Type II sodium channels in spinal cord astrocytes in situ: Immunocytochemical observations

The expression of sodium channel α-subunit isoforms in astrocytes in adult rat spinal cord and optic nerve was examined utilizing immunocytochemical methods with antibodies generated against conserved and subtype-specific sequences of the sodium channel. In adult rat spinal cord, astrocytes within the dorsal and ventral funiculi were immunolabelled with antibody SP20, which recognizes a conserved sequence within sodium channel types I, II, and III. In addition, astrocytes within these spinal cord white matter tracts were immunostained with antibody SP11-II, which recognizes sodium channel type II. Antibodies SP11-I and SP32-III, which are directed against subtype-specific sequences in sodium channel types I and III, respectively, did not label astrocytes in the dorsal and ventral funiculi of the spinal cord. In optic nerves, astrocytes were immunostained with antibody SP20. However, no detectable labelling of cells within the optic nerve was observed with antibodies SP11-I, SP11-II, and SP32-III. These observations demonstrate that sodium channel II is expressed by astrocytes in spinal cord white matter. Moreover, these data suggest that regional factors regulate the level of sodium channel isoform expression in astrocytes.     

BioMEMS Education at Louisiana Tech University

A course in BioMEMS was recently taught at the senior undergraduate and graduate level at Louisiana Tech University and the Institute for Micromanufacturing. The course focused on the basic principles and applications of MEMS technology in the area of biomedical microsystems. The course was well received and had an enrollment well above that expected. Student feedback and a review of the course evaluations indicated that the course was effective in achieving its aims. A poster session involving the presentation of student design projects at the end of the course proved to be a highlight and a valuable experience for all involved. While the course proved successful, suggestions for improvement of the course are outlined and a summary of student responses is presented. An overview of a BioMEMS short course based on the University course is also presented.     

Characteristics of Hidden Status Among Users of Crack, Powder Cocaine, and Heroin in Central Harlem

This article analyzes hidden status among crack, powder cocaine, and heroin users and setters, in contrast to more accessible users/sellers. Several sampling strategies acquired 657 users (N=559) and sellers (N=98). Indicators of hidden status were those who (1) paid rent in full in the last 30 days, (2) used nonstreet drug procurement. (3) had legal jobs, and (4) earned $1,000 or more in legal income in the last 30 days. Nearly half had at least one indicator: approximately 16% of users/sellers had two to four indicators. In logistic regression analyses, those who had not panhandled in the last 30 days, those who had used powder cocaine in the last 30 days, and those never arrested were the most likely to have hidden status, whether the analysis predicted those having any indicators or those having two to four indicators. The four indicators begin to operationally define hidden status among users of cocaine and heroin.     

A framework for evaluating image segmentation algorithms.

The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors-precision (reliability), accuracy (validity), and efficiency (viability)-need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit, repeat segmentation considering all sources of variation, and determine variations in figure of merit via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. In determining accuracy, it may be important to consider different 'landmark' areas of the structure to be segmented depending on the application. To assess efficiency, both the computational and the user time required for algorithm training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency factors have an influence on one another. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors, as illustrated in an example wherein two methods are compared in a particular application domain. The weight given to each factor depends on application.