Sediment Bacterial Community Structure under the Influence of Different Domestic Sewage Types
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- Journal of Microbiology and Biotechnology
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- 제30권9호
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- pp.1355-1366
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- 2020
Sediment bacterial communities are critical to the biogeochemical cycle in river ecosystems, but our understanding of the relationship between sediment bacterial communities and their specific input streams in rivers remains insufficient. In this study, we analyzed the sediment bacterial community structure in a local river receiving discharge of urban domestic sewage by applying Illumina MiSeq high-throughput sequencing. The results showed that the bacterial communities of sediments samples of different pollution types had similar dominant phyla, mainly Proteobacteria, Actinobacteria, Chloroflexi and Firmicutes, but their relative abundances were different. Moreover, there were great differences at the genus level. For example, the genus Bacillus showed statistically significant differences in the hotel site. The clustering of bacterial communities at various sites and the dominant families (i.e., Nocardioidaceae, and Sphingomonadaceae) observed in the residential quarter differed from other sites. This result suggested that environmentally induced species sorting greatly influenced the sediment bacterial community composition. The bacterial co-occurrence patterns showed that the river bacteria had a nonrandom modular structure. Microbial taxonomy from the same module had strong ecological links (such as the nitrogenium cycle and degradation of organic pollutants). Additionally, PICRUSt metabolic inference analysis showed the most important function of river bacterial communities under the influence of different types of domestic sewage was metabolism (e.g., genes related to xenobiotic degradation predominated in residential quarter samples). In general, our results emphasize that the adaptive changes and interactions in the bacterial community structure of river sediment represent responses to different exogenous pollution sources.
초연결사회를 구현하는 핵심 기술이자 정부와 기업의 새로운 신성장동력으로써 사물인터넷이 전 세계적으로 주목 받고 있다. 하지만 새로운 비즈니스 기회로써 받는 주목과는 대조적으로 기술적인 관점이 아닌 비즈니스 관점에서의 사물인터넷에 대한 학계의 연구는 미비한 수준에 머무르고 있으며, 사물인터넷 시장에 대한 객관적인 이해 또한 부족하다. 따라서 사물인터넷 서비스 시장의 실태를 파악하고, 신사업 기회를 포착 할 수 있는 연구가 필요하다. 본 연구는 사물인터넷 서비스 시장 활성화를 위한 기반연구로서 사물인터넷 시장에 대한 객관적인 통계 수집을 위한 사물인터넷 서비스 분류체계를 제시한다. 사물인터넷 서비스 분류체계는 사물인터넷 산업에 대한 포괄적인 분석을 위해 서비스 목적(IoT Purpose), 서비스 제공자(IoT Player), 서비스 영역(IoT Domain)의 관점으로 구성하였다. 문헌조사를 통해 총 117개의 사물인터넷 서비스를 수집하여 분류체계에 대입해 봄으로써 분류체계의 유용성을 검증하였고, 분류결과를 통해 사례 통계 기반의 사물인터넷 서비스 시장의 동향을 제시하였다. 본 연구는 향후 사물인터넷 서비스 활성화를 위한 기반 연구로써 활용될 수 있을 것으로 사료된다.
A cost analysis for hospitalized patients was performed based on the RBRVS in order to determine an appropriate nursing fee schedule. The study was conducted through three phases as follows: 1) Nursing activities provided for the inpatients currently in Korea were identified and classified using a taxonomy which was developed by our research team through the Delphi process. 2) The resource-based relative points for every nursing activity according to nursing time, mental effort and judgement, technical skill, physical effort and stress were determined through a survey of 300 clinical RNs working at 5 tertiary hospitals from May 25 to July 25. 1998. 3) The nursing cost of every nursing activity for hospitalized patients was estimated based on the RBRVS. As a result, 136 nursing activities were identified and classified by nursing processes and nursing domains. However, our classification system of nursing activities should continue to be refined, and all nursing practices should be standardized. The nursing activities were given resource-based relative points ranging from 100 to 400 points, then each nursing activity was assigned a value for the RBRVS, which was determined by the exponential function of 2resource-based relative point/100. Thus, a value of 2 was calculated for 100 points, 4 for 200 points, 8 for 300 points, and 16 for 400 points. Meanwhile, the unit cost of nursing was calculated as 170 Won. The nursing cost of 136 nursing activities was estimated using the RBRVS as shown in