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Na+/H+ antiporters are integral membrane proteins that are present in almost every cell and in every kingdom of life. They are essential for the regulation of intracellular pH-value, Na+-concentration and cell volume. These secondary active transporters exchange sodium ions against protons via an alternating access mechanism, which is not understood in full detail. Na+/H+ antiporters show distinct species-specific transport characteristics and regulatory properties that correlate with respective physiological functions. Here we present the characterization of the Na+/H+ antiporter NhaA from Salmonella enterica serovar Thyphimurium LT2, the causing agent of food-born human gastroenteritis and typhoid like infections. The recombinant antiporter was functional in vivo and in vitro. Expression of its gene complemented the Na+-sensitive phenotype of an E. coli strain that lacks the main Na+/H+ antiporters. Purified to homogeneity, the antiporter was a dimer in solution as accurately determined by size-exclusion chromatography combined with multi-angle laser-light scattering and refractive index monitoring. The purified antiporter was fully capable of electrogenic Na+(Li+)/H+-antiport when reconstituted in proteoliposomes and assayed by solid-supported membrane-based electrophysiological measurements. Transport activity was inhibited by 2-aminoperimidine. The recorded negative currents were in agreement with a 1Na+(Li+)/2H+ stoichiometry. Transport activity was low at pH 7 and up-regulation above this pH value was accompanied by a nearly 10-fold decrease of Km Na (16 mM at pH 8.5) supporting a competitive substrate binding mechanism. K+ does not affect Na+ affinity or transport of substrate cations, indicating that selectivity of the antiport arises from the substrate binding step. In contrast to homologous E. coli NhaA, transport activity remains high at pH values above 8.5. The antiporter from S. Typhimurium is a promising candidate for combined structural and functional studies to contribute to the elucidation of the mechanism of pH-dependent Na+/H+ antiporters and to provide insights in the molecular basis of species-specific growth and survival strategies.

Rapid expansion of smart metering technologies has enabled large-scale collection of electricity consumption data and created the foundation for sensor-based load forecasting on individual buildings or even the household level. With continuously growing energy consumption, the importance of energy management including load forecasting is increasing in order to remedy the energy effect on the environment. Numerous machine learning techniques have been proposed for sensor-based load forecasting but most are offline approaches: the model is trained once and then used to infer future consumption. However, these approaches are not able to adapt to concept drift: for example, their accuracy will degrade when the building use changes or new equipment is installed. Thus, an approach capable of learning from new data as they arrive is needed. This paper proposes adaptive online ensemble learning with Recurrent Neural Network (RNN) and ARIMA for load forecasting under concept drift. The RNN part of the ensembles consists of Online Adaptive RNN as its underlying RNN learner has the ability to model temporal dependencies present in load data while its online nature enables continuous learning from arriving data. The adaptation to the concept drift is improved by adding Rolling ARIMA to the ensemble. The performance of the proposed approach has been examined on the four individual homes with different degrees of concept drift. The results show that the proposed ensemble achieves better accuracy than its constituent algorithms alone and, moreover, the analysis demonstrates the need to examine load forecasting approaches in respect to how they handle concept drift.

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