The past couple of decades have seen a substantial increase in linguistic research that highlights the non-arbitrariness of language, as manifested in motivated sound–meaning correspondences. Yet one of the challenges of such studies is that there is a relative paucity of data-driven analyses, especially in the case of languages other than English, such as Hungarian, even though the proportion of at least partially motivated words in Hungarian vocabulary is substantial. We address this gap by investigating the relationship between Hungarian phoneme classes and positive/negative sentiment based on 3,023 word forms retrieved from the Hungarian Sentiment Lexicon. Our results indicate that positive polarity word forms tend to contain more vowels, front vowels, continuants, fricatives, palatals, and sibilants. On the other hand, negative sentiment polarity words tend to have more rounded vowels, plosives, and dorsal consonants. While our analysis provides strong evidence for a set of non-arbitrary form–meaning relationships, effect sizes also reveal that such associations tend to be fairly weak tendencies, and therefore sentiment polarity cannot be derived from the relative frequencies of phoneme classes in a deterministic fashion.

Neurodegenerative diseases are a pathologically, clinically and genetically diverse group of disorders without effective disease-modifying therapies. Pathologically, these disorders are characterised by disease-specific protein aggregates in neurons and/or glia and referred to as proteinopathies. Many neurodegenerative diseases show pathological overlap with the same abnormally deposited protein occurring in anatomically distinct regions, which give rise to specific patterns of cognitive and motor clinical phenotypes. Sequential distribution patterns of protein inclusions throughout the brain have been described. Rather than occurring in isolation, it is increasingly recognised that combinations of one or more proteinopathies with or without cerebrovascular disease frequently occur in individuals with neurodegenerative diseases. In addition, complex constellations of ageing-related and incidental pathologies associated with tau, TDP-43, Aβ, α-synuclein deposition have been commonly reported in longitudinal ageing studies. This review provides an overview of current classification of neurodegenerative and age-related pathologies and presents the spectrum and complexity of mixed pathologies in community-based, longitudinal ageing studies, in major proteinopathies, and genetic conditions. Mixed pathologies are commonly reported in individuals >65 years with and without cognitive impairment; however, they are increasingly recognised in younger individuals (<65 years). Mixed pathologies are thought to lower the threshold for developing cognitive impairment and dementia. Hereditary neurodegenerative diseases also show a diverse range of mixed pathologies beyond the proteinopathy primarily linked to the genetic abnormality. Cases with mixed pathologies might show a different clinical course, which has prognostic relevance and obvious implications for biomarker and therapy development, and stratifying patients for clinical trials.

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.