Boolean modeling of neural systems with point-process inputs and outputs.

This paper presents a novel modeling approach for neural systems with point-process inputs and outputs (binary time-series of 0's and 1's) that utilizes Boolean operators of modulo-2 multiplication and addition, corresponding to the logical AND and OR operations respectively. The form of the employed mathematical model is akin to a "Boolean-Volterra" model that contains the product terms of all relevant input lags in a hierarchical order, where terms of order higher than first represent nonlinear interactions among the various lagged values of each input point-process or among lagged values of various inputs (if multiple inputs exist) as they reflect on the output. The coefficients of this Boolean model are also binary variables that indicate the presence or absence of the respective term in each specific model/system.

Pathological voice assessment.

While there are number of guidelines and methods used in practice, there is no standard universally agreed upon system for assessment of pathological voices. Pathological voices are primarily labeled based on the perceptual judgments of specialists, a process that may result in different label(s) being assigned to a given voice sample. This paper focuses on the recognition of five specific pathologies.

Evidence for restriction of fluid and solute movement across the glomerular capillary wall by the subpodocyte space.

The glomerular filtration barrier (GFB) is generally considered to consist of three layers: fenestrated glomerular endothelium, glomerular basement membrane, and filtration slits between adjacent podocyte foot processes. Detailed anatomic examination of the GFB has revealed a novel abluminal structure, the subpodocyte space (SPS), identified as the labyrinthine space between the underside of podocyte cell body/primary processes and the foot processes. The SPS covers 50-65% of the filtration surface of the GFB, indicating that SPS may influence glomerular permeability.

DSP based image processing for retinal prosthesis.

The real-time image processing in retinal prosthesis consists of the implementation of various image processing algorithms like edge detection, edge enhancement, decimation etc. The algorithmic computations in real-time may have high level of computational complexity and hence the use of digital signal processors (DSPs) for the implementation of such algorithms is proposed here. This application desires that the DSPs be highly computationally efficient while working on low power.

MEMS shear stress sensors for cardiovascular diagnostics.

Coronary artery disease is the leading cause of morbidity and mortality in the industrialized nations. Both biochemical and biomechanical stimuli modulate the pathogenesis of coronary artery diseases. Shear stress acting on the lumen of blood vessels intimately modulates the biological activities of vascular endothelial cells (ECs).

Phenol injury-induced hypertension stimulates proximal tubule Na+/H+ exchanger activity.

Injection of 50 microl 10% phenol into rat renal cortex activates renal sympathetic nerve activity which provokes acute hypertension that persists for weeks. We have previously shown with membrane fractionation that phenol injury caused a redistribution of the main proximal tubule (PT) apical transporter NHE3 (Na+/H+ exchanger isoform 3) to low density membranes enriched in apical microvilli. The aim of this study was to determine whether phenol injury increases PT apical Na+/H+ exchanger (NHE) activity.

AMPK activation is not critical in the regulation of muscle FA uptake and oxidation during low-intensity muscle contraction.

To determine the role of AMP-activated protein kinase (AMPK) activation on the regulation of fatty acid (FA) uptake and oxidation, we perfused rat hindquarters with 6 mM glucose, 10 microU/ml insulin, 550 microM palmitate, and [14C]palmitate during rest (R) or electrical stimulation (ES), inducing low-intensity (0.1 Hz) muscle contraction either with or without 2 mM 5-aminoimidazole-4-carboxamide-1-beta-D-ribofuranoside (AICAR). AICAR treatment significantly increased glucose and FA uptake during R (P < 0.

Accurate estimation of the Fisher information matrix for the PET image reconstruction problem.

The Fisher information matrix (FIM) plays a key role in the analysis and applications of statistical image reconstruction methods based on Poisson data models. The elements of the FIM are a function of the reciprocal of the mean values of sinogram elements. Conventional plug-in FIM estimation methods do not work well at low counts, where the FIM estimate is highly sensitive to the reciprocal mean estimates at individual detector pairs.

Acute hypotension induced by aortic clamp vs. PTH provokes distinct proximal tubule Na+ transporter redistribution patterns.

Renal parathyroid hormone (PTH) action is often studied at high doses (100 microg PTH/kg) that lower mean arterial pressure significantly, albeit transiently, complicating interpretation of studies. Little is known about the effect of acute hypotension on proximal tubule Na(+) transporters. This study aimed to determine the effects of acute hypotension, induced by aortic clamp or by high-dose PTH (100 microg PTH/kg), on renal hemodynamics and proximal tubule Na/H exchanger isoform 3 (NHE3) and type IIa Na-P(i) cotransporter protein (NaPi2) distribution.

Inhibition of lipopolysaccharide-stimulated TNF-alpha promoter activity by S-adenosylmethionine and 5'-methylthioadenosine.

S-adenosylmethionine (SAM) is the principal biological methyl donor and precursor for polyamines. SAM is known to be hepatoprotective in many liver disease models in which TNF-alpha is implicated. The present study investigated whether and how SAM inhibited LPS-stimulated TNF-alpha expression in Kupffer cells (hepatic macrophages).

Expression of claudin-7 and -8 along the mouse nephron.

Claudins are integral membrane proteins of the tight junction that determine the magnitude and selectivity of paracellular permeability in epithelial tissues. The mammalian renal tubule exhibits considerable heterogeneity in the permeability properties of its different segments. To determine the nephron segment localization of claudin-7 and -8, immunofluorescence staining of mouse kidney sections was performed using isoform-specific antibodies.

Tumor necrosis factor-alpha inhibits peroxisome proliferator-activated receptor gamma activity at a posttranslational level in hepatic stellate cells.

Diminished activity of peroxisome proliferator-activated receptor gamma (PPARgamma) is implicated in activation of hepatic stellate cells (HSC), a critical event in the development of liver fibrosis. In the present study, we investigated PPARgamma regulation by TNF-alpha in an HSC line designated as BSC. In BSC, TNF-alpha decreased both basal and ligand (GW1929)-induced PPARgamma mRNA levels without changing its protein expression.

Approximate maximum likelihood hyperparameter estimation for Gibbs priors.

The parameters of the prior, the hyperparameters, play an important role in Bayesian image estimation. Of particular importance for the case of Gibbs priors is the global hyperparameter, beta, which multiplies the Hamiltonian. Here we consider maximum likelihood (ML) estimation of beta from incomplete data, i.

Adaptive scalar quantization without side information.

In this paper, we introduce a novel technique for adaptive scalar quantization. Adaptivity is useful in applications, including image compression, where the statistics of the source are either not known a priori or will change over time. Our algorithm uses previously quantized samples to estimate the distribution of the source, and does not require that side information be sent in order to adapt to changing source statistics.

A multiscale error diffusion technique for digital halftoning.

A new digital halftoning technique based on multiscale error diffusion is examined. We use an image quadtree to represent the difference image between the input gray-level image and the output halftone image. In iterative algorithm is developed that searches the brightest region of a given image via "maximum intensity guidance" for assigning dots and diffuses the quantization error noncausally at each iteration.

Volterra models and three-layer perceptrons.

This paper proposes the use of a class of feedforward artificial neural networks with polynomial activation functions (distinct for each hidden unit) for practical modeling of high-order Volterra systems. Discrete-time Volterra models (DVMs) are often used in the study of nonlinear physical and physiological systems using stimulus-response data. However, their practical use has been hindered by computational limitations that confine them to low-order nonlinearities (i.

Fast tree-structured nearest neighbor encoding for vector quantization.

This work examines the nearest neighbor encoding problem with an unstructured codebook of arbitrary size and vector dimension. We propose a new tree-structured nearest neighbor encoding method that significantly reduces the complexity of the full-search method without any performance degradation in terms of distortion. Our method consists of efficient algorithms for constructing a binary tree for the codebook and nearest neighbor encoding by using this tree.

Extension of the modified-histogramming method for multilevel Markov random fields.

A very useful method for the estimation of Markov random field (MRF) parameters has been proposed by H. Derin and H. Elliott (1987).

Unconstrained handwritten numeral recognition based on radial basis competitive and cooperative networks with spatio-temporal feature representation.

This paper presents a new approach to representation and recognition of handwritten numerals. The approach first transforms a two-dimensional (2-D) spatial representation of a numeral into a three-dimensional (3-D) spatio-temporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. A multiresolution critical-point segmentation method is then proposed to extract local feature points, at varying degrees of scale and coarseness.

Optimal solutions for cellular neural networks by paralleled hardware annealing.

An engineering annealing method for optimal solutions of cellular neural networks is presented. Cellular neural networks are very promising in solving many scientific problems in image processing, pattern recognition, and optimization by the use of stored program with predetermined templates. Hardware annealing, which is a paralleled version of mean-field annealing in analog networks, is a highly efficient method of finding optimal solutions of cellular neural networks.

Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images.

The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density.

Texture analysis and classification with tree-structured wavelet transform.

A multiresolution approach based on a modified wavelet transform called the tree-structured wavelet transform or wavelet packets is proposed. The development of this transform is motivated by the observation that a large class of natural textures can be modeled as quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, it is possible to zoom into any desired frequency channels for further decomposition.

Paralleled hardware annealing for optimal solutions on electronic neural networks.

Three basic neural network schemes have been extensively studied by researchers: the iterative networks, the backpropagation networks, and the self-organizing networks. Simulated annealing is a probabilistic hill-climbing technique that accepts, with a nonzero but gradually decreasing probability, deterioration in the cost function of the optimization problems. Hardware annealing, which combines the simulated annealing technique with continuous-time electronic neural networks by changing the voltage gain of neurons, is discussed.

A programmable analog VLSI neural network processor for communication receivers.

An analog VLSI neural network processor was designed and fabricated for communication receiver applications. It does not require prior estimation of the channel characteristics. A powerful channel equalizer was implemented with this processor chip configured as a four-layered perceptron network.