Researchers have developed a technique for activating biological signals through the skin of a living animal using light. In this illustration, ultraviolet light is shining through a pattern, initiating fluorescence in the biomaterial implanted in a biomaterial located under the skin of a living animal. (Click image for high-resolution version. Credit: García Laboratory at Georgia Tech)

When the disguised peptides are needed to launch biological processes, the researchers shine ultraviolet light onto the molecules through the skin, causing the “hat” structures to come off. That allows cells and other molecules to recognize and interact with the peptides on the surface of the material.

This light-activated triggering technique has been demonstrated in animal models, and if it can be made to work in humans, it could help provide more precise timing for processes essential to regenerative medicine, cancer treatment, immunology, stem cell growth, and a range of other areas. The research represents the first time biological signals presented on biomaterials have been activated by light through the skin of a living animal, and could provide a broader platform technology for launching and controlling biological processes in living animals.

“Many biological processes involve complex cascades of reactions in which the timing must be very tightly controlled,” said Andrés García, a Regents Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Tech and principal investigator for the project. “Until now, we haven’t had control over the sequence of events in the response to implanted materials. But with this technique, we can deliver a drug or particle with its signal in the ‘off’ position, then use light to turn the signal ‘on’ precisely when needed.”

Supported by the National Science Foundation and the National Institutes of Health, the research is reported in the December issue of the journal Nature Materials. It resulted from collaboration between scientists from Georgia Tech and the Max-Planck Institute in Germany through the Materials World Network Program.

When biomaterials are introduced into the body, they normally stimulate an immune system response immediately. But the researchers used molecular cages like hats to cover binding sites on the peptides that are normally recognized by cell receptors, preventing recognition by the animal’s cells. The cages were designed to detach and reveal the peptides when they encounter specific wavelengths of light.

Researchers have developed a technique for activating biological signals through the skin of a living animal using light. Georgia Tech Professor Andrés García, shown here in a Georgia Tech laboratory, is the project’s principal investigator. (Click image for high-resolution version. Credit: Nicole Cappello)

During the five-year project, the research team – which included Ted Lee and Jose Garcia from Georgia Tech and Aranzazu del Campo from Max-Planck – modified peptides that normally trigger cell adhesion to present the molecular cage in order to disguise them. They showed that disguised peptides introduced into animal models on biomaterials could trigger cell adhesion, inflammation, fibrous encapsulation, and vascularization responses when activated by light. They also showed that the location and timing of activation could be controlled inside the animal by simply shining light through the skin.

The work involved numerous controls to ensure that the triggering observed by the researchers was actually done by exposure of the peptides – not the light, or the removal of the protective cage. The researchers also had to demonstrate that the “hats” were stable enough that they didn’t come off spontaneously, but only when the link between the molecular cage and the peptide was severed by the ultraviolet light.

Among the experiments was use of the peptide to attract cells that would attach themselves to the biomaterial. “We showed that if we left the hat on, there would be few cells attracted to the material, García said. “But when we take the hat off, we recruited a lot of cells to the material. That shows we can activate the peptide, and that the activation has a biological consequence.”

Another experiment showed that the timing of peptide activation could affect the quantity of fibrosis, an immune system response that builds a protective capsule around an implanted biomaterial. By delaying the exposure of the peptides until after the bulk of the inflammation reaction had taken place, the thickness of the fibrosis capsule was significantly reduced, allowing it to be better incorporated into the body.

Researchers have developed a technique for activating biological signals through the skin of a living animal using light. Georgia Tech Professor Andrés García, shown here in a Georgia Tech laboratory with research on hydrogel materials, is the project’s principal investigator. (Click image for high-resolution version. Credit: Gary Meek)

In another experiment, the researchers showed that removing the hats could trigger the growth of blood vessels into the material. This vascularization is critical in regenerative medicine, but must take place at the right time to be successful.

“We showed that if you keep the hat on, you get no vessel in-growth into the material,” explained García. “But if we turn on the light, we get growth of new blood vessels into the material. We can control what happens and when it happens by when we expose the protective cages to light.”

In the future, photochemists at Max-Planck will be working on alternative cages that would be triggered by different wavelengths of light. As much as 90 percent of the ultraviolet light used in the experiments was lost in passing through the skin of the animal model, limiting the use of that wavelength to locations immediately below the skin.

Development of alternate “hats,” the molecular cages that protect the peptides, could allow sequential activation by light, and light activation of molecules at locations deeper inside the body.

Light, heat, and electricity have been used to trigger biological processes in vitro, García noted. Light is especially useful because it can be patterned to control processes spatially, which is also important because the processes must occur not only at the right time, but also the right place.

“The technique we developed is a general strategy that we can apply to other biological signals to see if they have similar spatio-temporal effects,” said García. “We see this as a beginning. From here, there are many, many applications that we can follow.”

In addition to those already mentioned, the research involved Ankur Singh, Edward Phelps and Asha Shekaran from Georgia Tech, and Julieta Paez, Simone Weis and Zahid Shafiq from the Max-Planck Institute. Lee now works for Dexcom, a San Diego-based company that focuses on continuous glucose monitoring systems for use by people with diabetes, and Singh is currently an assistant professor at Cornell University.

The research was supported by the Materials World Network Program of the National Science Foundation under grants DFG AOBJ 569628 and NSF DMR-0909002, by the National Institutes of Health under grants R01-AR062368 and R01-AR062920, and by the NIH Cell and Tissue NIH Biotechnology Training Grant T32-GM008433. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation or the National Institutes of Health.

CITATION: Lee, Ted, et al., “Light-triggered in vivo Activation of Adhesive Peptides Regulates Cell Adhesion, Inflammation and Vascularization of Biomaterials,” Nature Materials 2014.

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The Taj Mahal attracts millions of visitors each year. Researchers have determined how particulates in the air are discoloring the landmark. (Click image for high-resolution version. Credit: Mike Bergin)

Knowing the culprits in the discoloration is just the first step in cleaning up the Taj Mahal. Scientists now must determine where the particles are coming from to develop strategies for controlling them.

“Our team was able to show that the pollutants discoloring the Taj Mahal are particulate matter: carbon from burning biomass and refuse, fossil fuels, and dust – possibly from agriculture and road traffic,” said Michael Bergin, a professor in the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology. “We have also been able to show how these particles could be responsible for the brownish discoloration observed.”

Supported by the Indo U.S. Science and Technology Forum, the U.S. Environmental Protection Agency, and the National Science Foundation, the research was reported online December 3, 2014, in the journal Environmental Science & Technology. In addition to Georgia Tech, researchers from the Indian Institute of Technology at Kanpur (IIT-K), Archaeological Survey of India (ASI), and the University of Wisconsin, collaborated on the project.

Built in the 1600s by Mughal emperor Shah Jahan in memory of his third wife, Mumtaz Mahal, the structure is a mausoleum that includes a massive marble dome 115 feet high and minarets that reach 130 feet. Attracting millions of visitors each year, the Taj Mahal became a UNESCO World Heritage Site in 1983.

Workers clean a portion of the Taj Mahal complex. Marble on the right shows brownish discoloration caused by particles of carbon, and dust. (Click image for high-resolution version. Credit: Mike Bergin)

Beginning in the 1970s, observers noted a brownish cast to the white marble that makes up the structures. Today, routine cleaning, including the painstaking application and removal of a clay material, maintains the brightness of the marble. Air pollution had been suspected as the culprit responsible for the discoloration, but no systematic study had been done and the specific components of the air pollutants responsible for the discoloration and the mechanisms by which they discolor the surface had remained unknown.

To find out what was causing the color change, a team from four different institutions was assembled. In addition to Bergin, it included Sachchida Nand Tripathi and Tarun Gupta from the Indian Institute of Technology in Kanpur, India; K.S. Rana from the Archaeological Survey of India in Delhi; Martin M. Shafer, Ana M. Villalobos, and James J. Schauer from the University of Wisconsin’s Environmental Chemistry and Technology Program; and J. Jai Devi and Michael Mckenzie from Georgia Tech.

The researchers used air sampling equipment to measure what was in the air in the Taj Mahal complex from November 2011 through June 2012. Filters from the air-sampling equipment were analyzed for both fine particulate matter (smaller than 2.5 microns in diameter) and total suspended particulate matter. The analysis was done by scientists from both India and the United States.

Air sampling equipment located in this section of the Taj Mahal complex was used to determine what was causing discoloration of the landmark structure. (Click image for high-resolution version. Credit: Mike Bergin)

In addition, the researchers placed small samples of pristine marble onto the Taj Mahal at various locations near the main dome. After exposure to air pollutants over a two-month period, the samples were analyzed using an electron microscope to measure the size and the number of particles deposited on their surfaces as well as their elemental signatures. This information allowed the researchers to determine the likely composition of the particles.

The researchers found particles of dust, brown organic carbon and black carbon in the filters and on the marble samples, Bergin said. The carbon particles come from a variety of sources, including fuel combustion, cooking and brick-making, trash and refuse burning, and vehicle exhaust. The dust may come from local agricultural activities and vehicular traffic – or from distant sources.

To check their analysis, the researchers refined a model for showing how the surface reflectance of the building’s marble should change with the application of brown and black carbon particles, along with dust. The predictions of the model matched what was being observed on the Taj Mahal.

“We fundamentally showed how these particles change the color of the surface,” said Bergin, who is also associated with the School of Civil and Environmental Engineering at Georgia Tech. “We hope to share this model with others who could use it to determine how urban and natural environments are changing colors due to particulate pollution.”

Scaffolding covers a portion of the Taj Mahal complex as workers remove brownish discoloration caused by particles of carbon, and dust. (Click image for high-resolution version. Credit: Mike Bergin)

Now that researchers know what’s discoloring the Taj Mahal, the next step will be to identify the sources of the particles and plan control strategies. The sources could be local – and the government has already taken steps to reduce vehicle and industrial emissions in the area – or the particles could be coming from longer distances away from the region.

While the research focused only on the Taj Mahal itself, reducing particulate matter in the Agra region around the landmark would have additional benefits.

“Some of these particles are really bad for human health, so cleaning up the Taj Mahal could have a huge health benefit for people in the entire region,” said Bergin. “The health of humans and the health of the Taj Mahal are intertwined.”

This research was supported by the Indo U.S. Science and Technology Forum (IUSSTF), the U.S. Environmental Protection Agency (EPA) under grant RD83479901, and by the National Science Foundation (NSF) under PIRE grant 1243535. An opinions expressed are those of the authors and may not necessarily represent the official views of the IUSSTF, EPA or NSF.

A worker cleans a portion of the Taj Mahal complex. Marble on the right shows brownish discoloration caused by particles of carbon, and dust. (Click image for high-resolution version. Credit: Mike Bergin)

CITATION: Mike H. Bergin, et al., “The Discoloration of the Taj Mahal due to Particulate Carbon and Dust Deposition,” (Environmental Science & Technology, 2014). http://pubs.acs.org/doi/pdf/10.1021/es504005q

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The GTRI lightweight lidar prototype system uses a special green laser that penetrates water to considerable depths. GTRI researchers use it to study the best methods for producing accurate images of objects on the pool floor. (Click image for high-resolution version. Credit: Rob Felt)

A team at the Georgia Tech Research Institute (GTRI) has designed a new approach that could lead to bathymetric lidars that are much smaller and more efficient than the current full-size systems. The new technology, developed under the Active Electro-Optical Intelligence, Surveillance and Reconnaissance (AEO-ISR) project, would let modest-sized unmanned aerial vehicles (UAVs) carry bathymetric lidars, lowering costs substantially.

And, unlike currently available systems, AEO-ISR technology is designed to gather and transmit data in real time, allowing it to produce high-resolution 3-D undersea imagery with greater speed, accuracy, and usability.

These advanced capabilities could support a range of military uses such as anti-mine and anti-submarine intelligence and nautical charting, as well as civilian mapping tasks. In addition, GTRI’s new lidar could probe forested areas to detect objects under thick canopies.

“Lidar has completely revolutionized the way that ISR is done in the military – and also the way that precision mapping is done in the commercial world,” said Grady Tuell, a principal research scientist who is leading the work. “GTRI has extensive experience in atmospheric lidar going back 30 years, and we’re now bringing that knowledge to bear on a growing need for small, real-time bathymetric lidar systems.”

Active EO-ISR team members Eric Brown (left) and Ryan James examine the GTRI lightweight lidar prototype, which is gantry-mounted over a laboratory water tank. (Click image for high-resolution version. Credit: Rob Felt)

Tuell and his team have developed a new GTRI lightweight lidar, a prototype that has successfully demonstrated AEO-ISR techniques in the laboratory. The team has also completed a design for a deployable mid-size bathymetric device that is less than half the size and weight of current systems and needs half the electric power.

Measuring Laser Light

To simulate the movement of an actual aircraft, the prototype must be “flown” over a laboratory pool. To do this, the researchers install the lidar onto a gantry above a large water tank in Georgia Tech’s Woodruff School of Mechanical Engineering and then operate it in a manner that simulates flight.

The lidar utilizes a high-power green laser that can penetrate water to considerable depths. Firing a laser beam every 10,000th of a second, the proxy aircraft allows the team to study the best methods for producing accurate images of objects on the floor of the pool.

The ultimate goal is to obtain accurate reflectance from the sea floor, but the presence of water makes that difficult. To capture good images, the GTRI lightweight lidar must make a series of adjustments that let it measure reflected laser beams as if there were no water present.

The GTRI lightweight lidar prototype system uses a special green laser that penetrates water to considerable depths. GTRI researchers use it to study the best methods for producing accurate images of objects on the pool floor. (Click image for high-resolution version. Credit: Rob Felt)

One challenge is that when a tightly focused light beam such as a laser hits water, it loses speed and bends, a familiar underwater effect called refraction. Due to changes in the water’s surface, the angle of refraction varies constantly, and these changes in the refracted angle must be accounted for when computing the path of the light.

Another challenge is that the photons in the laser beam scatter in the water, like light from a car headlight hitting fog. The amount of this scattering depends on the water’s turbidity, which refers to the number of particles suspended in it. In addition, the water absorbs some of the light.

Because of these two effects, a lidar system receives back only a tiny signal when its laser beam bounces off an underwater surface such as the sea floor. The signal-conditioning and sensor-processing capabilities of the lightweight lidar must be sophisticated enough to detect that small returning signal in an overall sea and air environment that is very noisy – meaning that it’s filled with extraneous signals that interfere with the desired data.

Improving Critical Techniques

The ultimate product of a bathymetric lidar is a three-dimensional point cloud that describes the seafloor at high spatial resolution. Users of these data need to know the accuracy of each point.

GTRI’s Active EO-ISR team includes: (front row, left to right) Jason Zutty, Domenic Carr, Robert Ortman, Maha Hosain, Allison Mercer, Jack Wood; (back row, left to right) Thomas Craney, Bob Case, Chris Valenta, Grady Tuell, Eric Brown and Ryan James. (Click image for high-resolution version. Credit: Rob Felt)

GTRI’s researchers have devised a new approach for accuracy assessment called total propagated uncertainty (TPU). Using statistics, calculus, and linear algebra, the TPU technique propagates errors from the individual measurements – navigation, distance, and refraction angle – to estimate the accuracy of sea-floor measurements.

In a major milestone, the GTRI team was the first to demonstrate bathymetric lidar coordinate computation and TPU estimates in real time. To achieve the necessary processing speed, the team employs a mixed-mode computing environment composed of field programmable gate arrays (FPGAs), along with central-processing and graphics-processing units.

Each time a laser is fired, Tuell explained, it takes only a few nanoseconds for the beam to reach the bottom of the pool and bounce back. Once the beam returns, the lidar’s high-speed computer digitizes the returned beam and computes ranges, coordinates, and TPU before the next shot of the laser.

“In our laboratory tests, we’re computing about 37 million points per second – which is exceptionally fast for a lidar system and gives us a great deal of information about the sea floor in a very short period of time,” Tuell said. “The key is we’re using FPGAs to do the necessary signal conditioning and signal processing, and we’re doing it at exactly the time that we convert from an analog signal to a digital signal.”

A Deployable Design

Grady Tuell, a principal research scientist, leads GTRI’s research into novel bathymetric lidar systems. (Click image for high-resolution version. Credit: Rob Felt)

In addition to developing the proof-of-concept lidar prototype, the GTRI team has produced a CAD design for a deployable bathymetric device that is half the size and weight of current devices and has lower power needs. The immediate goal is to field such a mid-size device on a larger UAV such as an autonomous helicopter.

The longer-term aim is to use AEO-ISR technology to develop bathymetric lidars that could fly on small UAVs with payloads of 30 pounds or less. To help these lidars deliver maritime surveillance and mapping data in real time, most of the necessary signal processing would be done on the aircraft and only essential data would be transmitted to ground stations.

“We’ve provided a prototype that demonstrates the key technology, and we’ve completed a design for a mid-size design,” Tuell said. “In the future, we believe small bathymetric lidars will perform military tasks, and also civilian tasks such as county-level mapping, with increased convenience and at greatly reduced cost.”

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A GTRI team consisting of (left to right) Dan Campbell, Rob McColl, Jason Poovey, and David Ediger is bringing graph analytics to bear on a range of data-related challenges including social networks, surveillance intelligence, computer-network functionality, and industrial control systems. (Click image for high-resolution version).

Graph analysis is a prime tool for finding the needle in the data haystack. This potent technology – not to be confused with simple illustrations like bar graphs and pie charts – utilizes mathematical techniques that represent relationships in the data more efficiently than traditional statistical analyses.

Researchers at the Georgia Tech Research Institute (GTRI) are bringing graph analytics to bear on a range of data-related challenges. They’re developing advanced technology that can help investigate social networks, surveillance intelligence, computer-network functionality, industrial control systems, and more.

“Our first task is to look at the interesting properties of a graph – to find the important questions we can ask of that graph,” said Dan Campbell, a GTRI principal research engineer who heads the High Performance Computing Branch. “The second task is to find the answers as quickly as possible, and then put them to practical use.”

A graph is a type of data structure comprised of entities – meaning anything that can be represented digitally – and their relationships. In graph terminology, an entity is a vertex or a node; the connections between it and other vertices are edges or arcs. Graphs are constructed using software algorithms that represent both the data points and the relationships between them, and also enable computers to manipulate and analyze that information.

GTRI researchers make extensive use of a graph-analysis framework called STINGER, built specifically to tackle dynamic, ever-changing applications such as social networks and Internet traffic. STINGER was created by a team led by David A. Bader, a professor in the School of Computational Science and Engineering; key members of that team included David Ediger and Robert McColl, who are now part of Campbell’s GTRI group. STINGER, which is open-source software (STINGERgraph.com), continues to be developed at Georgia Tech and in the broader graph analytics community.

“We’ve done a great deal of work on analyzing openly available social media in real time,” said Ediger.”Social media analysis clearly has an important role to play in emergency response to both natural disasters like Hurricane Sandy and to potential terrorist attacks, and we’re actively researching applications in those areas, among others.”

STINGER helps support GTRI’s focus on streaming or dynamic-graph technology, which can store very large databases and then update them in real time as new data come in. This novel approach allows users to monitor social media on a massive scale, and can also be utilized to simulate very large networks.

Georgia Tech researchers have presented this technology at several recent conferences including the 1st Workshop on Parallel Programming for Analytics Applications, which was held in February in Orlando, Fla., in conjunction with the 19th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming.

“Unlike traditional graph databases, STINGER’s streaming-graph technology lets us store very big graphs and analyze them at high speed using fairly modest computing capability,” said Jason Poovey, a GTRI research scientist in Campbell’s group. “In half a terabyte of main memory – a pretty reasonable size today – we can handle billions of nodes and edges. Our benchmark tests show we can represent, update and analyze a graph in real time that’s essentially the size of all the data in Twitter.”

GTRI is focusing on multiple efforts in which graph analysis plays a key role. These projects include:

• Behavioral Modeling and Computational Social Systems (BMCSS) Strategic Initiative – A GTRI team led by senior research scientist Erica Briscoe has used STINGER to study real-time social media analytics, as part of research aimed at predicting human behavior on a large scale.
• BlackForest – Members of Campbell’s group are using graph analytics to support the BlackForest project led by GTRI researcher Chris Smoak. The aim of this externally funded project involves forming coherent intelligence pictures from disparate types of data obtained from multiple sources.
• Nextcache – This externally funded project focuses on developing new CPU, cache and memory designs tailored for graph-based applications.
• Real-time Business Intelligence – Using streaming graph technology, members of Campbell’s group are working with GTRI researcher Erica Briscoe to develop a business-intelligence dashboard that monitors social media in real time and helps businesses gauge consumer sentiment.
• XDATA – Working with researchers from the School of Computational Science and Engineering, GTRI senior research scientists Barry Drake and Richard Boyd are helping to address big-data challenges by studying the computational demands of processing machine-learning algorithms.

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