Research announcements from universities, hospitals and scientific institutions. Explore the stories, people and discoveries behind the headlines.
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A computational screen of 25 compounds from Andrographis paniculata identified SRJ09 as a candidate inhibitor of HDAC2, an enzyme involved in suppressing fetal hemoglobin production. The compound passed drug-likeness filters and showed stable binding over 5-nanosecond simulations, but has not been tested in cells or animals.
Read the releaseNorth Carolina State University researchers combined machine learning with CO2 laser ablation to create superomniphobic siloxane elastomers without chemical solvents. The material repels virtually any liquid and retains those properties at 400% strain across more than 5,000 stretch-release cycles. Published in Matter, the work offers a solvent-free manufacturing route for applications in soft robotics, artificial skin, and stretchable electronics.
Read the releaseResearchers at Korea Advanced Institute of Science and Technology developed an AI method that extracts spectral information from existing optical coherence tomography signals to detect lipid-rich plaques in coronary arteries - no hardware changes required. The deep learning model, validated in a rabbit atherosclerosis model against histopathology, learns from frame-level rather than pixel-level labels, dramatically reducing annotation burden for clinical training data.
Read the releaseUCSF and Wayne State University researchers gave eight AI chatbots the same preterm birth prediction task that took human data science teams months to complete. Four tools produced models matching or exceeding human performance on microbiome and blood data from over 1,000 pregnancies. The entire pro
Read the releaseComparing Chinese LLMs - BaiChuan, ChatGLM, Ernie Bot, and DeepSeek - against Western models on 145 questions about Chinese politics, Stanford and Princeton researchers found Chinese models significantly more likely to refuse, truncate, or fabricate responses. The gap persisted in English prompts, p
Read the releaseWhen social robots pair empathetic speech with background music, people rate them as more human-like and emotionally responsive. A PolyU study tested this combination across repeated interactions with Cantonese-speaking participants and found meaningful gains in perceived empathy - though the effect weakened over time as participants habituated to the music.
Read the releaseA research team led by professors at Korea University and Stanford University has developed the Water Sustainability Index (WSI), published in Nature Water on February 10, 2026. Analysis of the London Stock Exchange Group database found that while 14% of major companies reported greenhouse gas emissions, only 9% disclosed total water withdrawals and just 1% reported recycled water use. The WSI provides a transparent, location-adjusted formula that accounts for withdrawal volume, water scarcity, discharge quality, and reuse rates.
Read the releaseA study mapping the full atmospheric nitrogen budget of China's Erhai Lake Basin found that the region emits more than 10,700 metric tons of reactive nitrogen annually while absorbing only a fraction back through deposition - creating a net annual surplus of over 8,200 metric tons. Agricultural sources, primarily livestock and fertilizer, account for more than 90% of ammonia emissions; vehicles account for nearly all nitrogen oxide output. The findings identify the basin as a significant regional exporter of nitrogen pollution.
Read the releaseResearchers at Brazil's Federal University of Sao Carlos have developed a photocatalytic material that combines a zirconium metal-organic framework with silver pyrophosphate semiconductor, achieving over 95% removal of emerging water contaminants including antibiotics and industrial dyes. The material absorbs nearly seven times more photons in the visible spectrum than in UV, making it viable for solar-powered water treatment.
Read the releaseHigh-risk pregnancy specialists from Mount Sinai presented ten studies at the Society for Maternal-Fetal Medicine's 2026 Annual Pregnancy Meeting, including a machine learning model that identifies placenta accreta spectrum risk before conception and an AI tool for detecting severe fetal heart defects during routine ultrasound.
Read the releasePhotons disappear before they can be detected - and that has long made large photonic quantum states impractical. University of Illinois physicists propose a protocol that confirms each photon's existence first, then builds the entangled state from what survives. It works on hardware that already exists.
Read the releaseA single large semiconductor fabrication facility can generate thousands of cubic meters of PFAS-contaminated wastewater daily. A new NSF-funded review synthesizing over 160 studies finds the field's detection, separation, and destruction technologies are still developing to meet that challenge.
Read the releaseArtificial intelligence is increasingly able to simulate human behavior and answer online surveys and political polls, putting the reliability of survey-based research at risk. Consequences can be serious not only for science and research - online surveys are a cornerstone of modern social-science research - but also for policy and participation of people in democratic processes, as surveys are widely used in political polls. This is a worry expressed in a comment in Nature by three researchers ...
Read the releaseIn a study published in Robot Learning journal, researchers propose a new learning-based path planning framework that allows mobile robots to navigate safely and efficiently using a Transformer model. By learning from Improved RRT* with Reduced Random Map Size path-planning algorithms and combining this knowledge with a modified right-of-way rule, the system enables reliable navigation and replanning in dynamic multi-robot environments. Autonomous mobile robots are increasingly used in factories, warehouses, and service environments to transport materials and perform repetitive tasks. To operate safely, robots must plan collision-free ...
Read the releaseScientists have uncovered practical strategies that can significantly reduce harmful air pollution from composting while improving the quality of organic fertilizers. The findings, based on a large global meta-analysis, provide new evidence to help farmers, waste managers, and policymakers adopt climate-smart composting practices. The research, published in Environmental and Biogeochemical Processes, analyzed data from 135 scientific studies and 1,683 experimental observations worldwide. The study evaluated how different ...
Read the releaseANN ARBOR—A University of Michigan study has taken a fine-grained, long-term look at residential-area air pollution and how it relates to deteriorating mobility—and hindered recovery—for older Americans. By pairing and comparing the mobility and disability experiences of 29,790 participants in the national Health and Retirement Study with air quality exposures over 10 years, a team of veteran environmental health researchers found that people with long-term exposures were at greater risk of progressing from no physical function limitations to states of more physical function limitations ...
Read the releaseUNIVERSITY PARK, Pa. — A key problem facing artificial intelligence (AI) development is the vast amount of energy the technology requires, with some experts projecting AI datacenters to be responsible for over 13% of global electricity usage by 2028. According to Xingjie Ni, associate professor of electrical engineering at the Penn State School of Electrical Engineering and Computer Science, the key to addressing this roadblock could lie in computers powered by light instead of circuitry. Ni and his team recently developed a prototype device ...
Read the releaseCould a smart watch act as an early‑warning system for depression relapse? New research from McMaster University suggests that disruptions in a person’s sleep and daily activity routine, as detected through a simple wrist-worn device, can signal when there is increased risk of relapsing into major depression. The new research highlights a simple, yet powerful way to passively monitor relapse risk in people living with major depressive disorder (MDD), often detecting the probability of a relapse weeks or months before the episode occurs. Approximately 60 per cent of people with MDD relapse within five years, even with treatment. “Advances ...
Read the release“This is a crucial advance,” explains Ramón Aguado, a CSIC researcher at the Madrid Institute of Materials Science (ICMM) and one of the study's authors. “Our work is pioneering because we demonstrate that we can access the information stored in Majorana qubits using a new technique called quantum capacitance,” continues the scientist, who explains that this technique “acts as a global probe sensitive to the overall state of the system.” To better understand this achievement, Aguado explains that topological qubits are “like safe boxes for quantum ...
Read the releasePULLMAN, Wash. – A wearable biosensor developed by Washington State University researchers could improve wireless glucose monitoring for people with diabetes, making it more cost-effective, accurate, and less invasive than current models. The WSU researchers have developed a wearable and user-friendly sensor that uses microneedles and sensors to measure sugar in the fluid around cells, providing an alternative to continuous glucose monitoring systems. Reporting in the journal Analyst, the researchers were able to accurately ...
Read the releasePenn Engineers have developed a system that lets robots see around corners using radio waves processed by AI, a capability that could improve the safety and performance of driverless cars as well as robots operating in cluttered indoor settings like warehouses and factories. The system, called HoloRadar, enables robots to reconstruct three-dimensional scenes outside their direct line of sight, such as pedestrians rounding a corner. Unlike previous approaches to non-line-of-sight (NLOS) perception that rely on visible light, HoloRadar works reliably in darkness and under variable lighting conditions. “Robots ...
Read the releaseShima Mohebbi, Assistant Professor, Systems Engineering and Operations Research (SEOR), George Mason University; Milos Manic, Professor, Computer Science, Virginia Commonwealth University (VCU); and Babak Aslani, Postdoctoral Research Fellow, SEOR, George Mason, are studying agentic artificial intelligence (AI)-driven cybersecurity for critical manufacturing. The Commonwealth of Virginia is home to nearly 5,000 small- and medium-sized critical manufacturing entities, which collectively ...
Read the releasePost-stroke rehabilitation, particularly in restoring function to the wrist and hand (W/H), faces significant challenges due to compensatory movement patterns that develop in the shoulder and elbow joints. These compensations help individuals with daily tasks but often result in the learned disuse of distal muscles, hindering motor recovery. Effective rehabilitation requires not only restoring motor control but also improving sensorimotor integration (SMI) between the brain and the targeted muscles. However, current robotic rehabilitation systems often fail to adequately address both the motor pathways for movement control and the sensory pathways ...
Read the releaseResearchers have significantly enhanced an artificial intelligence tool used to rapidly detect bacterial contamination in food by eliminating misclassifications of food debris that looks like bacteria. Current methods to detect contamination of foods such as leafy greens, meat and cheese, which typically involve cultivating bacteria, often require specialized expertise and are time consuming — taking several days to a week. Luyao Ma, an assistant professor at Oregon State University, and her collaborators from the University of California, Davis, Korea University and Florida State University, have developed a deep learning-based model for rapid detection and ...
Read the releaseWith the rapid advancement of AI affecting all sectors of society, Columbia Engineering announced a new Master of Science in Artificial Intelligence (MSAI) program that combines core AI courses in computer science and engineering with a broad range of concentrations, through a partnership with many Columbia schools, to provide students with specialized domain-specific training. This new program comes at a time of unprecedented demand for talented graduates with solid foundational skills in AI and the ability ...
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