Accurately predicting arsenic (As) concentration in farmland soil on a large scale is essential for effectively preventing and managing soil pollution in agricultural areas, thereby safeguarding food security. Multispectral imaging presents a cost-effective and efficient method for monitoring As concentration across extensive farmland regions. Nevertheless, the underlying process and mechanisms determining the relationship between As concentration in farmland soil and spectral data remain uncertain. The primary aim of this study was to evaluate whether employing a hierarchical strategy (based on soil organic matter (SOM) and pH) results in more accurate prediction of As concentration in farmland soil than those employing nonhierarchical (global) models. Our results show that with respect to global models, the best prediction of As concentration was achieved using the convolutional neural network (CNN) model (validated ratio of the model performance to the interquartile distance (RPIQ) = 2.50), followed by the Cubist model (validated RPIQ = 2.19) and the extreme learning machine (ELM) model (validated RPIQ = 2.10). After SOM-based hierarchization, the Cubist model exhibited the highest prediction accuracy (validated coefficient of determination (R²) = 0.73), representing a 0.02 improvement in the R² compared with the that of global CNN model. The clay minerals ratio (CMR) was identified as the most important variable for predicting As concentration. Notably, the identification of high As concentration in the central old town areas underscores the importance of early soil contamination risk warnings on arable lands. These findings indicate that SOM-hierarchical machine learning models could serve as an effective approach to addressing the influence of soil environmental complications on spectral prediction of As concentration in farmland soil. By implementing this proposed method, soil environmental monitoring efforts can be significantly improved.

Islands represent distinctive geographical landscapes where cultural heritage, history, and ecological systems converge, offering critical insights into human–environment interactions. This study investigates how visual storytelling through digital tools such as the Historical Environment Map Viewer, Environment Digimap, Google Maps and Google Street View, and ArcGIS Field Maps can be employed to capture, interpret, and communicate islands’ landscape changes. By integrating historical environmental mapping, landscape change mapping, street map views, and field observations, this study creates a layered visual narrative that reveals shifts in land use, settlement patterns, and ecological features over time. Rathlin Island represents a distinctive island landscape, and this study uses visual storytelling as a tool to foster a broader public understanding of environmental conservation and engagement with the island’s ecologial challenges. The study demonstrates that multi-perspective, interdisciplinary methods provide valuable insights into the complex dynamics of landscape change, while also offering a comprehensive vision of sustainable future landscape on small islands.

The design of RNA-guided nucleases with properties not limited by evolution can expand programmable genome editing capabilities. However, generating diverse multi-domain proteins with robust enzymatic properties remains challenging. Here we use an artificial intelligence-driven strategy that couples structure-guided inverse protein folding with evolution-informed residue constraints to generate active, divergent variants of TnpB, a minimal CRISPR-Cas12-like nuclease. High-throughput functional screening of AI-generated variants yielded editors that retained or exceeded wild-type activity in bacterial, plant and human cells. Cryo-EM-based structure determination of the most divergent active variant revealed new stabilizing contacts in the RNA/DNA interfaces across conformational states, demonstrating the design potential of this approach. Together these results establish a strategy for creating non-natural RNA-guided nucleases and conformationally active nucleic acid binders, enlarging the designable protein space.