Screening of Human CYP1A2 and CYP3A4 Inhibitors from Seaweed In Silico and In Vitro

Phenolic compounds and carotenoids are potential inhibitors of cytochrome P450s. Sixteen known compounds, phenolic compounds and carotenoids from seaweed were examined for potential inhibitory capacity against CYP1A2 and CYP3A4 in silico and in vitro. Morin, quercetin, and fucoxanthin inhibited the enzyme activity of CYP1A2 and CYP3A4 in a dose-dependent manner. The IC₅₀ values of morin, quercetin, and fucoxanthin were 41.8, 22.5, and 30.3 μM for CYP1A2 and 86.6, 16.1, and 24.4 μM for CYP3A4, respectively. Siphonaxanthin and hesperidin did not show any significant effect on CYP1A2, but they slightly inhibited CYP3A4 activity at high concentrations. In silico modeling of CYP’s binding site revealed that the potential inhibitors bound in the cavity located above the distal surface of the heme prosthetic group through the 2a or 2f channel of CYPs. This study presents an approach for quickly predicting CYP inhibitory activity and shows the potential interactions of compounds and CYPs through in silico modeling.     

Genomic prediction for yields,processing and nutritional quality traits in cultivated potato (Solanum tuberosum L.)

Current potato breeding approaches are hampered by several factors including costly seed tubers, tetrasomic inheritance and inbreeding depression. Genomic selection (GS) demonstrated interesting results regardless of the ploidy level, and can be harnessed to circumvent these problems. In this work, three GS models were evaluated using 50,107 informative SilicoDArT markers and 11 traits in two values for cultivation and use (VCU) potato trials. Two key breeding problems modelled included predicting the performance of (i) new and unphenotyped clones (cross‐validation) and (ii) a VCU using another as training set (TS). GS models performed comparably. Cross‐validation accuracy was high for D35, D45, DMW and BVAL, in ascending order. Prediction accuracies of the VCUs were highly correlated, but the best prediction was obtained for the smaller VCU using the bigger as TS. Cross‐validation and VCU prediction accuracies were higher when bigger TSs were used. The findings herein indicate that GS can be attractively integrated in potato breeding, particularly in early clonal generations to predict and select for traits with low heritability which would otherwise require more testing years, environments and resources.     

Geostatistical interpolation and aggregation of crop growth model outputs

Many crop growth models require daily meteorological data. Consequently, model simulations can be obtained only at a limited number of locations, i.e. at weather stations with long-term records of daily data. To estimate the potential crop production at country level, we present in this study a geostatistical approach for spatial interpolation and aggregation of crop growth model outputs. As case study, we interpolated, simulated and aggregated crop growth model outputs of sorghum and millet in West-Africa. We used crop growth model outputs to calibrate a linear regression model using environmental covariates as predictors. The spatial regression residuals were investigated for spatial correlation. The linear regression model and the spatial correlation of residuals together were used to predict theoretical crop yield at all locations using kriging with external drift. A spatial standard deviation comes along with this prediction, indicating the uncertainty of the prediction. In combination with land use data and country borders, we summed the crop yield predictions to determine an area total. With spatial stochastic simulation, we estimated the uncertainty of that total production potential as well as the spatial cumulative distribution function. We compared our results with the prevailing agro-ecological Climate Zones approach used for spatial aggregation. Linear regression could explain up to 70% of the spatial variation of the yield. In three out of four cases the regression residuals showed spatial correlation. The potential crop production per country according to the Climate Zones approach was in all countries and cases except one within the 95% prediction interval as obtained after yield aggregation. We concluded that the geostatistical approach can estimate a country’s crop production, including a quantification of uncertainty. In addition, we stress the importance of the use of geostatistics to create tools for crop modelling scientists to explore relationships between yields and spatial environmental variables and to assist policy makers with tangible results on yield gaps at multiple levels of spatial aggregation.     

土耳其斯坦裂体吸虫23kDa蛋白基因的序列分析及抗原表位预测

为研究土耳其斯坦裂体吸虫23 kDa蛋白的生物学特性,以土耳其斯坦裂体吸虫cDNA为模版,利用PCR对23 kDa基因进行扩增,并将其克隆到T-easy载体后进行序列测定。利用生物信息学对其结构、抗原指数进行分析,并对其抗原表位进行预测。序列分析结果表明,该虫体的23 kDa基因长度为657 bp,A+T含量为57.38%,与曼氏血吸虫、埃及血吸虫和日本血吸虫的23 kDa基因的相似性分别为85.24%、83.71%和81.89%。蛋白二级结构分析表明,23 kDa蛋白经过4次跨膜,主要由6个α-螺旋、3个β-折叠、7个β转角和若干个无规则卷曲构成。抗原表位预测结果表明,该蛋白有3个B细胞抗原表位。综合分析土耳其斯坦裂体吸虫23 kDa蛋白是一种较好的抗原分子,是土耳其斯坦裂体吸虫疫苗的重要候选分子。     

鸡白细胞介素2的克隆及其序列分析

旨在从分子生物学角度进一步研究鸡免疫刺激因子白细胞介素2(IL-2),探究IL-2蛋白结构。根据GenBank发表的IL-2序列设计引物扩增IL-2基因,将其克隆到p GM-T载体后进行鉴定,鉴定正确的pT-IL-2重组质粒,然后对其进行序列分析。应用生物信息学软件对IL-2序列进行跨膜区、磷酸化位点、氨基酸序列、开放阅读框进行分析和IL-2蛋白的三级结构进行预测。核苷酸序列测定结果表明:IL-2基因片段为430 bp,编码143个氨基酸。生物信息学分析结果表明:本实验获得的IL-2基因编码的蛋白质中含有1个丝氨酸和3个苏氨酸,这可能成为蛋白激酶磷酸化的位点。IL-2蛋白可能在5~28位含有跨膜区。其蛋白二级、三级结构预测结果显示其属于亲水性蛋白,多为α-螺旋、β-转角和N端无规卷曲结构。该结果为IL-2的分子生物学探究奠定了基础。     

基于智能算法优化SVM的横向通风过程中温度场预测方法探究

将改进的智能预测技术应用于储粮横向通风过程中的粮堆温度预测,为粮食通风智能预测与决策提供了一种新思路。选取河北清苑国家粮食储备库冬季横向通风的实时监测数据,在分析主要影响因素的基础上,应用三种智能优化算法——网格寻优算法、GA遗传算法寻优、PSO粒子群算法,结合回归支持向量机理论,对粮堆的通风过程进行建模。结果表明,优化过的回归预测模型能较好地拟合粮食温度与其他变量之间的非线性关系,尤其是当样本数量较为有限时,该方法具有更高的拟合精度,更适合对储粮通风这一强非线性过程的预测研究,对于人工干预操作具有一定的现实指导意义。     

基于“3S”技术的辽宁省某尾矿库扩容工程生态影响预测与评价

以辽宁省某尾矿库为例,基于GIS技术,利用2010年的TM遥感影像及地形图,预测该尾矿库扩容后的生态影响。分析结果显示:尾矿库扩容工程对生态的影响主要体现在土地利用、植被、生态景观和地形地貌的变化4个方面。     

基于Lasso和BP神经网络的组合预测及其应用——以居民消费支出预测为例

在变量选择的基础上，构建基于Lasso方法和BP神经网络的预测模型，并对我国城乡居民的消费支出进行预测，结果显示：基于Lasso方法和BP神经网络的组合预测精度要明显高于BP神经网络、Lasso方法的预测精度；在2014~2020年，我国农村居民消费增长率有所提升，城镇居民消费增长率减缓，城乡居民消费增长率之间的差距呈下降趋势，但短期内城乡居民消费差距依然难以缓和。     

基于贡献度随机森林模型的公司债信用风险实证分析

运用贡献度随机森林方法（CRF）方法探讨公司债财务指标比率与其违约率的关系.运用连续属性离散化方法（OB）进行财务指标最优降维；运用WOE变换进行模型变量约简.研究表明，CRF模型的分类性能显著优于其他模型，测试集评估总体正确率达90.47%，AUC统计量、AR比率及K-S值分别提升了2.6%、7.6%、4.38%，变量贡献度量化了各财务指标对违约率影响，为诠释随机森林预测机制提供了依据.     

Bioinformatics Analysis of Bone Morphogenetic Protein 4 (BMP4) of Sheep

The assay was aimed to explore the biological characteristics of bone morphogenetic protein 4 (BMP4) of sheep,NCBI,DNAMAN DNAStar,TMHMM Server v.2.0,PsortⅡ,SignalP various bioinformatical softwares were used to speculate the physical and chemical properties,hydrophobic property,phosphorylation site,conservative structure domain,protein secondary structure of BMP4 protein.Also,the three-dimensional structure was forecasted with the SWISS-MODEL Workspace software.The results indicated that the BMP4 of sheep had high homologies with the BMP4 of various species.The encoded protein was a hydrophilic protein which was unstable.There was no transmembrane regions and it was likely to be located in the nucleus.What was more,there was signal peptide and eighteen phosphorylation sites.Through the forecast of functional domains,the protein had two functional domains,including the transforming growth factor-beta (TGF-beta) superfamily and TGF-beta propeptide superfamily.The result was consistent with the function of BMP4 gene family,it also demonstrated that BMP4 was a growth factor and it had the function of signal transduction.The amino acid homology between the predicted 3D structure of protein and template 3bmp.1.A was 88.29%.The bioinformatics analysis of BMP4 gene could provide reference for the further study in practice.