Insights into mechanism kinematics for protein motion simulation

Background: The high demanding computational requirements necessary to carry out protein motion simulations make it difficult to obtain information related to protein motion. On the one hand, molecular dynamics simulation requires huge computational resources to achieve satisfactory motion simulations. On the other hand, less accurate procedures such as interpolation methods, do not generate realistic morphs from the kinematic point of view. Analyzing a protein's movement is very similar to serial robots; thus, it is possible to treat the protein chain as a serial mechanism composed of rotational degrees of freedom. Recently, based on this hypothesis, new methodologies have arisen, based on mechanism and robot kinematics, to simulate protein motion. Probabilistic roadmap method, which discretizes the protein configurational space against a scoring function, or the kinetostatic compliance method that minimizes the torques that appear in bonds, aim to simulate protein motion with a reduced computational cost. Results: In this paper a new viewpoint for protein motion simulation, based on mechanism kinematics is presented. The paper describes a set of methodologies, combining different techniques such as structure normalization normalization processes, simulation algorithms and secondary structure detection procedures. The combination of all these procedures allows to obtain kinematic morphs of proteins achieving a very good computational cost-error rate, while maintaining the biological meaning of the obtained structures and the kinematic viability of the obtained motion. Conclusions: The procedure presented in this paper, implements different modules to perform the simulation of the conformational change suffered by a protein when exerting its function. The combination of a main simulation procedure assisted by a secondary structure process, and a side chain orientation strategy, allows to obtain a fast and reliable simulations of protein motion.

Co-occurrence flow for pedestrian detection

The last few years have seen considerable progress in pedestrian detection. Recent work has established a combination of oriented gradients and optic flow as effective features although the detection rates are still unsatisfactory for practical use. This paper introduces a new type of motion feature, the co-occurrence flow (CoF). The advance is to capture relative movements of different parts of the entire body, unlike existing motion features which extract internal motion in a local fashion. Through evaluations on the TUD-Brussels pedestrian dataset, we show that our motion feature based on co-occurrence flow contributes to boost the performance of existing methods. © 2011 IEEE.

Investigation of two-proton emission from excited states of the odd-Z nucleus P-28 by complete-kinematics measurements

An experiment to study exotic two-proton emission from excited levels of the odd-Z nucleus P-28 was performed at the National Laboratory of Heavy Ion Research-Radioactive Ion Beam Line (HIRFL-RIBLL) facility. The projectile P-28 at the energy of 46.5 MeV/u was bombarding a Au-197 target to populate the excited states via Coulomb excitation. Complete-kinematics measurements were realized by the array of silicon strip detectors and the CsI + PIN telescope. Two-proton events were selected and the relativistic-kinematics reconstruction was carried out. The spectrum of relative momentum and opening angle between two protons was deduced from Monte Carlo simulations. Experimental results show that two-proton emission from P-28 excited states less than 17.0 MeV is mainly two-body sequential emission or three-body simultaneous decay in phase space. The present simulations cannot distinguish these two decay modes. No obvious diproton emission was found.

Line Kinematics for Whole-Arm Manipulation

A Whole-Arm Manipulator uses every surface to both sense and interact with the environment. To facilitate the analysis and control of a Whole-Arm Manipulator, line geometry is used to describe the location and trajectory of the links. Applications of line kinematics are described and implemented on the MIT Whole-Arm Manipulator (WAM-1).

Model Selection for Solving Kinematics Problems

There has been much interest in the area of model-based reasoning within the Artificial Intelligence community, particularly in its application to diagnosis and troubleshooting. The core issue in this thesis, simply put, is, model-based reasoning is fine, but whence the model? Where do the models come from? How do we know we have the right models? What does the right model mean anyway? Our work has three major components. The first component deals with how we determine whether a piece of information is relevant to solving a problem. We have three ways of determining relevance: derivational, situational and an order-of-magnitude reasoning process. The second component deals with the defining and building of models for solving problems. We identify these models, determine what we need to know about them, and importantly, determine when they are appropriate. Currently, the system has a collection of four basic models and two hybrid models. This collection of models has been successfully tested on a set of fifteen simple kinematics problems. The third major component of our work deals with how the models are selected.