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Nature Connection: Providing a Pathway from Personal to Planetary HealthThe vast and growing challenges for human health and all life on Earth require urgent and deep structural changes to the way in which we live. Broken relationships with nature are at the core of both the modern health crisis and the erosion of planetary health. A declining connection to nature has been implicated in the exploitative attitudes that underpin the degradation of both physical and social environments and almost all aspects of personal physical, mental, and spiritual health.
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Surgery for rheumatic heart disease in the Northern Territory, Australia, 1997-2016: what have we gained?Between 1964 and 1996, the 10-year survival of patients having valve replacement surgery for rheumatic heart disease (RHD) in the Northern Territory, Australia, was 68%. As medical care has evolved since then, this study aimed to determine whether there has been a corresponding improvement in survival.
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Maternal diet modulates the infant microbiome and intestinal Flt3L necessary for dendritic cell development and immunity to respiratory infectionPoor maternal diet during pregnancy is a risk factor for severe lower respiratory infections in the offspring, but the underlying mechanisms remain elusive. Here, we demonstrate that in mice a maternal low-fiber diet led to enhanced LRI severity in infants because of delayed plasmacytoid dendritic cell recruitment and perturbation of regulatory T cell expansion in the lungs.
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Copy number variation in tRNA isodecoder genes impairs mammalian development and balanced translationThe number of tRNA isodecoders has increased dramatically in mammals, but the specific molecular and physiological reasons for this expansion remain elusive. To address this fundamental question we used CRISPR editing to knockout the seven-membered phenylalanine tRNA gene family in mice, both individually and combinatorially.
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Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq dataMachine learning (ML) algorithms are powerful tools that are increasingly being used for sepsis biomarker discovery in RNA-Seq data. RNA-Seq datasets contain multiple sources and types of noise (operator, technical and non-systematic) that may bias ML classification. Normalisation and independent gene filtering approaches described in RNA-Seq workflows account for some of this variability and are typically only targeted at differential expression analysis rather than ML applications.
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Reference exome data for Australian Aboriginal populations to support health-based researchOur data set provides a useful reference point for genomic studies on Aboriginal Australians
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Spotting sporotrichosis skin infection: The first Australian paediatric case seriesThese data highlight the importance of recognising Sporotrichosis in children outside an outbreak setting
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Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015Almost half of stillbirths could be potentially identified antenatally based on a combination of factors
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Lessons learned in genetic research with Indigenous Australian participantsWe reflect on the lessons learned from a recent genome‐wide association study of rheumatic heart disease with Aboriginal Australian participants
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Oestrogen amplifies pre-existing atopy-associated Th2 bias in an experimental asthma modelThe role of oestrogen in experimental atopic asthma, and guide future research on sex-related variations in atopic asthma susceptibility/intensity