Soil-landscape and climatic relationships in the middle Miocene of the Madrid Basin

The Miocene alluvial-lacustrine sequences of the Madrid Basin, Spain, formed in highly varied landscapes. The presence of various types of palaeosols allows assessment of the effects of local and external factors onsedimentation, pedogenesis and geomorphological development. In the northern, more arid, tectonicallyactive arca, soils were weakly developed in aggrading alluvial fans, dominated by mass flows. reflecting high sedimentation rates. In more distal parts of the fans and in playa lakes calcretes and dolocretes developed: the former were associated with Mg-poor fan sediments whitc: the latter formed on Mg-rich lake clays exposed during minar lake lowstands. The nonh-east part of the basin had a less arid climate. Alluvial fans in this area were dominated by stream Aood deposits, sourced by carbonate terrains. Floodplain and freshwater lakc deposits formed in distal areas. The high local supply of calcium carbonate may have contributed to the preferential developmenl on calcretes on the fans. Both the fan and floodplain palaeosols exhibit pedofacies relationships and more mature soils developed in settings more distant from the sediment sources. Palaeosols also developed on pond and lake margin carbonates, and led to the formation of palustrine limestones. The spatial distributions and stratigraphies of palaeosols in the Madrid Basin alluvial fans suggest that soil formation was controlled by local factors. These palaeosols differ from those seen in Quatemary fans. Which are characterized by climatically induced periods of stability and instability

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model—SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease