Serological evidence of bovine herpesvirus 1-related virus infection in the white-tailed deer population on Anticosti Island, Quebec

High white-tailed deer (Odocoileus virginianus) population densities and the occurrence of harsh environmental conditions are present on Anticosti Island, located in the Gulf of Saint-Lawrence (Quebec, Canada). This island is the northernmost region of white-tailed deer distribution in northeastern North America. The aim of this work was to determine whether a herpesvirus serologically related to bovine herpesvirus 1 (BHV1) may occur in a stressed white-tailed deer population. One hundred one deer sera were collected from apparently healthy animals during the hunting season from September to late November 1985. Fifty-three percent of tested deer were positive to the seroneutralization test using Colorado strain of BHV1 virus. Higher percentages of seropositivity were observed in animals of both sexes greater than 4-yr-old. Analysis of antibody titers in seropositive animals according to age suggests that BHV1-related viral infection is endemic in the Anticosti Island deer population. It is postulated that environmental stress may induce immunosuppression of certain infected and/or carrier animals in their population that shed virus for long periods of time.     

On problem solving with Hopfield neural networks

Hopfield and Tank have shown that neural networks can be used to solve certain computationally hard problems, in particular they studied the Traveling Salesman Problem (TSP). Based on network simulation results they conclude that analog VLSI neural nets can be promising in solving these problems. Recently, Wilson and Pawley presented the results of their simulations which contradict the original results and cast doubts on the usefulness of neural nets. In this paper we give the results of our simulations that clarify some of the discrepancies. We also investigate the scaling of TSP solutions found by neural nets as the size of the problem increases. Further, we consider the neural net solution of the Clustering Problem, also a computationally hard problem, and discuss the types of problems that appear to be well suited for a neural net approach.