Disposition, metabolism and mass balance of delafloxacin in healthy human volunteers following intravenous administration.

1. The pharmacokinetics and disposition of delafloxacin was investigated following a single intravenous (300 mg, 100 µCi) dose to healthy male subjects. 2. Mean Cmax, AUC0-∞, Tmax and t1/2 values for delafloxacin were 8.98 µg/mL, 21.31 µg h/mL, 1 h and 2.35 h, respectively, after intravenous dosing. 3. Radioactivity was predominantly excreted via the kidney with 66% of the radioactive dose recovered in the urine. Approximately 29% of the radioactivity was recovered in the faeces, giving an overall mean recovery of 94% administered radioactivity. 4. The predominant circulating components were identified as delafloxacin and a direct glucuronide conjugate of delafloxacin.

Fast eigenspace decomposition of images of objects with variation in illumination and pose.

Many appearance-based classification problems such as principal component analysis, linear discriminant analysis, and locally preserving projections involve computing the principal components (eigenspace) of a large set of images. Although the online expense associated with appearance-based techniques is small, the offline computational burden becomes prohibitive for practical applications. This paper presents a method to reduce the expense of computing the eigenspace decomposition of a set of images when variations in both illumination and pose are present. In particular, it is shown that the set of images of an object under a wide range of illumination conditions and a fixed pose can be significantly reduced by projecting these data onto a few low-frequency spherical harmonics, producing a set of "harmonic images." It is then shown that the dimensionality of the set of harmonic images at different poses can be further reduced by utilizing the fast Fourier transform. An eigenspace decomposition is then applied in the spectral domain at a much lower dimension, thereby significantly reducing the computational expense. An analysis is also provided, showing that the principal eigenimages computed assuming a single illumination source are capable of recovering a significant amount of information from images of objects when multiple illumination sources exist.

Eigendecomposition of images correlated on S(1), S(2), and SO3 using spectral theory.

Eigendecomposition represents one computationally efficient approach for dealing with object detection and pose estimation, as well as other vision-based problems, and has been applied to sets of correlated images for this purpose. The major drawback in using eigendecomposition is the off line computational expense incurred by computing the desired subspace. This off line expense increases drastically as the number of correlated images becomes large (which is the case when doing fully general 3-D pose estimation). Previous work has shown that for data correlated on S(1), Fourier analysis can help reduce the computational burden of this off line expense. This paper presents a method for extending this technique to data correlated on S(2) as well as SO3 by sampling the sphere appropriately. An algorithm is then developed for reducing the off line computational burden associated with computing the eigenspace by exploiting the spectral information of this spherical data set using spherical harmonics and Wigner-D functions. Experimental results are presented to compare the proposed algorithm to the true eigendecomposition, as well as assess the computational savings.