Coronavirus disease 2019 (COVID-19) is associated with autoimmunity and systemic inflammation. Patients with autoimmune rheumatic and musculoskeletal disease (RMD) may be at high risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In this review, based on evidence from the literature, as well as international scientific recommendations, we review the relationships between COVID-19, autoimmunity and patients with autoimmune RMDs, as well as the basics of a multisystemic inflammatory syndrome associated with COVID-19. We discuss the repurposing of pharmaceutics used to treat RMDs, the principles for the treatment of patients with autoimmune RMDs during the pandemic and the main aspects of vaccination against SARS-CoV-2 in autoimmune RMD patients.

We present novel methods for detecting lexical entailment in a fully rule-based and explainable fashion, by automatic construction of semantic graphs, in any language for which a crowd-sourced dictionary with sufficient coverage and a dependency parser of sufficient accuracy are available. We experiment and evaluate on both the Semeval-2020 lexical entailment task (Glavaš et al. (2020). Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 24–35) and the SherLIiC lexical inference dataset of typed predicates (Schmitt and Schütze (2019). Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 902–914). Combined with top-performing systems, our method achieves improvements over the previous state-of-the-art on both benchmarks. As a standalone system, it offers a fully interpretable model of lexical entailment that makes detailed error analysis possible, uncovering future directions for improving both the semantic parsing method and the inference process on semantic graphs. We release all components of our system as open source software.

In the Transdanubian Range, Pannonian Basin, Hungary, karstic sinkholes on a planation surface of Triassic carbonates are filled by grey clayey–silty kaolin deposits. The provenance and accumulation age of these strongly altered terrestrial karst-filling sediments are constrained by X-ray powder diffraction, heavy mineral analysis and zircon U–Pb dating. The heavy minerals of the Southern Bakony Mountains samples are dominated by the ultra-stable zircon–rutile–tourmaline association. Zircon U–Pb data indicate accumulation between 20 and 16 Ma. Furthermore, Archaean to Palaeogene grains were also determined, reflecting the principally fluvial recycling of Eocene bauxites and their cover sequences. In contrast, the sample from the Keszthely Hills consists almost exclusively of airborne material including zircons of 18–14 Ma, reflecting a dominant contribution from the Carpathian–Pannonian Neogene volcanism. The shift in the Miocene age components is inferred to have been caused by the landscape evolution and burial history of the planation surface remnants controlled by local block tectonics.