Objective To explore the genotyping characteristics of human fecal Escherichia coli(E. coli) and the relationships between antibiotic resistance genes(ARGs) and multidrug resistance(MDR) of E. coli in Miyun District, ...Objective To explore the genotyping characteristics of human fecal Escherichia coli(E. coli) and the relationships between antibiotic resistance genes(ARGs) and multidrug resistance(MDR) of E. coli in Miyun District, Beijing, an area with high incidence of infectious diarrheal cases but no related data.Methods Over a period of 3 years, 94 E. coli strains were isolated from fecal samples collected from Miyun District Hospital, a surveillance hospital of the National Pathogen Identification Network. The antibiotic susceptibility of the isolates was determined by the broth microdilution method. ARGs,multilocus sequence typing(MLST), and polymorphism trees were analyzed using whole-genome sequencing data(WGS).Results This study revealed that 68.09% of the isolates had MDR, prevalent and distributed in different clades, with a relatively high rate and low pathogenicity. There was no difference in MDR between the diarrheal(49/70) and healthy groups(15/24).Conclusion We developed a random forest(RF) prediction model of TEM.1 + baeR + mphA + mphB +QnrS1 + AAC.3-IId to identify MDR status, highlighting its potential for early resistance identification. The causes of MDR are likely mobile units transmitting the ARGs. In the future, we will continue to strengthen the monitoring of ARGs and MDR, and increase the number of strains to further verify the accuracy of the MDR markers.展开更多
Survival data with amulti-state structure are frequently observed in follow-up studies.An analytic approach based on a multi-state model(MSM)should be used in longitudinal health studies in which a patient experiences...Survival data with amulti-state structure are frequently observed in follow-up studies.An analytic approach based on a multi-state model(MSM)should be used in longitudinal health studies in which a patient experiences a sequence of clinical progression events.One main objective in the MSM framework is variable selection,where attempts are made to identify the risk factors associated with the transition hazard rates or probabilities of disease progression.The usual variable selection methods,including stepwise and penalized methods,do not provide information about the importance of variables.In this context,we present a two-step algorithm to evaluate the importance of variables formulti-state data.Three differentmachine learning approaches(randomforest,gradient boosting,and neural network)as themost widely usedmethods are considered to estimate the variable importance in order to identify the factors affecting disease progression and rank these factors according to their importance.The performance of our proposed methods is validated by simulation and applied to the COVID-19 data set.The results revealed that the proposed two-stage method has promising performance for estimating variable importance.展开更多
Magnetic Resonance Imaging(MRI)is one of the important resources for identifying abnormalities in the human brain.This work proposes an effective Multi-Class Classification(MCC)system using Binary Robust Invariant Scal...Magnetic Resonance Imaging(MRI)is one of the important resources for identifying abnormalities in the human brain.This work proposes an effective Multi-Class Classification(MCC)system using Binary Robust Invariant Scalable Keypoints(BRISK)as texture descriptors for effective classification.Atfirst,the potential Region Of Interests(ROIs)are detected using features from the acceler-ated segment test algorithm.Then,non-maxima suppression is employed in scale space based on the information in the ROIs.The discriminating power of BRISK is examined using three machine learning classifiers such as k-Nearest Neighbour(kNN),Support Vector Machine(SVM)and Random Forest(RF).An MCC sys-tem is developed which classifies the MRI images into normal,glioma,meningio-ma and pituitary.A total of 3264 MRI brain images are employed in this study to evaluate the proposed MCC system.Results show that the average accuracy of the proposed MCC-RF based system is 99.62%with a sensitivity of 99.16%and spe-cificity of 99.75%.The average accuracy of the MCC-kNN system is 93.65%and 97.59%by the MCC-SVM based system.展开更多
This paper aims to build an employee attrition classification model based on the Stacking algorithm.Oversampling algorithm is applied to address the issue of data imbalance and the Randomforest feature importance rank...This paper aims to build an employee attrition classification model based on the Stacking algorithm.Oversampling algorithm is applied to address the issue of data imbalance and the Randomforest feature importance ranking method is used to resolve the overfitting problem after data cleaning and preprocessing.Then,different algorithms are used to establish classification models as control experiments,and R-squared indicators are used to compare.Finally,the Stacking algorithm is used to establish the final classification model.This model has practical and significant implications for both human resource management and employee attrition analysis.展开更多
基金funded by the National Pathogen Identification Network project and Research on Key Technologies of Intelligent Monitoring,Early Warning and Tracing of Infectious Diseases in Miyun。
文摘Objective To explore the genotyping characteristics of human fecal Escherichia coli(E. coli) and the relationships between antibiotic resistance genes(ARGs) and multidrug resistance(MDR) of E. coli in Miyun District, Beijing, an area with high incidence of infectious diarrheal cases but no related data.Methods Over a period of 3 years, 94 E. coli strains were isolated from fecal samples collected from Miyun District Hospital, a surveillance hospital of the National Pathogen Identification Network. The antibiotic susceptibility of the isolates was determined by the broth microdilution method. ARGs,multilocus sequence typing(MLST), and polymorphism trees were analyzed using whole-genome sequencing data(WGS).Results This study revealed that 68.09% of the isolates had MDR, prevalent and distributed in different clades, with a relatively high rate and low pathogenicity. There was no difference in MDR between the diarrheal(49/70) and healthy groups(15/24).Conclusion We developed a random forest(RF) prediction model of TEM.1 + baeR + mphA + mphB +QnrS1 + AAC.3-IId to identify MDR status, highlighting its potential for early resistance identification. The causes of MDR are likely mobile units transmitting the ARGs. In the future, we will continue to strengthen the monitoring of ARGs and MDR, and increase the number of strains to further verify the accuracy of the MDR markers.
文摘Survival data with amulti-state structure are frequently observed in follow-up studies.An analytic approach based on a multi-state model(MSM)should be used in longitudinal health studies in which a patient experiences a sequence of clinical progression events.One main objective in the MSM framework is variable selection,where attempts are made to identify the risk factors associated with the transition hazard rates or probabilities of disease progression.The usual variable selection methods,including stepwise and penalized methods,do not provide information about the importance of variables.In this context,we present a two-step algorithm to evaluate the importance of variables formulti-state data.Three differentmachine learning approaches(randomforest,gradient boosting,and neural network)as themost widely usedmethods are considered to estimate the variable importance in order to identify the factors affecting disease progression and rank these factors according to their importance.The performance of our proposed methods is validated by simulation and applied to the COVID-19 data set.The results revealed that the proposed two-stage method has promising performance for estimating variable importance.
文摘Magnetic Resonance Imaging(MRI)is one of the important resources for identifying abnormalities in the human brain.This work proposes an effective Multi-Class Classification(MCC)system using Binary Robust Invariant Scalable Keypoints(BRISK)as texture descriptors for effective classification.Atfirst,the potential Region Of Interests(ROIs)are detected using features from the acceler-ated segment test algorithm.Then,non-maxima suppression is employed in scale space based on the information in the ROIs.The discriminating power of BRISK is examined using three machine learning classifiers such as k-Nearest Neighbour(kNN),Support Vector Machine(SVM)and Random Forest(RF).An MCC sys-tem is developed which classifies the MRI images into normal,glioma,meningio-ma and pituitary.A total of 3264 MRI brain images are employed in this study to evaluate the proposed MCC system.Results show that the average accuracy of the proposed MCC-RF based system is 99.62%with a sensitivity of 99.16%and spe-cificity of 99.75%.The average accuracy of the MCC-kNN system is 93.65%and 97.59%by the MCC-SVM based system.
文摘This paper aims to build an employee attrition classification model based on the Stacking algorithm.Oversampling algorithm is applied to address the issue of data imbalance and the Randomforest feature importance ranking method is used to resolve the overfitting problem after data cleaning and preprocessing.Then,different algorithms are used to establish classification models as control experiments,and R-squared indicators are used to compare.Finally,the Stacking algorithm is used to establish the final classification model.This model has practical and significant implications for both human resource management and employee attrition analysis.
