3,4,7,8-Tetra­methyl-1,10-phenanthrolin-1-ium nitrate monohydrate

In the crystal of the title compound, C16H17N2 +·NO3 −·H2O, the tetra­methyl-1,10-phenanthrolinium cations, nitrate anions and lattice water mol­ecules are all located on a mirror plane with the methyl H atoms of the cation equally disordered over two sites about the mirror plane. The cation, anion and water mol­ecule are linked by O—H⋯O and N—H⋯O hydrogen bonds into a sheet parallel to the bc plane. π–π stacking between phenanthroline ring systems is observed in the crystal structure, the centroid–centroid distance being 3.4745 (6) Å

A Phantom Study on Target Localization Accuracy Using Cone-Beam Computed Tomography

The purpose of this study is to evaluate the 3-dimensional target localization accuracy of cone-beam computed tomography (CBCT) using an on-board imager (OBI). An anthropomorphic pelvis phantom was used to simulate a range of offsets in the three translational directions and rotations around each of the three axes. After a translational or rotational offset was applied, a CBCT scan of the phantom was followed by image registration to detect the offsets in six degrees. The detected offsets were compared to the offset actually applied to give the detection error of the phantom position. Afterwards, the phantom was positioned by automatically moving the couch based on the detected offsets. A second CBCT scan followed by image registration was performed to give the residual error of the phantom positioning. On the average the detection errors and their standard deviations along the lateral, longitudinal and vertical axis are 0.3 ± 0.1, 0.3 ± 0.1 and 0.4 ± 0.1 mm respectively with respect to translational shifts ranging from 0 to 10 mm. The corresponding residual errors after positioning are 0.3 ± 0.1, 0.5 ± 0.1 and 0.3 ± 0.1 mm. For simulated rotational shifts ranging from 0 to 5 degrees, the average detection error and their standard deviation around lateral, longitudinal, and vertical axes are 0.1 ± 0.0, 0.2 ± 0.0, and 0.2 ± 0.0 degrees respectively. The residual errors after positioning are 0.4 ± 0.1, 0.6 ± 0.1, and 0.3 ± 0.1 mm along the lateral, longitudinal and vertical directions. These results indicate that target localization based on CBCT is capable of achieving sub-millimeter accuracy

Applications of graph theory in protein structure identification

There is a growing interest in the identification of proteins on the proteome wide scale. Among different kinds of protein structure identification methods, graph-theoretic methods are very sharp ones. Due to their lower costs, higher effectiveness and many other advantages, they have drawn more and more researchers’ attention nowadays. Specifically, graph-theoretic methods have been widely used in homology identification, side-chain cluster identification, peptide sequencing and so on. This paper reviews several methods in solving protein structure identification problems using graph theory. We mainly introduce classical methods and mathematical models including homology modeling based on clique finding, identification of side-chain clusters in protein structures upon graph spectrum, and de novo peptide sequencing via tandem mass spectrometry using the spectrum graph model. In addition, concluding remarks and future priorities of each method are given