Joint Materials/Solid Mechanics Seminar Series
Georgia Institute of Technology and emory University Atlanta, GA 30332 | |
Abstract: Over the last few years molecular biomechanics has emerged as a new field in which theoretical and experimental studies of the mechanics of proteins and nucleic acids have become a focus, and the importance of mechanical forces and motions to the fundamentals of biology and biochemistry has begun to be recognized. In particular, single-molecule biomechanics of DNA, and the mechanics and mechanochemical coupling of biomolecular motors has become a new frontier in life sciences. There is an increasing need for a more systematic study of the basic issues involved in molecular biomechanics, and a more active participation of researchers in applied mechanics. In this talk I will review some of the recent advances in molecular biomechanics, explore the molecular basis of mechanotransduction, and discuss the new concepts, issues, approaches and challenges in developing molecular biomechanics.
Center for Statistical Sciences
Abstract: Researchers have become increasingly interested in exploring dynamic relationships among brain regions involved in processing a given stimulus or mental task; the term "effective connectivity" (Friston, 1994) has been coined to denote the presence of such a relationship which is directed from one region toward another. An experimental paradigm commonly used for determining effective connectivity in functional MRI (fMRI) studies is the slow event-related design, which presents stimuli at sufficient temporal spacing for determining within-trial response trajectories of BOLD activation across regions. However, while a number of analytic methods for determining effective connectivity in fMRI studies have been devised, few are tailored for the specific characteristics of slow event-related designs, which include non-stationary BOLD response across the time course of a given trial and nesting of responses within trial and within subject. We propose an adaptation of vector autoregressive models (VAR), termed "event-related mixed-effect VAR" (EMVAR) models, tailored for exploring effective connectivity of multiple brain regions in slow event-related fMRI designs. We use EMVARs to generalize Granger causality analysis to non-stationary, multiply-nested data. Connectivity coefficients are estimated via two-dimensional basis expansions and allowed to vary randomly across subjects. We apply the EMVAR model to the analysis of data obtained from an fMRI experiment examining differences in effective connectivity between clinically depressed subjects and normal controls.
Scientific Computing Seminar
PDE Seminar
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