The National Academy of Engineering selected 'Imaging' as one of the greatest engineering achievements of the 20th century (Greatest Engineering Achievements of the 20th Century. 2009 (cited 2008, November 10); available from: http://www.greatachievements.org/). The combination of different imaging modalities and technologies for mapping bimolecular and/or biological processes within single cells or even whole organs has extraordinary potential for revolutionizing the diagnosis and treatment of pathophysiological disorders, and thus for mitigating the significant social and economic costs associated with the clinical management of disease. Such integrated imaging approaches will eventually lead to individualized programs for disease prevention through advanced diagnosis, risk stratification and targeted cell therapies resulting in more successful and efficient health care. The goal of this article is to provide readers with a current update of selected of state-of-the-art imaging modalities which would likely to lead to improved clinical outcomes if employed in an integrated approach, including use of ultramicrosensors to detect reactive oxygen/nitrogen species in a single cell, use of electron tomography to visualize and characterize cellular organization in three dimensions (3D), and molecular imaging strategies to assess naturally occurring and therapeutic peripheral and myocardial angiogenesis using targeted radiolabeled tracers.
I (cTnI) that cause hypertrophic cardiomyopathy (HCM) have been reported to change the contractility of cardiac myofilaments, but the underlying molecular mechanism remains elusive. In this study, Förster resonance energy transfer (FRET) was used to investigate the specific structural and kinetic e...
We investigate the asymptotic decrease of the Wannier functions for the
Valence and conduction band of graphene, both in the monolayer and the
Multilayer case. Since the decrease of the Wannier functions is characterised
By the structure of the Bloch eigenspaces around the Dirac points, we introduce...
We study the stability properties of nonlinear multi-task regression in
Reproducing Hilbert spaces with operator-valued kernels. Such kernels, a.k.a.
Multi-task kernels, are appropriate for learning prob- lems with nonscalar
Outputs like multi-task learning and structured out- put prediction. We sho...
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