BackgroundThe consumption of large amounts of dietary fats is one of the most important environmental factors contributing to the development of obesity and metabolic disorders. GPR120 and GPR40 are polyunsaturated fatty acid receptors that exert a number of systemic effects that are beneficial for metabolic and inflammatory diseases. Here, we evaluate the expression and potential role of hypothalamic GPR120 and GPR40 as targets for the treatment of obesity.MethodsMale Swiss (6-weeks old), were fed with a high fat diet (HFD, 60% of kcal from fat) for 4 weeks. Next, mice underwent stereotaxic surgery to place an indwelling cannula into the right lateral ventricle. intracerebroventricular (icv)-cannulated mice were treated twice a day for 6 days with 2.0 μL saline or GPR40 and GPR120 agonists: GW9508, TUG1197, or TUG905 (2.0 μL, 1.0 mM). Food intake and body mass were measured during the treatment period. At the end of the experiment, the hypothalamus was collected for real-time PCR analysis.ResultsWe show that both receptors are expressed in the hypothalamus; GPR120 is primarily present in microglia, whereas GPR40 is expressed in neurons. Upon intracerebroventricular treatment, GW9508, a non-specific agonist for both receptors, reduced energy efficiency and the expression of inflammatory genes in the hypothalamus. Reducing GPR120 hypothalamic expression using a lentivirus-based approach resulted in the loss of the anti-inflammatory effect of GW9508 and increased energy efficiency. Intracerebroventricular treatment with the GPR120- and GPR40-specific agonists TUG1197 and TUG905, respectively, resulted in milder effects than those produced by GW9508.ConclusionsGPR120 and GPR40 act in concert in the hypothalamus to reduce energy efficiency and regulate the inflammation associated with obesity. The combined activation of both receptors in the hypothalamus results in better metabolic outcomes than the isolated activation of either receptor alone.Electronic supplementary materialThe online version of this article (doi:10.1186/s12974-017-0869-7) contains supplementary material, which is available to authorized users.

New technologies and the ubiquitous use of smartphones have opened the possibilities for more convenient, affordable, fast, and safe options in urban transportation. This has led to the emergence of mobility-on-demand (MoD) systems, such as Uber and Lyft, which aim to provide fast and reliable mobility that is catered to individualistic needs. At the same time, automated vehicle (AV) technology has advanced at an impressive pace. Corporations, such as Google and Tesla (1), have been in a race to develop a fully automated vehicle. The combination of these two promising technologies, known as automated mobility-on-demand (AMoD), has recently attracted interest among both researchers and industry (for example, Uber (2) has started testing AV programs in several states in the US).The term AMoD (3) designates a service similar to MoD or taxi, with the difference that vehicle operations are driverless. AMoD combines the benefits of MoD and AVs in several aspects. First, operational cost is drastically reduced, given the complete removal of driver labor costs and superior energy efficiency of AVs. Furthermore, negative externalities, such as emissions, travel time uncertainty, and accidents, may also reduce, as already observed for MoD (4) and for AVs (5). The latter also observes that AMoD will increase road network utilization, making it possible to transport more passengers with less congestion, with respect to privately owned cars. Fagnant and Kockelman (6) found that AV benefits would amount to between $2,690 and $3,900 annually per vehicle, incorporating decreases in insurance, parking costs, and traffic congestion.It is clear that AMoD is a disruptive technology that will deeply impact the transportation system. Most of the literature (7-11) has focused on the efficiency of AMoD and AVs, in terms of road movement and fleet management. However, only a handful of recent studies have shown the importance 758630T RRXXX10.

ABSTRACT. The free fatty acid receptor 4 (FFA4 or GPR120) has appeared as an interesting potential target for the treatment of metabolic disorders. At present, most FFA4 ligands are carboxylic acids that are assumed to mimic the endogenous long-chain fatty acid agonists. Here, we report preliminary structure-activity relationship studies of a previously disclosed non-acidic sulfonamide FFA4 agonist. Mutagenesis studies indicate that the compounds are orthosteric agonists despite the absence of a carboxylate function. The preferred compounds showed full agonist activity on FFA4 and complete selectivity over FFA1, although a significant fraction of these non-carboxylic acids also showed partial antagonistic activity on FFA1. Studies in normal and diet-induced obese (DIO) mice with the preferred compound 34 showed improved glucose tolerance after oral dosing in an oral glucose tolerance test. Chronic dosing of 34 in DIO mice resulted in significantly increased insulin sensitivity and a moderate but significant reduction in bodyweight, effects that were also present in mice lacking FFA1 but absent in mice lacking FFA4.

Agent-based models have gained wide acceptance in transportation planning because with increasing computational power, large-scale people-centric mobility simulations are possible. Several modeling efforts have been reported in the literature on the demand side (with sophisticated activity-based models that focus on an individual’s day activity patterns) and on the supply side (with detailed representation of network dynamics through simulation-based dynamic traffic assignment models). This paper proposes an extension to a state-of-the-art integrated agent-based demand and supply model—SimMobility—for the design and evaluation of autonomous vehicle systems. SimMobility integrates various mobility-sensitive behavioral models in a multiple time-scale structure comprising three simulation levels: ( a) a long-term level that captures land use and economic activity, with special emphasis on accessibility; ( b) a midterm level that handles agents’ activities and travel patterns; and ( c) a short-term level that simulates movement of agents, operational systems, and decisions at a microscopic granularity. In that context, this paper proposes several extensions at the short-term and midterm levels to model and simulate autonomous vehicle systems and their effects on travel behavior. To showcase these features, the first-cut results of a hypothetical on-demand service with autonomous vehicles in a car-restricted zone of Singapore are presented. SimMobility was successfully used in an integrated manner to test and assess the performance of different autonomous vehicle fleet sizes and parking station configurations and to uncover changes in individual mobility patterns, specifically in regard to modal shares, routes, and destinations.

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