Seroprevalence and Severity of 2009 Pandemic Influenza A H1N1 in Taiwan

BACKGROUND: This study is to determine the seroprevalence of the pandemic influenza A H1N1 virus (pH1N1) in Taiwan before and after the 2009 pandemic, and to estimate the relative severity of pH1N1 infections among different age groups. METHODOLOGY/PRINCIPAL FINDINGS: A total of 1544 and 1558 random serum samples were collected from the general population in Taiwan in 2007 and 2010, respectively. Seropositivity was defined by a hemagglutination inhibition titer to pH1N1 (A/Taiwan/126/09) ≥1:40. The seropositivity rate of pH1N1 among the unvaccinated subjects and national surveillance data were used to compare the proportion of infections that led to severe diseases and fatalities among different age groups. The overall seroprevalence of pH1N1 was 0.91% (95% confidence interval [CI] 0.43-1.38) in 2007 and significantly increased to 29.9% (95% CI 27.6-32.2) in 2010 (p<0.0001), with the peak attack rate (55.4%) in 10-17 year-old adolescents, the lowest in elderly ≥65 years (14.1%). The overall attack rates were 20.6% (188/912) in unvaccinated subjects. Among the unvaccinated but infected populations, the estimated attack rates of severe cases per 100,000 infections were significantly higher in children aged 0-5 years (54.9 cases, odds ratio [OR] 4.23, 95% CI 3.04-5.90) and elderly ≥ 65 years (22.4 cases, OR 2.76, 95% CI 1.99-3.83) compared to adolescents aged 10-17 years (13.0 cases). The overall case-fatality rate was 0.98 per 100,000 infections without a significant difference in different age groups. CONCLUSIONS/SIGNIFICANCE: Pre-existing immunity against pH1N1 was rarely identified in Taiwanese at any age in 2007. Young children and elderly--the two most lower seroprotection groups showed the greatest vulnerability to clinical severity after the pH1N1 infections. These results imply that both age groups should have higher priority for immunization in the coming flu season

Intelligent Control for USV Based on Improved Elman Neural Network with TSK Fuzzy

In recent years, based on the rising of global personal safety demand and human resource cost considerations, development of unmanned vehicles to replace manpower requirement to perform high-risk operations is increasing. In order to acquire useful resources under the marine environment, a large boat as an unmanned surface vehicle (USV) was implemented. The USV is equipped with automatic navigation features and a complete substitute artificial manipulation. This USV system for exploring the marine environment has more carrying capacity and that measurement system can also be self-designed through a modular approach in accordance with the needs for various types of environmental conditions. The investigation work becomes more flexible. A catamaran hull is adopted as automatic navigation test with CompactRIO embedded system. Through GPS and direction sensor we not only can know the current location of the boat, but also can calculate the distance with a predetermined position and the angle difference immediately. In this paper, the design of automatic navigation is calculated in accordance with improved Elman neural network (ENN) algorithms. Takagi-Sugeno-Kang (TSK) fuzzy and improved ENN control are applied to adjust required power and steering, which allows the hull to move straight forward to a predetermined target position. The route will be free from outside influence and realize automatic navigation purpose

Pruning Quantized Unsupervised Meta-Learning DegradingNet Solution for Industrial Equipment and Semiconductor Process Anomaly Detection and Prediction

Machine- and deep-learning methods are used for industrial applications in prognostics and health management (PHM) for semiconductor processing and equipment anomaly detection to achieve proactive equipment maintenance and prevent process interruptions or equipment downtime. This study proposes a Pruning Quantized Unsupervised Meta-learning DegradingNet Solution (PQUM-DNS) for the fast training and retraining of new equipment or processes with limited data for anomaly detection and the prediction of various equipment and process conditions. This study utilizes real data from a factory chiller host motor, the Paderborn current and vibration open dataset, and the SECOM semiconductor open dataset to conduct experimental simulations, calculate the average value, and obtain the results. Compared to conventional deep autoencoders, PQUM-DNS reduces the average data volume required for rapid training and retraining by about 75% with similar AUC. The average RMSE of the predictive degradation degree is 0.037 for Holt–Winters, and the model size is reduced by about 60% through pruning and quantization which can be realized by edge devices, such as Raspberry Pi. This makes the proposed PQUM-DNS very suitable for intelligent equipment management and maintenance in industrial applications

Exploring the Impact of Different Types of Do-Not-Resuscitate Consent on End-of-Life Treatments among Patients with Advanced Kidney Disease: An Observational Study

Background: Patients with advanced kidney disease have a symptomatic and psychological burden which warrant renal supportive care or palliative care. However, the impact of do-not-resuscitate consent type (signed by patients or surrogates) on end-of-life treatments in these patients remains unclear. Objective: We aim to identify influential factors correlated with different do-not-resuscitate consent types in patients with advanced kidney disease and the impact of do-not-resuscitate consent types on various life-prolonging treatments. Methods: This was a retrospective observational study. We included patients aged 20 years and over, diagnosed with advanced kidney disease and receiving palliative and hospice care consultation services between January 2014 and December 2018 in a tertiary teaching hospital in Taiwan. We reviewed medical records and used logistic regression to identify factors associated with do-not-resuscitate consent types and end-of-life treatments. Results: A total of 275 patients were included, in which 21% signed their do-not-resuscitate consents. A total of 233 patients were followed until death, and 32% of the decedents continued hemodialysis, 75% underwent nasogastric (NG) tube placement, and 70% took antibiotics in their final seven days of life. Do-not-resuscitate consents signed by patients were associated with reduced life-prolonging treatments including feeding tube placement and antibiotic use in the last seven days (odd ratio and 95% confidence interval were 0.16, 0.07–0.34 and 0.33, 0.16–0.69, respectively) compared to do-not-resuscitate consents signed by surrogates. Conclusions: Do-not-resuscitate consent signed by patients and not by surrogates may reflect better patients’ autonomy and reduced life-prolonging treatments in the final seven days of patients with advanced kidney disease

Tumor Necrosis Factor-α 308.2 Polymorphism Is Associated with Advanced Hepatic Fibrosis and Higher Risk for Hepatocellular Carcinoma

BACKGROUND/AIMS: Host genetic factor and hepatic fibrosis may predispose to risk for hepatocellular carcinoma (HCC). This study aimed to assess the association between tumor necrosis factor (TNF) α polymorphism and hepatic fibrosis, and risk for HCC. METHODS: One hundred eight pairs of gender-matched and age-matched patients with HCC and unrelated healthy controls were genotyped for TNF308.2 and TNF238.2 alleles with polymerase chain reaction and direct sequencing. RESULTS: The frequency of TNF308.1/TNF308.2 genotype in cases was higher than that in controls [odds ratio (OR) = 4.37]. Multivariate analysis indicated that TNF308.2 allele (OR = 3.23), hepatitis B surface antigen (OR = 17.17), and antibodies to hepatitis C virus (OR = 45.52) were independent risk factors for HCC. Surrogate markers for significant fibrosis implied that cases with the TNF308.2 allele have more advanced liver fibrosis. Moreover, multivariate analysis indicated that cirrhosis with Child-Pugh grade C, low serum albumin, and low platelet count were independent risk factors for carrying the TNF308.2 allele. CONCLUSIONS: TNF308.2 allele carriage and chronic hepatitis B virus/hepatitis C virus infection are independent risk factors for HCC. Carriage of the TNF308.2 allele correlates with disease severity and hepatic fibrosis, which may contribute to a higher risk for HCC