目的构建纳米细菌Wistar雄性大鼠肾结石模型,并进行mirco-CT动态监测,为肾结石病因学的研究提供新的技术支持。方法将60只SPF级Wistar雄性大鼠随机分为纳米细菌组(尾静脉注射1.2 ml NB悬液,30只)和对照组(尾静脉注射1.2 ml生理盐水,30...目的构建纳米细菌Wistar雄性大鼠肾结石模型,并进行mirco-CT动态监测,为肾结石病因学的研究提供新的技术支持。方法将60只SPF级Wistar雄性大鼠随机分为纳米细菌组(尾静脉注射1.2 ml NB悬液,30只)和对照组(尾静脉注射1.2 ml生理盐水,30只),共计处理10 W,每周每组处死3只,并取两侧鲜活肾标本,在mirco-CT下进行检测,并记录阳性指标个数,同时做病理组织学检验,对结晶阳性动物实验模型进行统计分析。结果mirco-CT检测结果:截至第10周末,纳米细菌组大鼠肾高密度影检出4只,对照组未检出;病理组织学检验结果:10 w计数,纳米细菌组大鼠肾晶体阳性11只,截至第10周末,纳米细菌组大鼠肾晶体阳性率高于对照组,差异有统计学意义(P<0.001)。结论 mirco-CT与病理组织学检验结果进行比较分析,计算其Kappa值≈0.4199,一致性较差。原因系二者在宏观与微观两个层面分别对结石形成进行监测,其中mirco-CT可以在不破坏标本的情况下,构建更加直观立体的三维模型,对结石形成的动态观测具有重要意义。展开更多
The precise microscopic feature of carbon-carbon（C/C） composites is essential {or an accurate predic tion of their mechanical behavior. After fabrication, actual microscopic feature differs from simple ideal spatial...The precise microscopic feature of carbon-carbon（C/C） composites is essential {or an accurate predic tion of their mechanical behavior. After fabrication, actual microscopic feature differs from simple ideal spatial model. Micro computed lomography（CT） scan can well describe internal microstruetures of composites. Therefore, a reconstructed model is developed based on mireo-CT, by a series of prodcedures including extrac tlng components, generating new binary images and establishing a finite element （FE） model. Compared with the model designed by reconstructed commercial software MIMICS. the presented reconstructed FE model is superior in terms of high mesh quality and eontrollable mesh cluantity. The precision of the model is verified by experiment.展开更多
Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis.However,the complex internal structure of maize kernels presents several challenges.In CT(computed tomog...Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis.However,the complex internal structure of maize kernels presents several challenges.In CT(computed tomog-raphy)images,the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct,potentially leading to low segmentation accuracy or oversegmentation.Moreover,the blurred edges between the vitreous and starchy endosperm make segmentation difficult,often resulting in jagged segmentation outcomes.We propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize seeds.First,CT images are acquired using a multislice CT scanner.To improve the efficiency of maize kernel CT imaging,a batch scanning method is used.Individual kernels are accurately segmented from batch-scanned CT images using the Canny algorithm.Second,we modify the conventional architecture for high-quality segmentation of the vitreous and starchy endosperm in maize kernels.The conventional U-Net is modified by integrating the CBAM(convolutional block attention module)mechanism in the encoder and the SE(squeeze-and-excitation attention)mechanism in the decoder,as well as by using the focal-Tversky loss function instead of the Dice loss,and the boundary smoothing term is weighted as an additional loss term,named CSFTU-Net.The experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy endosperm.Finally,a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture,including the volume of the kernel(V),volume of the vitreous endosperm(VV),volume of starchy endosperm(SV),and ratios over their respective total kernel volumes(W/V and SV/V).The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels,offering valuable insights for maize breeding and processing.展开更多
基金supported by the National Natural Science Foundation of China (Nos.11272147,10772078)the Aviation Science Foundation (No.2013ZF52074)+1 种基金the State Key Laboratory of Mechanical Structural Mechanics and Control (No.0214G02)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘The precise microscopic feature of carbon-carbon（C/C） composites is essential {or an accurate predic tion of their mechanical behavior. After fabrication, actual microscopic feature differs from simple ideal spatial model. Micro computed lomography（CT） scan can well describe internal microstruetures of composites. Therefore, a reconstructed model is developed based on mireo-CT, by a series of prodcedures including extrac tlng components, generating new binary images and establishing a finite element （FE） model. Compared with the model designed by reconstructed commercial software MIMICS. the presented reconstructed FE model is superior in terms of high mesh quality and eontrollable mesh cluantity. The precision of the model is verified by experiment.
基金supported by the National Key Research and Development Program(2021YFD1200705)the Collaborative Innovation Center of the Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Science and Technology Innovation Special Construction Funded Program of the Beijing Academy of Agriculture and Forestry Sciences(KJCX20220401).
文摘Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis.However,the complex internal structure of maize kernels presents several challenges.In CT(computed tomog-raphy)images,the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct,potentially leading to low segmentation accuracy or oversegmentation.Moreover,the blurred edges between the vitreous and starchy endosperm make segmentation difficult,often resulting in jagged segmentation outcomes.We propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize seeds.First,CT images are acquired using a multislice CT scanner.To improve the efficiency of maize kernel CT imaging,a batch scanning method is used.Individual kernels are accurately segmented from batch-scanned CT images using the Canny algorithm.Second,we modify the conventional architecture for high-quality segmentation of the vitreous and starchy endosperm in maize kernels.The conventional U-Net is modified by integrating the CBAM(convolutional block attention module)mechanism in the encoder and the SE(squeeze-and-excitation attention)mechanism in the decoder,as well as by using the focal-Tversky loss function instead of the Dice loss,and the boundary smoothing term is weighted as an additional loss term,named CSFTU-Net.The experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy endosperm.Finally,a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture,including the volume of the kernel(V),volume of the vitreous endosperm(VV),volume of starchy endosperm(SV),and ratios over their respective total kernel volumes(W/V and SV/V).The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels,offering valuable insights for maize breeding and processing.
