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To compare the performance of the standard Historical Limits Method (HLM), with a modified HLM (MHLM), the Farrington-like Method (FLM), and the Serfling-like Method (SLM) in detecting simulated outbreak signals. We used weekly time series data from 12 infectious diseases from the U.S. Centers for Disease Control and Prevention’s National Notifiab   ...more

While some studies suggest cultural differences in visual processing, others do not, possibly because the complexity of their tasks draws upon high‐level factors that could obscure such effects. To control for this, we examined cultural differences in visual search for geometric figures, a relatively simple task for which the underlying mechanisms   ...more

This letter presents the development and evaluation of concurrent control of a robotic system for less-invasive treatment of osteolytic lesions behind an acetabular implant. This system implements safety constraints including a remote center of motion, virtual walls, and joint limits while operating through the screw holes of the acetabular implan   ...more

Public health institutions at local, regional, and national levels face evolving challenges with limited resources. Multiple forms of data are increasingly available, ranging from streaming statistical data to episodic reports of confirmed disease incidence. While technological tools for collecting and using these data proliferate, economic pressu   ...more

Biological response to blast overpressure is complex and results in various and potentially non-concomitant acute and long-term deficits to exposed individuals. Clinical links between blast severity and injury outcomes remain elusive and have yet to be fully described, resulting in a critical inability to develop associated protection and mitigati   ...more

We describe the recent development of assistive computer vision algorithms for use with the Argus II retinal prosthesis system. While users of the prosthetic system can learn and adapt to the limited stimulation resolution, there exists great potential for computer vision algorithms to augment the experience and significantly increase the utility   ...more

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Ashley Llorens
Ashley.Llorens@jhuapl.edu
240-228-0312

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Tricia Latham
Patricia.Latham@jhuapl.edu
240-228-8048

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