Affected by abundant PLTP : the atherogenic role of a lipid transfer protein in transgenic mice

__Abstract__ Atherosclerosis is a progressive disease of the large and medium-sized arteries. The disease is characterised by endothelial dysfunction, inflammation and the accumulation of fatty and fibrous substances in the vessel wall, resulting in thickening and loss of elasticity of the arteries. The word atherosclerosis has been derived from the Greek words "athera", porridge or gruel, and "skleros", hard or stiff. These words describe the external features of the lipid-loaded lesions that characterize the disease. Although atherosclerosis has been discovered in blood vessels of people living more than 3000 years ago, until the end of the 18th century its prevalence was very rare. During the 20th century, mortality caused by atherosclerosis strongly increased. Nowadays, complications of atherosclerosis are the main cause of death in the developed world, and are predicted to be the leading cause of death worldwide by the year 2020 (Fonarow, 2007). It is difficult to accurately determine the true frequency of atherosclerosis because it is a predominantly asymptomatic condition (Kavey et al., 2006). Early atherosclerotic lesions can already be found in the aorta shortly after birth, increasing in number during childhood. More advanced lesions begin to develop at an age of approximately 25 years. Generally, the clinical manifestations of the disease become apparent in the sixth decade of life

What model does MuZero learn?

Model-based reinforcement learning has drawn considerable interest in recent years, given its promise to improve sample efficiency. Moreover, when using deep-learned models, it is potentially possible to learn compact models from complex sensor data. However, the effectiveness of these learned models, particularly their capacity to plan, i.e., to improve the current policy, remains unclear. In this work, we study MuZero, a well-known deep model-based reinforcement learning algorithm, and explore how far it achieves its learning objective of a value-equivalent model and how useful the learned models are for policy improvement. Amongst various other insights, we conclude that the model learned by MuZero cannot effectively generalize to evaluate unseen policies, which limits the extent to which we can additionally improve the current policy by planning with the model