A rapid dot immunogold filtration assay (DIGFA) was adopted for specific immunodiagnosis of human cerebral angiostrongyliasis, using purified 31-kDa glycoprotein specific to Angiostrongylus cantonensis as diagnostic antigen and protein A colloidal gold conjugate as antigen-antibody detector. A total of 59 serum samples were assayed - 11 samples from clinically diagnosed patients with detectable A. cantonensis-specific antibody in immunoblotting; 23 samples from patients with other related parasitic diseases, i.e. gnathostomiasis (n= 8), cysticercosis (n= 5), toxocariasis (n= 2), filariasis (n= 4), paragonimiasis (n= 2) and malaria (n= 2); and 25 samples from normal healthy subjects. The sensitivity and specificity of DIGFA to detect anti-A. cantonensis specific antibodies in serologically confirmed angiostrongyliasis cases, were both 100%. No positive DIGFA was observed in cases with other parasitic diseases, and the healthy control subjects. The 3-min DIGFA is as sensitive and specific as the 3-h immunoblot test in angiostrongyliasis confirmed cases that revealed a 31-kDa reactive band. The gold-based DIGFA is more rapid and easier to perform than the traditional enzyme-linked immunosorbent assay (ELISA). The test utilizing purified A. cantonensis antigen is reliable and reproducible for specific immunodiagnosis of human infection with A. cantonensis - thus can be applied as an additional routine test for clinical diagnostic support. Large-scale sero-epidemiological studies in endemic communities in north-east Thailand are under way to evaluate its usefulness under field conditions.

There has wide academic and policy attention on the issue of scale economy and industrial agglomeration, with most of the attention paid to industrial geography concentration. This paper adopted a scale-independent and distance-based measurement method, K-density function or known as Duranton and Overman (DO) index, to study the manufacturing industries localization in Shanghai, which is the most representative economic development zone in China and East Asia. The result indicates the industry has a growing tendency of localization, and various spatial distribution patterns in different distances. Furthermore, the class of industry also show significant influence on the concentration pattern. Besides, the method has been coded and published on <i>GeoCommerce</i>, a visualization and analysis portal for industrial big data, to provide geoprocessing and spatial decision support.

ABSTRACT:We examined whether emotion expressed by users in social media can be influenced by stock market index or can predict the fluctuation of the stock market index. We collected the emotion data by using face detection technology and emotion cognition services for photos uploaded to Flickr. Each face's emotion was described in 8 dimensions the location was also recorded. An emotion score index was defined based on the combination of all 8 dimensions of emotion calculated by principal component analysis. The correlation coefficients between the stock market values and emotion scores are significant (R>0.59 with p < 0.01). Using Granger Causality analysis for cause and effect detection, we found that users' emotion is influenced by stock market value change. A multiple linear regression model was established (R-square=0.76) to explore the potential factors that influence the emotion score. Finally, a sensitivity map was created to show sensitive areas where human emotion is easily affected by the stock market changes. We concluded that in Manhattan region: (1) there is an obvious relationship between human emotion and stock market fluctuation; (2) emotion change follows the movements of the stock market; (3) the Times Square and Broadway Theatre are the most sensitive regions in terms of public emotional reaction to the economy represented by stock value.

Outdoor air pollution has become a more and more serious issue over recent years (He, 2014). Urban air quality is measured at air monitoring stations. Building air monitoring stations requires land, incurs costs and entails skilled technicians to maintain a station. Many countries do not have any monitoring stations and even lack any means to monitor air quality. Recent years, the social media could be used to monitor air quality dynamically (Wang, 2015; Mei, 2014). However, no studies have investigated the inter-correlations between real-space and cyberspace by examining variation in micro-blogging behaviors relative to changes in daily air quality. Thus, existing methods of monitoring AQI using micro-blogging data shows a high degree of error between real AQI and air quality as inferred from social media messages. <br><br> In this paper, we introduce a new geo-targeted social media analytic method to (1) investigate the dynamic relationship between air pollution-related posts on Sina Weibo and daily AQI values; (2) apply Gradient Tree Boosting, a machine learning method, to monitor the dynamics of AQI using filtered social media messages. Our results expose the spatiotemporal relationships between social media messages and real-world environmental changes as well suggesting new ways to monitor air pollution using social media.