New measure captures how scientists’ identities shift across academic disciplines
Peer-Reviewed Publication
Updates every hour. Last Updated: 20-Oct-2026 05:16 ET (20-Oct-2026 09:16 GMT/UTC)
Does the academic discipline of a scientist’s doctorate degree accurately capture their disciplinary affiliation decades later? Consider a scientist who earned a doctorate in physics 25 years ago who now studies climate change, publishes primarily in interdisciplinary journals and considers herself a climate scientist. Does “physics” still accurately describe where she fits in science? A new study led by researchers at Rice University and Michigan State University argues that the answer is often no.
A research team led by Professor Jiandong Ding at Fudan University has derived a set of equations for random degradation inspired by the classic Poisson raindrop problem, providing the first theoretical framework to quantify “acquired heterogeneity” — the uneven degradation that emerges during service rather than from manufacturing defects. Applying this framework to biodegradable metal stents, the team discovered that coating iron with polylactide (PLA) increases the corrosion rate by 3-fold but reduces corrosion inhomogeneity by 4,000-fold in blood-mimetic medium, paradoxically leading to later device fracture despite faster degradation. The resulting metal–polymer composite stent was validated in preclinical porcine studies, clinical trials involving 1,108 patients across multiple cohorts, and 5-year follow-up of 45 first-in-human cases, demonstrating safety, durability, and reliability. The theoretical framework is, in principle, applicable to any field where random spatiotemporal variability undermines system performance, from ship corrosion and bridge fracture to climate variability and big data analytics.
So far, quantum computers have been held back by their extreme sensitivity to errors and external disturbances. The longer a quantum operation takes, the greater the risk that computational errors will occur. Now, researchers at Chalmers University of Technology, in Sweden, have developed a new method that allows a wide range of advanced quantum operations to be carried out more than a thousand times faster. The breakthrough addresses a well-known bottleneck in the field and paves the way for fault-tolerant quantum computing.
Researchers at King’s College London have developed a new mathematical framework to test whether AI systems that appear to explain their decisions are genuinely transparent.
A new blood-based screening algorithm dramatically reduced the number of unnecessary PET scans needed to recruit patients at risk of Alzheimer’s disease for a clinical trial, according to research from scientists at the Keck School of Medicine of USC. Known as AHEAD 3-45, the clinical trial is a phase 3 international study testing whether earlier treatment with an approved drug for Alzheimer’s disease, known as lecanemab, could lead to better outcomes. Lecanemab removes sticky amyloid-beta protein aggregates that accumulate in the brain and are associated with the progression of Alzheimer’s disease. The drug can slow the clinical progression of the disease by about 30% but is currently prescribed only for patients who already have symptoms of cognitive decline. The AHEAD 3-45 trial specifically targets people with buildup of amyloid-beta, which happens early in the progression of Alzheimer's disease, sometimes decades before symptoms of cognitive impairment appear. However, only about 30% of cognitively healthy adults over 65 have amyloid levels high enough to qualify for trials like AHEAD. A major hurdle for recruiting patients to the trial is identifying and screening large numbers of people who may have hallmarks of the disease, but don’t yet have obvious symptoms such as memory loss. Before blood plasma screening was introduced, candidates for the AHEAD 3-45 trial had to navigate a screening process that could take up to three months from first visit to enrollment. After undergoing positron-emission tomography (PET), a costly imaging scan considered the gold standard for Alzheimer’s diagnosis, more than 70% of candidates would learn that they were ineligible for the trial. When the algorithm was used in recruiting participants for the study, it cut the rate of people who received a PET scan but did not qualify for the trial from more than 70% to 31%.
Associate Professor Kenta Kasai of Science Tokyo has developed a high-rate quantum low-density parity-check (LDPC) code by adapting design theory from classical communications and storage. By imposing orthogonality only where quantum mechanics requires it, the method retains the freedom used to control connection degrees, randomness, and short loops in classical LDPC codes. Overseas teams have already extended the design to neutral-atom hardware and broader code frameworks, referring to the resulting family as “Kasai codes.”