Despite the tremendous success of X-ray cryo-crystallography in recent decades, the transfer of crystals from the drops in which they are grown to diffractometer sample mounts remains a manual process in almost all laboratories. Here, the Shifter, a motorized, interactive microscope stage that transforms the entire crystal-mounting workflow from a rate-limiting manual activity to a controllable, high-throughput semi-automated process, is described. By combining the visual acuity and fine motor skills of humans with targeted hardware and software automation, it was possible to transform the speed and robustness of crystal mounting. Control software, triggered by the operator, manoeuvres crystallization plates beneath a clear protective cover, allowing the complete removal of film seals and thereby eliminating the tedium of repetitive seal cutting. The software, either upon request or working from an imported list, controls motors to position crystal drops under a hole in the cover for human mounting at a microscope. The software automatically captures experimental annotations for uploading to the user's data repository, removing the need for manual documentation. The Shifter facilitates mounting rates of 100–240 crystals per hour in a more controlled process than manual mounting, which greatly extends the lifetime of the drops and thus allows a dramatic increase in the number of crystals retrievable from any given drop without loss of X-ray diffraction quality. In 2015, the first in a series of three Shifter devices was deployed as part of the XChem fragment-screening facility at Diamond Light Source, where they have since facilitated the mounting of over 120 000 crystals. The Shifter was engineered to have a simple design, providing a device that could be readily commercialized and widely adopted owing to its low cost. The versatile hardware design allows use beyond fragment screening and protein crystallography.
The fields of Wearable Computing, Augmented Reality and Ubiquitous Computing are in principle highly convergent, as they all promise a utopian future in which the devices embedded in the environment, our bodies and our clothes will have reached a level integration such that we can intuitively perceive and interact with our environment. However; the reality as practised in research labs and limited commercial deployments has been that budgetary and technical constraints have actually kept these fields separate and distinct. One manifestation of this separation is in the choice of sensors used to build systems in each domain. A truly cross-disciplinary project has to incorporate sensors of much greater heterogeneity than has occurred heretofore. The way in which sensors are deployed results in spatial seams that can act as obstacles to the provision of services across different areas. This paper takes an architectural approach to handling events from different tracking systems and maintaining a consistent spatial model ofpeople and objects. The principal distinguishing feature is the automatic derivation ofdataflow network ofdistributed sensors, dynamically and at run-time, based on requirements expressed by clients.
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