A team of scientists from the University of Chicago Pritzker School of Molecular Engineering (UChicago PME), Harvard, Stony Brook University, and Quantinuum, built and tested a complete toolkit of operations using non-Abelian anyons, proving for the first time the broad utility of this approach. The research, published in Nature, demonstrated a so-called universal gate set—meaning that if you store information in these emergent versions of quarks, and you move them around, researchers can perform any desired quantum computation. The approach not only has the potential to lead to a general-purpose quantum computer, but also suggests a path to reliability.

About The Study: A study in JAMA presented Wednesday, July 15, at the annual Alzheimer’s Association International Conference reports that a blood test measuring phosphorylated tau 217 (P‑tau217) in plasma can help estimate an individual’s risk of developing cognitive impairment over the following 2, 5, and 10 years.

Across six cohorts, higher p-tau217 levels were consistently associated with increased risk of progression to cognitive impairment in cognitively unimpaired older adults, and the association remained significant after adjustment for amyloid PET.

The findings support P‑tau217 as a marker linked to amyloid and tau-related disease processes. If ongoing secondary prevention trials demonstrate that early intervention can delay or prevent cognitive decline, blood‑based biomarkers like P‑tau217 could help identify individuals most likely to benefit, authors note.

However, current Alzheimer’s Association clinical practice guidelines recommend against testing cognitively unimpaired older adults outside research studies or clinical trials.

Authors emphasize that the risk estimates are based on selected research cohorts, with greater confidence in 5‑year than 10‑year projections, and that current models do not fully account for vascular comorbidities or competing risk of death; additionally, P-tau217 may not fully reflect non-Alzheimer contributors to cognitive impairment, such as vascular or other neurodegenerative processes.

When can we trust the results we get from AI, and when is learning impossible? Researchers have shown that there are some problems that even the most powerful AI can reliably solve, no matter how much data it’s given.