Individuals born preterm show reduced exercise capacity and increased risk for pulmonary and cardiovascular diseases, but the impact of preterm birth on skeletal muscle, an inherently critical part of cardiorespiratory fitness, remains unknown. We evaluated the impacts of preterm birth-related conditions on the development, growth, and function of skeletal muscle using a recognized preclinical rodent model in which newborn rats are exposed to 80% oxygen from day 3 to 10 of life. We analyzed different hindlimb muscles of male and female rats at 10 days (neonatal), 4 weeks (juvenile) and 16 weeks (young adults). Neonatal high oxygen exposure increased the generation of reactive oxygen species and the signs of inflammation in skeletal muscles, which was associated with muscle fiber atrophy, fiber type shifting (reduced proportion of type I slow fibers and increased proportion of type IIb fast-fatigable fibers), and impairment in muscle function. These effects were maintained until adulthood. Fast-twitch muscles were more vulnerable to the effects of hyperoxia than slow-twitch muscles. Male rats, which expressed lower antioxidant defenses, were more susceptible than females to oxygen-induced myopathy. Overall, preterm birth-related conditions have long-lasting effects on the composition, morphology, and function of skeletal muscles; and these effects are sex-specific. Oxygen-induced changes in skeletal muscles could contribute to the reduced exercise capacity and to increased risk of diseases of preterm born individuals.
Feature selection is essential for effective visual recognition. We propose an efficient joint classifier learning and feature selection method that discovers sparse, compact representations of input features from a vast sea of candidates, with an almost unsupervised formulation. Our method requires only the following knowledge, which we call the feature sign-whether or not a particular feature has on average stronger values over positive samples than over negatives. We show how this can be estimated using as few as a single labeled training sample per class. Then, using these feature signs, we extend an initial supervised learning problem into an (almost) unsupervised clustering formulation that can incorporate new data without requiring ground truth labels. Our method works both as a feature selection mechanism and as a fully competitive classifier. It has important properties, low computational cost and excellent accuracy, especially in difficult cases of very limited training data. We experiment on large-scale recognition in video and show superior speed and performance to established feature selection approaches such as AdaBoost, Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.
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