Results 71 to 80 of about 1,669,292 (291)
Cancer treatment is associated with measurable acceleration of biological aging across epigenetic, telomere, senescence, and immune biomarkers. However, biomarker validation and interventional strategies remain limited, especially in hematologic malignancies, underscoring the need for standardized multi‐omic aging assessments and adequately powered ...
Moataz Ellithi +3 more
wiley +1 more source
Robust Sparse Recovery in Impulsive Noise via M-Estimator and Non-Convex Regularization
Robust sparse recovery aims at recovering a sparse signal or image from its compressed and contaminated measurements. Under the impulsive noise condition, the performance of traditional sparse recovery algorithms may deteriorate seriously for exploiting &
Le Gao +5 more
doaj +1 more source
ABSTRACT Objective Down syndrome regression disorder is a syndrome characterized by subacute loss of cognitive, behavioral, and functional abilities in individuals with Down syndrome. Electroencephalography abnormalities are frequently observed during evaluation, but it remains unclear whether these findings represent a dynamic marker of disease ...
Jonathan D. Santoro +14 more
wiley +1 more source
Performance Analysis for Sparse Support Recovery [PDF]
Submitted to IEEE Trans.
Gongguo Tang, Arye Nehorai
openaire +2 more sources
ABSTRACT Objective Treatment of disorders of consciousness (DoC) remains a major clinical challenge, and noninvasive, targeted modulation of deep brain structures has emerged as a promising therapeutic strategy. We aimed to evaluate the feasibility/safety and preliminary effects of thalamic temporal interference stimulation (TIS) targeting centromedian‐
Gengyao Hu +7 more
wiley +1 more source
Adaptive algorithm for sparse signal recovery [PDF]
Spike and slab priors play a key role in inducing sparsity for sparse signal recovery. The use of such priors results in hard non-convex and mixed integer programming problems. Most of the existing algorithms to solve the optimization problems involve either simplifying assumptions, relaxations or high computational expenses.
Fekadu L. Bayisa +3 more
openaire +2 more sources
ABSTRACT Objective Autoimmune glial fibrillary acidic protein astrocytopathy (GFAP‐A) is an inflammatory central nervous system disorder with variable outcomes. Relapse occurs in a subset of patients, but early predictors remain unclear. We aimed to identify admission‐available features associated with 1‐year recurrence and develop an interpretable ...
Qingting Hong +10 more
wiley +1 more source
A Unified Feasible SQP Framework for sparse and non-negative sparse recovery
Sparse signal recovery is a central problem in many areas, including medical imaging, remote sensing, machine learning, and data science. In many practical settings, the underlying signal is inherently non-negative, due to the physical nature of the ...
Mohammad Saeid Alamdari, Masoud Fatemi
doaj +1 more source
Alternating projection for sparse recovery
Reconstructing the sparse signal from a few linear measurements has attracted increasing attentions in recent years. In this study, the authors propose the alternating projection (AP) method for sparse signal recovery with learning the sparsity of the original signal.
Tao Sun 0005 +3 more
openaire +2 more sources
Objective We aimed to estimate the prevalence and cumulative incidence of hydroxychloroquine retinopathy (HCQ‐R) and its risk factors among patients receiving long‐term HCQ with rheumatic diseases through a systematic review and meta‐analysis of observational studies that used spectral‐domain optical coherence tomography (SD‐OCT) for screening ...
Narsis Daftarian +4 more
wiley +1 more source

