Exosomal NEDD4L derived from HG+oxLDL-induced vascular endothelial cells accelerates macrophage M1 polarization and oxLDL uptake by ubiquitinating IκBα and PPARγ.

Background: Vascular endothelial cell-derived exosomes are thought to mediate disease progression by regulating macrophage polarization. However, its mechanism in diabetes mellitus (DM)-related atherosclerosis (AS) progress is unclear.

Methods: High-glucose (HG) and oxLDL were used to induce human cardiac microvascular endothelial cells (HCMECs) to mimic DM-related AS model.

Federated Learning of Generalized Linear Causal Networks.

Causal discovery, the inference of causal relations among variables from data, is a fundamental problem of science. Nowadays, due to an increased awareness of data privacy concerns, there has been a shift towards distributed data collection, processing and storage. To meet the pressing need for distributed causal discovery, we propose a novel federated DAG learning method called distributed annealing on regularized likelihood score (DARLS) to learn a causal graph from data stored on multiple clients.

Optimizing Regularized Cholesky Score for Order-Based Learning of Bayesian Networks.

Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a novel structure learning method, annealing on regularized Cholesky score (ARCS), to search over topological sorts, or permutations of nodes, for a high-scoring Bayesian network. Our scoring function is derived from regularizing Gaussian DAG likelihood, and its optimization gives an alternative formulation of the sparse Cholesky factorization problem from a statistical viewpoint.