Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks

Enzymatic hydrolysis of cellulose was highly complex because of the unclear enzymatic mechanism and many factors that affect the heterogeneous system. Therefore, it is difficult to build a theoretical model to study cellulose hydrolysis by cellulase. Artificial neural network (ANN) was used to simulate and predict this enzymatic reaction and compared with the response surface model (RSM). The independent variables were cellulase amount X-1, substrate concentration X-2, and reaction time X-3, and the response variables were reducing sugar concentration Y-1 and transformation rate of the raw material Y-2. The experimental results showed that ANN was much more suitable for studying the kinetics of the enzymatic hydrolysis than RSM. During the simulation process, relative errors produced by the ANN model were apparently smaller than that by RSM except one and the central experimental points. During the prediction process, values produced by the ANN model were much closer to the experimental values than that produced by RSM. These showed that ANN is a persuasive tool that can be used for studying the kinetics of cellulose hydrolysis catalyzed by cellulase.

Quantum computation using artificial molecules

Two kinds of quantum computation systems using artificial molecules: quantum computer and quantum analog computer are described. The artificial molecule consists of two or three coupled quantum dots stacked along z direction and one single electron, In quantum computer, one-qubit and two-qubit gates are constructed by one molecule and two molecules, respectively. The coupling between two qubits in a quantum gate can be controlled by thin film electrodes. We also constructed a quantum analog computer by designing a three-dot molecule network and mapping a graph 3-colorability problem onto the network. The ground-state configuration of the single electrons in the network corresponds to one of the problem solutions, We numerically study the operations of the two kinds of the quantum computers and demonstrate that they quantum gates can perform the quantum computation and solve complex problems.

Artificial neural networks expecting more advanced microelectronics technology

　

Simultaneous determination of methylene blue and new methylene blue by slab optical waveguide spectroscopy and artificial neural networks

A slab optical waveguide (SOWG) has been used for study of adsorption of both methylene blue (MB) and new methylene blue (NMB) in liquid-solid interface. Adsorption characteristics of MB and NMB on both bare SOWG and silanized SOWG by octadecyltrichlorosilane (ODS) were compared. The simultaneous determinations of both MB and NMB were explored by flow injection SOWG spectrophotometric analysis and artificial neural networks (ANNs) for the first time. Concentrations of MB and NMB were estimated simultaneously with the ANNs. Results obtained with SOWG were compared with those got by conventional UV-visible spectrophotometry. (C) 2003 Elsevier Science B.V All rights reserved.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Hemoglobin conjugated micelles based on triblock biodegradable polymers as artificial oxygen carriers

An artificial oxygen carrier is constructed by conjugating hemoglobin molecules to biodegradable micelles. Firstly a series of triblock copolymers (PEG-PMPC-PLA) in which the middle block contains pendant propargyl groups were synthesized and characterized. After the amphiphilic copolymer was self-assembled into core-shell micelles in aqueous solution, azidized hemoglobin molecules protected by carbon monoxide (CO) were conjugated to the micelles via click reaction between the propargyl and azido groups. The conjugation causes an increase of the micelle's mean diameter. Maximum conjugation ratio is 250 wt% in the hemoglobin-conjugated micelles (HCMs). Oxygen-binding ability of the HCMs was demonstrated by converting the CO-binding state of the HCMs into O-2-binding state.

Prediction of molar absorptivities of color reagents and their color reactions with yttrium by artificial neural networks

The new topological indices A(x1)-A(x3) suggested in our laboratories were applied to the study of structure-property relationships between color reagents and their color reactions with yttrium. The topological indices of twenty asymmetrical phosphone bisazo derivatives of chromotropic acid were calculated. The work shows that QSPR can be used as a novel aid to predict the molar absorptivities of color reactions and in the long term to be helpful tool in-color reagent design. Multiple regression analysis and neural network were employed simultaneously in this study. The results demonstrated the feasibility and the effectiveness of the method.

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

Artificial neural network applied to diagnosis of lung cancer

Artificial neural network(ANN) approach was applied to classification of normal persons and lung cancer patients based on the metal content of hair and serum samples obtained by inductively coupled plasma atomic emission spectrometry (ICP-AES) for the two groups. This method was verified with independent prediction samples and can be used as an aiding means of the diagnosis of lung cancer. The case of predictive classification with one element missing in the prediction samples was studied in details, The significance of elements in hair and serum samples for classification prediction was also investigated.