Temperature-regulated expression of recombinant proteins in the tac promoter (Ptac) system was investigated. Expression levels of fungal xylanase and cellulase from N. patriciarum in E. coli strains containing the natural lacI gene under the control of the Ptac markedly increased with increasing cultivation temperature in the absence of a chemical inducer. The specific activities (units per milligram protein of crude enzyme) of the fungal xylanase and cellulase produced from recombinant E. coli strain pop2136 grown at 42 degrees C were about 4.5 times higher than those of the cells grown at 23 degrees C and were even slightly higher when compared with cells grown in the presence of the inducer isopropyl beta-D-thiogalactopyranoside. The xylanase expression level in the temperature-regulated Ptac system was about 35% of total cellular protein. However, this system can not be applied to E. coli strains containing lacIq, which confers over production of the lac repressor, for high-level expression of recombinant proteins. In comparison with the lambda PL system, the Ptac-based xylanase plasmid in E. coli pop2136 gave a considerably higher specific activity of the xylanase than did the best lambda PL-based construct using the same thermal induction procedure. The high-level expression of the xylanase using the temperature-regulated Ptac system was also obtained in 10-litre fermentation studies using a fed-batch process. These results unambiguously demonstrated that the temperature-modulated Ptac system can be used for overproduction of some non-toxic recombinant proteins. 相似文献
Surrogate model-assisted multi-objective genetic algorithms (MOGA) show great potential in solving engineering design problems since they can save computational cost by reducing the calls of expensive simulations. In this paper, a two-stage adaptive multi-fidelity surrogate (MFS) model-assisted MOGA (AMFS-MOGA) is developed to further relieve their computational burden. In the warm-up stage, a preliminary Pareto frontier is obtained relying only on the data from the low-fidelity (LF) model. In the second stage, an initial MFS model is constructed based on the data from both LF and high-fidelity (HF) models at the samples, which are selected from the preliminary Pareto set according to the crowding distance in the objective space. Then the fitness values of individuals are evaluated using the MFS model, which is adaptively updated according to two developed strategies, an individual-based updating strategy and a generation-based updating strategy. The former considers the prediction uncertainty from the MFS model, while the latter takes the discrete degree of the population into consideration. The effectiveness and merits of the proposed AMFS-MOGA approach are illustrated using three benchmark tests and the design optimization of a stiffened cylindrical shell. The comparisons between the proposed AMFS-MOGA approach and some existing approaches considering the quality of the obtained Pareto frontiers and computational efficiency are made. The results show that the proposed AMFS-MOGA method can obtain Pareto frontiers comparable to that obtained by the MOGA with HF model, while significantly reducing the number of evaluations of the expensive HF model.
Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily. 相似文献
