Centrality evolution of the charged-particle pseudorapidity density over a broad pseudorapidity range in Pb-Pb collisions at root s(NN)=2.76TeV

Peer reviewe

A Landform Evolution Model for the Mannen Area in Romsdal Valley, Norway

The Quaternary geology of western Norway’s landscape is the result of glacial and post-glacial sedimentation and erosional processes, a significant sea-level drop and high rock-slope failure activity. All these processes are represented within a small valley section below the Mannen rock-slope instability in Romsdal valley, western Norway. Here, exposure ages, Quaternary geological mapping and geophysical investigations permit the development of a paraglacial landscape evolution model. The model contextualises at least six catastrophic rock-slope failure events within the overall sequence of fjord-valley infilling following deglaciation. A transition from a wide basin-like valley into a strongly confined valley section led to the build-up of more than 40 m thick stratified drift, which was at least partly deposited within a marine environment. The morphology of these sediments features two distinct erosional levels, which are interpreted to be connected to tidal currents during post-glacial sea-level drop. The landform evolution model illustrates the importance of catastrophic rock-slope failures and the impact of strong tidal currents on the typical sediment fill in narrow, high-relief fjord valleys

Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study

Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven challenging. Developing and validating prognostic models using routinely collected data may provide new insights for treatment development and selection. However, due to the rarity of the cancer, it can be difficult to obtain sufficient data, especially from single centres, to develop and validate robust models. Moreover, multi-centre model development is hampered by ethical barriers and data protection regulations that often limit accessibility to patient data. Distributed (or federated) learning allows models to be developed using data from multiple centres without any individual-level patient data leaving the originating centre, therefore preserving patient data privacy. This work builds on the proof-of-concept three-centre atomCAT1 study and describes the protocol for the multi-centre atomCAT2 study, which aims to develop and validate robust prognostic models for three clinically important outcomes in anal cancer following chemoradiotherapy