The protozoan parasite Trypanosoma cruz causes the Chagas disease, which final outcome can be morbidity or death. The complexity of this infection is due to the many kinds of players involved in the immune response and to the variety of host cells targeted by the parasite. We built an ordinary differential equation model which includes aspects of innate and adaptive immune response to study the T. cruzi infection. The model also includes cardiomyocytes to represent how the infection affects the heart.

Standard reaction-diffusion systems are characterized by infinite velocities and no persistence in the movement of individuals, two conditions that are violated when considering living organisms. Here we consider a discrete particle model in which individuals move following a persistent random walk with finite speed and grow with logistic dynamics. We show that, when the number of individuals is very large, the individual-based model is well described by the continuous reactive Cattaneo equation (RCE), but for smaller values of the carrying capacity important finite-population effects arise.

Infection by Leishmania can cause diseases ranging from self-healing cutaneous to visceral dissemination that can lead to death if untreated. In order to explore the early phase of the infection and the role of macrophages, we implement a system of differential equations involving the major players in the innate immune response to leishmaniasis (i.e., parasites in the intracellular and free form, infected and uninfected macrophages, and NO/ROS). The model was adjusted and validated using data from C57BL/6, KO and SCID mice published in the literature.

A binary mixture saturating a horizontal porous layer, with large pores and uniformly heated from below, is considered. The instability of a vertical uid motion (throughflow) when the layer is salted by one salt (either from above or from below) is analyzed. Ultimately boundedness of solutions is proved, via the existence of positively invariant and attractive sets (i.e. absorbing sets). The critical Rayleigh numbers at which steady or oscillatory instability occurs, are recovered.

In this paper an in silico study of the behavior of an active polar emulsion is reported, focusing on the case of a highly off-symmetric ratio between the polar (active) and passive components, both for the extensile and contractile case. In absence of activity the system is characterized by an hexatic-ordered droplets phase. We find that small extensile activity is able to enhance the hexatic order in the array of droplets with respect to the passive case, while increasing activity aster-like rotating droplets appear.

A reaction-diffusion system governing the prey-predator interaction with hunting cooperation is investigated. Definitive boundedness of solutions is proved via the existence of positive invariants and attractive sets. Linear stability of the coexistence equilibria is performed and conditions guaranteeing the occurrence of Turing instability are found. Numerical simulations on the obtained results are provided.

We investigate the dynamics of a phase-separating binary fluid, containing colloidal dumbbells anchored to the fluid-fluid interface. Extensive lattice Boltzmann-immersed boundary method simulations reveal that the presence of soft dumbbells can significantly affect the curvature dynamics of the interface between phase-separating fluids, even though the coarsening dynamics is left nearly unchanged. In addition, our results show that the curvature dynamics exhibits distinct non-local effects, which might be exploited for the design of new soft mesoscale materials.

Many scientific applications require the solution of large and sparse linear systems of equations using Krylov subspace methods; in this case, the choice of an effective preconditioner may be crucial for the convergence of the Krylov solver. Algebraic MultiGrid (AMG) methods are widely used as preconditioners, because of their optimal computational cost and their algorithmic scalability. The wide availability of GPUs, now found in many of the fastest supercomputers, poses the problem of implementing efficiently these methods on high-throughput processors.

Graph Laplacian is a popular tool for analyzing graphs, in particular in graph partitioning and clustering. Given a notion of similarity (via an adjacency matrix), graph clustering refers to identifying different groups such that vertices in the same group are more similar compared to vertices across different groups. Data clustering can be reformulated in terms of a graph clustering problem when the given set of data is represented as a graph, also known as similarity graph.