A nationwide survey in Japan suggests cultural worldviews are more strongly linked to support for climate policies than to support for biodiversity conservation measures. While hierarchical worldviews were linked to lower perceived climate risks and weaker support for climate measures, these associations were weaker for climate-related biodiversity conservation approaches and not clearly observed for stand-alone conservation policies, pointing to potential common ground.

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.”

Eight technologists building five Singapore-based deep-tech start-ups have been selected for the inaugural cohort of Activate Global Fellows – Singapore, a national fellowship hosted by Nanyang Technological University, Singapore (NTU Singapore) to support promising science and technology ventures develop into globally competitive companies.

A research team comprising Associate Professor FUNAKOSHI Yohei, Associate Professor YAKUSHIJIN Kimikazu, Professor MINAMI Hironobu, and Associate Professor OHJI Goh of Kobe University, together with graduate student MASUDA Genki, then-graduate student IIZUMI Shunsuke, Associate Professor OHUE Masahito, and colleagues at the Institute of Science Tokyo, has developed a new analysis method, LM-QASAS. This method combines antibody language models with B-cell receptor (BCR) repertoires(*1) collected before and after vaccination to identify and track candidate BCR sequences that are likely to have responded to an immune stimulus, without using a database of known antigen-specific antibodies. LM-QASAS extends QASAS(*2; Quantification of Antigen-Specific Antibody Sequence), a method for quantitatively evaluating immune responses to a specific antigen through antigen-receptor repertoire analysis

This study develops a novel “Panning for Gold” framework that integrates Koopman analysis with clustering algorithms to identify representative water quality dynamic data for wastewater treatment plants (WWTPs). Using a full-scale Anaerobic-Anoxic-Oxic process in southern China as a case study, the research demonstrates that machine learning models can maintain predictive accuracy with 43.59% less training data, establishing a low-data-requirement paradigm for efficient water system management.

HieraScaffold is an AI framework that could help autonomous-driving and robotics teams create large, dynamic simulation environments with less reliance on costly real-world LiDAR data collection.

Finding better materials to store hydrogen safely and efficiently is key to clean energy, but AI models trained on incomplete data can recommend candidates that don't actually work in practice. An international team led by Tohoku University, drawing on insight from 69 researchers, has proposed a "physics-aware" framework that combines thermodynamic constraints, reproducible data, and real experimental feedback to make AI-guided materials discovery more reliable. The roadmap could help accelerate the search for practical hydrogen storage solutions to support future clean-energy systems.

Stowers Institute for Medical Research Investigator Julia Zeitlinger, Ph.D., and her team partnered with Google DeepMind on AlphaGenome Atlas, a new AI-generated resource released today that predicts the molecular effects of more than 9 billion possible single-letter DNA changes across the human genome. Zeitlinger's lab used the Atlas to map regulatory motifs — the short DNA sequences that help determine which genes are turned on and off — at base-pair resolution across hundreds of cell types, work that would be impractical to do experimentally. The one-petabyte resource is searchable through a web browser without writing code, allowing researchers to prioritize which genetic variants and biological mechanisms to investigate first. Researchers from Google DeepMind, Stowers, Broad Institute, the University of Exeter, Memorial Sloan Kettering Cancer Center and Stanford University contributed scientific input and tested applications of the resource.

Antibiotic resistance is increasingly recognized as a major public health and environmental challenge, yet the risks are not evenly distributed across aquatic environments. A new study published in Biocontaminant finds that river-type drinking water sources generally carry higher risks from antibiotics, antibiotic resistance genes, and resistant bacteria than reservoir-type sources, highlighting the importance of considering water source type when designing monitoring and pollution-control strategies.