In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.Despite this,to the best of our...In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.Despite this,to the best of our knowledge,no tool exists for such prediction.Therefore,under the rationale that drugs with similar structures have similar resistome profiles,we devel-oped two models,a deterministic model and a stochastic model,to predict the bacterial re-sistome likely to neutralize uncharacterized but potential chemical structures.The current version of the tool involves the prediction of a resistome for Escherichia coli and Pseudomonas aeruginosa.The deterministic model on omitting two diverse but relatively less characterized drug classes,polyketides and polypeptides showed an accuracy of 87%,a sensitivity of 85%,and a precision of 89%,whereas the stochastic model predicted antibiotic classes of the test set compounds with an accuracy of 72%,a sensitivity of 75%,and a precision of 83%.The models have been implemented in both a standalone package and an online server,uCAREChemSuite-CLI and uCARE Chem Suite,respectively.In addition to resistome prediction,the online version of the suite enables the user to visualize the chemical structure,classify compounds in 19 pre-defined drug classes,perform pairwise alignment,and cluster with database compounds using a graphical user interface.展开更多
利用Weather Research and Forecasting/Chemistry(WRF/Chem)空气质量模式模拟研究了山东地区2014年2月21~26日期间的中度细颗粒物(PM2.5)污染过程,并从模拟结果评估、分布及演变特征、与气象条件的关系等方面分析了PM2.5的模拟特征。...利用Weather Research and Forecasting/Chemistry(WRF/Chem)空气质量模式模拟研究了山东地区2014年2月21~26日期间的中度细颗粒物(PM2.5)污染过程,并从模拟结果评估、分布及演变特征、与气象条件的关系等方面分析了PM2.5的模拟特征。模拟研究结果表明,山东PM2.5积聚期间多为弱的偏南风控制,消散阶段受西北风控制,当北京—天津—河北(京津冀)一带同时存在更为严重的PM2.5污染时,西北冷空气的平流输送使得山东部分地区的PM2.5浓度在完全削弱前又出现了一个高峰值。污染期间山东全省平均PM2.5的模拟浓度为125μg m^(-3),伴随着地面3.0 m/s的低风速、370 m低边界层高度和70%左右的相对湿度,其中PM2.5的模拟值受边界层高度的影响最大。整个污染期间全省平均PM2.5模拟值高于监测值10%左右,但是对于局部站点300μg m^(-3)及以上的观测峰值,模式模拟结果明显偏低。模拟效果的评估结果是:山东南部最好、然后是山东半岛,山东中部、西北部地区较差。展开更多
文摘In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.Despite this,to the best of our knowledge,no tool exists for such prediction.Therefore,under the rationale that drugs with similar structures have similar resistome profiles,we devel-oped two models,a deterministic model and a stochastic model,to predict the bacterial re-sistome likely to neutralize uncharacterized but potential chemical structures.The current version of the tool involves the prediction of a resistome for Escherichia coli and Pseudomonas aeruginosa.The deterministic model on omitting two diverse but relatively less characterized drug classes,polyketides and polypeptides showed an accuracy of 87%,a sensitivity of 85%,and a precision of 89%,whereas the stochastic model predicted antibiotic classes of the test set compounds with an accuracy of 72%,a sensitivity of 75%,and a precision of 83%.The models have been implemented in both a standalone package and an online server,uCAREChemSuite-CLI and uCARE Chem Suite,respectively.In addition to resistome prediction,the online version of the suite enables the user to visualize the chemical structure,classify compounds in 19 pre-defined drug classes,perform pairwise alignment,and cluster with database compounds using a graphical user interface.
文摘利用Weather Research and Forecasting/Chemistry(WRF/Chem)空气质量模式模拟研究了山东地区2014年2月21~26日期间的中度细颗粒物(PM2.5)污染过程,并从模拟结果评估、分布及演变特征、与气象条件的关系等方面分析了PM2.5的模拟特征。模拟研究结果表明,山东PM2.5积聚期间多为弱的偏南风控制,消散阶段受西北风控制,当北京—天津—河北(京津冀)一带同时存在更为严重的PM2.5污染时,西北冷空气的平流输送使得山东部分地区的PM2.5浓度在完全削弱前又出现了一个高峰值。污染期间山东全省平均PM2.5的模拟浓度为125μg m^(-3),伴随着地面3.0 m/s的低风速、370 m低边界层高度和70%左右的相对湿度,其中PM2.5的模拟值受边界层高度的影响最大。整个污染期间全省平均PM2.5模拟值高于监测值10%左右,但是对于局部站点300μg m^(-3)及以上的观测峰值,模式模拟结果明显偏低。模拟效果的评估结果是:山东南部最好、然后是山东半岛,山东中部、西北部地区较差。
