It is difficult to reflect the properties of samples from the signal directly collected by the low field nuclear magnetic resonance (NMR) analyzer. People must obtain the relationship between the relaxation time and the original signal amplitude of every relaxation component by inversion algorithm. Consequently, the technology of T2 spectrum inversion is crucial to the application of NMR data. This study optimized the regularization factor selection method and presented the regularization algorithm for inversion of low field NMR relaxation distribution, which is based on the regularization theory of ill-posed inverse problem. The results of numerical simulation experiments by Matlab7.0 showed that this method could effectively analyze and process the NMR relaxation data.
Algorithms ; Humans ; Magnetic Resonance Spectroscopy

Objective To analyze the data of influenza A(H1N1) viruses surveillance and genetic characteristics from Taian City during 2005-2008,so a scientific basis can be provided for the prevention and treatment of influenza.Methods The specimens from Influenza-Like Illness(ILI) were collected.The viruses were isolated with MDCK cell and identified with HAI and RT-PCR.The product of PCR were sequenced.Then the sequences were analyzed through biometric software.Results A total of 121 influenza strains were obtained from 615 specimens,and 4 of them were identified as A(H1N1) subtype.There were 3 strains mutated on several sites.Compared with strains isolated in 2005,there were 5 and 8 mutations in the amino acid sequences of virus strains isolated in 2007 and 2008 respectively.And there were a total of 22 amino acid mutations compared with A/Brisbane/59/2007(H1N1).Conclusions Influenza type A(H1N1) are detected in Taian City.There are several mutations in the amino acid sequences of virus strains isolated in Taian. The antigenic drift of virus strains is due to accumulation of amino acid substitutions

Animals ; Asian Continental Ancestry Group ; genetics ; China ; epidemiology ; Ethnic Groups ; genetics ; Heart Defects, Congenital ; genetics ; metabolism ; Humans ; Intracellular Signaling Peptides and Proteins ; genetics ; metabolism ; Mice ; Muscle Proteins ; genetics ; metabolism ; RNA, Messenger ; genetics ; metabolism ; Risk Factors