Conserved histidine of metal transporter AtNRAMP1 is crucial for optimal plant growth under manganese deficiency at chilling temperatures

Manganese (Mn) is an essential nutrient required for plant growth, in particular in the process of photosynthesis. Plant performance is influenced by various environmental stresses including contrasting temperatures, light or nutrient deficiencies. The molecular responses of plants exposed to such stress factors in combination are largely unknown. 

Screening of 108 Arabidopsis thaliana (Arabidopsis) accessions for reduced photosynthetic performance at chilling temperatures was performed and one accession (Hog) was isolated. Using genetic and molecular approaches, the molecular basis of this particular response to temperature (GxE interaction) was identified. 

Hog showed an induction of a severe leaf chlorosis and impaired growth after transfer to lower temperatures. We demonstrated that this response was dependent on the nutrient content of the soil. Genetic mapping and complementation identified NRAMP1 as the causal gene. Chlorotic phenotype was associated with a histidine to tyrosine (H239Y) substitution in the allele of Hog NRAMP1. This led to lethality when Hog seedlings were directly grown at 4 degrees C. 

Chemical complementation and hydroponic culture experiments showed that Mn deficiency was the major cause of this GxE interaction. For the first time, the NRAMP-specific highly conserved histidine was shown to be crucial for plant performance.

In situ diagnostics and prognostics of solder fatigue in IGBT modules for electric vehicle drives

This paper proposes an in situ diagnostic and prognostic (D&P) technology to monitor the health condition of insulated gate bipolar transistors (IGBTs) used in EVs with a focus on the IGBTs' solder layer fatigue. IGBTs' thermal impedance and the junction temperature can be used as health indicators for through-life condition monitoring (CM) where the terminal characteristics are measured and the devices' internal temperature-sensitive parameters are employed as temperature sensors to estimate the junction temperature. An auxiliary power supply unit, which can be converted from the battery's 12-V dc supply, provides power to the in situ test circuits and CM data can be stored in the on-board data-logger for further offline analysis. The proposed method is experimentally validated on the developed test circuitry and also compared with finite-element thermoelectrical simulation. The test results from thermal cycling are also compared with acoustic microscope and thermal images. The developed circuitry is proved to be effective to detect solder fatigue while each IGBT in the converter can be examined sequentially during red-light stopping or services. The D&P circuitry can utilize existing on-board hardware and be embedded in the IGBT's gate drive unit.

Purification of a multidrug resistance transporter for crystallization studies

Crystallization of integral membrane proteins is a challenging field and much effort has been invested in optimizing the overexpression and purification steps needed to obtain milligram amounts of pure, stable, monodisperse protein sample for crystallography studies. Our current work involves the structural and functional characterization of the Escherichia coli multidrug resistance transporter MdtM, a member of the major facilitator superfamily (MFS). Here we present a protocol for isolation of MdtM to increase yields of recombinant protein to the milligram quantities necessary for pursuit of structural studies using X-ray crystallography. Purification of MdtM was enhanced by introduction of an elongated His-tag, followed by identification and subsequent removal of chaperonin contamination. For crystallization trials of MdtM, detergent screening using size exclusion chromatography determined that decylmaltoside (DM) was the shortest-chain detergent that maintained the protein in a stable, monodispersed state. Crystallization trials of MdtM performed using the hanging-drop diffusion method with commercially available crystallization screens yielded 3D protein crystals under several different conditions. We contend that the purification protocol described here may be employed for production of high-quality protein of other multidrug efflux members of the MFS, a ubiquitous, physiologically and clinically important class of membrane transporters.

Multidrug transporter proteins of the major facilitator superfamily: not only for drug efflux

Multidrug resistance in prokaryotes is due primarily to efflux of offending antimicrobials from the cell by representatives of several different families of integral membrane transporter proteins. Clearly, in evolutionary terms, these proteins did not arise specifically to pump human-made antimicrobials out of the cell and thereby confer resistance. Despite this, often only their role in antibiotic resistance is characterised and highlighted.
In recent years, however, a transition from the traditional anthropocentric perception of antibiotic resistance mechanisms in microorganisms has occurred, with naturally produced antimicrobials now generally regarded as physiologically important signalling molecules or sources of nutrition for bacteria rather than antimicrobial agents, and bacterial multidrug efflux proteins not merely as a defensive response to antimicrobials but as important players in fundamental physiological processes such as cellular homeostasis.
This emerging perspective supports the notion that a better understanding of the complexities of infection and multidrug resistance in bacteria can be achieved via a more detailed understanding of those physiological processes. In this chapter, we review the ‘true’ physiological roles of multidrug efflux proteins of the largest non-ATP-hydrolysing family of membrane transporters, the major facilitator superfamily, and explore the evidence for their function in processes such as pH and metal homeostasis, import and export of metabolites and biofilm formation

Non-convex dynamic economic/environmental dispatch with plug-in electric vehicle loads

Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

Analysis of electric vehicle charging using the traditional generation expansion planning analysis tool WASP-IV

Electric vehicles (EV) are proposed as a measure to reduce greenhouse gas emissions in transport and support increased wind power penetration across modern power systems. Optimal benefits can only be achieved, if EVs are deployed effectively, so that the exhaust emissions are not substituted by additional emissions in the electricity sector, which can be implemented using Smart Grid controls. This research presents the results of an EV roll-out in the all island grid (AIG) in Ireland using the long term generation expansion planning model called the Wien Automatic System Planning IV (WASP-IV) tool to measure carbon dioxide emissions and changes in total energy. The model incorporates all generators and operational requirements while meeting environmental emissions, fuel availability and generator operational and maintenance constraints to optimize economic dispatch and unit commitment power dispatch. In the study three distinct scenarios are investigated base case, peak and off-peak charging to simulate the impacts of EV’s in the AIG up to 2025.

Electric vehicle capacity forecasting model with application to load levelling

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

Residential electrical vehicle charging strategies: the good, the bad and the ugly

In recent years, a wide variety of centralised and decentralised algorithms have been proposed for residential charging of electric vehicles (EVs). In this paper, we present a mathematical framework which casts the EV charging scenarios addressed by these algorithms as optimisation problems having either temporal or instantaneous optimisation objectives with respect to the different actors in the power system. Using this framework and a realistic distribution network simulation testbed, we provide a comparative evaluation of a range of different residential EV charging strategies, highlighting in each case positive and negative characteristics.

Identification of Road Irregularities via Vehicle Accelerations

A periodic monitoring of the pavement condition facilitates a cost-effective distribution of the resources available for maintenance of the road infrastructure network. The task can be accurately carried out using profilometers, but such an approach is generally expensive. This paper presents a method to collect information on the road profile via accelerometers mounted in a fleet of non-specialist vehicles, such as police cars, that are in use for other purposes. It proposes an optimisation algorithm, based on Cross Entropy theory, to predict road irregularities. The Cross Entropy algorithm estimates the height of the road irregularities from vehicle accelerations at each point in time. To test the algorithm, the crossing of a half-car roll model is simulated over a range of road profiles to obtain accelerations of the vehicle sprung and unsprung masses. Then, the simulated vehicle accelerations are used as input in an iterative procedure that searches for the best solution to the inverse problem of finding road irregularities. In each iteration, a sample of road profiles is generated and an objective function defined as the sum of squares of differences between the ‘measured’ and predicted accelerations is minimized until convergence is reached. The reconstructed profile is classified according to ISO and IRI recommendations and compared to its original class. Results demonstrate that the approach is feasible and that a good estimate of the short-wavelength features of the road profile can be detected, despite the variability between the vehicles used to collect the data.