The apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3 (APOBEC3) proteins are cell-encoded cytidine deaminases, some of which, such as APOBEC3G (A3G) and APOBEC3F (A3F), act as potent human immunodeficiency virus type-1 (HIV-1) restriction factors. These proteins require packaging into HIV-1 particles to exert their antiviral activities, but the molecular mechanism by which this occurs is incompletely understood. The nucleocapsid (NC) region of HIV-1 Gag is required for efficient incorporation of A3G and A3F, and the interaction between A3G and NC has previously been shown to be RNA-dependent. Here, we address this issue in detail by first determining which RNAs are able to bind to A3G and A3F in HV-1 infected cells, as well as in cell-free virions, using the unbiased individual-nucleotide resolution UV cross-linking and immunoprecipitation (iCLIP) method. We show that A3G and A3F bind many different types of RNA, including HIV-1 RNA, cellular mRNAs and small non-coding RNAs such as the Y or 7SL RNAs. Interestingly, A3G/F incorporation is unaffected when the levels of packaged HIV-1 genomic RNA (gRNA) and 7SL RNA are reduced, implying that these RNAs are not essential for efficient A3G/F packaging. Confirming earlier work, HIV-1 particles formed with Gag lacking the NC domain (Gag ΔNC) fail to encapsidate A3G/F. Here, we exploit this system by demonstrating that the addition of an assortment of heterologous RNA-binding proteins and domains to Gag ΔNC efficiently restored A3G/F packaging, indicating that A3G and A3F have the ability to engage multiple RNAs to ensure viral encapsidation. We propose that the rather indiscriminate RNA binding characteristics of A3G and A3F promote functionality by enabling recruitment into a wide range of retroviral particles whose packaged RNA genomes comprise divergent sequences.

To foster the transition to more sustainable energy systems, policymakers have been approving measures to improve energy efficiency as well as promoting smart grids. In this setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. Yet, most fail to do so as they rely on conventional building energy management systems (BEMS) that have static temperature set points for heating and cooling equipment. In this paper, we investigate how effective policy measures are at improving building-level energy efficiency compared to a smart BEMS with dynamic temperature set points. To this end, we present an integrated optimisation model mimicking the smart BEMS that combines decisions on heating and cooling systems operations with decisions on energy sourcing. Using data from an Austrian and a Spanish building, we find that the smart BEMS results in greater reduction in energy consumption than a conventional BEMS with policy measures.

Environmental concerns have motivated governments in the European Union and elsewhere to set ambitious targets for generation from renewable energy (RE) technologies and to offer subsidies for their adoption along with priority grid access. However, because RE technologies like solar and wind power are intermittent, their penetration places greater strain on existing conventional power plants that need to ramp up more often. In turn, energy storage technologies, e.g., pumped hydro storage or compressed air storage, are proposed to offset the intermittency of RE technologies and to facilitate their integration into the grid. We assess the economic and environmental consequences of storage via a complementarity model of a stylized Western European power system with market power, representation of the transmission grid, and uncertainty in RE output. Although storage helps to reduce congestion and ramping costs, it may actually increase greenhouse gas emissions from conventional power plants in a perfectly competitive setting. Conversely, strategic use of storage by producers renders it less effective at curbing both congestion and ramping costs, while having no net overall impact on emissions.

The deregulation of electricity markets increases the financial risk faced by retailers who procure electric energy on the spot market to meet their customers' electricity demand. To hedge against this exposure, retailers often hold a portfolio of electricity derivative contracts. In this paper, we propose a multistage stochastic mean-variance optimisation model for the management of such a portfolio. To reduce computational complexity, we apply two approximations: we aggregate the decision stages and solve the resulting problem in linear decision rules (LDR). The LDR approach consists of restricting the set of recourse decisions to those affine in the history of the random parameters. When applied to mean-variance optimisation models, it leads to convex quadratic programs. Since their size grows typically only polynomially with the number of periods, they can be efficiently solved. Our numerical experiments illustrate the value of adaptivity inherent in the LDR method and its potential for enabling scalability to problems with many periods.