Designed Syntheses of Three {Ni6PW9}-Based Polyoxometalates,from Isolated Cluster to Cluster-Organic Helical Chain

Three new hexa-Ni-substituted Keggin-type polyoxometalates (POMs), [Ni₆(OH)₃- (DACH)₃(H₂O)₆(PW₉O₃₄)]·31H₂O (1), [Ni(DACH)₂][Ni₆(OH)₃(DACH)₃(HMIP)₂(H₂O)₂(PW₉O₃₄)]·56 H₂O (2), and [Ni(DACH)₂][Ni₆(OH)₃(DACH)₂(AP)(H₂O)₅(PW₉O₃₄)]·2H₂O (3) (DACH = 1,2-Diami- nocyclohexane, MIP = 5-Methylisophthalate, AP = Adipate) were successfully made in the presence of DACH under hydrothermal conditions. 1 is an isolated hexa-Ni-substituted Keggin unit decorated by DACH. In order to further construct POM cluster-organic frameworks (POMCOFs) on the basis of 1, by analyzing the steric hindrances and orientations of the POM units, the rigid HMIP and flexible AP ligands were successively incorporated, and another anionic monomeric POM 2 and the new 1D POM cluster organic chain (POMCOC) 3 were obtained. HMIP ligand still acts as a decorating group on the Ni₆ core of 2 but results in the different spatial arrangement of the {Ni₆PW₉} units. AP ligands in 3 successfully bridge adjacent isolated POM cluster units to 1D POMCOC with left-hand helices. The AP in 3 is the longest aliphatic carboxylic acid ligand in POMs, and the 1D POM cluster-AP helical chain represents the first 1D POMCOC with a helical feature.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

变系数计量经济学联立模型的局部线性工具向量估计及其性质

联立方程计量经济学模型在经济政策制定、经济结构分析和经济预测方面起着重要作用。本文首次提出了我国宏观经济的一类变系数联立模型,并建立了函数系数的局部线性工具向量估计,同时在时间点列固定设计、经济变量随机设计条件下,研究了估计量的大样本性质。与我国宏观经济经典线性联立模型相比,变系数联立模型拟合效果更优。另外它也有助于克服我国宏观经济数据不多而造成的非参数方法应用困难的现实情况。     

A MALDI-MS sensing chip prepared by non-covalent assembly for quantitation of acid phosphatase

A novel sensing chip was designed for MALDI-MS quantitation of acid phosphatase(ACP).The ACP sensing chip was constructed through non-covalent interaction of streptavidin and biotin for the assembly of biotinylated peptide substrate on biotinylated polyethylene-glycol(PEG)modified indium-tin oxide(ITO)slide.In the presence of ACP,the peptide substrate was dephosphorylated under acidic condition to generate a new mass signal.The quantitative assay of ACP was achieved with the mass signal ratio of product to the sum of product and left peptide substrate.Under optimal detection conditions,the ratio was linearly correlated with the concentration of ACP in the range of 0.05–12 g/L with a detection limit(LOD)of 0.04 g/L.The designed ACP sensing chip has been used to analyze ACP in complex clinical samples,which exhibited high selectivity,good repeatability,and admirably anti-interference ability.This work further demonstrates the concept of MS sensing and the application of MALDI-MS in quantitative analysis,and provides a convenient method for the quantitation of proteases in clinical diagnosis.     

华根霉菌丝体结合脂肪酶催化酯合成动力学拆分2-辛醇

研究了华根霉CCTCC M201021菌丝体结合脂肪酶(RCL)在非水相中催化酯合成动力学拆分外消旋2-辛醇的能力.发现RCL对该反应具有较好的光学选择性(E>100),辛酸和异辛烷分别是最佳的酰基供体和反应溶剂,体系水活度的减少对反应的光学选择性没有明显影响,但能显著提高反应初速度.在相同转化率下,通过添加3A分子筛降低体系水含量可使反应初始速度提高7.3倍.当底物浓度提高到0.230 mol/L,反应40 h时转化率达44.4%,产物酯的ee值为94.7%.与三种商品化脂肪酶进行了比较,发现在相同条件下RCL对2-辛醇的拆分不但具有较好的光学选择性(E=103.1),而且也表现出较高的反应初速度和转化率.     

不同品种胡萝卜中β-胡萝卜素的测定与分析

利用紫外分光光度法测定了27个不同品种,同一品种不同产地胡萝卜中所含的β-胡萝卜素含量。结果表明：品种中加一号胡萝卜所含β-胡萝卜素最高,达平均400．08mg／100g,其次是红芯六号,达平均272．86mg／100g,第三是含量较高的日本特选金笋六寸,平均含量达270．42mg／100g,石河子品种新胡萝卜一号所含β-胡萝卜素也较高。而同一品种不同产地的新黑田五寸胡萝卜所含隆胡萝卜素也有差别,但是都在150mg／100g左右。本实验的研究为胡萝卜育种,加工食品胡萝卜原材料的选材有重要的指导意义。     

棉花地上各组分干物质积累量的高光谱定量模型研究

测定了8个棉花主栽品种(其中2棉花品种为4水平种植密度)冠层多时相的反射光谱数据和对应的棉花地上茎、叶、生殖器官(花、蕾、铃)及冠层的干物质积累量;通过反射光谱数据与棉花冠层干物质积累量的逐步回归分析,确定与棉花冠层干物质积累量相关性最高的2个敏感波段——近红外934nm和红光673nm波段,采用这2个波段的反射率组成高光谱比值植被指数(RVI);基于RVI建立了6种函数形式的棉花地上各组分干物质积累量(茎、叶片、生殖组分、冠层)的反演模型,相关系数均达到了极显著的检验水平(α=1%,n=95),其中以构建的棉花地上茎组分干物质积累量的指数函数的反演模型相关系数为最高(R=0.8661**,RMSE=0.1293),在所构建的棉花地上各组分的干物质积累量模型中,均以指数函数、幂函数、对数函数和双曲线函数形式模型的预测精度为最高,研究结果表明,利用高光谱RVI可以实时、无损、快速、定量反演棉花地上各组分的干物质积累量,为高光谱遥感用于棉花长势监测和遥感估产提供了依据。     

大环分子衍生物在人工跨膜离子通道领域中的研究进展

大环分子如环糊精、冠醚等因具备独特的空腔结构,能够通过主-客体相互作用实现对分子、离子的特异性识别,因此常被应用于人工跨膜离子传输系统的研究和开发.与天然的通道蛋白相比,大环分子具有价格低廉、性质稳定、结构易于修饰和功能化等诸多优点,因而备受科研工作者们的关注.本综述详细介绍了大环分子在人工离子通道领域中的研究进展,按...     

Structural and Bonding Properties of AlnC4-/0 (n=2-4) Clusters:Anion Photoelectron Spectroscopy and Theoretical Calculations

We measured the photoelectron spectra of AlnC4-(n=2-4) clusters by using size-selected anion photoelectron spectroscopy.The structures of AlnC4-/0 (n=2-4) clust...     

基于行列异或的Arnold双置乱图像加密方法

针对目前加密图像单一以及加密算法安全性不高的问题,提出基于行列异或的Arnold双置乱图像加密方法。实施过程：首先根据Arnold变换公式对图像进行灰度空间置乱和位置空间置乱,然后进行行按位异或操作,其次,将上述结果再进行一次位置空间置乱,最后再进行列按位异或运算。该方法有效增强了传统Arnold双置乱的置乱效果,使图像得到充分扩散,最终得到密文图像。实验结果表明,本文方法能实现等长和不等长图像的置乱,改善了常规Arnold方法的置乱效果,有效降低了图像相邻像素间的相关性,保证了图像传输过程中的安全性和鲁棒性要求。