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To investigate the exercise intensity effects on rats' memory and learning, animals were divided into control, moderate training (MT), and overtraining (OT) groups. At training last week, learning and memory was assessed using Morris water maze (MWM) and passive avoidance (PA) tests. Finally, the rat's brains were removed for evaluating oxidative stress and inflammatory cytokines. Overtraining impaired animal's performance in MWM and PA tests. In MT group, hippocampal levels of interleukin 1 beta (IL-1β) and malondialdehyde (MDA) increased, and thiol contents in hippocampal and cortical tissues decreased compared to control. In OT group, tumor necrosis factor α, IL-1β, and C-reactive protein hippocampal levels increased, MDA and nitric oxide metabolite in hippocampal and cortical tissues increased, thiol contents, catalase and superoxide dismutase activity in hippocampal and cortical tissues decreased compared to control and MT groups. Overtraining might lead to learning and memory impairment by increasing the inflammatory cytokine and oxidative stress markers.

Conventional energy system models have limitations in evaluating complex choices for transitioning to low-carbon energy systems and preventing catastrophic climate change. To address this challenge, we propose a model that allows for the exploration of a broader design space. We develop a supervised machine learning surrogate of a capacity expansion model, based on residual neural networks, that accurately approximates the model’s outputs while reducing the computation cost by five orders of magnitude. This increased efficiency enables the evaluation of the sensitivity of the outputs to the inputs, providing valuable insights into system development factors for the Canadian electricity system between 2030 and 2050. To facilitate the interpretation and communication of a large number of surrogate model results, we propose an easy-to-interpret method using an unsupervised machine learning technique. Our analysis identified key factors and quantified their relationships, showing that the carbon tax and wind energy capital cost are the most impactful factors on emissions in most provinces, and are 2 to 4 times more impactful than other factors on the development of wind and natural gas generations nationally. Our model generates insights that deepen our understanding of the most impactful decarbonization policy interventions.