Cloning,expression and characterization of endo‐β‐1,4‐mannanase from Aspergillus fumigatus in Aspergillus sojae and Pichia pastoris

To be utilized in biomass conversion, including ethanol production and galactosylated oligosaccharide synthesis, namely prebiotics, the gene of extracellular endo‐β‐1,4‐mannanase (EC 3.2.1.78) of Aspergillus fumigatus IMI 385708 (formerly known as Thermomyces lanuginosus IMI 158749) was expressed first in Aspergillus sojae and then in Pichia pastoris under the control of the glyceraldehyde triphosphate dehydrogenase (gpdA ) and the alcohol oxidase (AOX1 ) promoters, respectively. The highest production of mannanase (352 U mL^?1) in A. sojae was observed after 6 days of cultivation. In P. pastoris, the highest mannanase production was observed 10 h after induction with methanol (61 U mL^?1). The fold increase in mannanase production was estimated as ～12‐fold and ～2‐fold in A. sojae and P. pastoris, respectively, when compared with A. fumigatus. Both recombinant enzymes showed molecular mass of about 60 kDa and similar specific activities (～350 U mg^?1 protein). Temperature optima were at 60°C and 45°C, and maximum activity was at pH 4.5 and 5.2 for A. sojae and P. pastoris, respectively. The enzyme from P. pastoris was more stable retaining most of the activity up to 50°C, whereas the enzyme from A. sojae rapidly lost activity above 40°C. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Design of experiments and artificial neural network linked genetic algorithm for modeling and optimization of L-asparaginase production by <Emphasis Type="Italic">Aspergillus terreus</Emphasis> MTCC 1782

The sequential optimization strategy for design of an experimental and artificial neural network (ANN) linked genetic algorithm (GA) were applied to evaluate and optimize media component for L-asparaginase production by Aspergillus terreus MTCC 1782 in submerged fermentation. The significant media components identified by Plackett-Burman design (PBD) were fitted into a second order polynomial model (R² = 0.910) and optimized for maximum L-asparaginase production using a five-level central composite design (CCD). A nonlinear model describing the effect of variables on L-asparaginase production was developed (R² = 0.995) and optimized by a back propagation NN linked GA. Ground nut oil cake (GNOC) flour 3.99% (w/v), sodium nitrate (NaNO₃) 1.04%, L-asparagine 1.84%, and sucrose 0.64% were found to be the optimum concentration with a maximum predicted L-asparaginase activity of 36.64 IU/mL using a back propagation NN linked GA. The experimental activity of 36.97 IU/mL obtained using the optimum concentration of media components is close to the predicted L-asparaginase activity of the ANN linked GA.     

Predictive modelling of Fe(III) precipitation in iron removal process for bioleaching circuits

In this study, the applicability of three modelling approaches was determined in an effort to describe complex relationships between process parameters and to predict the performance of an integrated process, which consisted of a fluidized bed bioreactor for Fe³⁺ regeneration and a gravity settler for precipitative iron removal. Self-organizing maps were used to visually evaluate the associations between variables prior to the comparison of two different modelling methods, the multiple regression modelling and artificial neural network (ANN) modelling, for predicting Fe(III) precipitation. With the ANN model, an excellent match between the predicted and measured data was obtained (R ² = 0.97). The best-fitting regression model also gave a good fit (R ² = 0.87). This study demonstrates that ANNs and regression models are robust tools for predicting iron precipitation in the integrated process and can thus be used in the management of such systems.     

Evaluation of Various Unstructured Kinetic Models for the Production of Protease by Bacillus sphaericus MTTC511

Bacillus sphaericus MTCC511 was used for the production of protease in submerged batch fermentation. Maximum protease activity of 1010 U/L was obtained during a fermentation period of 24 h under optimized conditions of 30 °C in a medium with an initial pH of 7 and at a shaking rate of 120 rpm. The maximum biomass obtained in the batch fermentation was 2.55 g/L after 16 h. Various unstructured models were analyzed to simulate the experimental values of microbial growth, protease activity and substrate concentration. The unstructured models, i.e. the Monod model for microbial growth, the Monod incorporated Luedeking‐Piret model for the production of protease and the Monod‐incorporated modified Luedeking‐Piret model for the utilization of substrate were capable of predicting the fermentation profile with high coefficient of determination (R²) values of 0.9967, 0.9402 and 0.9729, respectively. The results indicated that the unstructured models were able to describe the fermentation kinetics more effectively.     

Application of artificial neural network model for the development of optimized complex medium for phenol degradation using <Emphasis Type="Italic">Pseudomonas pictorum</Emphasis> (NICM 2074)

Biodegradation of phenol using Pseudomonas pictorum (NICM 2074) a potential biodegradant of phenol was investigated for its degrading potential under different operating conditions. The neural network input parameter set consisted of the same set of four levels of maltose (0.025, 0.05, 0.075 g/l), phosphate (3, 12.5, 22 g/l), pH (7, 8, 9) and temperature (30°C, 32°C, 34°C) on phenol degradation was investigated and a Artificial Neural Network (ANN) model was developed to predict the extent of degradation. The learning, recall and generalization characteristic of neural networks was studied using phenol degradation system data. The efficiency of the model generated by the ANN, was tested and compared with the results obtained from an established second order polynomial multiple regression analysis (MRA). Further, the two models (ANN and MRA) were used to predict the percentage of degradation of phenol for blind test data. Performance of both the models were validated in the cases of training and test data, ANN was recommended based on the following higher coefficient of determination R ²; lower standard error of residuals and lower mean absolute percentage deviation.     

Real‐time monitoring of high‐gravity corn mash fermentation using in situ raman spectroscopy

In situ Raman spectroscopy was employed for real‐time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm^?1 C–C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high‐ and low‐concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two‐set calibration models for starch (R² = 0.998, percent error = 2.5%) and ethanol (R² = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS‐based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman‐based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present. Biotechnol. Bioeng. 2013; 110: 1654–1662. © 2013 Wiley Periodicals, Inc.     

基于森林群落和光谱曲线特征分异的福建省森林生物量遥感反演

基于福建省Landsat8 OLI影像,利用混合像元分解模型筛选出"纯净"的植被像元,提取296个调查样地对应植被像元的红光和近红外波段的中心波长(分别CWR和CWNIR)及其对应的反射率(分别R和NIR),构建以(NIR-R)/(CWNIR-CWR)为特征指数的叶生物量回归模型。然后根据针叶林、阔叶林及针阔混交林叶生物量与干、枝、叶所组成的地上生物量的关系方程,结合福建省植被覆盖分类数据,估测了整个福建省针叶林、阔叶林、混交林的地上生物量,并绘制了福建省地上生物量分布图。结果表明:红光和近红外两个波段反射率和其中心波长所组成的斜率与叶生物量相关性显著,与针叶林、阔叶林、混交林叶生物量的精度分别达到70.55%、68.89%、51.75%,采用这种方法对福建省叶生物量和地上总生物量进行估算,并进行精度验证,其中,针叶林、阔叶林、混交林叶物量的模型误差(RMSE)分别达到29.2467 t/hm~2(R~2=66.64%)、14.0258 t/hm~2(R~2=61.13%)、10.1788 t/hm~2(R~2=55.43%),地上总生物量的模型精度分别达到49.8315 t/hm~2(R~2=54.65%)、45.1820 t/hm~2(R~2=49.01%)、41.5131 t/hm~2(R~2=38.79%),这说明,采用红光波段和近红外波段与其中心波长所组成的斜率估测森林叶生物量,进而估算其地上总生物量的方法是可行的。     

Modeling Grifola frondosa fungal growth during solid‐state fermentation

Grifola frondosa (maitake) is an edible and medicinal mushroom. Considering its increasing popularity, there are limited references for its cultivation. Previous studies demonstrated that carpophore formation is correlated directly with mycelial biomass. The development of a mathematical model for its growth under solid‐state fermentation (SSF) may help to predict the potential of different substrates for maitake production. G. frondosa growth and basidiome development was studied, using oak sawdust and corn bran as substrates. The fungal biomass content was determined by measuring N‐acetyl‐D ‐glucosamine (NAGA). It increased steadily for the first 80 days, to a maximum in coincidence with the first fruiting (60.5 μg NAGA/mg dry sample). Two mathematical models were selected to evaluate G. frondosa development, measuring reducing sugars consumption and NAGA synthesis, as an indirect assessment of fungal growth. Both models showed a good fit between predicted and experimental data: logistic model (R²=0.8896), two‐stage model (R²=0.8878), but the logistic model required a minor number of adjustment parameters.     

Applying a Novel Combination of Techniques to Develop a Predictive Model for Diabetes Complications

Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R², F-ratio and adjusted R² equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models.     

The use of ladder particle swarm optimisation for quantitative structure–activity relationship analysis of human immunodeficiency virus-1 integrase inhibitors

This contribution focuses on the use of ladder particle swarm optimisation (LPSO) on modelling of oxadiazole- and triazole-substituted naphthyridines as human immunodeficiency virus-1 integrase inhibitors. Artificial neural network (ANN) and Monte Carlo cross-validation techniques were combined with LPSO to develop a quantitative structure–activity relationship model. The techniques of LPSO, ANN and sample set partitioning based on joint x–y distances were applied as feature selection, mapping and model evaluation, respectively. The variables selected by LPSO were used as inputs of Bayesian regularisation ANN. The statistical parameters of correlation of deterministic, R ², and root-mean-square error for the test set were 0.876 and 0.23, respectively. Robustness of the model was confirmed by Y-randomisation method. Comparison of the LPSO–ANN results with those of stepwise multiple linear regression (MLR), LPSO–MLR and LPSO–MLR–ANN showed the superiority of LPSO–ANN. Inspection of the selected variables indicated that atomic mass, molecular size and electronic structure of the molecules play a significant role in inhibitory behaviour of oxadiazole- and triazole-substituted naphthyridines.     

Artificial neural networking to estimate the leaf area for invasive plant Wedelia trilobata

Leaf area are very important parameter for the understanding of growth and physiological responses of invasive plant species under different environmental factors. This study was conducted to build non-destructive leaf area model of Wedelia trilobata that were grown in greenhouse. Regression analysis and artificial neural network (ANN) approaches were used for the development of leaf area model with the help of leaf length and width of 262 plants samples. In selection of best method under both techniques, the lower value of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and higher value of R² were considered. According to the results it was found that ANN have higher value of (R² = 0.96) and lower value of error (MAE = 0.023, RMSE = 0.379, MAPE = 0.001) than regression analysis (R² = 0.94, MAE = 0.111, RMSE = 1.798, MAPE = 0.0005). It was concluded that error between predicted and actual value was less under ANN. Therefore, ANN model approach can be used as an alternating method for the estimation of leaf area. Through estimation of leaf area, invasive plant growth can predict under different environment conditions.     

Optimization of fermentation medium for triterpenoid production from <Emphasis Type="Italic">Antrodia camphorata</Emphasis> ATCC 200183 using artificial intelligence-based techniques

In this study, alteration in morphology of submergedly cultured Antrodia camphorata ATCC 200183 including arthroconidia, mycelia, external and internal structures of pellets was investigated. Two optimization models namely response surface methodology (RSM) and artificial neural network (ANN) were built to optimize the inoculum size and medium components for intracellular triterpenoid production from A. camphorata. Root mean squares error, R ², and standard error of prediction given by ANN model were 0.31%, 0.99%, and 0.63%, respectively, while RSM model gave 1.02%, 0.98%, and 2.08%, which indicated that fitness and prediction accuracy of ANN model was higher when compared to RSM model. Furthermore, using genetic algorithm (GA), the input space of ANN model was optimized, and maximum triterpenoid production of 62.84 mg l⁻¹ was obtained at the GA-optimized concentrations of arthroconidia (1.78 × 10⁵ ml⁻¹) and medium components (glucose, 25.25 g l⁻¹; peptone, 4.48 g l⁻¹; and soybean flour, 2.74 g l⁻¹). The triterpenoid production experimentally obtained using the ANN–GA designed medium was 64.79 ± 2.32 mg l⁻¹ which was in agreement with the predicted value. The same optimization process may be used to optimize many environmental and genetic factors such as temperature and agitation that can also affect the triterpenoid production from A. camphorata and to improve the production of bioactive metabolites from potent medicinal fungi by changing the fermentation parameters.     

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21

Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the effect of four independent variables, viz. glucose, sodium chloride (NaCl), temperature and induction time, on lipase production by a recombinant Escherichia coli BL21. The optimization and prediction capabilities of RSM and ANN were then compared. RSM predicted the dependent variable with a good coefficient of correlation determination (R ²) and adjusted R ² values for the model. Although the R ² value showed a good fit, absolute average deviation (AAD) and root mean square error (RMSE) values did not support the accuracy of the model and this was due to the inferiority in predicting the values towards the edges of the design points. On the other hand, ANN-predicted values were closer to the observed values with better R ², adjusted R ², AAD and RMSE values and this was due to the capability of predicting the values throughout the selected range of the design points. Similar to RSM, ANN could also be used to rank the effect of variables. However, ANN could not predict the interactive effect between the variables as performed by RSM. The optimum levels for glucose, NaCl, temperature and induction time predicted by RSM are 32 g/L, 5 g/L, 32°C and 2.12 h, and those by ANN are 25 g/L, 3 g/L, 30°C and 2 h, respectively. The ANN-predicted optimal levels gave higher lipase activity (55.8 IU/mL) as compared to RSM-predicted levels (50.2 IU/mL) and the predicted lipase activity was also closer to the observed data at these levels, suggesting that ANN is a better optimization method than RSM for lipase production by the recombinant strain.     

Construction of Predictive Models to Describe Apparent and Complex Viscosity Values of O/W Model System Meat Emulsions Using Adaptive Neuro – Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN)

This paper introduces an adaptive neuro ?C fuzzy inference system (ANFIS) and artificial neural networks (ANN) models to predict the apparent and complex viscosity values of model system meat emulsions. Constructed models were compared with multiple linear regression (MLR) modeling based on their estimation performance. The root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R ²) statistics were performed to evaluate the accuracy of the models tested. Comparison of the models showed that the ANFIS model performed better than the ANN and MLR models to estimate the apparent and complex viscosity values of the model system meat emulsions. Coefficients of determination (R ²) calculated for estimation performance of ANFIS modeling to predict apparent and complex viscosity of the emulsions were 0.996 and 0.992, respectively. Similar R ² values (0.991 and 0.985) were obtained when estimating the performance of the ANN model. In the present study, use of the constructed ANFIS models can be suggested to effectively predict the apparent and complex viscosity values of model system meat emulsions.     

Artificial neural network and response surface methodology: a comparative analysis for optimizing rice straw pretreatment and saccharification

Abstract

The present study demonstrates a comparative analysis between the artificial neural network (ANN) and response surface methodology (RSM) as optimization tools for pretreatment and enzymatic hydrolysis of lignocellulosic rice straw. The efficacy for both the processes, that is, pretreatment and enzymatic hydrolysis was evaluated using correlation coefficient (R²) & mean squared error (MSE). The values of R² obtained by ANN after training, validation, and testing were 1, 0.9005, and 0.997 for pretreatment and 0.962, 0.923, and 0.9941 for enzymatic saccharification, respectively. On the other hand, the R² values obtained with RSM were 0.9965 for cellulose recovery and 0.9994 for saccharification efficiency. Thus, ANN and RSM together successfully identify the substantial process conditions for rice straw pretreatment and enzymatic saccharification. The percentage of error for ANN and RSM were 0.009 and 0.01 for cellulose recovery and for 0.004 and 0.005 for saccharification efficiency, respectively, which showed the authority of ANN in exemplifying the non-linear behavior of the system.     

General description of Lake Peipsi-Pihkva

Neural networks and multiple linear regression models of the abundance of brown trout (Salmo trutta L.) on the mesohabitat scale were developed from combinations of physical habitat variables in 220 channel morphodynamic units (pools, riffles, runs, etc.) of 11 different streams in the central Pyrenean mountains. For all the 220 morphodynamic units, the determination coefficients obtained between the estimated and observed values of density or biomass were significantly higher for the neural network (r ² adjusted= 0.93 and r ² adjusted=0.92 (p<0.01) for biomass and density respectively with the neural network, against r ² adjusted=0.69 (p<0.01) and r ² adjusted = 0.54 (p<0.01) with multiple linear regression). Validation of the multivariate models and learning of the neural network developed from 165 randomly chosen channel morphodynamic units, was tested on the 55 other channel morphodynamic units. This showed that the biomass and density estimated by both methods were significantly related to the observed biomass and density. Determination coefficients were significantly higher for the neural network (r ² adjusted =0.72 (p<0.01) and 0.81 (p<0.01) for biomass and density respectively) than for the multiple regression model (r ² adjusted=0.59 and r ² adjusted=0.37 for biomass and density respectively). The present study shows the advantages of the backpropagation procedure with neural networks over multiple linear regression analysis, at least in the field of stochastic salmonid ecology.     

Characterization of an enantioselective amidase with potential application to asymmetric hydrolysis of (<Emphasis Type="Italic">R</Emphasis>, <Emphasis Type="Italic">S</Emphasis>)-2, 2-dimethylcyclopropane carboxamide

Amidase is a promising synthesis tool for chiral amides and related derivatives. In the present study, the biochemical properties of the Delftia tsuruhatensis CCTCC M 205114 enantioselective amidase were determined for its potential application in chiral amides synthesis. D. tsuruhatensis CCTCC M 205114 amidase was purified 105.2 fold with total activity recovery of 4.26%. The enzyme is a monomer with a subunit of approximately 50 kDa by analytical gel filtration HPLC and SDS–PAGE. It had a broad substrate spectrum and displayed high enantioselectivity against R-2, 2-dimethylcyclopropane carboxamide and R-mandelic amide. The amidase was applied to enantioselective hydrolysis of R-2, 2-dimethylcyclopropane carboxamide from racemic (R, S)-2, 2-dimethylcyclopropane carboxamide to accumulate S-2, 2-dimethylcyclopropane carboxamide. This enzyme did not require metal ions for the hydrolysis reaction. Its optimal pH and temperature were 8.0 and 35°C, respectively. The K _m and V _max of the amidase for R-2, 2-dimethylcyclopropane carboxamide were 2.54 mM and 8.37 μmol min⁻¹ mg protein⁻¹, respectively. After 60 min of the reaction, R-2, 2-dimethylcyclopropane carboxamide was completely hydrolyzed, generating S-2, 2-dimethylcyclopropane carboxamide with a yield of 45.9% and an e.e. of above 99%. Therefore, this amidase can serve as a promising producer for S-2, 2-dimethylcyclopropane carboxamide and other amides.     

Vegetal cover estimated by digital photographs related to biomass in a grassland site in northern Mexico

A regression model was used to determine the relationship between aerial herbaceous biomass and vegetation coverage estimated by digital images. Four samplings (n=36 each date) of vegetation cover and herbaceous biomass were performed during the growing season in 2011 in a grassland dominated by Bouteloua gracilis in La Cieneguilla, Municipality of Villa Hidalgo, Durango. Average production of dry biomass was 37.36 ± 9.66 g/m², and mean vegetation cover 30.02%. Dry biomass data were tested for normality using the test of Kolmogorov Smirnov, finding a lack of fit. The data were subjected to a logarithmic transformation and the model Ln(y) = 1.637926 + 0.08501X - 0.000586X² with an adjusted R² = 0.89 was found. In order to validate this model, another five samplings were carried out in 2013 at the same site during summer and autumn, using the same sampling size for each date as in 2011. Data collected in 2013 were analyzed with the model Ln (y) = β₀ + β₁X + β₂X². A comparison of regression coefficients was carried out between the 2011 and 2013 models with t (180+144-9-11-2=302, p<0.05) = 1.967. The results indicated that it is possible to use the 2011 regression model to estimate herbaceous aerial biomass from vegetation cover measurements with aerial photographs in La Cieneguilla site during summer and fall.