http://repub.eur.nl/res/aut/21556/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/38957/ 2013-01-29T00:00:00Z Background: For years, emerging infectious diseases have appeared worldwide and threatened the health of people. The emergence and spread of an infectious-disease outbreak are usually unforeseen, and have the features of suddenness and uncertainty. Timely understanding of basic information in the field, and the collection and analysis of epidemiological information, is helpful in making rapid decisions and responding to an infectious-disease emergency. Therefore, it is necessary to have an unobstructed channel and convenient tool for the collection and analysis of epidemiologic information in the field. Methodology/Principal Findings: Baseline information for each county in mainland China was collected and a database was established by geo-coding information on a digital map of county boundaries throughout the country. Google Maps was used to display geographic information and to conduct calculations related to maps, and the 3G wireless network was used to transmit information collected in the field to the server. This study established a decision support system for the response to infectious-disease emergencies based on WebGIS and mobile services (DSSRIDE). The DSSRIDE provides functions including data collection, communication and analyses in real time, epidemiological detection, the provision of customized epidemiological questionnaires and guides for handling infectious disease emergencies, and the querying of professional knowledge in the field. These functions of the DSSRIDE could be helpful for epidemiological investigations in the field and the handling of infectious-disease emergencies. Conclusions/Significance: The DSSRIDE provides a geographic information platform based on the Google Maps application programming interface to display information of infectious disease emergencies, and transfers information between workers in the field and decision makers through wireless transmission based on personal computers, mobile phones and personal digital assistants. After a 2-year practice and application in infectious disease emergencies, the DSSRIDE is becoming a useful platform and is a useful tool for investigations in the field carried out by response sections and individuals. The system is suitable for use in developing countries and low-income districts. http://repub.eur.nl/res/pub/19205/ 2010-05-01T00:00:00Z Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA + ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA + ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA + ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data. http://repub.eur.nl/res/pub/32393/ 2008-06-01T00:00:00Z Anaplasma phagocytophilum was detected by polymerase chain reaction in 13 (14.1%) of 92 rodents captured from a mountainous area of Zhejiang Province in southeastern China. The nucleotide sequences of 1442-bp, nearly entire 16S rRNA gene amplified from these rodents, had 100% identity, but varied from all known corresponding sequences of A. phagocytophilum deposited in GenBank. To further identify and classify the variant, fragments of 357-bp partial citrate synthase gene (gltA), 849-bp major surface protein 4 gene (msp4), and 443-bp groESL heat-shock operon gene, were amplified and analyzed. The nucleotide sequences of the partial gltA gene amplified from the rodents were identical to each other, but distinct from previously reported A. phagocytophilum sequences, as were msp4 and groESL. These findings indicate that the newly discovered agent represents a novel A. phagocytophilum variant. http://repub.eur.nl/res/pub/30549/ 2008-05-28T00:00:00Z Background: Since late 2003, highly pathogenic avian influenza (HPAI) outbreaks caused by infection with H5N1 virus has led to the deaths of millions of poultry and more than 10 thousands of wild birds, and as of 18-March 2008, at least 373 laboratory-confirmed human infections with 236 fatalities, have occurred. The unrestrained worldwide spread of this disease has caused great anxiety about the potential of another global pandemic. However, the effect of environmental factors influencing the spread of HPAI H5N1 virus is unclear. Methodology/Principal Findings: A database including incident dates and locations was developed for 128 confirmed HPAI H5N1 outbreaks in poultry and wild birds, as well as 21 human cases in mainland China during 2004-2006. These data, together with information on wild bird migration, poultry densities, and environmental variables (water bodies, wetlands, transportation routes, main cities, precipitation and elevation), were integrated into a Geographical Information System (GIS). A case-control design was used to identify the environmental factors associated with the incidence of the disease. Multivariate logistic regression analysis indicated that minimal distance to the nearest national highway, annual precipitation and the interaction between minimal distance to the nearest lake and wetland, were important predictive environmental variables for the risk of HPAI. A risk map was constructed based on these factors. Conclusions/Significance: Our study indicates that environmental factors contribute to the spread of the disease. The risk map can be used to target countermeasures to stop further spread of the HPAI H5N1 at its source.