MATH NEWS

May 16, 2012 TOPS IN TEACHING: Boas Honored as 2012 Presidential Professor www.science.tamu.edu Boas is one of five Presidential Professors for Teaching Excellence in the College of Science and the first recipient of Texas A&M University's most prestigious award for teaching performance from the Department of Mathematics. COLLEGE STATION -- Dr. Harold P. Boas, professor of mathematics, has been named a Presidential Professor for Teaching Excellence at Texas A&M University. Boas was honored at weekend commencement ceremonies along with his fellow award-winning colleague, Dr. Patricia Goodson, professor of health and kinesiology and director of POWER (Promoting Outstanding Writing for Excellence in Research) Services in the College of Education and Human Development. "Dr. Boas and Dr. Goodson personify the teaching qualities that we value so highly at Texas A&M -- caring for students, being dedicated and innovative and being leaders in their fields," said Texas A&M President R. Bowen Loftin. "They join a growing number of distinguished faculty who have had bestowed on them this special designation -- the university's highest form of recognition for teaching excellence." The prestigious award, established in 2003 by former Texas A&M President Robert M. Gates to underscore the importance of teaching at a major research university, provides for presentation each spring of two Presidential Professor for Teaching Excellence Awards, each with a $25,000 stipend that includes the title of "Presidential Professor for Teaching Excellence" -- a designation retained by the recipient for the remainder of his or her career. Boas has been a member of the faculty in the Texas A&M Department of Mathematics since 1984 and is known for his innovative approach to teaching and his lasting influence on students. He is the recipient of numerous awards and honors for teaching and scholarship, including the first Outstanding Teaching Award from the Department of Mathematics in 1994. He was a recipient of The Association of Former Students Distinguished Achievement Award in Teaching for the College of Science in 2009 and at the university level in 2010. Known as a professor and mentor who inspires his students, Boas has seen many of his former students become professors of mathematics. As one former student noted, "Dr. Boas defines what a true teacher is: someone who makes you feel as though you are learning with him, not for him." Another former student stated, "Not many days go by that I do not think of the influence that Dr. Boas has had on my life." According to university officials, this reputation stems from Boas' contributions to Texas A&M's teaching mission and his philosophy of teaching: love the students. He was a moving force behind several important curriculum development initiatives in the Department of Mathematics, including its distance education programs. Also, he has contributed significantly to the discipline of mathematics, as a member and chair of the Educational Testing Service's Committee of Examiners for the Mathematics Subject Test of the Graduate Record Examination and as the book review editor of the American Mathematical Monthly as well as an editor of Notices of the American Mathematical Society, among other activities. Nominations for the Presidential Professor awards are made by students, faculty members and deans in each of the university's colleges. Faculty Senate representatives review each nomination and narrow the list that is sent to the president for the final selections. Boas is the fifth College of Science faculty member to merit the coveted honor since its inception in 2003, the same year in which physics professor William H. Bassichis earned selection as one of the award's two inaugural recipients. Chemistry professors David E. Bergbreiter, the late John L. Hogg and Dr. Wendy L. Keeney-Kennicutt also received the honor in 2006, 2007 and 2009, respectively. -aTm- Contact: Lane Stephenson, Division of Marketing & Communications, (979) 845-4662 or l-stephenson@tamu.edu Stephenson Lane 2012-05-15 00:00:00 TOPS IN TEACHING: Boas Honored as 2012 Presidential Professor

May 16, 2012 Font for digits lets numbers punch their weight www.newscientist.com Sicily in number font with mount Etna in the north-east (Image: M. Nacent/U. Hinrichs/S. Carpendale) 12 May 2012 by Jacob Aron THE symbols we use to represent numbers are, mathematically speaking, arbitrary. Now there is a way to write numbers so that their areas equal their numerical values. The font, called FatFonts, could transform the art of data visualisation, allowing a single infographic to convey both a visual overview and exact values. "Scientific figures might benefit from this hybrid nature because scientists want both to see and to read data," says Miguel Nacenta, a computer scientist at the University of St Andrews, UK, who developed the concept with colleagues at the University of Calgary, Canada. Infographics are all the rage as a means to display information now that computers can gather and sort vast reams of data. However, fancy charts and images often obscure the actual data behind them. To get the best of both worlds, Nacenta's team designed a font in which a 2 has an area exactly twice that of a 1, a 3, triple, and so on. For single digit numbers, this was fairly straightforward. The team measured the area of each number and then thickened or thinned sections of it so that the total area scaled in proportion to each number's value. A digit like 2 did not need much thickening as it already covers a larger area than 1. By contrast 7 needed a lot of thickening as its total area is small relative to its value. To represent numbers with multiple digits, the team devised a system of nested digits. In the case of 489, the 8 sits inside the 4 and covers an area 10 times smaller, while the 9 sits inside the 8 and covers an area 10 times smaller still. This makes 999 the largest number possible with FatFonts, as the smallest part of a four digit number would be too little to see easily. To demonstrate the usefulness of the concept, Nacenta's team used FatFonts to create a map of Sicily in which each number represents the height of the ground at that geographical location (see images). For easy reading they scaled the heights so that the largest is represented by the number 99. As with existing heat maps, where areas associated with larger numbers appear proportionally darker, standing back from the map makes it easy to identify mountainous regions. However, the FatFonts map has the added advantage that moving closer lets you compare points numerically, too. FatFonts are most effective when printed on large, high-resolution wall displays, says Nacenta. These are big enough to see the individual numbers, as well as larger-scale trends. "It is a clever way of using numbers for visualisation," says Robert Kosara, an information visualisation researcher at the University of North Carolina at Charlotte, although he suspects FatFonts could be misleading in some cases. "The next step is to run a user study and compare it to a heat map and other techniques." Nacenta's team will present their work at the Advanced Visual Interfaces conference in Naples, Italy, later this month. "We are hoping that typeface designers and other people will pick up on our idea and create their own versions," says Nacenta. Font for digits lets numbers punch their weight

May 16, 2012 New twist on ancient math problem could improve medicine, microelectronics www.ns.umich.edu Joshua Anderson (right) and Sharon Glotzer, members of The Glotzer Group in the Laboratory for Computational Nanoscience & Soft Matter, demonstrate their new mathematical problem: the best way to fill a space with overlapping shapes while respecting the boundary. Image credit: Joseph Xu, College of Engineering Published on May 10, 2012 Written by Kate McAlpine, Phone: (734) 647-7085, E-mail: kmca@umich.edu ANN ARBOR, Mich.—A hidden facet of a math problem that goes back to timeworn Sanskrit manuscripts has just been exposed by nanotechnology researchers at the University of Michigan and the University of Connecticut. It turns out we've been missing a version of the famous "packing problem," and its new guise could have implications for cancer treatment, secure wireless networks, microelectronics and demolitions, the researchers say. Called the "filling problem," it seeks the best way to cover the inside of an object with a particular shape, such as filling a triangle with discs of varying sizes. Unlike the traditional packing problem, the discs can overlap. It also differs from the "covering problem" because the discs can't extend beyond the triangle's boundaries. "Besides introducing the problem, we also provided a solution in two dimensions," said Sharon Glotzer, U-M professor of chemical engineering. That solution makes it immediately applicable to treating tumors using fewer shots with radiation beams or speeding up the manufacturing of silicon chips for microprocessors. The key to solutions in any dimension is to find a shape's "skeleton," said Carolyn Phillips, a postdoctoral fellow at Argonne National Laboratory who recently completed her Ph.D. in Glotzer's group and solved the problem as part of her dissertation. "Every shape you want to fill has a backbone that goes through the center of the shape, like a spine," she said. For a pentagon, the skeleton looks like a stick-drawing of a starfish. The discs that fill the pentagon best will always have their centers on one of those lines. Junctions between lines in the skeleton are special points that Glotzer's team refers to as "traps." The pentagon only has one trap, right at its center, but more complicated shapes can contain multiple traps. In most optimal solutions, each trap has a disc centered over it, Phillips said. Other discs in the pattern change size and move around, depending on how many discs are allowed, but those over the traps are always the same. Phillips suspects that if a design uses enough discs, every trap will have a disc centered over it. In their paper, published online today in Physical Review Letters, the researchers report the rules for how to find the ideal size and spacing of the discs that fill a shape. In the future, they expect to reveal an algorithm that can take the desired shape and the number of discs, or the shape and percentage of the area to be filled, and spit out the best pattern to fill it. Extending the approach into three dimensions, Glotzer proposes that it could decide the placement of wireless routers in a building where the signal must not be available to a potential hacker in the parking lot. Alternatively, it could help demolition workers to set off precision explosions, ensuring that the blast covers the desired region but doesn't extend beyond a building's outer walls. Phillips expects filling solutions to be scientifically useful as well. Glotzer's team developed the new problem by trying to find a way to represent many-sided shapes for their computer models of nanoparticles. In addition to nanotechnology, biology and medicine often need models for complex shapes, such as those of proteins. "You don't want to model every single one of the thousands of atoms that make up this protein," Phillips said. "You want a minimal model that gives the shape, allowing the proteins to interact in a lock-and-key way, as they do in nature." The filling approach may prove a perfect fit for a variety of fields. The paper is titled "Optimal filling of shapes." Funding for this study included grants from the Department of Energy and the Department of Defense Related Links: Sharon Glotzer: http://che.engin.umich.edu/people/glotzer.html New twist on ancient math problem could improve medicine, microelectronics

May 16, 2012 Subways 'share universal structure', research suggests www.bbc.co.uk/news/ Slime moulds grow to seek "optimum" networks that parallel subway organisation 16 May 2012 A study of the world's largest subway networks has revealed that they are remarkably mathematically similar. The layouts seem to converge over time to a similar structure regardless of where or over how long they were built. The study, in the Journal of the Royal Society Interface, analysed 14 subway networks around the world. It found common distributions of stations within the networks, as well as common proportions of the numbers of lines, stations, and total distances. In some senses, it is unsurprising that the study found that networks tended over time to comprise a dense core of central stations with a number of lines radiating outward from it. By choosing the world's largest networks, from Beijing to Barcelona, the results were bound to represent networks that serve city centres with a dense collection of stations and bring commuters inward from more distant stations. But the analysis shows a number of less obvious similarities across all 14 networks. It found the total number of stations was proportional to the square of the number of lines - that is, a four-fold increase in station number would result in a doubling of the number of lines. The dense core of central stations all had the same average number of neighbours in the network, and in all cases, about half the total number of stations were found outside the core. In addition, the length of any one branch from the core's centre was about the same as twice the diameter of the core, and the number of stations at a given distance from the centre was proportional to the square of that distance. The authors analysed how the networks grew and added lines and stations, finding that they all converged over time to these similar structures. They authors point out that the similarities exist regardless of where the networks were, when they were begun, or how quickly they reached their current layout. "Although these (networks) might appear to be planned in some centralised manner, it is our contention here that subway systems like many other features of city systems evolve and self-organise themselves as the product of a stream of rational but usually uncoordinated decisions taking place through time," they wrote. The authors say that the systems do not appear to be "fractal". Fractal systems follow mathematical patterns that seem equivalent in a number of physical and social systems ranging from the movements of planets to the movements of depressed people, but they may or may not reflect a deeper, more universal organisational principle. Nevertheless, the team wrote that some underlying rule is likely to be driving the way subway systems end up worldwide. "The existence of unique long-time limit topological and spatial features is a universal signature that fundamental mechanisms, independent of historical and geographical differences, contribute to the evolution of these transportation networks," they wrote. Subways 'share universal structure', research suggests

May 16, 2012 Inside a mathematical proof lies literature, says Stanford's Reviel Netz news.stanford.edu The study of mathematics as text is a growing area of academic inquiry. (Photo illustration: Anna Cobb) Stanford Report, May 7, 2012 Stanford scholar Reviel Netz discusses why some of the greatest mathematicians were also some of classical history's most poetic storytellers. BY CORRIE GOLDMAN The Humanities at Stanford Like novelists, mathematicians are creative authors. With diagrams, symbolism, metaphor, double entendre and elements of surprise, a good proof reads like a good story. Reviel Netz, a professor of classics and, by courtesy, of philosophy, is especially interested in exploring the literary dimensions of the textual artifacts left by the likes of Archimedes and Euclid. Netz, one of the world's preeminent experts on the works of Archimedes, sees proofs as narratives that lead the reader turn by turn through an unfolding story that ends with a mathematical solution. In his book Ludic Proof: Greek Mathematics and the Alexandrian Aesthetic, Netz reveals the stunning stylistic similarities between Hellenistic poetry and mathematical texts from the same era. Earlier this spring, Netz and other scholars engaged in the study of mathematics as text gathered at Stanford to discuss this growing area of academic inquiry. How did you become interested in exploring the idea of mathematics as literature? I was always interested in the ways in which mathematics – or science in general – is concretely experienced: not the abstract logic of it but the heartbeat-by-heartbeat sense of getting the piece of knowledge through one's eyes, hands, thoughts. This gives rise very naturally to the thought of thinking of the taking-in of a mathematical text alongside the taking-in of a text in general, which is something literary scholars are sometimes interested in. You have said that a math proof is more focused on the properties of text than any other human endeavor, short of poetry. Mathematics is structured around texts – proofs – that have very rich protocols in terms of their textual arrangement, whether in the use of extra-verbal elements – diagrams – in the very layout, in the use of a particular formulaic language, in the structuring of the text. And its success or failure depends entirely on features residing in the text itself. It is really an activity very powerfully concentrated around the manipulation of written documents, more perhaps than anywhere else in science, and comparable, then, to modern poetry. Can parallels be drawn between pre-modern mathematics and pre-modern literature? Of course, and my book Ludic Proof: Greek Mathematics and the Alexandrian Aesthetic is precisely about the manner in which surprise, remarkable juxtapositions and the hybridization of genres are key norms for both mathematics and poetry in the culture of the Hellenistic Mediterranean. How does an aesthetic examination of a mathematical text differ from studying the mathematical information? When you study the information, you may translate the contents to whichever form suits you and consider its validity, axiomatic underpinning, etc; when you study its aesthetics, you must remain focused on the text as perceived historically. How do you define or identify literary-like elements like metaphors in a mathematical proof? Metaphor is fairly standard in mathematics. Mathematics can only become truly interesting and original when it involves the operation of seeing something as something else – a pair of similarly looking triangles, say, as a site for an abstract proportion; a diagonal crossing through the set of all real numbers. You have said that a proof can be seen as having a complex narrative and even elements of surprise much like how a story unfolds. Can you give me an example? You tell me, "I'm going to find the volume of a sphere." And then you do nothing of the kind, going instead through an array of unrelated results – a cone here, a funny polygon there, various proportion results and general problems; then you make a thought experiment that shows how a sphere is like a series of cones produced from a certain funny polygon and, lo and behold, all the results do allow one a very quick determination of the volume of the sphere. Here is surprise and narrative. That's Archimedes' "Sphere and Cylinder" proof; it's a typical mechanism in his works. Other authors are often much more sedate and progress in a more stately manner; this is Euclid's approach. Why is there a growing interest in studying the "aesthetic dimension of human endeavor," and why is this a valuable course of study, especially in relation to mathematics? There is an interest in the embodied experience of various fields, which gives rise to an interest in aesthetic. The unfortunate thing is that many scholars who study culture share this feeling but instead of actually engaging in the study of the aesthetic of a given culture – which would mean, essentially, a study of the formal properties of a historically situated group of artifacts – they engage in the ideas people had about aesthetics. Would you say there is an art to being a mathematician? Yes, it's a cliché really, nothing I came up with. Mathematicians are always intensely aware of the aesthetic dimension of their own craft. I basically just try to say this is no mere waxing lyrical, there's a concrete reality that needs to be studied. Media Contact Corrie Goldman, Stanford Humanities Outreach Officer: (650) 724-8156, corrieg@stanford.edu Dan Stober, Stanford News Service: (650) 721-6965, dstober@stanford.edu Inside a mathematical proof lies literature, says Stanford's Reviel Netz

May 06, 2012 Is Origami the Future of Tech? www.businessweek.com Photograph by Leonard Greco for Bloomberg Businessweek By Drake Bennett on May 03, 2012 In 1996 a young mathematician and computer scientist named Erik Demaine became fascinated by a magic trick that Harry Houdini used to do before he made his name as an escape artist. The magician would fold a piece of paper flat a few times, make one straight cut with a pair of scissors, and then unfold the paper to reveal a five-pointed star. Other magicians built on Houdini’s fold-and-cut method over the years, creating more intricate shapes: a single letter, for example, or a chain of stars. It’s an odd subject of study for a computer science professor, but Demaine had an unorthodox background. When he was hired by the Massachusetts Institute of Technology in 2001, he was, at 20, the youngest professor in the university’s history. Pale, thin, and soft-spoken, with a pickpocket’s long fingers and a fox-colored ponytail, Demaine was born in Halifax, Nova Scotia, and raised by his father, Martin, a renowned glass blower. When Demaine was six, he and his father started a puzzle company. When he was seven, his father took him out of school and they spent four years traveling the U.S., choosing their destinations together. They spent a few months in Florida, Demaine recalls, “because it was so flat and good for riding bicycles.” Martin home-schooled his son while they moved from place to place. The traveling stopped when Erik returned to Halifax to enroll in Dalhousie University at age 12. It was his father who brought the fold-and-cut problem to his attention when, still a teenager, Demaine was searching for a dissertation topic. It was clear that one could create complicated shapes by folding and cutting, but how complicated? What were the limits? It was a simple but potentially universal question, exactly the sort that most appealed to Erik. He worked on the problem for two years and in 1998 published a paper with his answer: There were no limits. Fold-and-cut allowed you to make any shape in the world, any collection of shapes, even, as long as they had straight sides. One could, in an angular font, create the entire text of this page with the right folds and the right cut. In a paper published two years later, Demaine expanded on this idea, extending it into three dimensions: Any faceted solid, he showed, no matter how complex or irregular, could be folded from a single uncut sheet of paper. Start with a piece of paper big enough, and you could model Notre Dame down to the last gargoyle. At least in theory. Demaine’s computational origami work assumed an idealized sheet of paper with a thickness of zero, a sheet that could be folded an unlimited number of times. Still, his method gave a sense of the possibilities of folding. He has since become an accomplished origami artist. He’s also made a robot from a single sheet of plastic. When we think of mass production, the image is of a factory floor. Take a car. The engine block is cast, either from iron or aluminum. The hood, doors, and roof are stamped out on 100-ton presses. Gears are carved from metal blocks by milling machines or punched out by dies. The console and interior handles are injection-molded or carved, the mats and seats woven or stitched together. Some of these processes date to the Industrial Revolution, others to the Iron Age. The natural world doesn’t use any of them. One of its favorite methods is to take something flat and fold it into a three-dimensional form. Flowers, leaves, wings, proteins, mountain ranges, eyelids, ears, DNA—all are created by folding. Today researchers in robotics, biology, math, and computer science are immersing themselves in that method. Scientists are looking at how materials and molecules wrinkle, drape, flex, and crease. They’re using folding to design everything from robots to cancer drugs, from airbags to mirrors for satellite telescopes. An Oxford University engineer named Zhong You has used origami to design better-crumpling car bumpers and flexible, low-cost stents. A team at Wake Forest University has used origami folding to create a fabric of densely layered nanotubes that can generate power from body heat. In a range of fields, fabrication by folding has the potential to be far faster, cheaper, and less energy-intensive than traditional methods and to work at very, very small scales, where even the most precise mills and lathes have all the accuracy of an earthquake. Makers of medical equipment and consumer electronics are looking at folding as a way to streamline manufacturing processes. “We have a paradigm where we want to build things by having a solid block and then etching away at the block until you get whatever shape that you want,” says William Shih, a Harvard University biochemistry professor. Think of Michelangelo chiseling his forms from boulders of marble, or a milling machine carving an engine part out of a hunk of steel. “The way that nature does things is different,” Shih says. “It uses a folding algorithm, and it’s something that seems to be very efficient. We can look to nature for inspiration.” Shih himself has designed devices at the nanoscale that assemble themselves out of DNA strands, a process known as DNA origami. Folding is, at heart, a geometry problem, and the groundwork for much of the new research is being laid by mathematicians. The increasingly ingenious applications, though, are driven by collaborations between engineers, scientists, and programmers: “Biologically inspired engineering” is an ambitious new way of doing science that treats living organisms like mechanical systems. Just as the diameter of a gear or the strength of a spring determines how a clock works, the shape and tensile qualities of folded proteins determine their roles in the countless processes that keep the human body running. Deciphering those relationships and building off of them are part of what the new science of folding is about. Two of Demaine’s collaborators are the roboticists Robert Wood of Harvard and Daniela Rus of MIT. Rus is one of the world’s leading researchers of “programmable matter”: substances that can change their own physical properties. Programmable materials could allow for the creation of devices that take on multiple dissimilar tasks, repair themselves, or evolve. The Defense Advanced Research Projects Agency, or Darpa, has a programmable matter project, and it helped fund the work that Wood, Rus, and Demaine have done together. Most programmable matter research has focused on small modular robots and other devices that can combine into various larger forms. Folding is a different approach, and it has the advantage of having been studied both by mathematicians such as Demaine and by generations of origami practitioners. As Rus, Wood, and Demaine wrote in a June 2010 paper in the Proceedings of the National Academy of Sciences, programmable matter that folds could be used to create a Swiss Army knife-like tool that could morph from a wrench to an antenna to a tripod. Rus has spoken of the possibility of kinetic maps that could reproduce the topography of the landscape they’re showing, and Demaine of solar panels reconfiguring themselves to more efficiently capture sunlight. The first challenge to realizing such machines is figuring out whether the sort of folding required is conceptually possible. Origami is all about crease patterns: choosing a fold pattern locks one into a single final form, whether it’s a crane or a flower. Demaine wanted to find out whether there was a crease pattern that was an exception to this, a sort of universal origami building block. He discovered it in a fold called the box pleat: a square grid of creases with alternating diagonals that origami artists had long known could be used to create a wide variety of different shapes. Demaine’s contribution, once again, was to prove just how wide that variety was. Using computational geometry, he proved that the box pleat worked as a three-dimensional pixel (a voxel). “If you want to make any shape out of little cubes, then this crease pattern is enough,” he says. Geometrically speaking, all you had to do was start with enough squares. In the summer of 2008, Rus, Wood, and the researchers in their labs set out to see if Demaine’s mathematical proof could be translated into an actual device. Their aim was a small prototype, a sheet that could fold itself into two simple shapes: an airplane and a boat. Paper thickness and paper size, the issues that always separate computational origami from real world folding, would only be exacerbated in this transformer robot. The “paper” would need circuitry, and the wires would have to be able to stretch at the robot’s joints, since each fold doubled the sheet’s thickness. In addition, the motors at the folds that would drive the transforming process would have to be very thin but also powerful, since they would be acting at the joint, the point of least leverage (think of pushing open a door right next to the hinge rather than at the side where the handle is). Over the next year the designers came up with solutions to each of the challenges. The final sheet, a square half a millimeter thick and 4 centimeters to a side, comprises 32 identical fiberglass triangles with silicone rubber joints between them. To create the wiring for the joints, Rus and Wood cut copper-laminated plastic into a mesh so it could expand, accordion-like, across the folds while still carrying a current. The robot’s muscles were thin foil strips made of a “shape memory” metal alloy that would either fold or straighten when electricity was run through it. Its robot brain was a sticker. Since his days as a University of California at Berkeley graduate student, Wood has been working on developing a life-size robotic bee. A tiny aerial robot, he argues, could be used to gather information in military and search-and-rescue operations, to explore hazardous environments, even to pollinate plants if bee populations continue to decline. Today, Wood’s RoboBee is part of an arms race of small drones: There’s also the Nano Hummingbird, built by AeroVironment for Darpa, and the 22-inch quadrotors being developed at the University of Pennsylvania (their feats of coordinated flying have made them YouTube (GOOG) sensations). The RoboBee remains a more rudimentary flyer; its designers are still working on getting it into a stable hover. The considerable challenge of sustained, controlled flight is exacerbated for the RoboBee by the fact that it’s a fraction of the size of its competitors—39 mm from wingtip to wingtip, 18 mm from head to tail, and one-sixtieth the weight of a quarter. The RoboBee’s miniature scale has also created fabrication problems. Until recently the robots had to be built by hand using microscopes, tweezers, and superglue. The laborious process took a year to learn, and even then the merest twitch of an assembler’s fingers could ruin the work. Nine out of 10 parts ended up defective. At Harvard, Wood and the graduate students in his lab were constantly looking for ways to improve the process. A couple of them, including Wood, have small children, and two years ago they realized they had a model for how to rethink their fabrication process right in their homes: pop-up books. “You open up the page, and out pops this complicated structure,” Wood says. “All of the assembly trajectories are built into that laminated two-dimensional structure.” The magic of pop-up is that the clumsiest infant can make it happen. The unfolding is easy; what’s tricky is making the cuts and folds beforehand that ensure that everything properly deploys. If the engineers in the lab could create an assembly method that did the same thing, they would be able to turn a painstaking task into a mass production process. The minirobots could be stamped out by the sheet. Two of Wood’s graduate students, Pratheev Sreetharan and J. Peter Whitney, set to work on pop-up assembly, poring over how-to books on pop-up design and trading e-mails with a German pop-up sculptor. Sreetharan, a physicist by training, took the lead in creating a production process for the RoboBee. He had to map out the dizzying choreography that would, in a single movement, lift every piece of the bee from two dimensions into three, without any of the parts colliding as they swung into place. He spent months on a computer design program diagramming the cuts he would have to make to guarantee the right folds, and he built large-scale models in cardboard and glue to test his ideas. “One of the strengths I brought to this,” says Sreetharan, “was the fortitude to just spend that long working on it.” The basic concept was to stack all of the bee’s building materials—multiple layers of carbon fiber (for the body), titanium (the wing frames), piezoelectric ceramic (to flap the wings), and a flexible plastic polyimide film (the joints)—one on top of the other like plywood, using dowels to align them. Each of the 18 strata was precision-cut with a laser, about 3,000 cuts per layer, and bonded to its neighbors at particular points with a solid adhesive. Some of the robot’s structural elements, such as the wing frames, were made from just one layer. Others emerged from the interaction of multiple strata—the joints, for example, are polyimide sandwiched between layers of carbon fiber, with small gaps cut out of the stiff carbon to allow for articulation, like the elbows in a suit of armor. The circuitry for sensing and controlling the RoboBee’s flight, Sreetharan says, can simply be printed onto some of the layers with the same techniques chip companies use to make circuit boards. By the spring of last year, Sreetharan thought he was close. He had come up with the idea of laser-cutting the carbon fiber around the bee into what he called an “assembly scaffold,” an intricate network of joints that would guide each component in the stack to its place as it rose, then could be easily cut away once the bee was locked into shape. One day in April, he laser-cut a prototype and, placing it under a microscope, began very slowly to raise it into shape, using a specialized jig and crank he had created. To his surprise, it worked nearly perfectly. Sreetharan is a pianist, and he remembers thinking as the bee emerged that there was something symphonic about the process. “It has so many different parts that are all basically in harmony,” he says. “Nothing in it is still. Everything happens together in such an ordered and controlled way.” His latest RoboBees can be popped into shape in less than a tenth of a second—he had to slow the process to make a video of it. Their predecessors took a week to make by hand. Wood is one of 17 founding faculty members of an ambitious research alliance at Harvard called the Wyss Institute, whose mission in large part is to work with researchers to commercialize their ideas. What particularly excites him and his colleagues about pop-up folding is that it potentially can be used to make all sorts of complex devices. “It’s a manufacturing strategy that we think is going to revolutionize everything from microelectronics to toy manufacturing,” says Donald Ingber, a Harvard Medical School professor and director of the Wyss. One of the companies working with Wood’s group is the Hong Kong-based toymaker WowWee, whose past hits include the Robosapien toy robot and Paper Jamz, a set of musical instruments made out of circuit-embedded paper. “We’re very interested, ” says Davin Sufer, the company’s chief technology officer. “We have a few product concepts that we’re working on together right now.” WowWee is looking at using pop-up fabrication to make toys that, like the RoboBee, would combine complex electronics with precision moving parts. “Toys are typically very labor intensive; we can save costs and make products more efficiently this way,” Sufer says. “We’re also looking at making products smaller and more compact than we could otherwise.” For many researchers the true measure of folding’s potential is the profusion of ways nature uses it. One of Wood’s colleagues and collaborators is L. Mahadevan, a Harvard mathematician who studies the fold algorithms of insect wings, leaves, and flowers. Also at the Wyss, a biophysicist named Shawn Douglas, working with immunologist Ido Bachelet, has built something called a DNA origami nanorobot, a drug-delivery device one-ten-thousandth of the width of the period at the end of this sentence. The bot has shown the ability, at least in petri dishes, to identify cancerous cells and release a payload of antibodies that kills them while leaving healthy cells untouched. Douglas and Bachelet built their smart bombs using DNA origami, which takes advantage of the way DNA base pairs—the teeth in the zipper of the double helix—bond to each other. The method involves mixing single strands of DNA to form a three-dimensional structural lattice. By determining the base pairs’ order, DNA origami designers can determine where the strands bond and thus the three-dimensional form the lattice folds itself into. It’s not really origami; it’s more like the world’s smallest, most complicated balloon animal. Researchers elsewhere are working on the puzzle of how proteins fold. The building blocks of human cells, proteins are strands of amino acids—they are extruded, spaghetti-like, from cellular machines called ribosomes. Biologists still don’t fully understand what determines the intricate shapes that the long molecules snap into once they’re completed. It’s a question with huge ramifications—Alzheimer’s, mad cow, and various cancers are thought to be caused by protein misfolding. One particularly promising approach to understanding protein folding is a website called Foldit, created by David Baker, a biochemist at the University of Washington. It’s a game—anyone can go to the website and play—that awards points for finding the most likely folding pattern for a protein. Certain players have proven to have a real knack for it, and their cumulative efforts have solved problems that have long stumped biochemists. The goal of researchers such as Baker isn’t just understanding how protein folding works and how it can go awry but also designing entirely new proteins. The forms proteins take determine their function—the corkscrew shape of actin is the mechanism by which muscles contract, the long, clingy arms of fibrin molecules form blood clots. Creating proteins that fold into new shapes could lead to compounds with new abilities: pharmaceuticals, chemical catalysts, molecules that take carbon dioxide out of the air or cause toxic compounds to break down. Baker has developed a protein that seems to block H1N1 flu infection, and Foldit players are at work on similar proteins for other flu viruses. As Baker describes it, what he and the Foldit players are doing is essentially protein origami. “Living systems have all these different things that they can do and all of these different chemical reactions that they can catalyze, but those are just the chemical reactions that were of interest during the evolution of life,” he says. “We in our modern world have a whole bunch of problems that were never encountered during evolution, so we’d like to be able to make new proteins that solve those problems.” Demaine has also studied protein folding. Along with advanced geometry, he’s using some of the same assumptions behind Foldit. “If we can find efficient, nice folding algorithms for proteins,” he says, “maybe nature is following those.” What excites him at the moment, though, is something called curved-crease folding, a method that dates to the 1920s and the artists and designers of the Bauhaus. Curving folds, it turns out, have a particular power: a simple pattern can create a menagerie of intricate three-dimensional shapes and can change the qualities of a piece of paper, giving it the ability to stretch and drape in new ways. Demaine is curious why that is. “There’s very little theory on curved-crease origami,” he says. He’s not sure whether anything useful will emerge from the work, and as a mathematician that’s not his concern. His father is now an artist-in-residence and visiting scientist at MIT, and together the two have created a series of curved-crease sculptures. Some are in the permanent collection of the Smithsonian American Art Museum, others were on view earlier this spring at a Manhattan gallery. They look like some sort of mutant mollusk rendered by M.C. Escher. They seem, at the same time, both impossible and perfectly natural. Is Origami the Future of Tech?

May 06, 2012 LBNL Scientist Elected to National Academy of Sciences www.hpcwire.com BERKELEY, Calif., May 3 -- John Bell, an applied mathematician and computational scientist who leads the Center for Computational Sciences and Engineering and the Mathematics and Computational Science Department at Lawrence Berkeley National Laboratory, has been elected to the National Academy of Sciences. Bell was one of 84 new members and 21 foreign associates announced by the National Academy of Sciences on May 1, 2012, in recognition of their distinguished and continuing achievements in original research. The National Academy of Sciences is a private organization of scientists and engineers dedicated to the furtherance of science and its use for the general welfare. It was established in 1863 by Congress, which can call on the Academy to act as an official adviser to the federal government, upon request, in any matter of science or technology. “John’s election to the National Academy of Sciences is great news and a fitting recognition of his work in developing algorithms to advance the study of a wide range of scientific problems,” said Katherine Yelick, Associate Laboratory Director for Computing Sciences at Berkeley Lab. “John and his team have helped scientists better understand problems ranging from combustion to carbon sequestration to supernovae. We’re proud to have him at Berkeley Lab.” Bell is well known for his contributions in the areas of finite difference methods, numerical methods for low Mach number flows, adaptive mesh refinement, interface tracking, and parallel computing and the application of these numerical methods to problems from a broad range of fields including combustion, shock physics, seismology, flow in porous media, and astrophysics. He is the co-author of more than 160 research papers. View his web page. Bell is the deputy director of the Department of Energy’s Combustion Exascale Co-Design Center, a five-year project to investigate numerical algorithms, data management and programming models needed to simulate combustion on future exascale computer architectures. In 2009, he was one of five Berkeley Lab mathematicians in the first group of researchers elected as fellows of the Society for Industrial and Applied Mathematics (SIAM). In 2005 he was awarded the Institute of Electrical and Electronics Engineering’s (IEEE) Sidney Fernbach Award “for outstanding contributions to the development of numerical algorithms, mathematical, and computational tools and on the application of those methods to conduct leading-edge scientific investigations in combustion, fluid dynamics, and condensed matter." In 2003, Bell and fellow NAS member Phillip Colella were co-recipients of the 2003 SIAM/ACM Prize in Computational Science and Engineering, awarded by SIAM and the Association for Computing Machinery (ACM) for “outstanding contributions to the development and use of mathematical and computational tools and methods for the solution of science and engineering problems.” Bell earned his M.S. and Ph.D. from Cornell University after receiving a B.S. from MIT, all in mathematics. He worked as a researcher at the Naval Surface Weapons Center and Exxon Production Research Company before joining Lawrence Livermore National Laboratory in 1986. In 1996, Bell and his group moved from LLNL to Berkeley Lab. LBNL Scientist Elected to National Academy of Sciences

May 06, 2012 Dr. David Eisenbud Named the Next Director of MSRI newswise.com Released: 5/4/2012 4:00 PM EDT Source: Mathematical Sciences Research Institute Distinguished UC Berkeley mathematician returns to lead international math institute Newswise — BERKELEY, California – Dr. Phillip Griffiths, chair of the Board of Trustees of the Mathematical Sciences Research Institute (MSRI, www.msri.org) announced today the appointment of Dr. David Eisenbud as the next director of MSRI, the prominent international math institute based in Berkeley, California. David Eisenbud’s four-year term at MSRI is effective August 1, 2013. Currently, Eisenbud is a professor in the Department of Mathematics at the University of California at Berkeley, and he is the director for Mathematics and the Physical Sciences at the Simons Foundation in New York. In fact, David Eisenbud is returning to helm MSRI, where he previously served as director for a 10-year tenure from 1997 to 2007. “David Eisenbud’s remarkable vision saw MSRI through a fantastic transformation in the first decade of the twenty-first century, and his intimate knowledge of its workings ensures that he will be an inspiring leader in his second Directorship,” commented Robert Bryant, Director of MSRI. “It is an exciting challenge for me to maintain the wonderful programs that Robert Bryant has supervised and to try to develop them further,” said David Eisenbud. “I’m honored to have been asked to return to this position, and I look forward to the work ahead.” Simultaneously with the directorship, Eisenbud will continue to serve on the faculty at UC Berkeley. David Eisenbud’s (re)appointment as director of MSRI follows a nationwide search process led by a committee of the Institute’s trustees chaired by Richard Schoen, which included Elwyn Berlekamp, Ruth Charney, Helmut Hofer, and Hugo Sonnenschein, and Robion Kirby, who represented UC Berkeley’s Math Department. After considering a number of excellent candidates, the committee presented its recommendation to MSRI’s Board, which made the final selection. “The search committee is very impressed with David Eisenbud’s deep commitment to the mission of MSRI and with his vision for the future of the Institute. With David’s strong leadership skills and firsthand experience, we feel that he is well positioned to provide the optimal mix of continuity and self-correction for MSRI during the next several years,” said Richard Schoen. David Eisenbud received his Ph.D. in mathematics in 1970 at the University of Chicago under Saunders Mac Lane and Chris Robson, and was on the faculty at Brandeis University before going to the University of California, Berkeley, where he has been professor of mathematics since 1997. Eisenbud has been a visiting professor at Harvard University and a research professor at the University of Bonn in Germany, at the Institut des Hautes Études Scientifiques in France and at the Institut Poincaré. Eisenbud was president of the American Mathematical Society from 2003 to 2005. He is now a director at Math for America, a foundation devoted to improving mathematics teaching. He chairs the editorial board of the journal Algebra and Number Theory, which he helped establish in 2006, and serves on several other editorial boards. Eisenbud has been a member of the board of Mathematical Sciences and their Applications (part of the National Research Council), and is a member of the U.S. National Committee of the International Mathematical Union. In 2006, he was elected a fellow of the American Academy of Arts and Sciences. Eisenbud’s major research contributions have focused on the study of algebraic curves and their moduli, and on the commutative algebra and algebraic geometry related to free resolutions. His mathematical interests also include topology and computer methods. “Since its founding thirty years ago, MSRI has taken on a central position in the mathematical sciences,” said David Eisenbud. “It is a world center for fundamental research and training brilliant young people. It is a place where mathematicians can collaborate with those from other sciences. It is a leader in encouraging the participation of minorities and women. It is important as a place that connects mathematical researchers with mathematics education, and as a place where the many connections of mathematics with art are appreciated and developed.” Eisenbud is the fifth director in the Institute’s 30-year history. He succeeds Robert Bryant, who has served as MSRI director since 2007 and is also a full professor at UC Berkeley. “David Eisenbud is an internationally recognized leader in the mathematics community and a leading authority in commutative algebra,” said Bryant. “Moreover, he has outstanding talent as an administrator and as an organizer, as evidenced by his enormously successful term as President of the American Mathematical Society and his first Directorship of MSRI.” PHOTO – A high-resolution photograph of David Eisenbud is available, by request. About MSRI: The Mathematical Sciences Research Institute (MSRI, http://www.msri.org), in Berkeley, California, is one of the world’s preeminent centers for research in the mathematical sciences and has been advancing mathematical research through workshops and conferences since its founding as an independent institute in 1982. Approximately 2,000 mathematicians visit the MSRI each year, and the Institute hosts about 85 leading researchers at any given time for stays of up to one academic year. The Institute has been funded primarily by the National Science Foundation with additional support from other government agencies, private foundations, corporations, individual donors, and 90 academic institutions. MSRI is involved in K-12 math education through its annual “Critical Issues in Mathematics Education” conferences for educators, math circles, math festivals, the National Association for Math Circles (NAMC) and its website (http://www.mathcircles.org), and Olympiad math competitions, in undergraduate education through its MSRI-UP program, and in public education through its “Conversations” series of public events. Dr. David Eisenbud Named the Next Director of MSRI

May 06, 2012 Unleashing the Power ‘Turing’s Cathedral,’ by George Dyson www.nytimes.com Alan W. Richards/Institute for Advanced Study, Princeton University Universal machine: John von Neumann and his high-speed computer, circa 1952. By WILLIAM POUNDSTONE Published: May 4, 2012 It’s anyone’s guess whether our digital world ends with a bang, a whimper or a singularity. One thing’s for sure: It began with a double entendre. The digital age can be traced to a machine built circa 1951 in Princeton, N.J. That machine was given the bureaucratic-­sounding name the Mathematical and Numerical Integrator and Computer, and was known by the acronym Maniac, meaning something wild and uncontrollable — which it proved to be. But the crucial double entendre was contained in the computer’s memory. For the first time, numbers could mean numbers or instructions. Data could be a noun or a verb. That turned out to be incredibly important, as George Dyson makes clear in his latest book, “Turing’s Cathedral,” a groundbreaking history of the Princeton computer. Though the English mathematician Alan Turing gets title billing, Dyson’s true protagonist is the Hungarian-­American John von Neumann, presented here as the Steve Jobs of early computers — a man who invented almost nothing, yet whose vision changed the world. Von Neumann was no stereotypical mathematician. He was urbane, witty, wealthy and (literally) entitled. At his 1926 doctoral exam, the mathematician David Hilbert is said to have asked but one question: “Pray, who is the candidate’s tailor?” He had never seen such beautiful evening clothes. Already one of the century’s great mathematicians, von Neumann pursued a career in academia before turning to consult on the building of bombs (and computers) during World War II. At the time, the Army had begun work on a “digital electronic computer” known as the Eniac that was programmed, via switches and cables, by hand. After Nagasaki, von Neumann sold the United States military on a more powerful “stored program” computer, one that could read coded sequences from high-speed memory and thus more rapidly, and automatically, run numerical simulations essential to the design of nuclear weapons. Von Neumann also sold his employer, the Institute for Advanced Study, on building the Faustian device in Princeton. Another institute scholar, the logician Kurt Gödel, is also a vital figure in Dyson’s story. Gödel is known for his “incompleteness theorem,” which demonstrated the existence of true statements that cannot be proved in any mathematically rigorous way. But it’s not Gödel’s conclusion that matters here so much as the trick he used to achieve it: He invented the mathematical double entendre. In his famous proof, numbers carry two meanings — the familiar one designating a quantity, and an encoded one designating a logical proposition (e.g., “No number can be multiplied by 0 to produce 7”). In this way Gödel commingled the data and “code” of arithmetic. Turing took this notion and in 1936 applied it to a hypothetical universe of “automata.” In his conception, a simple robot (a “Turing machine”) is supplied with a paper tape. The tape is a crude form of memory, its contents doubling as data and code. Turing proved that this minimalist design is a “universal” computer, capable of performing any calculation. The basic ideas of stored-program computers were therefore in place before von Neumann got to work. Yet it was he who had the prestige and the connections to turn the Turing machine into reality. Because city-destroying bombs couldn’t be built by trial and error, computers were required to simulate the physics of detonation and blast waves. A computer helped build the bomb, and the bomb necessitated ever more advanced computers. Von Neumann and two colleagues codified their machine’s architecture in a report issued in 1946. They could be called the fathers of the open-source movement, as they ultimately declined to seek any patents. Within a few years of the plans’ being shared, over a dozen siblings to the Princeton machine existed across the globe. Indeed, the processors in every cellphone, tablet and laptop still hew closely to von Neumann’s architecture. Not all the ideas von Neumann donated to the public domain were exclusively his. “Johnny was rephrasing our logic, but it was still the same logic,” John W. Mauchly, a creator of the Eniac, complained. Mauchly’s colleague John Eckert added: “He grasped what we were doing quite quickly. I didn’t know he was going to go out and more or less claim it as his own.” The Maniac was first tested in the summer of 1951, “with a thermonuclear calculation that ran for 60 days nonstop.” About 6 by 2 by 8 feet and weighing a trim half-ton, it was much smaller than the room-size Eniac. But it inherited some of its predecessor’s reliability issues. Dyson quotes engineers’ exasperated entries from the Princeton machine’s logbook. May 7, 1953: “What’s the use? good night.” June 14, 1953: “Damnit — I can be just as stubborn as this thing.” June 17, 1956: “the hell with it.” Like the nuclear physicist Edward Teller, von Neumann was an apparently unconflicted proponent of the bomb. At the Institute for Advanced Study, his hawkishness clashed with Einstein’s pacifism, and Einstein opposed building his computer there. Virginia Davis, wife of the logician Martin Davis, remembers writing “Stop the Bomb” in the dust on von Neumann’s car. But von Neumann’s second wife, Klári, recalled him being shaken by what his computer might wreak. One night in 1945, John announced, “What we are creating now is a monster whose influence is going to change history, provided there is any history left.” His biggest worry wasn’t the bomb, however, but, as Dyson writes, “the growing powers of machines.” Klári recalled prescribing “a couple of sleeping pills and a very strong drink.” “Turing’s Cathedral,” incorporating original research and reporting — Dyson interviewed several people present at the institute during von Neumann’s tenure there, including his own father, the physicist Freeman Dyson — is an expansive narrative wherein every character, place and idea rates a digression. A brief history of Olden Farm, the site of the Princeton computer, begins with the Lenni Lenape Indians and carries on through William Penn, George Washington and so forth. One of Dyson’s running jokes is the supposedly abominable climate: Princeton “in summer has been described as ‘like the inside of a dog’s mouth.’ ” Humidity caused the Maniac’s air-conditioning units to freeze solid with ice. The book brims with unexpected detail. Maybe the bomb (or the specter of the machines) affected everyone. Gödel believed his food was poisoned and starved himself to death. Turing, persecuted for his homosexuality, actually did die of poisoning, perhaps by biting into a cyanide-laced apple. Less well known is the tragic end of Klári von Neumann, a depressive Jewish socialite who became one of the world’s first machine-language programmers and enacted the grandest suicide of the lot, downing cocktails before walking into the Pacific surf in a black dress with fur cuffs. Dyson’s well-made sentences are worthy of these operatic contradictions. One example: “ ‘God does not play dice with the Universe,’ Albert Einstein advised physicist Max Born (Olivia Newton-­John’s grandfather) in 1936.” Unlike many historians, Dyson has no need to reach for contemporary relevance. He quotes Julian Bigelow, the Maniac’s chief engineer, in a passage that could serve as the book’s précis: “What von Neumann contributed” was “this unshakable confidence that said: ‘Go ahead, nothing else matters, get it running at this speed and this capability, and the rest of it is just a lot of nonsense.’ . . . People ordinarily of modest aspirations, we all worked so hard and selflessly because we believed — we knew — it was happening here and at a few other places right then, and we were lucky to be in on it. . . . A tidal wave of computational power was about to break and inundate everything in science and much elsewhere, and things would never be the same.” Unleashing the Power - 'Turing's Cathedral,' by George Dyson

May 06, 2012 Mathematics Transforming Bioresearch www.genengnews.com Feature Articles: May 1, 2012 (Vol. 32, No. 9) Mitzi Perdue Human and animal health, agriculture, energy production, environmental protection, and a host of other activities of interest to humans have something startling in common: the key to improvements in all of them is likely to require a significant computational component. As Darwin once said “Mathematics seems to endow one with something like a new sense,” and the powerful tools of mathematics are enabling those who are able to utilize this “new sense” to see connections, structures, and even possibilities that up to now have been invisible. What Darwin couldn’t have guessed is that the powerful tools of mathematics would be harnessed to extraordinary computational power, and that both would be needed to deal with the exponential increase in the volume of data coming from today’s laboratories and research institutes. The computational power itself is increasing at breathtaking rates. In our recent past, it took 30 years to determine the complete DNA sequence of a cold virus genome. Today a virus of the same size can be sequenced in minutes. We can now read more than 500x billion bases in a week, compared to 25,000 in 1990 and 5 million in 2000. “We are talking about exabytes [1018 bytes] of information,” marvels Leroy Hood, M.D., Ph.D., president and co-founder of the Institute for Systems Biology. Where are we going with our new ability to develop larger and larger amounts of data, while marrying this information with ever more sophisticated use of Darwin’s “new sense”? Revolutionary and Powerful For Dr. Hood, the results are revolutionary and they were powerfully encouraged by the Human Genome Project (HGP). “It was one of the most transforming events in biology, creating a whole cadre of mathematicians and computer scientists who applied their talents to biology,” he explains. “They expanded their views of biology and began analyzing many other types of biological questions.” According to Dr. Hood, the HGP meant a change in the relationship between mathematicians and biology. “Mathematicians have had a fascination with biology for a long time,” he continues, “but historically their contributions were limited.” The problem was too much of a top-down approach. “The great advantage of the Human Genome Project was, it was bottom up, where the objective was to define all the elements in a biological object (the 3 billion base-pairs of sequence of the 24 different human chromosomes) without consideration of hypothesis-driven questions,” he says. “It takes enormous amounts of bottom-up data to decipher biological complexity.” In Dr. Hood’s case, this has played out in a highly mathematical interdisciplinary systems approach to biological discovery, geared to handling extraordinary amounts of information. As just one example of the kinds of numbers in play, he points out that, “With a world population of seven billion people and with each individual having six billion nucleotides, you multiply these together and you are getting very large amounts of information.” The premise of the Institute’s work is that diseases result from perturbations of biological networks. These perturbations can arise from biological changes, such as mutations in the information of the genome, or from environmental influences, such as toxins or bacteria. Disease-perturbed networks both cause and reflect the progression of a disease. Thus, diseases can be diagnosed, treated, and prevented by understanding and intervening in the networks that underlie health and illness. One of the Institute’s strong points is establishing the computational infrastructure needed to analyze the thousands and eventually the millions of human genome sequences that will become available over the next 10 years. These computational tools will enable large-scale comparative analyses of human genomes and their attendant molecular, cellular, and phenotypic data. Dr. Hood cites two ultimate objectives of systems biology. The first is the ability to predict the behavior of a system with its emergent properties from knowing what kinds of perturbations that are applied to it. The second objective is the ability to understand the system in sufficient detail so it can be redesigned to create completely new emerging properties. “Being able to redesign systems whether it’s with genetics or whether it’s with drugs is going to be at the heart of the new kind of medicine that will emerge from systems biology—P4 Medicine, that is, predictive, personalized, preventive, and participatory medicine.” Dr. Hood has a quick example of how personalized medicine can work. “A friend of mine at Microsoft had a defect in vitamin D transporters and was suffering from osteoporosis. To reverse his osteoporosis, all he had to do was take 20 times the normal amount of Vitamin D.” Computational Oncology Like Dr. Hood, Franziska Michor, Ph.D., from the Harvard School of Public Health and the Dana-Farber Cancer Institute, also takes a computational approach to biology. Her focus is oncology, and as with Dr. Hood, mathematics allows her to see relationships that yield results never before achieved. “An exciting example of something we’ve just finished,” she says “is learning to use existing drugs to treat cancer more effectively.” Dr. Michor goes on to say that while we do have drugs that are effective in treating cancer, these drugs also exert a powerful selective force on the cancer cells. As with any organism under severe evolutionary pressure, some members of a cancer cell population may acquire molecular changes that strengthen the clan’s chances of survival. The result: drug-resistant cancers. Dr. Michor’s research involves learning ways to administer the drugs differently so resistance doesn’t arise. “We can make a mathematical model that allows us to study the evolution of resistance cells and then we can use this mathematical model to identify treatment strategies that prevent or delay resistance,” she says, adding that she does this by looking at acquired resistance as a problem in evolution. Mathematical modeling works well for modeling evolutionary processes, and such modeling can provide otherwise-invisible insights into the biology of cancer. To study the evolutionary dynamics of resistance under time-varying dosing schedules and pharmacokinetic effects, the populations of sensitive and resistant cells are modeled as multitype nonhomogeneous birth–death processes. The drug concentration may affect the birth and death rates of both the sensitive and resistant cell populations in continuous time. This flexible model allows Dr. Michor and her colleagues to consider the effects of generalized treatment strategies as well as detailed pharmacokinetic phenomena such as drug elimination and accumulation over multiple doses. Computational tools enable them to develop estimates for the probability of developing resistance and the size of the resistant cell population. With these estimates, they are able to optimize treatment schedules over a subspace of tolerated schedules that minimize the risk of disease progression due to resistance. In addition they can locate ideal schedules for controlling the population size of resistant clones in situations where resistance is inevitable. This evolutionary mathematical modeling approach in cancer treatment will soon be in clinical trials. Dr. Michor believes that the ability to design optimal treatment strategies for preventing drug resistance may increase the benefits of therapy for many different cancer types. Not Everyone’s Aboard Meanwhile, Dr. Michor is surprised by the resistance in the oncology community to mathematical techniques. “In physics or economics, it’s key to use math. In cancer research, while using statistics is accepted, it’s not a well-accepted idea to use math to see the underlying framework,” she points out. Dr. Michor suspects that oncology has not drawn many mathematicians because there are as yet few examples of successful applications in cancer biology. Odds are that with research like Dr. Michor’s, this will change. Heart of Biology “The great book of nature can be read only by those who know the language in which it was written. And this language is mathematics.” —Galileo Eric Lander, Ph.D., director of the Broad Institute, also takes a highly mathematical approach to investigating biology. “Mathematics is now at the heart of biology,” he states, and his approach is helping pioneer new ways to understand the basis of disease. In his view, mathematics has extraordinarily expanded our ability to see patterns and connections. “It seems inconceivable,” he emphasizes, “that we could analyze the trillions of data points that we now collect without mathematics.” The trillions of data points he and his colleagues at Broad are developing come from: mapping and sequencing human, and other genomes; understanding the functional elements encoded in genomes through comparative analysis; understanding the genetic variation in the human population and its relationship to disease susceptibility; understanding the distinctive cellular signatures of diseases and of response to drugs; and understanding the mutations underlying cancer. “Using mathematics,” according to Dr. Lander, “we can compare the differences across species to show what evolution has conserved and not conserved. We can compare in an unbiased way the similarities and differences among tumors that do and do not respond to a drug. We can do discovery science by taking large datasets and by comparing information; and we can uncover a range of surprising hypotheses from the data that we never would have guessed.” Among the surprising discoveries is the fact that “the majority of the functional sequences in the human genome encode regulatory elements that we had been completely blind to. They emerge from cross-species comparisons.” In addition, these discoveries have revealed that thousands of genes don’t code for proteins. For Dr. Lander, the ultimate goal is for sequencing to become so simple and inexpensive that it can be routinely deployed as a general-purpose tool throughout biomedicine. To fulfill this potential, the cost of whole-genome sequencing will need eventually to approach a few hundred U.S. dollars. “With new approaches under development and market-based competition,” he says, “these goals may be feasible within the next decade.” Mathematics, with the help of increasingly fast, powerful, and cost-effective technologies is providing us with an ability to see what have up to now been hidden patterns of life. These mathematical insights provide powerful new approaches for understanding disease mechanisms, pioneering new diagnostics techniques, and rethinking how drug targets should be chosen. Today’s use of Darwin’s “new sense” is laying the foundation for addressing in ways never before possible many of the serious societal problems involving biologically based systems. Mitzi Perdue is GEN’s new editor-at-large. Mathematics Transforming Bioresearch

May 06, 2012 Researchers use mathematics to fight cancer medicalxpress.com/news May 3, 2012 Using mathematical models, researchers in the Integrated Mathematical Oncology (IMO) program at Moffitt Cancer Center are focusing their research on the interaction between the tumor and its microenvironment and the "selective forces" in that microenvironment that play a role in the growth and evolution of cancer. According to Alexander R. A. Anderson, Ph.D., chair of the IMO, mathematical models can be useful tools for the study of cancer progression as related to understandings of tumor ecology. "Cancer is a complex disease driven by interactions between tumor cells and the tumor's microenvironment," Anderson said. "By developing mathematical models that describe how tumors grow and respond to changes in their surroundings (such as treatment), we can better understand how an individual patient might respond to a whole suite of different therapies." Robert Gillies, Ph.D., chair of imaging and metabolism at Moffitt, is working closely with Anderson and Robert Gatenby, M.D., chair of Diagnostic Imaging. They say it is important to pair tumor imaging with mathematical model building. "Imaging is a key to validate mathematical modeling," Gillies said. "Because imaging can be conducted over time, it affords us a good look at the actively changing systems in tumors that are predicted by the models." For Gatenby, because cancer is an evolving, always changing nonlinear system, it must be monitored over time and space. "Imaging noninvasively captures tumor changes, and the mathematical models, which are much more rigorous than language, can then be used in cancer research," Gatenby said. Clinical imaging and mathematical modeling combined will afford clinicians a valuable predictive tool. One tool will be familiar. Just as meteorologists develop "spaghetti models" from satellite images to predict the myriad possible paths of hurricanes, Anderson said, they will be able to generate similar models to inform clinicians about a patient's risk, which treatments may be best and whether recurrence is possible. "By incorporating specific information about a patient, such as the size of their tumor, the treatments they have had, the organ that the cancer is growing in, we can predict forward in time how the tumor will grow, shrink, and respond to different combinations of therapies. By the results of imaging, biological experiments and mathematical models, we are leading the world in patient-specific medicine," Anderson said. Mathematical models generated by IMO researchers are already finding clinical uses. Fibroblasts contribute to melanoma tumor growth "We used an integrated mathematical and experimental approach to investigate whether melanoma cells recruit, activate and stimulate fibroblasts to deposit certain proteins known to be pro-survival for melanoma cells," Anderson said. Fibroblasts, the most common connective tissue functioning in the extra cellular matrix, were known to be activated by and drawn to cancer cells. When they investigated the relationship between fibroblasts and tumors using mathematical models, Anderson and colleagues found that fibroblasts have direct effects on melanoma tumor behavior, including aiding tumor growth and tumor drug resistance. They published their findings in Molecular Pharmaceutics. Deadly glioblastomas better understood through mathematical models IMO researchers and colleagues also developed mathematical models for investigating the progression of glioma, an aggressive and fatal form of brain cancer. The mathematical models augment imaging and histologic grading of gliomas, graded on their blood vessel growth patterns (an angiogenic feature) and incorporating the tumor's cellular and microenvironmental changes. When the researchers observed a disparity between grading schemes and tumor activity observed through imaging, they developed a mathematical model based on changes in cell appearance, proliferation and invasion rates. The new model improved predictive and prognostic ability. "Being able to identify and predict patterns of dynamic changes in glioma histology as distinct from cellular changes in appearance and proliferation may provide a powerful clinical tool," Anderson said. They published this study in a recent issue of Cancer Research. Provided by H. Lee Moffitt Cancer Center & Research Institute Researchers use mathematics to fight cancer

May 06, 2012 Implants: A better fit through mathematics medicalxpress.com May 3, 2012 Individuals with implants may soon be able to feel the benefit of basic scientific knowledge in their own bodies. This is one of the findings of a translational research project conducted by the Austrian Science Fund FWF. The project demonstrated how 3D models and special mathematical methods could be used to improve the design and integration of implants in the body on a patient-specific basis. Data was gathered from computer and magnetic resonance tomography and used to generate 3D models specifically for shoulder joints and their replacements. The data was analysed in a procedure known as the finite element method, and possible individual optimisations were calculated. The project exemplifies the acute benefit of research findings from the Translational Research Programme, which ended at the close of the first quarter of 2012. Basic research forms the foundation for future applications, as illustrated by programmes like the Translational Research Programme. This programme, which the Austrian Science Fund (FWF) conducted on behalf of the country's Federal Ministry for Transport, Innovation and Technology (BMVIT), ran until early 2012 and served to accelerate the transfer of basic knowledge into practical applications: Applications which, first and foremost, improve the quality of people’s lives, in addition to creating economic value. Take project L526, for example. Shoulder to Shoulder: Mathematics & Medicine This project brought together basic scientific knowledge from the areas of mathematics, medicine and computer science with the aim of optimising replacement shoulder joints individually (patient-specific). Headed by Dr. Karl Entacher from Salzburg University of Applied Sciences and Dr. Peter Schuller-Götzburg from the Paracelsus Medical University in Salzburg, the project initially computed human shoulder joint models and then used them as the basis for the analytical simulation of varying load conditions. The team commenced by using imaging techniques to create the computer models. To this effect, computer tomography was used to build up images of human shoulder joints on a layer-by-layer basis. As Dr. Entacher explains: "Modern tomography techniques allow us to create images of an entire shoulder joint layer-by-layer, and the layer thicknesses that we can achieve today make excellent resolution possible. We were able to use this image data to create computer-generated 3D models of each patient's individual shoulder joint, forming the basis for our subsequent analysis." Finite Findings This subsequent analysis was based on a mathematical process called the finite element (FE) method. With this method, the objects to be analysed are depicted in small – but finite – elements. Their behaviour can then be computed numerically and simulated, taking into account variables such as material properties and load, as well as the limits of movement. In the process, it is possible to model the most varied conditions that the joint might face. Speaking about these conditions, Dr. Entacher comments: "Our aim was to simulate the implant at different positions and different angles in the body, as well as to simulate the anatomical make-up of different, individual patients." In fact, the model was so sophisticated that different types of tissue, such as soft tissue or different bone sections, could be selected. It was also possible to create virtual sections to move different parts of the bone or the implant to any given position. All in all, this enabled the scientists to gather valuable data for the patient-specific optimisation of shoulder and even tooth implants. This could provide future patients with important information on the positioning, the type or the performance of their implant before they have an operation. Commenting on the personal significance of the project and the end of the Translational Research Programme, Dr. Entacher says: "As a basic researcher, it is very satisfying to see how working with physicians and engineers can turn our findings into specific applications that can help people. In fact, I feel it provides a more personal perspective on personal development. In addition to this personal experience, the Translational Research Programme also makes a significant contribution to innovation culture in Austria. A contribution that will be missing in the future." Provided by Salzburg University of Applied Sciences Implants: A better fit through mathematics

May 06, 2012 Formula follows the evolution of writing styles www.newscientist.com 01 May 2012 by Sara Reardon Few novelists today would have a character say, "It is a far, far better rest that I go to than I have ever known." That is not only because few modern characters ponder death by guillotine, but also because writing styles have changed dramatically since Charles Dickens wrote A Tale of Two Cities in 1859. So how does literary style evolve? Surprisingly, clues lie in words with seemingly little meaning, such as "to" and "that". By analysing how writers use such "content-free" words, mathematician Daniel Rockmore and colleagues at Dartmouth College in Hanover, New Hampshire, were able to conduct the first, large-scale "stylometric" analysis of literature. Content-free words are indicative of writing style, Rockmore says. While two authors might use the same words to describe a similar event, they will use content-free "syntactic glue" to link their words in a different way. Using the Project Gutenberg digital library, Rockmore's team analysed 7733 English language works written since 1550, tracking how often and in what context content-free words appeared. As you might expect, they found that writers were strongly influenced by their predecessors. They also found that as the canon of literature grew, the reach of older works shrank. Authors in the earliest periods wrote in a very similar way to one another, the researchers found, probably because they all read the same small body of literature. But approaching the modern era, when more people were writing and more works were available from many eras and numerous styles, authors' styles were still very similar to those of their immediate contemporaries. "It's as if they find dialects in time," says Alex Bentley of the University of Bristol, UK, who was not involved in the study. "Content is what makes us distinctive, but content-free words put us in different groups." That writers should be most influenced by their contemporaries rather than the great works of the past is interesting, Rockmore says, because it challenges the reach of "classic" literature. When it comes to style at least, perhaps we aren't so strongly influenced by the classics after all. Journal reference: Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.1115407109 Formula follows the evolution of writing styles

May 06, 2012 First Light: NIST Researchers Develop New Way to Generate Superluminal Pulses www.nist.gov In four-wave mixing, researchers send "seed" pulses of laser light into a heated cell containing atomic rubidium vapor along with a separate "pump" beam at a different frequency. The vapor amplifies the seed pulse and shifts its peak forward, making it superluminal. At the same time, photons from the inserted beams interact with the vapor to generate a second pulse called the "conjugate." Its peak, too, can travel faster or slower depending on how the laser is tuned and the conditions inside the gain medium. (Credit: NIST) Researchers at the National Institute of Standards and Technology (NIST) have developed a novel way of producing light pulses that are "superluminal"—in some sense they travel faster than the speed of light.* The technique, called four-wave mixing, reshapes parts of light pulses and advances them ahead of where they would have been had they been left to travel unaltered through a vacuum. The new method could be used to improve the timing of communications signals and to investigate the propagation of quantum correlations. According to Einstein's special theory of relativity, light traveling in a vacuum is the universal speed limit. No information can travel faster than light. But there's kind of a loophole. A short burst of light arrives as a sort of (usually) symmetric curve like a bell curve in statistics. The leading edge of that curve can't exceed the speed of light, but the main hump, the peak of the pulse, can be skewed forward or backward, arriving sooner or later than it normally would. Recent experiments have generated "uninformed" faster-than-light pulses by amplifying the leading edge of the pulse and attenuating, or cutting off, the back end. The method introduces a great deal of noise with no great increase in the apparent speed. Four-wave mixing produces cleaner, less noisy pulses with a greater increase in speed by "re-phasing" or rearranging the light waves that make up the pulse. In four-wave mixing, researchers send 200-nanosecond-long "seed" pulses of laser light into a heated cell containing atomic rubidium vapor along with a separate "pump" beam at a different frequency from the seed pulses. The vapor amplifies the seed pulse and shifts its peak forward so that it becomes superluminal. At the same time, photons from the inserted beams interact with the vapor to generate a second pulse, called the "conjugate" because of its mathematical relationship to the seed. Its peak, too, can travel faster or slower depending on how the laser is tuned and the conditions inside the laser. In the experiment, the pulses' peaks arrived 50 nanoseconds faster than light traveling through a vacuum. One immediate application that the group would like to explore for this system is quantum discord. Quantum discord mathematically defines the quantum information shared between two correlated systems—in this case, the seed and conjugate pulses. By performing measurements of quantum discord between fast beams and reference beams, the group hopes to determine how useful this fast light could be for the transmission and processing of quantum information. * R. Glasser, U. Vogl and P. Lett. Stimulated generation of superluminal light pulses via four-wave mixing. Physical Review Letters, published online April 26, 2012. First Light: NIST Researchers Develop New Way to Generate Superluminal Pulses