Thymoquinone (TQ) is a major constituent of Nigella sativa oil with reported anti-oxidative activity and anti-inflammatory activity in animal cells. It also inhibits proliferation and induces programmed cell death (apoptosis) in human skin cancer cells. The present study sought to detect the influence of TQ on dividing cells of three plant systems and on expression of Bcl2-associated athanogene-like (BAG-like) genes that might be involved during the process of cell death. BAG genes are known for the regulation of diverse physiological processes in animals, including apoptosis, tumorigenesis, stress responses, and cell division. Synthetic TQ at 0.1 mg/mL greatly reduced wheat seed germination rate, whereas 0.2 mg/mL completely inhibited germination. An Evans blue assay revealed moderate cell death in the meristematic zone of Glycine max roots after 1 h of TQ treatment (0.2 mg/mL), with severe cell death occurring in this zone after 2 h of treatment. Light microscopy of TQ-treated (0.2 mg/mL) onion hairy root tips for 1 h revealed anti-mitotic activity and also cell death-associated changes, including nuclear membrane disruption and nuclear fragmentation. Transmission electron microscopy of TQ-treated cells (0.2 mg/ mL) for 1 h revealed shrinkage of the plasma membrane, leakage of cell lysate, degradation of cell walls, enlargement of vacuoles and condensation of nuclei. Expression of one BAGlike gene, previously associated with cell death, was induced 20 min after TQ treatment in Glycine max root tip cells. Thus, TQ has multiple effects, including cell death, on dividing plant cells and plants may serve as a useful system to further investigate the mechanisms underlying the response of eukaryotic cells to TQ.ß 2013 Published by Elsevier Masson SAS on behalf of Acade ´mie des sciences.

Global heating, depletions, and high cost of fossil fuels ensued the exploitation of AC sources of energy such as solar stamina. The peculiarities of photovoltaic PV module are a condition for dimensioning and designing a PV system. The causation for developing PV modules beneficial for electrical applications, this manner permits the development of new hefty-performances stand-alone PV system. PV ingredients are permitting the computation of the demeanor of the total system in medley scenarios. In this work, a comparison between calculation solar program and manual mathematical method are made according to Mosul-Iraq site.

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Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.