Abstract Shallow lowland lakes are critical components of the water cycle, providing an essential service function. However, the impacts of microcystin from phytoplankton communities on benthic macroinvertebrate community diversity and structure have seldom been investigated. During 2008-2012, the impacts of water environmental variables on the diversity of macrobenthic communities, including water temperature, pH, dissolved oxygen, transparency, conductivity, the permanganate index, Chlorophyll a, total phosphorus, total nitrogen, and microcystin-LR (L for leucine and R for arginine), were measured in a typical shallow lowland lake, Ge Lake. The results of the present study demonstrated that there were 31 benthic macroinvertebrate taxa in Ge Lake, including 7 oligochaetes, 7 Mollusca, 14 chironomids, and 3 other taxa. Among the macrobenthic taxa in the benthic community, opportunistic taxa such as Limnodrilus hoffmeisteri are present and can occur at greater densities in disturbed habitats. However, a significant reduction/disappearance of sensitive and clean taxa was observed among the benthic macroinvertebrates. Water temperature, dissolved oxygen, conductivity, ammonia nitrogen, transparency and total phosphorus were the main environmental variables influencing macrobenthic community structure, while water temperature, conductivity, ammonia nitrogen, Chlorophyll a, the permanganate index, total phosphorus and total nitrogen were the main factors that influenced macrobenthic community diversity indices (the numbers of taxa, Shannon’s diversity index, Margalef’s richness index, and Pielou’s evenness index). Our results underscore the severity of the effects of human activity on Ge Lake and strongly suggest that restoring the benthic invertebrate community to previous conditions would require the control and reduction of environmental pollutants and nutrients in Ge Lake.

The T-cell immunoglobulin and mucin domain (TIM) family is associated with autoimmune diseases, but its expression level in the immune cells of systemic lupus erythematosus (SLE) patients is not known. The aim of this study was to investigate whether the expression of TIM-3 mRNA is associated with pathogenesis of SLE. Quantitative real-time reverse transcription-polymerase chain reaction analysis (qRT-PCR) was used to determine TIM-1, TIM-3, and TIM-4 mRNA expression in peripheral blood mononuclear cells (PBMCs) from 132 patients with SLE and 62 healthy controls. The PBMC surface protein expression of TIMs in PBMCs from 20 SLE patients and 15 healthy controls was assayed by flow cytometry. Only TIM-3 mRNA expression decreased significantly in SLE patients compared with healthy controls (P<0.001). No significant differences in TIM family protein expression were observed in leukocytes from SLE patients and healthy controls (P>0.05). SLE patients with lupus nephritis (LN) had a significantly lower expression of TIM-3 mRNA than those without LN (P=0.001). There was no significant difference in the expression of TIM-3 mRNA within different classes of LN (P>0.05). Correlation of TIM-3 mRNA expression with serum IgA was highly significant (r=0.425, P=0.004), but was weakly correlated with total serum protein (rs=0.283, P=0.049) and serum albumin (rs=0.297, P=0.047). TIM-3 mRNA expression was weakly correlated with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI; rs=-0.272, P=0.032). Our results suggest that below-normal expression of TIM-3 mRNA in PBMC may be involved in the pathogenesis of SLE.

To effectively control and treat river water pollution, it is very critical to establish a water quality prediction system. Combined Principal Component Analysis (PCA), Genetic Algorithm (GA) and Back Propagation Neural Network (BPNN), a hybrid intelligent algorithm is designed to predict river water quality. Firstly, PCA is used to reduce data dimensionality. 23 water quality index factors can be compressed into 15 aggregative indices. PCA improved effectively the training speed of follow-up algorithms. Then, GA optimizes the parameters of BPNN. The average prediction rates of non-polluted and polluted water quality are 88.9% and 93.1% respectively, the global prediction rate is approximately 91%. The water quality prediction system based on the combination of Neural Networks and Genetic Algorithms can accurately predict water quality and provide useful support for realtime early warning systems.

This paper is considered with the robust stability problem for linear discrete-time systems with polytopic uncertainty and an interval time-varying delay in the state. On the basis of a novel Lyapunov-Krasovskii functional, new delay-range-dependent stability criteria are established by employing the free-weighting matrix approach and a Jensen-type sum inequality. It is shown that the newly proposed criteria can provide less conservative results than some existing ones. Numerical examples are given to illustrate the effectiveness of the proposed approach.