For five decades, scientists have known about a notorious cancer-causing enzyme called SRC. But they always assumed it only appeared on the inside of cells, where it sent signals that fueled tumor growth and stayed hidden from the immune system. But now researchers at UC San Francisco have discovered that the SRC enzyme also appears like a flag on the surface of bladder, colorectal, breast, pancreatic and probably many other tumor cells.

As cancer cells furiously divide, they produce a lot of garbage. In healthy cells, the trash gets broken down. But in tumors, the recycling system gets overwhelmed, and the cells expel some of their trash. This pushes the SRC onto the surface of the cell, where it is visible to potential therapies, like antibodies.

A robot that can locate lost items on command, the latest development at the Technical University of Munich (TUM), combines knowledge from the internet with a spatial map of its surroundings to efficiently find the objects being sought. The new robot from Prof. Angela Schoellig’s TUM Learning Systems and Robotics Lab looks like a broomstick on wheels with a camera mounted at the top. It is one of the first robots that not only integrates image understanding but also applies it to a clearly defined task.

To find a pair of glasses misplaced in the kitchen, for example, the robot has to look around and build a three-dimensional image of the room. The camera initially provides two-dimensional images, but these pixels also contain depth information...

Researchers have uncovered evidence for our sun joining a mass migration of similar “twins” leaving the core regions of our galaxy, 4 to 6 billion years ago. The team created and studied an unprecedentedly accurate catalog of stars and their properties using data from the European Space Agency’s Gaia satellite. Their discovery sheds light on the evolution of our galaxy, particularly the development of the rotating bar-like structure at its center.

While archaeology on Earth studies the human past, galactic archaeology traces the vast journey of stars and galaxies...

For years, the guiding assumption of artificial intelligence has been simple: an AI is only as good as the data it has seen. Feed it more, train it longer, and it performs better. Feed it less, and it stumbles. A new study from the USC Viterbi School of Engineering was accepted at the IEEE SoutheastCon 2026, taking place March 12–15. It suggests something far more surprising: with the right method in place, an AI model can dramatically improve its performance in territory it was barely trained on, pushing well past what its training data alone would ever allow.

The method was developed by Minda Li, a USC Viterbi undergraduate who has been pursuing research since her freshman year, working alongside her advisor Bhaskar Krishnamachari, a Faculty Fellow and S...