Capturing Cancer as Music: Cancer Mechanisms Expressed through Musification

The development of cancer is difficult to express on a simple and intuitive level due to its complexity. Since cancer is so widespread, raising public awareness about its mechanisms can help those affected cope with its realities, as well as inspire others to make lifestyle adjustments and screen for the disease. Unfortunately, studies have shown that cancer literature is too technical for the general public to understand. We found that musification, the process of turning data into music, remains an unexplored avenue for conveying this information. We explore the pedagogical effectiveness of musification through the use of an algorithm that manipulates a piece of music in a manner analogous to the development of cancer. We conducted two lab studies and found that our approach is marginally more effective at promoting cancer literacy when accompanied by a text-based article than text-based articles alone.

A calcium imaging large dataset reveals novel functional organization in macaque V4

The topological organization and feature preferences of primate visual area V4 have been primarily studied using artificial stimuli. Here, we combined large-scale calcium imaging with deep learning methods to characterize and understand how V4 processes natural images. By fitting a deep learning model to an unprecedentedly large dataset of columnar scale cortical responses to tens of thousands of natural stimuli and using the model to identify the images preferred by each cortical pixel, we obtained a detailed V4 topographical map of natural stimulus preference. The map contains distinct functional domains preferring a variety of natural image features, ranging from surface-related features such as color and texture to shape-related features such as edge, curvature, and facial features. These predicted domains were verified by additional widefield calcium imaging and single-cell resolution two-photon imaging. Our study reveals the systematic topological organization of V4 for encoding image features in natural scenes.