Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R²) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.     

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

Dynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.     

Toll-Like Receptor 7 Agonists: Chemical Feature Based Pharmacophore Identification and Molecular Docking Studies

Chemical feature based pharmacophore models were generated for Toll-like receptors 7 (TLR7) agonists using HypoGen algorithm, which is implemented in the Discovery Studio software. Several methods tools used in validation of pharmacophore model were presented. The first hypothesis Hypo1 was considered to be the best pharmacophore model, which consists of four features: one hydrogen bond acceptor, one hydrogen bond donor, and two hydrophobic features. In addition, homology modeling and molecular docking studies were employed to probe the intermolecular interactions between TLR7 and its agonists. The results further confirmed the reliability of the pharmacophore model. The obtained pharmacophore model (Hypo1) was then employed as a query to screen the Traditional Chinese Medicine Database (TCMD) for other potential lead compounds. One hit was identified as a potent TLR7 agonist, which has antiviral activity against hepatitis virus in vitro. Therefore, our current work provides confidence for the utility of the selected chemical feature based pharmacophore model to design novel TLR7 agonists with desired biological activity.     

Three-dimensional quantitative structure-activity relationship (3 D-QSAR) and docking studies on (benzothiazole-2-yl) acetonitrile derivatives as c-Jun N-terminal kinase-3 (JNK3) inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 44 (benzothiazole-2-yl) acetonitrile derivatives, inhibiting c-Jun N-terminal kinase-3 (JNK3). It includes molecular field analysis (MFA) and receptor surface analysis (RSA). The QSAR model was developed using 34 compounds and its predictive ability was assessed using a test set of 10 compounds. The predictive 3D-QSAR models have conventional r2 values of 0.849 and 0.766 for MFA and RSA, respectively; while the cross-validated coefficient r(cv)2 values of 0.616 and 0.605 for MFA and RSA, respectively. The results of the QSAR model were further compared with a structure-based analysis using docking studies with crystal structure of JNK3. Ligands bind in the ATP pocket and the hydrogen bond with GLN155 was found to be crucial for selectivity among other kinases. The results of 3D-QSAR and docking studies validate each other and hence, the combination of both methodologies provides a powerful tool directed to the design of novel and selective JNK3 inhibitors.     

Application of multivariate analysis to optimize function of cultured hepatocytes

Understanding the metabolic and regulatory pathways of hepatocytes is important for biotechnological applications involving liver cells, including the development of bioartificial liver (BAL) devices. To characterize intermediary metabolism in the hepatocytes, metabolic flux analysis (MFA) was applied to elucidate the changes in intracellular pathway fluxes of primary rat hepatocytes exposed to human plasma and to provide a comprehensive snapshot of the hepatic metabolic profile. In the current study, the combination of preconditioning and plasma supplementation produced distinct metabolic states. Combining the metabolic flux distribution obtained by MFA with methodologies such as Fisher discriminant analysis (FDA) and partial least squares or projection to latent structures (PLS) provided insights into the underlying structure and causal relationship within the data. With the aid of these analyses, patterns in the cellular response of the hepatocytes that contributed to the separation of the different hepatic states were identified. Of particular interest was the recognition of distal pathways that strongly correlated with a particular hepatic function. The hepatic functions investigated were intracellular triglyceride accumulation and urea production. This study illustrates a framework for optimizing hepatic function and a possibility of identifying potential targets for improving hepatic functions.     

Analysis of genetic effects of major genes and polygenes on quantitative traits. II. Genetic models for seed traits of crops

Genetic models for quantitative seed traits with effects of several major genes and polygenes, as well as their GE interaction, were proposed. Mixed linear model approaches were suggested for analyzing the genetic models. Monte Carlo simulations were conducted to evaluate unbiasedness and efficiency for estimating fixed effects and variance components of the embryo and the endosperm models, including effects of a major gene from an unbalanced modified diallel mating design with nine parents, respectively. Simulation results showed that estimates of generalized least squares (GLS) were unbiased and efficient, while those of ordinary least squares (OLS) were almost as good as GLS. Minimum norm quadratic unbiased estimation (MINQUE) could obtain unbiased estimates of the variance components. It was also suggested that precision of MINQUE estimation would be improved with augmentation of experimental size. Data from a modified diallel design in upland cotton ( Gossypium hirsutum L.) were used as a worked example to illustrate the parameter estimation.     

First report on exploring structural requirements of alpha and beta thymidine analogs for PfTMPK inhibitory activity using in silico studies

With the emergence of multi-drug resistance of the currently available antimalarial drugs including the “magic bullet” artemisinin derivatives in the market, there is an urgent need for discovery and development of new potent antimalarial molecules. The present work deals with quantitative structure–activity relationship (QSAR) modeling, pharmacophore mapping and docking studies of a series of 35 thymidine analogs as inhibitors of Plasmodium falciparum thymidylate kinase (PfTMPK), an enzyme that catalyzes phosphorylation of thymidine monophosphate (TMP) to thymidine diphosphate (TDP). The models were validated both internally and externally and significant statistical results were obtained, indicating the robustness and reliability of the developed models. The docking study was performed using the LigandFit option of receptor–ligand interactions protocol section available in Discovery Studio 2.1 where lower RMSD values (0.6931 Å) between the co-crystallized ligand and re-docked ligand assured that the ligand was bound in the same binding pocket. The QSAR, pharmacophore mapping and docking studies provide an understanding of important structural requirements or essential molecular properties, or features of molecules, and important binding interactions, and provide an important guidance for the chemist to synthesis of new molecules with improved PfTMPK inhibitory activity profile. This work revealed the importance of –NH-fragment, electrophilicity of the molecules and the number of oxygen atom towards the PfTMPK inhibitory activity of the molecules. To the best of our knowledge, this work presents the first QSAR and pharmacophore report for thymidine analogs which may serve as an efficient tool for the design and synthesis of potent molecules as PfTMPK inhibitors to address the increasing threat of multi-drug resistance against P. falciparum.     

Tandem mass spectrometry: a novel approach for metabolic flux analysis

The goal of metabolic flux analysis (MFA) is the accurate estimation of intracellular fluxes in metabolic networks. Here, we introduce a new method for MFA based on tandem mass spectrometry (MS) and stable-isotope tracer experiments. We demonstrate that tandem MS provides more labeling information than can be obtained from traditional full scan MS analysis and allows estimation of fluxes with better precision. We present a modeling framework that takes full advantage of the additional labeling information obtained from tandem MS for MFA. We show that tandem MS data can be computed for any network model, any compound and any tandem MS fragmentation using linear mapping of isotopomers. The inherent advantages of tandem MS were illustrated in two network models using simulated and literature data. Application of tandem MS increased the observability of the models and improved the precision of estimated fluxes by 2- to 5-fold compared to traditional MS analysis.     

New Methods for Characterization of Complex Receptor Systems Involving 3 or More Binding Sites: Application to Brain Opiate Receptors

Abstract

The problems of evaluating results based on the analysis of complex binding models are considered and new methods are proposed to provide independent confirmation of the existence of multiple sites. A new plotting format for the results from experiments involving two ligands is introduced, and its utility is demonstrated for a) finding initial estimates for nonlinear least squares curve fitting; b) presenting the results of multiple experiments; and c) giving a new means for evaluating the significance of a third site. The general problem of finding initial estimates for models involving three classes of sites, and strategies for using nonlinear least squares curve fitting algorithms to optimize the fit are considered.     

Comparative molecular field analysis of some pyridazinone-containing alpha1-antagonists

Diverse series of piperazines linked at N1 to 4, 5, or 6 positions of 3-(2H)-pyridazinone ring and at N4, by a suitable alkyl spacer, to some classical alpha1-adrenoceptor pharmacophore moieties, were tested in vitro for their alpha1-adrenoceptor antagonist activity. The modeling of their biological activity (pKb) by comparative molecular field analysis led to the development of a statistically significant partial least squares (PLS) model able to detect at 3-D level the main physicochemical interactions responsible for alpha1-adrenoceptor antagonist activity.     

Design of EGFR kinase inhibitors: a ligand-based approach and its confirmation with structure-based studies

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 100 anilinoquinazolines, inhibiting epidermal growth factor receptor (EGFR) kinase. The studies included molecular field analysis (MFA) and receptor surface analysis (RSA). The cross-validated r2 (r2cv) values are 0.81 and 0.79 for MFA and RSA, respectively. The predictive ability of these models was validated by 28 test set molecules. The results of the best QSAR model were further compared with structure-based investigations using docking studies with the crystal structure of EGFR kinase domain. The results helped to understand the nature of substituents at the 6- and 7-positions, thereby providing new guidelines for the design of novel inhibitors.     

Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows

Milk fatty acid (MFA) have already been used to model methane (CH₄) emissions from dairy cows. However, the data sets used to develop these models covered limited variation in dietary conditions, reducing the robustness of the predictions. In this study, a data set containing 140 observations from nine experiments (41 Holstein cows) was used to develop models predicting CH₄ expressed as g/day, g/kg dry matter intake (DMI) and g/kg milk. The data set was divided into a training (n=112) and a test data set (n=28) for model development and validation, respectively. A generalized linear mixed model was fitted to the data using the marginal R²_(m) and the Akaike information criterion to evaluate the models. The coefficient of determination of validation (R²_(v)) for different models developed ranged between 0.18 and 0.41. Form the intake-related parameters, only inclusion of total DMI improved the prediction (R²_(v)=0.58). In addition, in an attempt to further explore the relationships between MFA and CH₄ emissions, the data set was split into three categories according to CH₄ emissions: LOW (lowest 25% CH₄ emissions); HIGH (highest 25% CH₄ emissions); and MEDIUM (50% remaining observations). An ANOVA revealed that concentrations of several MFA differed for observations in HIGH compared with observations in LOW. Furthermore, the Gini coefficient was used to describe the MFA distribution for groups of MFA in each CH₄ emission category. The relative distribution of the MFA, particularly of the odd- and branched-chain fatty acids and mono-unsaturated fatty acids of observations in category HIGH differed from those in the other categories. Finally, in an attempt to validate the potential of MFA to identify cases of high or low emissions, the observations were re-classified into HIGH, MEDIUM and LOW according to the proportion of each individual MFA. The proportion of observations correctly classified were recorded. This was done for each individual MFA and for the calculated Gini coefficients, finding that a maximum of 67% of observations were correctly classified as HIGH CH₄ (trans-12 C18:1) and a maximum of 58% of observations correctly classified as LOW CH₄ (cis-9 C17:1). Gini coefficients did not improve this classification. These results suggest that MFA are not yet reliable predictors of specific amounts of CH₄ emitted by a cow, while holding a modest potential to differentiate cases of high or low emissions.     

基于DWT-GA-PLS的土壤碱解氮含量高光谱估测方法

以山东齐河县为研究区,实地采集土壤样本,在土样高光谱测试并进行一阶导数变换的基础上,先运用离散小波变换(DWT)对土壤光谱去噪降维,然后采用遗传算法(GA)筛选土壤碱解氮定量估测模型的参与变量,最后应用偏最小二乘(PLS)回归构建土壤碱解氮含量的估测模型.结果表明: 离散小波变换结合遗传算法和偏最小二乘法(DWT-GA-PLS)用于土壤碱解氮含量定量估测,不仅可压缩光谱变量、减少模型参与变量,而且可改善模型估测准确度;较之于采用土壤全谱,小波离散分解1～2层低频系数构建的模型在参与变量大幅减少的情况下,取得更准确或与之相当的预测结果,其中,基于第2层小波低频系数采用GA筛选变量构建的PLS模型的预测效果表现最好,预测R²达到0.85,RMSE为8.11 mg·kg^-1,RPD为2.53.说明DWT-GA-PLS用于土壤碱解氮含量高光谱定量估测的有效性.     

The discovery of inhibitors of Fas-mediated cell death pathway using the combined computational method

In this study, pharmacophore and 3D-QSAR models were developed for analogues of 3-substituted-benzofuran-2-carboxylate as inhibitors of Fas-mediated cell death pathways. Our pharmacophore model has good correspondence with experimental results and can explain the variance in biological activities coherently with respect to the structure of the data set compounds. The predictive power for our synthesized compounds were 0.96 for the pharmacophore model, 0.58 for the comparative molecular field analysis (CoMFA) model, and 0.57 for the comparative molecular similarity analysis (CoMSIA) model.     

Relationships between methane emission of Holstein Friesian dairy cows and fatty acids,volatile metabolites and non-volatile metabolites in milk

This study investigated the relationships between methane (CH₄) emission and fatty acids, volatile metabolites (V) and non-volatile metabolites (NV) in milk of dairy cows. Data from an experiment with 32 multiparous dairy cows and four diets were used. All diets had a roughage : concentrate ratio of 80 : 20 based on dry matter (DM). Roughage consisted of either 1000 g/kg DM grass silage (GS), 1000 g/kg DM maize silage (MS), or a mixture of both silages (667 g/kg DM GS and 333 g/kg DM MS; 333 g/kg DM GS and 677 g/kg DM MS). Methane emission was measured in climate respiration chambers and expressed as production (g/day), yield (g/kg dry matter intake; DMI) and intensity (g/kg fat- and protein-corrected milk; FPCM). Milk was sampled during the same days and analysed for fatty acids by gas chromatography, for V by gas chromatography–mass spectrometry, and for NV by nuclear magnetic resonance. Several models were obtained using a stepwise selection of (1) milk fatty acids (MFA), V or NV alone, and (2) the combination of MFA, V and NV, based on the minimum Akaike’s information criterion statistic. Dry matter intake was 16.8±1.23 kg/day, FPCM yield was 25.0±3.14 kg/day, CH₄ production was 406±37.0 g/day, CH₄ yield was 24.1±1.87 g/kg DMI and CH₄ intensity was 16.4±1.91 g/kg FPCM. The observed CH₄ emissions were compared with the CH₄ emissions predicted by the obtained models, based on concordance correlation coefficient (CCC) analysis. The best models with MFA alone predicted CH₄ production, yield and intensity with a CCC of 0.80, 0.71 and 0.69, respectively. The best models combining the three types of metabolites included MFA and NV for CH₄ production and CH₄ yield, whereas for CH₄ intensity MFA, NV and V were all included. These models predicted CH₄ production, yield and intensity better with a higher CCC of 0.92, 0.78 and 0.93, respectively, and with increased accuracy (C_b) and precision (r). The results indicate that MFA alone have moderate to good potential to estimate CH₄ emission, and furthermore that including V (CH₄ intensity only) and NV increases the CH₄ emission prediction potential. This holds particularly for the prediction model for CH₄ intensity.     

籼稻品质分析的近红外光谱模型建立及其应用研究

为了满足籼稻品质快速分析的需求,本研究利用籼稻精米粉近红外光谱建立了直链淀粉含量、蛋白质含量、碱消值、垩白度的回归预测模型.结果表明,本研究提供的预测模型具有良好的测定效果,用偏最小二乘法(PLS)获得的籼稻精米粉直链淀粉含量、蛋白质含量、碱消值、垩白度的回归模型和交叉验证显示最优校正决定系数(R~2)和交叉检验均方误差(RMSECV)分别为0.9561、1.55,0.9510、0.258,0.9076、0.283,0.9014、4.14.说明所建的近红外光谱预测模型具有实用价值.