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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.

Context:Pheochromocytomas and paragangliomas (PPGLs) in children are often hereditary and may present with different characteristics compared with adults. Hereditary PPGLs can be separated into cluster 1 and cluster 2 tumors due to mutations impacting hypoxia and kinase receptor signaling pathways, respectively.Objective:To identify differences in presentation of PPGLs between children and adults.Design:A retrospective cross-sectional clinical study.Setting:Seven tertiary medical centers.Patients:The study included 748 patients with PPGLs, including 95 with a first presentation during childhood. Genetic testing was available in 611 patients. Other data included locations of primary tumors, presence of recurrent or metastatic disease, and plasma concentrations of metanephrines and 3-methoxytyramine.Results:Children showed higher (P < 0.0001) prevalence than adults of hereditary (80.4% vs 52.6%), extra-adrenal (66.3% vs 35.1%), multifocal (32.6% vs 13.5%), metastatic (49.5% vs 29.1%), and recurrent (29.5% vs 14.2%) PPGLs. Tumors due to cluster 1 mutations were more prevalent among children than adults (76.1% vs 39.3%; P < 0.0001), and this paralleled a higher prevalence of noradrenergic tumors, characterized by relative lack of increased plasma metanephrine, in children than in adults (93.2% vs 57.3%; P < 0.0001).Conclusions:The higher prevalence of hereditary, extra-adrenal, multifocal, and metastatic PPGLs in children than adults represents interrelated features that, in part, reflect the lower age of disease presentation of noradrenergic cluster 1 than adrenergic cluster 2 tumors. The differences in disease presentation are important to consider in children at risk for PPGLs due to a known mutation or previous history of tumor.This study establishes the link between extraadrenal, multifocal, metastatic, reccurent, hereditary PPGLs to a higher prevalence of noradrenergic and cluster 1 tumors in pediatric than adults.

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.

Background While drugs have a limited role in the management of dental presentations, Australian dentists have high rates of inappropriate prescribing of antibiotics. There is also a lack of relevant drug resources for dentists in Australia. Our team developed Drugs4dent ® , a medicines decision support tool, that provides dentists with relevant drug knowledge, assists with appropriate prescribing and provides safety checks to reduce prescribing errors. The aim of this pilot study was to improve Drugs4dent ® with focus groups of dentists, and assess the acceptability, usability, and user experience, of Drugs4dent ® . Methods Focus groups of ten dentists were established to inform the improvement of Drugs4dent ® . Acceptability and usability testing of Drugs4dent ® was then undertaken with a further ten dentists using interviews and a survey. The survey was based on the Framework for Acceptability and System Usability Scale. Inductive thematic analysis was undertaken using Nvivo for the focus groups and interviews, and descriptive statistics for analysis of survey results. Results Dentists from the focus group and interviews found the content of Drugs4dent ® acceptable and useful for dentistry, recognising that similar drug information is currently not available. The majority agreed that Drugs4dent ® would improve their ability to prescribe according to guidance. Participants reported Drugs4dent ® was intuitive, and information was easy to locate. Most dentists preferred Drugs4dent ® integrated with their dental practice software. In the absence of this functionality, they preferred Drugs4dent ® as a standalone resource, without needing to input patient data. Drugs4dent ® was subsequently commercialised with MIMS Australia, to create the decision support tool: MIMS Drugs4dent ® . Conclusions Drugs4dent ® is the first dental medicines decision tool in Australia. The high acceptability and usability of the tool, and subsequent commercialisation indicates that MIMS Drugs4dent ® has substantial promise for the future, and can transform access to relevant drug information for Australian dentists.

To address privacy concerns, data in the blockchain should be encrypted in advance to avoid data access from all users in the blockchain. However, encrypted data cannot be directly retrieved, which hinders data sharing in the blockchain. Several works have been proposed to deal with this problem. However, the data retrieval in these schemes requires the participation of data owners and lacks finer-grained access control. In this paper, we propose an attribute-based keyword search scheme over the encrypted blockchain, which allows users to search encrypted files over the blockchain based on their attributes. In addition, we build a file chain structure to improve the efficiency of searching files with the same keyword. Security analysis proves the security of the proposed scheme. Theoretical analysis and experimental results in performance evaluation show that our scheme is feasible and efficient.

In recent years, one of the most popular techniques in the computer vision community has been the deep learning technique. As a data-driven technique, deep model requires enormous amounts of accurately labelled training data, which is often inaccessible in many real-world applications. A data-space solution is Data Augmentation (DA), that can artificially generate new images out of original samples. Image augmentation strategies can vary by dataset, as different data types might require different augmentations to facilitate model training. However, the design of DA policies has been largely decided by the human experts with domain knowledge, which is considered to be highly subjective and error-prone. To mitigate such problem, a novel direction is to automatically learn the image augmentation policies from the given dataset using Automated Data Augmentation (AutoDA) techniques. The goal of AutoDA models is to find the optimal DA policies that can maximize the model performance gains. This survey discusses the underlying reasons of the emergence of AutoDA technology from the perspective of image classification. We identify three key components of a standard AutoDA model: a search space, a search algorithm and an evaluation function. Based on their architecture, we provide a systematic taxonomy of existing image AutoDA approaches. This paper presents the major works in AutoDA field, discussing their pros and cons, and proposing several potential directions for future improvements.

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. 9EttwuCftMDTXMZ-D9rhTq Video abstract

Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex actions involving multiple people interacting with objects. Actions in team sports exhibit a different nature compared to other sports, since they tend to occur at a faster pace and involve more human-human interactions. As a result, research has typically not focused on the challenges of HAR in team sports. This paper comprehensively summarises HAR-related research and applications with specific focus on team sports such as football (soccer), basketball and Australian rules football. Key datasets used for HAR-related team sports research are explored. Finally, common challenges and future work are discussed, and possible research directions identified.

IntroductionThe 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 methodsInclusion 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.ResultsThe 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.ConclusionsThis 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 numberACTRN12613000794707.

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. A transient surge in serum mucosa‐associated cytokines was observed in children at genetic risk for type 1 diabetes followed from birth who developed islet autoantibodies, but not in children who did not develop islet autoantibodies. Our finding lends support to the view that mucosal, e.g., enteric, infection may drive the development of islet autoimmunity.