Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation.

Many methods have been developed for estimating the parameters of biexponential decay signals, which arise throughout magnetic resonance relaxometry (MRR) and the physical sciences. This is an intrinsically ill-posed problem so that estimates can depend strongly on noise and underlying parameter values. Regularization has proven to be a remarkably efficient procedure for providing more reliable solutions to ill-posed problems, while, more recently, neural networks have been used for parameter estimation.

Bacterially derived medical devices: How commercialization of bacterial nanocellulose and other biofabricated products requires challenging of standard industrial practices.

The objectives of innovation are often diametrically opposed to industrially standardized practices. The burgeoning field of Biofabrication represents one type of challenge that falls outside the norms of not only standardized industrial practices, but also those of Health Authorities. Biofabrication produces complex "biological products from raw materials such as living cells, molecules, extracellular matrices, and biomaterials" Mironov V, et al.

Compressed Sensing Electron Tomography for Determining Biological Structure.

There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials.

Efficient 2D MRI relaxometry using compressed sensing.

Potential applications of 2D relaxation spectrum NMR and MRI to characterize complex water dynamics (e.g., compartmental exchange) in biology and other disciplines have increased in recent years.

Solving 2D Fredholm Integral from Incomplete Measurements Using Compressive Sensing.

We present an algorithm to solve the two-dimensional Fredholm integral of the first kind with tensor product structure from a limited number of measurements, with the goal of using this method to speed up nuclear magnetic resonance spectroscopy. This is done by incorporating compressive sensing-type arguments to fill in missing measurements, using a priori knowledge of the structure of the data. In the first step we recover a compressed data matrix from measurements that form a tight frame, and establish that these measurements satisfy the restricted isometry property.

Schroedinger Eigenmaps for the analysis of biomedical data.

We introduce Schroedinger Eigenmaps (SE), a new semi-supervised manifold learning and recovery technique. This method is based on an implementation of graph Schroedinger operators with appropriately constructed barrier potentials as carriers of labeled information. We use our approach for the analysis of standard biomedical datasets and new multispectral retinal images.

Nonlinear gene cluster analysis with labeling for microarray gene expression data in organ development.

Background: The gene networks underlying closure of the optic fissure during vertebrate eye development are not well-understood. We use a novel clustering method based on nonlinear dimension reduction with data labeling to analyze microarray data from laser capture microdissected (LCM) cells at the site and developmental stages (days 10.5 to 12.

The future prospects of microbial cellulose in biomedical applications.

Microbial cellulose has proven to be a remarkably versatile biomaterial and can be used in wide variety of applied scientific endeavors, such as paper products, electronics, acoustics, and biomedical devices. In fact, biomedical devices recently have gained a significant amount of attention because of an increased interest in tissue-engineered products for both wound care and the regeneration of damaged or diseased organs. Due to its unique nanostructure and properties, microbial cellulose is a natural candidate for numerous medical and tissue-engineered applications.

Phantom cosmology as a simple model with dynamical complexity.

We study the Friedman-Robertson-Walker model with phantom fields modeled in terms of scalar fields. We apply the Ziglin theory of integrability and find that the flat model is nonintegrable. Then we cannot expect to determine simple analytical solutions of the Einstein equations.

Microbial cellulose--the natural power to heal wounds.

Microbial cellulose (MC) synthesized in abundance by Acetobacter xylinum shows vast potential as a novel wound healing system. The high mechanical strength and remarkable physical properties result from the unique nanostructure of the never-dried membrane. This article attempts to briefly summarize the recent developments and applications of MC in the emerging field of novel wound dressings and skin substitutes.