Spectral top-down recovery of latent tree models.

Modeling the distribution of high-dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying tree structure, given only observations of its terminal nodes. Many algorithms for tree recovery are computationally intensive, which limits their applicability to trees of moderate size.

Germline genetic variants are associated with development of insulin-dependent diabetes in cancer patients treated with immune checkpoint inhibitors.

Background: Immune checkpoint inhibitors (ICIs) have dramatically improved survival in patients with cancer but are often accompanied by severe immune-related adverse events (irAEs), which can sometimes be irreversible. Insulin-dependent diabetes is a rare, but life-altering irAE. Our purpose was to determine whether recurrent somatic or germline mutations are observed in patients who develop insulin-dependent diabetes as an irAE.

γδ T Cells Differentially Regulate Bone Loss in Periodontitis Models.

γδ T cells are nonclassical T lymphocytes representing the major T-cell population at epithelial barriers. In the gingiva, γδ T cells are enriched in epithelial regions adjacent to the biofilm and are considered to regulate local immunity to maintain host-biofilm homeostatic interactions. This delicate balance is often disrupted resulting in the development of periodontitis.

Spectral neighbor joining for reconstruction of latent tree Models.

A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of a set of observed organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a key challenge is to infer the underlying tree topology.