Signaling of RANK (receptor activator of nuclear factor kappa B) through its ligand RANKL appears critical in osteolysis associated with aseptic loosening (AL). The purpose of this study was to investigate the role of RANK in a murine osteolysis model developed in RANK knockout (RANK(-/-)) mice. Ultra high molecular weight polyethylene (UHMWPE) debris was introduced into established air pouches...

Standard face recognition algorithms that use standard feature extraction techniques always suffer from image performance degradation. Recently, singular value decomposition and low-rank matrix are applied in many applications,including pattern recognition and feature extraction. The main objective of this research is to design an efficient face recognition approach by combining many tech...

RANKL is a type II membrane protein bearing close homology to TNFSF members TRAIL, FasL, and TNF-a. has been definite for its key characteristic role in bone metabolism the immune system. It an apoptosis regulator gene, binding partner of osteoprotegerin OPG, ligand receptor RANK, it controls cell proliferation by modifying levels. Osteoblasts stromal stem cells activator NFκ-B (RANKL), binds a...

  In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...

In this note, a generalization of higher rank numerical range isintroduced and some of its properties are investigated

Abstract: In this paper we resume some results concerned our work about least-squares approximation on GaussLobatto points. We present explicit formulas for discrete orthogonal polynomials and give the three-term recurrence relation to construct such polynomials. We also show that the normal matrix on this set of nodes can be factorized as the sum of two symmetric matrices: a full rank matrix w...

Motivated by questions arising in signal processing, computational complexity, and other areas, we study the ranks and border ranks of symmetric tensors using geometric methods. We provide improved lower bounds for the rank of a symmetric tensor (i.e., a homogeneous polynomial) obtained by considering the singularities of the hypersurface defined by the polynomial. We obtain normal forms for po...