There is an increasing concern for potentially hazardous metals pollution, which can threaten crops production and human health. In this study, the spatial distribution and environmental risks of eight heavy metals in...There is an increasing concern for potentially hazardous metals pollution, which can threaten crops production and human health. In this study, the spatial distribution and environmental risks of eight heavy metals in surface soil samples collected from the paddy fields in Yongshuyu irrigation area, Northeast China were investigated. The mean concentrations of Pb, Cr, Cu, Ni, Zn, Cd, Hg and As were 34.6 ± 4.67, 82.8 ± 9.51, 17.3 ± 4.09, 21.2 ± 12.0, 88.6 ± 17.9, 0.18 ± 0.15, 0.22 ± 0.07 and 8.77 ± 2.47 mg/kg, respectively, which were slightly higher than their corresponding background values of Jilin Province, indicating enrichment of these metals in the paddy soils, especially for Ni, Cd and Hg. The spatial distribution of heavy metals was closely correlated with local anthropogenic activities, such as agricultural production, mining and transportation. The hot-spot areas of As and Cd were mainly concentrated in the up-midstream where were associated with agricultural activities. Cr and Cu showed similar spatial distributions with hot-spot areas distributed the whole irrigation area uniformly. Ni was mainly distributed in the downstream where Ni quarries concentrated, while the spatial distribution patterns of Hg was mainly located in the upstream and downstream where the soil was significantly influenced by irrigation and coal mining emission. The spatial distributions of Pb and Zn were mainly concentrated along the highway side. The pollution levels of Yongshuyu irrigation area were estimated through index of geo-accumulation(Igeo), Nemerow integrated pollution index(NIPI) and potential ecological risk index(PERI). The results showed that Cd and Hg were the main pollutants in the study area. Health risk assessment results indicated that children were in higher non-carcinogenic and carcinogenic risks than adults with the carcinogenic metal of As. Ingestion was the main exposure pathway to non-carcinogenic and carcinogenic risk for both adults and children. Principal component analysis(PCA) indicated that Cr and Cu were mainly from parent materials, while Cd and As were mainly affected by agricultural activities. Pb and Zn were controlled by traffic activities, and the accumulations of Ni and Hg were associated with mining activities. This study would be valuable for preventing heavy metals inputs and safety in rice production of the Songhua river basin.展开更多
Utilizing graph neural networks for knowledge embedding to accomplish the task of knowledge graph completion(KGC)has become an important research area in knowledge graph completion.However,the number of nodes in the k...Utilizing graph neural networks for knowledge embedding to accomplish the task of knowledge graph completion(KGC)has become an important research area in knowledge graph completion.However,the number of nodes in the knowledge graph increases exponentially with the depth of the tree,whereas the distances of nodes in Euclidean space are second-order polynomial distances,whereby knowledge embedding using graph neural networks in Euclidean space will not represent the distances between nodes well.This paper introduces a novel approach called hyperbolic hierarchical graph attention network(H2GAT)to rectify this limitation.Firstly,the paper conducts knowledge representation in the hyperbolic space,effectively mitigating the issue of exponential growth of nodes with tree depth and consequent information loss.Secondly,it introduces a hierarchical graph atten-tion mechanism specifically designed for the hyperbolic space,allowing for enhanced capture of the network structure inherent in the knowledge graph.Finally,the efficacy of the proposed H2GAT model is evaluated on benchmark datasets,namely WN18RR and FB15K-237,thereby validating its effectiveness.The H2GAT model achieved 0.445,0.515,and 0.586 in the Hits@1,Hits@3 and Hits@10 metrics respectively on the WN18RR dataset and 0.243,0.367 and 0.518 on the FB15K-237 dataset.By incorporating hyperbolic space embedding and hierarchical graph attention,the H2GAT model successfully addresses the limitations of existing hyperbolic knowledge embedding models,exhibiting its competence in knowledge graph completion tasks.展开更多
目的探讨采用定量CT联合MRI扩散加权成像(MR-DWI)预测肺癌表皮生长因子受体(EGFR)基因突变状态,并评估二者的诊断效能。方法选取47例经EGFR基因检测的肺腺癌患者,其中EGFR突变19例,未突变28例。全部患者治疗前均接受CT、MRI扫描,并取病...目的探讨采用定量CT联合MRI扩散加权成像(MR-DWI)预测肺癌表皮生长因子受体(EGFR)基因突变状态,并评估二者的诊断效能。方法选取47例经EGFR基因检测的肺腺癌患者,其中EGFR突变19例,未突变28例。全部患者治疗前均接受CT、MRI扫描,并取病理活检。将CAD定量分析结果及ADC值进行分析,并将有统计学意义的参数使用二元Logistic回归分析,用于筛选预测EGFR突变的影响因子。绘制ROC曲线,评估预测效能。结果两组间性别、年龄差异无统计学意义(P>0.05),病灶体积差异有统计学意义(P=0.020)。由2位放射科医师对ADC值测量的一致性较好(ICC=0.978)。EGFR突变型组与野生型组两组间ADC值差异有统计学意义(P=0.045)。肿瘤体积、ADC值均是预测肺腺癌EGFR基因突变状态的独立危险因素。联合ADC值与体积二者较单独ADC值、体积可提高预测EGFR突变状态的敏感度(84.2%vs 50.0%vs 64.3%)及AUC(0.805 vs 0.674 vs 0.701)。结论肿瘤体积、MR-DWI ADC值可用来无创的预测肺癌EGFR基因突变状态,联合二者可提高预测能力,为临床制定治疗策略提供影像学依据。展开更多
基金Under the auspices of ‘One-Three-Five’ Strategic Planning Principles of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(No.IGA-135-08)Research Foundation for Talents of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(No.Y6H1211001)+1 种基金National Natural Science Foundation(No.41701372)Jilin Provincial Natural Science Fund Subject(No.20180101318JC)
文摘There is an increasing concern for potentially hazardous metals pollution, which can threaten crops production and human health. In this study, the spatial distribution and environmental risks of eight heavy metals in surface soil samples collected from the paddy fields in Yongshuyu irrigation area, Northeast China were investigated. The mean concentrations of Pb, Cr, Cu, Ni, Zn, Cd, Hg and As were 34.6 ± 4.67, 82.8 ± 9.51, 17.3 ± 4.09, 21.2 ± 12.0, 88.6 ± 17.9, 0.18 ± 0.15, 0.22 ± 0.07 and 8.77 ± 2.47 mg/kg, respectively, which were slightly higher than their corresponding background values of Jilin Province, indicating enrichment of these metals in the paddy soils, especially for Ni, Cd and Hg. The spatial distribution of heavy metals was closely correlated with local anthropogenic activities, such as agricultural production, mining and transportation. The hot-spot areas of As and Cd were mainly concentrated in the up-midstream where were associated with agricultural activities. Cr and Cu showed similar spatial distributions with hot-spot areas distributed the whole irrigation area uniformly. Ni was mainly distributed in the downstream where Ni quarries concentrated, while the spatial distribution patterns of Hg was mainly located in the upstream and downstream where the soil was significantly influenced by irrigation and coal mining emission. The spatial distributions of Pb and Zn were mainly concentrated along the highway side. The pollution levels of Yongshuyu irrigation area were estimated through index of geo-accumulation(Igeo), Nemerow integrated pollution index(NIPI) and potential ecological risk index(PERI). The results showed that Cd and Hg were the main pollutants in the study area. Health risk assessment results indicated that children were in higher non-carcinogenic and carcinogenic risks than adults with the carcinogenic metal of As. Ingestion was the main exposure pathway to non-carcinogenic and carcinogenic risk for both adults and children. Principal component analysis(PCA) indicated that Cr and Cu were mainly from parent materials, while Cd and As were mainly affected by agricultural activities. Pb and Zn were controlled by traffic activities, and the accumulations of Ni and Hg were associated with mining activities. This study would be valuable for preventing heavy metals inputs and safety in rice production of the Songhua river basin.
基金the Beijing Municipal Science and Technology Program(No.Z231100001323004).
文摘Utilizing graph neural networks for knowledge embedding to accomplish the task of knowledge graph completion(KGC)has become an important research area in knowledge graph completion.However,the number of nodes in the knowledge graph increases exponentially with the depth of the tree,whereas the distances of nodes in Euclidean space are second-order polynomial distances,whereby knowledge embedding using graph neural networks in Euclidean space will not represent the distances between nodes well.This paper introduces a novel approach called hyperbolic hierarchical graph attention network(H2GAT)to rectify this limitation.Firstly,the paper conducts knowledge representation in the hyperbolic space,effectively mitigating the issue of exponential growth of nodes with tree depth and consequent information loss.Secondly,it introduces a hierarchical graph atten-tion mechanism specifically designed for the hyperbolic space,allowing for enhanced capture of the network structure inherent in the knowledge graph.Finally,the efficacy of the proposed H2GAT model is evaluated on benchmark datasets,namely WN18RR and FB15K-237,thereby validating its effectiveness.The H2GAT model achieved 0.445,0.515,and 0.586 in the Hits@1,Hits@3 and Hits@10 metrics respectively on the WN18RR dataset and 0.243,0.367 and 0.518 on the FB15K-237 dataset.By incorporating hyperbolic space embedding and hierarchical graph attention,the H2GAT model successfully addresses the limitations of existing hyperbolic knowledge embedding models,exhibiting its competence in knowledge graph completion tasks.
文摘目的探讨采用定量CT联合MRI扩散加权成像(MR-DWI)预测肺癌表皮生长因子受体(EGFR)基因突变状态,并评估二者的诊断效能。方法选取47例经EGFR基因检测的肺腺癌患者,其中EGFR突变19例,未突变28例。全部患者治疗前均接受CT、MRI扫描,并取病理活检。将CAD定量分析结果及ADC值进行分析,并将有统计学意义的参数使用二元Logistic回归分析,用于筛选预测EGFR突变的影响因子。绘制ROC曲线,评估预测效能。结果两组间性别、年龄差异无统计学意义(P>0.05),病灶体积差异有统计学意义(P=0.020)。由2位放射科医师对ADC值测量的一致性较好(ICC=0.978)。EGFR突变型组与野生型组两组间ADC值差异有统计学意义(P=0.045)。肿瘤体积、ADC值均是预测肺腺癌EGFR基因突变状态的独立危险因素。联合ADC值与体积二者较单独ADC值、体积可提高预测EGFR突变状态的敏感度(84.2%vs 50.0%vs 64.3%)及AUC(0.805 vs 0.674 vs 0.701)。结论肿瘤体积、MR-DWI ADC值可用来无创的预测肺癌EGFR基因突变状态,联合二者可提高预测能力,为临床制定治疗策略提供影像学依据。
