Antimicrobial and Cytotoxic Activity of Endophytic Fungi from Lagopsis supina

Zhang Dekui,Sun Weijian,Xu Wenjie,Ji Changbo,Zhou Yang,Sun Jingyi,Tian Yutong,Li Yanling,Zhao Fengchun,Tian Yuan 한국미생물·생명공학회 2023 Journal of microbiology and biotechnology Vol.33 No.4

In this study, five endophytic fungi belonging to the Aspergillus and Alternaria genera were isolated from Lagopsis supina. The antimicrobial activity of all fungal fermented extracts against Staphylococcus and Fusarium graminearum was tested using the cup-plate method. Among them, Aspergillus ochraceus XZC-1 showed the best activity and was subsequently selected for large-scale fermentation and bioactivity-directed separation of the secondary metabolites. Four compounds, including 2- methoxy-6-methyl-1,4-benzoquinone (1), 3,5-dihydroxytoluene (2), oleic acid (3), and penicillic acid (4) were discovered. Here, compounds 1 and 4 displayed anti-fungal activity against F. graminearum, F. oxysporum, F. moniliforme, F. stratum, Botrytis cinerea, Magnaporthe oryzae, and Verticillium dahliae with diverse MIC values (128–512 μg/ml), which were close to that of the positive control antifungal, actidione (64–128 μg/ml). Additionally, compounds 1 and 4 also exhibited moderate antibacterial activity against S. aureus, Listeria monocytogenes, Escherichia coli, and Salmonella enterica, with low MIC values (8–64 μg/ml). Moreover, compounds 1 and 4 displayed selective cytotoxicity against cancer cell lines as compared with the normal fibroblast cells. Therefore, this study proposes that the endophytic fungi from L. supina can potentially produce bioactive molecules to be used as lead compounds in drugs or agricultural antibiotics.

HSP70 attenuates compression-induced apoptosis of nucleus pulposus cells by suppressing mitochondrial fission via upregulating the expression of SIRT3

Hu Binwu,Wang Peng,Zhang Shuo,Liu Weijian,Lv Xiao,Shi Deyao,Zhao Lei,Liu Hongjian,Wang Baichuan,Chen Songfeng,Shao Zengwu 생화학분자생물학회 2022 Experimental and molecular medicine Vol.54 No.-

Compression-induced apoptosis of nucleus pulposus (NP) cells plays a pivotal role in the pathogenesis of intervertebral disc degeneration (IVDD). Recent studies have shown that the dysregulation of mitochondrial fission and fusion is implicated in the pathogenesis of a variety of diseases. However, its role in and regulatory effects on compression-induced apoptosis of NP cells have not yet been fully elucidated. Heat shock protein 70 (HSP70) is a major cytoprotective heat shock protein, but its physiological role in IVDD, especially its effect on mitochondrial fission and fusion, is still unknown. Herein, we found that compression could induce mitochondrial fission, which ultimately trigger apoptosis of NP cells via the mitochondrial apoptotic pathway. In addition, we identified the cytoprotective effects of HSP70 on NP cells, and we found that promoting the expression of HSP70 could protect NP cells from abnormal mechanical loading in vitro and in vivo. Finally, we showed that HSP70 inhibited compression-induced mitochondrial fission by promoting SIRT3 expression, thereby attenuating mitochondrial dysfunction and the production of reactive oxygen species and ultimately inhibiting the mitochondrial apoptotic pathway in NP cells. In conclusion, our results demonstrated that HSP70 could attenuate compression-induced apoptosis of NP cells by suppressing mitochondrial fission via upregulating SIRT3 expression. Promoting the expression of HSP70 might be a novel strategy for the treatment of IVDD.

Data anomaly detection for structural health monitoring using a combination network of GANomaly and CNN

Jiangpeng Shu,Gaoyang Liu,Yanbo Niu,Weijian Zhao,Yuan-Feng Duan 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

An active learning method with difficulty learning mechanism for crack detection

Zhicheng Zhang,Jiangpeng Shu,Jun Li,Jiawei Zhang,Weijian Zhao,Yuanfeng Duan 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is asignificant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320 × 320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.

Member capacity-based progressive collapse analysis of transmission towers under wind load

Yong-Quan Li,Yong Chen,Guohui Shen,Wenjuan Lou,Weijian Zhao,Hao Wang 한국풍공학회 2021 Wind and Structures, An International Journal (WAS Vol.33 No.4

The wind-induced collapse of transmission towers has raised many concerns. Progressive collapse analysis is recognized as a promising method for the assessment of the collapse-resistant capacity of the transmission tower. The finite element model of an actual transmission tower is firstly built for the analysis, in which the dynamic behavior of the member in failure is taken into account to be in accord with the actual tower collapse. The analysis considering the main design load cases is conducted in advance to determine the case under which the tower has the potential to collapse. The incremental dynamic analysis in association with the explicit time integration algorithm is employed to perform a progressive collapse analysis, where the wind loads are simulated by using the linear filtering method, and the developed failure criterion with axial force and bending moment involved is based on the stability bearing capacity of the members. It is found the tower collapse begins with the horizontal bracing member near the waist. Then, the adjacent members, including the leg members, fail sequentially, and the tower collapses eventually with a shear-type failure. The demand to capacity ratio (DCR) in terms of bearing capacity of the member is defined to quantify the structural behavior, the location of the member that has the potential to fail, and when the initial failure occurs are thereby identified. It is concluded that compared to the member capacity-based analysis, the ultimate strain-based analysis, which is most likely to be an inelastic dynamic analysis permitting a large deformation, may overestimate the bearing capacity of the structure in wind-induced collapse.