Strong genetic mutation overrides female protective effects in autism
Peer-Reviewed Publication
This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 3-Nov-2026 17:16 ET (3-Nov-2026 22:16 GMT/UTC)
Autism spectrum disorder affects males far more frequently than females, with diagnoses occurring roughly four times more often in boys. Scientists have long suspected that females may possess biological protective mechanisms that reduce vulnerability to autism, but direct experimental evidence has remained limited.
A joint research team from KAIST, Yonsei University, and Institute for Basic Science has now uncovered evidence that the severity of autism-related genetic mutations may play a key role in overcoming these protective effects. The researchers developed the world’s first viable homozygous CHD8-mutant mouse model and discovered that stronger mutations can dramatically alter the male–female pattern of autism-related symptoms.Harvard’s Visual Computing Group developed BRIDGE, a simulation system that converts standard standing-basketball footage into realistic wheelchair-basketball videos.
Ice-nucleating proteins naturally bind only to organic surfaces, so understanding whether they can bind to human-made materials can help with applications like deicing, creating artificial snow, cryo-medicine, and more. In Biointerphases, researchers found that the proteins connect to a surface in a layer of single molecules with their ice formation side facing out, allowing ice to grow atop the surface, and that they do not seem to care what type of material they are binding to; for both artificial and natural surfaces, the INPs bind in remarkably similar ways.
A new framework allows AI models that have already been trained to learn new tasks without sacrificing performance when performing old tasks. The framework, called CHEEM, also improves an AI model’s operating efficiency by using fewer computational steps to perform simpler tasks.