There is currently no effective medical treatment for temporomandibular joint osteoarthritis(TMJ-OA) due to a limited understanding of its pathogenesis. This study was undertaken to investigate the key role of transfo...There is currently no effective medical treatment for temporomandibular joint osteoarthritis(TMJ-OA) due to a limited understanding of its pathogenesis. This study was undertaken to investigate the key role of transforming growth factor-β(TGF-β)signalling in the cartilage and subchondral bone of the TMJ using a temporomandibular joint disorder(TMD) rat model, an ageing mouse model and a Camurati–Engelmann disease(CED) mouse model. In the three animal models, the subchondral bone phenotypes in the mandibular condyles were evaluated by μCT, and changes in TMJ condyles were examined by TRAP staining and immunohistochemical analysis of Osterix and p-Smad2/3. Condyle degradation was confirmed by Safranin O staining, the Mankin and OARSI scoring systems and type X collagen(Col X), p-Smad2/3 a and Osterix immunohistochemical analyses. We found apparent histological phenotypes of TMJ-OA in the TMD, ageing and CED animal models, with abnormal activation of TGF-βsignalling in the condylar cartilage and subchondral bone. Moreover, inhibition of TGF-β receptor I attenuated TMJ-OA progression in the TMD models. Therefore, aberrant activation of TGF-β signalling could be a key player in TMJ-OA development.展开更多
Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts itera...Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.展开更多
基于ICLV(Integrated Choice and Latent Variable)模型,结合2013年绍兴市居民出行调查数据,研究通勤者的方式选择行为,包括小汽车、公交车、摩托车和电动车等4种当地居民在日常生活中较常用的交通方式。模型不仅分析了可观测的个人及...基于ICLV(Integrated Choice and Latent Variable)模型,结合2013年绍兴市居民出行调查数据,研究通勤者的方式选择行为,包括小汽车、公交车、摩托车和电动车等4种当地居民在日常生活中较常用的交通方式。模型不仅分析了可观测的个人及家庭的社会经济属性对通勤方式的影响,而且通过潜变量模型构建通勤者个人对各类出行方式的态度等不可见因素,并将其纳入选择模型。结果表明,潜在的心理因素同样对方式选择行为有重要影响,并能够揭示选择行为的内在原因。该研究可为交通需求管理策略制定者以及交通规划者提供指导意见,从而促进交通系统的可持续发展。展开更多
目的了解脑卒中患者功能锻炼依从性的潜在剖面并探究不同剖面的影响因素。方法进行横断面研究,采用便利抽样法选取2020年11月至2021年11月上海市2所三级甲等医院的534例脑卒中患者。应用一般资料调查表、脑卒中患者功能锻炼依从性量表...目的了解脑卒中患者功能锻炼依从性的潜在剖面并探究不同剖面的影响因素。方法进行横断面研究,采用便利抽样法选取2020年11月至2021年11月上海市2所三级甲等医院的534例脑卒中患者。应用一般资料调查表、脑卒中患者功能锻炼依从性量表、脑卒中患者知识和态度问卷、脑卒中自我效能问卷对其进行调查。应用潜在剖面分析患者的功能锻炼依从性,采用多元Logistics回归分析不同潜在类别的影响因素。结果脑卒中患者功能锻炼依从性可分为三个潜在剖面:功能锻炼依从性较差组(24.7%)、功能锻炼依从性中等组(32.8%)、功能锻炼依从性良好组(42.5%)。知识、态度、自我效能、美国国立卫生研究院卒中量表(National Institute of Health stroke scale,NIHSS)评分、文化程度、婚姻状况、病程和居家辅助用具等是不同潜在剖面的影响因素(均P<0.05)。结论脑卒中患者的功能锻炼依从性有3个潜在剖面,医护人员可依据不同的人群特征给予针对性的干预策略,以提高其功能锻炼依从性。展开更多
随着众多具有传感功能的智能手机和可穿戴设备的普及,基于位置的服务得到了快速发展,其中基于位置的社交网络(location-based social networks,LBSN)逐渐被大多数人所接受,基于位置社交网络可以为人们提供兴趣点推荐服务。为了提供更加...随着众多具有传感功能的智能手机和可穿戴设备的普及,基于位置的服务得到了快速发展,其中基于位置的社交网络(location-based social networks,LBSN)逐渐被大多数人所接受,基于位置社交网络可以为人们提供兴趣点推荐服务。为了提供更加精准的兴趣点推荐服务,提出了一种融合的算法模型。通过隐语义分析算法来充分挖掘用户的历史行为,使用基于邻域的方法结合好友和地理位置等因素,然后在统一的框架中融合这两种推荐方式的结果,实现了对用户行为更好的预测。实验结果表明,提出的兴趣点推荐方法拥有较好的准确率和召回率。展开更多
High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurat...High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.展开更多
An improved Hybrid Collaborative Filtering algorithm(H-CF)is proposed,addressing the issues of data sparsity,low recommendation accuracy,and poor scalability present in traditional collaborative filtering algorithms.T...An improved Hybrid Collaborative Filtering algorithm(H-CF)is proposed,addressing the issues of data sparsity,low recommendation accuracy,and poor scalability present in traditional collaborative filtering algorithms.The core of H-CF is a linear weighted hybrid algorithm based on the Latent Factor Model(LFM)and the Improved Item Clustering and Similarity Calculation Collaborative Filtering Algorithm(ITCSCF).To begin with,the items are clustered based on their attribute dimension,which accelerates the computation of the nearest neighbor set.Subsequently,H-CF enhances the formula for scoring similarity by penalizing popular items and optimizing unpopular items.This improvement enhances the rationality of scoring similarity and reduces the impact of data sparseness.Furthermore,a weighting function is employed to combine the various improved algorithms.The balance factor of the weighting function is dynamically adjusted to attain the optimal recommendation list.To address the real-time and scalability concerns,the algorithm leverages the Spark big data distributed cluster computing framework.Experiments were conducted using the public dataset Movie Lens,where the improved algorithm’s performance was compared against the algorithm before enhancement and the algorithm running on a single machine.The experimental results demonstrate that the improved algorithm outperforms in terms of data sparsity,recommendation personalization,accuracy,recall,and efficiency.展开更多
基金supported by 2016JQ0054 and NSFC grants 81470711 to L.Z.National Key Research and Development Program of China 2016YFC1102700 to X.Z.
文摘There is currently no effective medical treatment for temporomandibular joint osteoarthritis(TMJ-OA) due to a limited understanding of its pathogenesis. This study was undertaken to investigate the key role of transforming growth factor-β(TGF-β)signalling in the cartilage and subchondral bone of the TMJ using a temporomandibular joint disorder(TMD) rat model, an ageing mouse model and a Camurati–Engelmann disease(CED) mouse model. In the three animal models, the subchondral bone phenotypes in the mandibular condyles were evaluated by μCT, and changes in TMJ condyles were examined by TRAP staining and immunohistochemical analysis of Osterix and p-Smad2/3. Condyle degradation was confirmed by Safranin O staining, the Mankin and OARSI scoring systems and type X collagen(Col X), p-Smad2/3 a and Osterix immunohistochemical analyses. We found apparent histological phenotypes of TMJ-OA in the TMD, ageing and CED animal models, with abnormal activation of TGF-βsignalling in the condylar cartilage and subchondral bone. Moreover, inhibition of TGF-β receptor I attenuated TMJ-OA progression in the TMD models. Therefore, aberrant activation of TGF-β signalling could be a key player in TMJ-OA development.
基金supported in part by the National Natural Science Foundation of China (6177249391646114)+1 种基金Chongqing research program of technology innovation and application (cstc2017rgzn-zdyfX0020)in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences
文摘Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.
文摘基于ICLV(Integrated Choice and Latent Variable)模型,结合2013年绍兴市居民出行调查数据,研究通勤者的方式选择行为,包括小汽车、公交车、摩托车和电动车等4种当地居民在日常生活中较常用的交通方式。模型不仅分析了可观测的个人及家庭的社会经济属性对通勤方式的影响,而且通过潜变量模型构建通勤者个人对各类出行方式的态度等不可见因素,并将其纳入选择模型。结果表明,潜在的心理因素同样对方式选择行为有重要影响,并能够揭示选择行为的内在原因。该研究可为交通需求管理策略制定者以及交通规划者提供指导意见,从而促进交通系统的可持续发展。
文摘目的了解脑卒中患者功能锻炼依从性的潜在剖面并探究不同剖面的影响因素。方法进行横断面研究,采用便利抽样法选取2020年11月至2021年11月上海市2所三级甲等医院的534例脑卒中患者。应用一般资料调查表、脑卒中患者功能锻炼依从性量表、脑卒中患者知识和态度问卷、脑卒中自我效能问卷对其进行调查。应用潜在剖面分析患者的功能锻炼依从性,采用多元Logistics回归分析不同潜在类别的影响因素。结果脑卒中患者功能锻炼依从性可分为三个潜在剖面:功能锻炼依从性较差组(24.7%)、功能锻炼依从性中等组(32.8%)、功能锻炼依从性良好组(42.5%)。知识、态度、自我效能、美国国立卫生研究院卒中量表(National Institute of Health stroke scale,NIHSS)评分、文化程度、婚姻状况、病程和居家辅助用具等是不同潜在剖面的影响因素(均P<0.05)。结论脑卒中患者的功能锻炼依从性有3个潜在剖面,医护人员可依据不同的人群特征给予针对性的干预策略,以提高其功能锻炼依从性。
文摘随着众多具有传感功能的智能手机和可穿戴设备的普及,基于位置的服务得到了快速发展,其中基于位置的社交网络(location-based social networks,LBSN)逐渐被大多数人所接受,基于位置社交网络可以为人们提供兴趣点推荐服务。为了提供更加精准的兴趣点推荐服务,提出了一种融合的算法模型。通过隐语义分析算法来充分挖掘用户的历史行为,使用基于邻域的方法结合好友和地理位置等因素,然后在统一的框架中融合这两种推荐方式的结果,实现了对用户行为更好的预测。实验结果表明,提出的兴趣点推荐方法拥有较好的准确率和召回率。
基金supported in part by the National Natural Science Foundation of China(61702475,61772493,61902370,62002337)in part by the Natural Science Foundation of Chongqing,China(cstc2019jcyj-msxmX0578,cstc2019jcyjjqX0013)+1 种基金in part by the Chinese Academy of Sciences“Light of West China”Program,in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciencesby Technology Innovation and Application Development Project of Chongqing,China(cstc2019jscx-fxydX0027)。
文摘High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.
基金Supported by the Natural Science Foundation of Jiangxi Province(20212BAB202018)Provincial Virtual Simulation Experiment Education Project of Jiangxi Education Department(2020-2-0048)the Science and Technology Research Project of Jiangxi Province Educational Department(GJJ210333)。
文摘An improved Hybrid Collaborative Filtering algorithm(H-CF)is proposed,addressing the issues of data sparsity,low recommendation accuracy,and poor scalability present in traditional collaborative filtering algorithms.The core of H-CF is a linear weighted hybrid algorithm based on the Latent Factor Model(LFM)and the Improved Item Clustering and Similarity Calculation Collaborative Filtering Algorithm(ITCSCF).To begin with,the items are clustered based on their attribute dimension,which accelerates the computation of the nearest neighbor set.Subsequently,H-CF enhances the formula for scoring similarity by penalizing popular items and optimizing unpopular items.This improvement enhances the rationality of scoring similarity and reduces the impact of data sparseness.Furthermore,a weighting function is employed to combine the various improved algorithms.The balance factor of the weighting function is dynamically adjusted to attain the optimal recommendation list.To address the real-time and scalability concerns,the algorithm leverages the Spark big data distributed cluster computing framework.Experiments were conducted using the public dataset Movie Lens,where the improved algorithm’s performance was compared against the algorithm before enhancement and the algorithm running on a single machine.The experimental results demonstrate that the improved algorithm outperforms in terms of data sparsity,recommendation personalization,accuracy,recall,and efficiency.
