The university will use a $500,000 grant from the National Science Foundation to give students, researchers and the public access to the collection of Carl Rettenmeyer and his wife Marian. The collection includes about 2 million specimens of ants and other critters that also live in army ant colonies. The Department of Ecology & Evolutionary Biology, where Carl Rettenmeyer worked from 1971 to 1996, will begin digitally cataloging the dead insects this summer, creating an electronic database of the collection and research notes that will be available online. "We have bulk samples of many colonies, thousands of vials with preserved things in them, and quite a few cabinets with pinned insects in them as well," Jane O'Donnell, the biologist who is managing the collection, said Wednesday. The ant colonies also are documented in 5,000 Kodachrome slides and about 30 hours of digital videotape. O'Donnell said the idea is to make the work accessible to everyone from elementary school students to the world's top ant researchers. The school is planning two exhibits, one about the ants and another about the Rettenmeyers. Carl, who died in 2009, also founded the Connecticut State Museum of Natural History at UConn. The school plans to build a large-scale model of an army ant that will welcome visitors to school's biology and physics building. There also will be 4-foot-long model ants placed on the side of the building to publicize the collection. The Rettenmeyers painstakingly documented the complex life of foraging army ant colonies. They became the first to discover many of the other organisms that live there, such as a special mite that attaches to the end of an ant's leg and serves as a sort of hiking boot for the insect, O'Donnell said. The school expects the additional research on the ants will uncover new details about colony life. The collection, O'Donnell said, also will be a valuable source of DNA for those studying the history of ants and their symbiotic systems. "We don't even know what some of the relationships with some of these other organisms are," she said. "We're hoping that by putting all of these details together and making it easier to study, some patterns will emerge." The school hopes to have the initial exhibition ready by early 2017. "We share billions of years of evolutionary history with these organisms, and we don't even really have an accounting of what's on the planet with us yet," O'Donnell said. "That's part of this." Explore further: Ants get their place in Smithsonian exhibit
News Article | March 4, 2016
For most of human history, the discovery of new materials has been a crapshoot. But now, UConn researchers have systematized the search with machine learning that can scan millions of theoretical compounds for qualities that would make better solar cells, fibers, and computer chips. The search for new materials may never be the same.
Fragile bones are usually an old person's affliction, but sometimes children are born with them. Now, a team of researchers led by UConn professor Ernesto Canalis has shown in mice that a specific gene can cause the disease, called Hajdu-Cheney syndrome. Overabundant bone-absorbing cells may be causing the disorder's characteristic bone loss, and the researchers hope to find a potential treatment. People born with Hajdu-Cheney syndrome develop misshapen skeletons and bones that quickly start to soften and fracture. Researchers knew Hajdu-Cheney was an inherited disease, but they weren't sure which genetic mutation caused it. They suspected it was in a gene called NOTCH2, which has a specific mutation that appears in people with the syndrome. But Hajdu-Cheney is very rare, and it might just have been a coincidence that families with Hajdu-Cheney also happen to carry an unusual variant of NOTCH2. To figure out whether the NOTCH2 variant really was responsible, Canalis and his colleagues replicated it in mice. The result, which will be reported in the 22 January issue of the Journal of Biological Chemistry, was essentially a mouse version of Hajdu-Cheney syndrome. "Until now, nobody understood why people afflicted with the disease had osteoporosis and fractures," said Canalis, a professor of orthopaedic surgery at UConn Health. His mice seem to provide the answers. They generate a larger pool of osteoclasts, cells that break down and resorb old bone. These cells also mature faster than they do in normal mice. So Hajdu-Cheney mice have far too much bone resorbed by their bodies, and new bone doesn't grow fast enough to replace it. This leads to mice with fragile bones, very similar to people with the disease. There are a few symptoms of the disease in humans - such as shortened fingers and oddly shaped skull bones - that the mice don't display. But overall, the mouse model is a very good model of the human disease, Canalis said. Knowing how the disease works also suggests how it may be treated. If people with Hajdu-Cheney have too many bone-resorbing cells, then it may help to suppress the formation or activity of those cells. And Canalis said scientists know how to do that. His group is currently working on treatments in mice. Hajdu-Cheney is an incredibly rare disease, with fewer than 100 cases ever described. But there are good scientific reasons to study it. It can illuminate the workings of bone formation and destruction, and give insight into a gene important to both the skeleton and the immune system. It could also possibly tell us about Alagille syndrome, another, much more common genetic disease associated with NOTCH2. But for Canalis, even if Hajdu-Cheney only affects a few people from a few families, what causes such suffering and how to abate it is worth searching for.
For most of human history, the discovery of new materials has been rather trial-and-error. But now, researchers from the University of Connecticut (UConn) have systematized the search by developing a machine learning tool that can scan millions of theoretical compounds for qualities that would make better solar cells, fibers and computer chips. The search for new materials may never be the same. No one knows why an early metallurgist decided to smelt a hunk of tin into some copper, but the resulting bronze alloy was harder and more durable than any material previously known. Most materials discovery over the ensuing 7000 years has been similarly random, guided largely by philosophy and chemical intuition. But with at least 95 stable elements, the number of possible combinations is enormous, and experimentation is an awfully inefficient way to find what you're looking for. Enter UConn materials scientist Ramamurthy 'Rampi' Ramprasad. Instead of randomly mixing chemicals to see what they do, Ramprasad designs them rationally, using machine learning to figure out which atomic configurations make a polymer a good electrical conductor or insulator. Polymers can have diverse electronic properties: they can be good insulators or good conductors. What controls these properties is mainly how the atoms in the polymer connect to each other. But until recently, no one had systematically related these properties to atomic configurations. So Ramprasad and his colleagues decided to do just that. First, they analyzed known polymers using laborious but accurate quantum mechanics-based calculations to figure out which arrangements of atoms confer which properties, and then quantified those atomic-level relationships via a string of numbers that fingerprint each polymer. Once they had those, they could conduct a computer search through any number of theoretical polymers to figure out which ones might have which properties. Then anyone looking for a polymer with a certain property could quickly scan the list and decide which theoretical polymers might be worth trying. For their project, Ramprasad's group looked at polymers made up of just seven molecular building blocks containing carbon, hydrogen, oxygen, nitrogen and sulfur: CH , C H , CO, O, NH, CS and C H S. These building blocks are found in common plastics such as polyethylene, polyesters and polyureas, and could theoretically produce an enormous variety of different polymers. Ramprasad's group decided at first to analyze just 283 simple polymers, each composed of a repeated four-block unit. They started from basic quantum mechanics, and calculated the three-dimensional atomic and electronic structures of each of those 283 four-block polymers. This is not trivial process, though: calculating the position of every electron and atom in a molecule with more than two atoms takes a powerful computer a significant chunk of time, which is why they only did it for 283 molecules. Once they had the three-dimensional structures, they could calculate what they really wanted to know: each polymer's properties. They calculated the band gap, which is the amount of energy it takes for an electron in the polymer to break free of its home atom and travel around the material, and the dielectric constant, which is a measure of the effect an electric field can have on the polymer. These properties determine how much electric energy each polymer can store in itself. Ramprasad's group then used this information to develop a much simpler, shorthand system that a computer could use to look at the building blocks of a polymer and how they connect to each other, and then make educated guesses about its properties. Computers deal with numbers, so first they had to define each polymer as a string of numbers, a sort of numerical fingerprint. Since there are seven possible building blocks, there are seven possible numbers, each indicating how many of each block type are contained in that polymer. But a simple number string doesn't convey enough information about the polymer's structure, so they added a second string of numbers to denote how many pairs there are of each combination of building blocks, such as NH-O or C H -CS. They then added a third string that described how many triplets, like NH-O-CH , there were. They arranged these strings as a three-dimensional matrix, which is a convenient way to describe such strings of numbers in a computer. Then they let the computer go to work. Using the library of 283 polymers that had been laboriously calculated using quantum mechanics, the machine compared each polymer's numerical fingerprint to its band gap and dielectric constant, and gradually 'learned' which building block combinations were associated with which properties. It could even map those properties onto a two-dimensional matrix of the polymer building blocks. Once the machine learned which atomic building block combinations gave rise to which properties, it no longer needed the quantum mechanics calculations of atomic structure. It could accurately evaluate the band gap and dielectric constant for any polymer made of any combination of those seven building blocks, using just the numerical fingerprint of its structure. Many of the predictions of quantum mechanics and the machine learning tool have already been validated by Ramprasad's UConn collaborators, chemistry professor Greg Sotzing and electrical engineering professor Yang Cao. Sotzing actually made several of the novel polymers, while Cao tested their properties; they came out just as Ramprasad's computations had predicted. "What's most surprising is the level of accuracy with which we can make predictions of the dielectric constant and band gap of a material using machine learning," says Ramprasad. "These properties are generally computed using quantum mechanical methods such as density functional theory, which are six to eight orders of magnitude slower." The group reported their polymer work in a recent paper in Scientific Reports; another paper on utilizing machine learning in a different manner – to discover laws that govern the dielectric breakdown of insulators – will be published in a forthcoming issue of Chemistry of Materials. The predicted properties of every polymer Ramprasad's group has evaluated so far is also freely available in their online data vault, Khazana, which also provides their machine learning apps to predict polymer properties on the fly. They are also uploading data and the machine learning tools from their Chemistry of Materials work, and from an additional recent paper on predicting the band gap of perovskites, which are inorganic compounds used in solar cells, lasers and light-emitting diodes. As a theoretical materials scientist, what Ramprasad wants to know is why materials behave the way they do. What about a polymer makes its dielectric constant just so? Or what makes an insulator withstand enormous electric fields without breaking down? But he also wants this understanding to be put to work designing new useful materials rationally, so he is making the results of his calculations freely available. This story is adapted from material from the University of Connecticut, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.
News Article | September 8, 2016
UConn researchers have developed a device that makes it easier to measure contaminant levels in water. With help from UConn's National Science Foundation Innovation Corps program, Accelerate UConn, marine geochemist Penny Vlahos and graduate student Joe Warren are now well on their way to commercializing their technology. Access to clean water is a major concern for nations around the globe. The new device can measure pollution in oceans, lakes, and rivers, and even in the home. "I was frustrated that we weren't measuring contaminants as often or as well as we should, just because it was too labor-intensive and costly," says Vlahos. "This device lets us test more bodies of water quickly, easily, and inexpensively, and yields results that better reflect the overall situation. The potential environmental impact is huge." Currently, if the quality of a body of water needs testing, a large sample is collected – between five and 20 liters of water – and is then transported to a lab for analysis. This process, known as "grab sampling," is labor-intensive and can be prohibitively expensive. As a result, small-scale testing by citizens who want to measure contaminants in a local stream or in their private wells isn't feasible. In contrast, Vlahos says her technology is so easy a child could do it. In fact, the device doesn't require collecting a water sample at all, because it uses a process called "passive sampling." The small, ecofriendly sampling device is placed directly into the body of water being tested, where it stays for a few hours and is then removed. Once back in the lab, it takes a little over two hours to conduct a full analysis of the water's target contaminant levels. Another important feature of the new technology is that it provides continuous sampling over time, which isn't possible with grab sampling. Since the device remains in the water, it gives a more representative picture of an aquatic environment's overall health, instead of the limited snapshot from grab sampling. Because of their low cost, a greater number of the semi-disposable passive samplers can be simultaneously deployed over a larger area to yield more comprehensive and informative data. The device currently measures a host of organic contaminants, such as industrial chemicals like PCBs (polychlorinated biphenyls), pesticides, synthetic chemicals that mimic hormones like estrogen, and even munition compounds from unexploded weapons that find their way into bodies of water. Vlahos has already tested the technology in a variety of aquatic and sedimentary environments, both nationally and internationally. Although Vlahos was confident in her technology's ability to improve on standard industry practices, she wasn't sure how to commercialize it or who the target customer would be. So she and Warren turned to Accelerate UConn, a program that helps entrepreneurial faculty and students at all UConn campuses validate their technology business ideas. The program was launched in May 2015, and is jointly operated by the Office of the Vice President for Research and the Connecticut Center for Entrepreneurship and Innovation (CCEI), housed in the School of Business. "Many UConn innovations have valuable, real-world applications, but our faculty and students need the right tools to successfully commercialize them," says Jeff Seemann, UConn vice president for research. "Accelerate UConn provides those tools, and helps promising technologies take those critical first steps towards the market, where they can benefit the state's citizens and economy." Warren, who has long aspired to be an entrepreneur, acted as the team's 'entrepreneurial lead.' He was responsible for attending weekly webinars, making presentations, and conducting interviews with potential customers to validate the researchers' assumptions about their technology and the market. He says he could relate to the program's Lean Launchpad methodology because of his background in science. "Even though commercializing a technology was totally new to me, the framework they provided was familiar," says Warren. "By running small iterations of 'experiments' on products or services with potential customers, you can really shape your business before you have to fully launch, and do a lot of the learning before you spend lots of time and money." The knowledge that participants gain from completing the Accelerate UConn program can serve as a stepping stone for additional funding through internal sources, like the UConn SPARK Technology Commercialization Fund, as well as external sources like federal Small Business Innovation Research grants. Warren recently won a $15,000 Summer Fellowship offered by CCEI, where he and Vlahos were able to build upon the progress they had made in Accelerate UConn. As a result, he was also selected to compete for an additional $15,000 provided through the Wolff New Venture Competition this September. Timothy B. Folta, UConn professor of business and CCEI faculty director, says entrepreneurs like Vlahos and Warren gain a new outlook on their products from participating in Accelerate UConn, and this can be important for their success as entrepreneurs. "One of the most potent criticisms of university technology commercialization is that technologists do not have a good understanding about whether customers really want their technology, because they are enamored with it," Folta says. "Accelerate UConn aims to correct this bias." Vlahos and Warren are not slowing down. They are actively seeking additional internal and external funding, are continuing to develop new applications for their device, and plan to conduct more pilot tests on the technology in the coming months.