| 1 | Epidemiological Characteristics of Plague in the Meriones unguiculatus Plague Focus--Inner Mongolia Autonomous Region,China,1950−2019显示文摘From 1950 to 2019,a total of 267 plague cases in humans were reported in the Inner Mongolia Autonomous Region with 133 deaths and 10,710 Yersinia pestis isolates;21 of these cases were reported from the Meriones unguiculatus(M.unguiculatus)plague focus with 6 deaths and 6,771 isolates.According to the plague situation in the Inner Mongolia Autonomous Region and the implementation of local preventive measures,the prevalence of plague in humans in the M.unguiculatus plague focus of the Inner Mongolia Autonomous Region could be divided into four stages.The first stage was the initial stage of plague prevention and control(1950–1959),with 0.80 cases annually and a case fatality rate of 37.50%.The second stage was the plague eradication stage(1960–1979),with 0.15 cases annually and a case fatality rate of 33.33%.The third stage was the plague surveillance stage(1980-1999),with 0.25 cases annually and a case fatality rate of 20.00%.The fourth stage is the comprehensive prevention and control stage under the emergency system(2000-2019),with 0.25 cases annually and a case fatality rate of 20.00%.The surveillance of rodent density(1981-2019)and studies on plague-related factors among M.unguiculatus have shown that the higher the M.unguiculatus density,the higher the nocturnal rodent capture rate(r=0.670,p<0.05)and the higher the indirect hemagglutination assay(IHA)positive rate of M.unguiculatus(r=0.344,p<0.05);the higher the percentage of hosts infected,the lower the M.unguiculatus density(r=-0.361,p<0.05)and the lower the IHA positive rate of M.unguiculatus(r=-0.337,p<0.05);the higher the percentage of nests infected with fleas,the lower the IHA positive rate of M.unguiculatus(r=-0.348,p<0.05).Together,these results suggest that it is necessary to simultaneously monitor the pathogens,serology,and vector index of M.unguiculatus to accurately reflect the plague prevalence among local animals.Although bubonic plague is the main plague type of the M.unguiculatus plague focus,severe pneumonic plague or septic plague may be secondary when the bubonic plague is misdiagnosed or not treated in time.In addition,the plague in animals is relatively virulent in the M.unguiculatus plague focus,and the risk of spreading to humans is higher.For plague in the Inner Mongolia Autonomous Region,comprehensive control efforts should be aimed at the M.unguiculatus focus,covering host animals,vector insects,and humans. | Boxi Liu Dayu Zhang Yuhuang Chen Zhaokai He Jun Liu Dongyue Lyu Weiwei Wu Ran Duan Shuai Qin Junrong Liang Huaiqi Jing Xin Wang | 2020 | China CDC weekly2020，2，49： | 3 |
| 2 | Consideration of the Local Correlation of Learning Behaviors to Predict Dropouts from MOOCs显示文摘Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected. | Yimin Wen Ye Tian Boxi Wen Qing Zhou Guoyong Cai Shaozhong Liu | 2020 | Tsinghua Science and Technology2020，25，3： | 2 |
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