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Motivation DataSHIELD is an open-source software infrastructure enabling the analysis of data distributed across multiple databases (federated data) without leaking individuals' information (non-disclosive). It has applications in many scientific domains, ranging from biosciences to social sciences and including high-throughput genomic studies. R is the language used to interact with (and build) DataSHIELD. This creates difficulties for researchers who do not have experience writing R code or lack the time to learn how to use the DataSHIELD functions. To help new researchers use the DataSHIELD infrastructure and to improve the user-friendliness for experienced researchers, we present ShinyDataSHIELD. Implementation ShinyDataSHIELD is a web application with an R backend that serves as a graphical user interface (GUI) to the DataSHIELD infrastructure. General features The version of the application presented here includes modules to perform: (i) exploratory analysis through descriptive summary statistics and graphical representations (scatter plots, histograms, heatmaps and boxplots); (ii) statistical modelling (generalized linear fixed and mixed-effects models, survival analysis through Cox regression); (iii) genome-wide association studies (GWAS); and (iv) omic analysis (transcriptomics, epigenomics and multi-omic integration). Availability ShinyDataSHIELD is publicly hosted online [ https://datashield-demo.obiba.org/ ], the source code and user guide are deposited on Zenodo DOI 10.5281/zenodo.6500323, freely available to non-commercial users under 'Commons Clause' License Condition v1.0. Docker images are also available [ https://hub.docker.com/r/brgelab/shiny-data-shield ]. [ABSTRACT FROM AUTHOR]

In this commentary article, we explore the needs of people with communication disability in relation to sustainable and inclusive communities. More green and public spaces is one strategy identified by the United Nations in the Sustainable Development Goals (SDGs) for creating sustainable and inclusive communities. We argue that through thoughtful design, these spaces could address the needs of people with communication disability and foster greater social interaction thereby supporting mental well-being. We present a model related to planning and design of green and public spaces that may provide a useful framework for considering the communication needs of people with a communication disability. Creative and practical approaches to planning green and public spaces, in consultation with local community members with lived experience of communication disability, helps to ensure inclusive meeting spaces that encourage connection over isolation. Well-designed green and public spaces provide opportunities for natural communication for people with communication disability and may decrease their sense of isolation, thereby reducing the risk of concomitant mental health problems. Future research is needed to explore how stakeholder consultation could enhance planning processes and address the specific needs of people with communication disability. This commentary focuses on sustainable cities and communities (SDG 11) and also addresses good health and well-being (SDG 3) and partnerships for the goals (SDG 17). [ABSTRACT FROM AUTHOR]

Background Growing evidence indicates a causal relationship between SARS-CoV-2 infection and myocarditis. Post-authorization safety data have also identified myocarditis as a rare safety event following mRNA COVID-19 vaccination, particularly among adolescent and young-adult males after dose 2. We further evaluated the potential risk by querying the Moderna global safety database for myocarditis/myopericarditis reports among mRNA-1273 recipients worldwide. Methods Myocarditis/myopericarditis reports from 18 December 2020 to 15 February 2022 were reviewed and classified. The reported rate after any known mRNA-1273 dose was calculated according to age and sex, then compared with a population-based incidence rate to calculate observed-to-expected rate ratios (RRs). Results During the study period, 3017 myocarditis/myopericarditis cases among 252 million mRNA-1273 recipients who received at least 1 dose were reported to the Moderna global safety database. The overall reporting rate was 9.23 per 100 000 person-years, which was similar to the expected reference rate (9.0 cases per 100 000 person-years; RR [95% confidence interval (CI)], 1.03 [.97–1.08]). When stratified by sex and age, observed rates were highest for males aged <40 years, particularly those 18–24 years (53.76 per 100 000 person-years), which was higher than expected (RR [95% CI], 3.10 [2.68–3.58]). When considering only cases occurring within 7 days of a known dose, the observed rate was highest for males aged 18–24 years after dose 2 (4.23 per 100 000 doses administered). Conclusions Myocarditis/myopericarditis rates were not higher than expected for the overall population of mRNA-1273 recipients but were higher than expected in males aged 18–24 years, with most cases occurring 7 days after dose 2. [ABSTRACT FROM AUTHOR]

Polybius (c. 200-118 BC) is a notoriously combative historian, gleefully attacking his fellow historians for their moral failings and intellectual crimes, and their works for their misleading errors and grave distortions. But Polybius sometimes appears guilty himself of some of the same faults he finds in others. This paper aims to assess Polybius' quality and reliability as a writer of history on his own terms, that is, according to the strict standards he sets for other historians, with the ultimate aim of determining whether his Histories marks a return to the high historiographical standards set by Polybius' famous predecessor Thucydides, or perpetrates the same historiographical high crimes and misdemeanours he accuses his contemporary historical writers of committing. [ABSTRACT FROM AUTHOR]

Motivation In multi-cohort machine learning studies, it is critical to differentiate between effects that are reproducible across cohorts and those that are cohort-specific. Multi-task learning (MTL) is a machine learning approach that facilitates this differentiation through the simultaneous learning of prediction tasks across cohorts. Since multi-cohort data can often not be combined into a single storage solution, there would be the substantial utility of an MTL application for geographically distributed data sources. Results Here, we describe the development of 'dsMTL', a computational framework for privacy-preserving, distributed multi-task machine learning that includes three supervised and one unsupervised algorithms. First, we derive the theoretical properties of these methods and the relevant machine learning workflows to ensure the validity of the software implementation. Second, we implement dsMTL as a library for the R programming language, building on the DataSHIELD platform that supports the federated analysis of sensitive individual-level data. Third, we demonstrate the applicability of dsMTL for comorbidity modeling in distributed data. We show that comorbidity modeling using dsMTL outperformed conventional, federated machine learning, as well as the aggregation of multiple models built on the distributed datasets individually. The application of dsMTL was computationally efficient and highly scalable when applied to moderate-size (n  < 500), real expression data given the actual network latency. Availability and implementation dsMTL is freely available at https://github.com/transbioZI/dsMTLBase (server-side package) and https://github.com/transbioZI/dsMTLClient (client-side package). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Excess mortality is an important measure of the scale of the coronavirus-2019 pandemic. It includes both deaths caused directly by the pandemic, and deaths caused by the unintended consequences of containment such as delays to accessing care or postponements of healthcare provision in the population. In 2020 and 2021, in England, multiple groups have produced measures of excess mortality during the pandemic. This paper describes the data and methods used in five different approaches to estimating excess mortality and compares their estimates. The fundamental principles of estimating excess mortality are described, as well as the key commonalities and differences between five approaches. Two of these are based on the date of registration: a quasi-Poisson model with offset and a 5-year average; and three are based on date of occurrence: a Poisson model without offset, the European monitoring of excess mortality model and a synthetic controls model. Comparisons between estimates of excess mortality are made for the period March 2020 through March 2021 and for the two waves of the pandemic that occur within that time-period. Model estimates are strikingly similar during the first wave of the pandemic though larger differences are observed during the second wave. Models that adjusted for reduced circulation of winter infection produced higher estimates of excess compared with those that did not. Models that do not adjust for reduced circulation of winter infection captured the effect of reduced winter illness as a result of mobility restrictions during the period. None of the estimates captured mortality displacement and therefore may underestimate excess at the current time, though the extent to which this has occurred is not yet identified. Models use different approaches to address variation in data availability and stakeholder requirements of the measure. Variation between estimates reflects differences in the date of interest, population denominators and parameters in the model relating to seasonality and trend. [ABSTRACT FROM AUTHOR]