For large databases, the research on improving the mining performance and precision is necessary, so many focuses of today on association rule mining are about new mining theories, algorithms and improvement to old methods. Association rules mining is a function of data mining research domain and arise many researchers interest to design a high efficient algorithm to mine association rules from transaction database. Generally all the frequent item sets discovery from the database in the process of association rule mining shares of larger, the price is also spending more. This paper introduces an improved aprior algorithm so called FP-growth algorithm that will help resolve two neck-bottle problems of traditional apriori algorithm and has more efficiency than original one. In theoretic research, An anatomy of two representative arithmetics of the Apriori and the FP Growth explains the mining process of frequent patterns item set. The constructing method of FP tree structure is provided and how it affects association rule mining is discussed. Experimental results show that the algorithm has higher mining efficiency in execution time, memory usage and CPU utilization than most current ones like Apriori.

Heterogeneous nature of real networks implies that different edges play different roles in network structure and functions, and thus to identify significant edges is of high value in both theoretical studies and practical applications. We propose the so-called second-order neighborhood (SN) index to quantify an edge's significance in a network. We compare SN index with many other benchmark methods based on 15 real networks via edge percolation. Results show that the proposed SN index outperforms other well-known methods.

Vibrio vulnificus is a Gram-negative, curved, obligate halophilic marine bacterium that exclusively exists in coastal seawaters. Previous studies revealed that V. vulnificus is one of the most dangerous foodborne zoonotic pathogens for human beings. However, it remains unknown whether marine mammals can be infected by V. vulnificus. In May 2016, a captive spotted seal (Phoca largha) died due to septicemia induced by V. vulnificus. Upon post-mortem examination, V. vulnificus was isolated, identified, and named as BJ-PH01. Further analysis showed that BJ-PH01 belongs to biotype 1 and the Clinical genotype. Furthermore, we performed an epidemiological investigation of V. vulnificus in six aquariums in northern China. As a result, V. vulnificus was successfully isolated from all investigated aquariums. The positive rates ranged from 20% to 100% in each investigated aquarium. During the investigation, 12 strains of V. vulnificus were isolated, and all 12 isolates were classified into biotype 1. Eleven of the 12 isolates belonged to the Clinical genotype, and one isolate belonged to the Environmental genotype. All 12 isolated V. vulnificus strains showed limited antibiotic resistance. Overall, our work demonstrated that V. vulnificus is frequently distributed in aquariums, thus constituting a threat to captive marine mammals and to public health.

The identification of profiled cancer-related genes plays an essential role in cancer diagnosis and treatment. Based on literature research, the classification of genetic mutations continues to be done manually nowadays. Manual classification of genetic mutations is pathologist-dependent, subjective, and time-consuming. To improve the accuracy of clinical interpretation, scientists have proposed computational-based approaches for automatic analysis of mutations with the advent of next-generation sequencing technologies. Nevertheless, some challenges, such as multiple classifications, the complexity of texts, redundant descriptions, and inconsistent interpretation, have limited the development of algorithms. To overcome these difficulties, we have adapted a deep learning method named Bidirectional Encoder Representations from Transformers (BERT) to classify genetic mutations based on text evidence from an annotated database. During the training, three challenging features such as the extreme length of texts, biased data presentation, and high repeatability were addressed. Finally, the BERT+abstract demonstrates satisfactory results with 0.80 logarithmic loss, 0.6837 recall, and 0.705 F -measure. It is feasible for BERT to classify the genomic mutation text within literature-based datasets. Consequently, BERT is a practical tool for facilitating and significantly speeding up cancer research towards tumor progression, diagnosis, and the design of more precise and effective treatments.