Review:DNA-damage response network at the crossroads of cell-cycle checkpoints, cellular senescence and apoptosis

Tissue homeostasis requires a carefully-orchestrated balance between cell proliferation, cellular senescence and cell death. Cells proliferate through a cell cycle that is tightly regulated by cyclin-dependent kinase activities. Cellular senescence is a safeguard program limiting the proliferative competence of cells in living organisms. Apoptosis eliminates unwanted cells by the coordinated activity of gene products that regulate and effect cell death. The intimate link between the cell cycle, cellular senes- cence, apoptosis regulation, cancer development and tumor responses to cancer treatment has become eminently apparent. Extensive research on tumor suppressor genes, oncogenes, the cell cycle and apoptosis regulatory genes has revealed how the DNA damage-sensing and -signaling pathways, referred to as the DNA-damage response network, are tied to cell proliferation, cell-cycle arrest, cellular senescence and apoptosis. DNA-damage responses are complex, involving “sensor” proteins that sense the damage, and transmit signals to “transducer” proteins, which, in turn, convey the signals to numerous “effector” proteins implicated in specific cellular pathways, including DNA repair mechanisms, cell-cycle checkpoints, cellular senescence and apoptosis. The Bcl-2 family of proteins stands among the most crucial regulators of apoptosis and performs vital functions in deciding whether a cell will live or die after cancer chemotherapy and irradiation. In addition, several studies have now revealed that members of the Bcl-2 family also interface with the cell cycle, DNA repair/recombination and cellular senescence, effects that are generally distinct from their function in apoptosis. In this review, we report progress in understanding the molecular networks that regulate cell-cycle checkpoints, cellular senescence and apoptosis after DNA damage, and discuss the influence of some Bcl-2 family members on cell-cycle checkpoint regulation.

Preliminary Analysis of the Relationship between the Magnitude Deviation of the Station and Site Response in the Fujian Digital Seismic Network

Using the records of 3,069 regional earthquake events from the Fujian Digital Seismic Network from October 2008 to December 2015,in which the magnitude of each of the events was measured by at least six stations,statistics are taken on the deviation between the magnitude of a single station and the average magnitude of the network. It is found that the magnitudes average deviation of each station is-0. 31-0. 68. Statistics are also taken for the period corresponding to the maximum amplitude of the record measured in each station for calculating the magnitude,and the dominant period gained is 0. 06s-0. 38s; site response of each seismic station is inverted using the Moya method,and it is found that the site response of 98 stations is in the bands of 1-20 Hz,suggesting that the site has an amplifying or suppressing effect on the signals in certain frequency bands;Considering the site response corresponding to the inherent 0. 8s period of the WoodAnderson pendulum seismograph,and comparing the magnitude deviation caused by the site response corresponding to the dominant period time of each station with the average magnitude deviation, we obtain that there is a good linear relationship between the magnitude deviation from the dominant period site response and the average deviation of the magnitude of each station,indicating that the magnitude deviation of a single station has a close relationship with the site response of the period corresponding to the maximum amplitude measured for calculating the magnitude.

Application of Risk Probability Evaluation Method to Offshore Platform Construction

Offshore project risk concerns many influence factors with complex relationship, and traditional methods cannot be used for the evaluation on risk probability. To deal with this problem, a new method was developed by the combination of improved technique for order preference by similarity ideal solution method, analytical hierarchy process method and the network response surface method. The risk probability was calculated by adopting network response surface analysis based on the state variable of a known event and its degree of membership.This quantification method was applied to an offshore platform project, Bonan oil and gas field project in Bohai Bay in June 2004.There were 7 sub-projects and each includes 4 risk factors.The values of 28 risk factors, ranging from 10^-6 to 10^-4, were achieved. This precision conforms to the international principle of as low as reasonably practically.The evaluation indicates that the values of comprehensive level of construction group and ability of technical personnel on the spot are relatively high among all risk factors, so these two factors should be paid more attention to in offshore platform construction.

Optimization of press bend forming path of aircraft integral panel

In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.

Design and Optimization of Press Bend Forming Path for Producing Aircraft Integral Panels with Compound Curvatures

In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element method （FEM） equivalent model, the surface curvature analysis, the artificial neural network response surface and the genetic algorithm. The method begins with analyzing the objective＇s shape curvature to determine the bending position. Then it optimizes the punch travel at each bending position by the following steps： （1） Establish a multi-step press bend forming FEM equivalent model, with which the FEM ex- periments designed with the Taguchi method are performed. （2） Construct a back-propagation （BP） neural network response surface with the data from the FEM experiments. （3） Use the genetic algorithm to optimize the neural network response surface as the objective function. Finally, this method is verified by press bending a complicated double-curvature grid-type stiffened panel and bears out its effectiveness and intrinsic worth in designing the press bend forming path.

Forming Path Optimization for Press Bending of Aluminum Alloy Aircraft Integral Panel

Because of the light weight,high stiffness and high structural efficiency,aluminium alloy integral panels are widely used on modern aircrafts.Press bend forming has many advantages,and it becomes a significant technique in aircraft manufacturing field.In order to design the press bend forming path for aircraft integral panels,we propose a novel optimization method which integrates the finite element method(FEM) equivalent model based on our previous study,the artificial neural network response surface,and the genetic algorithm. First,a multi-step press bend forming FEM equivalent model is established,with which the FEM experiments designed with Taguchi method are performed.Then,the backpropagation(BP) neural network response surface is developed with the sample data from the FEM experiments.Further more,genetic algorithm(GA) is applied with the neural network response surface as the objective function.Finally,experimental and simulation verifications are carried out on a single stiffener specimen.The forming error of the panel formed with the optimal path is only 5.37%and the calculating efficiency has been improved by 90.64%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.

Strength Prediction of Aluminum–Stainless Steel-Pulsed TIG Welding–Brazing Joints with RSM and ANN

Pulsed TIG welding–brazing process was applied to join aluminum with stainless steel dissimilar metals. Major parameters that affect the joint property significantly were identified as pulsed peak current, base current, pulse on time,and frequency by pre-experiments. A sample was established according to central composite design. Based on the sample,response surface methodology（RSM） and artificial neural networks（ANN） were employed to predict the tensile strength of the joints separately. With RSM, a significant and rational mathematical model was established to predict the joint strength.With ANN, a modified back-propagation algorithm consisting of one input layer with four neurons, one hidden layer with eight neurons, and one output layer with one neuron was trained for predicting the strength. Compared with RSM, average relative prediction error of ANN was ／10% and it obtained more stable and precise results.