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A new study on a 7,100-year-old skeleton from China has revealed a “ghost” lineage that only existed in theories until now. Skeleton of the early Neolithic woman, known as Xingyi_EN, unearthed at the Xingyi archaeological site in southwestern China’s Yunnan province. Her DNA links her to a deeply divergent human population that may have contributed to the ancestry of modern Tibetans. This study also reveals a distinct Central Yunnan ancestry connected to early Austroasiatic-speaking groups. This discovery makes Yunnan as a key region to understand the ancient genetic history of East and Southeast Asia. The detailed analysis of 127 human genomes from southwestern China is published in a study in the journal Science.

According to the study, radiocarbon dating indicates Xingyi_EN lived around 7,100 years ago and isotope analysis suggests she lived as a hunter-gatherer. Genetic sequencing revealed her ancestry from a deeply diverged human lineage—now named the Basal Asian Xingyi lineage. This lineage diverged from other modern human groups over 40,000 years ago and remained isolated for thousands of years without mixing with other populations.

This “ghost” lineage does not match DNA from Neanderthals or Denisovans but appears to have later contributed to the ancestry of some modern Tibetans. Xingyi_EN represents the first physical evidence of this previously unknown population.

Yunnan’s significance as a reservoir of deep human diversity

Most of the skeletons that the researchers sampled were dated between 1,400 and 7,150 years ago and came from Yunnan province, which today has the highest ethnic and linguistic diversity in all of China.

“Ancient humans that lived in this region may be key to addressing several remaining questions on the prehistoric populations of East and Southeast Asia,” the researchers wrote in the study. Those unanswered questions include the origins of people who live on the Tibetan Plateau, as previous studies have shown that Tibetans have northern East Asian ancestry.

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AI Model Learns to Predict Human Gait for Smarter, Pre-Trained Exoskeleton Control

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Scientists at Georgia Tech have created an AI technique that pre-trains exoskeleton controllers using existing human motion datasets, removing the need for lengthy lab-based retraining. The system predicts joint behavior and assistance needs, enabling controllers that work as well as hand-tuned versions. This advance accelerates prototype development and could improve…

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Scientists Build One of the Most Detailed Digital Simulations of the Mouse Cortex Using Japan’s Fugaku Supercomputer

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Researchers from the Allen Institute and Japan’s University of Electro-Communications have built one of the most detailed mouse cortex simulations ever created. Using Japan’s Fugaku supercomputer, the team modeled around 10 million neurons and 26 billion synapses, recreating realistic structure and activity. The virtual cortex offers a new platform for studying br…

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UC San Diego Engineers Create Wearable Patch That Controls Robots Even in Chaotic Motion

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UC San Diego engineers have developed a soft, AI-enabled wearable patch that can interpret gestures with high accuracy even during vigorous or chaotic movement. The armband uses stretchable sensors, a custom deep-learning model, and on-chip processing to clean motion signals in real time. This breakthrough could enable intuitive robot control for rehabilitation, indus…

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