A prototypic fluorescence array detector (FAD) has been designed and constructed which is capable of quantifying single-cell fluorescence emissions from a statistically significant population of cellsized objects (over lo3) on a solid substrate. The system is comprised of a cryogenically cooled CCD, 50 mW air-cooled argon ion laser, and optics that image a large (1 x 1 cm) field at 1:l (no magnification). The CCD is effectively treated as a two-dimensional array of 2.7 x lo5 independent 20 x 20 pm photodetectors, with each cell-sized object imaged across only a few CCD pixels. Algorithms have been developed for focusing, image segmentation, shading correction, and noise rejection; performance data for the FAD with fluorescent calibration beads are presented. The FAD is a simple alternative to microscope-based imaging cytometry, allowing large-field imaging without a scanning stage. 0 1994 Wiley-Liss, Inc.

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We present Illinois Extended Reality testbed (ILLIXR), the first fully open-source XR system and research testbed. ILLIXR enables system innovations with end-to-end co-designed hardware, compiler, OS, and algorithms, and driven by end-user perceived Quality-of-Experience (QoE) metrics. Using ILLIXR, we provide the first comprehensive quantitative analysis of performance, power, and QoE for a complete XR system and its individual components. We describe several implications of our results that propel new directions in architecture, systems, and algorithms research for domain-specific systems in general, and XR in particular.

The observation that men with sperm density greater than 10 million/ml had low probability of endocrinopathy led to a refinement in the evaluation of subfertility. Using statistical methods, we sought to provide a more accurate prediction of which patients have an endocrinopathy, and to report the outcome as the odds of having disease. In addition, by examining the parameters that influenced the model significantly, the underlying pathophysiology might be better understood. Records of 1035 men containing variables including testis volume, sperm density, motility as well as the presence of endocrinopathy were randomized into 'training' and 'test' data sets. We modeled the data set using linear and quadratic discriminant function analysis, logistic regression (LR) and a neural network. Wilk's regression analysis was performed to determine which variables influenced the model significantly. Of the four models investigated, LR and a neural network performed the best with receiver operating characteristic areas under the curve of 0.93 and 0.95, respectively, correlating to a sensitivity of 28% and a specificity of 99% for the LR model, and a sensitivity and specificity of 56 and 97% for the neural network model. Reverse regression yielded P-values for the testis volume and sperm density of o0.0001. The neural network and LR models accurately predicted the probability of an endocrinopathy from testis volume, sperm density and motility without serum assays. These models may be accessed via the Internet, allowing urologists to select patients for endocrinologic evaluation at