A PPARγ-FGF1 axis is required for adaptive adipose remodelling and metabolic homeostasis

Although feast and famine cycles illustrate that remodelling of adipose tissue in response to fluctuations in nutrient availability is essential for maintaining metabolic homeostasis, the underlying mechanisms remain poorly understood. Here we identify fibroblast growth factor 1 (FGF1) as a critical transducer in this process in mice, and link its regulation to the nuclear receptor PPARγ (peroxisome proliferator activated receptor γ), which is the adipocyte master regulator and the target of the thiazolidinedione class of insulin sensitizing drugs. FGF1 is the prototype of the 22-member FGF family of proteins and has been implicated in a range of physiological processes, including development, wound healing and cardiovascular changes. Surprisingly, FGF1 knockout mice display no significant phenotype under standard laboratory conditions. We show that FGF1 is highly induced in adipose tissue in response to a high-fat diet and that mice lacking FGF1 develop an aggressive diabetic phenotype coupled to aberrant adipose expansion when challenged with a high-fat diet. Further analysis of adipose depots in FGF1-deficient mice revealed multiple histopathologies in the vasculature network, an accentuated inflammatory response, aberrant adipocyte size distribution and ectopic expression of pancreatic lipases. On withdrawal of the high-fat diet, this inflamed adipose tissue fails to properly resolve, resulting in extensive fat necrosis. In terms of mechanisms, we show that adipose induction of FGF1 in the fed state is regulated by PPARγ acting through an evolutionarily conserved promoter proximal PPAR response element within the FGF1 gene. The discovery of a phenotype for the FGF1 knockout mouse establishes the PPARγ–FGF1 axis as critical for maintaining metabolic homeostasis and insulin sensitization.     

An ultra-relativistic outflow from a neutron star accreting gas from a companion

Collimated relativistic outflows-also known as jets-are amongst the most energetic phenomena in the Universe. They are associated with supermassive black holes in distant active galactic nuclei, accreting stellar-mass black holes and neutron stars in binary systems and are believed to be responsible for gamma-ray bursts. The physics of these jets, however, remains something of a mystery in that their bulk velocities, compositions and energetics remain poorly determined. Here we report the discovery of an ultra-relativistic outflow from a neutron star accreting gas within a binary stellar system. The velocity of the outflow is comparable to the fastest-moving flows observed from active galactic nuclei, and its strength is modulated by the rate of accretion of material onto the neutron star. Shocks are energized further downstream in the flow, which are themselves moving at mildly relativistic bulk velocities and are the sites of the observed synchrotron emission from the jet. We conclude that the generation of highly relativistic outflows does not require properties that are unique to black holes, such as an event horizon.     

A Machine Learning Approach for Mechanism Selection in Complex Negotiations

Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the characteristics of the underlying negotiation problem (e.g. on the complexity of participant’s utility functions, as well as the degree of conflict between participants). While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by (1) defining a set of scenario metrics to capture the relevant features of negotiation problems, (2) evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, (3) applying machine learning techniques to identify which mechanisms work best with which scenarios, and (4) demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.     

Intravenous delivery of a multi-mechanistic cancer-targeted oncolytic poxvirus in humans

The efficacy and safety of biological molecules in cancer therapy, such as peptides and small interfering RNAs (siRNAs), could be markedly increased if high concentrations could be achieved and amplified selectively in tumour tissues versus normal tissues after intravenous administration. This has not been achievable so far in humans. We hypothesized that a poxvirus, which evolved for blood-borne systemic spread in mammals, could be engineered for cancer-selective replication and used as a vehicle for the intravenous delivery and expression of transgenes in tumours. JX-594 is an oncolytic poxvirus engineered for replication, transgene expression and amplification in cancer cells harbouring activation of the epidermal growth factor receptor (EGFR)/Ras pathway, followed by cell lysis and anticancer immunity. Here we show in a clinical trial that JX-594 selectively infects, replicates and expresses transgene products in cancer tissue after intravenous infusion, in a dose-related fashion. Normal tissues were not affected clinically. This platform technology opens up the possibility of multifunctional products that selectively express high concentrations of several complementary therapeutic and imaging molecules in metastatic solid tumours in humans.