Understanding how life is adapting to urban environments represents an important challenge in evolutionary biology. In this work, we investigate a widely cited example of urban adaptation, Culex pi...

Humanity's evolutionary path thus far has been far from simple. It is not as straightforward as ape-like ancestor Homo sapiens. Quite the opposite, it's a complicated story of populations with various degrees of divergence and contact -- a tangled web. Despite many popular depictions, evolution does not follow a straight path. It's a meandering route of possibility, with plenty of side roads that may or may not curve back to the trajectory they sprung from.

Can the minds of machines teach us something new about what it means to be human? When it comes to the intricate story of our species' complex origins and evolution, it appears that they can. A recent study used machine learning technology to analyze eight leading models of human origins and evolution, and the program identified evidence in the human genome of a "ghost population" of human ancestors. The analysis suggests that a previously unknown and long-extinct group of hominins interbred with Homo sapiens in Asia and Oceania somewhere along the long, winding road of human evolutionary history, leaving behind only fragmented traces in modern human DNA. The study, published in Nature Communications, is one of the first examples of how machine learning can help reveal clues to our own origins.

The sequencing of ancient Neanderthal and Denisovan fossils supported introgression events into anatomically modern humans (AMH) (out of Africa). However, recent studies also support the presence of gene flow from AMH into Neanderthals, thus suggesting a complex hominin evolution. All modern humans are genetically related to each other at a time depth of up to 300 thousand years ago and share a common African root. The migratory routes used by AMH after the African diaspora and aspects of the interbreeding between AMH and presently extinct hominins living at the time in Eurasia (here referred as Eurasian Extinct Hominins, EEH) are still under debate. Recently, for the first time, deep learning has been successfully used to explain human history, paving the way for this technology to be applied in other questions in medicine, genomics, and evolution.