Environmental surveillance (ES) is a useful approach for monitoring circulating viruses, including polioviruses (PVs) and non-polio enteroviruses (NPEVs). In this study, the results of nine years of ES from 2013 to 20...Environmental surveillance (ES) is a useful approach for monitoring circulating viruses, including polioviruses (PVs) and non-polio enteroviruses (NPEVs). In this study, the results of nine years of ES from 2013 to 2021 at six sampling sites in three cities in Fujian Province, China, were summarized. It showed that the sewage samples contained abundant viruses, but the positive rate was affected by different sampling sites. From the 520 samples, 431 PVs, 1,713 NPEVs, and 281 human adenoviruses (HAdVs) were isolated. PV isolates had been markedly affected following the adjustment of the immunization strategy. All but one PV isolate were Sabin-like strains without wild PVs. One isolate was vaccine-derived PV type 3 with 10 variation points in theVP1 region. After May 2016, PV type 2 was no longer detected, and PV type 3 became a superior serotype. Of 1,713 NPEVs, 24 serotypes were identified, including echovirus11 (E11), E6, coxsackievirus B3 (CVB3), CVB5, E7, and E3 were the predominant serotypes (37.65%, 20.96%, 11.50%, 8.87%, 8.23%, and 7.06%, respectively). The temporal dynamic of the six common serotypes was inconsistent. E3 was frequently isolated, but the number of isolates was low, with no obvious peaks. E6, E7, and CVB3 exhibited periodic changes with a high peak every three to four years, and E11 only had one high peak lasting four years. Summer-fall peaks of the echoviruses and spring-winter peaks of CVB were observed in the monthly distribution of virus isolation. The infectious isolates of various serotypes of different species identified from the sewage samples showed that ES is an essential part of pathogen surveillance.展开更多
Network measures are useful for predicting fault-prone modules. However, existing work has not distinguished faults according to their severity. In practice, high severity faults cause serious problems and require fur...Network measures are useful for predicting fault-prone modules. However, existing work has not distinguished faults according to their severity. In practice, high severity faults cause serious problems and require further attention. In this study, we explored the utility of network measures in high severity faultproneness prediction. We constructed software source code networks for four open-source projects by extracting the dependencies between modules. We then used univariate logistic regression to investigate the associations between each network measure and fault-proneness at a high severity level. We built multivariate prediction models to examine their explanatory ability for fault-proneness, as well as evaluated their predictive effectiveness compared to code metrics under forward-release and cross-project predictions. The results revealed the following:(1) most network measures are significantly related to high severity fault-proneness;(2) network measures generally have comparable explanatory abilities and predictive powers to those of code metrics; and(3) network measures are very unstable for cross-project predictions. These results indicate that network measures are of practical value in high severity fault-proneness prediction.展开更多
基金funded by Fujian Health System Youth Backbone Talents training project(2013-ZQN-ZD-10)leading(key)project of social development in Fujian Province(2017Y0011)National Key Research and Development Program of China(2021YFC2302003)。
文摘Environmental surveillance (ES) is a useful approach for monitoring circulating viruses, including polioviruses (PVs) and non-polio enteroviruses (NPEVs). In this study, the results of nine years of ES from 2013 to 2021 at six sampling sites in three cities in Fujian Province, China, were summarized. It showed that the sewage samples contained abundant viruses, but the positive rate was affected by different sampling sites. From the 520 samples, 431 PVs, 1,713 NPEVs, and 281 human adenoviruses (HAdVs) were isolated. PV isolates had been markedly affected following the adjustment of the immunization strategy. All but one PV isolate were Sabin-like strains without wild PVs. One isolate was vaccine-derived PV type 3 with 10 variation points in theVP1 region. After May 2016, PV type 2 was no longer detected, and PV type 3 became a superior serotype. Of 1,713 NPEVs, 24 serotypes were identified, including echovirus11 (E11), E6, coxsackievirus B3 (CVB3), CVB5, E7, and E3 were the predominant serotypes (37.65%, 20.96%, 11.50%, 8.87%, 8.23%, and 7.06%, respectively). The temporal dynamic of the six common serotypes was inconsistent. E3 was frequently isolated, but the number of isolates was low, with no obvious peaks. E6, E7, and CVB3 exhibited periodic changes with a high peak every three to four years, and E11 only had one high peak lasting four years. Summer-fall peaks of the echoviruses and spring-winter peaks of CVB were observed in the monthly distribution of virus isolation. The infectious isolates of various serotypes of different species identified from the sewage samples showed that ES is an essential part of pathogen surveillance.
基金supported by National Natural Science Foundation of China (Grant Nos. 61472175, 61472178, 61272082, 61272080, 91418202)Natural Science Foundation of Jiangsu Province (Grant No. BK20130014)Natural Science Foundation of Colleges in Jiangsu Province (Grant No. 13KJB520018)
文摘Network measures are useful for predicting fault-prone modules. However, existing work has not distinguished faults according to their severity. In practice, high severity faults cause serious problems and require further attention. In this study, we explored the utility of network measures in high severity faultproneness prediction. We constructed software source code networks for four open-source projects by extracting the dependencies between modules. We then used univariate logistic regression to investigate the associations between each network measure and fault-proneness at a high severity level. We built multivariate prediction models to examine their explanatory ability for fault-proneness, as well as evaluated their predictive effectiveness compared to code metrics under forward-release and cross-project predictions. The results revealed the following:(1) most network measures are significantly related to high severity fault-proneness;(2) network measures generally have comparable explanatory abilities and predictive powers to those of code metrics; and(3) network measures are very unstable for cross-project predictions. These results indicate that network measures are of practical value in high severity fault-proneness prediction.
