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Anthony Finkelstein, James Hetherington, Linzhong Li, Ofer Margoninski, Peter Saffrey, Rob Seymour, and Anne Warner
By posing novel computational challenges and stretching the state of the art, bioinformatics has become the computing response to the molecular revolution in biology. But bioinformatics is only the first step in reshaping the life sciences. For further progress, we must return to the study of whole biological systems: the heart, cardiovascular system, brain, and liver.
Progress in systems biology will require computer scientists to work closely with life scientists and mathematicians. In contrast to the molecular biology revolution, computer science will actively engage in shaping systems biology. The prize to be attained is immense, ranging from in silico drug design and testing to individualized medicine that takes into account physiology and genetic profiles.
Intel's Proactive Health lab emerged from an anthropological study of households that had been early adopters of broadband technology. Almost every study participant over age 40 asked for technology to help with the care of aging parents. As the worldwide population over age 65 doubles in the next 20 years, the caregiving needs of this population will become an ever greater part of our personal lives and social healthcare costs.
The lab is applying digital home technologies to the development of "aging in place" personal health systems. These applications of wireless sensors, adaptive interfaces, real-time data capture, and context-aware feedback provide a rigorous testbed for digital home technologies. They also support an alternative to the costly "mainframe" healthcare that dominates current medical science and practice. Ultimately, aging-in-place research supports fundamental new ways of understanding both aging and disease processes to help us all better manage our health.
Alex (Sandy) Pentland
Until recently, researchers have had little success in extending healthcare into the home environment, yet there clearly is a huge demand for this service. Americans currently spend $27 billion on healthcare outside the formal medical establishment, which they find difficult, expensive, and painful to access. A dramatic shift in the composition of the US population makes it absolutely necessary to develop such distributed systems.
To address these demands, a research group at the MIT Media Lab has been developing healthwear, wearable systems with sensors that can continuously monitor the user's vital signs, motor activity, social interactions, sleep patterns, and other health indicators. The system's software can use the data from these sensors to build a personalized profile of the user's physical performance and nervous system activation throughout the entire day—providing a truly personal medical record that could revolutionize healthcare.
With the advent of fixed-rate broadband access, many home computers now connect to the Internet 24 hours a day. Users often configure these PCs to have private Internet protocol addresses, which home routers can translate to and from a single static or dynamic global IP address assigned by an Internet service provider.
The author proposes an alternative approach that provides remote access to various IP-ready sensors, computers, cameras, and microphones installed in the home environment to monitor the health and safety of bedridden quadriplegic patients. This system configures all its IP devices with private IP addresses to ensure that it remains isolated from network attacks on global ports, retains flexibility with respect to sensor changes, and minimizes the number of global IP addresses required. In a typical scenario, this meet-in-the-middle network will be useful for providing remote access from a corporate intranet to home.
Patrick T. Eugster, Rachid Guerraoui, Anne-Marie Kermarrec, and Laurent Massoulié
Epidemic algorithms have recently gained popularity as a potentially effective solution for disseminating information in large-scale systems, particularly peer-to-peer systems deployed on Internet or ad hoc networks. These algorithms mimic the spread of a contagious disease: Each process in a distributed system relays new information it has received to randomly chosen peers rather than to a server or cluster of servers in charge of forwarding it. In turn, each of these processes forwards the information to other randomly selected processes, and so on.
Although researchers have used epidemic algorithms in applications such as failure detection, data aggregation, and database replication, their general applicability to practical, Internet-wide systems remains unproven. The authors suggest possible solutions to four key problems—membership maintenance, network awareness, buffer management, and message filtering.