Selectivity for glucose,glucose-6-phosphate,and pyruvate in ternary mixtures from the multivariate analysis of near-infrared spectra

Near-infrared spectroscopy offers the potential for direct in situ analysis in complex biological systems. Chemical selectivity is a critical issue for such measurements given the extent of spectral overlap of overtone and combination spectra. In this work, the chemical basis of selectivity is investigated for a set of multivariate calibration models designed to quantify glucose, glucose-6-phosphate, and pyruvate independently in ternary mixtures. Near-infrared spectra are collected over the combination region (4,000–5,000 cm⁻¹) for a set of 60 standard solutions maintained at 37 °C. These standard solutions are composed of randomized concentrations (0.5–30 mM) of glucose, glucose-6-phosphate, and pyruvate. Individual calibration models are constructed for each solute by using the partial least-squares (PLS) algorithm with optimized spectral range and number of latent variables. The resulting standard errors are 0.90, 0.72, and 0.32 mM for glucose, glucose-6-phosphate, and pyruvate, respectively. A pure component selectivity analysis (PCSA) demonstrates selectivity for each solute in these ternary samples. The concentration of each solute is also predicted for each sample by using a set of net analyte signal (NAS) calibration models. A comparison of the PLS and NAS calibration vectors demonstrates the chemical basis of selectivity for these multivariate methods. Selectivity of each PLS and NAS calibration model originates from the unique spectral features associated with the targeted analyte. Overall, selectivity is demonstrated for each solute with an order of sensitivity of pyruvate > glucose-6-phosphate > glucose. Figure Combination near-infrared spectroscopy allows selective analytical measurements for glucose, glucose-6-phosphate, and pyruvate in ternary mixtures owing to the uniqueness of the individual absorption spectra for each solute     

非参数固定效应Panel Data模型的统计推断

本文主要讨论了非参数固定效应Panel data模型的估计与检验问题,首先我们利用Profile最小二乘方法得到了固定效应与非参数部分的估计;接着基于比较原假设与备择假设下模型拟合的残差平方和的思想针对固定效应的检验问题构造了检验统计量,并给出了计算检验p-值的F分布逼近法。     

Parameter extension for combined hybrid finite element methods and application to plate bending problems

Based on a weighted average of the modified Hellinger-Reissner principle and its dual, the combined hybrid finite element (CHFE) method was originally proposed with a combination parameter limited in the interval (0, 1). In actual computation this parameter plays an important role in adjusting the energy error of discretization models. In this paper, a novel expression of the combined hybrid variational form is used to show the relationship between the resultant method and some Galerkin/least-squares stabilized finite scheme for plate bending problems. The choice of combination parameter is then extended to (−∞, 0) ? (0, 1). Existence, uniqueness and convergence of the solution of discrete schemes are proved, and the advantage of the parameter extension in computation is discussed. As an application, improvement of Adini’s rectangular element by the CHFE approach is performed.     

NA样本半参数回归模型估计的矩相合性

考虑了误差为NA序列的半参数回归模型,利用非参数估计方法给出了模型参数的最小二乘估计和加权最小二乘估计,并在适当条件下得到了它们的矩相合性.     

Cooperative effect of monoclinic distortion and sinusoidal modulation in the martensitic structure of Ni2FeGa

The structural features of the “5M” martensitic phase in Ni₂FeGa alloys have been determined by electron diffraction using the multi-slice least-squares (MSLS) method. The results demonstrate that the “5M” phase contains an evident cooperative effect of monoclinic distortion and sinusoidal modulation along the [110]_c direction. Theoretical simulations based on our refined data suggest that the “5M” martensitic phase observed in Ni-Fe-Ga and Ni-Mn-Ga has visible common behaviors in both stacking sequence and local structural distortion. Considering the cooperative effect of monoclinic distortion and sinusoidal modulation, we demonstrate that the “7M” martensitic phase could adopt two equivalent structural phases corresponding with the stacking sequences of and , respectively.     

Analysis of split weighted least-squares procedures for pseudo-hyperbolic equations

In this paper, we introduce two novel split weighted least-squares finite element procedures for pseudo-hyperbolic equations arising in the modelling of nerve conduction process. By selecting the weighted least-squares functional properly, each procedure can be split into two independent symmetric positive definite sub-procedures. One of sub-procedures is for the primitive unknown variable, which is the same as the standard Galerkin finite element procedure and the other is for the introduced flux variable. Optimal order error estimates are developed and the numerical example is given to show the efficiency of the introduced schemes.     

Simultaneous spectrophotometric determination of drugs in pharmaceutical preparations using multiple linear regression and partial least-squares regression, calibration and prediction methods

Two new methods for the simultaneous determination of acetylsalicylic acid, acetaminophen and caffeine based on total absorbance measurements and their processing by multiple linear regession and partial least-squares regression are proposed. The concentration ranges used to construct the calibration matrix were 4.0-12.0, 2.0-10.0 and 0.9-6.0 μg ml⁻¹ for acetylsalicylic acid, acetaminophen and caffeine respectively. The proposed methods were validated by using a set of synthetic sample mixtures and subsequently applied to the determination of the three active principles in three different pharmaceutical preparations.     

Efficient experimental designs when most treatments are unreplicated

In early generation variety trials, large numbers of new breeders’ lines (varieties) may be compared, with each having little seed available. A so-called unreplicated trial has each new variety on just one plot at a site, but includes several replicated control varieties, making up around 10% and 20% of the trial. The aim of the trial is to choose some (usually around one third) good performing new varieties to go on for further testing, rather than precise estimation of their mean yields.Now that spatial analyses of data from field experiments are becoming more common, there is interest in an efficient layout of an experiment given a proposed spatial analysis and an efficiency criterion. Common optimal design criteria values depend on the usual C-matrix, which is very large, and hence it is time consuming to calculate its inverse. Since most varieties are unreplicated, the variety incidence matrix has a simple form, and some matrix manipulations can dramatically reduce the computation needed. However, there are many designs to compare, and numerical optimisation lacks insight into good design features. Some possible design criteria are discussed, and approximations to their values considered. These allow the features of efficient layouts under spatial dependence to be given and compared.     

A primal-dual interior-point method for linear programming based on a weighted barrier function

One motivation for the standard primal-dual direction used in interior-point methods is that it can be obtained by solving a least-squares problem. In this paper, we propose a primal-dual interior-point method derived through a modified least-squares problem. The direction used is equivalent to the Newton direction for a weighted barrier function method with the weights determined by the current primal-dual iterate. We demonstrate that the Newton direction for the usual, unweighted barrier function method can be derived through a weighted modified least-squares problem. The algorithm requires a polynomial number of iterations. It enjoys quadratic convergence if the optimal vertex is nondegenerate.The research of the second author was supported in part by ONR Grants N00014-90-J-1714 and N00014-94-1-0391.