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Su-Jun Sun,1 Peng Deng,1 Chun-E Peng,1 Hai-Yu Ji,2 Long-Fei Mao,1 Li-Zeng Peng1 1Key Laboratory of Agro-Products Processing Technology of Shandong Province, Key Laboratory of Novel Food Resources Processing Ministry of Agriculture, Institute of Agro-Food Science and Technology, Shandong Academy of Agricultural Sciences, Jinan, People’s Republic of China; 2Center for Mitochondria and Healthy Aging, College of Life Sciences, Yantai University, Yantai, People’s Republic of ChinaCorrespondence: Li-Zeng Peng, Tel +86-159-5412-8918, Email penglizeng@sdnu.edu.cnIntroduction: Chitosan is the product of the natural polysaccharide chitin removing part of the acetyl group, and exhibits various physiological and bioactive functions. Selenium modification has been proved to further enhance the chitosan bioactivities, and has been a hot topic recently.Methods: The present study aimed to investigate the potential inhibitory mechanism of selenium-modified chitosan (SMC) on HepG2 cells through MTT assays, morphological observation, annexin V–FITC/PI double staining, mitochondrial membrane potential determination, cell-cycle detection, Western blotting, and two-dimensional gel electrophoresis (2-DE).Results: The results indicated that SMC can induce HepG2 cell apoptosis with the cell cycle arrested in the S and G2/M phases and gradual disruption of mitochondrial membrane potential, reduce the expression of Bcl2, and improve the expression of Bax, cytochrome C, cleaved caspase 9, and cleaved caspase 3. Also, 2-DE results showed that tubulin α1 B chain, myosin regulatory light chain 12A, calmodulin, UPF0568 protein chromosome 14 open reading frame 166, and the cytochrome C oxidase subunit 5B of HepG2 cells were downregulated in HepG2 cells after SMC treatment.Discussion: These data suggested that HepG2 cells induced apoptosis after SMC treatment via blocking the cell cycle in the S and G2/M phases, which might be mediated through the mitochondrial apoptotic pathway. These results could be of benefit to future practical applications of SMC in the food and drug fields.Keywords: selenium-modified chitosan, antihepatoma activity, mitochondrial apoptotic pathway, differential protein analysis

With continuous development of artificial intelligence, text classification has gradually changed from a knowledge-based method to a method based on statistics and machine learning. Among them, it is a very important and efficient way to classify text based on the convolutional neural network (CNN) model. Text data are a kind of sequence data, while time sequentiality of the general text data is relatively weak, so text classification is usually less relevant to the sequential structure of the full text. Therefore, CNN-based text classification has gradually become a research hotspot when dealing with issues of text classification. For machine learning, especially deep learning, model interpretability has increasingly become the focus of academic research and industrial applications, and also become a key issue for further development and application of deep learning technology. Therefore, we recommend using the backtracking analysis method to conduct in-depth research on deep learning models. This paper proposes an analysis method for interpretability of a CNN text classification model. The method proposed by us can perform multi-angle analysis on the discriminant results of multi-classified text and multi-label classification tasks through backtracking analysis on model prediction results. Finally, the analysis results of the model can be displayed using visualization technology from multiple dimensions based on interpretability. The representative data set IMDB (Internet Movie Database) in text classification is verified by examples, and the results show that the model can be effectively analyzed when using our method.

The ethanol precipitation method has been widely-used for Dendrobium officinale polysaccharides preparation. However, the alcohol-soluble fractions have always been ignored, which causes significant wastes of resources and energies. In this study, the extraction, physicochemical properties, and immune regulation activity of an edible D. officinale polysaccharide (DOPs) isolated from the supernatant after 75% ethanol precipitation were systematically investigated. The structural characteristics determination results showed that DOPs was mainly composed of glucose and mannose at a molar ratio of 1.00:5.78 with an average molecular weight of 4.56 × 10 3 Da, which was made up of α-(1,3)-Glc p as the main skeleton, and the α-(1,4)-Glc p and β-(1,4)-Man p as the branches. Subsequently, the cyclophosphamide (CTX)-induced immunosuppressive mice model was established, and the results demonstrated that DOPs could dose-dependently protect the immune organs against CTX damage, improve the immune cells activities, and promote the immune-related cytokines (IL-2, IFN-γ and TNF-α) secretions. Furthermore, DOPs treatment also effectively enhanced the antioxidant enzymes levels (SOD, GSH-Px) in sera and livers, therefore weakening the oxidative damage of CTX-treated mice. Considering these above data, DOPs presented great potential to be explored as a natural antioxidant and supplement for functional foods.

Acoustic analysis is one of the important branches of biometric recognition technology widely used now. The mainly aim of the technology is to recognize the identity of person and judge the content of speech or diagnose the illness automatically according to the features extracted from the speaker's waveforms. All these features are related with the characteristics of speaker's physiological, pathological and psychological action. Speaker recognition study has its 50-year old history already, but acoustic analysis in diagnosing disease has been founded since 1970s. This paper introduces the main concept and research background of this diagnosing system generally and discusses the problems generated during processing. At last the prospect for the applications of acoustic analysis is forecasted.

This paper proposed a support vector machine (SVM) based classification method to identify diversified pathological voices. Sound signals were sampled from the pronunciation of a vowel "a" vocalized by 214 subjects, including 181 patients suffered from various dysphonias (such as polypoid degeneration, adductor spasmodic dysphonia, vocal fatigue, vocal tremor, vocal fold edema, hyperfunction, and erythema), and 33 healthy subjects. 25 acoustic parameters were calculated from the sampled data for each subject. The original acoustic dataset was first transformed via principal component analysis (PCA) method into a new feature space. To learn the identification boundary for healthy and pathological voices, a soft-margin SVM and three kinds of kernels were examined. The results under different combination of parameters and kernels were investigated. The effectiveness of SVM-based approach seems to be promising in the application of pathological voice identification.