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Knowledge of the mechanisms governing transfer, persistence, and recovery of trace evidence, together with background prevalence in the population of interest, and other task relevant information, is key for the forensic interpretation and reconstruction of what happened at the activity level. Up to now, this informational "toolkit" has largely been developed through empirical forensic studies on specific trace materials such as glass, textile fibers, and soil. Combined with the identified systemic siloing between disciplines, while valuable, such research tends to be very material-dependent, introducing specific parameters and interpretations that may have actually impeded the recognition of underlying foundational factors applicable to most material types. In Australia, there has been a renewed interest in developing a discipline-independent framework for the interpretation and/or reconstruction of trace evidence to interpret specific circumstances in casework. In this paper, we present a discipline agnostic "way of thinking" that has been anchored in foundational science underpinning the trace evidence discipline. Physical and mechanical material properties such as material geometry and surface topography, strength, stiffness, and hardness collectively influence contact interactions through underlying friction, wear, and lubrication cause and effect mechanisms. We discuss how these fundamental factors and parameters stemming from materials science and tribology may be adopted and adapted by forensic practitioners and researchers to contribute to a better understanding of transfer, persistence, and recovery mechanisms irrespective of evidence discipline and material type. Examples are provided to demonstrate the practical significance to real-life casework and academic research.

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Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In this work, we introduce Neuroimaging PheWAS (phenome-wide association study): a web-based system for searching over a wide variety of brain-wide imaging phenotypes to discover true system-level gene-brain relationships using a unified genotype-to-phenotype strategy. This design features a user-friendly graphical user interface (GUI) for anonymous data uploading, study definition and management, and interactive result visualizations as well as a cloud-based computational infrastructure and multiple state-of-art methods for statistical association analysis and multiple comparison correction. We demonstrated the potential of Neuroimaging PheWAS with a case study analyzing the influences of the apolipoprotein E (APOE) gene on various brain morphological properties across the brain in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Benchmark tests were performed to evaluate the system's performance using data from UK Biobank. The Neuroimaging PheWAS system is freely available. It simplifies the execution of PheWAS on neuroimaging data and provides an opportunity for imaging genetics studies to elucidate routes at play for specific genetic variants on diseases in the context of detailed imaging phenotypic data.

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The biomedical sciences have experienced an explosion of data which promises to overwhelm many current practitioners. Without easy access to data science training resources, biomedical researchers may find themselves unable to wrangle their own datasets. In 2014, to address the challenges posed such a data onslaught, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative. To this end, the BD2K Training Coordinating Center (TCC; bigdatau.org) was funded to facilitate both in-person and online learning, and open up the concepts of data science to the widest possible audience. Here, we describe the activities of the BD2K TCC and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials. ERuDIte now indexes over 9,500 resources. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required - in effect, using data science to inform training in data science. In so doing, the TCC seeks to democratize novel insights and discoveries brought forth via large-scale data science training.

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