Contemporary Wiradjuri relatedness in Peak Hill, New South Wales.

Wiradjuri Aboriginal people in Peak Hill, a small economically-declining town in central rural New South Wales, have been subjected to a century of government policies included segregation, assimilation, and forced relocations. Despite this local, colonial history Peak Hill Wiradjuri continue to experience daily life in a distinctively Wiradjuri way. To ‘be Wiradjuri’ is to be embedded within a complex web of close relationships that are socially, morally and emotionally developed with both kin and friends, human and non-human subjects. Despite dramatic social and cultural transformations in Wiradjuri meanings and practices of relatedness, the Wiradjuri social world and their ways of self-experience remain informed by past practices. To understand contemporary socialities, and thus the significance of these transformations, this thesis is an examination of the ways in which the moral and emotional order of relatedness governs relatedness, where daily lived experience of shared emotional states can be understood in terms of a language for the self and moral framework. Specifically, this thesis is an exploration of how Wiradjuri people negotiate relatedness in a space in which shared and contrasting Wiradjuri and non-Wiradjuri inter-subjectivities are experienced. This study draws on historical research and ethnographical fieldwork to move beyond an analysis of kinship in terms of structures, roles or values to explore the deeper foundations of emotions and states of being in everyday life

The Key Features of a Genetic Nondiscrimination Policy

Importance: Governments worldwide have become increasingly cognizant of the spread of genetic discrimination (negative treatment or harm on the basis of actual or presumed genetic characteristics). Despite efforts by a number of governments to establish regulations addressing this phenomenon, public concern about genetic discrimination persists. Objective: To identify key elements of an optimal genetic nondiscrimination policy and inform policymakers as they seek to allay genetic nondiscrimination and related public anxieties. Evidence Review: Sixty multidisciplinary experts from 20 jurisdictions worldwide were consulted to understand their views on effective genetic nondiscrimination policies. Following standard requirements of the Delphi method, 3 rounds of surveys over the course of 1.5 years were conducted. Round 1 focused on assessing participants' understanding of the intricacies of existing genetic nondiscrimination policies, while rounds 2 and 3 invited participants to reflect on specific means of implementing a more effective regime. A total of 60 respondents participated in the first round, 53 participated in round 2, and 43 participated in round 3. Findings: While responses varied across disciplines, there was consensus that binding regulations that reach across various sectors are most useful in preventing genetic discrimination. Overall, experts agreed that human rights-based approaches are well suited to preventing genetic discrimination. Experts also agreed that explicit prohibition of genetic discrimination within nondiscrimination policies can highlight the importance of genetic nondiscrimination as a fundamental right and ensure robust protection at a national level. While most participants believed the international harmonization of genetic nondiscrimination laws would facilitate data sharing worldwide, they also recognized that regulations must reflect the sociocultural differences that exist among regions. Conclusions and Relevance: As the reach of genetic discrimination continues to evolve alongside developments in genomics, strategic policy responses that are harmonious at the international and state levels will be critical to address this phenomenon. In seeking to establish comprehensive frameworks, policymakers will need to be mindful of regional and local circumstances that influence the need for and efficacy of unique genetic nondiscrimination approaches across diverse contexts

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT

Abstract: Background This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable.Materials and methods A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models.Results Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR.Conclusion We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules