The Antibacterial Assay of Tectorigenin with Detergents or ATPase Inhibitors against Methicillin-Resistant Staphylococcus aureus

Tectorigenin (TTR) is an O-methylated isoflavone derived from the rhizome of Belamacanda chinensis (L.) DC. It is known to perform a wide spectrum of biological activities such as antioxidant, anti-inflammatory, anti-tumor. The aim of this study is to examine the mechanism of antibacterial activity of TTR against methicillin-resistant Staphylococcus aureus (MRSA). The anti-MRSA activity of TTR was analyzed in combination assays with detergent, ATPase inhibitors, and peptidoglycan (PGN) derived from S. aureus. Transmission electron microscopy (TEM) was used to monitor survival characteristics and changes in S. aureus morphology. The MIC values of TTR against all the tested strains were 125 μg/mL. The OD(600) of each suspension treated with a combination of Triton X-100, DCCD, and NaN3 with TTR (1/10 × MIC) had been reduced from 68% to 80%, compared to the TTR alone. At a concentration of 125 μg/mL, PGN blocked antibacterial activity of TTR. This study indicates that anti-MRSA action of TTR is closely related to cytoplasmic membrane permeability and ABC transporter, and PGN at 125 μg/mL directly bind to and inhibit TTR at 62.5 μg/mL. These results can be important indication in study on antimicrobial activity mechanism against multidrug resistant strains

Dynamical-statistical seasonal prediction for western North Pacific typhoons based on APCC multi-models

This study aims at predicting the seasonal number of typhoons (TY) over the western North Pacific with an Asia-Pacific Climate Center (APCC) multi-model ensemble (MME)-based dynamical-statistical hybrid model. The hybrid model uses the statistical relationship between the number of TY during the typhoon season (July-October) and the large-scale key predictors forecasted by APCC MME for the same season. The cross validation result from the MME hybrid model demonstrates high prediction skill, with a correlation of 0.67 between the hindcasts and observation for 1982-2008. The cross validation from the hybrid model with individual models participating in MME indicates that there is no single model which consistently outperforms the other models in predicting typhoon number. Although the forecast skill of MME is not always the highest compared to that of each individual model, the skill of MME presents rather higher averaged correlations and small variance of correlations. Given large set of ensemble members from multi-models, a relative operating characteristic score reveals an 82 % (above-) and 78 % (below-normal) improvement for the probabilistic prediction of the number of TY. It implies that there is 82 % (78 %) probability that the forecasts can successfully discriminate between above normal (below-normal) from other years. The forecast skill of the hybrid model for the past 7 years (2002-2008) is more skillful than the forecast from the Tropical Storm Risk consortium. Using large set of ensemble members from multi-models, the APCC MME could provide useful deterministic and probabilistic seasonal typhoon forecasts to the end-users in particular, the residents of tropical cyclone-prone areas in the Asia-Pacific region.ope