基于人工神经网络-遗传算法的樟芝发酵培养基优化

采用优化模型对药用丝状真菌樟芝的复杂发酵过程进行建模,并获得最优发酵培养基组成.对樟芝发酵过程中的形态变化过程进行了观察,并分别采用人工神经网络(ANN)和响应面法(RSM)对樟芝发酵过程进行建模,同时采用遗传算法(GA)优化了发酵培养基组成.结果表明,ANN模型比RSM模型具有更好的实验数据拟合能力和预测能力,GA计算得到樟芝生物量理论最大值为6.2 g/L,并获得发酵最佳接种量及培养基组成:孢子浓度1.76× 105个/mL,葡萄糖29.1 g/L,蛋白胨9.4 g/L,黄豆粉2.8 g/L.在最佳培养条件下,樟芝生物量为(6.1±0.2)g/L.基于ANN-GA的优化方法可用于优化其他丝状真菌的复杂发酵过程,从而获得生物量或活性代谢产物.     

Genetic manipulation of the γ‐aminobutyric acid (GABA) shunt in rice: overexpression of truncated glutamate decarboxylase (GAD2) and knockdown of γ‐aminobutyric acid transaminase (GABA‐T) lead to sustained and high levels of GABA accumulation in rice kernels

Gamma‐aminobutyric acid (GABA) is a non‐protein amino acid commonly present in all organisms. Because cellular levels of GABA in plants are mainly regulated by synthesis (glutamate decarboxylase, GAD) and catabolism (GABA‐transaminase, GABA‐T), we attempted seed‐specific manipulation of the GABA shunt to achieve stable GABA accumulation in rice. A truncated GAD2 sequence, one of five GAD genes, controlled by the glutelin (GluB‐1) or rice embryo globulin promoters (REG) and GABA‐T‐based trigger sequences in RNA interference (RNAi) cassettes controlled by one of these promoters as well, was introduced into rice (cv. Koshihikari) to establish stable transgenic lines under herbicide selection using pyriminobac. T₁ and T₂ generations of rice lines displayed high GABA concentrations (2–100 mg/100 g grain). In analyses of two selected lines from the T₃ generation, there was a strong correlation between GABA level and the expression of truncated GAD2, whereas the inhibitory effect of GABA‐T expression was relatively weak. In these two lines both with two T‐DNA copies, their starch, amylose, and protein levels were slightly lower than non‐transformed cv. Koshihikari. Free amino acid analysis of mature kernels of these lines demonstrated elevated levels of GABA (75–350 mg/100 g polished rice) and also high levels of several amino acids, such as Ala, Ser, and Val. Because these lines of seeds could sustain their GABA content after harvest (up to 6 months), the strategy in this study could lead to the accumulation GABA and for these to be sustained in the edible parts.     

微粒群优化神经网络及其在环境评价中的运用

农业项目环境影响综合评价是目前新的研究领域,随着农业项目的增加,其环境影响的研究愈来愈重要.以某农业项目为例,运用PSO-BP进行农业项目环境评价;仿真和实验表明:微粒群优化神经网络,能够克服神经网络收敛速度慢,陷入局部最小的缺点;微粒群优化算法涉及的参数不多,但是微粒群优化结果是比较理想的.     

Separation of Subclasses of Human Serum High Density Lipoproteins by Zonal Ultracentrifugation

An improved one-step method for the preparative separation of three subfraotions of high-density lipoproteins from normal human serum has been developed. It employs the method of rate zonal ultracentrifugation in a Z-60 rotor using a discontinuous NaBr gradient in the density range of 1.0 - 1.4. The density gradients were monitored directly by a flow-through density meter allowing the direct read-out of the actual densities in the process of filling and emptying the rotor. The separation of the three density fractions from 5 to 15 ml serum was achieved during a single 12 hours run at 59.000 rpm. The three fractions showed characteristically different patterns on polyacrylamide gel electrophoresis and differences in their lipid and protein composition.     

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN)

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg^?1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R² (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.     

Characterization and immobilization on nickel-chelated Sepharose of a glutamate decarboxylase A from Lactobacillus brevis BH2 and its application for production of GABA

A gene encoding glutamate decarboxylase A (GadA) from Lactobacillus brevis BH2 was expressed in a His-tagged form in Escherichia coli cells, and recombinant protein exists as a homodimer consisting of identical subunits of 53?kDa. GadA was absolutely dependent on the ammonium sulfate concentration for catalytic activity and secondary structure formation. GadA was immobilized on the metal affinity resin with an immobilization yield of 95.8%. The pH optima of the immobilized enzyme were identical with those of the free enzyme. However, the optimum temperature for immobilized enzyme was 5?°C higher than that for the free enzyme. The immobilized GadA retained its relative activity of 41% after 30 reuses of reaction within 30?days and exhibited a half-life of 19 cycles within 19?days. A packed-bed bioreactor with immobilized GadA showed a maximum yield of 97.8% GABA from 50?mM l-glutamate in a flow-through system under conditions of pH 4.0 and 55?°C.     

Modeling and optimization of microbial hyaluronic acid production by Streptococcus zooepidemicus using radial basis function neural network coupling quantum‐behaved particle swarm optimization algorithm

Hyaluronic acid (HA) is a natural biopolymer with unique physiochemical and biological properties and finds a wide range of applications in biomedical and cosmetic fields. It is important to increase HA production to meet the increasing HA market demand. This work is aimed to model and optimize the amino acids addition to enhance HA production of Streptococcus zooepidemicus with radial basis function (RBF) neural network coupling quantum‐behaved particle swarm optimization (QPSO) algorithm. In the RBF‐QPSO approach, RBF neural network is used as a bioprocess modeling tool and QPSO algorithm is applied to conduct the optimization with the established RBF neural network black model as the objective function. The predicted maximum HA yield was 6.92 g/L under the following conditions: arginine 0.062 g/L, cysteine 0.036 g/L, and lysine 0.043 g/L. The optimal amino acids addition allowed HA yield increased from 5.0 g/L of the control to 6.7 g/L in the validation experiments. Moreover, the modeling and optimization capacity of the RBF‐QPSO approach was compared with that of response surface methodology (RSM). It was indicated that the RBF‐QPSO approach gave a slightly better modeling and optimization result compared with RSM. The developed RBF‐QPSO approach in this work may be helpful for the modeling and optimization of the other multivariable, nonlinear, time‐variant bioprocesses. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

An artificial neural network model of the landscape pattern in Shanghai metropolitan region, China

To characterize the urbanization pattern quantitatively, a study on the mechanisms of the landscape pattern formation could facilitate the understanding on urban landscape patterns and processes, the ecological and socioeconomic consequences of urbanization, as well as the establishment of more effective strategies for landscape management. In this study, we integrated a Geographic Information System (GIS)-based analysis on landscape pattern with an artificial neural network (ANN) to quantitatively characterize the urbanization pattern of the metropolitan area of Shanghai, China, and to establish an ANN model that could preferably simulate the responses of urban landscape pattern to the natural and socioeconomic factors such as residence area, road density, population density, urban development history and the Huangpu River as an element of economic change. Our results showed that the ANN model seems appropriate for studying the nonlinear relationship among the forcing factors of urbanization and the urban landscape patterns, which provided an effective and practical approach for further understanding the mechanisms of the landscape formation pattern and the reciprocal relationship between landscape spatial pattern and ecological process. __________ Translated from Acta Ecologica Sinica, 2005, 25(5): 958–964 [译自: 生态学报, 2005, 25(5): 958–964]     

An artificial neural network model of the landscape pattern in Shanghai metropolitan region,China

To characterize the urbanization pattern quantitatively,a study on the mechanisms of the landscape pattern formation could facilitate the understanding on urban landscape patterns and processes,the ecological and socioeconomic consequences of urbanization,as well as the establishment of more effective strategies for landscape management.In this study,we integrated a Geographic Information System (GIS)-based analysis on landscape pattern with an artificial neural network (ANN) to quantitatively characterize the urbanization pattern of the metropolitan area of Shanghai,China,and to establish an ANN model that could preferably simulate the responses of urban landscape pattern to the natural and socioeconomic factors such as residence area,road density,population density,urban development history and the Huangpu River as an element of economic change.Our results showed that the ANN model seems appropriate for studying the nonlinear relationship among the forcing factors of urbanization and the urban landscape patterns,which provided an effective and practical approach for further understanding the mechanisms of the landscape formation pattern and the reciprocal relationship between landscape spatial pattern and ecological process.