The ability to analyze gene expression at the single-cell level — known as single-cell RNA sequencing (scRNA-seq) — has transformed life sciences, driving discoveries across immunology, oncology, and developmental biology. Over 40,000 studies have leveraged this technique to map the complex diversity of cells within tissues and organisms.

Yet beneath this explosive growth lies a persistent problem: clustering instability. When researchers attempt to group cells by expression patterns to identify cell types or disease states, they often face inconsistent results — even when analyzing the same dataset repeatedly.

Inaccurate clustering can lead to misclassifying normal cells as cancerous or missing rare but critical cell types — jeopardizing interpretation and therapeutic decisions. This “reliability crisis” forces scientists to rerun analyses or rely on computationally expensive pipelines to extract trustworthy insights.

Now, a research team led by Professor KIM Jae Kyoung of the Korea Advanced Institute of Science and Technology (KAIST) and the Institute for Basic Science (IBS) has developed a solution: a mathematical framework named scICE (single-cell Inconsistency Clustering Estimator).

Researchers from The University of Osaka have developed a method to visualize and reconstruct individual urban plants in different seasons using AI and street view images. The novel Seasonal Species-Specific Plant View Index allows urban designers to enhance the ecological sustainability and the well-being of city dwellers by incorporating diverse plant configurations, colors, and seasonality into current and future urban green spaces.

In August 2024, the World Health Organization declared a second “Public Health Emergency of International Concern” for mpox. The current outbreak in Africa is driven mainly by the clade I variant, with multiple countries reporting their first-ever mpox cases of this more severe strain. Nagoya University researchers and their collaborators have found that measuring the amount of virus in the blood when skin lesions first appear can help predict whether patients will experience mild or severe progression of the disease. The study analyzes viral loads during early infection to predict how sick patients will get, potentially improving treatment strategies for future outbreaks. 

Harvard scientists have described a particular movement observed mostly in young, teenaged anacondas, called an S-start. A mathematical model shows that young anacondas, as opposed to babies and adults, exist in a “goldilocks zone” of relative weight and strength to allow them to execute the movement.

Unprecedented experiment reproduces phase transition in system with short-range interactions, challenging traditional predictions of physics

Quantum computers still face a major hurdle on their pathway to practical use cases: their limited ability to correct the arising computational errors. To develop truly reliable quantum computers, researchers must be able to simulate quantum computations using conventional computers to verify their correctness – a vital yet extraordinarily difficult task. Now, in a world-first, researchers from Chalmers University of Technology in Sweden, the University of Milan, the University of Granada, and the University of Tokyo have unveiled a method for simulating specific types of error-corrected quantum computations – a significant leap forward in the quest for robust quantum technologies.

New research reveals a surprising geometric link between human and machine learning. A mathematical property called convexity may help explain how brains and algorithms form concepts and make sense of the world.