Quantitative real-time PCR (qPCR) was applied to rapid screening of positive plasmid clones. Insert-specific primer pairs were used in qPCR colony screening, and false positive colonies could easily be distinguished f...Quantitative real-time PCR (qPCR) was applied to rapid screening of positive plasmid clones. Insert-specific primer pairs were used in qPCR colony screening, and false positive colonies could easily be distinguished from true positive ones by comparing their Ct values. In addition, qPCR is particularly suitable when amplicon is small (<150 bp). This method is sensitive, simple and fast, obviates the need for gel electrophoresis, and is a cost-effective alternative to the traditional PCR approach.展开更多
As one of the most widely used assays in biological research,an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process.To speed up the colony counting,a machine learn...As one of the most widely used assays in biological research,an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process.To speed up the colony counting,a machine learning method is presented for counting the colony forming units(CFUs),which is referred to as CFUCounter.This cellcounting program processes digital images and segments bacterial colonies.The algorithm combines unsupervised machine learning,iterative adaptive thresholding,and local-minima-based watershed segmentation to enable an accurate and robust cell counting.Compared to a manual counting method,CFUCounter supports color-based CFU classification,allows plates containing heterologous colonies to be counted individually,and demonstrates overall performance(slope 0.996,SD 0.013,95%CI:0.97–1.02,p value<1e-11,r=0.999)indistinguishable from the gold standard of point-and-click counting.This CFUCounter application is open-source and easy to use as a unique addition to the arsenal of colony-counting tools.展开更多
利用快速扩展随机树算法(Rapidly-exploring random tree,RRT)进行路径规划时,在狭窄复杂区域与空旷障碍区域融合环境下,存在随机性大、搜索时间长、路径曲折等问题。为此,提出了一种基于蚁群的环境分区目标偏置RRT算法。首先,采用分环...利用快速扩展随机树算法(Rapidly-exploring random tree,RRT)进行路径规划时,在狭窄复杂区域与空旷障碍区域融合环境下,存在随机性大、搜索时间长、路径曲折等问题。为此,提出了一种基于蚁群的环境分区目标偏置RRT算法。首先,采用分环境的随机概率采样并结合人工势场的目标偏向扩展策略,以提高算法收敛速度,增强算法搜索能力。其次,为解决规划路径曲折且冗余点多的问题,提出改进蚁群寻优路径,并结合跳点筛选策略及三次B样条以消除冗余点平滑最终路径。最后,改进后的算法与A*算法、目标偏向RRT算法进行了对比分析。仿真结果表明:改进后的算法节点耗费量降低了54.8%,时间平均缩短了75.88%,从而验证了算法的有效性。展开更多
文摘Quantitative real-time PCR (qPCR) was applied to rapid screening of positive plasmid clones. Insert-specific primer pairs were used in qPCR colony screening, and false positive colonies could easily be distinguished from true positive ones by comparing their Ct values. In addition, qPCR is particularly suitable when amplicon is small (<150 bp). This method is sensitive, simple and fast, obviates the need for gel electrophoresis, and is a cost-effective alternative to the traditional PCR approach.
基金This research was funded by a VPR Special Research Grant entitled Potential of a Site-Specific DNA Interstrand Crosslink.
文摘As one of the most widely used assays in biological research,an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process.To speed up the colony counting,a machine learning method is presented for counting the colony forming units(CFUs),which is referred to as CFUCounter.This cellcounting program processes digital images and segments bacterial colonies.The algorithm combines unsupervised machine learning,iterative adaptive thresholding,and local-minima-based watershed segmentation to enable an accurate and robust cell counting.Compared to a manual counting method,CFUCounter supports color-based CFU classification,allows plates containing heterologous colonies to be counted individually,and demonstrates overall performance(slope 0.996,SD 0.013,95%CI:0.97–1.02,p value<1e-11,r=0.999)indistinguishable from the gold standard of point-and-click counting.This CFUCounter application is open-source and easy to use as a unique addition to the arsenal of colony-counting tools.
基金Natural Science Foundation of Shaanxi Province(No.2019JM-286)。
文摘利用快速扩展随机树算法(Rapidly-exploring random tree,RRT)进行路径规划时,在狭窄复杂区域与空旷障碍区域融合环境下,存在随机性大、搜索时间长、路径曲折等问题。为此,提出了一种基于蚁群的环境分区目标偏置RRT算法。首先,采用分环境的随机概率采样并结合人工势场的目标偏向扩展策略,以提高算法收敛速度,增强算法搜索能力。其次,为解决规划路径曲折且冗余点多的问题,提出改进蚁群寻优路径,并结合跳点筛选策略及三次B样条以消除冗余点平滑最终路径。最后,改进后的算法与A*算法、目标偏向RRT算法进行了对比分析。仿真结果表明:改进后的算法节点耗费量降低了54.8%,时间平均缩短了75.88%,从而验证了算法的有效性。
