Simulations for Cancer Prevention

Carcinogens seem to be everywhere, from automobile exhaust to secondhand smoke. With cancer the second leading cause of death in the US, research into various carcinogens may be the first step in preventing cancer from developing. Now, with the help of researchers at NYU, HPC may give us the necessary tools to curb cancer development. High performance computing resources help researchers model those airborne cancerous chemicals, known as polycyclic aromatic hydrocarbon (PAH), and their effect on DNA strands in human cells.

Carcinogens, or chemicals that manipulate the DNA of cells in such a way that the cells replicate uncontrollably leading to tumors, can be broken into two categories. Some of these chemicals destabilize the actual DNA strands, making them easier to defend against. Others, however, actually create stronger bonds between the strands than there exists between normal DNA, making them particularly effective at propagating themselves. Modeling complex forces and interactions is no small feat, as it was necessary to garner the coordinates of the structures over a period of time.

More information:

http://www.hpcwire.com/hpcwire/2012-11-07/computer_simulations_shed_light_on_cancer_prevention.html

Brain-Controlled Computer Cursors

When a paralyzed person imagines moving a limb, cells in the part of the brain that controls movement still activate as if trying to make the immobile limb work again. Despite neurological injury or disease that has severed the pathway between brain and muscle, the region where the signals originate remains intact and functional. In recent years, neuroscientists and neuroengineers working in prosthetics have begun to develop brain-implantable sensors that can measure signals from individual neurons, and after passing those signals through a mathematical decode algorithm, can use them to control computer cursors with thoughts. The work is part of a field known as neural prosthetics. A team of Stanford researchers have now developed an algorithm, known as ReFIT, which vastly improves the speed and accuracy of neural prosthetics that control computer cursors. In side-by-side demonstrations with rhesus monkeys, cursors controlled by the ReFIT algorithm doubled the performance of existing systems and approached performance of the real arm. Better yet, more than four years after implantation, the new system is still going strong, while previous systems have seen a steady decline in performance over time. The system relies on a silicon chip implanted into the brain, which records ‘action potentials’ in neural activity from an array of electrode sensors and sends data to a computer.

The frequency with which action potentials are generated provides the computer key information about the direction and speed of the user’s intended movement. The ReFIT algorithm that decodes these signals represents a departure from earlier models. In most neural prosthetics research, scientists have recorded brain activity while the subject moves or imagines moving an arm, analyzing the data after the fact. The Stanford team wanted to understand how the system worked ‘online’, under closed-loop control conditions in which the computer analyzes and implements visual feedback gathered in real time as the monkey neurally controls the cursor to toward an onscreen target. The system is able to make adjustments on the fly when while guiding the cursor to a target, just as a hand and eye would work in tandem to move a mouse-cursor onto an icon on a computer desktop. If the cursor were straying too far to the left, for instance, the user likely adjusts their imagined movements to redirect the cursor to the right. The team designed the system to learn from the user’s corrective movements, allowing the cursor to move more precisely than it could in earlier prosthetics. To test the new system, the team gave monkeys the task of mentally directing a cursor to a target — an onscreen dot — and holding the cursor there for half a second. ReFIT performed vastly better than previous technology in terms of both speed and accuracy. The path of the cursor from the starting point to the target was straighter and it reached the target twice as quickly as earlier systems, achieving 75 to 85 percent of the speed of real arms.

More information:

http://engineering.stanford.edu/research-profile/leap-forward-brain-controlled-computer-cursors

3D Smartphone Gesture

The clickwheel of the first iPod worked by measuring electric field disturbances in one dimension. The first iPhone touch screen functioned similarly, but in two dimensions. Microchip Technology, a large U.S. semiconductor manufacturer, is releasing the first controller that uses electrical fields to make 3D measurements.

The low-power chip makes it possible to interact with mobile devices and a host of other consumer electronics using hand gesture recognition, which today is usually accomplished with camera-based sensors. A key limitation is that it only recognizes motions, such as a hand flick or circular movement, within a six-inch range.

More information:

http://www.technologyreview.com/news/507161/a-new-chip-to-bring-3-d-gesture-control-to-smartphones/

Memory-Making Connection

A model that shows how connections in the brain must change to form memories could help to develop artificial cognitive computers. Exactly how memories are stored and accessed in the brain is unclear. Neuroscientists, however, do know that a primitive structure buried in the center of the brain, called the hippocampus, is a pivotal region of memory formation. Here, changes in the strengths of connections between neurons, which are called synapses, are the basis for memory formation. Networks of neurons linking up in the hippocampus are likely to encode specific memories.

Since direct tests cannot be performed in the brain, experimental evidence for this process of memory formation is difficult to obtain but mathematical and computational models can provide insight. To this end, researchers at the A*STAR Institute for Infocomm Research, Singapore, have developed a model that sheds light on the exact synaptic conditions required in memory formation. Since direct tests cannot be performed in the brain, experimental evidence for this process of memory formation is difficult to obtain but mathematical and computational models can provide insight.

More information:

http://www.sciencedaily.com/releases/2012/11/121108140847.htm

Smart as a Bird

Cornell researchers have created an autonomous flying robot that is as smart as a bird when it comes to maneuvering around obstacles. Able to guide itself through forests, tunnels or damaged buildings, the machine could have tremendous value in search-and-rescue operations. Small flying machines are already common, and GPS technology provides guidance. Now, researchers are tackling the hard part: how to keep the vehicle from slamming into walls and tree branches. Human controllers can't always react swiftly enough, and radio signals may not reach everywhere the robot goes. The test vehicle is a quadrotor, a commercially available flying machine about the size of a card table with four helicopter rotors. Researchers have already programmed quadrotors to navigate hallways and stairwells. But in the wild, current methods aren't accurate enough at large distances to plan a route around obstacles.

They are building on methods previously developed to turn a flat video camera image into a 3D model of the environment using such cues as converging straight lines. They also trained the robot with 3D pictures of such obstacles as tree branches, poles, fences and buildings; the robot's computer learns the characteristics all the images have in common, such as color, shape, texture and context. The resulting set of rules for deciding what is an obstacle is burned into a chip before the robot flies. In flight the robot breaks the current 3D image of its environment into small chunks based on obvious boundaries, decides which ones are obstacles and computes a path through them as close as possible to the route it has been told to follow, constantly making adjustments as the view changes. It was tested in 53 autonomous flights in obstacle-rich environments -- including Cornell's Arts Quad -- succeeding in 51 cases, failing twice because of winds.

More information:

http://www.news.cornell.edu/stories/Oct12/RescueRobot.html