选取我国7个栽培玉米亚种材料,进行5 S rDNA的非转录间隔区(nontranscribed intergenic spacer,NTS)的序列分析,比较7个亚种材料NTS序列差异并进行聚类分析,探讨其亲缘关系。研究结果表明:7个材料的NTS区GC平均含量为45.67%,核苷酸位点...选取我国7个栽培玉米亚种材料,进行5 S rDNA的非转录间隔区(nontranscribed intergenic spacer,NTS)的序列分析,比较7个亚种材料NTS序列差异并进行聚类分析,探讨其亲缘关系。研究结果表明:7个材料的NTS区GC平均含量为45.67%,核苷酸位点变异位点个数1～15,转换/颠换率为0.83～2.0,特用玉米材料均存在不同程度的缺失;7个材料主要聚为两大类,第一类群中包括甜质、马齿、硬粒、爆裂和蜡质5个亚种材料,第二类群中包括粉质和甜粉2个亚种材料。同时利用荧光原位杂交技术(fluorescence in situ hybridization,FISH)对5 S rDNA进行定位,探针标记分别采用荧光素标记和生物素标记。结果表明:生物素标记检测系统灵敏度高、杂交信号强,更适合于5 S rDNA重复序列的定位检测。展开更多
Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as...Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.展开更多
2015年11月18日,英飞凌科技股份公司（FSE：IFX/OTCQX：IFNNY）推出全新S5系列,进一步增强其IGBT的性能。全新推出的这个产品系列立足于超薄晶圆TRENCHSTOPTM5 IGBT,专门针对开关频率高达40 k Hz的工业设备的交流-直流电力转换装置而开...2015年11月18日,英飞凌科技股份公司（FSE：IFX/OTCQX：IFNNY）推出全新S5系列,进一步增强其IGBT的性能。全新推出的这个产品系列立足于超薄晶圆TRENCHSTOPTM5 IGBT,专门针对开关频率高达40 k Hz的工业设备的交流-直流电力转换装置而开发。这类工业设备主要包括光伏逆变器（PV）和不间断电源（UPS）。S5系列器件能够满足制造商实现不低于98%的系统效率级别,展开更多
[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer bloc...[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer block),用于不同成熟度苹果检测。首先改进YOLOv5s的多尺度目标检测层,在Prediction中构建检测160×160特征图的检测头,提高小尺寸的不同成熟度苹果的检测精度;其次在Backbone结构中融合Swin Transformer Block,加强同级成熟度的苹果纹理特征融合,弱化纹理特征分布差异带来的消极影响,提高模型泛化能力;最后将Neck结构的Conv模块替换为动态卷积模块ODConv,细化局部特征映射,实现局部苹果细粒度特征的充分提取。基于不同成熟度苹果数据集进行试验,验证改进模型的性能。[结果]改进模型SODSTR-YOLOv5s检测的精确率、召回率、平均精度均值分别为89.1%、95.5%、93.6%,高、中、低成熟度苹果平均精度均值分别为94.1%、93.1%、93.7%,平均检测时间为16 ms,参数量为7.34 M。相比于YOLOv5s模型,改进模型SODSTR-YOLOv5s精确率、召回率、平均精度均值分别提高了3.8%、5.0%、2.9%,参数量和平均检测时间分别增加了0.32 M和5 ms。[结论]改进模型SODSTR-YOLOv5s提升了在自然环境下对不同成熟度苹果的检测能力,能较好地满足实际采摘苹果的检测要求。展开更多
文摘选取我国7个栽培玉米亚种材料,进行5 S rDNA的非转录间隔区(nontranscribed intergenic spacer,NTS)的序列分析,比较7个亚种材料NTS序列差异并进行聚类分析,探讨其亲缘关系。研究结果表明:7个材料的NTS区GC平均含量为45.67%,核苷酸位点变异位点个数1～15,转换/颠换率为0.83～2.0,特用玉米材料均存在不同程度的缺失;7个材料主要聚为两大类,第一类群中包括甜质、马齿、硬粒、爆裂和蜡质5个亚种材料,第二类群中包括粉质和甜粉2个亚种材料。同时利用荧光原位杂交技术(fluorescence in situ hybridization,FISH)对5 S rDNA进行定位,探针标记分别采用荧光素标记和生物素标记。结果表明:生物素标记检测系统灵敏度高、杂交信号强,更适合于5 S rDNA重复序列的定位检测。
基金the Research Management Center,Xiamen University Malaysia under XMUM Research Program Cycle 4(Grant No.XMUMRF/2019-C4/IECE/0012).
文摘Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.
文摘2015年11月18日,英飞凌科技股份公司（FSE：IFX/OTCQX：IFNNY）推出全新S5系列,进一步增强其IGBT的性能。全新推出的这个产品系列立足于超薄晶圆TRENCHSTOPTM5 IGBT,专门针对开关频率高达40 k Hz的工业设备的交流-直流电力转换装置而开发。这类工业设备主要包括光伏逆变器（PV）和不间断电源（UPS）。S5系列器件能够满足制造商实现不低于98%的系统效率级别,
文摘[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer block),用于不同成熟度苹果检测。首先改进YOLOv5s的多尺度目标检测层,在Prediction中构建检测160×160特征图的检测头,提高小尺寸的不同成熟度苹果的检测精度;其次在Backbone结构中融合Swin Transformer Block,加强同级成熟度的苹果纹理特征融合,弱化纹理特征分布差异带来的消极影响,提高模型泛化能力;最后将Neck结构的Conv模块替换为动态卷积模块ODConv,细化局部特征映射,实现局部苹果细粒度特征的充分提取。基于不同成熟度苹果数据集进行试验,验证改进模型的性能。[结果]改进模型SODSTR-YOLOv5s检测的精确率、召回率、平均精度均值分别为89.1%、95.5%、93.6%,高、中、低成熟度苹果平均精度均值分别为94.1%、93.1%、93.7%,平均检测时间为16 ms,参数量为7.34 M。相比于YOLOv5s模型,改进模型SODSTR-YOLOv5s精确率、召回率、平均精度均值分别提高了3.8%、5.0%、2.9%,参数量和平均检测时间分别增加了0.32 M和5 ms。[结论]改进模型SODSTR-YOLOv5s提升了在自然环境下对不同成熟度苹果的检测能力,能较好地满足实际采摘苹果的检测要求。
