ABSTRACT
Atherosclerosis is the underlying cause of cardiovascular diseases that are responsible for many deaths in the world, and the early diagnosis of atherosclerosis is highly desirable. The existing imaging methods, however, are not capable of detecting the early stage of atherosclerosis development due to their limited spatial resolution. Using piezoresponse force microscopy (PFM), we show that the piezoelectric response of an aortic wall increases as atherosclerosis advances, while the stiffness of the aorta shows a less evident correlation with atherosclerosis. Furthermore, we show that there is strong correlation between the coercive electric field necessary to switch the polarity of the artery and the development of atherosclerosis. Thus by measuring the electromechanical coupling of the aortic wall, it is possible to probe atherosclerosis at the early stage of its development, not only improving the spatial resolution by orders of magnitude, but also providing comprehensive quantitative information on the biomechanical properties of the artery.
Subject(s)
Aorta/pathology , Aorta/physiopathology , Atherosclerosis/pathology , Atherosclerosis/physiopathology , Animals , Apolipoproteins E/genetics , Atherosclerosis/genetics , Biomechanical Phenomena , Disease Progression , Electrochemistry , Mice , Mice, Inbred C57BL , Mice, Knockout , Microscopy, Atomic ForceABSTRACT
We evaluate the thermodynamic properties of the 4-state antiferromagnetic Potts model on the Union-Jack lattice using tensor-based numerical methods. We present strong evidence for a previously unknown, "entropy-driven," finite-temperature phase transition to a partially ordered state. From the thermodynamics of Potts models on the diced and centered diced lattices, we propose that finite-temperature transitions and partially ordered states are ubiquitous on irregular lattices.
ABSTRACT
We propose a second renormalization group method to handle the tensor-network states or models. This method dramatically reduces the truncation error of the tensor renormalization group. It allows physical quantities of classical tensor-network models or tensor-network ground states of quantum systems to be accurately and efficiently determined.
