Automated component analysis in DOSY NMR using information criteria.

This study introduces a model selection technique based on Bayesian information criteria for estimating the number of components in a mixture during Diffusion-Ordered Spectroscopy (DOSY) Nuclear Magnetic Resonance (NMR) data analysis. As the accuracy of this technique is dependent on the efficiency of parameter estimators, we further investigate the performance of the Weighted Least Squares (WLS) and Maximum a Posteriori (MAP) estimators. The WLS method, enhanced with meticulously tuned L2-regularization, effectively detects components when the difference in self-diffusion coefficients is more than two-fold, especially when the component with the smaller coefficient has a larger weight ratio.

INFLUENZA SURVEILLANCE IN RUSSIA BASED ON EPIDEMIOLOGICAL AND LABORATORY DATA FOR THE PERIOD FROM 2005 TO 2012.

Exchange of information on and sharing of influenza viruses through the GISRS network has great significance for understanding influenza virus evolution, recognition of a new pandemic virus emergence and for preparing annual WHO recommendations on influenza vaccine strain composition. Influenza surveillance in Russia is based on collaboration of two NICs with 59 Regional Bases. Most epidemiological and laboratory data are entered through the internet into the electronic database at the Research Institute of Influenza (RII), where they are analyzed and then reported to the Ministry of Public Health of Russia.