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Developing fit-for-purpose funding models for rural settings: Lessons from the evaluation of a step-up/step-down service in regional AustraliaSub-acute mental health community services provide a bridging service between hospital and community care. There is limited understanding of the local factors that influence success, and of the funding implications of delivering services in rural areas.
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Reliability of the Commonly Used and Newly-Developed Autism MeasuresThe aim of the present study was to compare scale and conditional reliability derived from item response theory analyses among the most commonly used, as well as several newly developed, observation, interview, and parent-report autism instruments.
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Inter-rater reliability and agreement of the General Movement Assessment and Motor Optimality Score-Revised in a large population-based samplePrechtl's General Movement Assessment (GMA) at fidgety age (3-5 months) is a widely used tool for early detection of cerebral palsy. Further to GMA classification, detailed assessment of movement patterns at fidgety age is conducted with the Motor Optimality Score-Revised.
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Arriving at the empirically based conceptualization of restricted and repetitive behaviors: A systematic review and meta-analytic examination of factor analysesAn empirically based understanding of the factor structure of the restricted and repetitive behaviors (RRB) domain is a prerequisite for interpreting studies attempting to understand the correlates and mechanisms underpinning RRB and for measurement development. Therefore, this study aimed to conduct a systematic review and meta-analysis of RRB factor analytic studies.
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Gender non-conformity in childhood and adolescence and mental health through to adulthood: A longitudinal cohort study, 1995-2018Few studies have examined associations between gender non-conformity (GNC) in childhood or adolescence and mental health outcomes later in life. This study examined associations between GNC and mental health over multiple time points in childhood and adolescence, and GNC in childhood and/or adolescence and mental health in adulthood.
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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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The relationship between pitch contours in infant-directed speech and early signs of autism in infancyMother-infant interactions during the first year of life are crucial to healthy infant development. The infant-directed speech (IDS), and specifically pitch contours, used by mothers during interactions are associated with infant language and social development.
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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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Pulmonary bacteriophage and cystic fibrosis airway mucus: friends or foes?For those born with cystic fibrosis (CF), hyper-concentrated mucus with a dysfunctional structure significantly impacts CF airways, providing a perfect environment for bacterial colonization and subsequent chronic infection. Early treatment with antibiotics limits the prevalence of bacterial pathogens but permanently alters the CF airway microenvironment, resulting in antibiotic resistance and other long-term consequences.
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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.