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Sphingosine 1-phosphate (S1P) is a potent bioactive sphingolipid binding to specific G protein-coupled receptors expressed in several organs. The relevance of S1P-S1P receptor axis in the pathophysiology of immune and nervous systems has encouraged the development of S1P receptor modulators for the treatment of neurological, autoimmune and/or inflammatory disorders. Currently, four S1P receptor modulators are approved drugs for multiple sclerosis (MS), an inflammatory disorder of the central nervous system. As main pharmacologic effect, these treatments induce lymphopenia due to the loss of responsiveness to S1P gradients guiding lymphocyte egress from lymphoid organs into the bloodstream. Recent data point to immunological effects of the S1P modulators beyond the inhibition of lymphocyte trafficking. Further, these drugs may cross the blood-brain barrier and directly target CNS resident cells expressing S1P receptors. Here we review the role of S1P signalling in neuroimmunology at the light of the evidences generated from the study of the mechanism of action of S1P receptor modulators in MS and integrate this information with findings derived from neuroinflammatory animal models and in vitro observations. These insights can direct the application of therapeutic approaches targeting S1P receptors in other disease areas. [ABSTRACT FROM AUTHOR]

Russia's invasion of Ukraine fuelled an energy crisis, which considerably impacted Europe given its heavy reliance on Russian natural gas imports. This study uses an ensemble of four global integrated assessment models, which are further soft-linked to two sectoral models, and explores the synergies and trade-offs among three approaches to living without Russian gas in Europe: (a) replacing with other gas imports, (b) boosting domestic energy production, and (c) reducing demand and accelerating energy efficiency. We find that substituting Russian gas from other trade partners would miss an opportunity to accelerate decarbonisation in end-use sectors while risking further fossil-fuel lock-ins, despite featuring the lowest gas price spikes and potentially reducing heating costs for end-users in the near term. Boosting domestic, primarily renewable, energy production on the other hand would instead require considerable investments, potentially burdening consumers. Energy demand reductions, however, could offer considerable space for further emissions cuts at the lowest power-sector investment costs; nonetheless, an energy efficiency-driven strategy would also risk relocation of energy-intensive industries, an aspect of increasing relevance to EU policymakers. • Three 'corner' strategies to replacing Russian natural gas in the EU are explored. • EU climate targets can still be reached in all three strategies, but costs increase. • Promoting energy efficiency or domestic renewables can boost end-use decarbonisation. • An 'energy efficiency first' response may save millions € in annual electricity costs. • Southern Europe is the most vulnerable to high electricity prices across strategies. [ABSTRACT FROM AUTHOR]

The mass-scale integration of electric vehicles into the power system is a key pillar of the European energy transition agenda. Yet, the extent to which such integration would represent a burden for the power system of each member country is still an unanswered question. This is mainly due to a lack of accurate and context-specific representations of aggregate mobility and charging patterns for large electric vehicle fleets. Here, we develop and validate against empirical data an open-source model that simulates such patterns at high (1-min) temporal resolution, based on easy-to-gather, openly accessible data. We hence apply the model – which we name RAMP-mobility – to 28 European countries, showing for the first time the existence of marked differences in mobility and charging patterns across those, due to a combination of weather and socio-economic factors. We hence quantify the impact that fully-electric car fleets would have on the demand to be met by each country's power system: an uncontrolled deployment of electric vehicles would increase peak demand in the range 35–51%, whilst a plausible share of adoption of smart charging strategies could limit the increase to 30–41%. On the contrary, plausible technology (battery density) and infrastructure (charging power) developments would not provide substantial benefits. Efforts for electric vehicles integration should hence primarily focus on mechanisms to support smart vehicle-to-grid interaction. The approach is applicable generally beyond Europe and can provide policy makers with quantitatively reliable insights about electric vehicles impact on the power system. • We generate validated mobility and charging patterns for electric vehicles in Europe. • Hourly patterns vary substantially across countries due to weather and user behaviour. • Electric vehicles may increase peak power systems demand by 36–51%. • Plausible shares of smart-charging vehicles may limit such an increase to 30–41%. • Smart-charging substantially outperforms battery and infrastructure developments. [ABSTRACT FROM AUTHOR]

The decarbonisation of residential heat through integration with the power system and deployment of refurbishment policies is at the core of European energy policies. Yet, heat-electricity integration may be challenged, in practice, by the large variability of heat demand across weather years. Current approaches for residential heat demand simulation fail to provide insights about the extent of such variability across many weather years and about the benefits potentially brought about by nearly zero-energy buildings. To fill this gap, this work develops an open-source space-heating demand simulation workflow that is applicable to any country's building stock. The workflow, based on a well-established lumped-parameter thermodynamic model, allows capturing sub-national weather-year variability and the mitigation effects of refurbishment. For Italy, different weather years lead to variations in heat demand up to 2 TWh/day, lasting for several days. Moreover, some weather regimes produce spatial asymmetries that may further complicate heat-electricity integration. The refurbishment of about 55% of buildings constructed before 1975 could substantially mitigate such oscillations, leading to a 31–37% reduction of yearly heat demand, primarily in colder regions. Intra-day heat demand variations, driven by user behaviour, are not substantially impacted by refurbishment, calling for the simultaneous deployment of flexible heat generating technologies. • Heat demand models fail to capture weather variability and refurbishment benefits. • This study provides an open, context-adaptable workflow that allows to do so. • For Italy, large and long-lasting demand variations occur across weather regimes. • Refurbishment can reduce absolute demand by up to 37% and mitigate oscillations. • All such effects vary spatially and must be evaluated at sub-national resolution. [ABSTRACT FROM AUTHOR]

Thanks to their modularity and their capacity to adapt to different contexts, hybrid microgrids are a promising solution to decrease greenhouse gas emissions worldwide. To properly assess their impact in different settings at country or cross-country level, microgrids must be designed for each particular situation, which leads to computationally intractable problems. To tackle this issue, a methodology is proposed to create surrogate models using machine learning techniques and a database of microgrids. The selected regression model is based on Gaussian Processes and allows to drastically decrease the computation time relative to the optimal deployment of the technology. The results indicate that the proposed methodology can accurately predict key optimization variables for the design of the microgrid system. The regression models are especially well suited to estimate the net present cost and the levelized cost of electricity (R 2 = 0.99 and 0.98). Their accuracy is lower when predicting internal system variables such as installed capacities of PV and batteries (R 2 = 0.92 and 0.86). A least-cost path towards 100% electrification coverage for the Bolivian lowlands mid-size communities is finally computed, demonstrating the usability and computational efficiency of the proposed framework. • A novel methodology to create surrogate models for energy planning is presented. • Gaussian regression is compared to multi-variable regression. • The surrogate models for Economic indicators have a high prediction accuracy. • PV and Batteries installed capacity have lower prediction accuracy. • Surrogate models are created and coupled to a rural energy planning tool. [ABSTRACT FROM AUTHOR]