BACKGROUND Malignant triton tumors(MTTs)comprise a subgroup of malignant peripheral nerve sheath tumors(MPNSTs)that exhibits rhabdomyosarcomatous differen-tiation and follow an aggressive course.MTTs are primarily loc...BACKGROUND Malignant triton tumors(MTTs)comprise a subgroup of malignant peripheral nerve sheath tumors(MPNSTs)that exhibits rhabdomyosarcomatous differen-tiation and follow an aggressive course.MTTs are primarily located along peripheral nerves.Cases of MTTs in the abdominal wall have not been reported.MTT has a poorer prognosis than classic MPNSTs,and accurate diagnosis necessitates a keen understanding of the clinical history and knowledge of its differential diagnosis intricacies.Treatment for MTTs mirrors that for MPNSTs and is predominantly surgical.CASE SUMMARY A 49-year-old woman presented with a subcutaneous mass in her lower abdo-minal wall and a pre-existing surgical scar that had grown slowly over 3-4 months before the consultation.She had previously undergone radical hysterectomy and concurrent chemo-radiotherapy for cervical cancer approximately 5 years prior to the consultation.Abdominal computed tomography(CT)showed a 1.3 cm midline mass in the lower abdomen with infiltration into the rectus abdominis muscle.There was no sign of metastasis(T1N0M0).An incisional biopsy identified sporadic MTT of the lower abdomen.A comprehensive surgical excision with a 3 cm margin inclusive of the peritoneum was executed.Subse-quently,the general surgeon utilized an approach akin to the open peritoneal onlay mesh technique.The patient underwent additional treatment with an excision shaped as a mini-abdominoplasty for the skin defect.No complications arose,and annual follow-up CTs did not show signs of recurrence or metastasis.CONCLUSION An abdominal MTT was efficaciously treated with extensive excision and abdominal wall reconstruction,eliminating the need for postoperative radiotherapy.展开更多
讨论了基于高光谱成像技术光谱及纹理特征在识别早期柑橘黄龙病中的应用。使用一套近地高光谱成像系统采集了176枚柑橘叶片的高光谱图像作为实验样品,其中健康叶片60枚,黄龙病叶片60枚,缺锌叶片56枚。手工选取每幅叶片高光谱图像的病斑...讨论了基于高光谱成像技术光谱及纹理特征在识别早期柑橘黄龙病中的应用。使用一套近地高光谱成像系统采集了176枚柑橘叶片的高光谱图像作为实验样品,其中健康叶片60枚,黄龙病叶片60枚,缺锌叶片56枚。手工选取每幅叶片高光谱图像的病斑位置作为样品感兴趣区域（regions of interest,ROI）,计算其平均光谱反射率,并以此作为样品的反射光谱,光谱范围为396～1 010nm。样品光谱分别经过主成分分析（PXA）及连续投影算法（SPA）进行数据降维,再结合最小二乘支持向量机（LS-SVM）分类器建立分类模型。相比原始光谱,由PCA选取的前四个主成分及SPA选取的一组最佳波长组合（630.4,679.4,749.4和899.9 nm）建立的模型拥有更好的分类识别能力,其对三类柑橘叶片平均预测准确率分别为89.7%和87.4%。同时,从被选四个波长的每幅灰度图像中提取6个灰度直方图的纹理特征以及9个灰度共生矩阵的纹理特征再次构建分类模型。经SPA优选的10个纹理特征值进一步提高了分类效果,对三类柑橘叶片的识别正确率达到了100%,93.3%和92.9%。实验结果表明,同时包含光谱信息及空间纹理信息的高光谱图像在柑橘黄龙病的识别中显示了很大的潜力。展开更多
In this study, hyperspectral images were used to detect a fungal disease in apple leaves called Marssonina blotch(AMB). Estimation models were built to classify healthy, asymptomatic and symptomatic classes using part...In this study, hyperspectral images were used to detect a fungal disease in apple leaves called Marssonina blotch(AMB). Estimation models were built to classify healthy, asymptomatic and symptomatic classes using partial least squares regression(PLSR), principal component analysis(PCA), and linear discriminant analysis(LDA) multivariate methods. In general, the LDA estimation model performed the best among the three models in detecting AMB asymptomatic pixels, while all the models were able to detect the symptomatic class. LDA correctly classified asymptomatic pixels and LDA model predicted them with an accuracy of 88.0%. An accuracy of 91.4% was achieved as the total classification accuracy. The results from this work indicate the potential of using the LDA estimation model to identify asymptomatic pixels on leaves infected by AMB.展开更多
A biclustering algorithm extends conventional clustering techniques to extract all of the meaningful subgroups of genes and conditions in the expression matrix of a microarray dataset. However, such algorithms are ver...A biclustering algorithm extends conventional clustering techniques to extract all of the meaningful subgroups of genes and conditions in the expression matrix of a microarray dataset. However, such algorithms are very sensitive to input parameters and show poor scalability. This paper proposes a scalable unsupervised biclustering framework, SUBic, to find high quality constant-row biclusters in an expression matrix effectively. A one-dimensional clustering algorithm is proposed to partition the attributes, that is, columns of an expression matrix into disjoint groups based on the similarity of expression values. These groups form a set of short transactions and are used to discover a set of frequent itemsets each of which corresponds to a bicluster. However, a bicluster may include any attribute whose expression value is not similar enough to others, so a bicluster refinement is used to enhance the quality of a bicluster by removing those attributes based on its distribution of expression values. The performance of the proposed method is comparatively analyzed through a series of experiments on synthetic and real datasets.展开更多
At an early immature growth stage of citrus, a hyperspectral camera of 369–1042 nm was employed to acquire 30 hyperspectral images in order to detect immature green fruit within citrus trees under natural illuminatio...At an early immature growth stage of citrus, a hyperspectral camera of 369–1042 nm was employed to acquire 30 hyperspectral images in order to detect immature green fruit within citrus trees under natural illumination conditions. First, successive projections algorithm(SPA) were implemented to select 677, 804,563, 962, and 405 nm wavebands and to construct multispectral images from the original hyperspectral images for further processing. Then, histogram threshold segmentation using NDVI of 804 and 677 nm was implemented to remove image backgrounds. Three slope parameters, calculated from the pairs 405 and 563 nm, 563 and 677 nm, and 804 and 962 nm were used to construct a classifier to identify the potential citrus fruit. Then, a marker-controlled watershed segmentation based on wavelet transform was applied to obtain potential fruit areas.Finally, a green fruit detection model was constructed according to Grey Level Co-occurrence Matrix(GLCM)texture features of the independent areas. Three supervised classifiers, logistic regression, random forest and support vector machine(SVM) were developed using texture features. The detection accuracies were 79%, 75%, and 86% for the logistic regression, random forest, and SVM models, respectively. The developed algorithm showed a great potential for identifying immature green citrus for an early yield estimation.展开更多
文摘BACKGROUND Malignant triton tumors(MTTs)comprise a subgroup of malignant peripheral nerve sheath tumors(MPNSTs)that exhibits rhabdomyosarcomatous differen-tiation and follow an aggressive course.MTTs are primarily located along peripheral nerves.Cases of MTTs in the abdominal wall have not been reported.MTT has a poorer prognosis than classic MPNSTs,and accurate diagnosis necessitates a keen understanding of the clinical history and knowledge of its differential diagnosis intricacies.Treatment for MTTs mirrors that for MPNSTs and is predominantly surgical.CASE SUMMARY A 49-year-old woman presented with a subcutaneous mass in her lower abdo-minal wall and a pre-existing surgical scar that had grown slowly over 3-4 months before the consultation.She had previously undergone radical hysterectomy and concurrent chemo-radiotherapy for cervical cancer approximately 5 years prior to the consultation.Abdominal computed tomography(CT)showed a 1.3 cm midline mass in the lower abdomen with infiltration into the rectus abdominis muscle.There was no sign of metastasis(T1N0M0).An incisional biopsy identified sporadic MTT of the lower abdomen.A comprehensive surgical excision with a 3 cm margin inclusive of the peritoneum was executed.Subse-quently,the general surgeon utilized an approach akin to the open peritoneal onlay mesh technique.The patient underwent additional treatment with an excision shaped as a mini-abdominoplasty for the skin defect.No complications arose,and annual follow-up CTs did not show signs of recurrence or metastasis.CONCLUSION An abdominal MTT was efficaciously treated with extensive excision and abdominal wall reconstruction,eliminating the need for postoperative radiotherapy.
基金The Citrus Research and Development Council,USA,National Natural Science Foundation for Young Scholars of China(31301240)
文摘讨论了基于高光谱成像技术光谱及纹理特征在识别早期柑橘黄龙病中的应用。使用一套近地高光谱成像系统采集了176枚柑橘叶片的高光谱图像作为实验样品,其中健康叶片60枚,黄龙病叶片60枚,缺锌叶片56枚。手工选取每幅叶片高光谱图像的病斑位置作为样品感兴趣区域（regions of interest,ROI）,计算其平均光谱反射率,并以此作为样品的反射光谱,光谱范围为396～1 010nm。样品光谱分别经过主成分分析（PXA）及连续投影算法（SPA）进行数据降维,再结合最小二乘支持向量机（LS-SVM）分类器建立分类模型。相比原始光谱,由PCA选取的前四个主成分及SPA选取的一组最佳波长组合（630.4,679.4,749.4和899.9 nm）建立的模型拥有更好的分类识别能力,其对三类柑橘叶片平均预测准确率分别为89.7%和87.4%。同时,从被选四个波长的每幅灰度图像中提取6个灰度直方图的纹理特征以及9个灰度共生矩阵的纹理特征再次构建分类模型。经SPA优选的10个纹理特征值进一步提高了分类效果,对三类柑橘叶片的识别正确率达到了100%,93.3%和92.9%。实验结果表明,同时包含光谱信息及空间纹理信息的高光谱图像在柑橘黄龙病的识别中显示了很大的潜力。
基金supported by the National Academy of Agricultural Science,Rural Development Administration,Republic of Korea.
文摘In this study, hyperspectral images were used to detect a fungal disease in apple leaves called Marssonina blotch(AMB). Estimation models were built to classify healthy, asymptomatic and symptomatic classes using partial least squares regression(PLSR), principal component analysis(PCA), and linear discriminant analysis(LDA) multivariate methods. In general, the LDA estimation model performed the best among the three models in detecting AMB asymptomatic pixels, while all the models were able to detect the symptomatic class. LDA correctly classified asymptomatic pixels and LDA model predicted them with an accuracy of 88.0%. An accuracy of 91.4% was achieved as the total classification accuracy. The results from this work indicate the potential of using the LDA estimation model to identify asymptomatic pixels on leaves infected by AMB.
基金supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education,Science and Technology (MEST) of Korea under Grant No. 2011-0016648
文摘A biclustering algorithm extends conventional clustering techniques to extract all of the meaningful subgroups of genes and conditions in the expression matrix of a microarray dataset. However, such algorithms are very sensitive to input parameters and show poor scalability. This paper proposes a scalable unsupervised biclustering framework, SUBic, to find high quality constant-row biclusters in an expression matrix effectively. A one-dimensional clustering algorithm is proposed to partition the attributes, that is, columns of an expression matrix into disjoint groups based on the similarity of expression values. These groups form a set of short transactions and are used to discover a set of frequent itemsets each of which corresponds to a bicluster. However, a bicluster may include any attribute whose expression value is not similar enough to others, so a bicluster refinement is used to enhance the quality of a bicluster by removing those attributes based on its distribution of expression values. The performance of the proposed method is comparatively analyzed through a series of experiments on synthetic and real datasets.
基金funded by the National Natural Science Foundation of China (31360291)China Scholarship Council (201408625069) and University of Florida
文摘At an early immature growth stage of citrus, a hyperspectral camera of 369–1042 nm was employed to acquire 30 hyperspectral images in order to detect immature green fruit within citrus trees under natural illumination conditions. First, successive projections algorithm(SPA) were implemented to select 677, 804,563, 962, and 405 nm wavebands and to construct multispectral images from the original hyperspectral images for further processing. Then, histogram threshold segmentation using NDVI of 804 and 677 nm was implemented to remove image backgrounds. Three slope parameters, calculated from the pairs 405 and 563 nm, 563 and 677 nm, and 804 and 962 nm were used to construct a classifier to identify the potential citrus fruit. Then, a marker-controlled watershed segmentation based on wavelet transform was applied to obtain potential fruit areas.Finally, a green fruit detection model was constructed according to Grey Level Co-occurrence Matrix(GLCM)texture features of the independent areas. Three supervised classifiers, logistic regression, random forest and support vector machine(SVM) were developed using texture features. The detection accuracies were 79%, 75%, and 86% for the logistic regression, random forest, and SVM models, respectively. The developed algorithm showed a great potential for identifying immature green citrus for an early yield estimation.
