen independent technological innovation, quicken the pace to deepsea oil and gas, and continue to research key core technologies for oil and gas reserves and production increase (e.g. continuous localization of drilling and completion technologies, equipment and materials in complex fields), commingled gas production and test and green energy transformation (e.g. geothermal energy), so as to make greater contributions to ensure national energy security and build a maritime power.

Published
2022
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78. Predictors of cognitive impairment in newly diagnosed Parkinson’s disease with normal cognition at baseline: A 5-year cohort study

Author

Jing Chen, Danhua Zhao, Qi Wang, Junyi Chen, Chaobo Bai, Yuan Li, Xintong Guo, Baoyu Chen, Lin Zhang, and Junliang Yuan

Subjects
Parkinson’s disease, mild cognitive impairment, dementia, Montreal cognitive assessment, neuropsychological test, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Background and objectiveCognitive impairment (CI) is a substantial contributor to the disability associated with Parkinson’s disease (PD). We aimed to assess the clinical features and explore the underlying biomarkers as predictors of CI in patients with newly diagnosed PD (NDPD; less than 2 years).MethodsWe evaluated the cognitive function status using the Montreal Cognitive Assessment (MoCA) and a battery of neuropsychological tests at baseline and subsequent annual follow-up for 5 years from the Parkinson’s Progression Markers Initiative (PPMI) database. We assessed the baseline clinical features, apolipoprotein (APO) E status, β-glucocerebrosidase (GBA) mutation status, cerebrospinal fluid findings, and dopamine transporter imaging results. Using a diagnosis of CI (combined mild cognitive impairment and dementia) developed during the 5-year follow-up as outcome measures, we assessed the predictive values of baseline clinical variables and biomarkers. We also constructed a predictive model for the diagnosis of CI using logistic regression analysis.ResultsA total of 409 patients with NDPD with 5-year follow-up were enrolled, 232 with normal cognitive function at baseline, and 94 patients developed CI during the 5-year follow-up. In multivariate analyses, age, current diagnosis of hypertension, baseline MoCA scores, Movement disorder society Unified PD Rating Scale part III (MDS-UPDRS III) scores, and APOE status were associated with the development of CI. Predictive accuracy of CI using age alone improved by the addition of clinical variables and biomarkers (current diagnosis of hypertension, baseline MoCA scores, and MDS-UPDRS III scores, APOE status; AUC 0.80 [95% CI 0.74–0.86] vs. 0.71 [0.64–0.77], p = 0.008). Cognitive domains that had higher frequencies of impairment were found in verbal memory (12.6 vs. 16.8%) and attention/processing speed (12.7 vs. 16.9%), however, no significant difference in the prevalence of CI at annual follow-up was found during the 5-year follow-up in NDPD patients.ConclusionIn NDPD, the development of CI during the 5-year follow-up can be predicted with good accuracy using a model combining age, current diagnosis of hypertension, baseline MoCA scores, MDS-UPDRS III scores, and APOE status. Our study underscores the need for the earlier identification of CI in NDPD patients in our clinical practice.

Published
2023
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79. Impulsivity trait mediates the relationship between white matter integrity of prefrontal–striatal circuits and the severity of dependence in alcoholism

Author

Fei Wu, Ping Dong, Guowei Wu, Jiahui Deng, Zhaojun Ni, Xuejiao Gao, Peng Li, Bing Li, Junliang Yuan, and Hongqiang Sun

Subjects
alcohol dependence, impulsivity, systematic striatal circuits, probabilistic tractography, diffusion tensor imaging, Psychiatry, RC435-571
Abstract

BackgroundAlcohol dependence (AD) remains one of the major public health concerns. Impulsivity plays a central role in the transfer from recreational alcohol use to dependence and relapse. White matter dysfunction has been implicated in alcohol addiction behaviors and impulsivity. However, little is known about the role of systematic striatal structural connections underlying the mechanism of impulsive traits in AD.MethodsIn our study, we used seed-based classification by probabilistic tractography with five target masks of striatal circuits to explore the differences in white matter integrity (fractional anisotropy, FA) in AD male patients (N = 51) and healthy controls (N = 27). We mainly explored the correlation between FA of the striatal circuits and impulsive traits (Barratt Impulsiveness Scale, BIS-11), and the mediation role of impulsivity in white matter integrity and the severity of alcohol dependence.ResultsCompared with healthy controls, AD showed much lower FA in the left and right striatum–supplementary motor area (SMA) and left striatum–amygdala. We also found the decreased FA of right striatum-vlPFC was correlated with higher impulsivity. Besides, the relationship between reduced FA of right striatum-vlPFC and severity of dependence could be mediated by impulsivity.ConclusionIn our study, we found disrupted white matter integrity in systematic striatal circuits in AD and the decreased FA of right striatum-vlPFC was correlated with higher impulsivity in AD. Our main findings provide evidence for reduced white matter integrity of systematic striatal circuits and the underlying mechanisms of impulsivity in male AD individuals.

Published
2022
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80. Research on Economic Development Stage and Marginal Effects of Trade and FDI on Economic Growth in China

Author

Limin Yao, Junliang Yu, and Chaobo Bao

Subjects
Internationalization, Economic growth, Policy mix, Promotion effect, Economics, Foreign direct investment, Per capita income, China, Panel data
Abstract

Since reform and opening up. exports and FDI, as two main internationalization patterns, successfully promote China's economic growth and increase the per capita income. In the mean time, it widens the regional economic gap. Whether exports and FDI are still two engines to promote China’s economic growth in a new stage of development? This paper collects provincial panel data from 1987 to 2010 in China with three economic areas, the east, the middle, and the west, and divides them into two periods, before entry into WTO and after, to study the stage of economic development’s growth effect on trade and FDI empirically. It concludes that the developed eastern area’s exports have the strongest promotion effect on economy, but its promotion effect is weakening now and weaker than the imports’ promotion effect which is still increasing now, in the compared undeveloped mid and west of China. Furthermore, FDI’s growth effects on all the three economic areas are increasing. In a word, foreign trade and FDI’s growth effects change periodically in the different stages of development, so the policy mix of trade and FDI need to be adjusted with economic development.

Published
2013

81. Clinical characteristics of Lewy body dementia in Chinese memory clinics

Author

Jinghuan Gan, Shuai Liu, Xiaodan Wang, Zhihong Shi, Lu Shen, Xudong Li, Qihao Guo, Junliang Yuan, Nan Zhang, Yong You, Yang Lv, Dongming Zheng, Yong Ji, and China Lewy Body Disease Collaborative Alliance

Subjects
Lewy body dementia, Dementia with Lewy bodies, Parkinson’s disease dementia, Prevalence, Gender, Molecular image, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Lewy body dementia (LBD), consisting of dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD), is the second most common type of neurodegenerative dementia in older people. The current study aimed to investigate the clinical characteristics of LBD in Chinese memory clinics. Methods A total of 8405 dementia medical records were reviewed, revealing 455 patients with LBD. Demographic data, neuropsychological scores, and the scale for Medial Temporal lobe Atrophy (MTA) were then analyzed from nine memory clinics in the China Lewy Body Disease Collaborative Alliance. Results The clinical proportion of LBD among the subjects and among all dementia types was 5.4% (4.9–5.9%) and 7.3% (6.7–8.0%), respectively, with a mean onset age of 68.6 ± 8.4 years. Patients with DLB comprised 5.6% (n = 348, age of onset 69.1 ± 8.3), while PDD comprised 1.7% (n = 107, age of onset 66.7 ± 8.8) of all dementia cases. There were slightly more males than females with DLB (n = 177, 50.9%) and PDD (n = 62, 57.9%). Patients with DLB had a poorer performance compared to those with PDD on the MMSE (16.8 ± 7.1 vs. 19.5 ± 5.7, p = 0.001), the MoCA (11.4 ± 6.6 vs. 14.0 ± 5.8, p

Published
2021
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82. Coupling of Lattice Boltzmann and Volume of Fluid Approaches to Study the Droplet Behavior at the Gas Diffusion Layer/Gas Channel Interface

Author

Andersson, Martin, Beale, Steven B., Froning, Dieter, and, Junliang Yu, and Lehnert, Werner

Abstract

A typical polymer electrolyte fuel cell (PEFC) flow field consists of micro/minichannels. The continues removal of liquid water from the cathode channels is a critical topic, as water droplets forming in the channels may block the transport of gaseous oxygen to the active sites, which not only gives an uneven current distribution and substantial loss of performance, but also, increases degradation rates and unstable operation. Water generated by the electrochemical reactions condenses, depending on temperature mainly, into liquid form, potentially flooding various part of the PEFC. The aim of this work is to obtain an increased understanding of the droplet behavior, especially at the gas diffusion layer (GDL) interface, by the coupling of Lattice Boltzmann (LB) and Volume of Fluid (VOF) approaches, for gas channels in PEFCs. A multiscale environment is established with input parameters in the VOF model being extracted from in-house LB calculations. It is clear that the contact angle as well as the size of the liquid droplet vary with positions at the GDL surface, depending on the stochastic GDL geometry. A VOF model describing one straight channel with one gas inlet, one liquid inlet (at the GDL surface) and one two-phase outlet is employed.

Published
2018

83. The reliability and validity of a novel Chinese version simplified modified Rankin scale questionnaire (2011)

Author

Junliang Yuan, Yunxiao Wang, Wenli Hu, and Askiel Bruno

Subjects
Modified Rankin scale, Simplified modified Rankin scale questionnaire, Stroke, China, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background The modified Rankin Scale (mRS) is a key global outcome measure after stroke internationally. The latest English version of the simplified modified Rankin scale questionnaire (smRSq)(2011) is a reliable and valid tool in scoring the mRS after stroke. In order to use this tool in Chinese patients, we translated it into Chinese and tested its clinimetric properties. Methods The English version smRSq (2011) was translated into Chinese by a standard process. We recruited 300 consecutive hospitalized ischemic stroke patients in the department of neurology, Beijing Chaoyang Hospital. Six randomly paired raters scored the conventional mRS, the novel Chinese version smRSq (2011), the National Institutes of Health Stroke Scale (NIHSS), and the Barthel index (BI) in-person. Inter-rater reliability and validity were assessed. Results Among the 300 ischemic stroke patients, mean age was 64.9 ± 12.1 years, and 220 (73%) were male. For inter-rater reliability of the smRSq (2011), the percent agreement among the paired raters was 87%, the kappa (κ) was 0.84 (95% CI, 0.79–0.88), and the weighted kappa (κw) was 0.96 (95% CI, 0.95–0.98). The percent agreement between the smRSq (2011) scores and the conventional mRS scores was 55%, κ = 0.47 (95% CI, 0.40–0.54), and κw = 0.91 (95% CI, 0.89–0.93). In construct validity testing, the Spearman’s correlation coefficients comparing the smRSq (2011) scores with the NIHSS and the BI scores were 0.83 (P

Published
2020
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84. The Impact of Rotating Night Shift Work and Daytime Recharge on Cognitive Performance Among Retired Nurses

Author

Jinghuan Gan, Xiao-Dan Wang, Zhihong Shi, Junliang Yuan, Meiyun Zhang, Shuai Liu, Fei Wang, Yong You, Peifei Jia, Lisha Feng, Junying Xu, Jinhong Zhang, Wenzheng Hu, Zhichao Chen, and Yong Ji

Subjects
shift work, cognitive impairment, dementia, sleep disorders, daytime sleepiness, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

IntroductionThe exact relationship between long-term shift work (SW) and cognitive impairment (CI) has been poorly understood. The effects of the long-term rotating night SW (RNSW) combining daytime recharge (DTR) on cognitive function were investigated.MethodsA total 920 retired nurses and 656 retired female teachers aged ≥50 years were analyzed. Participants who worked at least once per week for 8 hat night for more than 1 year were defined as the SW group, and those without a regular nighttime shift were defined as the control group. The associations among duration, frequency, and DTR of RNSW, and neuropsychological assessments were ascertained by regression models.ResultsParticipants with RNSW had a significantly higher proportion of mild CI (MCI), both amnestic MCI (aMCI) (14.4% in 11–20 years, p < 0.05, and 17.8% in > 20 years, p < 0.001) and non-amnestic MCI (naMCI) (8.1% in 11–20 years, p < 0.05), as well as dementia (1.5% in 1–10 years, and 11.7% in > 20 years, p < 0.05) compared to controls (8.4% with aMCI, 4.4% with naMCI, and 7.0% with dementia, respectively). There were significant negative relationships between general times of night SW and scores of Mini-Mental State Examination (MMSE) (R squared = 0.01, p = 0.0014) and Montreal Cognitive Assessment (MoCA) (R squared = 0.01, p = 0.0054). Participants with ≥1 h of DTR and ≥ 11 years of RNSW were about 2-fold more likely to experience MCI compared with the subjects in the control group, especially with 3–5 h (odds ratio [OR]: 2.35; 95% confidence interval: 1.49–3.68, p < 0.001).ConclusionThe long-term RNSW was associated with a higher risk of CI, especially aMCI and dementia, and the problem cannot be improved by DTR.

Published
2022
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85. SIRT3 Enhances the Protective Role of Propofol in Postoperative Cognitive Dysfunction via Activating Autophagy Mediated by AMPK/mTOR Pathway

Author

Shang Li, Yi Zhou, Huaying Hu, Xiaodong Wang, Junjun Xu, Chaobo Bai, Junliang Yuan, and Dongliang Zhang

Subjects
postoperative cognitive dysfunction, sirt3, propofol, autophagy, Biochemistry, QD415-436, Biology (General), QH301-705.5
Abstract

Background: Postoperative cognitive dysfunction (POCD) is a common complication after surgery and anesthesia. In this study, we aimed to determine the neuroprotective mechanism of Sirtuin 3 (SIRT3) and propofol in POCD. Methods: The cognitive dysfunction models in C57BL/6J mice were induced and treated, then cognitive function of mice were tested using morris water maze and novel object recognition tests. Primary neurons were stimulated by lipopolysaccharide (LPS) to mimic neuroinflammation during POCD. Meanwhile, cells were treated with propofol. 3-methyladenine (3-MA) was administrated to inhibit autophagy in neurons. SIRT3 overexpression vector was constructed to upregulate SIRT3. Biomarker changes in inflammation, oxidative stress and autophagy were determined in vivo and in vitro. Results: Propofol enhanced the spatial cognitive ability and novel objective recognition of POCD mice. Inflammation and oxidative stress were observed in the hippocampus, which were inhibited by propofol treatment. During POCD, SIRT3 expression and autophagy in the hippocampus was decreased; propofol activated autophagy and upregulated SIRT3. In LPS-stimulated neurons, SIRT3 upregulation enhanced the anti-inflammation and anti-oxidative stress roles of propofol; SIRT3 elevated propofol-activated autophagy in neurons undergoing LPS administration. Moreover, 3-MA reversed propofol-induced biomarker changes in inflammation, oxidative stress and autophagy in LPS-stimulated neurons. In POCD mice, SIRT3 upregulation enhanced the cognitive function during propofol treatment; SIRT3 overexpression elevated the inhibitory role of propofol in inflammation, oxidative stress and autophagy. AMPK/mTOR pathway was activated in response to propofol treatment and SIRT3 enhanced the signaling activation. Conclusions: SIRT3 enhances the protective effect of propofol on POCD by triggering autophagy that eliminates oxidative stress and inhibits the production of pro-inflammatory cytokines.

Published
2022
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86. Higher Total Cerebral Small Vessel Disease Burden Was Associated With Mild Cognitive Impairment and Overall Cognitive Dysfunction: A Propensity Score-Matched Case–Control Study

Author

Xuanting Li, Junliang Yuan, Wei Qin, Lei Yang, Shuna Yang, Yue Li, and Wenli Hu

Subjects
cerebral small vessel disease, mild cognitive impairment, propensity score matching, susceptibility weighted imaging, neuropsychology, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Background and ObjectiveThe combination of neuroimaging and cognition characteristics may provide complementary information for early identification of mild cognitive impairment (MCI). This study aimed to establish the clinical relevance between cerebral small vessel disease (CSVD) burden and MCI and further explored the cognitive characteristics linked to CSVD applying a propensity score matching (PSM) approach.MethodsThe study was designed as a case–control study. All the subjects underwent the standard clinical assessments, neuropsychological testing battery (including global cognition, memory, executive function, and speed and motor control domains), and brain magnetic resonance imaging (MRI). A 1:2 nearest-neighbor matching approach without replacement was employed with a caliper of 0.15 in the PSM approach.ResultsA total of 84 MCI patients and 186 cognitively normal controls were included in this study. After PSM, 74 MCI patients and 129 controls were successfully matched, and the covariate imbalance was well eliminated. Compared with controls, the MCI group had more severe CSVD burden. In the binary logistic regression analysis, CSVD was associated with MCI after adjusting for all confounders. The results of multivariate linear regression analyses showed that higher total MRI CSVD burden was related to the deficit of cognitive performance in global cognition and three important cognitive domains after adjusting for all confounders.ConclusionCerebral small vessel disease was an independent risk factor of MCI. Moreover, higher total MRI CSVD burden was associated with the overall cognitive impairment among middle-aged and elderly Chinese adults.

Published
2021
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87. The combined presence of hypertension and vitamin D deficiency increased the probability of the occurrence of small vessel disease in China

Author

Junzeng Si, Kuibao Li, Peiyan Shan, and Junliang Yuan

Subjects
25-hydroxy vitamin D, Small vessel disease, Ischemic stroke, Hypertension, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background The exact relationship between 25-hydroxyvitamin D [25(OH) D] levels and small vessel disease (SVD) are not clear in China. The aim of this study was to determine such the association between 25(OH) D and SVD in China. Methods We retrospectively enrolled 106 patients with SVD and 115 controls between Jan 2017 and Dec 2017. All the subjects were categorized into three subgroups according to the level of 25 (OH) D: vitamin D deficiency ( 20 ng/ml). Results Among 106 SVD patients, 80 (75.5%) were men and the mean age was 61.6 ± 13.2 years. The deficiency of 25(OH) D was observed in 76 (71.7%) of SVD patients and 47 (40.9%) of controls (P = 0.001). Compared with controls, patients with SVD were more likely to be male, a stroke history, smokers, with hyperlipidemia, higher systolic and diastolic blood pressure and low-density lipoprotein, and lower of 25(OH)D level (P

Published
2019
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88. Antibody-LGI 1 autoimmune encephalitis manifesting as rapidly progressive dementia and hyponatremia: a case report and literature review

Author

Xuanting Li, Junliang Yuan, Lei Liu, and Wenli Hu

Subjects
Autoimmune encephalitis, Limbic encephalitis, Leucine-rich glioma inactivated 1, Cognitive impairment, Hyponatremia, Arterial spin labeling, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Anti leucine-rich glioma inactivated 1 (LGI1) encephalitis is a rare autoimmune encephalitis (AE), characterized by acute or subacute cognitive impairment, faciobrachial dystonic seizures, psychiatric disturbances and hyponatremia. Antibody-LGI 1 autoimmune encephalitis (anti-LGI1 AE) has increasingly been recognized as a primary autoimmune disorder with favorable prognosis and response to treatment. Case presentation Herein, we reported a male patient presenting as rapidly progressive dementia and hyponatremia. He had antibodies targeting LGI1 both in the cerebrospinal fluid and serum, which demonstrated the diagnosis of typical anti-LGI1 AE. The scores of Mini-Mental State Examination and Montreal Cognitive Assessment were 19/30 and 15/30, respectively. Cranial magnetic resonance images indicated hyperintensities in bilateral hippocampus. The findings of brain arterial spin labeling and Fluorine-18-fluorodeoxyglucose positron emission tomography showed no abnormal perfusion/metabolism. After the combined treatment of intravenous immunoglobulin and glucocorticoid, the patient’s clinical symptoms improved obviously. Conclusions This case raises the awareness that a rapid progressive dementia with predominant memory deficits could be induced by immunoreactions against LGI1. The better recognition will be great importance for the early diagnosis, essential treatment, even a better prognosis.

Published
2019
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89. Glial Cell-Based Vascular Mechanisms and Transplantation Therapies in Brain Vessel and Neurodegenerative Diseases

Author

Yingying Zhao, Shuanglin Wang, Xiaopeng Song, Junliang Yuan, Dong Qi, Xiaohuan Gu, Michael Yaoyao Yin, Zhou Han, Yanbing Zhu, Zhandong Liu, Yongbo Zhang, Ling Wei, and Zheng Zachory Wei

Subjects
neurovascular unit, blood brain barrier, vascular neurology, ischemic stroke, local cerebral blood flow, vascular progenitor cells, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Neurodevelopmental and neurodegenerative diseases (NDDs) with severe neurological/psychiatric symptoms, such as cerebrovascular pathology in AD, CAA, and chronic stroke, have brought greater attention with their incidence and prevalence having markedly increased over the past few years. Causes of the significant neuropathologies, especially those observed in neurological diseases in the CNS, are commonly believed to involve multiple factors such as an age, a total environment, genetics, and an immunity contributing to their progression, neuronal, and vascular injuries. We primarily focused on the studies of glial involvement/dysfunction in part with the blood-brain barrier (BBB) and the neurovascular unit (NVU) changes, and the vascular mechanisms, which have been both suggested as critical roles in chronic stroke and many other NDDs. It has been noted that glial cells including astrocytes (which outnumber other cell types in the CNS) essentially contribute more to the BBB integrity, extracellular homeostasis, neurotransmitter release, regulation of neurogenic niches in response to neuroinflammatory stimulus, and synaptic plasticity. In a recent study for NDDs utilizing cellular and molecular biology and genetic and pharmacological tools, the role of reactive astrocytes (RACs) and gliosis was demonstrated, able to trigger pathophysiological/psychopathological detrimental changes during the disease progression. We speculate, in particular, the BBB, the NVU, and changes of the astrocytes (potentially different populations from the RACs) not only interfere with neuronal development and synaptogenesis, but also generate oxidative damages, contribute to beta-amyloid clearances and disrupted vasculature, as well as lead to neuroinflammatory disorders. During the past several decades, stem cell therapy has been investigated with a research focus to target related neuro-/vascular pathologies (cell replacement and repair) and neurological/psychiatric symptoms (paracrine protection and homeostasis). Evidence shows that transplantation of neurogenic or vasculogenic cells could be achieved to pursue differentiation and maturation within the diseased brains as expected. It would be hoped that, via regulating functions of astrocytes, astrocytic involvement, and modulation of the BBB, the NVU and astrocytes should be among major targets for therapeutics against NDDs pathogenesis by drug and cell-based therapies. The non-invasive strategies in combination with stem cell transplantation such as the well-tested intranasal deliveries for drug and stem cells by our and many other groups show great translational potentials in NDDs. Neuroimaging and clinically relevant analyzing tools need to be evaluated in various NDDs brains.

Published
2021
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90. Wellbore Fracture Mode and Fracture Pressure Drilled in Depleted Reservoir

Author

Kai Zhao, Junliang Yuan, Ming Liu, Chuanliang Yan, Liangbin Dou, Gang Bi, Runsen Li, and Wei Gao

Subjects
Geology, QE1-996.5
Abstract

Drilling fluid loss in depleted reservoir has been an import issue faced by further tapping the potential of old oil fields. Accurate evaluation of the fracture pressure is the foundation to avoid mud loss. Traditional views suggest that tensile failure is the only fracture mode and the fracture pressure should be determined by a tensile failure criterion, which are not suitable for wells drilled in the depleted reservoir. In this paper, the analysis focuses on the fracture mode and fracture pressure in depleted reservoir, and case studies show that three fracture modes may first occur, and the fracture mode will be changed with reservoir depletion which highly depends on reservoir depletion degree, well azimuth and deviation angle, and the in situ stress state; different failure criteria at different stages of reservoir depletion should be selected to accurately evaluate the fracture pressure. For the vertical well, fracture pressure is no longer a single linear reduction with reservoir depletion; instead, a three-step and two-step reduction may appear, and for the directional well, the fracture pressure is not always decreased; the other patterns such as increase and first increase then decrease may also appear for the wells drilled in reverse and strike fault stress regimes.

Published
2021
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91. The structural basis for interhemispheric functional connectivity: Evidence from individuals with agenesis of the corpus callosum

Author

Junliang Yuan, Xiaopeng Song, Elliot Kuan, Shuangkun Wang, Long Zuo, Dost Ongur, Wenli Hu, and Fei Du

Subjects
Corpus callosum, Functional magnetic resonance imaging, Functional connectivity, Voxel-mirrored homotopic connectivity, Diffusion tensor imaging, Structural connectivity, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Agenesis of the corpus callosum (AgCC) is a rare congenital malformation characterized by partial or complete absence of the corpus callosum (CC). The effects of AgCC on cerebral structural and functional networks are not clear. We aimed to utilize AgCC as a model to characterize the relationship between brain structure and function. Diffusion tensor imaging and resting-state fMRI data were collected from nine AgCC and ten healthy subjects. The interhemispheric functional connectivity (FC) was quantified using a voxel-mirrored-homotopic-connectivity (VMHC) method, and its correlation with the number (FN) and fractional anisotropy (FA) of the fibers crossing the CC was calculated. Graph-based network analyses of structural and functional topologic properties were performed. AgCC subjects showed markedly reduced VMHC compared to controls. VMHC was significantly correlated with the FN and FA of the fibers crossing the CC. Structural network analyses revealed impaired global properties, but intact local properties in AgCC compared to controls. Functional network analyses showed no significant difference in network properties between the groups. Finally, in both groups, brain areas with more fiber connections were more likely to build a positive FC with each other, while areas with decreased white matter connections were more likely to result in negative FC. Our observations demonstrate that interhemispheric FC is highly dependent on CC structure. Increased alternative intrahemispheric SC might be a compensatory mechanism in AgCC that helps to maintain normal global brain function. Our study provides insights into the underlying neurological pathophysiology of brain malformations, thereby helping to elucidate the structure–function relationship of normal human brain.

Published
2020
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92. Clinical features and the degree of cerebrovascular stenosis in different types and subtypes of cerebral watershed infarction

Author

Yue Li, Man Li, Xiaoyu Zhang, Shuna Yang, Huimin Fan, Wei Qin, Lei Yang, Junliang Yuan, and Wenli Hu

Subjects
Cerebral watershed infarction, cerebrovascular stenosis, hemodynamic impairment, clinical features, prognosis, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Whether there are differences in pathogenesis among different types and subtypes of cerebral watershed infarction (WSI) is controversial since they have been combined into a single group in most previous studies. Methods We prospectively identified 340 supratentorial WSI patients at Beijing Chao-Yang Hospital, Capital Medical University, China and classified them based on diffusion-weighted imaging(DWI) templates. Baseline characteristics, clinical courses and neuroradiological features were compared among patients with different types and subtypes of WSI. Results We identified 92 patients with cortical watershed infarction (CWI), 112 with internal watershed infarction (IWI) and 136 with mixed-type infarction. Compared with CWI patients, more IWI patients had critical stenosis of internal carotid artery (ICA) (P

Published
2017
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93. Mild encephalitis/encephalopathy with reversible splenial lesion (MERS) in adults-a case report and literature review

Author

Junliang Yuan, Shuna Yang, Shuangkun Wang, Wei Qin, Lei Yang, and Wenli Hu

Subjects
Mild encephalitis/encephalopathy with reversible splenial lesion, Adult-onset MERS, Encephalitis, Encephalopathy, Corpus callosum, Reversible plenial lesion, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Mild encephalitis/encephalopathy with reversible splenial lesion (MERS) is a rare clinico-radiological entity characterized by the magnetic resonance imaging (MRI) finding of a reversible lesion in the corpus callosum, sometimes involved the symmetrical white matters. Many cases of child-onset MERS with various causes have been reported. However, adult-onset MERS is relatively rare. The clinical characteristics and pathophysiologiccal mechanisms of adult-onset MERS are not well understood. We reviewed the literature on adult-onset MERS in order to describe the characteristics of MERS in adults and to provide experiences for clinician. Methods We reported a case of adult-onset MERS with acute urinary retension and performed literature search from PubMed and web of science databases to identify other adult-onset MERS reports from Januarary 2004 to March 2016. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed on selection process. And then we summarized the clinico-radiological features of adult-onset MERS. Results Twenty-nine adult-onset MERS cases were reviewed from available literature including the case we have. 86.2% of the cases (25/29) were reported in Asia, especially in Japan. Ages varied between 18 and 59 years old with a 12:17 female-to-male ratio. The major cause was infection by virus or bacteria. Fever and headache were the most common clinical manifestation, and acute urinary retention was observed in 6 patients. All patients recovered completely within a month. Conclusion Adult-onset MERS is an entity with a broad clinico-radiological spectrum because of the various diseases and conditions. There are similar characteristics between MERS in adults and children, also some differences.

Published
2017
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94. External Validation of the Diagnosis of TIA (DOT) Score for Identification of TIA in a Chinese Population

Author

Junliang Yuan, Zejin Jia, Yangguang Song, and Wenli Hu

Subjects
transient ischemic attack, diagnosis of TIA score, Dawson score, ischemic stroke, diffusion weighted imaging, Neurology. Diseases of the nervous system, RC346-429
Abstract

Background: Recently, the [diagnosis of transient ischemic attack (TIA), DOT] score has been recognized to be a new tool for non-specialists to diagnose TIA more accurately with the sensitivity and specificity being 89 and 76%, respectively. However, the DOT score has not yet been validated externally in patients with TIA in China.Methods: We retrospectively enrolled 500 consecutive patients with transient neurological symptoms, who were admitted to the Department of Neurology, Beijing Chaoyang Hospital and underwent magnetic resonance imaging (MRI) between Jan 2016 and Dec 2018. Patients with transient neurological symptoms were divided into two subgroups: TIA mimic group (N = 140, 28%) and definite cerebrovascular events group including tissue-based TIA (DWI negative, N = 252, 50.4%) and minor stroke (DWI positive, N = 108, 21.6%). The demographic data, clinical characteristics, laboratory findings, and scores of Dawson and DOT were compared between the two groups.Results: A total of 500 patients with transient neurological symptoms (mean age, 61.1 ± 12.8) were enrolled and 70% (N = 350) were male. Comparing with TIA mimic groups, patients with cerebrovascular events group were more likely to have higher diastolic blood pressure, uric acid and homocysteine, more motor weakness and speech abnormalities, and also scored higher using the Dawson and DOT. The area under the curve (AUC) was 0.728 for DOT, with a sensitivity of 70.3% and specificity of 62.9%, respectively.Conclusion: In patients with transient neurological symptoms, our findings showed that the DOT score had relatively good calibration and discrimination to identify of TIA in a Chinese Population. As a novel tool of TIA identification, further validations are needed in multiple centers with larger samples in China.

Published
2019
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95. Incidence and Predictors of Acute Ischemic Lesions on Brain Magnetic Resonance Imaging in Patients With a Clinical Diagnosis of Transient Ischemic Attack in China

Author

Junliang Yuan, Zejin Jia, Yangguang Song, Shuna Yang, Yue Li, Lei Yang, Wei Qin, and Wenli Hu

Subjects
transient ischemic attack, ischemic stroke, diffusion-weighted imaging, ABCD3-I score, dawson score, Neurology. Diseases of the nervous system, RC346-429
Abstract

Background: The associations between the clinical characteristics and diffusion-weighted imaging (DWI) positivity in patients with a clinical diagnosis of transient ischemic attack (TIA) are still poorly understood. The purpose of our study was to determine the incidence of TIA related acute infarction by DWI, and to determine the underlying predictors of DWI positivity in TIA patients.Methods: Between Jan 2017 and Dec 2018, we retrospectively enrolled 430 patients with a clinical diagnosis of TIA who underwent DWI. Patients were divided into those with acute ischemic lesions (DWI positive group) and those without (DWI negative group). The clinical characteristics, laboratory data, and imaging parameters were compared between the two groups.Results: A total of 430 time-based TIA patients (mean age, 61.4 ± 13.0) were enrolled in this study. About 126 (29.3%) of TIA patients had a DWI positive lesion in our series. Comparing TIA patients with positive DWI to those with negative DWI, acute lesions were more likely to be more male, have higher hyperlipidemia and a smoking history, more speech abnormalities and increased motor weakness; and higher systolic and diastolic blood pressure, homocysteine, fasting blood glucose, and the scores of ABCD2, ABCD3, ABCD3-I, and Dawson. Several independent predictors of DWI positivity were identified with logistic regression analysis: motor weakness (odds ratio 4.861, P = 0.021), speech abnormalities (odds ratio 4.029, P = 0.024), and ABCD3-I (odds ratio 13.141, P = 0.001). ABCD3-I showed the greatest area under the ROC curve, with a sensitivity of 85.7% and specificity of 72.4%.Conclusion: In patients with a clinical diagnosis of TIA, 29.3% demonstrated acute DWI lesions on brain magnetic resonance imaging (MRI). They were associated with motor weakness, speech abnormalities and higher ABCD3-I score at admission.

Published
2019
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96. The significant effects of cerebral microbleeds on cognitive dysfunction: An updated meta-analysis.

Author

Xuanting Li, Junliang Yuan, Lei Yang, Wei Qin, Shuna Yang, Yue Li, Huimin Fan, and Wenli Hu

Subjects
Medicine, Science
Abstract

Accumulated data suggests that cerebral microbleeds (CMBs) play an important role in the decline of cognitive function, but the results remain inconsistent. In the current study, we aimed to investigate the association between CMBs and cognitive function, as well as the various effects of CMBs on different domains of cognition.We searched through the databases of PubMed, Embase, Cochrane Library, and ScienceDirect. After a consistency test, the publication bias was evaluated and a sensitivity analysis was performed with combined odds ratios (OR) and standardized mean difference (SMD) of CMBs.A meta-analysis of 25 studies with 9343 participants total was conducted. Patients with CMBs had higher incidence of cognitive impairment (OR:3.5410; 95% confidence interval [CI] [2.2979, 5.4567], p

Published
2017
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97. Brain Atrophy Correlates with Severe Enlarged Perivascular Spaces in Basal Ganglia among Lacunar Stroke Patients.

Author

Xiaoyu Zhang, Lingling Ding, Lei Yang, Wei Qin, Junliang Yuan, Shujuan Li, and Wenli Hu

Subjects
Medicine, Science
Abstract

Enlarged perivascular spaces (EPVS) correlate with cognitive impairment and incident dementia. However, etiologies for severe basal ganglia EPVS (BG-EPVS) are still unclear. Our aim was to investigate the independent risk factors for severe BG-EPVS in patients with acute lacunar stroke.We prospectively identified patients with lacunar stroke (diameter on DWI ≤ 20mm) from Jan 2011 to May 2015. Patients with severe BG-EPVS were identified on T2 weighted MRI. Age (± 1 year) and sex matched controls were also recruited in the same population (two controls for one case). Vascular risk factors, clinical data, EPVS in centrum semiovale (rated 0 to 4), white matter hyperintensities (WMH) (by Fazekas scale), brain atrophy (rated 0 to 6) were compared between two groups. Logistic regression was performed to determine independent risk factors for severe BG-EPVS.During study period, 89 patients with severe BG-EPVS and 178 matched controls were included. Vascular risk factors did not differ between two groups. Patients with severe BG-EPVS had lower level of HbA1c and diastolic BP at admission, but presented with larger infarct size, more severe WMH (including total WMH, periventricular WMH and deep WMH) and brain atrophy. In logistic regression, brain atrophy (OR = 1.40; 95%CI 1.13, 1.73) and deep WMH (OR = 1.88; 95%CI 1.24, 2.83) were independent risk factors for severe BG-EPVS.Brain atrophy and deep WMH are independent risk factors for severe BG-EPVS, supporting the hypothesis that brain atrophy may be associated with the development of EPVS in basal ganglia.

Published
2016
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98. Risk Factors for Multiple Organ Dysfunction Syndrome in Severe Stroke Patients.

Author

Wei Qin, Xiaoyu Zhang, Shuna Yang, Yue Li, Junliang Yuan, Lei Yang, Shujuan Li, and Wenli Hu

Subjects
Medicine, Science
Abstract

Severe stroke patients have poor clinical outcome which may be associated with development of multiple organ dysfunction syndrome (MODS). Therefore, the aim of our study was to investigate independent risk factors for development of MODS in severe stroke patients.Ninety seven severe stroke patients were prospective recruited from Jan 2011 to Jun 2015. The development of MODS was identified by Sequential Organ Failure Assessment (SOFA) score (score ≥ 3, at least two organs), which was assessed on day 1, 4, 7, 10 and 14 after admission. Baseline characteristics, Acute Physiology and Chronic Health Evaluation (APACHE) II score, Glasgow coma score (GCS) and cerebral imaging parameters were collected at admission. Cox regression was performed to determine predictors for the development of MODS. Medical complications after admission and in-hospital mortality were also investigated.33 (34%) patients were in MODS group and 64 (66%) were in non-MODS group within 14 days after admission. Patients in MODS group had more smoker (51.5% vs 28.1%, p = 0.023), higher NIHSS score (23.48 ± 6.12 vs 19.81 ± 4.83, p = 0.004), higher APACHE II score (18.70 ± 5.18 vs 15.64 ± 4.36, p = 0.003) and lower GCS score (6.33 ± 2.48 vs 8.14 ± 2.73, p = 0.002). They also had higher rate of infarction in multi vascular territories (36.4% vs 10.9%, p = 0.003). The most common complication in all patients was pulmonary infection, while complication scores were comparable between two groups. Patients with MODS had higher in-hospital mortality (69.7% vs 9.4%, p = 0.000). In Cox regression, NIHSS score (RR = 1.084, 95% CI 1.019-1.153) and infarction in multi vascular territories (RR = 2.345 95% CI 1.105-4.978) were independent risk factors for development of MODS.In acute phase of stroke, NIHSS score and infarction in multi vascular territories predicted MODS in severe stroke patients. Moreover, patients with MODS had higher in-hospital mortality, suggesting that early identification of MODS is critical important.

Published
2016
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99. The Functional Study of a Chinese Herbal Compounded Antidepressant Medicine--Jie Yu Chu Fan Capsule on Chronic Unpredictable Mild Stress Mouse Model.

Author

Lingling Ding, Xiaoyu Zhang, Hongliang Guo, Junliang Yuan, Shujuan Li, Wenli Hu, Teresa Golden, and Ning Wu

Subjects
Medicine, Science
Abstract

Jie Yu Chu Fan capsule (JYCF) is a new compounded Chinese herbal medicine for the treatment of depression. The present study was designed to explore the antidepressant effects and the possible mechanisms of JYCF by using chronic unpredictable mild stress (CUMS) mouse model and comparing results to that of fluoxetine. Behavioral tests including an open field test, sucrose preference test and forced swim test were performed to evaluate the antidepressant effects of JYCF. The concentrations of monoamine neurotransmitters and metabolic products including norepinephrine (NE), 5-hydroxytryptamine (5-HT), dopamine (DA), 5-hydroxyindoleacetic acid (5-HIAA), homovanillic acid (HVA) and 3,4-dihydroxyphenylacetic acid (DOPAC) in the cerebral cortex and hippocampus of mice were determined by means of high performance liquid chromatography with electrochemical detection (HPLC-EC). The results show that a successful mouse CUMS model was established through 5 weeks of continuous unpredictable stimulation, as indicated by the significant decrease in sucrose preference and locomotor activity and increase in immobility time in the forced swim test. Chronic treatment of JYCF (1.25, 2.5 and 5 g/kg) and fluoxetine (20 mg/kg) significantly reversed the CUMS-induced behavioral abnormalities. JYCF (1.25, 2.5 and 5 g/kg) significantly increased NE in CUMS mouse prefrontal cortex (P < 0.01, P < 0.01, P < 0.05 respectively) and 5-HT in hippocampus (P < 0.05). In summary, our findings suggest that JYCF exerts comparable antidepressant-like effects to that of fluoxetine in CUMS mice. Besides, the antidepressant-like effect of JYCF is mediated by the increase of monoaminergic transmitters including 5-HT and NE.

Published
2015
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100. Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective

Author

Junhao Wen, Qingyu Xiong, Junliang Yu, Jundong Li, Min Gao, and Junwei Zhang

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Information Systems and Management, Computer science, 05 social sciences, 050301 education, 02 engineering and technology, Recommender system, Minimax, Data science, Computer Science Applications, Theoretical Computer Science, Machine Learning (cs.LG), Computer Science - Information Retrieval, Adversarial system, Artificial Intelligence, Control and Systems Engineering, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0503 education, Software, Generative grammar, Information Retrieval (cs.IR)
Abstract

Recommender systems (RSs) now play a very important role in the online lives of people as they serve as personalized filters for users to find relevant items from an array of options. Owing to their effectiveness, RSs have been widely employed in consumer-oriented e-commerce platforms. However, despite their empirical successes, these systems still suffer from two limitations: data noise and data sparsity. In recent years, generative adversarial networks (GANs) have garnered increased interest in many fields, owing to their strong capacity to learn complex real data distributions; their abilities to enhance RSs by tackling the challenges these systems exhibit have also been demonstrated in numerous studies. In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy. To gain a comprehensive understanding of these research efforts, we review the corresponding studies and models, organizing them from a problem-driven perspective. More specifically, we propose a taxonomy of these models, along with their detailed descriptions and advantages. Finally, we elaborate on several open issues and current trends in GAN-based RSs., 24 pages, 12 figures, 8 tables, the journal of Information Sciences

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