Mouse Anti-Hev b5 (Major latex allergen) monoclonal antibody, clone 5

We investigated the seroprevalence and incidence of hepatitis E virus (HEV) infection in men who have sex with men (MSM) who have been exposed to pre-exposure prophylaxis (PrEP) against HIV as sexual transmission of HEV has been suggested. A total of 147 PrEP-using MSM and 147 blood donors matched for sex, age and geographical area were tested for anti-HEV IgG and IgM. Among them, 135 have been followed for 1 year, at the end of which serological tests for HEV were performed retrospectively on stored samples. Laboratory data on sexual transmitted infections (STIs) and viral hepatitis, including hepatitis A virus (HAV), were collected. Baseline seroprevalence rates in PrEP users were 42.2% (anti-HEV IgG) and 3.4% (anti-HEV IgM). Those of the control blood donors were similar (anti-HEV IgG 43.5% and anti-HEV IgM 4.1%). There was no incident of HEV infection despite the rates of bacterial STIs (incidence rate (IR) = 46.6%) and HAV infection (IR = 15.8%). Age was the only risk factor associated with anti-HEV IgG seropositivity at baseline and at the end of follow-up. Sexual transmission does not seem to be a major route of HEV infection in MSM, unlike HAV.

Hybrid electric vehicles (HEVs) are overactuated systems in that they include two power sources: a battery pack and an internal combustion engine. This feature of HEVs is exploited in this article to achieve accurate identification of battery parameters/states. By actively injecting currents, the state of charge, state of health, and other battery parameters can be estimated in a specific sequence to improve identification performance when compared to the case where all parameters and states are estimated concurrently using baseline currents. A dynamic programming strategy is developed to provide the benchmark results regarding how to balance the conflicting objectives corresponding to the identification and system efficiency. The tradeoff between different objectives is presented to optimize the current profile so that the richness of the signal can be ensured and the good fuel economy can be achieved. In addition, simulation results show that the root-mean-square error of the estimation can be decreased by up to 100% at a cost of less than a 2% increase in fuel consumption. With the proposed simultaneous identification and control algorithm, the parameters/states of the battery can be monitored to ensure safe and efficient operation of the battery for HEVs.