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.     

Prolonged deterioration of ischemic brain energy metabolism and acidosis associated with hyperglycemia: human cerebral infarction studied by serial 31P NMR spectroscopy

We report on a patient with a large ischemic hemispherical stroke studied serially by 31P nuclear magnetic resonance spectroscopy. Persistent hyperglycemia was associated with prolonged acidosis in ischemic brain and failure of high-energy phosphate metabolism to recover. These in vivo human data support the concept that hyperglycemia adversely affects ischemic brain metabolism, pH, and clinical outcome.