Tuberculosis (TB) is a potentially fatal infectious disease that, in Aotearoa New Zealand (NZ), inequitably affects Asian, Pacific, Middle Eastern, Latin American, and African (MELAA), and Māori people. Medical research involving genome sequencing of TB samples enables more nuanced understanding of disease strains and their transmission. This could inform highly specific health interventions. However, the collection and management of TB isolate samples for research are currently informed by monocultural biomedical models often lacking key ethical considerations. Drawing on a qualitative kaupapa (...) Māori–consistent study, this paper reports on preliminary discussions with groups of Māori, Pacific, and Afghan people in NZ, whose communities have been harmed by TB and TB stigma. Participants’ discussions highlight key concerns and meanings that ought to inform the development of guidelines and a more robust consultative process for the governance of how TB isolate samples are collected and used both retrospectively and in future research. We argue for ethical processes to be culturally nuanced and community-generated, flexible and meaningful, and situated in relation to the physical and symbolic effects of TB. We discuss the significance of Indigenous data sovereignty, rights, and kāwanatanga (governorship) in shaping a multicultural data sovereignty model. (shrink)

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates. BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ’learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the (...) relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. (shrink)

Background More than one in 10 of all prisoners in England and Wales are Foreign Nationals. This article discusses whether the research applications to one London prison are aimed at understanding a prisoner population characterised by significant multinational and multilingual complexity. Methods We studied all accessible documents relating to research undertaken at a women's prison between 2005 and 2009 to assess the involvement of Foreign National prisoners and women with limited English. The source of information was prison research applications and (...) protocols. We also looked at available final research reports and journal articles. Results Two key findings emerged from this study. First, studies at this prison frequently excluded Foreign National prisoners and women with limited English. Second, Foreign National prisoners were often clustered as a homogeneous category in the research reports reviewed. This is despite their diverse cultural backgrounds, their variable immigration status and their differing competence in English, all of which affect their lives. Conclusions The failure to include and/or identify social subgroups of the population can undermine the value of research, including, in the case of the study prison, funded health research. This can compromise associated needs assessments and service delivery, particularly important in already disadvantaged populations; this may encourage and/or perpetuate a range of health inequalities. There is a pressing need to examine cultural exclusion in other health and criminal justice settings, to assess the ways in which—and the extent to which—such exclusion may compromise the merit of proposed and completed health and social research. (shrink)