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AbstractHere we are reporting a new bioactive flavanone glycoside. The compound having molecular formula C21H22O11, melting point 123-125°C, and ESI-MS m/z 451 was isolated from the methanolic extract of Tradescantia SpathaceaSw. Its characterization and structure elucidation was done with the help of various chemical degradation, color reactions, and spectral data like UV-Visible, FT-IR, electrospray ionization mass spectrometry technique, nuclear magnetic resonance spectroscopy, etc. It was recognized as 5,7,3’4’-tetrahydroxy flavanone 3’-O-D-mannopyranoside. This study was also designed to examine the efficacy of an isolated compound against various bacteria which are responsible for food spoilage and pathogens in humans. The organic flavanone glycoside of methanolic extract exhibited a promising antibacterial activity towards Escherichia coli(gram negative) MTCC 724, Bacillus subtilis MTCC 441, Staphylococcus aureusMTCC 737, Pseudomonas aerugenosaMTCC 424, and Salmonella typhimuriumMTCC 3224. A new flavanone glycoside possesses potent anti-bacterial activity so it can be utilized as a potent source of antibacterial agents for food and pharmaceutical interest.

Abstract: The method of Cohen, Ma and Sackrowitz (2012) [3] is applied to the problems of multiple testing and simultaneous interval estimation of correlated correlations. A large sample method is assumed and thus the basic statistics considered are Fisher’s statistics. The asymptotic joint distribution of the statistics is derived and utilized in the procedure. An example using data from Westfall and Young (1993) [10] is provided. [Copyright &y& Elsevier]

Abstract: One-term Edgeworth Expansions for the studentized version of compound Poisson processes are developed. For a suitably defined bootstrap in this context, the so called one-term Edgeworth correction by bootstrap is also established. The results are applicable for constructing second-order correct confidence intervals (which make correction for skewness) for the parameter “mean reward per unit time”.

We propose and study the bootstrap confidence regions for multivariate parameters based on Tukey’s depth. The bootstrap is based on the normalized or Studentized statistic formed from an independent and identically distributed random sample obtained from some unknown distribution in Rq. The bootstrap points are deleted on the basis of Tukey’s depth until the desired confidence level is reached. The proposed confidence regions are shown to be second order balanced in the context discussed by Beran. We also study the asymptotic consistency of Tukey’s depth‐based bootstrap confidence regions. The applicability of the method proposed is demonstrated in a simulation study.

The product-limit estimator and its quantile process are represented as i.i.d. mean processes, with a remainder of ordern−3/4(logn)3/4a.s. Corresponding bootstrap versions of these representations are given, which can help one visualize how the bootstrap procedure operates in this set up.