A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images are digitally viewable, analyzable, and shareable, and are widely used for Artificial Intelligence (AI) algorithm development. WSIs play an important role in pathology for disease diagnosis and oncology for cancer research, but are also applied in neurology, veterinary medicine, hematology, microbiology, dermatology, pharmacology, toxicology, immunology, and forensic science. When assembling cohorts for AI training or validation, it is essential to know the content of a WSI. However, no standard currently exists for this metadata, and such a selection has largely relied on manual inspection, which is not suitable for large collections with millions of objects. We propose a general framework to generate 2D index maps (tissue maps) that describe the morphological content of WSIs using common syntax and semantics to achieve interoperability between catalogs. The tissue maps are structured in three layers: source, tissue type, and pathological alterations. Each layer assigns WSI segments to specific classes, providing AI-ready metadata. We demonstrate the advantages of this standard by applying AI-based metadata extraction from WSIs to generate tissue maps and integrating them into a WSI archive. This integration enhances search capabilities within WSI archives, thereby facilitating the accelerated assembly of high-quality, balanced, and more targeted datasets for AI training, validation, and cancer research.

OBJECTIVE: Biobanks and biomolecular resources are increasingly central to data-driven biomedical research, encompassing not only metadata but also granular, sample-related data from diverse sources such as healthcare systems, national registries, and research outputs. However, the lack of a standardised, machine-readable format for representing such data limits interoperability, data reuse and integration into clinical and research environments. While MIABIS provides a conceptual model for biobank data, its abstract nature and reliance on heterogeneous implementations create barriers to practical, scalable adoption. This study presents a pragmatic, operational implementation of MIABIS focused on enabling real-world exchange and integration of sample-level data. METHODS: We systematically evaluated established data exchange standards, comparing HL7 FHIR and OMOP CDM with respect to their suitability for structuring sample-related data in a semantically robust and machine-readable form. Based on this analysis, we developed a FHIR-based representation of MIABIS that supports complex biobank structures and enables integration with federated data infrastructures. Supporting tools, including a Python library and an implementation guide, were created to ensure usability across diverse research and clinical contexts. RESULTS: We created nine interoperable FHIR profiles covering core MIABIS entities, ensuring consistency with FHIR standards. To support adoption, we developed an open-source Python library that abstracts FHIR interactions and provides schema validation for MIABIS-compliant data. The library was integrated into an ETL tool in operation at Czech Node of BBMRI-ERIC, European Biobanking and Biomolecular Resources Research Infrastructure, to demonstrate usability with real-world sample-related data. Separately, we validated the representation of MIABIS entities at the organisational level by converting the data structures of BBMRI-ERIC Directory into FHIR, demonstrating compatibility with federated data infrastructures. CONCLUSION: This work delivers a machine-readable, interoperable implementation of MIABIS, enabling the exchange of both organisational and sample-level data across biobanks and health information systems. By integrating MIABIS with HL7 FHIR, we provide a host of reusable tools and mechanisms for further evolution of the data model. Combined, these benefits can help with the integration into clinical and research workflows, supporting data discoverability, reuse, and cross-institutional collaboration in biomedical research.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

Abstract Histopathology research quickly evolves thanks to advances in whole slide imaging (WSI) and artificial intelligence (AI). However, existing WSI viewers are tailored either for clinical or research environments, but none suits both. This hinders the adoption of new methods and communication between the researchers and clinicians. The paper presents xOpat, an open‐source, browser‐based WSI viewer that addresses these problems. xOpat supports various data sources, such as tissue images, pathologists' annotations, or additional data produced by AI models. Furthermore, it provides efficient rendering of multiple data layers, their visual representations, and tools for annotating and presenting findings. Thanks to its modular, protocol‐agnostic, and extensible architecture, xOpat can be easily integrated into different environments and thus helps to bridge the gap between research and clinical practice. To demonstrate the utility of xOpat, we present three case studies, one conducted with a developer of AI algorithms for image segmentation and two with a research pathologist.

MOTIVATION: Biobanks are indispensable for large-scale genetic/epidemiological studies, yet it remains difficult for researchers to determine which biobanks contain data matching their research questions. RESULTS: To overcome this, we developed a new matching algorithm that identifies pairs of related data elements between biobanks and research variables with high precision and recall. It integrates lexical comparison, Unified Medical Language System ontology tagging and semantic query expansion. The result is BiobankUniverse, a fast matchmaking service for biobanks and researchers. Biobankers upload their data elements and researchers their desired study variables, BiobankUniverse automatically shortlists matching attributes between them. Users can quickly explore matching potential and search for biobanks/data elements matching their research. They can also curate matches and define personalized data-universes. AVAILABILITY AND IMPLEMENTATION: BiobankUniverse is available at http://biobankuniverse.com or can be downloaded as part of the open source MOLGENIS suite at http://github.com/molgenis/molgenis. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recent advances in processing capabilities of commodity hardware enabled deployment of high-performance collaborative environments in various application fields, where maximum image quality and interactivity are critical. In this paper, we focus on user-empowered collaborative environments that rely on information available to the users and that can be obtained from the end-nodes connected to the network. We build upon our previous work on scheduling strategies for multi-point data distribution, while utilizing media transcoding during the distribution and partial network topology knowledge that may be provided by the users. The novelty introduced by this paper focuses on improving scheduling scalability by employing local search metaheuristics, allowing us to maintain systems comprising more than twice as many applications compared to the previous approaches.

Interactive collaborative environments built upon high quality video applications require minimum capture-to-display latency to maintain feeling of natural communication. In practice, end users often encounter interruptions due to network congestion, which they could not avoid without knowledge of physical network infrastructure. We solve the problem of application level multipoint routing without assuming accurate knowledge of network link capacities, but rather relying on their estimates by network inference and bandwidth measurement tools. Our approach can handle uncertainties caused by both unreliable bandwidth measurements and unexpected capacity sharing. Support for on-the-fly transcoding of data streams on multicast agents allows per-recipient adaptation of video quality and bandwidth. We have implemented an ant colony optimization algorithm in the Co Universe middleware for orchestration of collaborative environments. Experiments show very good quality of results on most practical use cases and ability to solve significantly larger instances than optimal approaches based on mixed integer programming.

High-quality video-mediated communication uses data streams with high bitrates, often comparable to capacity of network links, it also demands latency to be as low as possible. The problem of finding the optimal solution to multi-point data distribution on application level while minimizing distribution latency is NP-complete. This paper presents a novel approach to solving the problem using local search methods. Experimental evaluation within the Co Universe framework shows that the proposed algorithm is capable of solving problems an order of magnitude larger than previously available approaches, while maintaining high quality of the solutions. The proposed approach also makes initial attempts toward handling of system dynamics and minimizing the impact on already participating users.

High-quality video, both uncompressed and compressed with relatively low compression ratio, is used in interactive environments, where minimum end-to-end latency is required. The media streams planning solves multi-point data routing for such applications even when bit rate of streams is comparable to capacity of network links. Recent advances in acceleration of high-throughput low-latency video processing enable on-the-fly video transcoding even for 4K and 8K video streams, thus allowing individual adaptation of data stream for each recipient without affecting the others. Stimulated by these advances, we present a novel media streams planning with transcoding on network nodes, and a mixed integer programming approach to solve the problem optimally. Our approach also enhances traditional end-to-end network model of the whole system with known topology of certain network regions. Our solution was implemented in the Co Universe, which is middleware for orchestration of collaborative environments. Measurements show that performance of the new solver is sufficient for interactive solving of most practical use-cases, thus enabling self-organization of high-performance collaborative environments.

This paper gives a brief overview of UltraGrid---a high-quality video transmission software. Employing a commodity PC and Mac hardware, UltraGrid allows for transmission of video in resolutions ranging through HD up to 2160p/4K with end-to-end latency as low as 75ms. The high-quality is achieved either by transmitting uncompressed video streams or streams with low compression ratio. UltraGrid was originally developed as a research tool to demonstrate capabilities of 10Gbps networks and to study multi-point data distribution in high-speed network environments. Through years it has evolved into an open-source project intended to support applications having quality demands beyond the competence of standard videoconferencing or streaming tools. Nowadays, the main application areas are collaborative environments, medical, cinematography and broadcasting applications, as well as various educational activities.

Embedded Block Coding with Optimal Truncation (EBCOT) is the fundamental and computationally very demanding part of the compression process of JPEG2000 image compression standard. In this paper, we present a reformulation of the context modeling of EBCOT that allows full parallelization for massively parallel architectures such as GPUs with their single instruction multiple threads architecture. We prove that the reformulation is equivalent to the EBCOT specification in JPEG2000 standard. Behavior of the reformulated algorithm is demonstrated using NVIDIA CUDA platform and compared to other state-of-the-art implementations.

Networking infrastructure is a vital part of virtual computer clusters. This paper describes VirtCloud, a system for interconnecting virtual clusters in a state-wide network based on advanced features available in academic networks. The system supports dynamic creation of virtual clusters without the need of run-time administrative privileges on the backbone core network, encapsulation of the clusters, controlled access to external sources for cluster hosts, full user access to the clusters, and optional publishing of the clusters. The paper describes architecture of the system, and prototype implementation in MetaCenter (Czech national Grid infrastructure) using Czech national research network CESNET2. Feasibility of the concept is evaluated through a series of measurements demonstrating that the network performance of the system is satisfactory.

We present a videoconferencing tool, GColl, which aims to support collaboration among remote groups of participants. GColl supports mutual gaze as well as partial gaze awareness for all participants, while still retaining a modest technical requirements: a camera and an echo-canceling microphone at

Current collaborative tools are often not able to profit from existing systems for user management. It is therefore necessary for collaborative systems to administrate their users using their own solutions, which may not be adequate in terms of scalability or security. Many users may also experience problems working with authentication credentials (e.g. digital certificates) employed by collaborative systems. In this paper, we propose a general framework to provide easy-to-use yet secure access to collaborative systems, which offers a general middleware layer to accommodate various types of collaborative tools. The framework utilizes the emerging model of federations, which allows to provide a user-friendly means of logging in to a collaborative system as well as a solid basis for specifying access control policies. The framework handles all security aspects in a transparent way without requiring the users to perform complicated tasks. Using user attributes maintained in the federation, it is also possible to implement efficient and dynamic group management of the collaborating users.

This article describes a distributed classroom experiment carried out by five universities in the US and Europe at the beginning of 2007. This experiment was motivated by the emergence of new digital media technology supporting uncompressed high-definition video capture, transport and display as well as the networking services required for its deployment across wide distances. The participating institutes have designed a distributed collaborative environment centered around the new technology and applied it to join the five sites into a single virtual classroom where a real course has been offered to the registered students. Here we are presenting the technologies utilized in the experiment, the results of a technology evaluation done with the help of the participating students and we identify areas of future improvements of the system. While there are a few hurdles in the path of successfully deploying this technology on a large scale, our experiment shows that the new technology is sustainable and the significant quality improvements brought by it can help build an effective distributed and collaborative classroom environment.