4,265 research outputs found
Associated production of the heavy charged gauge boson and a top quark at LHC
In the context of topflavor seesaw model, we study the production of the
heavy charged gauge boson associated with a top quark at the LHC.
Focusing on the searching channel , we carry out a full simulation of the signal and the
relevant standard model backgrounds. The kinematical distributions of final
states are presented. It is found that the backgrounds can be significantly
suppressed by sets of kinematic cuts, and the signal of the heavy charged boson
might be detected at the LHC with TeV. With a integrated
luminosity of \LL= 100 , a signal significance can be
achieved for TeV.Comment: 16 pages, 6 figure
Time scale analysis of receptor enzyme activity : irreversible inhibition sometimes exhibits incubation-time independence
At early drug discovery, purified protein-based assays are often used to characterise compound potency. As far as dose response is concerned, it is often thought that a time-independent inhibitor is reversible and a time-dependent inhibitor is irreversible. Using a simple kinetics model, we investigate the legitimacy of this. Our model-based analytical analysis and numerical studies reveal that dose response of an irreversible inhibitor may appear time-independent under certain parametric conditions. Hence, time-independence cannot be used as evidence for inhibitor reversibility. Furthermore, we also analysed how the synthesis and degradation of a target receptor affect drug inhibition in an in vitro cell-based assay setting. Indeed, these processes may also influence dose response of an irreversible inhibitor in such a way that it appears time-independent under certain conditions. Hence, time-independent dose response in a cell assay also needs careful considerations. It is necessary to formulate a suitable model for analysis of protein-based assay and in vitro cell assay data to ensure a consistent understanding
Hypergraph Neural Networks
In this paper, we present a hypergraph neural networks (HGNN) framework for
data representation learning, which can encode high-order data correlation in a
hypergraph structure. Confronting the challenges of learning representation for
complex data in real practice, we propose to incorporate such data structure in
a hypergraph, which is more flexible on data modeling, especially when dealing
with complex data. In this method, a hyperedge convolution operation is
designed to handle the data correlation during representation learning. In this
way, traditional hypergraph learning procedure can be conducted using hyperedge
convolution operations efficiently. HGNN is able to learn the hidden layer
representation considering the high-order data structure, which is a general
framework considering the complex data correlations. We have conducted
experiments on citation network classification and visual object recognition
tasks and compared HGNN with graph convolutional networks and other traditional
methods. Experimental results demonstrate that the proposed HGNN method
outperforms recent state-of-the-art methods. We can also reveal from the
results that the proposed HGNN is superior when dealing with multi-modal data
compared with existing methods.Comment: Accepted in AAAI'201
Investigating receptor enzyme activity using time-scale analysis
At early drug discovery, purified protein-based assays are often used to characterise compound potency. In the context of dose response, it is often perceived that a time-independent inhibitor is reversible and a time-dependent inhibitor is irreversible. The legitimacy of this argument is investigated using a simple kinetics model, where it is revealed by model-based analytical analysis and numerical studies that dose response of an irreversible inhibitor may appear time-independent under certain parametric conditions. Hence, the observation of time-independence cannot be used as sole evidence for identification of inhibitor reversibility. It has also been discussed how the synthesis and degradation of a target receptor affect drug inhibition in an in vitro cell-based assay setting. These processes may also influence dose response of an irreversible inhibitor in such a way that it appears time-independent under certain conditions. Furthermore, model-based steady-state analysis reveals the complexity nature of the drug-receptor process
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