In the current study we investigated the antibacterial activity of fragrance ingredients against Legionella pneumophila, a causative agent of severe pneumonia. Among the 41 different fragrance ingredients tested, we found that the natural fragrance ingredients oakmoss (OM) and birch tar oil (BT), which contain many components, exhibit potent antibacterial activity. The minimum inhibitory concentration (MIC, % (v/v)) of OM and BT were 0.0020 and 0.0024, respectively and were lower than that of cinnamic aldehyde (0.0078), which has been previously shown to possess high antimicrobial activity. In a time-kill assay of OM and BT at MIC and two times MIC, the colony forming units (CFU) of the microbe were reduced to between 10 ؊ ؊3 to 10 ؊ ؊4 of the original CFU after 1 h co-incubation. After this time, the CFU gradually decreased in number, but remained above detection levels even after a 48-h co-incubation, except for BT at two times MIC. In contrast, at a concentration of 0.1% OM and BT (approximately 50 times MIC), CFU were not detected after co-incubation for 1 h. Another 18 fragrance ingredients including ketone, aldehyde, lactone, acid, phenol derivative, aliphatic alcohol and quinoline also exhibited a lesser degree of antibacterial activity against L. pneumophila at a MIC of less than 0.10.
This article presents a new method for fitting a digital line or plane to a given set of points in a 2D or 3D image in the presence of noise by maximizing the number of inliers, namely the consensus set. By using a digital model instead of a continuous one, we show that we can generate all possible consensus sets for model fitting. We present a deterministic algorithm that efficiently searches the optimal solution with time complexity O(N d log N) for dimension d, where d 5 2,3, together with space complexity O(N) where N is the number of points.
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