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Host Species as a Strong Determinant of the Intestinal Microbiota of Fish Larvae
Xuemei Li,Yuhe Yu,Weisong Feng,Qingyun Yan,Yingchun Gong 한국미생물학회 2012 The journal of microbiology Vol.50 No.1
We investigated the influence of host species on intestinal microbiota by comparing the gut bacterial community structure of four cohabitating freshwater fish larvae, silver carp, grass carp, bighead carp, and blunt snout bream, using denaturing gradient gel electrophoresis (DGGE) of the amplified 16S and 18S rRNA genes. Similarity clustering indicated that the intestinal microbiota derived from these four fish species could be divided into four groups based on 16S rRNA gene similarity, whereas the eukaryotic 18S rRNA genes showed no distinct groups. The water sample from the shared environment contained microbiota of an independent group as indicated by both 16S and 18S rRNA genes segments. The bacterial community structures were visualized using rank-abundance plots fitted with linear regression models. Results showed that the intestinal bacterial evenness was significantly different between species (P<0.05) and between species and the water sample (P<0.01). Thirty-five relatively dominant bands in DGGE patterns were sequenced and grouped into five major taxa: Proteobacteria (26), Actinobacteria (5), Bacteroidetes (1), Firmicutes (2), and Cyanobacterial (1). Six eukaryotes were detected by sequencing 18S rRNA genes segments. The present study suggests that the intestines of the four fish larvae, although reared in the same environment, contained distinct bacterial populations, while intestinal eukaryotic microorganisms were almost identical.
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( Wei Song ),( Ning Feng ),( Yifei Tian ),( Simon Fong ),( Kyungeun Cho ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.1
Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user’s comfort and improving the user’s experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.
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