Distinct Unfolding and Refolding Pathways of Ribonuclease A Revealed by Heating and Cooling Temperature Jumps

Heating and cooling temperature jumps (T-jumps) were performed using a newly developed technique to trigger unfolding and refolding of wild-type ribonuclease A and a tryptophan-containing variant (Y115W). From the linear Arrhenius plots of the microscopic folding and unfolding rate constants, activation enthalpy (ΔH#), and activation entropy (ΔS#) were determined to characterize the kinetic transition states (TS) for the unfolding and refolding reactions. The single TS of the wild-type protein was split into three for the Y115W variant. Two of these transition states, TS1 and TS2, characterize a slow kinetic phase, and one, TS3, a fast phase. Heating T-jumps induced protein unfolding via TS2 and TS3; cooling T-jumps induced refolding via TS1 and TS3. The observed speed of the fast phase increased at lower temperature, due to a strongly negative ΔH# of the folding-rate constant. The results are consistent with a path-dependent protein folding/unfolding mechanism. TS1 and TS2 are likely to reflect X-Pro114 isomerization in the folded and unfolded protein, respectively, and TS3 the local conformational change of the β-hairpin comprising Trp115. A very fast protein folding/unfolding phase appears to precede both processes. The path dependence of the observed kinetics is suggestive of a rugged energy protein folding funne

A proposal to estimate the motion of an underwater vehicle through visual mosaicking

This thesis proposes a solution to the problem of estimating the motion of an Unmanned Underwater Vehicle (UUV). Our approach is based on the integration of the incremental measurements which are provided by a vision system. When the vehicle is close to the underwater terrain, it constructs a visual map (so called "mosaic") of the area where the mission takes place while, at the same time, it localizes itself on this map, following the Concurrent Mapping and Localization strategy. The proposed methodology to achieve this goal is based on a feature-based mosaicking algorithm. A down-looking camera is attached to the underwater vehicle. As the vehicle moves, a sequence of images of the sea-floor is acquired by the camera. For every image of the sequence, a set of characteristic features is detected by means of a corner detector. Then, their correspondences are found in the next image of the sequence. Solving the correspondence problem in an accurate and reliable way is a difficult task in computer vision. We consider different alternatives to solve this problem by introducing a detailed analysis of the textural characteristics of the image. This is done in two phases: first comparing different texture operators individually, and next selecting those that best characterize the point/matching pair and using them together to obtain a more robust characterization. Various alternatives are also studied to merge the information provided by the individual texture operators. Finally, the best approach in terms of robustness and efficiency is proposed. After the correspondences have been solved, for every pair of consecutive images we obtain a list of image features in the first image and their matchings in the next frame. Our aim is now to recover the apparent motion of the camera from these features. Although an accurate texture analysis is devoted to the matching pro-cedure, some false matches (known as outliers) could still appear among the right correspon-dences. For this reason, a robust estimation technique is used to estimate the planar transformation (homography) which explains the dominant motion of the image. Next, this homography is used to warp the processed image to the common mosaic frame, constructing a composite image formed by every frame of the sequence. With the aim of estimating the position of the vehicle as the mosaic is being constructed, the 3D motion of the vehicle can be computed from the measurements obtained by a sonar altimeter and the incremental motion computed from the homography. Unfortunately, as the mosaic increases in size, image local alignment errors increase the inaccuracies associated to the position of the vehicle. Occasionally, the trajectory described by the vehicle may cross over itself. In this situation new information is available, and the system can readjust the position estimates. Our proposal consists not only in localizing the vehicle, but also in readjusting the trajectory described by the vehicle when crossover information is obtained. This is achieved by implementing an Augmented State Kalman Filter (ASKF). Kalman filtering appears as an adequate framework to deal with position estimates and their associated covariances. Finally, some experimental results are shown. A laboratory setup has been used to analyze and evaluate the accuracy of the mosaicking system. This setup enables a quantitative measurement of the accumulated errors of the mosaics created in the lab. Then, the results obtained from real sea trials using the URIS underwater vehicle are shown.