Thirty uropathogenic E. coli isolates were isolated from hospitalized and non hospitalized patients, complaining of urinary tract infections, of Al-Kadhymia Teaching Hospital and subjected to tRNA extraction. A method of tRNA extraction was modified by adding sodium dodecyl sulfate (SDS) instead of urea. Polyacrylamide gel electrophoresis and two methods of staining, ethidium bromide staining and silver staining, as well as spectrophotometric detection were used.

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting

Semliki Forest Virus (SFV), a member of the Alphavirus genus in the Togaviridae family, is a small-enveloped, positive-sense single-stranded RNA (+ssRNA) virus. The virus is spread by mosquitos and can infect humans, resulting in mild febrile disease with symptoms that include fever, myalgia, arthralgia, persistent headaches and asthenia.  Virulent strains of SFV in mice cause lethal encephalitis by infecting neurons in the central nervous system. In on-going experiments in the research group using a focused siRNA screen we have investigated the role of deubiquitylases (DUBs) during SFV infection (as a model alphavirus) and monitored the effect of DUB depletion on cell viability after infection. We identified a group of DUBs that h