Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations—inspired by Channel Islands’ foundations laws—provide a workable model for good data governance not only in the Channel Islands, but also elsewhere. A key advantage of this model—in addition to leveraging existing legislation and building on established precedent—is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper, therefore, is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice—and provide key design principles that should be used to guide the design and development of any data foundation.

Vaccination remains the best strategy to reduce invasive meningococcal disease. This study evaluated an investigational tetanus toxoid-conjugate quadrivalent meningococcal vaccine (MenACYW-TT) vs. a licensed tetanus toxoid-conjugate quadrivalent meningococcal vaccine (MCV4-TT) (NCT02955797). Healthy toddlers aged 12–23 months were included if they were either meningococcal vaccine-naïve or MenC conjugate (MCC) vaccine-primed (≥1 dose of MCC prior to 12 months of age). Vaccine-naïve participants were randomised 1:1 to either MenACYW-TT (n = 306) or MCV4-TT (n = 306). MCC-primed participants were randomised 2:1 to MenACYW-TT (n = 203) or MCV4-TT (n = 103). Antibody titres against each of the four meningococcal serogroups were measured by serum bactericidal antibody assay using the human complement. The co-primary objectives of this study were to demonstrate the non-inferiority of MenACYW-TT to MCV4-TT in terms of seroprotection (titres ≥1:8) at Day 30 in both vaccine-naïve and all participants (vaccine-naïve and MCC-primed groups pooled). The immune response for all four serogroups to MenACYW-TT was non-inferior to MCV4-TT in vaccine-naïve participants (seroprotection: range 83.6–99.3% and 81.4–91.6%, respectively) and all participants (seroprotection: range 83.6–99.3% and 81.4–98.0%, respectively). The safety profiles of both vaccines were comparable. MenACYW-TT was well-tolerated and demonstrated non-inferior immunogenicity when administered to MCC vaccine-primed and vaccine-naïve toddlers.

Seeds of Abrus precatorius L. (Fabaceae) were used as weight measure by Indigenous people. Where, the seeds were referred as Ratti; a traditional Indian unit of mass measurement. Seed weight fluctuates depending upon age, moisture, storage-period/conditions. Therefore, use of seeds as a weighing unit become dubious and need to be validated. For this purpose, seeds of A. precatorious were subjected to different moisture conditions and periodically monitored. Surprisingly, there was no change in seed weight was observed, indicating the impermeability of seed coat. The later was confirmed by scarification of seed coat which resulted in 53% increase in seed weight against 0% in control. Further, presence of a potent toxin (abrin) in the seed coat protects it from pests and microbes, and contributes to the maintenance of impermeability for longer period of time. The data validates the use of A. precatorious seeds as a weighing unit (ratti) by the indigenous people and discussed.

We consider conditions for strict stationarity and ergodicity of a class of multivariate BEKK processes $(X_t : t=1,2,\ldots )$ and study the tail behavior of the associated stationary distributions. Specifically, we consider a class of BEKK-ARCH processes where the innovations are assumed to be Gaussian and a finite number of lagged $X_t$’s may load into the conditional covariance matrix of $X_t$. By exploiting that the processes have multivariate stochastic recurrence equation representations, we show the existence of strictly stationary solutions under mild conditions, where only a fractional moment of $X_t$ may be finite. Moreover, we show that each component of the BEKK processes is regularly varying with some tail index. In general, the tail index differs along the components, which contrasts with most of the existing literature on the tail behavior of multivariate GARCH processes. Lastly, in an empirical illustration of our theoretical results, we quantify the model-implied tail index of the daily returns on two cryptocurrencies.

An increase in oyster aquaculture as a sustainable method of shellfish production is one response to overharvest and degradation of natural oyster reefs over the past century. Successful aquaculture production requires determining the environmental conditions optimal for oyster growth. In this study, the salinity, temperature, chlorophyll a concentration and the growth of Crassostrea virginica were monitored at four locations within the Mission-Aransas Estuary, Texas (USA), a shallow subtropical estuary influenced by relatively low freshwater inflow. Mean growth of the oyster shell (0.205 mm d–1 and 0.203 g d–1) and soft tissues (3.447 mg d–1) was highest when salinity was low (mean = 15.5) and chlorophyll a concentration was high (8.4 μg l–1). Oyster growth also varied temporally with periods of spawning. In low-inflow estuaries such as the Mission-Aransas Estuary, oyster farms should be sited close to river mouths so that oysters can benefit from freshwater inflows and lower salinities.

People living in urban slums or informal settlements are among the most vulnerable communities, highly susceptible to coronavirus disease 2019 (COVID-19) infection and vulnerable to the consequences of the measures taken to control the spread of the virus. Fear and stigma related to infection, mistrust between officials and the population, the often-asymptomatic nature of the disease is likely to lead to under-reporting. We conducted a cross-sectional study to determine the seroprevalence of COVID-19 infection in a large slum in South India 3 months after the index case and recruited 499 adults (age >18 years). The majority (74.3%) were females and about one-third of the population reported comorbidities. The overall seroprevalence of IgG antibody for COVID-19 was 57.9% (95% CI 53.4–62.3). Age, education, occupation and the presence of reported comorbidities were not associated with seroprevalence (P-value >0.05). Case-to-undetected-infections ratio was 1:195 and infection fatality rate was calculated as 2.94 per 10 000 infections. We estimated seroprevalence of COVID-19 was very high in our study population. The focus in this slum should shift from infection prevention to managing the indirect consequences of the pandemic. We recommend seroprevalence studies in such settings before vaccination to identify the vulnerability of COVID-19 infection to optimise the use of insufficient resources. It is a wake-up call to societies and nations, to dedicate paramount attention to slums into recovery and beyond – to build, restore and maintain health equity for the ‘Health and wellbeing of all’.

We discuss the existence and uniqueness of stationary and ergodic nonlinear autoregressive processes when exogenous regressors are incorporated into the dynamic. To this end, we consider the convergence of the backward iterations of dependent random maps. In particular, we give a new result when the classical condition of contraction on average is replaced with a contraction in conditional expectation. Under some conditions, we also discuss the dependence properties of these processes using the functional dependence measure of Wu (2005, Proceedings of the National Academy of Sciences 102, 14150–14154) that delivers a central limit theorem giving a wide range of applications. Our results are illustrated with conditional heteroscedastic autoregressive nonlinear models, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes, count time series, binary choice models, and categorical time series for which we provide many extensions of existing results.

Environmental gains of electric cars can be optimized with the use of lightweight and recyclable magnesium in the vehicle’s structural components. Ductility improvement of low-density Mg-Al alloys will extend their use in automotive body applications. The authors achieved 63% ductility improvement in Mg-6wt%Al with trace Y (1.5 ppm) due to the β-phase refinement and predicted that higher levels would not perform as well. As predicted, 0.3wt% of Y addition investigated in this study led to lower mechanical performance and β-phase refinement than those obtained with trace additions. The tensile ductility and yield strength increased by ~13% and 16%, respectively, and the compression strain to fracture by ~22%. Scanning electron and optical microscopy, X-Rays diffraction, mechanical testing and thermodynamic calculations were used to investigate the effect of 0.3wt% Y on the microstructure of Mg-6wt%Al. The matrix dissolution revealed the close association of the Al2Y and the β-Mg17Al12 phases.

Ever since the World Health Organization (WHO) declared the new coronavirus disease 2019 (COVID-19) as a pandemic, there has been a public health debate concerning medical resources and supplies including hospital beds, intensive care units (ICU), ventilators and protective personal equipment (PPE). Forecasting COVID-19 dissemination has played a key role in informing healthcare professionals and governments on how to manage overburdened healthcare systems. However, forecasting during the pandemic remained challenging and sometimes highly controversial. Here, we highlight this challenge by performing a comparative evaluation for the estimations obtained from three COVID-19 surge calculators under different social distancing approaches, taking Lebanon as a case study. Despite discrepancies in estimations, the three surge calculators used herein agree that there will be a relative shortage in the capacity of medical resources and a significant surge in PPE demand if the social distancing policy is removed. Our results underscore the importance of implementing containment interventions including social distancing in alleviating the demand for medical care during the COVID-19 pandemic in the absence of any medication or vaccine. The paper also highlights the value of employing several models in surge planning.

Gene methylation is one means of controlling tissue gene expression, but it is unknown what pathways influencing Alzheimer’s disease (AD) are controlled this way. We compared normal and AD brain tissue data for gene expression (mRNAs) and gene methylation profiling. We identified methylated differentially expressed genes (MDEGs). Protein-protein interaction (PPI) of the MDEGs showed 18 hypermethylated low-expressed genes (Hyper-LGs) involved in cell signaling and metabolism; also 10 hypomethylated highly expressed (Hypo-HGs) were involved in regulation of transcription and development. Molecular pathways enriched in Hyper-LGs included neuroactive ligand-receptor interaction pathways. Hypo-HGs were notably enriched in pathways including hippo signaling. PPI analysis also identified both Hyper-LGs and Hypo-HGs, as hub proteins. Our analysis of AD datasets identified Hyper-LGs, Hypo-HGs, and transcription factors linked to these genes. These pathways, which may participate in Alzheimer’s disease development, may be affected by treatments that influence gene methylation patterns.

Mortality projection and forecasting of life expectancy are two important aspects of the study of demography and life insurance modelling. We demonstrate in this work the existence of long memory in mortality data. Furthermore, models incorporating long memory structure provide a new approach to enhance mortality forecasts in terms of accuracy and reliability, which can improve the understanding of mortality. Novel mortality models are developed by extending the Lee–Carter (LC) model for death counts to incorporate a long memory time series structure. To link our extensions to existing actuarial work, we detail the relationship between the classical models of death counts developed under a Generalised Linear Model (GLM) formulation and the extensions we propose that are developed under an extension to the GLM framework known in time series literature as the Generalised Linear Autoregressive Moving Average (GLARMA) regression models. Bayesian inference is applied to estimate the model parameters. The Deviance Information Criterion (DIC) is evaluated to select between different LC model extensions of our proposed models in terms of both in-sample fits and out-of-sample forecasts performance. Furthermore, we compare our new models against existing models structures proposed in the literature when applied to the analysis of death count data sets from 16 countries divided according to genders and age groups. Estimates of mortality rates are applied to calculate life expectancies when constructing life tables. By comparing different life expectancy estimates, results show the LC model without the long memory component may provide underestimates of life expectancy, while the long memory model structure extensions reduce this effect. In summary, it is valuable to investigate how the long memory feature in mortality influences life expectancies in the construction of life tables.

We construct a double common factor model for projecting the mortality of a population using as a reference the minimum death rate at each age among a large number of countries. In particular, the female and male minimum death rates, described as best-performance or best-practice rates, are first modelled by a common factor model structure with both common and sex-specific parameters. The differences between the death rates of the population under study and the best-performance rates are then modelled by another common factor model structure. An important result of using our proposed model is that the projected death rates of the population being considered are coherent with the projected best-performance rates in the long term, the latter of which serves as a very useful reference for the projection based on the collective experience of multiple countries. Our out-of-sample analysis shows that the new model has potential to outperform some conventional approaches in mortality projection.

One of the main concerns about the fast spreading coronavirus disease 2019 (Covid-19) pandemic is how to intervene. We analysed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) isolates data using the multifractal approach and found a rich in viral genome diversity, which could be one of the root causes of the fast Covid-19 pandemic and is strongly affected by pressure and health index of the hosts inhabited regions. The calculated mutation rate (mr) is observed to be maximum at a particular pressure, beyond which mr maintains diversity. Hurst exponent and fractal dimension are found to be optimal at a critical pressure (Pm), whereas, for P > Pm and P < Pm, we found rich genome diversity relating to complicated genome organisation and virulence of the virus. The values of these complexity measurement parameters are found to be increased linearly with health index values.

In various applications of heavy-tail modelling, the assumed Pareto behaviour is tempered ultimately in the range of the largest data. In insurance applications, claim payments are influenced by claim management and claims may, for instance, be subject to a higher level of inspection at highest damage levels leading to weaker tails than apparent from modal claims. Generalizing earlier results of Meerschaert et al. (2012) and Raschke (2020), in this paper we consider tempering of a Pareto-type distribution with a general Weibull distribution in a peaks-over-threshold approach. This requires to modulate the tempering parameters as a function of the chosen threshold. Modelling such a tempering effect is important in order to avoid overestimation of risk measures such as the value-at-risk at high quantiles. We use a pseudo maximum likelihood approach to estimate the model parameters and consider the estimation of extreme quantiles. We derive basic asymptotic results for the estimators, give illustrations with simulation experiments and apply the developed techniques to fire and liability insurance data, providing insight into the relevance of the tempering component in heavy-tail modelling.

We prove the Erdős–Sós conjecture for trees with bounded maximum degree and large dense host graphs. As a corollary, we obtain an upper bound on the multicolour Ramsey number of large trees whose maximum degree is bounded by a constant.

We present the comparative characterisation of 195 non-aureus staphylococci (NAS) isolates obtained from sheep (n = 125) and humans (n = 70) in Sardinia, Italy, identified at the species level by gap gene polymerase chain reaction (PCR) followed by restriction fragment length polymorphism analysis with AluI. Isolates were tested phenotypically with a disc diffusion method and genotypically by PCR, for resistance to 11 antimicrobial agents including cationic antiseptic agents. Among the ovine isolates, Staphylococcus epidermidis (n = 57), S. chromogenes (n = 29), S. haemolyticus (n = 17), S. simulans (n = 8) and S. caprae (n = 6) were the most prevalent species, while among human isolates, S. haemolyticus (n = 28) and S. epidermidis (n = 26) were predominant, followed by S. lugdunensis and S. hominis (n = 4). Of the 125 ovine isolates, 79 (63.2%) did not carry any of the resistance genes tested, while the remainder carried resistance genes for at least one antibiotic. The highest resistance rates among ovine isolates were recorded against tetracycline (20.8%), and penicillin (15.2%); none was resistant to methicillin and two exhibited multidrug resistance (MDR); one of which was positive for the antiseptic resistance smr gene. By contrast, most human isolates (59/70, 84.3%) were resistant to ⩾1 antimicrobials, and 41 (58.6%) were MDR. All 52 (74.3%) penicillin-resistant isolates possessed the blaZ gene, and 33 of 70 (47.1%) harboured the mec gene; of these, seven were characterised by the Staphylococcal Chromosomal Cassette (SCCmec) type IV, 6 the type V, 5 of type III and one representative each of type I and type II. The majority (57.1%) was erythromycin-resistant and 17 isolates carried only the efflux msrA gene, 11 the methylase ermC gene and an equal number harboured both of the latter genes. Moreover, 23 (32.8%) were tetracycline-resistant and all but one possessed only the efflux tetK gene. qacA/B and smr genes were detected in 27 (38.6%) and 18 (25.7%) human NAS, respectively. These results underline a marked difference in species distribution and antimicrobial resistance between ovine and human-derived NAS.

This paper describes the epidemiology of coronavirus disease 2019 (COVID-19) in Northern Ireland (NI) between 26 February 2020 and 26 April 2020, and analyses enhanced surveillance and contact tracing data collected between 26 February 2020 and 13 March 2020 to estimate secondary attack rates (SAR) and relative risk of infection among different categories of contacts of individuals with laboratory confirmed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection. Our results show that during the study period COVID-19 cumulative incidence and mortality was lower in NI than the rest of the UK. Incidence and mortality were also lower than in the Republic of Ireland (ROI), although these observed differences are difficult to interpret given considerable differences in testing and surveillance between the two nations. SAR among household contacts was 15.9% (95% CI 6.6%–30.1%), over 6 times higher than the SAR among ‘high-risk’ contacts at 2.5% (95% CI 0.9%–5.4%). The results from logistic regression analysis of testing data on contacts of laboratory-confirmed cases show that household contacts had 11.0 times higher odds (aOR: 11.0, 95% CI 1.7–70.03, P-value: 0.011) of testing positive for SARS-CoV-2 compared to other categories of contacts. These results demonstrate the importance of the household as a locus of SARS-CoV-2 transmission, and the urgency of identifying effective interventions to reduce household transmission.