Early- and advanced non-enzymatic glycation in diabetic vascular complications: the search for therapeutics

Cardiovascular disease is a common complication of diabetes and the leading cause of death among people with diabetes. Because of the huge premature morbidity and mortality associated with diabetes, prevention of vascular complications is a key issue. Although the exact mechanism by which vascular damage occurs in diabetes in not fully understood, numerous studies support the hypothesis of a causal relationship of non-enzymatic glycation with vascular complications. In this review, data which point to an important role of Amadori-modified glycated proteins and advanced glycation endproducts in vascular disease are surveyed. Because of the potential role of early- and advanced non-enzymatic glycation in vascular complications, we also described recent developments of pharmacological inhibitors that inhibit the formation of these glycated products or the biological consequences of glycation and thereby retard the development of vascular complications in diabetes

Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

Influence maximization is a widely studied topic in network science, where the aim is to reach the maximum possible number of nodes, while only targeting a small initial set of individuals. It has critical applications in many fields, including viral marketing, information propagation, news dissemination, and vaccinations. However, the objective does not usually take into account whether the final set of influenced nodes is fair with respect to sensitive attributes, such as race or gender. Here we address fair influence maximization, aiming to reach minorities more equitably. We introduce Adversarial Graph Embeddings: we co-train an auto-encoder for graph embedding and a discriminator to discern sensitive attributes. This leads to embeddings which are similarly distributed across sensitive attributes. We then find a good initial set by clustering the embeddings. We believe we are the first to use embeddings for the task of fair influence maximization. While there are typically trade-offs between fairness and influence maximization objectives, our experiments on synthetic and real-world datasets show that our approach dramatically reduces disparity while remaining competitive with state-of-the-art influence maximization methods