Biomarker selection has played an increasingly important part in modern medicine with advances of omics techniques. Kohonen self-organizing map is a well-established variable reduction algorithm in identifying significant biomarkers based on variable clustering. However, high dimensionality but small sample size of omics data makes self-organizing map-based model problematic in terms of selection stability and reproducibility. A novel feature screening system is presented in this study by coupling bootstrap with synergy self-organizing map-based orthogonal partial least squares discriminant analysis for stable and biologically meaningful metabolic biomarker selection. https://www.selleckchem.com/products/prt062607-p505-15-hcl.html In the proposed feature screening system, particle swarm optimization algorithm is utilized to configure synergy self-organizing map-based orthogonal partial least squares discriminant analysis to perform the combination of clusters in a heuristic learning manner, enabling flexible selection of more informative features cost-effectively. Based on the paradigm of ensemble feature selection, bootstrap is adopted to explore significant variables consistently identified across multiple feature selectors rather than a single one. The feasibility of the novel feature screening system is evaluated by two most common inherited metabolic diseases, methylmalonic academia and propionic academia, using urinary metabolomics data. With the desirable classification performance, the proposed feature screening system outperforms simpler techniques in the identification of more features closely correlated with the metabolic mechanisms and the stability of selected candidate biomarkers against sample variations. Besides, the novel feature screening system greatly degrades the sensitivity of identified candidate biomarkers to the network size of self-organizing map, benefiting the identification of a suitable and stable final candidate biomarker list.This study reports a novel and environmentally friendly method based on bar adsorptive microextraction (BAμE) with cork pellet as extraction phase for the determination of methylparaben, ethylparaben, propylparaben and butylparaben in river water samples. This natural approach consists of a cork pellet recycled from wine stoppers used as biosorbent material to replace the traditional BAμE device. The analytical determinations were performed using a high-performance liquid chromatography-diode array detector (HPLC-DAD). Parameters such as type of desorption solvent, desorption and extraction time, sample pH and ionic strength were carefully optimized through univariate and multivariate approaches. Cork pellets of 15 mm length were inserted into vials containing 15 mL of water sample adjusted at pH 3 and 25% (w/v) of NaCl. The extraction step was carried out under agitation for 45 min followed by liquid desorption with 120 μL of methanolacetonitrile (11 v/v) for 30 min. Satisfactory analytical performance was obtained with coefficients of determination ranging from 0.9921 for methylparaben to 0.9994 for propylparaben; intraday precision ranged from 6.7 to 18.3%, and interday precision varied from 7.2 to 20.0%. Accuracy was assessed through relative recovery assays and varied from 53 to 124%.In this work, the correlations between retention behavior and lipophilicity of a large set of hydrophilic neutral and ionic analytes were studied based on three hydrophilic interaction liquid chromatography (HILIC) stationary phases, including zwitterionic, crosslinked diol and triazole stationary phases. It was found that HILIC, due to the diversity of retention mechanism, is a more complex chromatography separation mode than reversed-phase liquid chromatography (RPLC) which has been widely accepted for lipophilicity assessment. Because electrostatic interactions contributed to the overall retention of the charged solutes on all three stationary phases, ion-strength of the mobile phase kept the same during the whole experiment. After the correlations between retention factor log k and water volume fraction Φ were investigated, the mixed retention model was revealed to be more suitable for HILIC retention behavior than other single models including partitioning and adsorption model. Moreover, in order to bridge the relationship between HILIC log k and lipophilicity parameter log D, net charge ne and Abraham solvation parameter were introduced in the quantitative structure-retention relationship (QSRR) model. Although the correlation coefficients between log D and log k were still moderate, the significant improvement in correlation has made HILIC a potential choice as the complement of RPLC for log D measurement.Porous microstructure materials are considered good candidates for the development of highly sensitive and fast humidity sensors. In this regard, we prepared polyaniline (PANI) decorated Cu-ZnS porous microsphere structures (PMSs) for the fabrication of humidity sensors. PANI coated Cu-ZnS PMSs were synthesized by a hydrothermal method and in situ polymerization process. The synthesized PMSs were characterized by different techniques to study the structural, morphological and surface absorption properties. Several compositions for the PANI/Cu-ZnS PMS were investigated, which were then compared with pure PANI. The experimental observations demonstrate that a PANI/1%Cu-ZnS PMS has better sensitivity, fast response and good stability compared to pure PANI and other PANI/CuZnS compositions. Finally, a PANI/1% Cu-ZnS PMS was found to be optimized for humidity sensors due to its well distributed roughness, porosity and hydrophilicity. The average response and recovery times for PANI/1% Cu-ZnS were found to be 42 s and 24 s, respectively, which outperform recent results.Histamine (HA) plays an important role in food safety supervision and is also involved in various physiological functions. Accurate and rapid detection of HA in real sample is count for much as this is the significant prerequisite for its effective monitoring. In this study, we fabricated an electrochemical sensor to detect HA via the pyrolysis of the hydrothermal Ni-MOF (metal-organic frameworks), in which the obtained Ni@C material was deployed as the sensing agent. Ni@C was comprehensively characterized in terms of its morphology, constitution, as well as its electrochemical behavior. The as-prepared sensor (Ni@C/GCE) features excellent electrocatalytic activities. It was also observed that the electrochemical property of the sensor was substantially improved because Ni@C afforded an enlarged active surface and accelerated electron transport. This sensor affords amperometric analysis in the linear range of 10-3-100 μM HA with a 3.2 × 10-4 μM low detection limit (S/N = 3). Many important features, including decent anti-interference, reproducibility, stability, and reliability, were also observed.