Since December 2019, there has been an outbreak of novel coronavirus (2019-nCoV) infection in China. Two cases of neonates with positive 2019-nCoV tests have been reported. Due to the immature immune system and the possibility of vertical transmission from mother to infant, neonates have become a high-risk group susceptible to 2019-nCoV, which emphasize a close cooperation from both perinatal and neonatal pediatrics. In neonatal intensive care unit (NICU), to prevent and control infection, there should be practical measures to ensure the optimal management of children potentially to be infected. According to the latest 2019-nCoV national management plan and the actual situation, the Chinese Neonatal 2019-nCoV expert working Group has put forward measures on the prevention and control of neonatal 2019-nCoV infection.

Genomic sequencing technologies have revolutionized mutation detection of the genetic diseases in the past few years. In recent years, the third generation sequencing (TGS) has been gaining insight into more genetic diseases owing to the single molecular and real time sequencing technology. This paper reviews the genomic sequencing revolutionary history first and then focuses on the genetic diseases discovered through the TGS and the clinical effects of the TGS, which is followed by the discussion of the improvement in the bioinformatic analysis for the TGS and its limitations. In summary, the TGS has been enhancing the diagnostic accuracy of genetic diseases in molecular level as well as paving a new way for basic researches and therapies.

Abstract-In wireless sensor networks, it is already noted that nearby sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the research of in-network data aggregation. In this paper, an -local spatial clustering algorithm for sensor networks is proposed. By measuring the spatial correlation between data sampled by different sensors, the algorithm constructs a dominating set as the sensor network backbone used to realize the data aggregation based on the information description/summarization performance of the dominators. In order to evaluate the performance of the algorithm a pattern recognition scenario over environmental data is presented. The evaluation shows that the resulting network achieved by our algorithm can provide environmental information at higher accuracy compared to other algorithms.