Investigation of the effects of 24 bio-matrices on the LC-MS/MS analysis of morinidazole

This study compares and evaluates the effect of various matrices on liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) analysis. Permanent post-column infusion (PCI) was used to quantify matrix effects. In this way, the suppressed or enhanced signal of the target material resulting from different co-eluting matrix components could be assessed. Twenty-four biological samples from in vivo and in vitro experiments were selected for this study. In addition, 7 sample components were further analyzed after sample preparation by protein precipitation. Multiple regression analysis was used to investigate the collinear relationship between matrix effects and co-eluted components at different time intervals. We found that salt was the dominant factor which impacted changes in signal detection. In order to eliminate it, we used ammonium formate as a modifier of the mobile phase which resulted in charge-state redistribution profiles so that a homogeneous matrix formed. By employing pulse gradient chromatography in the presence of 5 mM ammonium formate, favorable improvements of enhanced signal intensity and reduced matrix effects were obtained. These experiments also indicated the feasibility of using analogue IS during bio-analysis which contributed to an overall faster assay that would be suitable for drug discovery and development purposes.     

Solvent and thiourea adsorption/intercalation effects on the solid-state electrochemistry of α-phase nickel hydroxide nanoparticles

Nanoparticles of α-phase nickel hydroxide were synthesized by a single-step hydrothermal method using urea as the hydrolytic agent. Precipitated powders were of pure turbostratic α-phase as confirmed by x-ray diffraction profile. The ageing of α-Ni(OH)₂ in 1.0 M alkali solutions is investigated for pure non-intercalated α-Ni(OH)₂ and thiourea intercalated/absorbed α-phase nanomaterials. The α-Ni(OH)₂ powder immobilized on the surface of graphite electrodes shows a gradual α→β phase transformation with continuous voltammetric cycling, and the concentration gradient of water that exists in the layered-double-hydroxide-like interlayers of α-phase and the solution was shown to play a crucial role on the high electrochemical activity of this phase nickel hydroxide. To understand the role of water in the ageing process, concomitant entries of non-aqueous solvents like ethanol and acetonitrile along with thiourea were effected. Cyclic voltammetric measurements of thiourea-treated α-Ni(OH)₂ samples revealed that hydroxyl ion influx during the anodic oxidation depends on the counter flux of solvent molecules, and if the intercalated the solvent is acetonitrile, then the electrochemical activity of α-Ni(OH)₂ reduced drastically; Q _a/Q _c>1 for water as solvent in the interlayers α-Ni(OH)₂ and Q _a/Q _c<1 for ethanol and acetonitrile as solvents. The α-phase gets stabilized in the presence of thiourea with water and ethanol as co-intercalates. Transmission electron microscope images of α-Ni(OH)₂ and thiourea-treated samples show a change in particle size and morphology. Elemental CHNS analysis confirms the presence of sulphur in the thiourea intercalated samples.     

Extending the Iterative Convex Minorant Algorithm to the Cox Model for Interval-Censored Data

Abstract

The iterative convex minorant (ICM) algorithm proposed by Groeneboom and Wellner is fast in computing the NPMLE of the distribution function for interval censored data without covariates. We reformulate the ICM as a generalized gradient projection method (GGP), which leads to a natural extension to the Cox model. It is also easily extended to support Tibshirani's Lasso method. Some simulation results are also shown. For illustration we reanalyze two real datasets.     

Parallel preconditioned conjugate gradient algorithm on GPU

We propose a parallel implementation of the Preconditioned Conjugate Gradient algorithm on a GPU platform. The preconditioning matrix is an approximate inverse derived from the SSOR preconditioner. Used through sparse matrix–vector multiplication, the proposed preconditioner is well suited for the massively parallel GPU architecture. As compared to CPU implementation of the conjugate gradient algorithm, our GPU preconditioned conjugate gradient implementation is up to 10 times faster (8 times faster at worst).     

An Inexact Newton Method Derived from Efficiency Analysis

We consider solving an unconstrained optimization problem by Newton-PCG like methods in which the preconditioned conjugate gradient method is applied to solve the Newton equations. The main question to be investigated is how efficient Newton-PCG like methods can be from theoretical point of view. An algorithmic model with several parameters is established. Furthermore, a lower bound of the efficiency measure of the algorithmic model is derived as a function of the parameters. By maximizing this lower bound function, the parameters are specified and therefore an implementable algorithm is obtained. The efficiency of the implementable algorithm is compared with Newtons method by theoretical analysis and numerical experiments. The results show that this algorithm is competitive.Mathematics Subject Classification: 90C30, 65K05.This work was supported by the National Science Foundation of China Grant No. 10371131, and Hong Kong Competitive Earmarked Research Grant CityU 1066/00P from Hong Kong University Grant Council     

受控AR模型中参数估计误差的一致有界性分析

本针对系统为受控AR模型，其参数估计采用随机梯度算法时，用鞅收敛定理的推广形式分析了它的收敛性，得到了参数估计误差一致有界的结果．     

Hybrid Conjugate Gradient Algorithm for Unconstrained Optimization

In this paper a new hybrid conjugate gradient algorithm is proposed and analyzed. The parameter β _k is computed as a convex combination of the Polak-Ribière-Polyak and the Dai-Yuan conjugate gradient algorithms, i.e. β _k ^N =(1−θ _k )β _k ^PRP +θ _k β _k ^DY . The parameter θ _k in the convex combination is computed in such a way that the conjugacy condition is satisfied, independently of the line search. The line search uses the standard Wolfe conditions. The algorithm generates descent directions and when the iterates jam the directions satisfy the sufficient descent condition. Numerical comparisons with conjugate gradient algorithms using a set of 750 unconstrained optimization problems, some of them from the CUTE library, show that this hybrid computational scheme outperforms the known hybrid conjugate gradient algorithms. N. Andrei is a member of the Academy of Romanian Scientists, Splaiul Independenţei nr. 54, Sector 5, Bucharest, Romania.     

Equivalent operator preconditioning for elliptic problems

The numerical solution of linear elliptic partial differential equations most often involves a finite element or finite difference discretization. To preserve sparsity, the arising system is normally solved using an iterative solution method, commonly a preconditioned conjugate gradient method. Preconditioning is a crucial part of such a solution process. In order to enable the solution of very large-scale systems, it is desirable that the total computational cost will be of optimal order, i.e. proportional to the degrees of freedom of the approximation used, which also induces mesh independent convergence of the iteration. This paper surveys the equivalent operator approach, which has proven to provide an efficient general framework to construct such preconditioners. Hereby one first approximates the given differential operator by some simpler differential operator, and then chooses as preconditioner the discretization of this operator for the same mesh. In this survey we give a uniform presentation of this approach, including theoretical foundation and several practically important applications for both symmetric and nonsymmetric equations and systems, and some nonlinear examples in the context of Newton linearization. Dedicated to the memory of Gene Golub for his friendly manner and for his broad interest and significant impact on numerical analysis.     

关于拉格朗日乘数法的几何意义

利用梯度和方向导数的概念讨论函数在曲线或曲面上的变化率,从而给出拉格朗日乘数法的一个直观的几何解释.