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

　

大熊猫“盛林1号”放归监测

大熊猫(Ailuropoda melanoleuca)是我国特有的珍稀濒危物种，国家Ⅰ级重点保护野生动物，被称为“国宝”。目前，大熊猫被局限在我国中西部的岷山、邛崃、大相岭、小相岭、凉山和秦岭6大山系中。对大熊猫的保护和研究，我国政府、保护生物学科研人员、社会各界及国际保护组织都做了大量的工作。根据全国三次大熊猫调查结果显示，大熊猫栖息地片段化现象依然存在，形成多个隔离的大熊猫小种群。尤其在小相岭、大相岭、岷山B和岷山C种群，大熊猫数量较少，且栖息地破碎，面临较大威胁。有的山系大熊猫种群数量些已低于最小可存活大熊猫种群的数量，如果不采取人工措施，这些种群的大熊猫存在灭绝的危险。 将圈养大熊猫放归野外，以补充野外大熊猫种群数量，增加其遗传多样性，复壮和扩大野生大熊猫种群，是大熊猫人工繁育的最终目标。为降低放归的风险性，在放归人工繁育大熊猫前，将救护存活的野生大熊猫先有计划放归野外，并对其进行跟踪监测，对积累大熊猫放归经验，进一步研究大熊猫野外生物学习性，丰富放归地大熊猫种群遗传多样性，为人工繁育大熊猫放归野外夯实基础，具有十分重要的意义。2005年8月8日，国家林业局和四川省人民政府联合将救护野生大熊猫“盛林1号”放归于龙溪－虹口国家级自然保护区内岷山B大熊猫种群栖息地，并进行系统监测研究。成功的积累了一些放归经验和放归大熊猫的生物学资料，为人工繁育大熊猫的放归奠定了一定基础。 2005年8月至2007年6月期间，我们采用GPS无线电项圈、粪便DNA检测和红外线自动触发相机陷阱的方法，对大熊猫“盛林１号”进行了追踪监测，获得了以下成果： 1.通过分析“盛林1号”放归后了活动趋势和采用两种贝叶斯方法，利用目前五大山系的已有微卫星遗传数据，检测“盛林１号”与五大山系的遗传关系的远近，推测其来源于邛崃山系的可能性较大。 2.收集了大量“盛林1号”野外生境选择数据。我们认为“盛林1号”放归后经历了应急期、初步稳定期、长途迁徙期三个阶段（这可能是今后放归大熊猫都必经的三个时期），并与当地大熊猫种群已发生交流。目前“盛林１号”仍在寻找适合的巢域。 3.结合过去监测数据分析，在放归区域大熊猫和羚牛尽管同域分布，但由于食性不同，对微生境选择还是有着很大差异，因此保护管理对策要有针对性。 4.“盛林1号”的放归是成功的。救护大熊猫异地放归工作应继续开展，但要改进放归后的监测技术。要改进现有对人工饲养大熊猫野化培训方法和放归方式，才能真正将人工繁殖个体放归野外。 Giant Panda (Ailuropoda melanoleuca) is an endangered species endemic to China. It was listed as National Protected I Class Species and is crowned as “National treasure” of China. The populations of Giant Panda are limited in 6 mountain system in Center-West of China, i.e. Mingshan, Mt. Qionglai, Mt. Daxiangling，Mt. Xiaoxiangling, Mt. Liangshan and Mt. Qinling. The results of the Third National Survey on Giant Panda showed that the habitats of Giant Panda is still fracted and Giant Panda population is divided into several isolated small populations. Population B from Mt. Daxiangling, Mt. Xiaoxiangling and Mt. Mingshan and Population C from Mt. Mingshan are very small with very fracted habitat and are more endangered. Several populations in those mountain systems are smaller than Minimum Viable Population of Giant Panda. It is very possible that those populations will be extinct without artificial help. The ultimate Goal of Reintroduction caged Giant Panda to wild is to increase wild population size and genetics diversity and rebuild and expand wild Giant Panda population. It is of significant to return rescued wild Giant Panda to wild and monitor their behavior before reintroduction artificial reproduced Giant Panda. It will increase our knowledge on reintroduction of Giant Panda. Aug 8th, 2005, “Shenglin 1”, a rescued wild Giant Panda was returned to Longxi-Hongkou National Nature Reservoir, which is habitat of Giant Panda Population B of Mt. Mingshan. A systematic monitor was carried out on “Shenglin 1”, and the successful return enriched our biological knowledge on Giant Panda reintroduction. It will be very help for future conservation work on reintroduce artificial reproduced Giant Panda. “Shenglin 1” was tracked with GPS collar, DNA in feces and infrared-trigged camera from Aug 2005 to Jun 2007. 1. Locomotion behavior and microsatellites comparison with Giant Panda from the 5 mountain systems indicated that “Shenglin 1” is possibly from Mt. Qionglai. 2. Habitat usage of “Shenglin 1” was studied. It was suggested that there were 3 phases after return, i.e. emergency response, preliminary stable phase and long distance locomotion, which could be a general process for other returned Giant Panda. It was indicated that there was some interaction between “Shenglin 1” and local population. “Shenglin 1” is seeking for suitable home range now. 3. Monitor data also indicated that microhabitat preference of Giant Panda and takin (Budorcas taxicolor) are different because of different diet, though they are sympatric. It was suggested that conservation management for the two species should be plan in particular. 4. The reintroduction of “Shenglin 1” is a successful case. The program of return rescued Giant Panda to other habitats is of value and should be continued. However, more improvement is needed for the monitor technique. More improvement is need for feralization and returning before we return artificial reproduced Giant Panda to wild.

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.