Abstract: Intensity of natural and man-made disasters is increasing day by day. Disaster is one of the major threats that affects the sustainable development of tourist attractions. Big data and Internet of Things(IoT) will greatly improve the disaster management. Based on the Big Data and IoT, a tourism attraction disaster management system is designed, divided into several stages namely pre-disaster early warning prevention, disaster mitigation, recovery and reconstruction after disaster and updating disaster planning. Then, the system flow is analysed, as well as the system structure is constructed. Additional, system function and its operation flow are introduced, including disaster warning, disaster relief, disaster assessment, real-time monitoring and supporting disaster planning functions. Finally, an application case is introduced. Research intends to improve tourism area disasters management.

The Coronavirus disease 2019 (COVID-19) pandemic began in Jan. 2020 in Wuhan, China with a new coronavirus designated SARS-CoV-2. The principle cause of death from COVID-19 disease quickly emerged as Acute Respiratory Distress Syndrome (ARDS). A key ARDS pathogenic mechanism is the “Cytokine Storm”. This is a dramatic increase in the blood of inflammatory cytokines. In the last 2 years of the pandemic new pathology has emerged in COVID-19 survivors in which a variety of long-term symptoms emerge. This condition is called “Long COVID”. The spike protein on the surface of the virus (target for the new mRNA/DNA vaccines) is composed of joined S1-S2 subunits. Upon S1 bind-ing to the human ACE2 receptor on cells, the S1 subunit is cleaved and the S2 subunit me-diates entry of the virus. The S1 protein is then released into the blood, which might be one of the pivotal triggers for initiation and/or perpetuation of the cytokine storm. In this study, we tested the hypothesis that the spike S1 protein may activate inflammatory sig-naling and cytokine production independent of the virus. Our data support a potential role for spike S1 activation of inflammatory signaling and cytokine production in human lung and intestinal epithelial cells in culture. These data support a potential role for the SARS-CoV-2 spike S1 protein in COVID-19 pathogenesis.

Cotton (Gossypium hirsutum Linn.) is widely cultivated in China. The polyphagous insect Adelphocoris suturalis (Jakovlev) is a serious insect pest in cotton growing regions. Plants have evolved sophisticated systems to cope with herbivore attacks. However, the cotton defense mechanisms induced by A. suturalis feeding have lagged behind. We carried out untargeted proteomic analysis using the iTARQ technique and metabolomics based on LC-MS/MS analysis of cotton leaves fed upon by A. suturalis. Proteomic analysis identified 775 upregulated proteins and 477 downregulated proteins in plants that were infested by A. sututralis compared to the controls. Metabolomic analysis identified 50 differentially expressed metabolites in the positive ion mode and 14 in the negative ion mode compared to the controls. The tryptophan metabolism pathway was significantly changed in both the positive and negative ion mode in the metabolomics analysis. The alpha-linolenic acid pathway was significantly changed in both the proteomic and metabolomics analyses. Furthermore, the result was validated by RT-qPCR analysis of 5 related genes involved in alpha-linolenic acid pathway. These results indicate that tryptophan metabolism and the alpha-linolenic acid pathway may be important in cotton defense against herbivores and would enhance our understanding of plant defenses induced by A. sututrali feeding.