为了开发丙酮酸高产菌株,以大肠杆菌MG1655为出发菌株,通过基因敲除阻断副产物途径构建了产丙酮酸大肠杆菌工程菌KLPP。进一步利用p UT Mini-Tn5载体进行转座子随机突变,构建了含有7 197个单克隆的突变体文库。使用基于丙酮酸的二硝基...为了开发丙酮酸高产菌株,以大肠杆菌MG1655为出发菌株,通过基因敲除阻断副产物途径构建了产丙酮酸大肠杆菌工程菌KLPP。进一步利用p UT Mini-Tn5载体进行转座子随机突变,构建了含有7 197个单克隆的突变体文库。使用基于丙酮酸的二硝基苯肼显色法,建立了96孔板-酶标仪快速筛选方法,经过两轮的筛选,成功筛选到了6个突变体菌株,比KLPP丙酮酸产量提高了38%、31%、19%、28%、44%和14%。利用全基因组重测序确定了其转座子插入的位置,进而确定了可能影响丙酮酸产量的基因位点,为后续菌株改造工作奠定了基础。展开更多
A novel data-driven method based on Gaussian mixture model （GMM） and distance evaluation technique （DET） is proposed to predict the remaining useful life （RUL） of rolling bearings. The data sets are clustered by...A novel data-driven method based on Gaussian mixture model （GMM） and distance evaluation technique （DET） is proposed to predict the remaining useful life （RUL） of rolling bearings. The data sets are clustered by GMM to divide all data sets into several health states adaptively and reasonably. The number of clusters is determined by the minimum description length principle. Thus, either the health state of the data sets or the number of the states is obtained automatically. Meanwhile, the abnormal data sets can be recognized during the clustering process and removed from the training data sets. After obtaining the health states, appropriate features are selected by DET for increasing the classification and prediction accuracy. In the prediction process, each vibration signal is decomposed into several components by empirical mode decomposition. Some common statis- tical parameters of the components are calculated first and then the features are clustered using GMM to divide the data sets into several health states and remove the abnormal data sets. Thereafter, appropriate statistical parameters of the generated components are selected using DET. Finally, least squares support vector machine is utilized to predict the RUL of rolling bearings.Experimental results indicate that the proposed method reliably predicts the RUL of rolling bearings.展开更多
文摘为了开发丙酮酸高产菌株,以大肠杆菌MG1655为出发菌株,通过基因敲除阻断副产物途径构建了产丙酮酸大肠杆菌工程菌KLPP。进一步利用p UT Mini-Tn5载体进行转座子随机突变,构建了含有7 197个单克隆的突变体文库。使用基于丙酮酸的二硝基苯肼显色法,建立了96孔板-酶标仪快速筛选方法,经过两轮的筛选,成功筛选到了6个突变体菌株,比KLPP丙酮酸产量提高了38%、31%、19%、28%、44%和14%。利用全基因组重测序确定了其转座子插入的位置,进而确定了可能影响丙酮酸产量的基因位点,为后续菌株改造工作奠定了基础。
基金Acknowledgements The authors gratefully acknowledge the support of the National Key Research and Development Program of China （Grant No. 2016YFF0203400）, the National Natural Science Foundation of China （Grant Nos. 51575168 and 51375152）, the Project of National Science and Technology Supporting Plan （Grant No. 2015BAF32B03）, and the Science Research Key Program of Educational Department of Hunan Province of China （Grant No. 16A180）. The authors appreciate the support provided by the Collaborative Innovation Center of Intelligent New Energy Vehicle, the Hunan Collaborative Innovation Center for Green Car.
文摘A novel data-driven method based on Gaussian mixture model （GMM） and distance evaluation technique （DET） is proposed to predict the remaining useful life （RUL） of rolling bearings. The data sets are clustered by GMM to divide all data sets into several health states adaptively and reasonably. The number of clusters is determined by the minimum description length principle. Thus, either the health state of the data sets or the number of the states is obtained automatically. Meanwhile, the abnormal data sets can be recognized during the clustering process and removed from the training data sets. After obtaining the health states, appropriate features are selected by DET for increasing the classification and prediction accuracy. In the prediction process, each vibration signal is decomposed into several components by empirical mode decomposition. Some common statis- tical parameters of the components are calculated first and then the features are clustered using GMM to divide the data sets into several health states and remove the abnormal data sets. Thereafter, appropriate statistical parameters of the generated components are selected using DET. Finally, least squares support vector machine is utilized to predict the RUL of rolling bearings.Experimental results indicate that the proposed method reliably predicts the RUL of rolling bearings.
