Gas chromatography‐mass spectrometric method for the screening and quantification of illicit drugs and their metabolites in human urine using solid‐phase extraction and trimethylsilyl derivatization

A simple and rapid GC‐MS method has been developed for the screening and quantification of many illicit drugs and their metabolites in human urine by using automatic SPE and trimethylsilylation. Sixty illicit drugs, including parent drugs and their metabolites that are possibly abused in Korea, can be monitored by this method. Among them, 24 popularly abused illicit drugs were selected for quantification. Very delicate optimizations were carried out in SPE, trimethylsilylation derivatization, and GC/MS to enable such remarkable achievements. Trimethylsilylated analytes were well separated within 21 min by GC‐MS. In the validation results, the LOD of all the analytes were in the range of 2–75 ng/mL. The LOQ of the quantified analytes were in the range of 5–98 ng/mL. The linearity (r²) of the quantified analytes ranged 0.990–1.000 in each concentration range between 10 and 1000 ng/mL. The mean recoveries ranged from 62 to 126% at three different concentrations of each analyte. The inter‐day and inter‐person accuracies were within ?13.3～14.9%, and ?10.1～13.0%, respectively, and the inter‐day and inter‐person precisions were less than 12.9%. The method was reliable and efficient for the screening and quantification of abused illicit drugs in routine urine analysis.     

A novel (18)O inverse labeling-based workflow for accurate bottom-up mass spectrometry quantification of proteins separated by gel electrophoresis

In the present work we report on a novel and fast protocol for accurate bottom-up protein quantification that overcomes the drawbacks of in-gel digestion and MALDI analysis, while maintaining their benefits. It relies on the following steps: (i) gel electrophoresis separation of proteins, (ii) fast in-gel protein digestion with trypsin, (iii) (18)O-labeling through the decoupled method, (iv) quantification through selected peptides previously chosen using the (18)O inverse labeling approach and that, finally, (v) it takes advantage of software specifically developed to select the peptides that will drive the quantification of the protein in an automated mode. We have accurately quantified the following six proteins: glycogen phosphorylase, BSA, ovalbumin, carbonic anhydrase, trypsin inhibitor, and α-lactalbumin. As a case study we have quantified the protein vitellogenin in plasma of Cyprinus carpio exposed to high levels of estrogens. The proposed new protocol was validated against the traditional ELISA method; both were found to provide comparable results (non-parametric Mann-Whitney U-test).     

Composition determination of binary polymer mixtures by size exclusion chromatography with light scattering detection

<正>Base on the principle of absolute quantification of size exclusion chromatography(SEC),a light scattering(LS) detector coupled with a concentration detector(refractive index detector) is utilized to determine the compositions of complicated binary mixtures.A theoretical analysis predicts that the response factors for both LS and RI detectors are linear functions with the composition of any specified polymer mixtures in the binary polymer mixtures.Two pairs of complicated binary mixtures were used to test the theory mentioned in the present paper,and the experimental results show an excellent accordance with the theory.     

ICP-AES测定钛合金中铝、钼和锆的不确定度评定

对钛合金中铝、钼和锆含量测定过程中的不确定度来源进行了辨别,并在此基础上评定了分析结果的不确定度.测量不确定度主要来源于试样称量、标准溶液配制、校准曲线拟合、各种玻璃量器的使用及测量重复性.依据不确定度评定的步骤,计算得到了各分量标准不确定度及合成标准不确定度.     

直读光谱法测定不锈钢中铬的不确定度评定

利用光电直读光谱仪测定不锈钢中铬含量,对其在测量过程中不确定度产生的原因进行了分析,并对测量结果的不确定度进行了评定。     

Unification theory of classical statistical uncertainty relation and quantum uncertainty relation and its applications

General classical statistical uncertainty relation is deduced and generalized to quantum uncertainty relation. We give a general unification theory of the classical statistical and quantum uncertainty relations, and prove that the classical limit of quantum mechanics is just classical statistical mechanics. It is shown that the classical limit of the general quantum uncertainty relation is the general classical uncertainty relation. Also, some specific applications show that the obtained theory is self-consistent and coincides with those from physical experiments.     

Assessment of clinical data of nonlinear stochastic deconvolution versus block-circulant singular value decomposition for quantitative dynamic susceptibility contrast magnetic resonance imaging

Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) allows the noninvasive assessment of brain hemodynamics alterations by quantifying, via deconvolution, the cerebral blood flow (CBF) and mean transit time (MTT). Singular value decomposition (SVD) and block-circulant SVD (cSVD) are the most widely adopted deconvolution method, although they bear some limitations, including unphysiological oscillations in the residue function and bias in the presence of delay and dispersion between the tissue and the arterial input function. A nonlinear stochastic regularization (NSR) has been proposed, which performs better than SVD and cSVD on simulated data both in the presence and absence of dispersion. Moreover, NSR allows to quantify the dispersion level. Here, cSVD and NSR are compared for the first time on a group of nine patients with severe atherosclerotic unilateral stenosis of internal carotid artery before and after carotid stenting to investigate the effect of arterial dispersion. According to region of interest-based analysis, NSR characterizes the pathologic tissue more accurately than cSVD, thus improving the quality of the information provided to physicians for diagnosis. In fact, in 7 (78%) of the 9 subjects, CBF and MTT maps provided by NSR allow to correctly identify the pathologic hemisphere to the physician. Moreover, by emphasizing the difference between pathologic and healthy tissues, NSR may be successfully used to monitor the subject's recovery after the treatment and/or surgery. NSR also generates dispersion level and non-dispersed CBF and MTT maps. The dispersion level provides information on CBF and MTT estimates reliability and may also be used as a clinical indicator of pathological tissue state complementary to CBF and MTT, thus increasing the clinical information provided by DSC-MRI analysis.     

Minmax robustness for multi-objective optimization problems

In real-world applications of optimization, optimal solutions are often of limited value, because disturbances of or changes to input data may diminish the quality of an optimal solution or even render it infeasible. One way to deal with uncertain input data is robust optimization, the aim of which is to find solutions which remain feasible and of good quality for all possible scenarios, i.e., realizations of the uncertain data. For single objective optimization, several definitions of robustness have been thoroughly analyzed and robust optimization methods have been developed. In this paper, we extend the concept of minmax robustness (Ben-Tal, Ghaoui, & Nemirovski, 2009) to multi-objective optimization and call this extension robust efficiency for uncertain multi-objective optimization problems. We use ingredients from robust (single objective) and (deterministic) multi-objective optimization to gain insight into the new area of robust multi-objective optimization. We analyze the new concept and discuss how robust solutions of multi-objective optimization problems may be computed. To this end, we use techniques from both robust (single objective) and (deterministic) multi-objective optimization. The new concepts are illustrated with some linear and quadratic programming instances.     

The analytic hierarchy process with stochastic judgements

The analytic hierarchy process (AHP) is a widely-used method for multicriteria decision support based on the hierarchical decomposition of objectives, evaluation of preferences through pairwise comparisons, and a subsequent aggregation into global evaluations. The current paper integrates the AHP with stochastic multicriteria acceptability analysis (SMAA), an inverse-preference method, to allow the pairwise comparisons to be uncertain. A simulation experiment is used to assess how the consistency of judgements and the ability of the SMAA-AHP model to discern the best alternative deteriorates as uncertainty increases. Across a range of simulated problems results indicate that, according to conventional benchmarks, judgements are likely to remain consistent unless uncertainty is severe, but that the presence of uncertainty in almost any degree is sufficient to make the choice of best alternative unclear.     

Reasoning about norms under uncertainty in dynamic environments

The behaviour of norm-autonomous agents is determined by their goals and the norms that are explicitly represented inside their minds. Thus, they require mechanisms for acquiring and accepting norms, determining when norms are relevant to their case, and making decisions about norm compliance. Up until now the existing proposals on norm-autonomous agents assume that agents interact within a deterministic environment that is certainly perceived. In practise, agents interact by means of sensors and actuators under uncertainty with non-deterministic and dynamic environments. Therefore, the existing proposals are unsuitable or, even, useless to be applied when agents have a physical presence in some real-world environment. In response to this problem we have developed the n-BDI architecture. In this paper, we propose a multi-context graded BDI architecture (called n-BDI) that models norm-autonomous agents able to deal with uncertainty in dynamic environments. The n-BDI architecture has been experimentally evaluated and the results are shown in this paper.