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$_1$ 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 $L_2$. 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 $B_c \rightarrow D_s(1968) \ell \bar{\ell}$ and $B_c\rightarrow D_s^*(2317) \ell \bar{\ell}$ 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 $B_c$ 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