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Introduction The Environmental Determinants of Islet Autoimmunity (ENDIA) Study is an ongoing Australian prospective cohort study investigating how modifiable prenatal and early-life exposures drive the development of islet autoimmunity and type 1 diabetes (T1D) in children. In this profile, we describe the cohort’s parental demographics, maternal and neonatal outcomes and human leukocyte antigen (HLA) genotypes.Research design and methods Inclusion criteria were an unborn child, or infant aged less than 6 months, with a first-degree relative (FDR) with T1D. The primary outcome was persistent islet autoimmunity, with children followed until a T1D diagnosis or 10 years of age. Demographic data were collected at enrollment. Lifestyle, clinical and anthropometric data were collected at each visit during pregnancy and clinical pregnancy and birth data were verified against medical case notes. Data were compared between mothers with and without T1D. HLA genotyping was performed on the ENDIA child and all available FDRs.Results The final cohort comprised 1473 infants born to 1214 gestational mothers across 1453 pregnancies, with 80% enrolled during pregnancy. The distribution of familial T1D probands was 62% maternal, 28% paternal and 11% sibling. The frequency of high-risk HLA genotypes was highest in T1D probands, followed by ENDIA infants, and lowest among unaffected family members. Mothers with T1D had higher rates of pregnancy complications and perinatal intervention, and larger babies of shorter gestation. Parent demographics were comparable to the Australian population for age, parity and obesity. A greater percentage of ENDIA parents were Australian born, lived in a major city and had higher socioeconomic advantage and education.Conclusions This comprehensive profile provides the context for understanding ENDIA’s scope, methodology, unique strengths and limitations. Now fully recruited, ENDIA will provide unique insights into the roles of early-life factors in the development of islet autoimmunity and T1D in the Australian environment.Trial registration number ACTRN12613000794707.

ABSTRACT Aims/Introduction Autoantibodies to pancreatic islet antigens identify young children at high risk of type 1 diabetes. On a background of genetic susceptibility, islet autoimmunity is thought to be driven by environmental factors, of which enteric viruses are prime candidates. We sought evidence for enteric pathology in children genetically at‐risk for type 1 diabetes followed from birth who had developed islet autoantibodies (“seroconverted”), by measuring mucosa‐associated cytokines in their sera. Materials and Methods Sera were collected 3 monthly from birth from children with a first‐degree type 1 diabetes relative, in the Environmental Determinants of Islet Autoimmunity (ENDIA) study. Children who seroconverted were matched for sex, age, and sample availability with seronegative children. Luminex xMap technology was used to measure serum cytokines. Results Of eight children who seroconverted, for whom serum samples were available at least 6 months before and after seroconversion, the serum concentrations of mucosa‐associated cytokines IL‐21, IL‐22, IL‐25, and IL‐10, the Th17‐related cytokines IL‐17F and IL‐23, as well as IL‐33, IFN‐γ, and IL‐4, peaked from a low baseline in seven around the time of seroconversion and in one preceding seroconversion. These changes were not detected in eight sex‐ and age‐matched seronegative controls, or in a separate cohort of 11 unmatched seronegative children. Conclusions In a cohort of children at risk for type 1 diabetes followed from birth, a transient, systemic increase in mucosa‐associated cytokines around the time of seroconversion lends support to the view that mucosal infection, e.g., by an enteric virus, may drive the development of islet autoimmunity.

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.

Abstract Background The gut microbiome changes in response to a range of environmental conditions, life events and disease states. Pregnancy is a natural life event that involves major physiological adaptation yet studies of the microbiome in pregnancy are limited and their findings inconsistent. Pregnancy with type 1 diabetes (T1D) is associated with increased maternal and fetal risks but the gut microbiome in this context has not been characterized. By whole metagenome sequencing (WMS), we defined the taxonomic composition and function of the gut bacterial microbiome across 70 pregnancies, 36 in women with T1D. Results Women with and without T1D exhibited compositional and functional changes in the gut microbiome across pregnancy. Profiles in women with T1D were distinct, with an increase in bacteria that produce lipopolysaccharides and a decrease in those that produce short-chain fatty acids, especially in the third trimester. In addition, women with T1D had elevated concentrations of fecal calprotectin, a marker of intestinal inflammation, and serum intestinal fatty acid-binding protein (I-FABP), a marker of intestinal epithelial damage. Conclusions Women with T1D exhibit a shift towards a more pro-inflammatory gut microbiome during pregnancy, associated with evidence of intestinal inflammation. These changes could contribute to the increased risk of pregnancy complications in women with T1D and are potentially modifiable by dietary means. Video abstract

The idea of big data has gained extensive attention from governments and academia all over the world. It is especially relevant for the establishment of a smart city environment combining complex heterogeneous data with data analytics and artificial intelligence (AI) technology. Big data is generated from many facilities and sensor networks in smart cities and often streamed and stored in the cloud storage platform. Ensuring the integrity and subsequent auditability of such big data is essential for the performance of AI-driven data analysis. Recent years has witnessed the emergence of many big data auditing schemes that are often characterized by third party auditors (TPAs). However, the TPA is a centralized entity, which is vulnerable to many security threats from both inside and outside the cloud. To avoid this centralized dependency, we propose a decentralized big data auditing scheme for smart city environments featuring blockchain capabilities supporting improved reliability and stability without the need for a centralized TPA in auditing schemes. To support this, we have designed an optimized blockchain instantiation and conducted a comprehensive comparison between the existing schemes and the proposed scheme through both theoretical analysis and experimental evaluation. The comparison shows that lower communication and computation costs are incurred with our scheme than with existing schemes.

Abstract Background Computational bioinformatics workflows are extensively used to analyse genomics data, with different approaches available to support implementation and execution of these workflows. Reproducibility is one of the core principles for any scientific workflow and remains a challenge, which is not fully addressed. This is due to incomplete understanding of reproducibility requirements and assumptions of workflow definition approaches. Provenance information should be tracked and used to capture all these requirements supporting reusability of existing workflows. Results We have implemented a complex but widely deployed bioinformatics workflow using three representative approaches to workflow definition and execution. Through implementation, we identified assumptions implicit in these approaches that ultimately produce insufficient documentation of workflow requirements resulting in failed execution of the workflow. This study proposes a set of recommendations that aims to mitigate these assumptions and guides the scientific community to accomplish reproducible science, hence addressing reproducibility crisis. Conclusions Reproducing, adapting or even repeating a bioinformatics workflow in any environment requires substantial technical knowledge of the workflow execution environment, resolving analysis assumptions and rigorous compliance with reproducibility requirements. Towards these goals, we propose conclusive recommendations that along with an explicit declaration of workflow specification would result in enhanced reproducibility of computational genomic analyses.

AbstractBackground: The Environmental Determinants of Islet Autoimmunity (ENDIA) pregnancy-birth cohort investigates the developmental origins of type 1 diabetes (T1D), with recruitment between 2013 and 2019. ENDIA is the first study in the world with comprehensive data and biospecimen collection during pregnancy, at birth and through childhood from at-risk children who have a first-degree relative with T1D. Environmental exposures are thought to drive the progression to clinical T1D, with pancreatic islet autoimmunity (IA) developing in genetically susceptible individuals. The exposures and key molecular mechanisms driving this progression are unknown. Persistent IA is the primary outcome of ENDIA; defined as a positive antibody for at least one of IAA, GAD, ZnT8 or IA2 on two consecutive occasions and signifies high risk of clinical T1D.Method: A nested case-control (NCC) study design with 54 cases and 161 matched controls aims to investigate associations between persistent IA and longitudinal omics exposures in ENDIA. The NCC study will analyse samples obtained from ENDIA children who have either developed persistent IA or progressed to clinical T1D (cases) and matched control children at risk of developing persistent IA. Control children were matched on sex and age, with all four autoantibodies absent within a defined window of the case’s onset date. Cases seroconverted at a median of 1.37 years (IQR 0.95, 2.56). Longitudinal omics data generated from approximately 16,000 samples of different biospecimen types, will enable evaluation of changes from pregnancy through childhood.Conclusions: This paper describes the ENDIA NCC study, omics platform design considerations and planned univariate and multivariate analyses for its longitudinal data. Methodologies for multivariate omics analysis with longitudinal data are discovery-focused and data driven. There is currently no single multivariate method tailored specifically for the longitudinal omics data that the ENDIA NCC study will generate and therefore omics analysis results will require either cross validation or independent validation.KEY MESSAGESThe ENDIA nested case-control study will utilize longitudinal omics data on approximately 16,000 samples from 190 unique children at risk of type 1 diabetes (T1D), including 54 who have developed islet autoimmunity (IA), followed during pregnancy, at birth and during early childhood, enabling the developmental origins of T1D to be explored.

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.