Symptom burden, coagulopathy and heart disease after acute SARS-CoV-2 infection in primary practice.

SETANTA (Study of HEarT DiseAse and ImmuNiTy After COVID-19 in Ireland) study aimed to investigate symptom burden and incidence of cardiac abnormalities after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19 and to correlate these results with biomarkers of immunological response and coagulation. SETANTA was a prospective, single-arm observational cross-sectional study condcuted in a primary practice setting, and prospectively registered with ClinicalTrials.gov (identifier: NCT04823182).

Valence and interactions in judicial voting.

The collective statistics of voting on judicial courts present hints about their inner workings. Many approaches for studying these statistics, however, assume that judges' decisions are conditionally independent: a judge reaches a decision based on the case at hand and his or her personal views. In reality, judges interact.

Heterogeneous message passing for heterogeneous networks.

Message passing (MP) is a computational technique used to find approximate solutions to a variety of problems defined on networks. MP approximations are generally accurate in locally treelike networks but require corrections to maintain their accuracy level in networks rich with short cycles. However, MP may already be computationally challenging on very large networks and additional costs incurred by correcting for cycles could be prohibitive.

Belief propagation for permutations, rankings, and partial orders.

Many datasets give partial information about an ordering or ranking by indicating which team won a game, which item a user prefers, or who infected whom. We define a continuous spin system whose Gibbs distribution is the posterior distribution on permutations, given a probabilistic model of these interactions. Using the cavity method, we derive a belief propagation algorithm that computes the marginal distribution of each node's position.

Belief propagation for networks with loops.

Belief propagation is a widely used message passing method for the solution of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical models, but it suffers from the serious shortcoming that it works poorly in the common case of networks that contain short loops. Here, we provide a solution to this long-standing problem, deriving a belief propagation method that allows for fast calculation of probability distributions in systems with short loops, potentially with high density, as well as giving expressions for the entropy and partition function, which are notoriously difficult quantities to compute. Using the Ising model as an example, we show that our approach gives excellent results on both real and synthetic networks, improving substantially on standard message passing methods.