The miR-310/13 cluster antagonizes ß-catenin function in the regulation of germ and somatic cell differentiation in the Drosophila testis.

MicroRNAs (miRNAs) are regulators of global gene expression and function in a broad range of biological processes. Recent studies have suggested that miRNAs can function as tumor suppressors or oncogenes by modulating the activities of evolutionarily conserved signaling pathways that are commonly dysregulated in cancer. We report the identification of the miR-310 to miR-313 (miR-310/13) cluster as a novel antagonist of Wingless (Drosophila Wnt) pathway activity in a functional screen for Drosophila miRNAs. We demonstrate that miR-310/13 can modulate Armadillo (Arm; Drosophila ß-catenin) expression and activity by directly targeting the 3'-UTRs of arm and pangolin (Drosophila TCF) in vivo. Notably, the miR-310/13-deficient flies exhibit abnormal germ and somatic cell differentiation in the male gonad, which can be rescued by reducing Arm protein levels or activity. Our results implicate a previously unrecognized function for miR-310/13 in dampening the activity of Arm in early somatic and germline progenitor cells, whereby inappropriate/sustained activation of Arm-mediated signaling or cell adhesion may impact normal differentiation in the Drosophila male gonad.

Using Mobility Data to Understand and Forecast COVID19 Dynamics.

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.