Methylmercury in fish from the South China Sea: Geographical distribution and biomagnification

We conducted a large-scale investigation of methylmercury (MeHg) in a total of 628 marine wild fish covering 46 different species collected from the South China Sea between 2008 and 2009. Biological and ecological characteristics such as size (length and wet weight), feeding habit, habitat, and stable isotope (delta N-15) were examined to explain MeHg bioaccumulation in marine fish and their geographical distribution. MeHg levels in the muscle tissues of the 628 individuals ranged from 0.010 to 1.811 mu g/g dry wt. Log(10)MeHg concentration was significantly related to their length and wet weight. Feeding habit and habitat were the primary factors influencing MeHg bioaccumulation. Demersal fish were more likely to be contaminated with MeHg than the epipelagic and mesopelagic varieties. Linear relationships were obtained between Log(10)(MeHg) and delta N-15 only for one location, indicating that biomagnification was site-specific. Results from this study suggest that dietary preference and trophic structure were the main factors affecting MeHg bioaccumulation in marine fish from the South China Sea. (C) 2013 Elsevier Ltd. All rights reserved

Research on sliding mode controller of the high-speed maglev train under aerodynamic load

The high-speed maglev train will be subjected to extremely obvious aerodynamic load and instantaneous aerodynamic impact during passing another train, which brings significant challenges to the train's suspension stability and safe operation. It's necessary to consider the influence of aerodynamic load and shock waves in the design of suspension control algorithms. Traditional proportion integration differentiation (PID) control cannot follow the change of vehicle parameters or external disturbance, which is easy to cause suspension fluctuation and instability. To improve the suspension stability and vibration suppression of the high-speed maglev train under aerodynamic load and impact, we design a siding mode controller introducing the primary suspension's deformation to replace the aerodynamic load on the electromagnet. Furthermore, we establish the train's dynamic simulation model with three vehicles and compare the designed controller and the PID controller for their performance in controlling the model suspension stability in the presence of the train operating in open air. Simulation results show that the sliding mode control (SMC) method proposed in this paper can effectively restrain the electromagnet fluctuation of the train under aerodynamic loads

Research on sliding mode controller of the high-speed maglev train under aerodynamic load

The high-speed maglev train will be subjected to extremely obvious aerodynamic load and instantaneous aerodynamic impact during passing another train, which brings significant challenges to the train's suspension stability and safe operation. It's necessary to consider the influence of aerodynamic load and shock waves in the design of suspension control algorithms. Traditional proportion integration differentiation (PID) control cannot follow the change of vehicle parameters or external disturbance, which is easy to cause suspension fluctuation and instability. To improve the suspension stability and vibration suppression of the high-speed maglev train under aerodynamic load and impact, we design a siding mode controller introducing the primary suspension's deformation to replace the aerodynamic load on the electromagnet. Furthermore, we establish the train's dynamic simulation model with three vehicles and compare the designed controller and the PID controller for their performance in controlling the model suspension stability in the presence of the train operating in open air. Simulation results show that the sliding mode control (SMC) method proposed in this paper can effectively restrain the electromagnet fluctuation of the train under aerodynamic loads

Bacterial Communities in the Rhizospheres of Three Mangrove Tree Species from Beilun Estuary, China.

The bacterial communities played important roles in the high productivity mangrove ecosystem. In this study, we investigated the vertical distributions of rhizosphere bacteria from three mangrove species (Bruguiera gymnorrhiza, Kandelia candel and Aegiceras corniculatum) in Beilun Estuary, China using high throughput DNA pyrosequencing of the 16S rRNA gene. Phylogenetic analysis showed that bacterial communities from mangrove rhizosphere sediments were dominated by Proteobacteria (mostly Deltaproteobacteria and Gammaproteobacteria), followed by Chloroflexi, Bacteroidetes, Planctomycetes and Acidobacteria. However, the ANOVA analysis on Shannon and Chao1 indices indicated that bacterial communities among sediments of the three mangrove species varied more strongly than the sampling depths. In addition, the PCA result demonstrated that the bacterial communities could be separated into three groups according to the mangrove species. Moreover, the dominated orders Rhodospirillales, GCA004 and envOPS12 were significantly different among sediments of the three mangrove species. The results of this study provided valuable information about the distribution feature of rhizosphere bacteria from Chinese mangrove plants and shed insights into biogeochemical transformations driven by bacteria in rhizosphere sediments

Sequencing information and diversity estimates for rhizosphere samples collected from three mangrove tree species (<i>B</i>. <i>gymnorrhiza</i>, <i>K</i>. <i>candel</i> and <i>A</i>. <i>corniculatum</i>) obtained by pyrosequencing.

<p>Sequencing information and diversity estimates for rhizosphere samples collected from three mangrove tree species (<i>B</i>. <i>gymnorrhiza</i>, <i>K</i>. <i>candel</i> and <i>A</i>. <i>corniculatum</i>) obtained by pyrosequencing.</p

Relative abundance of the 30 most dominant bacterial groups on the order level.

<p>Symbols (*) represent significant differences among sediments of the three mangrove species (p<0.05).</p

The analysis of statistical significant differences about Shannon and Chao1 indices for mangrove species and sampling depths (p<0.05).

<p>The analysis of statistical significant differences about Shannon and Chao1 indices for mangrove species and sampling depths (p<0.05).</p