Background: According to the 7 th edition of the American Joint Committee on Cancer(AJCC) staging system, over50% of patients with nasopharyngeal carcinoma(NPC) have N1 disease at initial diagnosis. However, patients ...Background: According to the 7 th edition of the American Joint Committee on Cancer(AJCC) staging system, over50% of patients with nasopharyngeal carcinoma(NPC) have N1 disease at initial diagnosis. However, patients with N1 NPC are relatively under-researched, and the metastasis risk of this group is not well-stratified. This study aimed to evaluate the prognostic values of gross tumor volume of metastatic regional lymph node(GTVnd) and pretreatment serum copy number of Epstein-Barr virus(EBV) DNA in predicting distant metastasis of patients with N1 NPC, and to develop an integrated prognostic model that incorporates GTVnd and EBV DNA copy number for this group of patients.Methods: The medical records of 787 newly diagnosed patients with nonmetastatic, histologically proven N1 NPC who were treated at Sun Yat-sen University Cancer Center between November 2009 and February 2012 were analyzed. Computed tomography-derived GTVnd was measured using the summation-of-area technique. Blood samples were collected before treatment to quantify plasma EBV DNA. The receiver operating characteristic(ROC) curve analysis was used to evaluate the cut-off point for GTVnd, and the area under the ROC curve was used to assess the predicted validity of GTVnd. The survival rates were assessed by Kaplan-Meier analysis, and the survival curves were compared using a log-rank test. Multivariate analysis was conducted using the Cox proportional hazard regression model.Results: The 5-year distant metastasis-free survival(DMFS) rates for patients with GTVnd > 18.9 vs.≤ 18.9 mL were82.2% vs. 93.2%(P < 0.001), and for patients with EBV DNA copy number > 4000 vs. < 4000 copies/mL were 83.5% vs.93.9%(P < 0.001). After adjusting for GTVnd, EBV DNA copy number, and T category in the Cox regression model, both GTVnd > 18.9 mL and EBV DNA copy number > 4000 copies/mL were significantly associated with poor prognosis(both P < 0.05). According to combination of GTVnd and EBV DNA copy number, all patients were divided into low-,moderate-, and high-risk groups, with the 5-year DMFS rates of 96.1,87.4, and 73.8%, respectively(P < 0.001). Multivariate analysis confirmed the prognostic value of this model for distant metastatic risk stratification(hazard ratio [HR],4.17; 95% confidence interval [CI] 2.34-7.59; P < 0.001).Conclusions: GTVnd and serum EBV DNA copy number are independent prognostic factors for predicting distant metastasis in NPC patients with N1 disease. The prognostic model incorporating GTVnd and EBV DNA copy number may improve metastatic risk stratification for this group of patients.展开更多
This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two pe...This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.展开更多
阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood...阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood AgGrEgation,GraphNAGE)的AD分类新方法.首先进行图数据建模,将AD数据样本表示为图数据.采用基于互信息(Mutual Information,MI)的特征选择方法,从样本的114维大脑皮层与皮层下感兴趣区域(Cerebral Cortex and Subcortical Regions Of Interest,CCS-ROI)的体积特征中选取重要性高的体积特征,并将其用于节点建模.提出基于相似性度量的关系建模方法,利用重要性高的体积特征、遗传基因、人口统计信息和认知评分对样本之间的关系进行建模.进而构建GraphNAGE,针对每个节点,基于与该节点相关的边的权重进行最近邻域采样,然后使用均值聚合方法对采样得到的邻居节点和中心节点的数据进行聚合,最后通过一个全连接层和一个Softmax层实现AD分类.在TADPOLE(The Alzheimer’s Disease Prediction Of Longitudinal Evolution)数据集上进行实验,结果表明:本文提出的AD分类方法的准确率(ACCuracy,ACC)为98.20%,F_(1)分数为97.34%,曲线下面积(Area Under Curve,AUC)为97.80%.实验结果表明:本文提出的AD分类方法充分利用了AD数据样本之间的相关性,其性能优于传统的基于机器学习、深度学习和图神经网络(Graph Neural Network,GNN)的AD分类方法.展开更多
Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the comm...Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.展开更多
In this paper, the dynamic properties of a discrete predator-prey model are discussed. The properties of non-hyperbolic fixed points and hyperbolic fixed points of the model are analyzed. First, by using the classic S...In this paper, the dynamic properties of a discrete predator-prey model are discussed. The properties of non-hyperbolic fixed points and hyperbolic fixed points of the model are analyzed. First, by using the classic Shengjin formula, we find the existence conditions for fixed points of the model. Then, by using the qualitative theory of ordinary differential equations and matrix theory we indicate which points are hyperbolic and which are non-hyperbolic and the associated conditions.展开更多
基金supported by Grants from the National Natural Science Foundation of China(Nos.81372409,81402532)the Sun Yat-sen University Clinical Research 5010 Program(No.2012011)
文摘Background: According to the 7 th edition of the American Joint Committee on Cancer(AJCC) staging system, over50% of patients with nasopharyngeal carcinoma(NPC) have N1 disease at initial diagnosis. However, patients with N1 NPC are relatively under-researched, and the metastasis risk of this group is not well-stratified. This study aimed to evaluate the prognostic values of gross tumor volume of metastatic regional lymph node(GTVnd) and pretreatment serum copy number of Epstein-Barr virus(EBV) DNA in predicting distant metastasis of patients with N1 NPC, and to develop an integrated prognostic model that incorporates GTVnd and EBV DNA copy number for this group of patients.Methods: The medical records of 787 newly diagnosed patients with nonmetastatic, histologically proven N1 NPC who were treated at Sun Yat-sen University Cancer Center between November 2009 and February 2012 were analyzed. Computed tomography-derived GTVnd was measured using the summation-of-area technique. Blood samples were collected before treatment to quantify plasma EBV DNA. The receiver operating characteristic(ROC) curve analysis was used to evaluate the cut-off point for GTVnd, and the area under the ROC curve was used to assess the predicted validity of GTVnd. The survival rates were assessed by Kaplan-Meier analysis, and the survival curves were compared using a log-rank test. Multivariate analysis was conducted using the Cox proportional hazard regression model.Results: The 5-year distant metastasis-free survival(DMFS) rates for patients with GTVnd > 18.9 vs.≤ 18.9 mL were82.2% vs. 93.2%(P < 0.001), and for patients with EBV DNA copy number > 4000 vs. < 4000 copies/mL were 83.5% vs.93.9%(P < 0.001). After adjusting for GTVnd, EBV DNA copy number, and T category in the Cox regression model, both GTVnd > 18.9 mL and EBV DNA copy number > 4000 copies/mL were significantly associated with poor prognosis(both P < 0.05). According to combination of GTVnd and EBV DNA copy number, all patients were divided into low-,moderate-, and high-risk groups, with the 5-year DMFS rates of 96.1,87.4, and 73.8%, respectively(P < 0.001). Multivariate analysis confirmed the prognostic value of this model for distant metastatic risk stratification(hazard ratio [HR],4.17; 95% confidence interval [CI] 2.34-7.59; P < 0.001).Conclusions: GTVnd and serum EBV DNA copy number are independent prognostic factors for predicting distant metastasis in NPC patients with N1 disease. The prognostic model incorporating GTVnd and EBV DNA copy number may improve metastatic risk stratification for this group of patients.
文摘This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.
文摘阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood AgGrEgation,GraphNAGE)的AD分类新方法.首先进行图数据建模,将AD数据样本表示为图数据.采用基于互信息(Mutual Information,MI)的特征选择方法,从样本的114维大脑皮层与皮层下感兴趣区域(Cerebral Cortex and Subcortical Regions Of Interest,CCS-ROI)的体积特征中选取重要性高的体积特征,并将其用于节点建模.提出基于相似性度量的关系建模方法,利用重要性高的体积特征、遗传基因、人口统计信息和认知评分对样本之间的关系进行建模.进而构建GraphNAGE,针对每个节点,基于与该节点相关的边的权重进行最近邻域采样,然后使用均值聚合方法对采样得到的邻居节点和中心节点的数据进行聚合,最后通过一个全连接层和一个Softmax层实现AD分类.在TADPOLE(The Alzheimer’s Disease Prediction Of Longitudinal Evolution)数据集上进行实验,结果表明:本文提出的AD分类方法的准确率(ACCuracy,ACC)为98.20%,F_(1)分数为97.34%,曲线下面积(Area Under Curve,AUC)为97.80%.实验结果表明:本文提出的AD分类方法充分利用了AD数据样本之间的相关性,其性能优于传统的基于机器学习、深度学习和图神经网络(Graph Neural Network,GNN)的AD分类方法.
基金supported by the National Natural Science Foundation of China(71271018)
文摘Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.
文摘In this paper, the dynamic properties of a discrete predator-prey model are discussed. The properties of non-hyperbolic fixed points and hyperbolic fixed points of the model are analyzed. First, by using the classic Shengjin formula, we find the existence conditions for fixed points of the model. Then, by using the qualitative theory of ordinary differential equations and matrix theory we indicate which points are hyperbolic and which are non-hyperbolic and the associated conditions.
文摘目的:本研究旨在建立甲状腺乳头状癌(Papillary Thyroid Carcinoma,PTC)患者大量中央区淋巴结转移[(high-volume Central Lymph Node Metastasis,hv-CLNM)中央区淋巴结转移数量>5]的预测模型。方法:纳入2021年1月—2024年6月于石河子大学第一附属医院行手术的579例PTC患者。数据按照7∶3比例随机分为训练集(n=406例)和验证集(n=173例),根据训练集术后CLNM数量分为hv-CLNM组(n=23例)和非hv-CLNM组(n=383例)。采用独立样本t检验、曼-惠特尼检验和卡方检验等比较组间差异。运用单变量和多变量逻辑回归分析筛选独立危险因素并绘制列线图,利用ROC曲线下面积(Area Under Curve,AUC)、校准曲线和决策曲线评估列线图模型效能。结果:差异分析显示,2组间年龄、性别、肿瘤大小、病灶数目、微钙化和TSH水平差异有统计学意义(P<0.05)。单变量和多变量逻辑回归显示,男性、年龄小于55岁、肿瘤大小≥7 mm、多灶性肿瘤、伴有微钙化以及TSH水平较高是hv-CLNM的预测因子(P<0.05)。根据多因素结果构建的列线图模型具有较高的诊断效能,其训练集AUC:0.894(95%CI:0.817~0.970),验证集AUC:0.801(95%CI:0.578~1.000)。校准曲线和决策曲线验证模型较好的一致性和临床效用。结论:基于本中心数据构建的TSH联合超声特征模型对PTC患者hv-CLNM有较高的预测价值,未来还需要进一步验证和优化。
