Specific and rapid identification of the Pheretima aspergillum by loop-mediated isothermal amplification.

Guang-dilong (Pheretima aspergillum) is a traditional Chinese animal medicine that has been used for thousands of years in China. In the present study, we purposed to establish a new rapid identification method for Guang-dilong. We provided a useful technique, loop-mediated isothermal amplification (LAMP), to differentiate Guang-dilong from other species. Four specific LAMP primers were designed based on mitochondrial cytochrome c oxidase I (COI) gene sequences of Guang-dilong. LAMP reaction, containing DNA template, four primers, 10× Bst DNA polymerase reaction buffer, dNTPs, MgSO4, and Bst DNA polymerase, was completed within 60 min at 63°C. The LAMP product can be visualized by adding SYBR Green I or detected by 2% gel electrophoresis. LAMP technology was successfully established for rapid identification of Guang-dilong. In addition, DNA template concentration of 675 fg/µl was the detection limit of LAMP in Guang-dilong, which was 1000-times higher than conventional PCR. The simple, sensitive, and convenient LAMP technique is really suited for on-site identification of Guang-dilong in herbal markets.

Anomaly detection based on a dynamic Markov model.

Anomaly detection in sequence data is becoming more and more important in a wide variety of application domains such as credit card fraud detection, health care in medical field, and intrusion detection in cyber security. In the existing anomaly detection approaches, Markov chain techniques are widely accepted for their simple realization and few parameters. However, the short memory property of a classical Markov model ignores the interaction among data, and the long memory property of a higher order Markov model clouds the relationship between the previous data and current test data, and reduces the reliability of the model. Besides, both of these models cannot successfully describe the sequences changing with a tendency. In this paper, we propose an anomaly detection approach based on a dynamic Markov model. This approach segments sequence data by a sliding window. In the sliding window, we define the states of data according to the value of the data and establish a higher order Markov model with a proper order consequently, to balance the length of the memory property and keep up with the trend of sequences. In addition, an anomaly substitution strategy is proposed to prevent the detected anomalies from impacting the building of the models and keep anomaly detection continuously. The experimental results using simulated datasets and real-world datasets have demonstrated that the proposed approach improves the adaptability and stability of anomaly detection in sequence data.