Automatic image processing methods are a prerequisite to efficiently analyze the large amount of image data produced by computed tomography (CT) scanners during cardiac exams. This paper introduces a model-based approach for the fully automatic segmentation of the whole heart (four chambers, myocardium, and great vessels) from 3-D CT images. Model adaptation is done by progressively increasing the degrees-of-freedom of the allowed deformations. This improves convergence as well as segmentation accuracy. The heart is first localized in the image using a 3-D implementation of the generalized Hough transform. Pose misalignment is corrected by matching the model to the image making use of a global similarity transformation. The complex initialization of the multicompartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model. Finally, a deformable adaptation is performed to accurately match the boundaries of the patient's anatomy. A mean surface-to-surface error of 0.82 mm was measured in a leave-one-out quantitative validation carried out on 28 images. Moreover, the piecewise affine transformation introduced for mesh initialization and adaptation shows better interphase and interpatient shape variability characterization than commonly used principal component analysis.

We have developed a rapid semiquantitative model for evaluating the relative susceptibilities of different sites on drug molecules to metabolism by cytochrome P450 3A4. The model is based on the energy necessary to remove a hydrogen radical from each site, plus the surface area exposure of the hydrogen atom. The energy of hydrogen radical abstraction is conventionally measured by AM1 semiempirical molecular orbital calculations. AM1 calculations show the following order of radical stabilities for the hydrogen atom abstractions: sp2 centers > heteroatom sp3 centers > carbon sp3 centers. Since AM1 calculations are too time intensive for routine work, we developed a statistical trend vector model, which is used to estimate the AM1 abstraction energy of a hydrogen atom from its local atomic environment. We carried out AM1 and trend vector calculations on 50 CYP3A4 substrates whose major sites of metabolism are known in the literature. A plot of the lowest hydrogen radical formation energy versus its sterically accessible surface area exposure for these 50 substrates shows that only those hydrogen atoms with solvent accessible surface area exposure > or = 8.0 A(2) are susceptible to CYP3A4-mediated metabolism. This approach forms the basis for our general model, which predicts sites on drugs that are susceptible to cytochrome P450 3A4-mediated hydrogen radical abstraction followed by a hydroxylation reaction. This model, in conjunction with specific enzyme site binding requirements, can aid in identifying possible sites of metabolism catalyzed by other cytochrome P450 enzymes.

Cytochromes P450 3A4, 2D6, and 2C9 metabolize a large fraction of drugs. Knowing where these enzymes will preferentially oxidize a molecule, the regioselectivity, allows medicinal chemists to plan how best to block its metabolism. We present QSAR-based regioselectivity models for these enzymes calibrated against compiled literature data of drugs and drug-like compounds. These models are purely empirical and use only the structures of the substrates, in contrast to those models that simulate a specific mechanism like hydrogen radical abstraction, and/or use explicit models of active sites. Our most predictive models use three substructure descriptors and two physical property descriptors. Descriptor importances from the random forest QSAR method show that other factors than the immediate chemical environment and the accessibility of the hydrogen affect regioselectivity in all three isoforms. The cross-validated predictions of the models are compared to predictions from our earlier mechanistic model (Singh et al. J. Med. Chem. 2003, 46, 1330-1336) and predictions from MetaSite (Cruciani et al. J. Med. Chem. 2005, 48, 6970-6979).

Abstract-Modern mobile and embedded devices are required to be increasingly energy-efficient while running more sophisticated tasks, causing the CPU design to become more complex and employ more energy-saving techniques. This has created a greater need for fast and accurate power estimation frameworks for both run-time CPU energy management and design-space exploration. We present a statistically rigorous and novel methodology for building accurate run-time power models using Performance Monitoring Counters (PMCs) for mobile and embedded devices, and demonstrate how our models make more efficient use of limited training data and better adapt to unseen scenarios by uniquely considering stability. Our robust model formulation reduces multicollinearity, allows separation of static and dynamic power, and allows a 100× reduction in experiment time while sacrificing only 0.6% accuracy. We present a statistically detailed evaluation of our model, highlighting and addressing the problem of heteroscedasticity in power modeling. We present software implementing our methodology and build power models for ARM Cortex-A7 and Cortex-A15 CPUs, with 3.8% and 2.8% average error, respectively. We model the behavior of the nonideal CPU voltage regulator under dynamic CPU activity to improve modeling accuracy by up to 5.5% in situations where the voltage cannot be measured. To address the lack of research utilizing PMC data from real mobile devices, we also present our data acquisition method and experimental platform software. We support this work with online resources including software tools, documentation, raw data and further results.