34 research outputs found
Genetic variation and exercise-induced muscle damage: implications for athletic performance, injury and ageing.
Prolonged unaccustomed exercise involving muscle lengthening (eccentric) actions can result in ultrastructural muscle disruption, impaired excitation-contraction coupling, inflammation and muscle protein degradation. This process is associated with delayed onset muscle soreness and is referred to as exercise-induced muscle damage. Although a certain amount of muscle damage may be necessary for adaptation to occur, excessive damage or inadequate recovery from exercise-induced muscle damage can increase injury risk, particularly in older individuals, who experience more damage and require longer to recover from muscle damaging exercise than younger adults. Furthermore, it is apparent that inter-individual variation exists in the response to exercise-induced muscle damage, and there is evidence that genetic variability may play a key role. Although this area of research is in its infancy, certain gene variations, or polymorphisms have been associated with exercise-induced muscle damage (i.e. individuals with certain genotypes experience greater muscle damage, and require longer recovery, following strenuous exercise). These polymorphisms include ACTN3 (R577X, rs1815739), TNF (-308 G>A, rs1800629), IL6 (-174 G>C, rs1800795), and IGF2 (ApaI, 17200 G>A, rs680). Knowing how someone is likely to respond to a particular type of exercise could help coaches/practitioners individualise the exercise training of their athletes/patients, thus maximising recovery and adaptation, while reducing overload-associated injury risk. The purpose of this review is to provide a critical analysis of the literature concerning gene polymorphisms associated with exercise-induced muscle damage, both in young and older individuals, and to highlight the potential mechanisms underpinning these associations, thus providing a better understanding of exercise-induced muscle damage
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Multi-agent cooperative systems applied to precision applications
Regulatory agencies are imposing limits and constraints to protect the operator and/or the environment. While generally necessary, these controls also tend to increase cost and decrease efficiency and productivity. Intelligent computer systems can be made to perform these hazardous tasks with greater efficiency and precision without danger to the operators. The Idaho national Engineering and Environmental Laboratory and the Center for Self-Organizing and Intelligent Systems at Utah State University have developed a series of autonomous all-terrain multi-agent systems capable of performing automated tasks within hazardous environments. This paper discusses the development and application of cooperative small-scale and large-scale robots for use in various activities associated with radiologically contaminated areas, prescription farming, and unexploded ordinances
Software Pipelining via Stochastic Search Algorithms
The scheduling of loops for architectures which support instruction level parallelism is an important area of research. Many polynomial time, heuristic algorithms for software pipelining have been proposed for this NPcomplete problem. In this research, genetic algorithms and simulated annealing are used to test the feasibility of applying artificial intelligence techniques to the problem of software pipelining. Both algorithms are iterative search algorithms which adjust their response based on feedback from the fitness function. Results indicate these techniques are superior to deterministic polynomial time algorithms. Key Words: software pipelining, instruction-level parallelism, Petri nets, cyclic scheduling, artificial intelligence, genetic algorithm, simulated annealing. 1 Introduction Frequently code optimization concentrates on loops as loop execution dominates the total execution time of most programs. Software pipelining is a loop optimization technique in which the body of t..
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Mobile Robotic Teams Applied to Precision Agriculture
The Idaho National Engineering and Environmental Laboratory (INEEL) and Utah State University�s Center for Self-Organizing and Intelligent Systems (CSOIS) have developed a team of autonomous robotic vehicles applicable to precision agriculture. A unique technique has been developed to plan, coordinate, and optimize missions in large structured environments for these autonomous vehicles in real-time. Two generic tasks are supported: 1) Driving to a precise location, and 2) Sweeping an area while activating on-board equipment. Sensor data and task achievement data is shared among the vehicles enabling them to cooperatively adapt to changing environmental, vehicle, and task conditions. This paper discusses the development of the autonomous robotic team, details of the mission-planning algorithm, and successful field demonstrations at the INEEL