BioTuring’s BBrowser is a software solution that helps scientists effectively analyze single-cell omics data. It combines big data with big computation and modern data visualization to create a unique platform where scientists can interact and obtain important biological insights from the massive amounts of single-cell data. BBrowser has three main components: a curated single-cell database, a big-data analytics layer, and a data visualization module. BBrowser is available for download at: https://bioturing.com/bbrowser/download.

Media use cases for emergency services require mission-critical levels of reliability for the delivery of media-rich services such as video streaming. With the upcoming deployment of the Fifth Generation (5G) networks, a wide variety of applications and services with heterogeneous performance requirements are expected to be supported, and any migration of missioncritical services to 5G networks presents significant challenges in the Quality of Service (QoS), for emergency service operators. This paper presents a novel SliceNet framework, based on advanced and customisable network slicing to address some of the highlighted challenges in migrating eHealth telemedicine services to 5G networks. An overview of the framework outlines the technical approaches in beyond the-state-of-the-art network slicing. Subsequently, the paper emphasises the design and prototyping of a media-centric eHealth use case, focusing on a set of innovative enablers towards achieving end-to-end QoS-aware network slicing capabilities, required by this demanding use case. Experimental results empirically validate the prototyped enablers and demonstrate the applicability of the proposed framework in such media-rich use cases.

In order to compete for a prominent market share, network operators and service providers should retain and increase the verticals' subscription, catering to their needs in order to differentiate themselves from competitors. In this scenario, verticals' satisfaction arises of paramount importance. As such, user experience is becoming a reliable indicator for service providers and telecommunication operators to convey overall end-to-end system functioning. To properly estimate end user satisfaction, operators and service providers require efficient means for quality monitoring and estimation at all layers, in conjunction with mechanisms able to maintain said quality at optimum levels. Given these factors, this paper proposes a mechanism for Quality of Perception (QoP) estimation in e-Health services, enabling the QoP-aware management of network slices fulfilling the requirements of supported services. To this end, the paper proposes a cognitive-based architecture which allows for the collection and monitoring of verticals' data to estimate QoP and provides mechanisms to re-configure the underlying network slices according to the monitored quality levels. A machine learning (ML) model is introduced that aims to forecast any future degradation in the quality perceived by vertical users. In case of a predicted degradation, the proposed architecture reacts and triggers the necessary remedial actions, referred as actuations. In order to evaluate the developed ML model and to showcase the

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