667 research outputs found
Macrophage mal1 deficiency suppresses atherosclerosis in low-density lipoprotein receptor-null mice by activating peroxisome proliferator-activated receptor-γ-regulated genes
Objective- The adipocyte/macrophage fatty acid-binding proteins aP2 (FABP4) and Mal1 (FABP5) are intracellular lipid chaperones that modulate systemic glucose metabolism, insulin sensitivity, and atherosclerosis. Combined deficiency of aP2 and Mal1 has been shown to reduce the development of atherosclerosis, but the independent role of macrophage Mal1 expression in atherogenesis remains unclear. Methods and Results- We transplanted wild-type (WT), Mal1, or aP2 bone marrow into low-density lipoprotein receptor-null (LDLR) mice and fed them a Western diet for 8 weeks. Mal1→LDLR mice had significantly reduced (36%) atherosclerosis in the proximal aorta compared with control WT→LDLR mice. Interestingly, peritoneal macrophages isolated from Mal1-deficient mice displayed increased peroxisome proliferator-activated receptor-γ (PPARγ) activity and upregulation of a PPARγ-related cholesterol trafficking gene, CD36. Mal1 macrophages showed suppression of inflammatory genes, such as COX2 and interleukin 6. Mal1→LDLR mice had significantly decreased macrophage numbers in the aortic atherosclerotic lesions compared with WT→LDLR mice, suggesting that monocyte recruitment may be impaired. Indeed, blood monocytes isolated from Mal1→LDLR mice on a high-fat diet had decreased CC chemokine receptor 2 gene and protein expression levels compared with WT monocytes. Conclusion- Taken together, our results demonstrate that Mal1 plays a proatherogenic role by suppressing PPARγ activity, which increases expression of CC chemokine receptor 2 by monocytes, promoting their recruitment to atherosclerotic lesions. © 2011 American Heart Association, Inc
Non-linear regression models for Approximate Bayesian Computation
Approximate Bayesian inference on the basis of summary statistics is
well-suited to complex problems for which the likelihood is either
mathematically or computationally intractable. However the methods that use
rejection suffer from the curse of dimensionality when the number of summary
statistics is increased. Here we propose a machine-learning approach to the
estimation of the posterior density by introducing two innovations. The new
method fits a nonlinear conditional heteroscedastic regression of the parameter
on the summary statistics, and then adaptively improves estimation using
importance sampling. The new algorithm is compared to the state-of-the-art
approximate Bayesian methods, and achieves considerable reduction of the
computational burden in two examples of inference in statistical genetics and
in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and
Computin
Automatic service categorisation through machine learning in emergent middleware
The modern environment of mobile, pervasive, evolving services presents a great challenge to traditional solutions for enabling interoperability. Automated solutions appear to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to determine compatibility, as a precursor to interaction, come at a substantial computational cost, especially when checks are performed between systems in unrelated domains. To overcome this, we apply machine learning to extract high-level functionality information through text categorisation of a system's interface description. This categorisation allows us to restrict the scope of compatibility checks, giving an overall performance gain when conducting matchmaking between systems. We have evaluated our approach on a corpus of web service descriptions, where even with moderate categorisation accuracy, a substantial performance benefit can be found. This in turn improves the applicability of our overall approach for achieving interoperability in the Connect project
Confirmation of a pi_1^0 Exotic Meson in the \eta \pi^0 System
The exclusive reaction , at 18 GeV has been studied with a partial wave analysis on a sample
of 23~492 events from BNL experiment E852. A mass-dependent fit
is consistent with a resonant hypothesis for the wave, thus providing
evidence for a neutral exotic meson with , a mass of MeV, and a width of MeV. New
interpretations of the meson exotics in neutral system observed in
E852 and Crystal Barrel experiments are discussed.Comment: p3, rewording the paragraph (at the bottom) about the phase
variations. p4, rewording paragrath "The second method ..." . p4, at the
bottom of paragrath "The third method ..." added consistent with the results
of methods 1 and 2
Exotic Meson Production in the System observed in the Reaction at 18 GeV/c
This letter reports results from the partial wave analysis of the
final state in collisions at 18GeV/c.
Strong evidence is observed for production of two mesons with exotic quantum
numbers of spin, parity and charge conjugation, in the decay
channel . The mass MeV/c^2 and
width MeV/c^2 of the first state are consistent
with the parameters of the previously observed . The second
resonance with mass MeV/c^2 and width MeV/c^2 agrees very well with predictions from theoretical
models. In addition, the presence of is confirmed with mass MeV/c^2 and width MeV/c^2
and a new state, , is observed with mass
MeV/c^2 and width MeV/c^2. The decay properties of
these last two states are consistent with flux tube model predictions for
hybrid mesons with non-exotic quantum numbers
Observation of Pseudoscalar and Axial Vector Resonances in pi- p -> K+ K- pi0 n at 18 GeV
A new measurement of the reaction pi- p -> K+ K- pi0 n has been made at a
beam energy of 18 GeV. A partial wave analysis of the K+ K- pi0 system shows
evidence for three pseudoscalar resonances, eta(1295), eta(1416), and
eta(1485), as well as two axial vectors, f1(1285), and f1(1420). Their observed
masses, widths and decay properties are reported. No signal was observed for
C(1480), an IG J{PC} = 1+ 1{--} state previously reported in phi pi0 decay.Comment: 7 pages, 6 figs, to be submitted to Phys. Let
Effect of Initial Disturbance on The Detonation Front Structure of a Narrow Duct
The effect of an initial disturbance on the detonation front structure in a
narrow duct is studied by three-dimensional numerical simulation. The numerical
method used includes a high resolution fifth-order weighted essentially
non-oscillatory scheme for spatial discretization, coupled with a third order
total variation diminishing Runge-Kutta time stepping method. Two types of
disturbances are used for the initial perturbation. One is a random disturbance
which is imposed on the whole area of the detonation front, and the other is a
symmetrical disturbance imposed within a band along the diagonal direction on
the front. The results show that the two types of disturbances lead to
different processes. For the random disturbance, the detonation front evolves
into a stable spinning detonation. For the symmetrical diagonal disturbance,
the detonation front displays a diagonal pattern at an early stage, but this
pattern is unstable. It breaks down after a short while and it finally evolves
into a spinning detonation. The spinning detonation structure ultimately formed
due to the two types of disturbances is the same. This means that spinning
detonation is the most stable mode for the simulated narrow duct. Therefore, in
a narrow duct, triggering a spinning detonation can be an effective way to
produce a stable detonation as well as to speed up the deflagration to
detonation transition process.Comment: 30 pages and 11 figure
Formation of Nanopits in Si Capping Layers on SiGe Quantum Dots
In-situ annealing at a high temperature of 640°C was performed for a low temperature grown Si capping layer, which was grown at 300°C on SiGe self-assembled quantum dots with a thickness of 50 nm. Square nanopits, with a depth of about 8 nm and boundaries along 〈110〉, are formed in the Si capping layer after annealing. Cross-sectional transmission electron microscopy observation shows that each nanopit is located right over one dot with one to one correspondence. The detailed migration of Si atoms for the nanopit formation is revealed by in-situ annealing at a low temperature of 540°C. The final well-defined profiles of the nanopits indicate that both strain energy and surface energy play roles during the nanopit formation, and the nanopits are stable at 640°C. A subsequent growth of Ge on the nanopit-patterned surface results in the formation of SiGe quantum dot molecules around the nanopits
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