Conformational Free-Energy Landscapes for a Peptide in Saline Environments

AbstractThe conformations that proteins adopt in solution are a function of both their primary structure and surrounding aqueous environment. Recent experimental and computational work on small peptides, e.g., polyK, polyE, and polyR, have highlighted an interesting and unusual behavior in the presence of aqueous ions such as ClO4−, Na+, and K+. Notwithstanding the aforementioned studies, as of this writing, the nature of the driving force induced by the presence of ions and its role on the conformational stability of peptides remains only partially understood. Molecular-dynamics simulations have been performed on the heptapeptide AEAAAEA in NaCl and KCl solutions at concentrations of 0.5, 1.0, and 2.0 M. Metadynamics in conjunction with a three-dimensional model reaction coordinate was used to sample the conformational space of the peptide. All simulations were run for 2 μs. Free-energy landscapes were computed over the model reaction coordinate for the peptide in each saline assay as well as in the absence of ions. Circular dichroism spectra were also calculated from each trajectory. In the presence of Na+ and K+ ions, no increase in helicity is observed with respect to the conformation in pure water

Optical flow estimation using discontinuity conforming filters

The discontinuities and the large image displacements pose some of the hardest problems in flow estimation. This paper uses a set of filters that change shape to avoid blending of the constraints across discontinuity boundaries. This is done by using an incompatibility measure of the constraints of neighbouring pixels. The algorithm is embedded in a coarse to fine multigrid scheme to address the problem of large displacements. We report results on real images which show that the algorithm works very well. The support of the NSERC (App. No. OGP0046645) is gratefully acknowledged. 1

Tracking Based Motion Segmentation under Relaxed Statistical Assumptions

Many Computer Vision algorithms employ the sum of pixel-wise squared differences between two patches as a statistical measure of similarity. This silently assumes that the noise in every pixel is independent. We present a method that involves a much more general noise model with relaxed independence assumptions but without significant increase in the computational requirements. We apply this technique to the problem of motion segmentation that uses tracking to estimate the motion of each region and then we employ our statistic to classify every pixel as part of a segment or the background. We tested several versions of the algorithm on a variety of image sequences (indoor and outdoor, real and synthetic, constant and varying lighting, stationary and moving camera, one of them with known ground truth) with very good results

1 An Adaptive-Sampling Algorithm for Object Representation

We present a novel adaptive-sampling algorithm for spectral signature generation. This algorithm is designed to increase inter-object discrimination and reduce featurevector dimensionality. Our algorithm is applied to a Gaborfeature based multi-resolutional object detection and recognition scheme. In this context we study and analyze the detection and identification of unknown objects in a complex background. Iterative, off-line optimization methods are employed to reduce computational demands during the learning phase. Our representation scheme takes into account all items in a given object library. It selects sample-point sets that maximize the inter-object distance. Thus, the presented method increases identification robustness and can reduce the size of signature vectors

Understanding noise sensitivity in structure from motion

Solutions to the structure from motion problem have been shown to be very sensitive to measurement noise and the respective motion and geometry configuration. Statistical error analysis has become an invaluable tool in analyzing the sensitivity phenomenon. This paper presents a unifying approach to the problems of statistical bias, correlated noise, choice of error metrics, geometric instabilities and information fusion exploring several assumptions commonly used in motion estimation and reviews several promising techniques for motion estimation. The techniques are based on a small number of principles of statistics and perturbation theory. The analyticity of the approach enables the design of alternatives overcoming the observed instabilities

Motion Segmentation and Tracking

This paper presents a noveltracking based motion segmentation algorithm. The tracking is done by fitting successively more elaborate models of optical flowonthe tracked region and the segmentation is done by extracting the regions of the image that are consistent with the computed model of flow. The method can track objects in image sequences with moving background, taken by a hand-held camera, tolerate up to 30 pixels interframe motion and takes 0.3 seconds per frame pair of size 320 x 240 pixels on a 500 Mhz Sun Blade 100 workstation