MendelR is a fully automated R package specifically developed for Mendelian randomization (MR) studies, designed to address the technical challenges of causal inference in biomedical research. As a powerful causal inference method, Mendelian randomization can effectively reduce confounding bias in observational studies. However, its complex data processing workflow, diverse analytical approaches, and stringent hypothesis-testing requirements often deter researchers. MendelR integrates the latest eQTL and pQTL databases, along with multi-omics resources, to provide a one-stop solution covering data acquisition, preprocessing, and instrumental variable selection, through to various MR analysis methods, thereby significantly lowering the technical barriers to research. This toolkit supports not only standard two-sample MR analysis but also encompasses advanced applications, including mediation analysis, multivariate MR, drug target analysis, and multi-omics integration. It also incorporates rigorous sensitivity analyses and multiple-testing correction mechanisms to ensure the reliability of research findings. With its user-friendly interface, fully automated workflow, and diverse visualization features, MendelR offers an efficient and accurate MR analysis platform for researchers at all levels, from beginners to seasoned professionals, thereby advancing the in-depth development of causal relationship research in the biomedical field.

An increase in nitrogen dioxide (NO₂) levels in the air is associated with a 7% rise in the risk of cardiac arrest over four days. Particulate matter (PM₂.₅ and PM₁₀) elevates the risk on the same day as the peak.

Generative AI (GenAI) is rapidly transforming higher education. This study explores the imperative for curriculum reform to effectively integrate these powerful tools of GenAI into education and prepare students for an AI-driven world.

This study examined the evolution of digital education policy in the United Kingdom (UK) from 2008 to 2024 based on a discourse analysis of 21 policy documents retrieved from the UK government’s official website, GOV.UK. 

The work introduces a large-language-model framework that predicts a student’s future performance and generates natural-language diagnoses from only a handful of activity records, thereby eliminating the data hunger and opacity that have long limited knowledge-tracing systems.

Qing Wei and colleagues from the College of Engineering, China Agricultural University, systematically elaborated on the innovative applications of neural networks in agricultural product drying, offering new insights to address industry pain points. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025620).