ABSTRACT
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. The MAP method, strengthened by a substantial database of prior information, exhibits outstanding precision, decreasing this threshold to 1.5 times. Both estimators provide weight ratio estimates with standard deviations of approximately around 1 percentage point, although the MAP method tends to overestimate the component with a larger self-diffusion coefficient. Deviations from the expected values can exceed 10 percentage points, often due to inaccuracies in component detection. The error estimates are determined using data resampling techniques derived from a large-scale 1000-point experiment and an additional five measurements from a single-component mixture. This approach allowed us to thoroughly examine data distribution characteristics, thereby laying a robust groundwork for future refinement efforts.
ABSTRACT
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. Simultaneously, data are introduced into WHO's Flu Net and Euro Flu, both electronic databases. Annual influenza epidemics of moderate intensity were registered during four pre-pandemic seasons. Children aged 0-2 and 3-6 years were the most affected groups of the population. Influenza registered clinically among hospitalized patients with respiratory infections for the whole epidemic period varied between 1.3 and 5.4% and up but to 18.5-23.0% during the peak of the two pandemic waves caused by influenza A(H1N1) pdm 09 virus and to lesser extent (2.9 to 8.5%) during usual seasonal epidemics. Most epidemics were associated with influenza A(H1N1), A(H3N2) and B co-circulation. During the two pandemic waves (in 2009-2010 and 2010-2011) influenza A(H1N1) pdm 09 predominated. It was accompanied by a rapid growth of influenza morbidity with a significant increase of both hospitalization and mortality. The new pandemic virus displaced the previous seasonal A(H1N1) virus completely. As a rule, most of the influenza viruses circulating in Russia were antigenic ally related to the strains recommended by WHO for vaccine composition for the Northern hemisphere with the exception of two seasons when an unexpected replacement of the influenza B Victoria lineage by Yamagata lineage (2007-2008) and the following return of Victoria lineage viruses (2008-2009) was registered. Influenza surveillance in Russia was improved as a result of enhancing capacity to international standards and the introduction of new methods in NICs such as rRT-PCR diagnosis, regular testing of influenza viruses for susceptibility to antivirals, phylogenetic analysis as well as organization of sentinel surveillance in a number of Regional Base Laboratories. Improvements promoted rapid recognition of the appearance a new pandemic virus in the country and enhancement of confirmation tests in investigation of influenza related death cases.
