Crystal Structures of Cif from Bacterial Pathogens Photorhabdus luminescens and Burkholderia pseudomallei

A pre-requisite for bacterial pathogenesis is the successful interaction of a pathogen with a host. One mechanism used by a broad range of Gram negative bacterial pathogens is to deliver effector proteins directly into host cells through a dedicated type III secretion system where they modulate host cell function. The cycle inhibiting factor (Cif) family of effector proteins, identified in a growing number of pathogens that harbour functional type III secretion systems and have a wide host range, arrest the eukaryotic cell cycle. Here, the crystal structures of Cifs from the insect pathogen/nematode symbiont Photorhabdus luminescens (a γ-proteobacterium) and human pathogen Burkholderia pseudomallei (a β-proteobacterium) are presented. Both of these proteins adopt an overall fold similar to the papain sub-family of cysteine proteases, as originally identified in the structure of a truncated form of Cif from Enteropathogenic E. coli (EPEC), despite sharing only limited sequence identity. The structure of an N-terminal region, referred to here as the ‘tail-domain’ (absent in the EPEC Cif structure), suggests a surface likely to be involved in host-cell substrate recognition. The conformation of the Cys-His-Gln catalytic triad is retained, and the essential cysteine is exposed to solvent and addressable by small molecule reagents. These structures and biochemical work contribute to the rapidly expanding literature on Cifs, and direct further studies to better understand the molecular details of the activity of these proteins

Intelligence in electricity networks for embedding renewables and distributed generation

Over the course of the 20th century, electrical power systems have become one of the most complex systems created by mankind. Electricity has made a transition from a novelty, to a convenience, to an advantage, and finally to an absolute necessity. The electricity infrastructure consists of two highly-interrelated subsystems for commodity trade and physical delivery. To ensure the infrastructure is up and running in the first place, the increasing electricity demand poses a serious threat. Additionally, two other trends force a change in infrastructure management. Firstly, there is a shift toward intermittent sources, which gives rise to a higher influence of weather patterns on generation. At the same time, introducing more combined heat and power generation (CHP) couples electricity production to heat demand patterns. Secondly, the location of electricity generation relative to the load centers is changing. Large-scale generation from wind is migrating towards and into the seas and oceans, and, with the increase of distributed generators (DG), the generation capacity embedded in the (medium and low voltage) distribution networks is rising. Due to these developments, intelligent distributed coordination will be essential to ensure the efficient operation of this critical infrastructure in the future. As compared to traditional grids, operated in a top-down manner, these novel grids will require bottom-up control. As field test results have shown, intelligent distributed coordination can be beneficial to both energy trade and active network management. In future power grids, these functions need to be combined in a dual-objective coordination mechanism. To exert this type of control, alignment of power systems with communication network technology as well as computer hardware and software in shared information architectures will be necessary

PowerMatcher:multiagent control in the electricity infrastructure

Different driving forces push the electricity production towards decentralization. As a result, the current electricity infrastructure is expected to evolve into a network of networks, in which all system parts communicate with each other and influence each other. Multi-agent systems and electronic markets form an appropriate technology needed for control and coordination tasks in the future electricity network. We present the PowerMatcher, a market-based control concept for supply and demand matching (SDM) in electricity networks. In a presented simulation study is shown that the simultaneousness of electricity production and consumption can be raised substantially using this concept. Further, we present a field test with medium-sized electricity producing and consuming installations controlled via this concept, currently in preparation

A heterarchic hybrid coordination approach for congestion management using the DREAM framework

Software agent-based strategies using micro-economic theory like PowerMatcher[1] have been utilized to coordinate demand and supply matching for electricity. Virtual power plants (VPPs) using these strategies have been tested in living lab environments on a scale of up to hundreds of households. So far, the coordination configuration of a VPP is fixed in these settings. The DREAM [2] framework architecture uses heterarchies to make parts of a VPP flexible in coordination strategy depending on the current operational grid status. In this way, a sub-VPP, serving one coordination objective, can decouple from and couple to an overarching VPP with another coordination objective dynamically.\u3cbr/\u3e In this paper a grid congestion simulation with an overarching VPP coordinating demand and supply for electricity market optimization [3] and a sub-VPP reacting to a heat-pump congestion event in winter and a PV overproduction event in summer is described. The simulation was run in a static simulator [4]. The LV-segment consisted of ‘flameless’ residential areas with well-insulated homes with primarily heat pumps for heating and some renovated homes with local gas-fired co-generators of heat and electricity. Households additionally had solar cells, batteries and EV charging units. The goal of the additional coordination sub-VPP was to solve grid stability issues like congestion due to heat pump loads in winter and overproduction by PV in summer in this physical part locally, while the rest of the cluster remained unaffected and still optimizing for the commercial goal.\u3cbr/\u3e The results were analyzed in terms of infringement of comfort parameters and performance in adapting the flexible load and generation. It appeared, substantial load shedding and load shifting of devices is possible to show the synergy in solving the grid stability issues evenly sharing the discomfort to the individual heating devices. By changing their charging strategy, the new algorithm also showed heat storage and electricity storage devices providing additional support. \u3cbr/\u3e \u3cbr/\u3eINTRODUCTION\u3cbr/\u3e The simplest definition of flexibility can be given as a bandwidth around a required momentary power value of a connection or a device. The flexibility can be discriminated in a part to be used in normal operation, that, for example, might be expressed in a PowerMatcher bid-curve [1]. This part may be utilized by a commercial party in normal grid operation to balance the portfolio. The timescale for it to be mobilized is in the order of tens of seconds to minutes. Another part can be utilized as a contingency reserve by a grid operator to prevent the operation of the power system to go into a critical situation. The second part typically has to be used in the seconds scale. In micro-economic terms, primary processes, using electrical energy, have different utilities for flexibility on these different time scales. This economic utility can be translated in a price that can be used for coordination.\u3cbr/\u3

An assessment of the influence of demand response on demand elasticity in electricity retail market

A transition towards a sustainable society is currently ongoing. In the electrical power system, this is reflected by the increasing share of renewable energy sources (RES). The weather dependence of some RES results in intermittent and volatile behaviour, thus matching supply and demand has become a challenge. Demand response (DR) is an emerging field which enables society to coop with upcoming challenges. One of the explored concepts to perform the DR is the price elasticity of demand. This paper presents a model to compute price elasticity matrices for typical households consisting of dispatchable appliances and a base load. Simulations of the model will demonstrate the price elastic response for different scenarios