Firefly as a novel swarm intelligence variable selection method in spectroscopy

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle.     

Detection and quantification of plasma amyloid-β by selected reaction monitoring mass spectrometry

Amyloid-β (Aβ) in human plasma was detected and quantified by an antibody-free method, selected reaction monitoring mass spectrometry (SRM-MS) in the current study. Due to its low abundance, SRM-based quantification in 10 μL plasma was a challenge. Prior to SRM analysis, human plasma proteins as a whole were digested by trypsin and high pH reversed-phase liquid chromatography (RPLC) was used to fractionate the tryptic digests and to collect peptides, Aβ_1–5, Aβ_6–16, Aβ_17–28 and Aβ_29–40(42) of either Aβ_1–40 or Aβ_1–42. Among those peptides, Aβ_17–28 was selected as a surrogate to measure the total Aβ level. Human plasma samples obtained from triplicate sample preparations were analyzed, obtaining 4.20 ng mL⁻¹ with a CV of 25.3%. Triplicate measurements for each sample preparation showed CV of <5%. Limit of quantification was obtained as 132 pM, which corresponded to 570 pg mL⁻¹ of Aβ_1–40. Until now, most quantitative measurements of Aβ in plasma or cerebrospinal fluid have required antibody-based immunoassays. Since quantification of Aβ by immunoassays is highly dependent on the extent of epitope exposure due to aggregation or plasma protein binding, it is difficult to accurately measure the actual concentration of Aβ in plasma. Our diagnostic method based on SRM using a surrogate peptide of Aβ is promising in that actual amounts of total Aβ can be measured regardless of the conformational status of the biomarker.     

Modern analytical supercritical fluid chromatography using columns packed with sub-2 μm particles: A tutorial

This tutorial provides an overview of the possibilities, limitations and analytical conditions of modern analytical supercritical fluid chromatography (SFC) using columns packed with sub-2 μm particles. In particular, it gives a detailed overview of commercially available modern SFC instrumentation and the detectors that can be employed (UV, MS, ELSD, FID, etc.). Some advice on the choice of the stationary phase dimensions and chemistries, the nature of the mobile phase (choice of organic modifier and additives) and its flow rate as well as the backpressure and temperature are also provided. Finally, several groups of potentially problematic compounds, including lipophilic compounds, hydrophilic substances and basic drugs, are discussed in detail. All these families of analytes can be resolved with SFC but require specific analytical conditions.     

A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration

Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective methods which can select an optimal variables subset. In this study, a strategy that considers the possible interaction effect among variables through random combinations was proposed, called iteratively retaining informative variables (IRIV). Moreover, the variables are classified into four categories as strongly informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both the strongly and weakly informative variables in every iterative round until no uninformative and interfering variables exist. Three datasets were employed to investigate the performance of IRIV coupled with partial least squares (PLS). The results show that IRIV is a good alternative for variable selection strategy when compared with three outstanding and frequently used variable selection methods such as genetic algorithm-PLS, Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS) and competitive adaptive reweighted sampling (CARS). The MATLAB source code of IRIV can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.     

Gas chromatography–mass spectroscopy optimization by computer simulation,application to the analysis of 93 volatile organic compounds in workplace ambient air

GC–MS optimization method including both advantages from chromatographic separation and mass spectrometric detection was designed for a set of 93 volatile organic compounds. Only a few experiments were necessary to determine the thermodynamic retention parameters for all compounds on a RTX-VMS column. From these data, computer simulation was used in order to predict the retention times of the compounds in temperature programmed gas chromatography. Then, an automatic selection of ions from the NIST database was performed and compared to the optimum conditions (full separation of VOC). This simulation-selection procedure was used to screen a numerous set of GC and MS conditions in order to quickly design a GC–MS method whatever the set of compounds considered.     

Optimal payoff under the generalized dual theory of choice

We consider portfolio optimization under a preference model in a single-period, complete market. This preference model includes Yaari’s dual theory of choice and quantile maximization as special cases. We characterize when the optimal solution exists and derive the optimal solution in closed form when it exists. The optimal portfolio yields an in-the-money payoff when the market is good and zero payoff otherwise. Finally, we extend our portfolio optimization problem by imposing a dependence structure with a given benchmark payoff.     

Overview of Feature Screening Methods for Ultra-high Dimensional Data

With the improvement of data collection and storage capacity, ultra-high dimensional data\ucite{9}, that is, dimensionality with the exponential growth of samples appears in many scientific neighborhoods. At this time, penalized variable selection methods generally encounter three challenges: computational expediency, statistical accuracy, and algorithmic stability, which are limited in handling ultra-high dimensional problems. Fan and Lv\ucite{9} proposed the method of ultra-high dimensional feature screening, and achieved a lot of research results in the past ten years, which has become the most popular field of research in statistics. This paper mainly introduces the related work of ultra-high dimensional screening method from four aspects: the screening methods with model hypothesis, including parametric, non-parametric and semi-parametric model hypothesis, model-free hypothesis, and screening methods for special data. Finally, we briefly discuss the existing problems of ultra-high dimensional screening methods and some future directions.     

A Parameterized Class of Complex Nonsymmetric Algebraic Riccati Equations

In this paper, by introducing a definition of parameterized comparison matrix of a given complex square matrix, the solvability of a parameterized class of complex nonsymmetric algebraic Riccati equations (NAREs) is discussed. The existence and uniqueness of the extremal solutions of the NAREs is proved. Some classical numerical methods can be applied to compute the extremal solutions of the NAREs, mainly including the Schur method, the basic fixed-point iterative methods, Newton's method and the doubling algorithms. Furthermore, the linear convergence of the basic fixed-point iterative methods and the quadratic convergence of Newton's method and the doubling algorithms are also shown. Moreover, some concrete parameter selection strategies in complex number field for the doubling algorithms are also given. Numerical experiments demonstrate that our numerical methods are effective.     

考虑农户“以次充好”行为的生鲜电商销售模式选择研究

考虑农户“以次充好”行为和电商企业抽样检测机制的基础上,运用博弈理论分别建立自营和平台两种销售模式下生鲜电商供应链的决策模型,对比研究了不同销售模式下电商企业抽样检测前后各方的最优决策及其期望收益。研究发现:(1)虽然电商企业的抽样检测将会降低农户“以次充好”的比例,提升在线销售生鲜品的品质,但却不一定带来自身和农户的收益增加;(2)无论自营模式还是平台模式,消费者的追溯赔偿和电商企业的严惩措施都会对农户“以次充好”行为起到抑制作用;(3)当农户存在“以次充好”行为时,抽检费用、内外部惩罚、信誉损失以及平台使用费等因素都将影响电商企业和农户对销售模式的选择。