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It’s easy to solve a 3×3 Rubik’s cube, says Shantanu Chakrabartty, the Clifford W. Murphy Professor and vice dean for research and graduate education in the McKelvey School of Engineering at Washington University in St. Louis. Just learn and memorize the steps then execute them to arrive at the solution.

Computers are already good at this kind of procedural problem solving. Now, Chakrabartty and his collaborators have developed a tool that can go beyond procedure to discover new solutions to complex in logistics to .

Chakrabartty and his collaborators introduced NeuroSA, a problem-solving neuromorphic architecture modeled on how human neurobiology functions, but that leverages quantum mechanical behavior to find optimal solutions—guaranteed—and find those solutions more reliably than state-of-the-art methods.

Combinatorial Optimization problems can be solved by investigating the ground states of particular Ising models. Here, the authors developed a neuromorphic architecture to ensure asymptotic convergence to the ground state of an Ising problem and to consistently produce high-quality solutions.

A protein identified nearly 40 years ago for its ability to stimulate the production of red blood cells plays a surprising, critical role in dampening the immune system’s response to cancer.

Blocking the activity of the protein turns formerly “cold,” or immune-resistant, liver tumors in mice into “hot” tumors teeming with cancer-fighting immune cells. When combined with an immunotherapy that further activates these immune cells against the cancer, the treatment led to complete regression of existing liver tumors in most mice. Treated animals lived for the duration of the experiment. In contrast, control animals survived only a few weeks.

“This is a fundamental breakthrough in our understanding of how the immune system is turned off and on in cancer,” said the senior author published the work in Science. “I could not be more excited about this discovery, and I hope treatments that target the mechanism we uncovered will quickly move forward to human trials.”

To understand what drives disease progression in tissues, scientists need more than just a snapshot of cells in isolation—they need to see where the cells are, how they interact, and how that spatial organization shifts across disease states. A computational method called MESA (Multiomics and Ecological Spatial Analysis), detailed in a study published in Nature Genetics, is helping researchers study diseased tissues in more meaningful ways.

The work details the results of a collaboration among researchers from MIT, Stanford University, Weill Cornell Medicine, the Ragon Institute of MGH, MIT, and Harvard, and the Broad Institute of MIT and Harvard, and was led by the Stanford team.

MESA brings an ecology-inspired lens to tissue analysis. It offers a pipeline to interpret spatial omics data—the product of cutting-edge technology that captures molecular information along with the location of cells in tissue samples. This data provides a high-resolution map of tissue “neighborhoods,” and MESA helps make sense of the structure of that map.