Leinamycin is a thiol dependent DNA alkylating agent which shows very potent activity against various cancer cell lines. This natural compound forms guanine adducts (N7) in DNA which are converted into a basic sites and simultaneously generates Reactive Oxygen Species (ROS), to produce DNA strand breaks in human cancer cells. In present study, eight different strains isolated from Iraqi soils were taxonomically assigned as Streptomyces.atroolivaceous. Remarkably the strain named as THS-44 was distinguished in productivity in comparison with other strains; the amount of leinamycin was 50.98 mg/l. In this study, we assessed the cytotoxic activity of leinamycin against RD and ANM3 cancer cell line in compare with REF cell line as a normal control. leinamycin revealed a potent activity against growth of both RD , and AMN3 cell lines with inhibition rate 93.56%, and 86.67%) respectively at 100mg/ml as a highest concentration. Moreover, leinamycin from HS-44 strain was mediate apoptotic effects on cancer cells without affecting on the normal cell line REF; a clear cut difference in the % of fragmented DNA was found between both RD, and AMN3 malignant cells, and REF normal cell line (89.16, and 83.1466, and 13.75; p< 0.001, and p< 0.001) respectively after exposure to 4.5μg/ml, leinamycin for 24hrs of incubation period.
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The temperature control process of electric heating furnace (EHF) systems is a quite difficult and changeable task owing to non-linearity, time delay, time-varying parameters, and the harsh environment of the furnace. In this paper, a robust temperature control scheme for an EHF system is developed using an adaptive active disturbance rejection control (AADRC) technique with a continuous sliding-mode based component. First, a comprehensive dynamic model is established by using convection laws, in which the EHF systems can be characterized as an uncertain second order system. Second, an adaptive extended state observer (AESO) is utilized to estimate the states of the EHF system and total disturbances, in which the observer gains are updated
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Markov chains are an application of stochastic models in operation research, helping the analysis and optimization of processes with random events and transitions. The method that will be deployed to obtain the transient solution to a Markov chain problem is an important part of this process. The present paper introduces a novel Ordinary Differential Equation (ODE) approach to solve the Markov chain problem. The probability distribution of a continuous-time Markov chain with an infinitesimal generator at a given time is considered, which is a resulting solution of the Chapman-Kolmogorov differential equation. This study presents a one-step second-derivative method with better accuracy in solving the first-order Initial Value Problem
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