CATEGORY OF MODALITY IN ENGLISH AND TATAR PROVERBS: COMPARATIVE ASPECT

Purposes: The article discusses one of the most significant and contradictory categories in linguistics, the category of modality, using the proverbs of two different structural languages like English and Tatar. Methodology: The basic methods of scientific research are the statistics method, method of comparative and quantities analyses of the data, method of description. Implications/Applications A systematic study of the complex of multilevel means of expressing the category of modality will help to study the mechanism of action of this category as a functional-semantic subsystem of the language, to determine its essence, volume and boundaries in such different structural languages as English and Tatar. Results: The results of the study allow us to conclude that the representation of the category of modality is similar in languages ​​of different structures, which may become the basis for assuming the similarity of semantic processes in both languages. Novelty: The problem of determining modality is still debatable, and research on how to express it in different languages is relevant. The authors give a classification of the selected proverbs; determine the criteria for comparison, on the basis of which determine the general and various ways of expressing modality in folklore texts

A review of applications of the Bayes factor in psychological research

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The paper is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Crowdfunding as a Source for Social Enterprise Financing : Advantages and Disadvantages Experienced by Social Entrepreneurs

Social Enterprises face funding challenges. As investors focus too narrowly on risk and return, social enterprises may struggle to compete with commercial enterprises for investment capital. In this context, lending and equity crowdfunding have not been sufficiently examined, and its growing importance for business financing makes it valuable to understand its implications for social enterprises. This study collects qualitative data and uses thematic analysis to identify advantages and disadvantages that social entrepreneurs experience when using lending or equity crowdfunding. By conducting six semi-structured interviews we identified nine major advantages which are Viable funding option, Publicity and marketing, Engagement creation, Access to impact-minded investors, Alignment with company principles, Higher valuation of the company, Tests market viability, Favourable power balance towards investors and Large pool of capital; and five major disadvantages which includes Higher costs, Large number of investors, Inexperienced investors, Public exposure & Efficiency concerns. We discuss that crowdfunding represents values that are attractive for social enterprises. Further, crowdfunding sometimes offer higher valuation or more capital to social enterprises, compared to other funding sources. We see that several advantages are especially important in business’s startup phase. However, crowdfunding can also cause greater stress on the management team, and require time and resources. Entrepreneurs also need to consider factors such as public embarrassment when campaigns fail.

Separating the wheat from the chaff:Bayesian regularization in dynamic social networks

In recent years there has been an increasing interest in the use of relational event models for dynamic social network analysis. The basis of these models is the concept of an “event”, defined as a triplet of time, sender, and receiver of some social interaction. The key question that relational event models aim to answer is what drives the pattern of social interactions among actors. Researchers often consider a very large number of predictors in their studies (including exogenous effects, endogenous network effects, and interaction effects). However, employing an excessive number of effects may lead to overfitting and inflated Type-I error rates. Moreover, the fitted model can easily become overly complex and the implied social interaction behavior difficult to interpret. A potential solution to this problem is to apply Bayesian regularization using shrinkage priors to recognize which effects are truly nonzero (the “wheat”) and which effects can be considered as (largely) irrelevant (the “chaff”). In this paper, we propose Bayesian regularization methods for relational event models using four different priors for both an actor and a dyad relational event model: a flat prior model with no shrinkage, a ridge estimator with a normal prior, a Bayesian lasso with a Laplace prior, and a horseshoe prior. We apply these regularization methods in three different empirical applications. The results reveal that Bayesian regularization can be used to separate the wheat from the chaff in models with a large number of effects by yielding considerably fewer significant effects, resulting in a more parsimonious description of the social interaction behavior between actors in dynamic social networks, without sacrificing predictive performance

Allergic diseases and immunodeficiencies in children, lessons learnt from COVID-19 pandemic by 2022: A statement from the EAACI-section on pediatrics

By the April 12, 2022, the COVID‐19 pandemic had resulted in over half a billion people being infected worldwide. There have been 6.1 million deaths directly due to the infection, but the pandemic has had many more short‐ and long‐term pervasive effects on the physical and mental health of the population. Allergic diseases are among the most prevalent noncommunicable chronic diseases in the pediatric population, and health‐care professionals and researchers were seeking answers since the beginning of pandemic. Children are at lower risk of developing severe COVID‐19 or dying from infection. Allergic diseases are not associated with a higher COVID‐19 severity and mortality, apart from severe/poorly controlled asthma. The pandemic disrupted routine health care, but many mitigation strategies, including but not limited to telemedicine, were successfully implemented to continue delivery of high‐standard care. Although children faced a multitude of pandemic‐related issues, allergic conditions were effectively treated remotely while reduction in air pollution and lack of contact with outdoor allergens resulted in improvement, particularly respiratory allergies. There is no evidence to recommend substantial changes to usual management modalities of allergic conditions in children, including allergen immunotherapy and use of biologicals. Allergic children are not at greater risk of multisystem inflammatory syndrome development, but some associations with Long COVID were reported, although the data are limited, and further research is needed. This statement of the EAACI Section on Pediatrics provides recommendations based on the lessons learnt from the pandemic, as available evidence

A review of applications of the Bayes factor in psychological research

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines