Oxalate disrupts monocyte and macrophage cellular function via Interleukin-10 and mitochondrial reactive oxygen species (ROS) signaling.

Oxalate is a small compound found in certain plant-derived foods and is a major component of calcium oxalate (CaOx) kidney stones. Individuals that consume oxalate enriched meals have an increased risk of forming urinary crystals, which are precursors to CaOx kidney stones. We previously reported that a single dietary oxalate load induces nanocrystalluria and reduces monocyte cellular bioenergetics in healthy adults.

Deep learning of contagion dynamics on complex networks.

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data.

Geometric evolution of complex networks with degree correlations.

We present a general class of geometric network growth mechanisms by homogeneous attachment in which the links created at a given time t are distributed homogeneously between a new node and the existing nodes selected uniformly. This is achieved by creating links between nodes uniformly distributed in a homogeneous metric space according to a Fermi-Dirac connection probability with inverse temperature β and general time-dependent chemical potential μ(t). The chemical potential limits the spatial extent of newly created links.

Exact analytical solution of irreversible binary dynamics on networks.

In binary cascade dynamics, the nodes of a graph are in one of two possible states (inactive, active), and nodes in the inactive state make an irreversible transition to the active state, as soon as their precursors satisfy a predetermined condition. We introduce a set of recursive equations to compute the probability of reaching any final state, given an initial state, and a specification of the transition probability function of each node. Because the naive recursive approach for solving these equations takes factorial time in the number of nodes, we also introduce an accelerated algorithm, built around a breath-first search procedure.