Characterization of Antioxidant Activity of Momordica Charantia Fruit by Infrared-Based Fingerprinting

Momordica charantia is widely consumed edible fruit. The food and pharmaceutical industries use it as a natural antioxidant. However, the quality control of M. charantia-based medicinal products is questionable due to the complexity of metabolites in this fruit. Hence, this study has developed a statistical model in predicting the antioxidant value through the 2, 2-diphenyl-1 picrylhydrazyl radical scavenging activity and ferric reducing antioxidant power based on infrared spectroscopy with attenuated total reflectance. This technique was reliably used for quality control. Six ethanol extracts (0, 20, 40, 60, 80, and 100% in water) of this plant’s fruit were prepared. The radical scavenging and ferric reducing antioxidant power activities were measured and the chemical profiling of the extracts was fingerprinted by infrared spectroscopy between 4,000 and 600?cm^?1 at a resolution of 4?cm^?1. Statistical analysis was developed by correlating the bioactivity and infrared spectra of each extract using orthogonal partial least square discriminant analysis. The C–N, C?O, C–O, C–H, and OH infrared signals were positively correlated with biological activity. The antioxidant activity of the fruit of M. charantia may be due to the presence of several antioxidants that work synergistically.     

Rapid identification of Candida albicans in blood by combined capillary electrophoresis and fluorescence in situ hybridization

A CE method based on whole‐cell molecular labeling via fluorescence in situ hybridization was developed for the detection of Candida albicans in whole blood. Removal of potentially interfering red blood cells (RBC) with a simple hypotonic/detergent lysis step enabled us to detect and quantitate contaminating C. albicans cells at concentrations that were orders of magnitude lower than background RBC counts (∼7.0×10⁹ RBC/mL). In the presence of the lysed blood matrix, yeast cells aggregated without the use of a blocking plug to stack the cells. Short (15 min) hybridizations yielded bright Candida‐specific fluorescence in situ hybridization signals, enabling us to detect as few as a single injected cell. The peak area response of the stacked Candida cells showed a strong linear correlation with cell concentrations determined by plate counts, up to ∼10⁷ CFU/mL (or ∼1×10⁴ injected cells). This rapid and quantitative method for detecting Candida in blood may have advantageous applications in both human and veterinary diagnostics.     

Adaptive Bayesian Nonstationary Modeling for Large Spatial Datasets Using Covariance Approximations

Gaussian process models have been widely used in spatial statistics but face tremendous modeling and computational challenges for very large nonstationary spatial datasets. To address these challenges, we develop a Bayesian modeling approach using a nonstationary covariance function constructed based on adaptively selected partitions. The partitioned nonstationary class allows one to knit together local covariance parameters into a valid global nonstationary covariance for prediction, where the local covariance parameters are allowed to be estimated within each partition to reduce computational cost. To further facilitate the computations in local covariance estimation and global prediction, we use the full-scale covariance approximation (FSA) approach for the Bayesian inference of our model. One of our contributions is to model the partitions stochastically by embedding a modified treed partitioning process into the hierarchical models that leads to automated partitioning and substantial computational benefits. We illustrate the utility of our method with simulation studies and the global Total Ozone Matrix Spectrometer (TOMS) data. Supplementary materials for this article are available online.