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This paper is concerned with the problem of robust H∞ output feedback control for a class of uncertain discrete-time delayed nonlinear stochastic systems with missing measurements. The parameter uncertainties enter into all the system matrices, the time-varying delay is unknown with given low and upper bounds, the nonlinearities satisfy the sector conditions, and the missing measurements are described by a binary switching sequence that obeys a conditional probability distribution. The problem addressed is the design of an output feedback controller such that, for all admissible uncertainties, the resulting closed-loop system is exponentially stable in the mean square for the zero disturbance input and also achieves a prescribed H∞ performance level. By using the Lyapunov method and stochastic analysis techniques, sufficient condition are first derived to guarantee the existence of the desired controllers, and then the controller parameters are characterized in terms of linear matrix inequalities (LMIs). A numerical example is exploited to show the usefulness of the results obtained.

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.This paper is concerned with analysis problem for the global exponential stability of a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, by employing a new Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable. Therefore, the global exponential stability of the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany

Microbial conversion of inorganic mercury (IHg) to methylmercury (MeHg) is a significant environmental concern because of the bioaccumulation and biomagnification of toxic MeHg in the food web. Laboratory incubation studies have shown that, despite the presence of large quantities of IHg in cell cultures, MeHg biosynthesis often reaches a plateau or a maximum within hours or a day by an as yet unexplained mechanism. Here we report that mercuric Hg(II) can be taken up rapidly by cells of Desulfovibrio desulfuricans ND132, but a large fraction of the Hg(II) is unavailable for methylation because of strong cellular sorption. Thiols, such as cysteine, glutathione, and penicillamine, added either simultaneously with Hg(II) or after cells have been exposed to Hg(II), effectively desorb or mobilize the bound Hg(II), leading to a substantial increase in MeHg production. The amount of thiol-desorbed Hg(II) is strongly correlated to the amount of MeHg produced (r = 0.98). However, cells do not preferentially take up Hg(II)-thiol complexes, but Hg(II)-ligand exchange between these complexes and the cell-associated proteins likely constrains Hg(II) uptake and methylation. We suggest that, aside from aqueous chemical speciation of Hg(II), binding and exchange of Hg(II) between cells and complexing ligands such as thiols and naturally dissolved organics in solution is an important controlling mechanism of Hg(II) bioavailability, which should be considered when predicting MeHg production in the environment.

Microbial methylation and demethylation are two competing processes controlling the net production and bioaccumulation of neurotoxic methylmercury (MeHg) in natural ecosystems. Although mercury (Hg) methylation by anaerobic microorganisms and demethylation by aerobic Hg-resistant bacteria have both been extensively studied, little attention has been given to MeHg degradation by anaerobic bacteria, particularly the iron-reducing bacterium Geobacter bemidjiensis Bem. Here we report, for the first time, that the strain G. bemidjiensis Bem can mediate a suite of Hg transformations, including Hg(II) reduction, Hg(0) oxidation, MeHg production and degradation under anoxic conditions. Results suggest that G. bemidjiensis utilizes a reductive demethylation pathway to degrade MeHg, with elemental Hg(0) as the major reaction product, possibly due to the presence of genes encoding homologues of an organomercurial lyase (MerB) and a mercuric reductase (MerA). In addition, the cells can strongly sorb Hg(II) and MeHg, reduce or oxidize Hg, resulting in both time and concentration-dependent Hg species transformations. Moderate concentrations (10-500 μM) of Hg-binding ligands such as cysteine enhance Hg(II) methylation but inhibit MeHg degradation. These findings indicate a cycle of Hg methylation and demethylation among anaerobic bacteria, thereby influencing net MeHg production in anoxic water and sediments.

Despite the vital role of microorganisms for ecosystem functioning and human welfare, our understanding of their global diversity and biogeographical patterns lags significantly behind that of plants and animals. We conducted a meta‐analysis including ~600 soil samples from all continents to evaluate the biogeographical patterns and drivers of bacterial diversity in terrestrial ecosystems at the global scale. Similar to what has been found with plants and animals, the diversity of soil bacteria in the Southern Hemisphere decreased from the equator to Antarctica. However, soil bacteria showed similar levels of diversity across the Northern Hemisphere. The composition of bacterial communities followed dissimilar patterns between hemispheres, as the Southern and Northern Hemispheres were dominated by Actinobacteria and Acidobacteria, respectively. However, Proteobacteria was co‐dominant in both hemispheres. Moreover, we found a decrease in soil bacterial diversity with altitude. Climatic features (e.g., high diurnal temperature range and low temperature) were correlated with the lower diversity found at high elevations, but geographical gradients in soil total carbon and species turnover were important drivers of the observed latitudinal patterns. We thus found both parallels and differences in the biogeographical patterns of aboveground vs. soil bacterial diversity. Our findings support previous studies that highlighted soil pH, spatial influence, and organic matter as important drivers of bacterial diversity and composition. Furthermore, our results provide a novel integrative view of how climate and soil factors influence soil bacterial diversity at the global scale, which is critical to improve ecosystem and earth system simulation models and for formulating sustainable ecosystem management and conservation policies.

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2005 Elsevier Ltd.In this Letter, the global asymptotic stability analysis problem is investigated for a class of neural networks with discrete and distributed time-delays. The purpose of the problem is to determine the asymptotic stability by employing some easy-to-test conditions. It is shown, via the Lyapunov–Krasovskii stability theory, that the class of neural networks under consideration is globally asymptotically stable if a quadratic matrix inequality involving several parameters is feasible. Furthermore, a linear matrix inequality (LMI) approach is exploited to transform the addressed stability analysis problem into a convex optimization problem, and sufficient conditions for the neural networks to be globally asymptotically stable are then derived in terms of a linear matrix inequality, which can be readily solved by using the Matlab LMI toolbox. Two numerical examples are provided to show the usefulness of the proposed global stability condition.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany