Big data for metabolic pathways; wristband seizure detection; garbage-collecting robots.
Drilling into metabolic details with big data
In a development that may help researchers find new therapeutic targets for cancer and other diseases, Rice University researchers have created a fast computational method to model tissue-specific metabolic pathways.
The team explained that metabolic pathways are immense networks of biochemical reactions that keep organisms functioning and are also implicated in many diseases, and present the kind of challenges “big data” projects are designed to address.
In recent years, computer scientists have built ways to model these networks in humans, particularly since the 2007 introduction of the first genome-scale model of human metabolic pathways, but the big picture doesn’t have all the answers. For example, a pathway in the liver might not act the same way as an identical chain in the muscle.
To this end, the Rice lab of bioengineer Amina Qutub designed an algorithm — Cost Optimization Reaction Dependency Assessment (CORDA) — to model metabolic pathways specific to their home tissues.
The researchers expect their algorithm will be a broad tool to model cell- and tissue-specific metabolism as CORDA yields tissue-specific models that are more comprehensive and functional than previous algorithms, and allows for quick comparison of models to understand differences across tissues.
Wristband monitors, alerts grand mal seizures
In order to help people with epilepsy, and survive sudden unexpected death in epilepsy, a company called Empatica, co-founded by MIT professor and wearables pioneer Rosalind Picard, has developed a medical-quality consumer wristband that monitors stress signals to detect potentially deadly seizures and alert wearers and caregivers, so they can intervene.
Researchers worldwide are using a scientific version of the wristband, called the E4, which also measures other signals, to study epilepsy and other neurological and psychiatric conditions. Numerous academic papers are now published, showing that the combined electrodermal activity (EDA), also known as skin conductance, and motion data collected from the wrist improve the accuracy of seizure detection over using only motion data.
Empatica is preparing to release Embrace, “a consumer-looking, but medical-quality device” for monitoring stress and seizures, Picard said.
Apart from detecting seizures, anyone can also use the wristbands to monitor stress levels — which is important for good health, given that chronic stress has been linked to numerous health issues such as heart disease, obesity, and diabetes. Stress signals reach every organ of your body, so these stress signals are potentially influencing everything.
The company aims to aid people suffering from epilepsy by helping them better alert loved ones. An app that comes with the device that lets wearers and others monitor when the person might be having a grand mal seizure.
The wristbands resemble watches but have a solid silver or black face. Sensors underneath the face track pulse, body motion, temperature, and EDA, which involves subtle electrical changes across the skin. Boosts in EDA, without accompanying changes in motion, can signal stress. In people with epilepsy, a sharp rise in both signals could indicate a severe, potentially life-threatening seizure.
When the wristband detects a seizure, it vibrates, and the wearer can respond. If the wearer becomes unconscious, which happens with the most dangerous seizures, and doesn’t respond quickly, the app sends an alert to a designated individual.
Drone-assisted, garbage-collecting robot
Students from three universities have collaborated with the Volvo Group and the waste recycling company, Renova, on a robot that automatically collects and empties refuse bins. A drone on the roof of the refuse truck scans the area and helps the robot to find the bins.
The project is a collaboration between the Volvo Group, Chalmers University of Technology, Mälardalen University, Penn State University, and Renova.
Through the student project, Robot-based Autonomous Refuse (ROAR) handling, the intent was to demonstrate how smart machines will soon be able to communicate with each other to facilitate everyday life in a large number of areas – not just refuse handling.
In technical terms, a prerequisite for the robot’s work is that it already knows the neighborhood in the form of a map of both the maneuverable area and likely bin locations. The robot then uses a number of different sensors to keep itself positioned within this map, enabling it to automatically perform its tasks. The sensors include GPS, LiDAR (a system similar to radar but using infrared light instead of radio waves), cameras, and IMU data, which uses accelerometers and gyroscope for navigation as well as odometry where motion sensors measure the position changes over time.
It took the students and researchers from the three participating universities only four months to design and build the prototype robot that automatically collects and empties the refuse bins. Mälardalen University was responsible for designing the robot itself and Penn State University developed the web based 3D interface that allows the driver to monitor the situation and, if need be, control the robot.