Plate reconstructions in the Arctic region based on joint analysis of gravity, magnetic, and seismic anomalies

Based on the analysis of various geophysical data, namely, free-air gravity anomalies, magnetic anomalies, upper mantle seismic tomography images, and topography/bathymetry maps, we single out the major structural elements in the Circum Arctic and present the reconstruction of their locations during the past 200 million years. The configuration of the magnetic field patterns allows revealing an isometric block, which covers the Alpha-Mendeleev Ridges and surrounding areas. This block of presumably continental origin is the remnant part of the Arctida Plate, which was the major tectonic element in the Arctic region in Mesozoic time. We believe that the subduction along the Anyui suture in the time period from 200 to 120 Ma caused rotation of the Arctida Plate, which, in turn, led to the simultaneous closure of the South Anyui Ocean and opening of the Canadian Basin. The rotation of this plate is responsible for extension processes in West Siberia and the northward displacement of Novaya Zemlya relative to the Urals-Taimyr orogenic belt. The cratonic-type North American, Greenland, and European Plates were united before 130 Ma. At the later stages, first Greenland was detached from North America, which resulted in the Baffin Sea, and then Greenland was separated from the European Plate, which led to the opening of the northern segment of the Atlantic Ocean. The Cenozoic stage of opening of the Eurasian Basin and North Atlantic Ocean is unambiguously reconstructed based on linear magnetic anomalies. The counter-clockwise rotation of North America by an angle of ~. 15° with respect to Eurasia and the right lateral displacement to 200-250 km ensure an almost perfect fit of the contours of the deep water basin in the North Atlantic and Arctic Oceans.</p

Plain-language medical vocabulary for precision diagnosis

Letter to the edito

The GA4GH Phenopacket schema defines a computable representation of clinical data.

n the clinical domain, substantial work has been dedicated to the development of computational phenotypes.1 Traditionally, these approaches have largely relied on rule-based methods and large sources of clinical data to identify cohorts of patients with or without a specific disease.2–5 However, they were not developed to enable deep phenotyping of abnormalities, to facilitate computational analysis of interpatient phenotypic similarity, or to support computational decision support. To address this, the Global Alliance for Genomics and Health6 (GA4GH) has developed the Phenopacket schema, which supports exchange of computable longitudinal case-level phenotypic information for diagnosis of and research on all types of disease, including Mendelian and complex genetic diseases, cancer, and infectious diseases. A Phenopacket characterizes an individual person or biosample, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments (Fig 1). The Phenopacket software is available at https://github.com/phenopackets/