Implementation of a re-linkage to care strategy in patients with chronic hepatitis C who were lost to follow-up in Latin America.

To achieve WHO's goal of eliminating hepatitis C virus (HCV), innovative strategies must be designed to diagnose and treat more patients. Therefore, we aimed to describe an implementation strategy to identify patients with HCV who were lost to follow-up (LTFU) and offer them re-linkage to HCV care. We conducted an implementation study utilizing a strategy to contact patients with HCV who were not under regular follow-up in 13 countries from Latin America.

Entropic determination of the phase transition in a coevolving opinion-formation model.

We study an opinion formation model by the means of a coevolving complex network where the vertices represent the individuals, characterized by their evolving opinions, and the edges represent the interactions among them. The network adapts to the spreading of opinions in two ways: not only connected agents interact and eventually change their thinking but an agent may also rewire one of its links to a neighborhood holding the same opinion as his. The dynamics, based on a global majority rule, depends on an external parameter that controls the plasticity of the network.

Glassy properties of frustrated arrays of nonlinear devices.

We study linear arrays of different number of quartic oscillators shaped in the form of a ring when Gaussian noise (temperature) is added. Frustration is introduced through periodic boundary conditions and repulsive, directional interactions between neighboring oscillators. We show that these systems have similar dynamic properties than the arrays of fluxgates magnetometers.

Two classes of bipartite networks: nested biological and social systems.

Bipartite graphs have received some attention in the study of social networks and of biological mutualistic systems. A generalization of a previous model is presented, that evolves the topology of the graph in order to optimally account for a given contact preference rule between the two guilds of the network. As a result, social and biological graphs are classified as belonging to two clearly different classes.