Abstract: Near-infrared fluorescence tomography using molecularly targeted lifetime-sensitive, fluorescent contrast agents have applications for early-stage cancer diagnostics. Yet, although the measurement of fluorescent lifetime imaging microscopy (FLIM) is extensively used in microscopy and spectroscopy applications, demonstration of fluorescence lifetime tomography for medical imaging is limited to two-dimensional studies. Herein, the feasibility of three-dimensional fluorescence-lifetime tomography on clinically relevant phantom volumes is established, using (i) a gain- modulated intensified charge coupled device (CCD) and modulated laser diode imaging system, (ii) two fluorescent contrast agents, e.g., Indocyanine green and 3-3'-Diethylthiatricarbocyanine iodide differing in their fluorescence lifetime by 0.62 ns, and (iii) a two stage approximate extended Kalman filter reconstruction algorithm. Fluorescence measurements of phase and amplitude were acquired on the phantom surface under different target to background fluorescence absorption (70:1, 100:1) and fluorescence lifetime (1:1, 2.1:1) contrasts at target depths of 1.4–2 cm. The Bayesian tomography algorithm was employed to obtain three-dimensional images of lifetime and absorption owing to the fluorophores.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).