We investigated the possibility of infecting normal adult human hepatocytes maintained in pure cultures or in cocultures with hepatitis B virus (HBV). Several assays with different infectious sera and hepatocyte populations from various donors identified only limited HBV replication, with significant variations from one cell preparation to another. The addition of 1.5% dimethyl sulfoxide to the culture medium markedly enhanced the infection process. Indeed, hepatitis B e antigen secretion, the appearance of both HBV DNA replicative forms and major HBV transcripts, and the release of complete HBV particles into the medium were demonstrated. It is possible that the significant increase in intracellular HBV DNA in dimethyl sulfoxidetreated cells was related to enhanced adsorption of the virus. When viral particles produced by a transfected HepG2 cell line were used to infect normal hepatocytes, the same results were obtained. In addition, comparative assays with hepatocytes from three different donors showed that although high amounts of intracellular viral DNA were found in all cases, viral replicative intermediates were visualized in only one case. These findings suggest that this HBV-producing cell line could serve as a reproducible source of infectious virus and that primary culturing of human hepatocytes represents a unique tool for analyzing intracellular regulating factors which, in addition to the penetration step, modulate HBV replication.

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Abstract-We introduce a methodology to predict when and where link additions/upgrades have to take place in an IP backbone network. Using SNMP statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent PoPs and look at its evolution at time scales larger than one hour. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales.Our methodology relies on the wavelet multiresolution analysis and linear time series models. Using wavelet multiresolution analysis, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12 hour time scale.We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order AutoRegressive Integrated Moving Average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12 hour time scale yields accurate estimates for at least six months in the future.

Abstract-We introduce a methodology to predict when and where link additions/upgrades have to take place in an IP backbone network. Using SNMP statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent PoPs and look at its evolution at time scales larger than one hour. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales.Our methodology relies on the wavelet multiresolution analysis and linear time series models. Using wavelet multiresolution analysis, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12 hour time scale.We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order AutoRegressive Integrated Moving Average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12 hour time scale yields accurate estimates for at least six months in the future.