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To respond to environmental changes, plants have developed complex mechanisms that allow them to rapidly perceive and respond to abiotic stresses. Late embryogenesis abundant (LEA) proteins are a large and diverse family that play important roles in environmental stress tolerance in plants. Dehydrins belong to group II LEA proteins, which are considered stress proteins involved in the formation of plants’ protective reactions to dehydration. Some studies have demonstrated that dehydrins could binding metal ions or lipid vesicles. In vitro experiments revealed that dehydrins could protect the activity of enzyme from damage caused by environmental stress. Although many studies have been conducted to understand their roles in abiotic stresses, the molecular function of dehydrins is still unclear. In this review, to generate new ideas for elucidating dehydrins’ functions, we highlight the functional characteristics of dehydrins to understand their roles under environmental stress in plants.

Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining.

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