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Genotyping Characteristics of Human Fecal Escherichia coli and Their Association with Multidrug Resistance in Miyun District, Beijing

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Biomedical and Environmental Sciences 2023年第5期36卷 406-417页

作者： ZHANG Wei Wei ZHU Xiao Lin DENG Le Le HAN Ya Jun LI Zhuo Wei WANG Jin Long CHEN Yong Liang WANG Ao Lin TIAN Er Li CHENG Bin XU Lin Hua CHEN Yi Cong TIAN Li Li HE Guang Xue National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and PreventionBeijing 102206China Miyun District Center for Disease Control and Prevention Beijing 101520China Berger Bioengineering Co. LtdShanghai 201401China Xicheng District Commission of Health of Beijing Municipality Beijing 100044China Miyun District Hospital Beijing 101520China Hebei North University Zhangjiakou 075000HeibeiChina

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.

关键词： E.coli Multidrug resistance Whole-genome sequencing Antibiotic resistance genes randomforest

Multi Class Brain Cancer Prediction System Empowered with BRISK Descriptor

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Intelligent Automation & Soft Computing 2023年第5期36卷 1507-1521页

作者： Madona B.Sahaai G.R.Jothilakshmi E.Praveen V.Hemath Kumar Department of Electronics and Communication Engineering VISTASChennaiTamil NaduIndia

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.

关键词： Braincancer BRISKdescriptor randomforest multi-classclassiﬁcation brain image analysis

Employee Attrition Classification Model Based on Stacking Algorithm

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Psychology Research 2023年第6期13卷 279-285页

作者： CHEN Yanming LIN Xinyu ZHAN Kunye Shantou University South China Normal University Shenzhen University

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.

关键词： employee attrition classification model machine learning ensemble learning oversampling algorithm randomforest stacking algorithm

Impacts of predictor variables and species models on simulating Tamarix ramosissima distribution in Tarim Basin, northwestern China

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Journal of Plant Ecology 2012年第3期5卷 337-345页

作者： Qiang Zhang Xinshi Zhang State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of Sciences20 NanxincunXiangshanBeijing 100093China Graduate University of Chinese Academy of Sciences 19A YuquanluBeijing 100049China

Aims Preserving and restoring Tamarix ramosissima is urgently required in the Tarim Basin,Northwest China.Using species distribution models to predict the biogeographical distribution of species is regularly used in conservation and other management activities.However,the uncertainty in the data and models inevitably reduces their prediction power.The major purpose of this study is to assess the impacts of predictor variables and species distribution models on simulating T.ramosissima distribution,to explore the relationships between predictor variables and species distribution models and to model the potential distribution of T.ramosissima in this basin.Methods Three models—the generalized linear model(GLM),classification and regression tree(CART)and Random Forests—were selected and were processed on the BIOMOD platform.The presence/absence data of T.ramosissima in the Tarim Basin,which were calculated from vegetation maps,were used as response variables.Climate,soil and digital elevation model(DEM)data variables were divided into four datasets and then used as predictors.The four datasets were(i)climate variables,(ii)soil,climate and DEM variables,(iii)principal component analysis(PCA)-based climate variables and(iv)PCA-based soil,climate and DEM variables.Important Findings The results indicate that predictive variables for species distribution models should be chosen carefully,because too many predictors can reduce the prediction power.The effectiveness of using PCA to reduce the correlation among predictors and enhance the modelling power depends on the chosen predictor variables and models.Our results implied that it is better to reduce the correlating predictors before model processing.The Random Forests model was more precise than the GLM and CART models.The best model for T.ramosissima was the Random Forests model with climate predictors alone.Soil variables considered in this study could not significantly improve the model’s prediction accuracy for T.ramosissima.The potential distribution area of T.ramo

关键词： species distribution model Tamarix ramosissima generalized linear models classification and regression trees randomforest

基于randomforest分类方法的购买预测研究

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江苏通信 2021年第6期37卷 72-74页

作者：姜潇蔚杨达霏中国邮政集团有限公司南京市分公司中博信息技术研究院有限公司

本文针对银行客户,提出一种基于randomforest的银行客户分类模型,通过基于randomforest的分类方法对银行客户已存在数据进行处理,并应用labelencoder对数据进行预处理从而提高分类精确度,得到客户对产品是否具有购买倾向。结合了RandomF... 详细信息

本文针对银行客户,提出一种基于randomforest的银行客户分类模型,通过基于randomforest的分类方法对银行客户已存在数据进行处理,并应用labelencoder对数据进行预处理从而提高分类精确度,得到客户对产品是否具有购买倾向。结合了randomforest的分类能力和labelencoder量化数据能力,使用葡萄牙银行机构数据进行验证,实验结果表明本文提出的客户购买预测模型精度更高。

关键词： randomforest 机器学习客户分类

基于数据挖掘和randomforest算法的助学金分类研究

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中国市场 2019年第3期 50-52页

作者：王雨萌武小军罗雅晨同济大学上海201804

在普及高等教育的过程中,发放助学金作为帮助贫困学生完成学业的重要手段之一。目前国内高校的助学金发放准则存在一定的问题,因此如何在高校缺乏学生的真实家庭情况以及助学金的金额有限的背景下,将助学金发放到最需要帮助的学生手上... 详细信息

在普及高等教育的过程中,发放助学金作为帮助贫困学生完成学业的重要手段之一。目前国内高校的助学金发放准则存在一定的问题,因此如何在高校缺乏学生的真实家庭情况以及助学金的金额有限的背景下,将助学金发放到最需要帮助的学生手上成为亟待解决的问题。文章以某高校大学生一卡通脱敏数据为原始数据,采用大数据挖掘技术和机器学习算法等AI技术进行拟合训练,以期有效预测需要帮助的学生,发挥助学金的作用。

关键词：数据挖掘 randomforest 机器学习助学金

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基于多组学数据的乳腺癌预后预测模型构建

应用数学进展 2022年第9期11卷 6723-6729页

作者：苏婕怡青岛大学山东青岛

本文主要从UCSC Xena数据库中已经整理好的关于TCGA数据库的乳腺癌数据中,挑选了拷贝数变异、RNA基因表达量、RNA外显子表达量三个组学方面的数据。首先,基于三个组学数据的维度远大于样本量的特征,分别对三个组学的数据进行方差阈值过... 详细信息

本文主要从UCSC Xena数据库中已经整理好的关于TCGA数据库的乳腺癌数据中,挑选了拷贝数变异、RNA基因表达量、RNA外显子表达量三个组学方面的数据。首先,基于三个组学数据的维度远大于样本量的特征,分别对三个组学的数据进行方差阈值过滤,初步筛选过滤掉变化幅度不大的变量,再使用mRMR进行滤波式的变量选择方法,即最大化特征与分类变量之间的相关性,最小化特征之间的相关性,各自筛选得到50个变量。对于离散型的天数表型数据,采用阈值方法将其转化为0-1分类变量,最终将因变量与自变量进行合并,并划分测试集、训练集,使用svm、XGBoost、Logistic、randomforest四种方法对结果变量进行预后预测,并采用特定的指标对这四种算法进行比较,运用在训练集上,最终得到XGBoost、Logistic两种算法的预测效果要优于svm、randomforest。

关键词：多组学 mRMR XGBoost svm Logistic randomforest 变量选择预后预测

基于随机森林的水库大坝基础设施安全评估方法

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基于随机森林的水库大坝基础设施安全评估方法

基于K-means和Random Forest的WiFi室内定位方法

2022（第十届）中国水利信息化技术论坛

作者：丁炜徐毅金有杰张日水利部南京水利水文自动化研究所靖江市水利局规划建设科

水库大坝安全评估在降低水库大坝溃坝概率、保障水库正常运行方面发挥着重要的作用。本文基于随机森林方法构建基于数据驱动的水库大坝基础设施安全评估模型,减少人工干预,实现安全评估高效化、智能化。首先预处理水库大坝基础数据资料... 详细信息

水库大坝安全评估在降低水库大坝溃坝概率、保障水库正常运行方面发挥着重要的作用。本文基于随机森林方法构建基于数据驱动的水库大坝基础设施安全评估模型,减少人工干预,实现安全评估高效化、智能化。首先预处理水库大坝基础数据资料,构建训练数据集和测试数据集,然后构建水库大坝基础设施安全评估模型并利用训练数据训练模型,利用GridSearch和Cross-validation确定模型最优情况的参数,并通模型评价指标和多种算法对比结果全面评估模型性能。实验结果表明:基于随机森林的安全评估模型在测试数据上的准确度率为90.54%,模型准确度相较于ANN、KNN、SVM算法高出4.87%、18.59%、37.93%,满足实际应用的需求。

关键词：水库大坝安全等级评估 randomforest Gridsearch Cross-validation

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控制工程 2017年第4期24卷 787-792页

作者：李军何星蔡云泽徐琴上海交通大学自动化系系统控制与信息处理教育部重点实验室上海200240 国网上海市电力公司电力科学研究院上海200437

为了减小室内环境因素对室内WiFi定位的影响,降低定位成本,提高定位精度以及扩大定位区域,通过对室内定位系统和机器学习算法的讨论,提出了一种基于K-means和Random Forest融合的WiFi室内定位算法。针对室内WiFi信号强度分布的特点,该... 详细信息

为了减小室内环境因素对室内WiFi定位的影响,降低定位成本,提高定位精度以及扩大定位区域,通过对室内定位系统和机器学习算法的讨论,提出了一种基于K-means和Random Forest融合的WiFi室内定位算法。针对室内WiFi信号强度分布的特点,该算法通过K-means聚类改进算法对数据进行初始分类,然后使用Random Forest对初始分类结果进行二次分类。实验结果表明,该定位算法的定位精度在2米以内的概率为89.1%,达到预期的定位效果,同时对缺失值数据具有较好的适应能力。

关键词：室内定位 WiFi randomforest K-means 多模融合