Arterial stiffness and cardiovascular risk factors

Background: As the human body ages, the arteries gradually lose their elasticity and become stiffer. Although inevitable, this process is influenced by hereditary and environmental factors. Interestingly, many classic cardiovascular risk factors affect the arterial stiffness. During the last decade, accelerated arterial stiffening has been recognized as an important cardiovascular risk factor associated with increased mortality as well as with several chronic disorders. Objectives: This thesis examines the role of arterial stiffness in relation to variations in a physiological feature in healthy individuals. In addition, the effect on arterial stiffness of an acute transitory disease and the effect of a chronic disease are studied. Furthermore, the thesis analyzes the prognostic value of a marker of arterial stiffness in individuals with chronic disease. Finally, a potential method of reducing arterial stiffness is evaluated. Material and study design: The first study examines pulse wave reflection and pulse wave velocity in relation to muscle fibre distribution in healthy middle-aged men. In the second study, pulse wave reflection in women with current or previous preeclampsia is compared to a healthy control group. The effect of aging on the different blood pressure indices in patients with type 1 diabetes is examined in the third study, whereas the fourth paper studies the relation between these blood pressure indices and mortality in type 2 diabetes. The fifth study evaluates how intake of a fermented milk product containing bioactive peptides affects pulse wave reflection in individuals with mild hypertension. Results and conclusions: Muscle fibre type distribution is not an independent determinant of arterial stiffness in middle-aged males. Pulse wave reflection is increased in pregnant women with preeclampsia, but not in previously preeclamptic non-pregnant women. Patients with type 1 diabetes have a higher and more rapidly increasing pulse pressure, which suggests accelerated arterial stiffening. In elderly type 2 diabetic patients, very high and very low levels of pulse pressure are associated with higher mortality. Intake of milk-derived bioactive peptides reduces pulse wave reflection in hypertensive males but not in hypertensive females.

On Random Planar Curves and Their Scaling Limits

Planar curves arise naturally as interfaces between two regions of the plane. An important part of statistical physics is the study of lattice models. This thesis is about the interfaces of 2D lattice models. The scaling limit is an infinite system limit which is taken by letting the lattice mesh decrease to zero. At criticality, the scaling limit of an interface is one of the SLE curves (Schramm-Loewner evolution), introduced by Oded Schramm. This family of random curves is parametrized by a real variable, which determines the universality class of the model. The first and the second paper of this thesis study properties of SLEs. They contain two different methods to study the whole SLE curve, which is, in fact, the most interesting object from the statistical physics point of view. These methods are applied to study two symmetries of SLE: reversibility and duality. The first paper uses an algebraic method and a representation of the Virasoro algebra to find common martingales to different processes, and that way, to confirm the symmetries for polynomial expected values of natural SLE data. In the second paper, a recursion is obtained for the same kind of expected values. The recursion is based on stationarity of the law of the whole SLE curve under a SLE induced flow. The third paper deals with one of the most central questions of the field and provides a framework of estimates for describing 2D scaling limits by SLE curves. In particular, it is shown that a weak estimate on the probability of an annulus crossing implies that a random curve arising from a statistical physics model will have scaling limits and those will be well-described by Loewner evolutions with random driving forces.

Rolling tachyons, random matrices, and Coulomb gas thermodynamics

Time-dependent backgrounds in string theory provide a natural testing ground for physics concerning dynamical phenomena which cannot be reliably addressed in usual quantum field theories and cosmology. A good, tractable example to study is the rolling tachyon background, which describes the decay of an unstable brane in bosonic and supersymmetric Type II string theories. In this thesis I use boundary conformal field theory along with random matrix theory and Coulomb gas thermodynamics techniques to study open and closed string scattering amplitudes off the decaying brane. The calculation of the simplest example, the tree-level amplitude of n open strings, would give us the emission rate of the open strings. However, even this has been unknown. I will organize the open string scattering computations in a more coherent manner and will argue how to make further progress.

Rigorous scaling law for the heat current in disordered harmonic chain

We study the energy current in a model of heat conduction, first considered in detail by Casher and Lebowitz. The model consists of a one-dimensional disordered harmonic chain of n i.i.d. random masses, connected to their nearest neighbors via identical springs, and coupled at the boundaries to Langevin heat baths, with respective temperatures T_1 and T_n. Let EJ_n be the steady-state energy current across the chain, averaged over the masses. We prove that EJ_n \sim (T_1 - T_n)n^{-3/2} in the limit n \to \infty, as has been conjectured by various authors over the time. The proof relies on a new explicit representation for the elements of the product of associated transfer matrices.

Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.