High Heritability Is Compatible with the Broad Distribution of Set Point Viral Load in HIV Carriers.

Set point viral load in HIV patients ranges over several orders of magnitude and is a key determinant of disease progression in HIV. A number of recent studies have reported high heritability of set point viral load implying that viral genetic factors contribute substantially to the overall variation in viral load. The high heritability is surprising given the diversity of host factors associated with controlling viral infection. Here we develop an analytical model that describes the temporal changes of the distribution of set point viral load as a function of heritability. This model shows that high heritability is the most parsimonious explanation for the observed variance of set point viral load. Our results thus not only reinforce the credibility of previous estimates of heritability but also shed new light onto mechanisms of viral pathogenesis

Discrete Fracture Model with Anisotropic Load Sharing

A two-dimensional fracture model where the interaction among elements is modeled by an anisotropic stress-transfer function is presented. The influence of anisotropy on the macroscopic properties of the samples is clarified, by interpolating between several limiting cases of load sharing. Furthermore, the critical stress and the distribution of failure avalanches are obtained numerically for different values of the anisotropy parameter $\alpha$ and as a function of the interaction exponent $\gamma$. From numerical results, one can certainly conclude that the anisotropy does not change the crossover point $\gamma_c=2$ in 2D. Hence, in the limit of infinite system size, the crossover value $\gamma_c=2$ between local and global load sharing is the same as the one obtained in the isotropic case. In the case of finite systems, however, for $\gamma\le2$, the global load sharing behavior is approached very slowly

A model for hedging load and price risk in the Texas electricity market

Energy companies with commitments to meet customers’ daily electricity demands face the problem of hedging load and price risk. We propose a joint model for load and price dynamics, which is motivated by the goal of facilitating optimal hedging decisions, while also intuitively capturing the key features of the electricity market. Driven by three stochastic factors including the load process, our power price model allows for the calculation of closed-form pricing formulas for forwards and some options, products often used for hedging purposes. Making use of these results, we illustrate in a simple example the hedging benefit of these instruments, while also evaluating the performance of the model when fitted to the Texas electricity market