593 research outputs found
RNA-Binding protein HuR and the members of miR-200 family play an unconventional role in the regulation of c-Jun mRNA
Post-transcriptional gene regulation is a fundamental step for coordinating cellular response in a variety of processes. RNA-binding proteins (RBPs) and microRNAs (miRNAs) are the most important factors responsible for this regulation. Here we report that different components of the miR-200 family are involved in c-Jun mRNA regulation with the opposite effect. While miR-200b inhibits c-Jun protein production, miR-200a tends to increase the JUN amount through a stabilization of its mRNA. This action is dependent on the presence of the RBP HuR that binds the 3′UTR of c-Jun mRNA in a region including the mir-200a binding site. The position of the binding site is fundamental; by mutating this site, we demonstrate that the effect is not micro-RNA specific. These results indicate that miR-200a triggers a microRNA-mediated stabilization of c-Jun mRNA, promoting the binding of HuR with c-Jun mRNA. This is the first example of a positive regulation exerted by a microRNA on an important oncogene in proliferating cells
Ariel - Volume 8 Number 1
Executive Editor
James W. Lockard, Jr.
Issue Editor
Michael J. Grimes
Business Manager
Neeraj K. Kanwal
Managing Editor
Edward H. Jasper
University News
Richard J. Perry
World News
William D.B. Hiller
Opinions
Elizabeth A. McGuire
Features
Patrick P. Sokas
Sports Desk
Shahab S. Minassian
Managing Associate
Brenda Peterson
Photography
Robert D. Lehman, Jr.
Graphics
Christine M. Kuhnl
Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
We introduce a class of interatomic potential models that can be
automatically generated from data consisting of the energies and forces
experienced by atoms, derived from quantum mechanical calculations. The
resulting model does not have a fixed functional form and hence is capable of
modeling complex potential energy landscapes. It is systematically improvable
with more data. We apply the method to bulk carbon, silicon and germanium and
test it by calculating properties of the crystals at high temperatures. Using
the interatomic potential to generate the long molecular dynamics trajectories
required for such calculations saves orders of magnitude in computational cost.Comment: v3-4: added new material and reference
Ariel - Volume 8 Number 3
Executive Editor
James W. Lockard, Jr.
Business Manager
Neeraj K. Kanwal
University News
Richard J . Perry
World News
Doug Hiller
Opinions
Elizabeth A. McGuire
Features
Patrick P. Sokas
Sports Desk
Shahab S. Minassian
Managing Editor
Edward H. Jasper
Managing Associate
Brenda Peterson
Photography Editor
Robert D. Lehman. Jr.
Graphics
Christine M. Kuhnl
Deep interactive evolution
This paper describes an approach that combines generative adversarial
networks (GANs) with interactive evolutionary computation (IEC). While GANs can
be trained to produce lifelike images, they are normally sampled randomly from
the learned distribution, providing limited control over the resulting output.
On the other hand, interactive evolution has shown promise in creating various
artifacts such as images, music and 3D objects, but traditionally relies on a
hand-designed evolvable representation of the target domain. The main insight
in this paper is that a GAN trained on a specific target domain can act as a
compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes
do resemble valid domain artifacts). Once such a GAN is trained, the latent
vector given as input to the GAN's generator network can be put under
evolutionary control, allowing controllable and high-quality image generation.
In this paper, we demonstrate the advantage of this novel approach through a
user study in which participants were able to evolve images that strongly
resemble specific target images.Comment: 16 pages, 5 figures, Published at EvoMUSART EvoStar 201
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