LDL-cholesterol (LDL-C) concentration is currently determined in most clinical laboratories by the Friedewald calculation. This approach has several limitations and may not meet the current total error requirement in LDL-C measurement of < or = 12%. We evaluated the analytical and clinical performance of the direct N-geneous LDL-C assay (Equal Diagnostics). The N-geneous method correlated highl...

We examined the analytical performance of eight compact systems for measuring total cholesterol: AccuMeter, Cobas Ready, Discovery f2, DT60, L-D-X, Reflotron, QCA, and Vision. We determined average bias at two decision levels, the mean absolute bias, and the percentage of results differing from the comparison method results by > 8.9% allowable total error limit for multiple reagent lots. Averag...

BACKGROUND LDL-cholesterol (LDL-C) concentrations currently are determined in most clinical laboratories using the Friedewald calculation. This approach has several limitations and may not always meet the current total error recommendation in LDL-C measurement of </=12% established by the National Cholesterol Education Program. METHODS In a multicenter study, we evaluated the analytical and c...

Time-domain equalizers (TEQs) have been applied extensively to shorten the channel impulse response, thus enhancing the transmission efficiency of multitone and multicarrier systems with cyclic prefix. Recent developments on TEQs have mainly been focused on minimizing the additive noise power, minimizing the ISI power or maximizing the total throughput. This paper takes a different approach by ...

Before the wide deployment of underwater sensor networks becomes a reality, one of the challenges that needs to be resolved is efficient error recovery in the presence of high error rates, node mobility and long propagation delays. In this paper, we propose an efficient error-recovery scheme that carefully couples network coding and multipath routing. Through an analytical study, we provide gui...

This paper presents the multiclass classifier based on analytical center of feasible space (MACM). This multiclass classifier is formulated as quadratic constrained linear optimization and does not need repeatedly constructing classifiers to separate a single class from all the others. Its generalization error upper bound is proved theoretically. The experiments on benchmark datasets validate t...