MOTIVATION: Accurately characterizing expressed genetic variation at the single-cell level is essential for understanding transcriptional heterogeneity, allelic regulation, and mutational dynamics within complex tissues. However, few tools enable comprehensive visualization and quantitative analysis of expressed variants across individual cells. RESULTS: scSNViz is an R package for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from cell-barcoded single-cell RNA sequencing (scRNA-seq) data. The software supports estimation of variant allele fractions, clustering of SNV expression profiles, and 2D and 3D visualization of individual SNVs or user-defined SNV groups. Beyond visualization, scSNViz facilitates investigation of cell-, cluster-, or lineage-specific variant expression patterns, as well as allelic dynamics including imprinting, random allele inactivation, and transcriptional bursting. It interoperates seamlessly with established single-cell frameworks-Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling-enabling integrative multi-omic analyses of expressed variation. AVAILABILITY AND IMPLEMENTATION: scSNViz is implemented in R and freely available at https://github.com/HorvathLab/scSNViz (DOI: 10.5281/zenodo.17307516). The package includes comprehensive documentation and example workflows designed for users with limited bioinformatics experience.

OBJECTIVES: Federated Ecosystems for Analytics and Standardized Technologies (FEAST) is a modular, cloud-based platform developed through the ARPA-H Biomedical Data Fabric initiative to enable secure, federated analysis of real-world biomedical data. To guide and iteratively refine its modular design, the FEAST team conducted a cross-institutional survey to systematically identify and prioritize research needs related to authorized-access data across diverse biomedical domains. This study presents a structured synthesis of submitted use cases to uncover infrastructure gaps, data integration challenges, and translational opportunities. The results from the survey inform both front-end user-facing functionality and backend data requirements, shaping how the interface supports user interactions, data types, and compliance with security and interoperability standards. MATERIALS AND METHODS: A structured survey form was distributed to researchers affiliated with participating institutions, including DNA-HIVE, The George Washington University (GW-FEAST), Weill Cornell Medicine, Vanderbilt University Medical Center, Georgetown University, European Bioinformatics Institute, and Kaiser Permanente. Respondents completed standardized fields describing the data types of interest, project goals, analytic methods, and perceived technical barriers. The collected responses were curated and analyzed to identify common needs related to privacy, interoperability, scalability, and workflow reproducibility. RESULTS: The survey compiled 61 use cases spanning genomics, imaging, clinical phenotyping, EHR-driven analytics, and precision medicine. Common themes included the need for multi-modal data integration, HL7 FHIR-based secure access, federated model training without PII retention, and containerized microservices for scalable deployment. Convergent needs across institutions emphasized consistent demand for FAIR-compliant infrastructure and readiness for real-world data analytics. CONCLUSION: The FEAST Use Cases survey provides a cross-sectional view of biomedical informatics priorities grounded in real-world data needs. The findings offer a strategic blueprint for developing federated, privacy-preserving infrastructure to support secure, collaborative, and scalable biomedical research.

The aim of this research is to design and implement a Solar Virtual Reality environment (SolarVR) for inter-cognitive and intra-cognitive communication by connecting users and sensors to a real-world solar panel plant for remote monitoring, maintenance and collaboration. The paper outlines the development of a VR solution which can be utilized for remote monitoring and communication, skills training and science education.

This paper describes the architecture and implementation of a distributed autonomous gardening system. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. Task allocation, sensing and manipulation are distributed in the system and de-centrally coordinated. We describe the architecture of this system and present experimental results for navigation, object recognition and manipulation.