Evaluate how steaming and sulfur fumigation change the microstructure, physicochemical properties and in vitro digestibility of Gastrodia elata Bl. starch

The sulfur dioxide gas (SO2) generated by sulfur burning can improve the appearance quality of food and enhance the storage time. However, excessive sulfur dioxide will pollute the environment and cause deterioration of food quality, and even the high residual levels can increase the risk of cancer. As Gastrodia elata Blume is prone to corruption during processing, sulfur fumigation is often used for preservation. In this study, spectral analysis and Texture Profile Analysis (TPA) were used to investigate the effects of traditional sulfur fumigation processing on the morphology quality, edible quality and structural characteristics of G. elata. The results showed that compared with direct drying, the pH decreased by 0.399 of the sulfur fumigated after steamed treatment G. elata, and the morphology quality, pasting ability and gel edible quality of the starch were significantly improved. In addition, it was suggested that sulfur fumigation after steaming could promote the release of molecular chains from starch granules and thus enhance the cross-linking between molecules, which explained the reason for the improve of starch edible quality. This study can provide technical and theoretical support for improving the quality of starch rich foods, replacing sulfur fumigation and reducing potential environmental hazards

Optimized deep learning based single-phase broken fault type identification for active distribution networks

Single-phase broken faults occur frequently, affecting the reliability of distribution network. In order to effectively identify the fault type of single-phase broken fault, this paper proposes a new identification method, which is based on the combination of variational mode decomposition and stacked auto encoder with double optimization (AO-VMD-PSO-SAE). Firstly, the zero sequence voltage, which collected in line, is decomposed into a set of variational modal components. Nextly, the stack automatic encoder is used to conduct unsupervised training on the denoised data to establish a depth learning model, and the AO optimization algorithm and the PSO optimization algorithm are used to determine the super parameters in the model.​ Finally, simulation results supported and the validity of the method was verified. What the results show is that the proposed model named AO-VMD-PSO-SAE can accurately predict the types of single-phase broken fault under noise interference

Targeting AMPK signalling pathway with natural medicines for atherosclerosis therapy: an integration of <i>in silico</i> screening and <i>in vitro</i> assay

<p>An integration of virtual screening and kinase assay was reported to identify AMPK kinase inhibitors from various natural medicines.The activation of AMP-activated protein kinase (AMPK) signalling pathway plays a central role in the pathologic progression of atherosclerosis (AS). Targeting the AMPK is thus considered as a potential therapeutics to attenuate AS. Here, we report the establishment of a synthetic pipeline that integrates <i>in silico</i> virtual screening and <i>in vitro</i> kinase assay to discover new lead compounds of AMPK inhibitors. The screening is performed against a large-size pool of structurally diverse natural products, from which a number of compounds are inferred as promising candidates, and few of them are further tested <i>in vitro</i> by using a standard kinase assay protocol to determine their inhibitory potency against AMPK. With this scheme we successfully identify five potent AMPK inhibitors with IC₅₀ values at micromolar level. We also examine the structural basis and molecular mechanism of nonbonded interaction network across the modelled complex interface of AMPK kinase domain with a newly identified natural medicine.</p