Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm.

Large volumes of automatic identification system (AIS) data provide new ideas and methods for ship data mining and navigation behavior pattern analysis. However, large volumes of big data have low unit values, resulting in the need for large-scale computing, storage, and display. Learning efficiency is low and learning direction is blind and untargeted. Therefore, key feature point (KFP) extraction from the ship trajectory plays an important role in fields such as ship navigation behavior analysis and big data mining. In this paper, we propose a ship spatiotemporal KFP online extraction algorithm that is applied to AIS trajectory data. The sliding window algorithm is modified for application to ship navigation angle deviation, position deviation, and the spatiotemporal characteristics of AIS data. Next, in order to facilitate the subsequent use of the algorithm, a recommended threshold range for the corresponding two parameters is discussed. Finally, the performance of the proposed method is compared with that of the Douglas-Peucker (DP) algorithm to assess its feature extraction accuracy and operational efficiency. The results show that the proposed improved sliding window algorithm can be applied to rapidly and easily extract the KFPs from AIS trajectory data. This ability provides significant benefits for ship traffic flow and navigational behavior learning.

Morphological and molecular identification of two strains of dermatophytes.

In this study, we used traditional morphological and molecular identification methods to preliminarily identify two strains of dermatophytes. The two strains were observed under the microscope. And then the dermatophytes were cultured on Sabouraud's dextrose agar (SDA). The 18S rRNA regions of the two dermatophyte strains were amplified by polymerase chain reaction (PCR), and the PCR products were sequenced and compared with GenBank data. BLAST tools and DNAMAN software were used to analyze the sequences. To further determine highly homologous sequences, a phylogenetic tree was constructed using the Neighbor-Joining method. The two strains of dermatophytes were identified by traditional morphological identification as Epidermophyton floccosum and Microsporum ferrugineum. The 18S rRNA sequence analyses showed high similarities to Cladosporium cladosporioides isolate C115LM-UFPR and Ascomycete sp. LB68A1A2. Epidermophyton and Cladosporium belong to dermatophyte, while Microsporum ferrugineum and Ascomycete belong to microsporum. The two novel strains of dermatophytes were therefore identified as Cladosporium cladosporioides isolate C115LM-UFPR (JN650537, Cladosporium) and Ascomycete sp. LB68A1A2 (AY770409, Ascomycete sp).

[Expression of bcl-2 gene in EBV-transformed human gastric epithelial cell line GES-1].

OBJECTIVE: To detect bcl-2 gene expression in Epstein-Barr virus (EBV)-infected human gastric epithelial cell line GES-1 for understanding the role of bcl-2 gene in the carcinogenesis of EBV-associated gastric carcinoma. METHODS: Akata 1061 cells producing recombined EBV carrying neomycin resistance gene (NEOr) was used to mediate the EBV infection of human gastric epithelial cell line GES-1 via close contact, with the empty plasmid pcDNA3-transfected GES-1 cells via lipofectamine method as a control. The EBV-infected and pcDNA3-transfected cells were cloned by limited dilution and the positive clones selected with G418. Immunocytochemical staining was performed to detect the expressions of EBNA1 and Bcl-2 protein. RESULTS: Bcl-2 protein expression was detected in EBV-infected cells but not in the control cells. CONCLUSION: EBV infection can increase Bcl-2 expression in gastric epithelial cells, and such cell transformation effect of EBV is related to the overexpression of bcl-2 gene.