Henan University of Technology

About: Henan University of Technology is a education organization based out in Zhengzhou, China. It is known for research contribution in the topics: Catalysis & Chemistry. The organization has 7648 authors who have published 6503 publications receiving 73067 citations. The organization is also known as: Hénán Gōngyè Dàxué.

Study on the binding of chlorogenic acid to pepsin by spectral and molecular docking.

Abstract: The interaction of pepsin with chlorogenic acid (CHA) was investigated using fluorescence, UV/vis spectroscopy and molecular modeling methods. Stern–Volmer analysis indicated that the fluorescence quenching of pepsin by CHA resulted from a static mechanism, and the binding constant was 1.1846 × 105 and 1.1587 × 105 L/mol at 288 and 310 K, respectively. The distance between donor (pepsin) and acceptor (CHA) was calculated to be 2.39 nm and the number of binding sites for CHA binding on pepsin was ~ 1. The results of synchronous fluorescence and three-dimensional fluorescence showed that binding of CHA to pepsin could induce conformational changes in pepsin. Molecular docking experiments found that CHA bonded with pepsin in the area of the hydrophobic cavity with Van der Waals' forces or hydrogen bonding interaction, which were consistent with the results obtained from the thermodynamic parameter analysis. Furthermore, the binding of CHA can inhibit pepsin activity in vitro. Copyright © 2013 John Wiley & Sons, Ltd.

Special Issue Editorial: Cognitively Inspired Computing for Knowledge Discovery

Abstract: Knowledge discovery is an emerging topic in many domains addressing a variety of methodologies for extracting useful knowledge from data. In an era of explosive data growth, together with wide-spreading powerful distributive and parallel computing, we are faced with an urgent demand for research and development of more efficient, effective and smart methodologies. On the other hand, it is also crucially challenging to extract, summarize, and even generate knowledge due to the large-scale, noisy, heterogeneous nature of big data. To this end, significant efforts have been reported in the literature on social networks, computer vision, data science, machine learning, data mining, statistical analysis, and fast computing. A number of successful models have recently emerged and led to great impact in the field. Interestingly, despite the diverse research topics and applications, these works recognize that cognitivelyinspired mechanisms should be investigated in order to make the algorithms more intelligent, powerful, and effective in extracting insightful knowledge, from huge amounts of heterogeneous Big data.

Iron-catalyzed regioselective direct coupling of aromatic aldehydes with coumarins leading to 3-aroyl coumarins

Abstract: An efficient protocol for iron-catalyzed cross-coupling of coumarins with aromatic aldehydes has been developed. The various 3-aroyl coumarins were selectively afforded in moderate yields. Some notable features of this protocol are high efficiency, wide functional group tolerance, and commercially available and cheap aromatic aldehydes and coumarins as starting materials. Furthermore, these reaction conditions were also applicable to N-methyl quinolinones, affording the corresponding 3-aroyl quinolinone derivatives in good yields.

Rapid and Non-destructive Determination of Moisture Content of Peanut Kernels Using Hyperspectral Imaging Technique

Abstract: Moisture content (MC) is a fundamental and very important quality indicator of peanut, which has significant influence on the overall quality of peanut in the process of storage. This study aimed to investigate the potential of hyperspectral imaging technique in the spectral range I (400–1000 nm) and spectral range II (1000–2500 nm) for predicting the MC of peanut kernels non-destructively. Hyperspectral images were obtained, and the corresponding spectral data was extracted. The calibration models were built between the extracted spectral data and the measured MC using partial least squares regression (PLSR) analysis. The established PLSR models using the full wavelengths showed good performance with determination coefficient (R 2 p) of 0.908 and 0.906, and root mean square errors by prediction (RMSEP) of 0.063 and 0.063 %, respectively. Optimal wavelengths were then selected based on the regression coefficients of the established PLSR model. The simplified PLSR models established only using identified optimal wavelengths also showed good performance with R 2 p of 0.910 and 0.900 and RMSEP of 0.061 and 0.060 %, respectively. The best PLSR model, established only using six optimal wavelengths (409, 508, 590, 663, 924, and 974 nm) selected from the spectral range I, was used to shift the spectrum of each pixel into its MC value for visualizing the distribution map of MC in peanut kernels. The results demonstrated that hyperspectral imaging technique in tandem with chemometrics analysis has the potential for rapid and non-destructive prediction of MC in peanut kernels.