Background: tendon and skeletal muscle function adapts to physical training of resistive nature, but it is unknown to what extent persons with genetically altered connective tissue-who have a higher than normal tendon extensibility-will obtain any effect upon their tendon and muscle when undergoing muscle strength training. We investigated patients with classical Ehlers Danlos Syndrome (EDS) (collagen type V defect) who display articular hypermobility, skin extensibility and tissue fragility. Methods: subjects underwent strength training 3 times a week for 4 months and were tested before and after intervention in regards to muscle strength, tendon mechanical properties, and muscle function. Results: three subjects completed the scheduled 48 sessions and had no major adverse events. Mean isometric leg extension force and leg extensor power both increased by 8 and 11% respectively (358 to 397 N, and 117 to 123 W). The tendon stiffness was tested and an average increase in response to physical training, from 1795 to 2519 N/mm was found. On average, the training loads both in upper and lower body exercises increased by around 30% over the training period. When testing balance, the average sway-area of the participants decreased by 26% (0.144 to 0.108 m 2). On the subscale of CIS20 the participants lowered their average subjective fatigue score from 33 to 25. Conclusion: in this small pilot study, heavy resistance training was both feasible and effective in classic Ehlers Danlos patients, and the results indicated that both tendon and skeletal muscle properties can be improved also in this patient group when they are subjected to resistance training.
The dihydroazulene/vinylheptafulvene (DHA/VHF) photocouple is a promising candidate for molecular solar heat batteries, storing and releasing energy in a closed cycle. Much work has been done on improving the energy storage capacity and the half-life of the high-energy isomer via substituent functionalization, but similarly important is keeping these improved properties in common polar solvents, along with being soluble in these, which is tied to the dipole properties. However, the number of possible derivatives makes an overview of this combinatorial space impossible both for experimental work and traditional computational chemistry. Due to the time-consuming nature of running many thousands of computations, we look to machine learning, which bears the advantage that once a model has been trained, it can be used to rapidly estimate approximate values for the given system. Applying a convolutional neural network, we show that it is possible to reach good agreement with traditional computations on a scale that allows us to rapidly screen tens of thousands of the DHA/VHF photocouple, eliminating bad candidates and allowing computational resources to be directed toward meaningful compounds.
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