UNLABELLED Contemporary natural history data in Duchenne muscular dystrophy (DMD) is needed to assess care recommendations and aid in planning future trials. METHODS The Cooperative International Neuromuscular Research Group (CINRG) DMD Natural History Study (DMD-NHS) enrolled 340 individuals, aged 2-28 years, with DMD in a longitudinal, observational study at 20 centers. Assessments obtained...

In Duchenne muscular dystrophy (DMD), dystrophin mutation leads to progressive lethal skeletal muscle degeneration. For unknown reasons, dystrophin deficiency does not recapitulate DMD in mice (mdx), which have mild skeletal muscle defects and potent regenerative capacity. We postulated that human DMD progression is a consequence of loss of functional muscle stem cells (MuSC), and the mild mous...

To identify serum biomarkers that allow monitoring of disease progression and treatment effects in Duchenne muscular dystrophy (DMD) patients, levels of matrix metalloproteinase-9 (MMP-9), tissue inhibitors of metalloproteinase-1 (TIMP-1) and osteopontin (OPN) were determined in 63 DMD patients on corticosteroid therapy. These proteins were selected for their role in the pathogenesis of muscula...

Duchenne muscular dystrophy (DMD) is secondary to loss-of-function mutations in the dystrophin gene. The causes underlying the progression of DMD, differential muscle involvement, and the discrepancies in phenotypes among species with the same genetic defect are not understood. The mdx mouse, an animal model with dystrophin mutation, has a milder phenotype. This article reviews the available in...

Elastic topological states have been receiving increased attention in numerous scientific and engineering fields due to their defect-immune nature, resulting applications of vibration control information processing. Here, we present the data-driven discovery elastic using dynamic mode decomposition (DMD). The DMD spectrum modes are retrieved from propagation relevant along boundary, where natur...

We analyze the performance of dynamic mode decomposition (DMD)-based approximations stochastic Koopman operator for random dynamical systems where either dynamics or observables are affected by noise. For many DMD algorithms, presence noise can introduce a bias in operator, leading to poor dynamics. In particular, methods using time delayed observables, such as Hankel DMD, biased when random. n...

We report on the development of a digital-micromirror-device (DMD)-based multishot snapshot spectral imaging (DMD-SSI) system as an alternative to current piezostage-based multishot coded aperture snapshot spectral imager (CASSI) systems. In this system, a DMD is used to implement compressive sensing (CS) measurement patterns for reconstructing the spatial/spectral information of an imaging sce...

Members Esen Akpek, MD Richard Brasington, MD, FACR Michael Brennan, DDS, MHS Steven E. Carsons, MD* Nancy L. Carteron, MD, FACR Troy Daniels, DDS, MS* H. Kenneth Fisher, MD, FACP, FCCP Gary Foulks, MD, FACS Theresa Lawrence Ford, MD S. Lance Forstot, MD Philip C. Fox, DDS* Robert I. Fox, MD, PhD, FACP* Tara Mardigan, MS, MPH, RD Austin Mircheff, PhD John Daniel Nelson, MD, FACS Kelly Nichols, ...

Four data-driven low-order modeling approaches, Dynamic mode decomposition (DMD) and three other variations (optimal decomposition, total-least-squares DMD high-order DMD), are used to capture the spatio-temporal evolution of fluid–structure interactions. These methods applied experimental data obtained in a flow over flexible membrane wing its elastic deformation. Spectral coherence indicates ...