Water access as a required public health intervention to fight COVID-19 in the Pacific Islands

In 2015, 193 United Nations (UN) member countries adopted Agenda 2030 as an agreed framework for sustainable development to 2030 [1], with seventeen sustainable development goals (SDGs). For health, water and sanitation, the relevant goals are SDG 3, to "ensure healthy lives and promote well-being for all at all ages", and SDG 6, to "ensure availability and sustainable management of water and sanitation for all"

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

Water and health interlinkages of the sustainable development goals in remote Indigenous Australia

Australia has committed to the 17 Sustainable Development Goals (SDGs) goals under the UN's 2030 Agenda. However, these goals may not be fully achieved in Australia under current policy settings. Australia reports success in achieving the goal for quality and access to safe drinking water and sanitation (SDG 6), though for Australians living in remote Indigenous communities, the experience is very different. Furthermore, the burden of disease is higher in remote communities (SDG 3). Many of these diseases are waterborne or hygiene-related, including prevalence in some remote Indigenous communities of endemic trachoma eye infection, preventable through access to functioning water services and available soap. This research provides a case for identifying, then understanding the interlinkages between SDGs 3, 6, and others locally, as well as nationally. This will enable governments to enact policies for long-term sustainable solutions for remotely-located and marginalised peoples in Australia in line with Agenda 2030 commitments

Multiparameter optimisation of a magneto-optical trap using deep learning

Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. Here, we implement a deep artificial neural network to optimise the magneto-optic cooling and trapping of neutral atomic ensembles. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used. Despite this, the solutions outperform best known solutions producing higher optical densities

Stopped and stationary light with cold atomic ensembles and machine learning

Quantum information systems demand methods for the storage and manipulation of qubits. For optical qubits, atomic ensembles provide a potential platform for such operations. In this work, we demonstrate a stopped light optical quantum memory with efficiency up to 87%. We also demonstrate and visualise stationary light, which could potentially enhance weak optical nonlinearities. At the heart of our experiments is a laser-cooled atomic ensemble, which has recently been optimised with the help of a machine learning system that uses an artificial neural network

On the relative abundance of autopolyploids and allopolyploids.

The prevalence of autopolyploids in angiosperms has long been a subject of debate. Meurountzing (1936) and Darlington (1937) conclude d that autopolyploids were common and important evolutionary entities. However, Clausen et al. (1945) and Stebbins (1947) subsequently considered them rare, in part because the criteria upon which interpretations of autopolyploidy were rendered were not rigorous. This position was reiterated by Grant (1981) decades later, although evidence was mounting that autopolyploid taxa might be important in natural populations (Lewis, 1980). As cytological and genetic data have accumulated, it has become increasingly apparent that the latter view is likely to be correct (Soltis et al., 2004b, 2007, 2010). However, it still appears that the majority of polyploids are allopolyploids (Parisod et al., 2010; Soltis et al., 2010), even though Ramsey & Schemske (1998, p. 467) conclude that 'the rate of autopolyploid formation may often be higher than the rate of allopol yploid formation.' In this letter we survey the literature to assess whether allopolyploids are indeed the prevailing cytotype in nature. Using our new estimates for the incidence of autopolyploidy and allopolyploidy, we discuss some of the evolutionary dynamics that may be driving their frequencies in nature. Finally, we suggest avenues for future research on polyploidy that build on our results and other recent progress in the field