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Hypoxia is a critical aspect of the glioma microenvironment and has been associated with poor prognosis and resistance to various therapies. However, the mechanisms responsible for hypoxic survival of glioma cells remain unclear. Recent studies strongly suggest that microRNAs act as critical mediators of the hypoxic response. We thus hypothesized their(More)
ACKNOWLEDGEMENT The timely and successful completion of the book could hardly be possible without the helps and supports from a lot of individuals. I will take this opportunity to thank all of them who helped me either directly or indirectly during this important work. First of all I wish to express my sincere gratitude and due respect to my supervisor Dr.(More)
Enhancing the robustness and accuracy of time series forecasting models is an active area of research. Recently, Artificial Neural Networks (ANNs) have found extensive applications in many practical forecasting problems. However, the standard backpropagation ANN training algorithm has some critical issues, e.g. it has a slow convergence rate and often(More)
OBJECTIVE To determine whether umbilical cord blood glucose correlates with subsequent hypoglycaemia after birth in infants of well-controlled diabetic mothers. METHODOLOGY Thirty-eight term infants of well-controlled diabetic mothers were enrolled. Five mothers had pre-existing diabetes. Of the 33 gestational diabetic mothers, 16 were managed on insulin(More)
Microorganisms attach to surfaces, start multiplying, and develop biofilms. Biofilm-associated cells can be differentiated from their suspended counterparts by the generation of an extracellular polymeric substance (EPS) matrix, reduced growth rates, and up- and downregulation of their specific genes. The attachment of microorganisms is a complex process(More)
Properly comprehending and modeling the dynamics of financial data has indispensable practical importance. The prime goal of a financial time series model is to provide reliable future forecasts which are crucial for investment planning, fiscal risk hedging, governmental policy making, etc. These time series often exhibit notoriously haphazard movements(More)
Recently, the Particle Swarm Optimization (PSO) technique has gained much attention in the field of time series forecasting. Although PSO trained Artificial Neural Networks (ANNs) performed reasonably well in stationary time series forecasting, their effectiveness in tracking the structure of non-stationary data (especially those which contain trends or(More)