基于均匀设计的主成分分析-支持向量机模型及其在几丁质酶最适pH建模中的应用

采用主成分分析法对样本数据集进行预处理,将得到的新样本数据集输入支持向量机,籍均匀设计,构建了几丁质酶氨基酸组成和最适pH的数学模型。当惩罚系数C为10,epsilon值为0.7,Gamma值为0.5,模型对pH值拟合的平均绝对百分比误差为3.76%,同时具有良好的预测效果,预测的平均绝对误差为0.42个pH单位。该方法比用BP神经网络方法效果更佳。     

甘蓝夜蛾几丁质酶基因cDNA序列的克隆与序列分析

昆虫几丁质酶在害虫生物防治中具有很大的发展潜力。以甘蓝夜蛾Mamestra brassicae L.预蛹期幼虫整个虫体为材料提取总RNA,利用RT-PCR和cDNA末端快速扩增技术(RACE),扩增得到其几丁质酶的cDNA序列。该序列含有2826个碱基,包括1个1689个碱基的开放阅读框,预测编码1个含562个氨基酸的多肽,分子量约为62.6kDa,等电点为5.30。推导得到的氨基酸序列含有2个N-位糖基化位点,22个O-位糖基化位点,氨基酸序列与其他昆虫,尤其是鳞翅目昆虫的几丁质酶高度同源。获得的甘蓝夜蛾几丁质酶基因cDNA序列已经登录GenBank并获得登录号FJ436415。     

产几丁质酶菌株SWCH-6的筛选、鉴定及其产酶条件的优化研究

从汕头海湾养殖区域的海底沉积物中分离到1株几丁质酶活性较高的菌株, 命名为SWCH-6, 根据菌株的形态特征、生理生化特征和16S rDNA 序列, 确定该菌株为嗜水气单胞菌(Aeromonas hydrophlilla)。采用单因素优化方法结合正交实验, 得到菌株SWCH-6产几丁质酶的最佳发酵条件：胶体几丁质25.0 g/L, 胰蛋白胨10.0 g/L, 陈海水1.0 L, pH 8.5, 32℃, 150 r/min培养72 h; 在该条件下酶活力达0.39 U/mL。此外, 菌株所产几丁质酶的最适催化pH 5.0; 最适催化温度为40℃; Cu2+、Fe3+及表面活性剂Tween-80能增强该酶的催化活性; Zn2+、Mn2+及表面活性剂SDS、洗衣粉对该酶的催化活性有抑制作用, 与其它几丁质酶存在着一些不同。     

小地老虎几丁质酶基因的克隆与序列分析

利用昆虫几丁质酶对几丁质的调控作用破坏几丁质新陈代谢的平衡来防治害虫, 在生物防治策略中具有很大的发展潜力。从处于预蛹期的小地老虎Agrotls ipsilon (Hufnagel)体中肠内提取总的RNA, 经反转录, 利用cDNA末端快速扩增技术（RACE）获得了几丁质酶基因的cDNA序列。该基因序列已经登录GenBank并获得登录号为EU035316。该序列长度为2 823个碱基, 含有一个1 674个碱基的开放读码框。开放读码框编码558个氨基酸残基, 预测的分子量为62.5 kDa, 等电点5.12。推导得到的氨基酸序列含有2个N-位糖基化位点,20个O-位糖基化位点, 含有2个几丁质酶所具有的保守序列：N-端的催化区和C-端的几丁质结合区。氨基酸序列与其他昆虫, 特别是鳞翅目昆虫的几丁质酶高度同源。     

α-葡萄糖苷酶三维结构的同源模建与分子设计

目的:该文预测了来源于嗜热脂肪芽孢杆菌(Bacillus stearothermophilus)ATCC 12016编码α-葡萄糖苷酶序列的三维结构,设计突变位点,构建突变体模型,并对预测三维结构与突变体进行了评估与分析.方法:分析了α-葡萄糖苷酶的核酸序列与蛋白序列,确定了α-葡萄糖苷酶蛋白序列的同源性与保守区特征;利用来源于蜡状芽胞杆菌(Bacillus cereus)ATCC 7064寡聚-1,6-葡萄糖苷酶三维蛋白结构作为模板,同时基于同源模建方法对α-葡萄糖苷酶序列的三维结构与突变突变体模型进行了结构预测.结果:对预测的三维结构与突变体进行评估与分析,表明预测与设计的结构达到合理化标准.结论:基于以上研究结果,构建α-葡萄糖苷酶的三维结构模型是合理的,为蛋白质工程应用建立了理论研究平台.     

豇豆几丁质酶N端序列测定及与其它植物的比较

通过自动 Edman降解程序 ,测定了经诱导、纯化的豇豆几丁质酶 N端 1 0个氨基酸的序列 ,并将该序列与其它植物几丁质酶 N端相应部分的氨基酸序列进行了比较分析。结果表明 ,该豇豆 ( Vigna sesquipedalis)几丁质酶 N端 1 0个氨基酸的序列为 EQCGSQAGGA,与 类几丁质酶同一部分同感序列同源性高达 1 0 0 % ;而与 、 及 类几丁质酶的相应序列均无同源性。结合考虑此酶的等电点 ( 8.3)及分子量 ( 33k D) ,可推测该豇豆几丁质酶属于 类几丁质酶。其 N端序列的高度保守性提示 ,该段序列可作为 类几丁质酶的一段主要特征序列 ,并可据其合成核酸探针 ,以分离、克隆其它 类几丁质酶编码基因。     

昆虫来源的几丁质酶的分离纯化及酶学性质

几丁质酶在真菌和昆虫的生理和发育过程中起着关键作用,该酶本身及其酶抑制剂是获取生物农药的重要途径。本研究从蚕蛹体内提取几丁质粗酶,经硫酸铵分级沉淀和Sephadex G-150分离得到几丁质酶。用SDS-PAGE测得该酶的分子量为88kDa。水解胶体几丁质的Km值为22.3μmol/L。酶反应的最适温度为45℃,最适pH值为6.0,金属离子和有机试剂对几丁质酶活性都有影响,其中高浓度的Mn2+对酶有较强的激活作用,而Cu2+、SDS则有较强的抑制作用。研究结果为基于几丁质酶的生物农药筛选研究奠定了基础。     

绿色木霉内切几丁质酶cDNA的克隆及其编码蛋白的序列分析

根据已克隆的内切几丁质酶基因序列的同源性比较,设计引物,采用PCR技术从绿色木霉基因组中分离出一个大小为1467bp的特异DNA片段,采用RT．PeR技术从绿色木霉总RNA中分离出大小约1276bp的eDNA片段。序列对比后发现该内切几丁质酶DNA含有三个内含子,大小分别为52bp,69bp,64bp。同源性分析表明其全长eDNA序列和已经报道的内切几丁质酶序列的同源性高达95％以上,预测其编码蛋白的氨基酸序列含424个氨基酸残基,分子量为46kDa,氨基酸序列分析表明该内切几丁质酶164～172位氨基酸是其活性中心,用同源建模法模拟其空间结构模型,为进一步研究其作用机制奠定了良好基础。     

棘孢木霉几丁质酶tachi2 基因的原核表达及酶学性质研究

目的:实现棘孢木霉(Trichoderma asperellum)几丁质酶基因tachi2的原核高效表达,研究几丁质酶Tachi2的酶学性质.方法:利用PCR技术扩增得到几丁质酶基因tachi2,将其克隆到原核表达载体pEHISTEV中,测序后,转化大肠杆菌BL21感受态细胞,经异丙基硫代-β-D-半乳糖苷(IPTG)诱导后进行Tachi2蛋白的纯化和复性.用纯化的目的蛋白Tachi2进行几丁质酶酶学性质的研究.结果:tachi2基因在重组大肠杆菌中正确表达,其主要以包涵体形式存在;重组蛋白Tachi2分子量约为44kDa,经过纯化和复性后得到的Tachi2有较高的几丁质酶活性.该酶的最适温度为40℃,最适pH值为7.0,几丁质酶在40℃以下比较稳定、pH 6～9时酶有较高活性,受Cu2和Zn2+的强烈抑制.结论:成功实现了棘孢木霉几丁质酶基因tachi2的原核高效表达,表达纯化了重组蛋白,明确了几丁质酶Tachi2的酶学性质,为该几丁质酶的进一步开发利用和深入研究奠定了基础.     

粘质沙雷氏菌XJ-01几丁质酶合成条件的优化

目的:对粘质沙雷氏菌(Serratia marcescens)XJ-01几丁质酶合成条件优化.方法:研究了碳源、氮源、温度、pH等单个因素对该菌几丁质酶合成的影响,并通过正交实验确定了该菌的最适酶合成条件.结果:该茵几丁质酶最优合成条件为:胶体几丁质5g/L、硫酸铵5 g/L、培养温度32℃、最适pH 8.结论:优化了S.marcescens XJ-01几丁质酶的合成条件.     

木聚糖酶氨基酸组成与其最适pH的神经网络模型

籍均匀设计（UD）方法,构建了G/11家族木聚糖酶氨基酸组成和最适pH的神经网络（NNs）模型。当学习速率为0.09、动态参数为0.4、Sigmoid参数为0.98,隐含层结点数为10时,该模型对最适pH的拟合和预测平均绝对百分比误差可分别达到3.02%和4.06%,均方根误差均为0.19个pH单位,平均绝对误差分别为0.11和0.19个pH单位。该结果比文献报道的用逐步回归方法好。     

Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria

A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency of Lanaset Red (LR) G on Chara contraria based on 2304 experimental sets. The effects of operating variables (particle size, adsorbent dosage, pH regimes, dye concentration, and contact time) were studied to optimize the sorption conditions of this dye. The operating variables were used as the input to the constructed neural network to predict the dye uptake at any time as the output. This adsorbent was characterized by FTIR. Pseudo second-order model was also fitted to the experimental data. According to values of error analyses and determinations coefficient, the ANN was more appropriate to describe this adsorption process. Result of this model indicated that pH regimes had the highest importance effect (49%) on the dye uptake.     

A model of photosystem II for the analysis of a fast fluorescence rise in plant leaves

The polyphasic patterns of fluorescence induction rise in pea leaves in vivo and after the treatment with ionophores have been studied using a plant efficiency analyzer. To analyze in detail photosystem II (PS II) electron transfer processes, an extended PS II model was applied, which included the sums of exponential functions to specify explicitly the light-driven formation of the transmembrane electric potential (delta psi(t)) as well as pH in the lumen (pHL(t)) and stroma (pHs(t)). PS II model parameters and numerical coefficients in delta psi(t), and pHs(t) were evaluated to fit fluorescence induction data for different experimental conditions: leaf in vivo or after ionophore treatment at low or high light intensity. The model imitated changes in the pattern of fluorescence induction rise due to the elimination of transmembrane potential in the presence of ionophores, when delta psi = 0 and pHL(t), pHS(t) altered to small extent relative to control values in vivo, with maximum delta psi(t) approximately 90 MB and delta psi(t) approximately 40 MB, for the stationary state at deltapH aproximately equal to 1.8. As the light intensity was increased from 300 to 1200 micromol x m(-2) x s(-1), the heat dissipation rate constants increased threefold for nonradiative recombination of P680+Phe- and by approximately 30% for P680+Q(A)-. The parameters delta psi, pH(S) and pH(L) were analyzed as factors of PS II redox state populations and fluorescence yield. The kinetic mechanism of qE quenching is discussed, which is related with light induced pH(L) lumen acidification, when Q(A)- and P680+ recombination probability increases to regulate the QA reduction.     

Maximum likelihood prediction of lake acidity based on sedimented diatoms

Abstract. As an example of ecological gradient analysis, Gaussian response functions, with Poisson or quasi-Poisson error distribution, were fitted for diatom taxa on a pH gradient. It is possible to predict or infer the pH of lake water from the fitted curves using the method of maximum likelihood, which is easily implemented in standard non-linear regressionprograms. Due to overdis-persion with respect to the Poisson distribution, moment estimates forthe negative binomial distribution were also applied, both in estimating the species response curves and in prediction. Simulations indicated that the theoretical maximum precision (measuredby standard deviation of prediction errors) in our data set was 0.17 pH units. The observed errors were much greater (SD 0.35 to 0.43). It seems that roughly equal proportions of the excess error were caused (1) by systematic differences between the training (estimation) data and the validation (prediction) data, and (2) from a misspecified model. It is suggested that the error due to model misspecification consists of inadequacy of the presumed error distribution and of inadequacy of the simple Gaussian response function.     

Inclusion of fractal dimension in four machine learning algorithms improves the prediction accuracy of mean weight diameter of soil

The study of soil mean weight diameter (MWD), essential for sustainable soil management, has recently received much attention. As the estimation of MWD is challenging, labor-intensive, and time-consuming, there is a crucial need to develop a predictive estimation method to generate helpful information required for the soil health assessment to save time and cost involved in soil analysis. Pedotransfer functions (PTFs) are used to estimate parameters that are ‘difficult to measure’ and time-consuming with the help of ’easy to measure’ parameters. In the current study, empirical PTFs, i.e., multi-linear regression (MLR), and four machine learning based PTFs, i.e., artificial neural network (ANN), support vector machine (SVM), classification and regression trees (CART), and random forest (RF) were used for mean weight diameter prediction in Karnal district of Haryana, India. A total of 121 soil samples from 0‐15 and 15‐30 cm soil depths were collected from seventeen villages of Nilokheri, Nissing, and Assandh blocks of Karnal district. Soil parameters such as bulk density (BD), fractal dimension (D), soil texture (i.e., sand, silt, and clay), organic carbon (OC), and glomalin content were used as the input variables. Two input combinations, i.e., one with texture data (dataset 1) and the other with fractal dimension data replacing texture (dataset 2), were used, and the complete dataset (121) was divided into training and testing datasets in a 4:1 ratio. The model performance was evaluated by statistical parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R²). The comparison results showed that including the fractal dimension in the input dataset improved the prediction capability of ANN, SVM, and RF. MLR and CART showed lower predictive ability than the other three approaches (i.e., ANN, SVM, and RF). In the training dataset, RMSE (mm) for the SVM model was 8.33% lower with D than with texture as the input, whereas, in the testing dataset, it was 16.67% lower. Because SVM is more flexible and effectively captures non-linear relationships, it performed better than the other models in predicting MWD. As seen in this study, the SVM model with input data D is the best in its class and has a high potential for MWD prediction in the Karnal district of Haryana, India.     

基于混合效应的大别山地区杉木树高-胸径模型比较

基于安徽省大别山区马鬃岭林场杉木人工林30块样地1087组数据,选用7个常用树高-胸径(H-D)模型(线性模型、Chapman-Richards模型、Logistic模型等),采用最小二乘法拟合并选出最优基础模型(式11,只含D变量的Chapman-Richards模型),然后基于该模型构建含林分变量优势木平均高度、密度的H-D模型(式12),同时考虑样地水平的随机效应,分别基于式11、12构建混合模型(式13、14),并用幂函数、指数函数消除误差异方差,利用决定系数(R²)、均方根误差(RMSE)、平均绝对误差(MAE)和平均相对误差绝对值(MAPE)等指标来评价模型的拟合与预测能力,最终获取最优树高预测模型.结果表明:含林分变量的模型的拟合精度(式12,R²=0.863、RMSE=1.381、MAE=0.971)优于基础模型(式11,R²=0.827、RMSE=1.554、MAE=0.101).对于误差方差,幂函数、指数函数均能较好地消除异方差,但幂函数相对最好.混合模型的拟合与预测能力均优于式11、12,但混合模型(式13、14)之间的拟合与预测精度相差不大.基于混合效应的H-D模型(式13)能够较好地描述不同林分间H-D关系的差异,实际运用中可选用该模型来预测杉木树高,具有较高的预测精度.     

The Variability in Circadian Phase and Amplitude Estimates Derived from Sequential Constant Routines

Both the constant routine (CR) and the dim light melatonin onset have been suggested as reliable methods to determine circadian phase from a single circadian cycle. However, both techniques lack published studies quantifying the intercycle variability in their phase resolution. To address this question eight healthy male subjects participated in two CRs, 7 days apart. Circadian phase was determined using 3-min samples of core body temperature and two hourly urinary sulphatoxy melatonin excretion rates. Phase and amplitude were estimated using simple (24 h) and complex (24 + 12 h) cosinor models of temperature data and the onset, offset, and a distance-weighted-least-squares (DWLS) fitted acrophase for the melatonin metabolite. The variability in phase estimates was measured using the mean absolute difference between successive CRs. Using the simple 24 h model of temperature data, the mean absolute phase difference was 51 min (SD = 35 min). Using the complex model, the mean absolute phase difference was 62 min (SD = 35 min). Using the DWLS fitted acrophase for the melatonin metabolite, the mean absolute phase difference between CR1 and CR2 was 40 min (SD = 26 min). The results indicate that for CRs a week apart, the mean absolute difference in an individual's phase estimate can vary by 40-60 min depending on the choice of dependent measure and analytic technique. In contrast to the intraindi-vidual variability, the group results showed considerably less variability. The mean algebraic difference between CRs, using temperature- or melatonin-derived estimates, was less than 5 min, and well within the range of normal measurement error.     

The variability in circadian phase and amplitude estimates derived from sequential constant routines.

Both the constant routine (CR) and the dim light melatonin onset have been suggested as reliable methods to determine circadian phase from a single circadian cycle. However, both techniques lack published studies quantifying the intercycle variability in their phase resolution. To address this question eight healthy male subjects participated in two CRs, 7 days apart. Circadian phase was determined using 3-min samples of core body temperature and two hourly urinary sulphatoxy melatonin excretion rates. Phase and amplitude were estimated using simple (24 h) and complex (24 + 12 h) cosinor models of temperature data and the onset, offset, and a distance-weighted-least-squares (DWLS) fitted acrophase for the melatonin metabolite. The variability in phase estimates was measured using the mean absolute difference between successive CRs. Using the simple 24 h model of temperature data, the mean absolute phase difference was 51 min (SD = 35 min). Using the complex model, the mean absolute phase difference was 62 min (SD = 35 min). Using the DWLS fitted acrophase for the melatonin metabolite, the mean absolute phase difference between CR1 and CR2 was 40 min (SD = 26 min). The results indicate that for CRs a week apart, the mean absolute difference in an individual's phase estimate can vary by 40-60 min depending on the choice of dependent measure and analytic technique. In contrast to the intraindividual variability, the group results showed considerably less variability. The mean algebraic difference between CRs, using temperature- or melatonin-derived estimates, was less than 5 min, and well within the range of normal measurement error.     

Kinetic analysis of a chitinase from red sea bream, Pagrus major

Kinetic analysis was done on the 46-kDa chitinase (EC 3.2.1.14) purified from the stomach of red sea bream, Pagrus major, using glycolchitin and N-acetylchitooligosaccharides (GlcNAc(n), n=2-6) as substrates. High activity was observed at two pHs, such as 2.5 and 9.0, toward glycolchitin as seen in other insect chitinases, and also at both pH 2.5 and 5.0 even toward a short substrate, N-acetylchitopentasaccharide. Allosamidin competitively inhibited chitinase with Ki value of 0.0214 microM at pH 2.5 and 0.0024 microM at pH 9.0 in the reaction of glycolchitin. Substrate inhibition was observed in the reaction of N-acetylchitopentasaccharide. The anomeric forms of the products from N-acetylchitooligosaccharides were analyzed to be beta anomer by the high pressure liquid chromatography (HPLC) method. The data for both beta-anomer formation and allosamidin inhibition suggest that red sea bream chitinase belongs to family 18 of glycosyl hydrolases. This suggestion is also supported by the results for the N-terminal amino acid sequence.