Direct determination of glyphosate using hydrophilic interaction chromatography with coulometric detection at copper microelectrode

A simple, rapid, and low-cost coulometric method for direct detection of glyphosate using hydrophilic interaction chromatography is presented. The principle of detection is based on the enhancement of the anodic current of copper microelectrode in the presence of complexing agents, such as glyphosate, with the formation of a soluble Cu(II) complex. Under optimized conditions, the limit of detection (S/R = 3) for glyphosate was 0.1 mg L⁻¹ (0.59 μM) without any preconcentration method. The calibration curve has been found linear in all concentration range tested (from limit of detection to 34 mg L⁻¹) with an excellent correlation coefficient (0.9999). The present method was successfully applied for the determination of glyphosate in fruit juices without any kind of extraction, clean-up, or preconcentration step, with recoveries of 92 and 90% for apple and grape juice, respectively.     

基于纳米金探针和基因芯片的DNA检测新方法

运用荧光纳米金探针和基因芯片杂交建立一种新的DNA检测方法. 荧光纳米金探针表面标记有两种DNA探针: 一种为带有Cy5荧光分子的信号探针BP1, 起信号放大作用; 另一种为与靶DNA一部分互补的检测探针P532, 两种探针比例为5∶1. 当靶DNA存在时, 芯片上捕捉探针(与靶DNA的另一部分互补)通过碱基互补配对结合靶DNA, 将靶DNA固定于芯片上; 荧光纳米金探针通过检测探针与靶DNA及芯片结合, 在芯片上形成“三明治”复合结构, 最后通过检测信号探针上荧光分子的信号强度来确定靶DNA的量. 新方法检测灵敏度高, 可以检测浓度为1 pmol/L的靶DNA, 操作简单, 检测时间短. 通过改进纳米金探针的标记和优化杂交条件, 可进一步提高核酸检测的灵敏度, 这将在核酸检测方面具有重要的应用价值.     

Applications of Raman-based techniques to on-site and in-vivo analysis

Raman-based technologies have proved to be excellent tools for on-site and in-vivo analysis, due to the non-invasive nature of their detection, their capability of providing structure information, their high tolerance to aqueous samples, the ultra-sensitivity of surface-enhanced Raman scattering (SERS) and resonance Raman scattering (RRS), the high spatial resolution of tip-enhanced Raman scattering (TERS), and the ultrashort spectra-acquisition time for coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS).In this review, we highlight the recent success of Raman-based technologies in various applications, including:

• (1) 

  on-site surface analysis and chemical-reaction monitoring;

• (2) 

  on-site identification of cultural objects, archeological studies and planetary science;

• (3) 

  in-vivo analysis of cells and microorganisms;

• (4) 

  in-vivo diagnosis inside human and animal bodies;

• (5) 

  in-vivo fast Raman imaging and mapping;

• (6) 

  the study of SERS processes; and,

• (7) 

  assessment of nanomaterial safety.

     

Rapid determination of trace polycyclic aromatic hydrocarbons in particulate matter using accelerated solvent extraction followed by ultra high performance liquid chromatography with fluorescence detection

A method has been developed for the trace analysis of polycyclic aromatic hydrocarbons, which are known as persistent organic pollutants and ubiquitous constituents of fine particulate matter that causes growing airborne pollution. The method, which was especially for samples of airborne particulate matter less than 2.5 μm in diameter, utilized accelerated solvent extraction and ultra high performance liquid chromatography with fluorescence detector. Four principal parameters of accelerated solvent extraction were optimized to obtain maximum extraction efficiency. Using the established synergetic programs of gradient elution and fluorescence wavelength switching, a rapid separation was achieved in 6.56 min with good linearity for 15 polycyclic aromatic hydrocarbons (coefficient of determination above 0.999). The limits of detection ranged from 0.833 to 10.0 pg/m³. The precision of the method expressed as inter‐day relative standard deviation ranged from 0.2 to 1%, which was calculated from nine repetitive measurements of 8.00 μg/L analytes. Average spiked recoveries ranged from 71.6 to 97.7%, with the exception of naphthalene. The rapid, sensitive, and accurate method can meet the pressing needs of health risk assessment and increasingly heavy daily tasks of air quality monitoring.     

Toll-like receptors for pathogen detection in water: challenges and benefits

The need for effective and efficient methods for pathogen detection in water is as serious as ever due to the health risk posed to human population by the consumption of pathogen-contaminated water. One of the important research streams which have been focused on by researchers for development of novel techniques for this purpose is biosensor technologies. Using different bio-recognition elements and transduction methodologies, biosensors have the potential to detect their analyte of interest in a fast and highly specific manner. Different pathogenic agents can be recognised by toll-like receptors (TLRs). The innate immune system of higher organisms employs TLRs for triggering intracellular signalling and induction of the expression of immune response genes. In this report, we explore the challenges associated with employing TLRs for pathogen detection in water samples. Although methods using TLR expressing cells also have been discussed, the focus of this review is on using TLR proteins as the bio-recognition elements in biosensors.     

Ensemble Approach for Detection of Depression Using EEG Features

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.     

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.     

Estimating Non-Gaussianity of a Quantum State by Measuring Orthogonal Quadratures

We derive the lower bounds for a non-Gaussianity measure based on quantum relative entropy (QRE). Our approach draws on the observation that the QRE-based non-Gaussianity measure of a single-mode quantum state is lower bounded by a function of the negentropies for quadrature distributions with maximum and minimum variances. We demonstrate that the lower bound can outperform the previously proposed bound by the negentropy of a quadrature distribution. Furthermore, we extend our method to establish lower bounds for the QRE-based non-Gaussianity measure of a multimode quantum state that can be measured by homodyne detection, with or without leveraging a Gaussian unitary operation. Finally, we explore how our lower bound finds application in non-Gaussian entanglement detection.     

基于彩色结构光的自由曲面三维重建方法

彩色结构光三维重建过程中,系统的非线性耦合以及待测曲面的拓扑结构等均会对结构光解码产生影响,从而导致调制条纹漏检和颜色码误识别.为解决这一问题,提出一种基于彩色编码结构光的三维重建方法.利用YUV颜色通道对调制条纹进行滤波差分投影处理,通过调制条纹的波形分布提取中心特征线;利用颜色聚类方法精确获取调制条纹的颜色码信息值...