BackgroundJapanese encephalitis (JE) is very prevalent in China, but the incidence of JE among children has been greatly reduced by extensive promotion of vaccinations. The incidence of JE among adults, however, has increased in some parts of China.Methods/Principal FindingsData on JE in mainland China, in terms of incidence, gender, and age, were collected between 2004 and 2014. We conducted spatial and temporal analyses on data from different age groups. Generally, children aged 0–15 years still represent the major population of JE cases in China, despite the gradual decrease in incidence over years. However, the incidence of JE among adults in several provinces is notably higher than the national average, especially during the epidemic waves in 2006, 2009, and 2013. The JE cases in the 0–15-year-old group are distributed mainly in the area south of the Yangtze River, with peak incidence occurring from July to September. In the adult group, especially for those over 40 years old, the JE cases are concentrated mainly in the area north of the Yangtze River. JE incidence in the adult group in September and October is significantly greater compared to the other groups. Further analysis using Local Indicators of Spatial Association (LISA) reveals that the distribution of adult JE cases in the six provinces north of the Yangtze River, between north 30–35° latitude and east 110–130° longitude, is a hotspot for adult JE cases.Conclusions/SignificanceThe rate of JE case increase for adults is much greater than for children and has become a public health issue. Therefore, studies on the necessity and feasibility of vaccinating adults who live in JE-endemic areas, but have never been vaccinated for JE, should become a new focus of JE prevention in the future.
Ethnopharmacological relevance The novel coronavirus disease (COVID-19) outbreak in Wuhan has imposed a huge influence in terms of public health and economy on society. However, no effective drugs or vaccines have been developed so far. Traditional Chinese Medicine (TCM) has been considered as a promising supplementary treatment of this disease due to its clinically proven performance in many severe diseases, like severe acute respiratory syndrome (SARS). Meanwhile, many reports suggest that the side-effects (SE) of TCM prescriptions cannot be ignored in treating COVID-19 as it often leads to dramatic degradation of the patients’ physical condition. Systematic evaluation of TCM regarding its latent SE becomes a burning issue. Aim In this study, we used an ontology-based side-effect prediction framework (OSPF) developed from our previous work and Artificial Neural Network (ANN)-based deep learning, to evaluate the TCM prescriptions officially recommended by China for the treatment of COVID-19. Materials and methods The OSPF developed from our previous work was implemented in this study, where an ontology-based model separated all ingredients in a TCM prescription into two categories: hot and cold. A database was created by converting each TCM prescription into a vector which contained ingredient dosages, corresponding hot/cold attribution and safe/unsafe labels. This allowed for training of the ANN model. A safety indicator (SI), as a complement to SE possibility, was then assigned to each TCM prescription. According to the proposed SI, from high to low, the recommended prescription list could be optimized. Furthermore, in interest of expanding the potential treatment options, SIs of other well-known TCM prescriptions, which are not included in the recommended list but are used traditionally to cure flu-like diseases, are also evaluated via this method. Results Based on SI, QFPD-T, HSBD-F, PMSP, GCT-CJ, SF-ZSY, and HSYF-F were the safest treatments in the recommended list, with SI scores over 0.8. PESP, QYLX-F, JHQG-KL, SFJD-JN, SHL-KFY, PESP1, XBJ-ZSY, HSZF-F, PSSP2, FFTS-W, and NHSQ-W were the prescriptions most likely to be unsafe, with SI scores below 0.1. In the additional lists of other TCM prescriptions, the indicators of XC-T, SQRS-S, CC-J, and XFBD-F were all above 0.8, while QF-Y, XZXS-S, BJ-S, KBD-CJ, and QWJD-T’s indicators were all below 0.1. Conclusions In total, there were 10 TCM prescriptions with indicators over 0.8, suggesting that they could be considered in treating COVID-19, if suitable. We believe this work could provide reasonable suggestions for choosing proper TCM prescriptions as a supplementary treatment for COVID-19. Furthermore, this work introduces a novel and informative method which could help create recommendation list of TCM prescriptions for the treatment of other diseases.
This paper describes the design and implementation of a C++-based framework for two-layer uniform facet elastic object for real-time simulation based on physics modeling methods. The two-layered elastic object consists of inner and outer elastic mass-spring surfaces and compressible internal pressure. The density of the inner layer can be set differently from the density of the outer layer; the motion of the inner layer can be opposite to the motion of the outer layer. These special features, which cannot be achieved by a single layered object, result in improved imitation of a soft body, such as tissue's liquid non-uniform deformation. The inertial behavior of the elastic object is well illustrated in environments with gravity and collisions with walls, ceiling, and floor. The collision detection is defined by elastic collision penalty method and the motion of the object is guided by the Ordinary Differential Equation computation. Users can interact with the modeled objects, deform them, and observe the response to their action in real-time and we provide an extensible framework and its implementation for comparative studies of different physical-based modeling and integration algorithm implementations.
Advanced driver assistance systems with SAE Level 2 automated capabilities have entered the vehicle marketplace. Such driving automation systems (DASs) have the potential to fundamentally change the driving experience through automated lateral and longitudinal vehicle control. However, drivers may not use DASs as intended because of their misunderstanding of the systems’ capabilities and limitations. Moreover, the real-world use and effects of this novel technology on transportation safety are largely unknown. To investigate driver interactions with driving automation, the study examined existing naturalistic driving data collected from 50 participants who drove personally owned vehicles with Level 2 DASs for 12 months. It was found that 47 out of 235 safety-critical events (SCEs) involved DAS use. An in-depth analysis of 47 SCEs revealed that people misused DASs in 57% of SCEs (e.g., engaged in secondary tasks, used the systems not on highways or with hands off the wheel). During 13% of SCEs, the systems neither reacted to the situation nor warned the driver. A post-study survey showed that the participants found DASs useful and usable. However, the greater the positive attitude toward DAS features, the more participants felt comfortable engaging in secondary tasks. This is a potential unintended side effect of Level 2 DASs given that they still rely on the human driver’s supervision. This study also captured some scenarios where DASs did not meet driver expectations in typical driving situations, such as approaching stopped vehicles and negotiating curves. The findings may inform the development of human-machine interfaces and training programs to reduce the unintended use of DASs and their safety consequences.
3D graphics power-point-like slides in OpenGL is a way to present a demo or a teaching item. We focus on how the softbody objects are modeled and rendered with the intent to make a base for teaching, learning, and education of/for computer graphics and integration with the softbody framework. This is work-in-progress on the implementation and publication of this integration and demonstration work.
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