An integrated framework for flood disaster information extraction and analysis leveraging social media data: A case study of the Shouguang flood in China.

Flood disasters cause significant casualties and economic losses annually worldwide. During disasters, accurate and timely information is crucial for disaster management. However, remote sensing cannot balance temporal and spatial resolution, and the coverage of specialized equipment is limited, making continuous monitoring challenging. Real-time disaster-related information shared by social media users offers new possibilities for monitoring. We propose a framework for extracting and analyzing flood information from social media, validated through the 2018 Shouguang flood in China. This framework innovatively combines deep learning techniques and regular expression matching techniques to automatically extract key flood-related information from Weibo textual data, such as problems, floodings, needs, rescues, and measures, achieving an accuracy of 83 %, surpassing traditional models like the Biterm Topic Model (BTM). In the spatiotemporal analysis of the disaster, our research identifies critical time points during the disaster through quantitative analysis of the information and explores the spatial distribution of calls for help using Kernel Density Estimation (KDE), followed by identifying the core affected areas using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. For semantic analysis, we adopt the Latent Dirichlet Allocation (LDA) algorithm to perform topic modeling on Weibo texts from different regions, identifying the types of disasters affecting each township. Additionally, through correlation analysis, we investigate the relationship between disaster rescue requests and response measures to evaluate the adequacy of flood response measures in each township. The research results demonstrate that this analytical framework can accurately extract disaster information, precisely identify critical time points in flood disasters, locate core affected areas, uncover primary regional issues, and further validate the sufficiency of response measures, therefore enhancing the efficiency in collecting disaster information and analytical capabilities.

Fusion of cellobiose phosphorylase and potato alpha-glucan phosphorylase facilitates substrate channeling for enzymatic conversion of cellobiose to starch.

We previously reported an in vitro enzymatic pathway for conversion of nonfood cellulose to starch (PNAS,110 (18): 7182-7187, 2013), in which the two sequential enzymes cellobiose phosphorylase (CBP) from Clostridium thermocellum and potato alpha-glucan phosphorylase (PGP) from Solanum tuberosum were the two key enzymes responsible for the whole conversion rate. In this work CBP and PGP were fused to form a large enzyme and it turned out that the fusion protein could exhibit a good bifunctionality when PGP moiety was put at the N-terminus and CBP moiety at the C-terminus (designated as PGP-CBP). Although the coupled reaction rate of PGP-CBP was decreased by 23.0% compared with the free enzymes, substrate channeling between the two active sites in PGP-CBP was formed, demonstrated by the introduction of the competing enzyme of PGP to the reaction system. The potential of PGP-CBP fusion enzyme being applied to the conversion of cellulose to amylose was discussed.