10,953 research outputs found
Image interpolation using Shearlet based iterative refinement
This paper proposes an image interpolation algorithm exploiting sparse
representation for natural images. It involves three main steps: (a) obtaining
an initial estimate of the high resolution image using linear methods like FIR
filtering, (b) promoting sparsity in a selected dictionary through iterative
thresholding, and (c) extracting high frequency information from the
approximation to refine the initial estimate. For the sparse modeling, a
shearlet dictionary is chosen to yield a multiscale directional representation.
The proposed algorithm is compared to several state-of-the-art methods to
assess its objective as well as subjective performance. Compared to the cubic
spline interpolation method, an average PSNR gain of around 0.8 dB is observed
over a dataset of 200 images
Learning Points and Routes to Recommend Trajectories
The problem of recommending tours to travellers is an important and broadly
studied area. Suggested solutions include various approaches of
points-of-interest (POI) recommendation and route planning. We consider the
task of recommending a sequence of POIs, that simultaneously uses information
about POIs and routes. Our approach unifies the treatment of various sources of
information by representing them as features in machine learning algorithms,
enabling us to learn from past behaviour. Information about POIs are used to
learn a POI ranking model that accounts for the start and end points of tours.
Data about previous trajectories are used for learning transition patterns
between POIs that enable us to recommend probable routes. In addition, a
probabilistic model is proposed to combine the results of POI ranking and the
POI to POI transitions. We propose a new F score on pairs of POIs that
capture the order of visits. Empirical results show that our approach improves
on recent methods, and demonstrate that combining points and routes enables
better trajectory recommendations
Upconversion Nanoparticle-Based Cell Membrane-Coated cRGD Peptide Bioorthogonally Labeled Nanoplatform for Glioblastoma Treatment
Glioblastoma is hard to be eradicated partly because of the obstructive blood-brain barrier (BBB) and the dynamic autophagy activities of glioblastoma. Here, hydroxychloroquine (HDX)-loaded yolk-shell upconversion nanoparticle (UCNP)@Zn0.5Cd0.5S nanoparticle coating with the cyclic Arg-Gly-Asp (cRGD)-grafted glioblastoma cell membrane for near-infrared (NIR)-triggered treatment of glioblastoma is prepared for the first time. [email protected] (abbreviated as YSN, yolk-shell nanoparticle) under NIR radiation will generate reactive oxygen species for imposing cytotoxicity. HDX, the only available autophagy inhibitor in clinical studies, can enhance cytotoxicity by preventing damaged organelles from being recycled. The cRGD-decorated cell membrane allowed the HDX-loaded nanoparticles to efficiently bypass the BBB and specifically target glioblastoma cells. Exceptional treatment efficacy of the NIR-triggered chemotherapy and photodynamic therapy was achieved in U87 cells and in the mouse glioblastoma model as well. Our results provided proof-of-concept evidence that HDX@YSN@CCM@cRGD could overcome the delivery barriers and achieve targeted treatment of glioblastoma
BatMeth: improved mapper for bisulfite sequencing reads on DNA methylation
10.1186/gb-2012-13-10-R82Genome Biology1310-GNBL
A Scoping Review of Health Literacy Measurement Tools in the Context of Cardiovascular Health
publishedVersio
Efficient Resolution of Anisotropic Structures
We highlight some recent new delevelopments concerning the sparse
representation of possibly high-dimensional functions exhibiting strong
anisotropic features and low regularity in isotropic Sobolev or Besov scales.
Specifically, we focus on the solution of transport equations which exhibit
propagation of singularities where, additionally, high-dimensionality enters
when the convection field, and hence the solutions, depend on parameters
varying over some compact set. Important constituents of our approach are
directionally adaptive discretization concepts motivated by compactly supported
shearlet systems, and well-conditioned stable variational formulations that
support trial spaces with anisotropic refinements with arbitrary
directionalities. We prove that they provide tight error-residual relations
which are used to contrive rigorously founded adaptive refinement schemes which
converge in . Moreover, in the context of parameter dependent problems we
discuss two approaches serving different purposes and working under different
regularity assumptions. For frequent query problems, making essential use of
the novel well-conditioned variational formulations, a new Reduced Basis Method
is outlined which exhibits a certain rate-optimal performance for indefinite,
unsymmetric or singularly perturbed problems. For the radiative transfer
problem with scattering a sparse tensor method is presented which mitigates or
even overcomes the curse of dimensionality under suitable (so far still
isotropic) regularity assumptions. Numerical examples for both methods
illustrate the theoretical findings
Potassium channel dysfunction in human neuronal models of Angelman syndrome
Disruptions in the ubiquitin protein ligase E3A (UBE3A) gene cause Angelman syndrome (AS). Whereas AS model mice have associated synaptic dysfunction and altered plasticity with abnormal behavior, whether similar or other mechanisms contribute to network hyperactivity and epilepsy susceptibility in AS patients remains unclear. Using human neurons and brain organoids, we demonstrate that UBE3A suppresses neuronal hyperexcitability via ubiquitin-mediated degradation of calcium- and voltage-dependent big potassium (BK) channels. We provide evidence that augmented BK channel activity manifests as increased intrinsic excitability in individual neurons and subsequent network synchronization. BK antagonists normalized neuronal excitability in both human and mouse neurons and ameliorated seizure susceptibility in an AS mouse model. Our findings suggest that BK channelopathy underlies epilepsy in AS and support the use of human cells to model human developmental diseases
B_c meson rare decays in the light-cone quark model
We investigate the rare decays
and in the framework of the
light-cone quark model (LCQM). The transition form factors are calculated in
the space-like region and then analytically continued to the time-like region
via exponential parametrization. The branching ratios and longitudinal lepton
polarization asymmetries (LPAs) for the two decays are given and compared with
each other. The results are helpful to investigating the structure of
meson and to testing the unitarity of CKM quark mixing matrix. All these
results can be tested in the future experiments at the LHC.Comment: 9 pages, 11 figures, version accepted for publication in EPJ
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