Oxygenation in the rabbit myocardium: assessment with susceptibility-dependent MR imaging

PURPOSE: To determine the feasibility of using hemoglobin (Hb) desaturation as an indicator of myocardial oxygenation. MATERIALS AND METHODS: High-resolution gradient-echo nuclear magnetic resonance (MR) images of isolated, blood-perfused rabbit hearts were obtained at various blood oxygenation levels. The hearts were perfused at 37 degrees C with a Langendorff apparatus modified for nuclear MR imaging. The perfusate contained bovine red blood cells in a cardioplegic solution that eliminated motion artifacts and minimized arteriovenous oxygenation differences. Hb saturation was varied (7%-100%) randomly. Perfusion pressure was continuously monitored, and blood samples were obtained. RESULTS: There was a substantial correlation between image signal intensity in the myocardium and Hb saturation in the blood, believed to be due to susceptibility effects of the paramagnetic species deoxyhemoglobin. CONCLUSION: Direct and noninvasive determination of regional Hb saturation with susceptibility-dependent MR imaging may provide information regarding regional myocardial O2 content.     

Effect of the sulfonylurea glyburide on glycogen synthase activity in alloxan-induced diabetic rat adipocytes

1. The effect of glyburide (glibenclamide) treatment in vivo on the adipose tissue glycogen synthase activity of type II diabetic rats has been studied. 2. Three week treatment of diabetic animals with glyburide (5 mg/kg orally, in saline) increased adipose glycogen synthase activity and decreased blood glucose levels. 3. These results demonstrate that the sulfonylurea glyburide is capable of exerting direct insulin-like effect on adipose glycogen-synthase activity of type II diabetic rats in vivo.     

Use of center of gravity with the common vector approach in isolated word recognition

In this paper, the subspace based classifier, common vector approach (CVA), with the center of gravity (COG) method is used for isolated word recognition. Since the CVA classifier is sensitive to shifts through the time axis, endpoint detection becomes extremely important for the recognition of isolated words. The COG method eliminates the need for endpoint detection. The effects of the COG method and a classical endpoint detection algorithm on the recognition rates of isolated words are investigated. The experimental results show that the COG method yields slightly higher recognition rates than the endpoint detection method in the TI-digit database when CVA is used.     

Design optimization of an enzymatic assay in an electrokinetically-driven microfluidic device

Microfluidic systems are increasingly popular for rapid and cheap determinations of enzyme assays and other biochemical analysis. In this study reduced order models (ROM) were developed for the optimization of enzymatic assays performed in a microchip. The model enzyme assay used was β-galactosidase (β-Gal) that catalyzes the conversion of Resorufin β-d-galactopyranoside (RBG) to a fluorescent product as previously reported by Hadd et al. (Anal Chem 69(17): 3407–3412, 1997). The assay was implemented in a microfluidic device as a continuous flow system controlled electrokinetically and with a fluorescence detection device. The results from ROM agreed well with both computational fluid dynamic (CFD) simulations and experimental values. While the CFD model allowed for assessment of local transport phenomena, the CPU time was significantly reduced by the ROM approach. The operational parameters of the assay were optimized using the validated ROM to significantly reduce the amount of reagents consumed and the total biochip assay time. After optimization the analysis time would be reduced from 20 to 5.25 min which would also resulted in 50% reduction in reagent consumption.     

Quadrilateral meshes with provable angle bounds

In this paper, we present an algorithm that utilizes a quadtree data structure to construct a quadrilateral mesh for a simple polygonal region in which no newly created angle is smaller than 18.43^° (=arctan(\frac13)){{18.43}}^{\circ} ({=}\hbox{arctan}(\frac{1}{3})) or greater than 171.86^° (=135^° + 2arctan(\frac13)){{171.86}}^{\circ} ({=}{{135}}^{\circ} + 2\hbox{arctan}(\frac{1}{3})). This is the first known result, to the best of our knowledge, on a direct quadrilateral mesh generation algorithm with a provable guarantee on the angles.     

A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences

Various deterministic and latent structure approaches for combining forms of multidimensional scaling and cluster analysis have been previously discussed. A new clusterwise three-way unfolding methodology for the analysis of two-way or three-way metric dominance/preference data is proposed. The purpose of this proposed methodology is to simultaneously estimate a joint space of stimuli and cluster ideal point representations, as well as the clusters themselves, such that the geometry underlying the clusterwise model renders some indication of the underlying structure in the data. In the three-way case, it is shown how multiple ideal points can represent preference change over contexts or situations. Partitions, overlapping clusters, stationary and context dependent preference representations are allowed. After a literature review of related methodological research, the technical details of the proposed three-way clusterwise spatial unfolding model are presented in terms of modeling context/situational dependent preferences (i.e., preferences for various stimuli collected over the same set of subjects over time, situation, etc.). The psychological basis for the models is provided in terms of the extensive behavioral decision theory and consumer psychology literature on contextual preferences and situational effects. An application to a data set exploring preferences for breakfast/snack food data over a number of different usage situations is then presented, followed by a discussion on future potential research directions.     

Sequential enzymatic quantification of two sugars in a single microchannel

This paper presents the implementation of a multiple analyte enzyme assay, based on the sequential injection of the different enzyme solutions, in an electrokinetic driven microfluidic chip. The assay methodology for the simultaneous quantification of d-glucose and d-fructose was reported in previous publications but here the real integration of both enzyme assays was achieved. When assays were executed separately, good reproducibility was observed with average CV values of 5.2% and 4.5% for the d-glucose and d-fructose assay, respectively. Next, the assays for the quantification of d-glucose and d-fructose were integrated simultaneously on chip, where each assay was executed consecutively in the same microreactor by applying a specific sequence of potentials at the reservoirs. This article proves the integration of a sequential based quantification approach in continuous microfluidic chips with electrokinetic actuation.     

Catalytic combustion of carbon tetrachloride

The catalytic combustion of carbon tetrachloride (CCl4) by metal oxide catalysts coated on the monolith support was investigated. The prepared catalysts were tested at temperatures between 300 and 800 degrees C and at varying gas hourly space velocity (GHSV) values with an excess air ratio of 3100%. The catalyst, whose composition was 18% Cr2O3, 2% Ce2O3 and 80% gamma-Al2O3, was found to almost completely oxidize CCl4. The operating conditions proposed are 5702 h(-1) for GHSV, 3100% excess air and a temperature slightly higher than 800 degrees C. The reaction rate expression was found to be independent of oxygen partial pressure but strongly dependent on CCl4 partial pressure.     

Projection based method for segmentation of human face and its evaluation

We detect facial features and then circumscribe each facial feature with the smallest rectangle possible by using vertical and horizontal gray value projections of pixels. The result is evaluated with respect to the manually located enclosing rectangle on the images of a publicly available database.     

The kernel common vector method: a novel nonlinear subspace classifier for pattern recognition.

The common vector (CV) method is a linear subspace classifier method which allows one to discriminate between classes of data sets, such as those arising in image and word recognition. This method utilizes subspaces that represent classes during classification. Each subspace is modeled such that common features of all samples in the corresponding class are extracted. To accomplish this goal, the method eliminates features that are in the direction of the eigenvectors corresponding to the nonzero eigenvalues of the covariance matrix of each class. In this paper, we introduce a variation of the CV method, which will be referred to as the modified CV (MCV) method. Then, a novel approach is proposed to apply the MCV method in a nonlinearly mapped higher dimensional feature space. In this approach, all samples are mapped into a higher dimensional feature space using a kernel mapping function, and then, the MCV method is applied in the mapped space. Under certain conditions, each class gives rise to a unique CV, and the method guarantees a 100% recognition rate with respect to the training set data. Moreover, experiments with several test cases also show that the generalization performance of the proposed kernel method is comparable to the generalization performances of other linear subspace classifier methods as well as the kernel-based nonlinear subspace method. While both the MCV method and its kernel counterpart did not outperform the support vector machine (SVM) classifier in most of the reported experiments, the application of our proposed methods is simpler than that of the multiclass SVM classifier. In addition, it is not necessary to adjust any parameters in our approach.