ISSN 0253-2778

CN 34-1054/N

2022 Vol. 52, No. 9

2022-9 Contents
2022, 52(9): 1-2.
2022-9 Abstract
2022, 52(9): 1-2.
Life Sciences
IITZ-01 activates NLRP3 inflammasome by inducing mitochondrial damage
Wenxin Hu, Wei Jiang
2022, 52(9): 1. doi: 10.52396/JUSTC-2022-0090
NLRP3 inflammasome can be activated by a variety of pathogen activators (including components of bacteria, viruses and fungi) or “danger signals” (including abnormal metabolites and environmental components), so its activation mechanism is extremely complex. IITZ-01 is a lysosomotropic molecule that can disrupt lysosomal functions. We found that IITZ-01 can activate inflammasome at a low concentration. Then, we determined that IITZ-01 is a specific activator of NLRP3 inflammasome through inflammasome stimulation, ELISA, Western blot and other experiments. Mechanistically, NLRP3 inflammasome activation induced by IITZ-01 is independent of direct binding and ion flow but dependent on mitochondrial damage and mROS accumulation. This study suggests that a lysosomotropic compound can activate NLRP3 inflammasome by impairing mitochondrial functions.
Sphingosine-1-phosphate induces Ca2+ mobilization via TRPC6 channels in SH-SY5Y cells and hippocampal neurons
Haotian Wu, Bingqian Lin, Canjun Li, Wenping Zeng, Lili Qu, Chunlei Cang
2022, 52(9): 2. doi: 10.52396/JUSTC-2022-0014
Sphingosine-1-phosphate (S1P) is a widely expressed biologically active sphingolipid that plays an important role in cell differentiation, migration, proliferation, metabolism and apoptosis. S1P activates various signaling pathways, some of which evoke Ca2+ signals in the cytosol. Few studies have focused on the mechanism by which S1P evokes Ca2+ signals in neurons. Here, we show that S1P evokes global Ca2+ signals in SH-SY5Y cells and hippocampal neurons. Removal of extracellular calcium largely abolished the S1P-induced increase in intracellular Ca2+, suggesting that the influx of extracellular Ca2+ is the major contributor to this process. Moreover, we found that S1P-induced Ca2+ mobilization is independent of G protein-coupled S1P receptors. The TRPC6 inhibitor SAR7334 suppressed S1P-induced calcium signals, indicating that the TRPC6 channel acts as the downstream effector of S1P. Using patch-clamp recording, we showed that S1P activates TRPC6 currents. Two Src tyrosine kinase inhibitors, Src-I1 and PP2, dramatically inhibited the activation of TRPC6 by S1P. Taken together, our data suggest that S1P activates TRPC6 channels in a Src-dependent way to induce Ca2+ mobilization in SH-SY5Y cells and hippocampal neurons.
Comprehensive bioinformatic analysis of key genes and signaling pathways in glioma
Xiaoming Zhang, Mengyuan Jiang, Shenfeng Tang, Chaoshi Niu, Shanshan Hu
2022, 52(9): 3. doi: 10.52396/JUSTC-2022-0010
The identification of specific survival-related differentially expressed genes (DEGs) is a method for uncovering therapeutic approaches for various cancers, including glioma. However, the key target genes associated with the occurrence and development of gliomas remain unknown. In this study, we performed bioinformatics analysis on 17 GSE datasets and identified DEGs correlated with glioma. A total of 74 mutual-DEGs with downregulated expression in gliomas compared with that in normal brain tissues were found in 17 datasets. These DEGs were related to GABAergic synaptic transmission, chloride transmembrane transport, glutamate secretion, and gamma-aminobutyric acid signaling pathway. Gamma-aminobutyric acid type A receptor subunit gamma 2 (GABRG2) was identified as a hub gene in the protein-protein interaction network. GABRG2 exhibited lower expression in IDH wild-type astrocytoma than that in IDH mutant astrocytoma and indicated poor prognosis in glioma patients. GABRG2 may contribute to the progression of glioma by affecting GABA receptor-related pathways and is a potential biomarker for the diagnosis and treatment of glioma.
Evolutionary game analysis of promoting the development of green logistics under government regulation
Yu Dong, Tingting Yang
2022, 52(9): 4. doi: 10.52396/JUSTC-2022-0067
Due to the strong negative externalities of traditional logistics, the green logistics that developed from traditional logistics has the advantages of saving resources and protecting the environment. However, in the competitive market environment, enterprises will not implement green logistics based on their own revenues and competitiveness and, instead, will choose the best choice from the actions of a series of internal and external factors. To explore the effect of various factors on the implementation of green logistics by enterprises, this study constructs a tripartite evolutionary game model of the governments, logistics enterprises, and users from the perspective of the participants in the process of logistics greening and analyzes the evolutionarily stable strategies of each participant under different situations. Netlogo software is used to simulate and analyze the initial willingness of the participants, the intensity of government subsidies and fines, and the probability that the enterprises’ speculative behaviors are founded on the system’s evolutionary paths and results. The results demonstrate that the initial willingness of the governments, logistics enterprises, and users to participate has different effects on the evolutionary results of the system. Government subsidy and fine measures significantly impact the strategic choices of enterprises and users. Compared with users, enterprises are more sensitive to government subsidies, and compared with fines, government subsidies have a greater impact on enterprises’ behavior choices. Moreover, the governments should strengthen the publicity of green logistics, formulate judgement standards and an evaluation system for green enterprise logistics, and restrain the speculative behaviors of enterprises.
Info. & Intelligence
Self-supervised human semantic parsing for video-based person re-identification
Wei Wu, Jiawei Liu
2022, 52(9): 5. doi: 10.52396/JUSTC-2021-0212
Video-based person re-identification is an important research topic in computer vision that entails associating a pedestrian’s identity with non-overlapping cameras. It suffers from severe temporal appearance misalignment and visual ambiguity problems. We propose a novel self-supervised human semantic parsing approach (SS-HSP) for video-based person re-identification in this work. It employs self-supervised learning to adaptively segment the human body at pixel-level by estimating motion information of each body part between consecutive frames and explores complementary temporal relations for pursuing reinforced appearance and motion representations. Specifically, a semantic segmentation network within SS-HSP is designed, which exploits self-supervised learning by constructing a pretext task of predicting future frames. The network learns precise human semantic parsing together with the motion field of each body part between consecutive frames, which permits the reconstruction of future frames with the aid of several customized loss functions. Local aligned features of body parts are obtained according to the estimated human parsing. Moreover, an aggregation network is proposed to explore the correlation information across video frames for refining the appearance and motion representations. Extensive experiments on two video datasets have demonstrated the effectiveness of the proposed approach.
The surrounding vehicles behavior prediction for intelligent vehicles based on Att-BiLSTM
Yunqing Gao, Juping Zhu, Hongbo Gao
2022, 52(9): 6. doi: 10.52396/JUSTC-2021-0155
A surrounding vehicles behavior prediction method was presented for intelligent vehicles. The surrounding vehicles’ behavior is hard to predict since the significant uncertainty of vehicle driving and environmental changes. This method adopts bidirectional long short-term memory (BiLSTM) model combined with an encoder to ensure the memory of long-time series training. By constructing an attention mechanism based on BiLSTM, we consider the importance of different information which could guarantee the encoder’s memory under long sequence. The designed attention-bidirectional LSTM (Att-BiLSTM) model is adopted to ensure the surrounding vehicles’ prediction accuracy and effectiveness.