AimsTo investigate, for a given energy expenditure (EE) rise, the differential effects of glucagon infusion and cold exposure on brown adipose tissue (BAT) activation in humans.MethodsIndirect calorimetry and supraclavicular thermography was performed in 11 healthy male volunteers before and after: cold exposure; glucagon infusion (at 23 °C); and vehicle infusion (at 23 °C). All volunteers underwent 18F‐fluorodeoxyglucose (18F‐FDG) positron emission tomography (PET)/CT scanning with cold exposure. Subjects with cold‐induced BAT activation on 18F‐FDG PET/CT (n = 8) underwent a randomly allocated second 18F‐FDG PET/CT scan (at 23 °C), either with glucagon infusion (n = 4) or vehicle infusion (n = 4).ResultsWe observed that EE increased by 14% after cold exposure and by 15% after glucagon infusion (50 ng/kg/min; p < 0.05 vs control for both). Cold exposure produced an increase in neck temperature (+0.44 °C; p < 0.001 vs control), but glucagon infusion did not alter neck temperature. In subjects with a cold‐induced increase in the metabolic activity of supraclavicular BAT on 18F‐FDG PET/CT, a significant rise in the metabolic activity of BAT after glucagon infusion was not detected. Cold exposure increased sympathetic activation, as measured by circulating norepinephrine levels, but glucagon infusion did not.ConclusionsGlucagon increases EE by a similar magnitude compared with cold activation, but independently of BAT thermogenesis. This finding is of importance for the development of safe treatments for obesity through upregulation of EE.
Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.
The future of high-performance computing is likely to rely on the ability to efficiently exploit huge amounts of parallelism. One way of taking advantage of this parallelism is to formulate problems as "embarrassingly parallel" MonteCarlo simulations, which allow applications to achieve a linear speedup over multiple computational nodes, without requiring a super-linear increase in inter-node communication. However, such applications are reliant on a cheap supply of high quality random numbers, particularly for the three main maximum entropy distributions: uniform, used as a general source of randomness; Gaussian, for discrete-time simulations; and exponential, for discrete-event simulations. In this paper we look at four different types of platform: conventional multi-core CPUs (Intel Core2); GPUs (NVidia GTX 200); FPGAs (Xilinx Virtex-5); and Massively Parallel Processor Arrays (Ambric AM2000). For each platform we determine the most appropriate algorithm for generating each type of number, then calculate the peak generation rate and estimated power efficiency for each device.
The multivariate Gaussian distribution is often used to model correlations between stochastic time-series, and can be used to explore the effect of these correlations across N time-series in Monte-Carlo simulations. However, generating random correlated vectors is an O ( N 2 ) process, and quickly becomes a computational bottleneck in software simulations. This article presents an efficient method for generating vectors in parallel hardware, using N parallel pipelined components to generate a new vector every N cycles. This method maps well to the embedded block RAMs and multipliers in contemporary FPGAs, particularly as extensive testing shows that the limited bit-width arithmetic does not reduce the statistical quality of the generated vectors. An implementation of the architecture in the Virtex-4 architecture achieves a 500MHz clock-rate, and can support vector lengths up to 512 in the largest devices. The combination of a high clock-rate and parallelism provides a significant performance advantage over conventional processors, with an xc4vsx55 device at 500MHz providing a 200 times speedup over an Opteron 2.6GHz using an AMD optimised BLAS package. In a case study in Delta-Gamma Value-at Risk, an RC2000 accelerator card using an xc4vsx55 at 400MHz is 26 times faster than a quad Opteron 2.6GHz SMP.
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