The potential role of pneumococcal conjugate vaccine in reducing acute respiratory inflammation in community-acquired pneumococcal pneumonia
JOURNAL OF BIOMEDICAL SCIENCE
Authors: Shen, Ching-Fen; Wang, Shih-Min; Chi, Hsin; Huang, Yi-Chuan; Huang, Li-Min; Huang, Yhu-Chering; Lin, Hsiao-Chuan; Ho, Yu-Huai; Hsiung, Chao A.; Liu, Ching-Chuan
Abstract
Background Pneumococcal conjugate vaccine (PCV) reduces both invasive pneumococcal disease (IPD) and other pneumococcal infections worldwide. We investigated the impact of stepwise implementation of childhood PCV programs on the prevalence of pneumococcal pneumonia, severity of acute inflammation, and associations between breakthrough pneumonia and pneumococcal serotypes in Taiwan. Methods In total, 983 children diagnosed with community-acquired pneumococcal pneumonia were enrolled between January 2010 and December 2015. Results Proportions of pneumococcal vaccinations increased each year in age-stratified groups with PCV7 (32.2%) as the majority, followed by PCV13 (12.2%). The proportion of pneumococcal pneumonia decreased each year in age-stratified groups, especially in 2-5 year group. Serotype 19A is the leading serotype either in vaccinated (6.4%) or unvaccinated patients (5.2%). In particular, vaccinated patients had significantly higher lowest WBC, lower neutrophils, lower lymphocytes and lower CRP values than non-vaccinated patients (p < 0.05). After stratifying patients by breakthrough infection, those with breakthrough pneumococcal infection with vaccine coverage serotypes had more severe pneumonia disease (p < 0.05). Conclusion Systematic childhood pneumococcal vaccination reduced the prevalence of community-acquired pneumococcal pneumonia, especially in 2-5 year group. Serotype 19A was the major serotype for all vaccine types in patients with pneumococcal pneumonia and severity of acute inflammatory response was reduced in vaccinated patients.
A dynamic Bayesian network based methodology for fault diagnosis of subsea Christmas tree
APPLIED OCEAN RESEARCH
Authors: Liu, Peng; Liu, Yonghong; Cai, Baoping; Wu, Xinlei; Wang, Ke; Wei, Xiaoxuan; Xin, Chao
Abstract
A subsea Christmas tree (XT) is an extremely important part of a subsea production system. The safety-fault of subsea XT indicates that no major safety incidents are difficult to diagnose. To identify the faulty components and distinguishing the fault types, including the blocking, leakage, and especially safety-fault, we present a dynamic Bayesian networks (DBN)-based fault diagnosis methodology of subsea XT considering component degradation and safety-fault. As the performance of components degrades over time, the diagnosis results can differ at different times for the given identical fault symptoms. DBNs are established to model the dynamic degradation of components in a system under additional information by using the failure rate, and fault diagnosis is conducted through a backward analysis of DBNs. Three fault diagnosis cases of subsea XT system are investigated. In case 1, when safety-fault occur on surface control subsea safety valve (SCSSV) and production main valve (PWV) components, the absolute difference in the posterior and prior probabilities of safety-fault for SCSSV and PWV was >50%. In case 2, when the blocking and leakage occur in SCSSV and annular main valve (AMV) components, respectively, the absolute difference between the posterior and prior probabilities of blocking for the SCSSV was > 30%, and the absolute difference between the posterior and prior probabilities of leakage for AMV was >30%. In case 3, when the fault occur in production control valve (PCV) and chemical injection valve 1 (CIV1) components, respectively, the absolute difference between the posterior and prior probabilities of leakage for PCV and CIV1 was > 60%. Three fault diagnosis cases validate the accuracy and effectiveness of the proposed methodology. This method is appropriate in providing maintenance instructions to engineers.