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All known life is homochiral. DNA and RNA are made from “righthanded” nucleotides, and proteins are made from “left-handed” amino acids. Driven by curiosity and plausible applications, some researchers had begun work toward creating lifeforms composed entirely of mirror-image biological molecules. Such mirror organisms would constitute a radical departure from known life, and their creation warrants careful consideration. The capability to create mirror life is likely at least a decade away and would require large investments and major technical advances; we thus have an opportunity to consider and preempt risks before they are realized. Here, we draw on an indepth analysis of current technical barriers, how they might be eroded by technological progress, and what we deem to be unprecedented and largely overlooked risks. We call for broader discussion among the global research community, policy-makers, research funders, industry, civil society, and the public to chart an appropriate path forward. 

Intrinsically safe designs and a staged transparent development process will be essential 

Hand-to-Face transmission has been estimated to be a minority, yet non-negligible, vector of COVID-19 transmission and a major vector for multiple other pathogens. At the same time, as it cannot be effectively addressed with mainstream protection measures, such as wearing masks or tracing contacts, it remains largely untackled. To help address this issue, we have developed Saving Face - an app that alerts users when they are about to touch their faces, by analyzing the distortion patterns in the ultrasound signal emitted by their earphones. The system only relies on pre-existing hardware (a smartphone with generic earphones), which allows it to be rapidly scalable to billions of smartphone users worldwide. This paper describes the design, implementation and evaluation of the system, as well as the results of a user study testing the solution's accuracy, robustness, and user experience during various day-to-day activities (93.7% Sensitivity and 91.5% Precision, N=10). While this paper focuses on the system's application to detecting hand-to-face gestures, the technique can also be applicable to other types of gestures and gesture-based applications.