[目的]采用生物信息学方法预测鼠李糖乳杆菌LGG细胞壁蛋白和功能分析。[方法]以鼠李糖乳杆菌LGG基因组编码蛋白序列为研究对象,采用Phobius和Signal P 4.0软件分析该菌株的细胞壁蛋白,同时采用COG功能数据库对预测的细胞壁蛋白进行功能...[目的]采用生物信息学方法预测鼠李糖乳杆菌LGG细胞壁蛋白和功能分析。[方法]以鼠李糖乳杆菌LGG基因组编码蛋白序列为研究对象,采用Phobius和Signal P 4.0软件分析该菌株的细胞壁蛋白,同时采用COG功能数据库对预测的细胞壁蛋白进行功能分析。[结果]鼠李糖乳杆菌LGG基因组中含有41个细胞壁蛋白,这些蛋白的功能分析结果显示,41个细胞壁蛋白中,25个蛋白没有功能注释,16个有功能注释,主要与细胞壁和细胞膜的生物合成,碳水化合物的代谢与转运,蛋白质的翻译后修饰等功能有关。[结论]该研究从结构和功能上分析鼠李糖乳杆菌细胞壁蛋白,为分析益生菌适应环境的分子特征打下基础。展开更多
The semantic segmentation of a brain tumor is the essential stage in medical treatment planning. Due to the different characteristics of tumors, one of the main difficulties in image segmentation is the severe imbalan...The semantic segmentation of a brain tumor is the essential stage in medical treatment planning. Due to the different characteristics of tumors, one of the main difficulties in image segmentation is the severe imbalance between classes. Also, a dataset with imbalanced classes is a common problem in multimodal 3D brain MRIs. Despite these problems, most studies in brain tumor segmentation are biased toward the overrepresented tumor class (majority class) and ignore the small size tumor class (minority class). In this paper, we propose an improved loss function Weighted Focal Loss (WFL), based on 3D U-Net to enhance the prediction of brain tumor segmentation. Using our proposed loss function (WFL) solves the imbalance between classes and the imbalance between weights by giving higher weights to the minority and lower weights to the majority. After assigning these weights to different pixel values, our work is able to resolve pixel degradation, which is one of the limitations of the loss function during model training. Based on our experiments, the proposed function (WFL) on the 3D U-Net model for high-grade glioma (HGG) and low-grade glioma (LGG) in the Brain Tumor Segmentation Challenge (BraTS) 2019 dataset has shown promising results for tumor core (TC), whole tumor (WT) and enhanced tumor (ET) with average dice scores of HGG: 0.830, 0.913, 0.815 and Dice scores of LGG for TC: 0.731, WT: 0.775 and ET: 0.685. Moreover, we deployed our training on BraTS 2020 in which we obtained mean Dice scores of HGG: TC: 0.843, WT: 0.892, ET: 0.871 and Dice scores of LGG: 0.7501, 0.7985, 0.6103 for TC, WT and ET, respectively.展开更多
文摘[目的]采用生物信息学方法预测鼠李糖乳杆菌LGG细胞壁蛋白和功能分析。[方法]以鼠李糖乳杆菌LGG基因组编码蛋白序列为研究对象,采用Phobius和Signal P 4.0软件分析该菌株的细胞壁蛋白,同时采用COG功能数据库对预测的细胞壁蛋白进行功能分析。[结果]鼠李糖乳杆菌LGG基因组中含有41个细胞壁蛋白,这些蛋白的功能分析结果显示,41个细胞壁蛋白中,25个蛋白没有功能注释,16个有功能注释,主要与细胞壁和细胞膜的生物合成,碳水化合物的代谢与转运,蛋白质的翻译后修饰等功能有关。[结论]该研究从结构和功能上分析鼠李糖乳杆菌细胞壁蛋白,为分析益生菌适应环境的分子特征打下基础。
文摘The semantic segmentation of a brain tumor is the essential stage in medical treatment planning. Due to the different characteristics of tumors, one of the main difficulties in image segmentation is the severe imbalance between classes. Also, a dataset with imbalanced classes is a common problem in multimodal 3D brain MRIs. Despite these problems, most studies in brain tumor segmentation are biased toward the overrepresented tumor class (majority class) and ignore the small size tumor class (minority class). In this paper, we propose an improved loss function Weighted Focal Loss (WFL), based on 3D U-Net to enhance the prediction of brain tumor segmentation. Using our proposed loss function (WFL) solves the imbalance between classes and the imbalance between weights by giving higher weights to the minority and lower weights to the majority. After assigning these weights to different pixel values, our work is able to resolve pixel degradation, which is one of the limitations of the loss function during model training. Based on our experiments, the proposed function (WFL) on the 3D U-Net model for high-grade glioma (HGG) and low-grade glioma (LGG) in the Brain Tumor Segmentation Challenge (BraTS) 2019 dataset has shown promising results for tumor core (TC), whole tumor (WT) and enhanced tumor (ET) with average dice scores of HGG: 0.830, 0.913, 0.815 and Dice scores of LGG for TC: 0.731, WT: 0.775 and ET: 0.685. Moreover, we deployed our training on BraTS 2020 in which we obtained mean Dice scores of HGG: TC: 0.843, WT: 0.892, ET: 0.871 and Dice scores of LGG: 0.7501, 0.7985, 0.6103 for TC, WT and ET, respectively.
