Structure-based discovery of fiber-binding compounds that reduce the cytotoxicity of amyloid beta.

Amyloid protein aggregates are associated with dozens of devastating diseases including Alzheimer's, Parkinson's, ALS, and diabetes type 2. While structure-based discovery of compounds has been effective in combating numerous infectious and metabolic diseases, ignorance of amyloid structure has hindered similar approaches to amyloid disease. Here we show that knowledge of the atomic structure of one of the adhesive, steric-zipper segments of the amyloid-beta (Aβ) protein of Alzheimer's disease, when coupled with computational methods, identifies eight diverse but mainly flat compounds and three compound derivatives that reduce Aβ cytotoxicity against mammalian cells by up to 90%. Although these compounds bind to Aβ fibers, they do not reduce fiber formation of Aβ. Structure-activity relationship studies of the fiber-binding compounds and their derivatives suggest that compound binding increases fiber stability and decreases fiber toxicity, perhaps by shifting the equilibrium of Aβ from oligomers to fibers. DOI:http://dx.doi.org/10.7554/eLife.00857.001

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

New frontiers in international strategy

This paper is based on a panel we organized at the "First Annual Conference on Emerging Research Frontiers in International Business Studies", organized by the Journal of International Business Studies (JIBS), to discuss several new lines of research in international strategy. Four lines of research are developed: The strategic implications of semiglobalization, the impact of institutional voids in international strategy, primitives and levels of analysis in international business, and strategies for the base of the pyramid. Taken together, these studies aim to open a new frontier in our understanding of International Strategy, based on the co-location of firms as places and a key element in international business.international business; semiglobalization; internal strategy; base pyramid; institutions; competitiveness;

System Architecture Design Using Multi-Criteria Optimization

System architecture is defined as the description of a complex system in terms of its functional requirements, physical elements and their interrelationships. Designing a complex system architecture can be a difficult task involving multi-faceted trade-off decisions. The system architecture designs often have many project-specific goals involving mix of quantitative and qualitative criteria and a large design trade space. Several tools and methods have been developed to support the system architecture design process in the last few decades. However, many conventional problem solving techniques face difficulties in dealing with complex system design problems having many goals. In this research work, an interactive multi-criteria design optimization framework is proposed for solving many-objective system architecture design problems and generating a well distributed set of Pareto optimal solutions for these problems. System architecture design using multi-criteria optimization is demonstrated using a real-world application of an aero engine health management (EHM) system. A design process is presented for the optimal deployment of the EHM system functional operations over physical architecture subsystems. The EHM system architecture design problem is formulated as a multi-criteria optimization problem. The proposed methodology successfully generates a well distributed family of Pareto optimal architecture solutions for the EHM system, which provides valuable insights into the design trade-offs. Uncertainty analysis is implemented using an efficient polynomial chaos approach and robust architecture solutions are obtained for the EHM system architecture design. Performance assessment through evaluation of benchmark test metrics demonstrates the superior performance of the proposed methodology

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural architecture search (NAS), as an emerging field, has demonstrated its capabilities in discovering powerful neural network architectures, which motivates us to explore its potential for CTR predictions. Due to 1) diverse unstructured feature interactions, 2) heterogeneous feature space, and 3) high data volume and intrinsic data randomness, it is challenging to construct, search, and compare different architectures effectively for recommendation models. To address these challenges, we propose an automated interaction architecture discovering framework for CTR prediction named AutoCTR. Via modularizing simple yet representative interactions as virtual building blocks and wiring them into a space of direct acyclic graphs, AutoCTR performs evolutionary architecture exploration with learning-to-rank guidance at the architecture level and achieves acceleration using low-fidelity model. Empirical analysis demonstrates the effectiveness of AutoCTR on different datasets comparing to human-crafted architectures. The discovered architecture also enjoys generalizability and transferability among different datasets