Multichannel blind deconvolution using a generalized Gaussian source model

In this paper, we present an algorithm for the problem of multi-channel blind deconvolution which can adapt to un-known sources with both sub-Gaussian and super-Gaussian probability density distributions using a generalized gaussian source model. We use a state space representation to model the mixer and demixer respectively, and show how the parameters of the demixer can be adapted using a gradient descent algorithm incorporating the natural gradient extension. We also present a learning method for the unknown parameters of the generalized Gaussian source model. The performance of the proposed generalized Gaussian source model on a typical example is compared with those of other algorithm, viz the switching nonlinearity algorithm proposed by Lee et al. [8]. © Association for Scientific Research

Multichannel Blind Deconvolution Using a Generalized Gaussian Source Model

In this paper, we present an algorithm for the problem of multi-channel blind deconvolution which can adapt to un-known sources with both sub-Gaussian and super-Gaussian probability density distributions using a generalized gaussian source model. We use a state space representation to model the mixer and demixer respectively, and show how the parameters of the demixer can be adapted using a gradient descent algorithm incorporating the natural gradient extension. We also present a learning method for the unknown parameters of the generalized Gaussian source model. The performance of the proposed generalized Gaussian source model on a typical example is compared with those of other algorithm, viz the switching nonlinearity algorithm proposed by Lee et al. [8]. © Association for Scientific Research

Impact of depth of clinical response on outcomes of acute myeloid leukemia patients in first complete remission who undergo allogeneic hematopoietic cell transplantation

Acute myeloid leukemia (AML) patients often undergo allogeneic hematopoietic cell transplantation (alloHCT) in first complete remission (CR). We examined the effect of depth of clinical response, including incomplete count recovery (CRi) and/or measurable residual disease (MRD), in patients from the Center for International Blood and Marrow Transplantation Research (CIBMTR) registry. We identified 2492 adult patients (1799 CR and 693 CRi) who underwent alloHCT between January 1, 2007 and December 31, 2015. The primary outcome was overall survival (OS). Multivariable analysis was performed to adjust for patient-, disease-, and transplant-related factors. Baseline characteristics were similar. Patients in CRi compared to those in CR had an increased likelihood of death (HR: 1.27; 95% confidence interval: 1.13-1.43). Compared to CR, CRi was significantly associated with increased non-relapse mortality (NRM), shorter disease-free survival (DFS), and a trend toward increased relapse. Detectable MRD was associated with shorter OS, shorter DFS, higher NRM, and increased relapse compared to absence of MRD. The deleterious effects of CRi and MRD were independent. In this large CIBMTR cohort, survival outcomes differ among AML patients based on depth of CR and presence of MRD at the time of alloHCT. Further studies should focus on optimizing post-alloHCT outcomes for patients with responses less than CR