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One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, attempts are made to extend the scope of application of the one-class classification technique to Statistical Process Control (SPC) problems. New multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II analysis in SPC are proposed. These charts use a monitoring statistic to represent the degree of being an outlier as obtained through one-class classification. The control limits of the proposed charts are established based on the empirical level of significance on the percentile, estimated by the bootstrap method. A simulation study is conducted to illustrate the limitations of current one-class classification control charts and demonstrate the effectiveness of the proposed control charts.

This study presents three feature selection methods for identifying the metabolite features in nuclear magnetic resonance spectra that contribute to the distinction of samples among varying nutritional conditions. Principal component analysis, Fisher discriminant analysis, and Partial Least Square Discriminant Analysis (PLS-DA) were used to calculate the importance of individual metabolite feature in spectra. Moreover, an Orthogonal Signal Correction (OSC) filter was used to eliminate unnecessary variations in spectra. We evaluated the presented methods by comparing the ability of classification based on the features selected by each method. The result showed that the best classification was achieved from an OSC-PLS-DA model.

DP. Individual variation in macronutrient regulation measured by proton magnetic resonance spectroscopy of human plasma. Am J Physiol Regul Integr Comp Physiol 297: R202-R209, 2009. First published May 20, 2009 doi:10.1152/ajpregu.90757.2008.-Proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy of plasma provides a global metabolic profiling method that shows promise for clinical diagnostics. However, cross-sectional studies are complicated by a lack of understanding of intraindividual variation, and this limits experimental design and interpretation of data. The present study determined the diurnal variation detected by 1 H NMR spectroscopy of human plasma. Data reduction methods revealed three time-of-day metabolic patterns, which were associated with morning, afternoon, and night. Major discriminatory regions for these time-of-day patterns included the various kinds of lipid signals , and the region between 3 and 4 ppm heavily overlapped with amino acids that had ␣-CH and ␣-CH2. The phasing and duration of time-of-day patterns were variable among individuals, apparently because of individual difference in food processing/digestion and absorption and clearance of macronutrient energy sources (fat, protein, carbohydrate). The times of day that were most consistent among individuals, and therefore most useful for cross-sectional studies, were fasting morning (0830 -0930), postprandial afternoon (1430 -1630), and nighttime samples (0430 -0530). Importantly, the integrated picture of metabolism provided by 1 H-NMR spectroscopy of plasma suggests that this approach is suitable to study complex regulatory processes, including eating patterns/eating disorders, upper gastrointestinal functions (gastric emptying, pancreatic, biliary functions), and absorption/clearance of macronutrients. Hence, 1 H-NMR spectroscopy of plasma could provide a global metabolic tolerance test to assess complex processes involved in disease, including eating disorders and the range of physiological processes causing dysregulation of energy homeostasis. metabolomics; diurnal variation; eating disorders; gastrointestinal regulation GLOBAL METABOLIC PROFILING coupled with bioinformatic methods offers an approach to study the integration of macronutrient energy metabolism and can be useful to detect disease, toxicity, and nutritional deficiency (11,23,30,36). A variety of metabolic profiling methods are available, including gas chromatography-mass spectrometry, liquid chromatographymass spectrometry, and NMR spectroscopy (10,31,39). In view of its capability to handle multiple specimens in a high-throughput, semiautomated system, 1 H-NMR may offer unique insight into the in vivo metabolism of macronutrients (1, 2, 9). Dysfunction in macronutrient metabolism, i.e., carbohydrate, fat, protein, and alcohol, is relevant to obesity, metabolic syndrome, diabetes, cardiovascular disease and other common pathological conditions (19,25).Pioneering studies by Nicholson and coworkers (3, 24, 27, 35) established the utility of 1 H NMR spectroscopy f...

This research develops a novel data-integrated simulation to evaluate nurse-patient assignments (SIMNA) based on a real data set provided by a northeast Texas hospital. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree models, data mining tools for prediction and classification, were used to develop five tree structures: (a) four classification trees from which transition probabilities for nurse movements are determined, and (b) a regression tree from which the amount of time a nurse spends in a location is predicted based on factors such as the primary diagnosis of a patient and the type of nurse. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Results obtained from SIMNA to evaluate nurse-patient assignments in Medical/Surgical unit I of the northeast Texas hospital are discussed.