Antigenic and genetic typing of Whataroa viruses in Australia

We recently characterized three novel alphaviruses isolated from mosquitoes captured in New South Wales, Australia. Initial cross-neutralization studies revealed antigenic similarity to the Sindbis virus (SINV)-like Whataroa virus (WHAV), heretofore found only in New Zealand. Nucleotide sequence analysis showed that the WHAV-Iike viruses shared >99% nucleotide sequence similarity with each other, and 96-97% similarity with prototype WHAV. Enzyme-linked immunosorbent assay reactions of a panel of monoclonal antibodies to SINV showed that the novel WHAV-Iike viruses displayed identical binding patterns and were antigenically distinct from all SINV isolates examined. Although these viruses displayed a similar binding pattern to prototype WHAV, three monoclonal antibodies discriminated them from the New Zealand virus. Our results suggest that these novel alphaviruses are antigenic variants of WHAV and represent the first reported isolations of this virus from outside New Zealand. The monoclonal antibodies used in this study will be useful for typing new SINV and SINV-like isolates.

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Biological, antigenic and phylogenetic characterization of the flavivirus Alfuy

Alfuy virus (ALFV) is classified as a subtype of the flavivirus Murray Valley encephalitis virus (MVEV); however, despite preliminary reports of antigenic and ecological similarities with MVEV, ALFV has not been associated with human disease. Here, it was shown that ALFV is at least 10(4)-fold less neuroinvasive than MVEV after peripheral inoculation of 3-week-old Swiss outbred mice, but ALFV demonstrates similar neurovirulence. In addition, it was shown that ALFV is partially attenuated in mice that are deficient in alpha/beta interferon responses, in contrast to MVEV which is uniformly lethal in these mice. To assess the antigenic relationship between these viruses, a panel of monoclonal antibodies was tested for the ability to bind to ALFV and MVEV in ELISA. Although the majority of monoclonal antibodies recognized both viruses, confirming their antigenic similarity, several discriminating antibodies were identified. Finally, the entire genome of the prototype strain of ALFV (MRM3929) was sequenced and phylogenetically analysed. Nucleotide (73%) and amino acid sequence (83 %) identity between ALFV and IMVEV confirmed previous reports of their close relationship. Several nucleotide and amino acid deletions and/or substitutions with putative functional significance were identified in ALFV, including the abolition of a conserved glycosylation site in the envelope protein and the deletion of the terminal dinucleotide 5'-CUOH-3' found in all other members of the genus. These findings confirm previous reports that ALFV is closely related to IMVEV, but also highlights significant antigenic, genetic and phenotypic divergence from MVEV. Accordingly, the data suggest that ALFV is a distinct species within the serogroup Japanese encephalitis virus.

Overcoming original antigenic sin to generate new CD8 T cell IFN-gamma responses in an antigen-experienced host

The failure to mount effective immunity to virus variants in a previously virus-infected host is known as original antigenic sin. We have previously shown that prior immunity to a virus capsid protein inhibits induction by immunization of an IFN-gamma CD8(+) T cell response to an epitope linked to the capsid protein. We now demonstrate that capsid protein-primed CD4(+) T cells secrete IL-10 in response to capsid protein presented by dendritic cells, and deviate CD8+ T cells responding to a linked MHC class I-restricted epitope to reduce IFN-gamma production. Neutralizing IL-10 while delivering further linked epitope, either in vitro or in vivo, restores induction by immunization of an Ag-specific IFN-gamma response to the epitope. This finding demonstrates a strategy for overcoming inhibition of MHC class I epitopes upon immunization of a host already primed to Ag, which may facilitate immunotherapy for chronic viral infection or cancer.