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Search Results: 1 - 10 of 401771 matches for " Mikaela M. Byrne "
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Epigenetic modulation in the treatment of atherosclerotic disease
Mikaela M. Byrne,Anthony W. Ryan
Frontiers in Genetics , 2014, DOI: 10.3389/fgene.2014.00364
Abstract: Cardiovascular disease is the single largest cause of death in the western world and its incidence is on the rise globally. Atherosclerosis, characterized by the development of atheromatus plaque, can trigger luminal narrowing and upon rupture result in myocardial infarction or ischemic stroke. Epigenetic phenomena are a focus of considerable research interest due to the role they play in gene regulation. Epigenetic mechanisms such as DNA methylation and histone acetylation have been identified as potential drug targets in the treatment of cardiovascular disease. miRNAs are known to play a role in gene silencing, which has been widely investigated in cancer. In comparison, the role they play in cardiovascular disease and plaque rupture is not well understood. Nutritional epigenetic modifiers from dietary components, for instance sulforaphane found in broccoli, have been shown to suppress the pro-inflammatory response through transcription factor activation. This review will discuss current and potential epigenetic therapeutics for the treatment of cardiovascular disease, focusing on the use of miRNAs and dietary supplements such as sulforaphane and protocatechuic aldehyde.
Asymptotic quantization for probability measures on Riemannian manifolds
Mikaela Iacobelli
Mathematics , 2014,
Abstract: In this paper we study the quantization problem for probability measures on Riemannian manifolds. Under a suitable assumption on the growth at infinity of the measure we find asymptotic estimates for the quantization error, generalizing the results on $\mathbb{R}^d.$ Our growth assumption depends on the curvature of the manifold and reduces, in the flat case, to a moment condition. We also build an example showing that our hypothesis is sharp.
Raciocínio contrafactual e modelos mentais
Byrne,Ruth M. J.; Quelhas,Ana Cristina;
Análise Psicológica , 1999,
Abstract: the central idea in this study is that ?... thinking about matters of fact and thinking about matters of possibility and impossibility are based on similar sorts of mental representations and cognitive processes? (byrne, 1997, p. 107). that is to say that people reason by constructing and revising mental models (e.g., johnson-laird, & byrne, 1991). counterfactual conditionals require reasoners to keep in mind not only what is presupposed to be true, but also what is suppositionally true but factually false (byrne, 1997, p. 117; cf. johnson-laird, & byrne, 1991, pp. 72-73). and the hypothesis that the initial representation of a counterfactual conditional is more explicit than the initial representation of a factual conditional, allows the prediction that modus tollens and denial of the antecedent inferences would be made more frequently from the counterfactual than from the factual conditionals. byrne and tasso (in press) found evidence for those predictions. in the present study, we look for replication of the data found by byrne and tasso, and we add some hypothesis related with the latencies to understand both kinds of conditionals, and to choose a conclusion. we use neutral conditionals like ?if there was a circle, then there was a triangle?, and we presented to participants the four conditional syllogisms in the superlab program.
Possibilities for carbon sequestration in Irish forests. COST E21 Workshop. Contribution of forests and forestry to mitigate greenhouse effects. Joensuu (Finland). 28-30 Sep 2000
Byrne K.A.,Perks M.
Biotechnologie, Agronomie, Société et Environnement , 2000,
Abstract: Ireland has a rapidly expanding forest estate which covers some 9/ of the land area. It is government policy to increase this to 17/ by the year 2030. Preliminary studies suggest that forestry activities have the potential to contribute significantly to the mitigation of greenhouse gas emissions. Although some studies have been carried out the determination of the carbon stores and sinks in Irish forests will require a considerable research effort in the future. A key aspect of such studies will be field based studies which measure all components of the carbon cycle and their relationship to climatic and environmental conditions as well as management practices. Many of these issues will be addressed in the recently announced research programme of the Council for Forest Research and Development (COFORD).
Robust inverse energy cascade and turbulence structure in three-dimensional layers of fluid
D. Byrne,H. Xia,M. Shats
Physics , 2011, DOI: 10.1063/1.3638620
Abstract: Here we report the first evidence of the inverse energy cascade in a flow dominated by 3D motions. Experiments are performed in thick fluid layers where turbulence is driven electromagnetically. It is shown that if the free surface of the layer is not perturbed, the top part of the layer behaves as quasi-2D and supports the inverse energy cascade regardless of the layer thickness. Well below the surface the cascade survives even in the presence of strong 3D eddies developing when the layer depth exceeds half the forcing scale. In a bounded flow at low bottom dissipation, the inverse energy cascade leads to the generation of a spectral condensate below the free surface. Such coherent flow can destroy 3D eddies in the bulk of the layer and enforce the flow planarity over the entire layer thickness.
Turbulence damping as a measure of the flow dimensionality
M. Shats,D. Byrne,H. Xia
Physics , 2010, DOI: 10.1103/PhysRevLett.105.264501
Abstract: The dimensionality of turbulence in fluid layers determines their properties. We study electromagnetically driven flows in finite depth fluid layers and show that eddy viscosity, which appears as a result of three-dimensional motions, leads to increased bottom damping. The anomaly coefficient, which characterizes the deviation of damping from the one derived using a quasi-two-dimensional model, can be used as a measure of the flow dimensionality. Experiments in turbulent layers show that when the anomaly coefficient becomes high, the turbulent inverse energy cascade is suppressed. In the opposite limit turbulence can self-organize into a coherent flow.
Ring geometries, Two-Weight Codes and Strongly Regular Graphs
E. Byrne,M. Greferath,T. Honold
Mathematics , 2007,
Abstract: It is known that a linear two-weight code $C$ over a finite field $\F_q$ corresponds both to a multiset in a projective space over $\F_q$ that meets every hyperplane in either $a$ or $b$ points for some integers $a
Optimizing The Integrator Step Size for Hamiltonian Monte Carlo
M. J. Betancourt,Simon Byrne,Mark Girolami
Statistics , 2014,
Abstract: Hamiltonian Monte Carlo can provide powerful inference in complex statistical problems, but ultimately its performance is sensitive to various tuning parameters. In this paper we use the underlying geometry of Hamiltonian Monte Carlo to construct a universal optimization criteria for tuning the step size of the symplectic integrator crucial to any implementation of the algorithm as well as diagnostics to monitor for any signs of invalidity. An immediate outcome of this result is that the suggested target average acceptance probability of 0.651 can be relaxed to $0.6 \lesssim a \lesssim 0.9$ with larger values more robust in practice.
Between ordinary and deeply religious - Re/Negotiating the 'religious' and the 'secular' in the Finnish parliamentary debate on assisted reproduction,
Eva-Mikaela Kinnari
Graduate Journal of Social Science , 2009,
Abstract:
Metric learning for graph-based label propgation
Pauline Wauquier,Mikaela Keller
Computer Science , 2015,
Abstract: The efficiency of graph-based semi-supervised algorithms depends on the graph of instances on which they are applied. The instances are often in a vectorial form before a graph linking them is built. The construction of the graph relies on a metric over the vectorial space that help define the weight of the connection between entities. The classic choice for this metric is usually a distance measure or a similarity measure based on the euclidean norm. We claim that in some cases the euclidean norm on the initial vectorial space might not be the more appropriate to solve the task efficiently. We propose an algorithm that aims at learning the most appropriate vectorial representation for building a graph on which the task at hand is solved efficiently.
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