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Antibodies in human sera specific to hypervariable region 1 of hepatitis C virus can block viral attachment

It has been postulated that antibodies specific to the hypervariable region 1 (HVR1) within the putative envelop protein E2 of hepatitis C virus (HCV) can neutralize virus. We studied such antibodies in sera of patients who were infected in a single-source outbreak by a contaminated anti-D immunoglobulin preparation (HCV-AD78). The nucleotide sequences of cDNAs encoding HVR1 of HCV-AD78 were determined. The four major variants (HVR1.A, B, C, and D) were expressed as fusion proteins in Escherichia coli. Sixty-seven percent of sera contained antibodies to HVR1.A. Sera unrelated to infection of the outbreak also recognized HVR1.A but to a lesser extent (15%), suggesting that not all HVR1-specific antibodies are absolutely isolate-specific. Antibodies directed against individual variants of HVR1 were found in sera obtained early postinfection (p.i.) (< or = 1 year) but also in sera obtained several years later. An in vitro binding assay of HCV to tissue culture cells was employed to further characterize these sera. Five of seven sera that were obtained early p.i. prevented binding of HCV to cells. Preincubation of such sera with HVR1-specific fusion proteins restored binding of HCV to cells in four of five sera. These findings suggest that the majority of neutralizing antibodies are directed against HVR1.     

Differentiation of vaccine and wild-type polioviruses using polymerase chain reaction and restriction enzyme analysis

Genomic amplification followed by selective digestion of restriction enzymes was used to differentiate polioviruses. The method was based on conserved and variable components of the 5'-noncoding region. The differences between Sabin vaccine and wild-type viruses made it possible to identify rapidly an isolated poliovirus as vaccine-related or wild-type virus. A total of 60 isolates and strains were tested and all of them were correctly identified. This method is recommended as a sensitive, specific and rapid way to differentiate polioviruses in clinical isolates and environmental samples.     

A Unifying Discussion of Correlation Analysis for Complex Random Vectors

The assessment of multivariate association between two complex random vectors is considered. A number of correlation coefficients based on three popular correlation analysis techniques, namely canonical correlation analysis, multivariate linear regression, and partial least squares, are reviewed and connected to performance measures in signal processing and communications, such as mean-squared estimation error, mutual information, and signal-to-noise ratio (SNR). For complex data, there are three types of correlation coefficients, which account for rotational, reflectional, and total (i.e., rotational and reflectional) dependencies between two random vectors. These three types are defined and analyzed for different correlation coefficients, and a numerical example is given. It is often required to compare two complex random vectors in a lower-dimensional subspace. For the large class of increasing, Schur-convex correlation coefficients, it is shown that the low-rank approximations of two random vectors maximizing a particular correlation coefficient are determined only by the constraints imposed on the correlation analysis technique. In this context, the correlation spread is defined as a normalized measure of how much of the overall correlation is contained in a low-dimensional subspace.