Expression and activities of cyclins and cyclin-dependent kinases in developing rat ventricular myocytes

The molecular mechanisms responsible for the alterations in proliferative capacity of cardiac myocytes during development remain unknown; however, cell cycle dependent molecules may be involved. We have determined the expression of cyclins A, D1–3and E, and cyclin-dependent kinases (CDKs) 2, 4, 5 and 6 and cdc2 in freshly isolated rat cardiac myocytes from fetal (18 days gestation), neonatal (2 days post-natal) and adult animals by immunoblotting. Our results show a dramatic decrease in expression of these proteins during normal cardiac development, such that levels are highest in fetal myocytes but are significantly down-regulated in adult cells (P<0.05, in each case). We also have determined thein vitrokinase activities of cdc2, CDK2, CDK4, CDK5 and CDK6 immunocomplexes in fetal, neonatal and adult myocytes. There was a consistent and significant loss of cdc2, CDK2, CDK4 and CDK6 kinase activities in adult cardiac cell lysates (5.3-, 10.6-, 1.5- and 1.9-fold decreases, respectively) when compared to neonatal samples (P<0.05); CDK5 activity showed a similar trend but failed to reach significance. In conclusion, our results show that the expression and activities of various positive regulators of the cell cycle are down-regulated significantly during development of the cardiac myocyte, concomitant with the loss of proliferative capacity in adult myocytes. Down-regulation of these proteins may be pivotal in the withdrawal of the cardiac myocyte from the cell cycle.

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.