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  1. Article ; Online: Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.

    Yi Feng / Xiya Cui / Jingjing Lv / Bingyu Yan / Xin Meng / Li Zhang / Yanhui Guo

    PLoS ONE, Vol 18, Iss 3, p e

    2023  Volume 0282928

    Abstract: Background Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of ...

    Abstract Background Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of diseases. However, only using historical incidence data for prediction can not get good results. This study analyzes the influence of meteorological factors on the incidence of hepatitis E, which are used to improve the accuracy of incidence prediction. Methods We extracted the monthly meteorological data, incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We employ GRA method to analyze the correlation between the incidence and meteorological factors. With these meteorological factors, we achieve a variety of methods for incidence of hepatitis E by LSTM and attention-based LSTM. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results Duration of sunshine and rainfall-related factors(total rainfall, maximum daily rainfall) are more relevant to the incidence of hepatitis E than other factors. Without meteorological factors, we obtained 20.74%, 19.50% for incidence in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we obtained 14.74%, 12.91%, 13.21%, 16.83% for incidence, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.83%. Without meteorological factors, we achieved 20.41%, 19.39% for cases in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we achieved 14.20%, 12.49%, 12.72%, 15.73% for cases, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.92%. More detailed results are ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 333
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Prediction of hepatitis E using machine learning models.

    Yanhui Guo / Yi Feng / Fuli Qu / Li Zhang / Bingyu Yan / Jingjing Lv

    PLoS ONE, Vol 15, Iss 9, p e

    2020  Volume 0237750

    Abstract: Background Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, ... ...

    Abstract Background Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared. Methods Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Prediction of hepatitis E using machine learning models

    Yanhui Guo / Yi Feng / Fuli Qu / Li Zhang / Bingyu Yan / Jingjing Lv / Tao Song

    PLoS ONE, Vol 15, Iss

    2020  Volume 9

    Abstract: Background Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, ... ...

    Abstract Background Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared. Methods Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: Assessing the Influence of Dietary History on Gut Microbiota

    Yang, Bo / Chang Ye / Bingyu Yan / Xionglei He / Ke Xing

    Current microbiology. 2019 Feb., v. 76, no. 2

    2019  

    Abstract: Diet is known to play a major role in determining the composition and function of the gut microbiota. Previous studies have often focused on the immediate effects of dietary intervention. How dietary history prior to a given dietary intervention ... ...

    Abstract Diet is known to play a major role in determining the composition and function of the gut microbiota. Previous studies have often focused on the immediate effects of dietary intervention. How dietary history prior to a given dietary intervention influences the gut microbiota is, however, not well understood. To assess the influence of dietary history, in this study, mice with different dietary histories were subjected to the same dietary interventions, and the gut microbial communities of these mice were characterized by 16S rDNA sequencing. We found that dietary history played a long-lasting role in the composition of the gut microbiota when the dietary switch was moderate. In sharp contrast, such effects nearly vanished when the diet was switched to certain extreme dietary conditions. Interestingly, the abundance of Akkermansia, a bacterial genus associated with loss of body weight, was elevated dramatically in mice subjected to a diet composed exclusively of meat. Our results revealed a more complex picture of the influence of dietary history on gut microbiota than anticipated.
    Keywords body weight changes ; diet history ; intestinal microorganisms ; meat ; mice ; microbial communities ; nutritional intervention ; ribosomal DNA ; sequence analysis
    Language English
    Dates of publication 2019-02
    Size p. 237-247.
    Publishing place Springer US
    Document type Article
    ZDB-ID 134238-1
    ISSN 1432-0991 ; 0343-8651
    ISSN (online) 1432-0991
    ISSN 0343-8651
    DOI 10.1007/s00284-018-1616-8
    Database NAL-Catalogue (AGRICOLA)

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  5. Article: A hepatitis E outbreak by genotype 4 virus in Shandong province, China

    Zhang, Li / Aiqiang Xu / Bingyu Yan

    Vaccine. 2016 July 19, v. 34, no. 33

    2016  

    Abstract: Hepatitis E vaccine was available in China in 2012, but the priority population for immunization is not clear. In 2013, a hepatitis E outbreak occurred in a company of Shandong province, China where most employees moved from other provinces and dined at ... ...

    Abstract Hepatitis E vaccine was available in China in 2012, but the priority population for immunization is not clear. In 2013, a hepatitis E outbreak occurred in a company of Shandong province, China where most employees moved from other provinces and dined at the company’s cafeteria. A total of fourteen (19%, 14/73) case-patients were identified, and three of them had symptomatic infection with one death. The proportion of symptomatic infection was much higher among those aged ⩾50years than those aged <50years (2/2 vs. 1/12, P=0.03), and higher in males than females (3/8 vs. 0/6, P=0.21). Food in the company’s cafeteria might be the possible source of the outbreak. The findings from this outbreak investigation indicate that individuals aged ⩾50years, particularly males, might be the population of top priority for hepatitis E vaccination in China.
    Keywords cafeterias ; death ; females ; genotype ; hepatitis E ; human resources ; males ; outbreak investigation ; vaccination ; vaccines ; viruses ; China
    Language English
    Dates of publication 2016-0719
    Size p. 3715-3718.
    Publishing place Elsevier Ltd
    Document type Article
    ZDB-ID 605674-x
    ISSN 1873-2518 ; 0264-410X
    ISSN (online) 1873-2518
    ISSN 0264-410X
    DOI 10.1016/j.vaccine.2016.06.010
    Database NAL-Catalogue (AGRICOLA)

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  6. Article ; Online: Metatranscriptomic Analysis Reveals an Imbalance of Hepatopancreatic Flora of Chinese Mitten Crab Eriocheir sinensis with Hepatopancreatic Necrosis Disease

    Zeen Shen / Dhiraj Kumar / Xunmeng Liu / Bingyu Yan / Ping Fang / Yuchao Gu / Manyun Li / Meiping Xie / Rui Yuan / Yongjie Feng / Xiaolong Hu / Guangli Cao / Renyu Xue / Hui Chen / Xiaohan Liu / Chengliang Gong

    Biology, Vol 10, Iss 462, p

    2021  Volume 462

    Abstract: Hepatopancreas necrosis disease (HPND) of the Chinese mitten crab Eriocheir sinensis causes huge economic loss in China. However, the pathogenic factors and pathogenesis are still a matter of dissension. To search for potential pathogens, the ... ...

    Abstract Hepatopancreas necrosis disease (HPND) of the Chinese mitten crab Eriocheir sinensis causes huge economic loss in China. However, the pathogenic factors and pathogenesis are still a matter of dissension. To search for potential pathogens, the hepatopancreatic flora of diseased crabs with mild symptoms, diseased crabs with severe symptoms, and crabs without visible symptoms were investigated using metatranscriptomics sequencing. The prevalence of Absidia glauca and Candidatus Synechococcus spongiarum decreased, whereas the prevalence of Spiroplasma eriocheiris increased in the hepatopancreatic flora of crabs with HPND. Homologous sequences of 34 viral species and 4 Microsporidian species were found in the crab hepatopancreas without any significant differences between crabs with and without HPND. Moreover, DEGs in the hepatopancreatic flora between crabs with severe symptoms and without visible symptoms were enriched in the ribosome, retinol metabolism, metabolism of xenobiotics by cytochrome P450, drug metabolism—cytochrome P450, biosynthesis of unsaturated fatty acids, and other glycan degradation. Moreover, the relative abundance of functions of DEDs in the hepatopancreatic flora changed with the pathogenesis process. These results suggested that imbalance of hepatopancreatic flora was associated with crab HPND. The identified DEGs were perhaps involved in the pathological mechanism of HPND; nonetheless, HPND did not occur due to virus or microsporidia infection.
    Keywords Eriocheir sinensis ; hepatopancreas necrosis disease ; metatranscriptomics sequencing ; hepatopancreatic flora ; Biology (General) ; QH301-705.5
    Subject code 580
    Language English
    Publishing date 2021-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Temporal trend of hepatitis B surface mutations in the post-immunization period

    Bingyu Yan / Jingjing Lv / Yi Feng / Jiaye Liu / Feng Ji / Aiqiang Xu / Li Zhang

    Scientific Reports, Vol 7, Iss 1, Pp 1-

    9 years of surveillance (2005–2013) in eastern China

    2017  Volume 8

    Abstract: Abstract Limited information is available about the temporal trend in the prevalence and evolution of hepatitis B virus (HBV) S-gene mutations in the post-immunization era in China. From 2005 to 2013, 1077 hepatitis B cases under 15 years of age reported ...

    Abstract Abstract Limited information is available about the temporal trend in the prevalence and evolution of hepatitis B virus (HBV) S-gene mutations in the post-immunization era in China. From 2005 to 2013, 1077 hepatitis B cases under 15 years of age reported through Chinese National Notifiable Disease Reporting System (NNDRS) were successfully sequenced of S-gene in Shandong province, China. A total of 97 (9.01%) cases had amino acid (aa) substitution in the “α” determinant of HBsAg. The yearly prevalence from 2005 to 2013 maintained at a relatively stable level, and showed no significant change (P > 0.05). Multivariate logistic regression analysis demonstrated that the prevalence of “α” mutations was independently associated with the maternal HBsAg status (P < 0.05), and not with surveillance year and hepatitis B vaccination (P > 0.05). The hottest mutation position was aa126 (I126S/N and T126A, 29.63%), and aa 145 (G145R/A, 25.93%). Mutated residue 126 tended to occur less frequent, while that of residue 145 was more frequent with increasing year. Our data showed that there was no increase in the frequency of HBV “α” mutations over time during the post-immunization period. However, long-term vaccination might enhance the change of HBV mutational pattern, and G145 mutation was becoming dominant.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2017-07-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Hepatitis E Virus in Yellow Cattle, Shandong, Eastern China

    Bingyu Yan / Li Zhang / Lianfeng Gong / Jingjing Lv / Yi Feng / Jiaye Liu / Lizhi Song / Qing Xu / Mei Jiang / Aiqiang Xu

    Emerging Infectious Diseases, Vol 22, Iss 12, Pp 2211-

    2016  Volume 2212

    Keywords hepatitis E ; hepatitis E virus ; viruses ; zoonoses ; yellow cattle ; cow ; Medicine ; R ; Infectious and parasitic diseases ; RC109-216
    Language English
    Publishing date 2016-12-01T00:00:00Z
    Publisher Centers for Disease Control and Prevention
    Document type Article ; Online
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