Mission-critical systems often require some testing to occur while the system is running. In many cases, this involves taking parts of the system off-line temporarily to apply the tests. However, hazards that occur during regular processor execution require the addition of stall cycles to maintain program correctness. These stall cycles generally perform no other function. In this paper, we focus on testing the ALU during those stall cycles to identify new errors or defects that arise during program execution due to aging and increased temperature that may slow down the circuitry or cause permanent defects. We investigate the time to detection of a fault (both stuck-at and transition) that may have caused silent data corruption. In addition, we identify the relationship between the programs running and the list of functional faults and how this impacts the test set length. Finally, we discuss area and performance impacts for the physical implementation of the approach.

BACKGROUND: Conformational transitions are implicated in the biological function of many proteins. Structural changes in proteins can be described approximately as the relative movement of rigid domains against each other. Despite previous efforts, there is a need to develop new domain segmentation algorithms that are capable of analysing the entire structure database efficiently and do not require the choice of protein-dependent tuning parameters such as the number of rigid domains. RESULTS: We develop a graph-based method for detecting rigid domains in proteins. Structural information from multiple conformational states is represented by a graph whose nodes correspond to amino acids. Graph clustering algorithms allow us to reduce the graph and run the Viterbi algorithm on the associated line graph to obtain a segmentation of the input structures into rigid domains. In contrast to many alternative methods, our approach does not require knowledge about the number of rigid domains. Moreover, we identified default values for the algorithmic parameters that are suitable for a large number of conformational ensembles. We test our algorithm on examples from the DynDom database and illustrate our method on various challenging systems whose structural transitions have been studied extensively. CONCLUSIONS: The results strongly suggest that our graph-based algorithm forms a novel framework to characterize structural transitions in proteins via detecting their rigid domains. The web server is available at http://azifi.tz.agrar.uni-goettingen.de/webservice/ .

MOTIVATION: Large-scale conformational changes in proteins are implicated in many important biological functions. These structural transitions can often be rationalized in terms of relative movements of rigid domains. There is a need for objective and automated methods that identify rigid domains in sets of protein structures showing alternative conformational states. RESULTS: We present a probabilistic model for detecting rigid-body movements in protein structures. Our model aims to approximate alternative conformational states by a few structural parts that are rigidly transformed under the action of a rotation and a translation. By using Bayesian inference and Markov chain Monte Carlo sampling, we estimate all parameters of the model, including a segmentation of the protein into rigid domains, the structures of the domains themselves, and the rigid transformations that generate the observed structures. We find that our Gibbs sampling algorithm can also estimate the optimal number of rigid domains with high efficiency and accuracy. We assess the power of our method on several thousand entries of the DynDom database and discuss applications to various complex biomolecular systems. AVAILABILITY AND IMPLEMENTATION: The Python source code for protein ensemble analysis is available at: https://github.com/thachnguyen/motion_detection CONTACT: : [email protected].

Since introducing Internet-based distance education programs in 1996, Drexel University has gained recognition as an online education leader. Remaining at the vanguard means finding innovative, automated solutions to determine which students are contributing to thoughtful discussion, helping faculty engage with online students more efficiently, and spending less time managing ever more complex Learning Management Systems (LMS). We introduce ForumDash, a BBLearn plugin for the Blackboard LMS1, designed to enhance online learning. Through its three visualization tools, ForumDash shows instructors which students are contributing, struggling, or distracted, thereby helping instructors target their efforts, save time managing online courses, and scale course tools up to the level of Massive Open Online Courses (MOOCs). ForumDash also provides students with performance feedback, showing them whether their participation levels are satisfactory. Initial testing with two Drexel University Online courses produced positive feedback, and larger scale testing is in progress.