All posts by University of Maryland

Making “Smart” Cells Smarter

Post Syndicated from University of Maryland original https://spectrum.ieee.org/biomedical/devices/making-smart-cells-smarter

For years, scientists have explored ways to alter the cells of microorganisms in efforts to improve how a wide range of products are made – including medicines, fuels, and even beer. By tapping into the world of metabolic engineering, researchers have also developed techniques to create “smart” bacteria capable of carrying out a multitude of functions that impact processes involved in drug delivery, digestion, and even water decontamination.

But, altering the genetic and regulatory processes that take place within cells presents challenges.

To start, cells are already programmed to carry out their normal, everyday processes with maximum efficiency; any alterations that engineers make to increase a cell’s production of a certain substance can, in turn, upset these processes and overburden the cell.

To address this problem, William E. Bentley, an A. James Clark School of Engineering professor and director of the Robert E. Fischell Institute for Biomedical Devices, is working with a team of researchers to focus on engineering microbial consortia, wherein cell subpopulations are engineered to work together to carry out a desired function. This strategy – which others in the field have also explored – allows engineers to design specialized cells and divvy up the target workload among a group of cells.

The tradeoff is that directing a cell consortium to carry out specific tasks requires engineers to somehow regulate how many of each cell subpopulation are present. Until now, there’s been little research focused on developing devices or systems to automatically regulate the compositions of cellular subpopulations within a consortium. Generally, studies of cell consortia have required engineers to use painstaking manual or expensive external controller systems to strike that balance.

Bentley and his team are focused on reengineering cells so that they’re able to coordinate their subpopulation densities autonomously. Their technique was highlighted in a Nature Communications paper published on Sept. 11.

“The key concept is that groups of cells can be engineered to self-regulate their composition, and no outside input is needed,” Bentley said. “For example, there’s no way to ensure that the bacteria engineered for use in the gastrointestinal tract will actually be retained or behave as we expect. And you can’t use convenient means such as magnetic or electrical fields to regulate bacteria in the gut, so why not incorporate the self-regulation property into the bacteria themselves?”

Like others in the field, Bentley and members of his Biomolecular and Metabolic Engineering Lab previously investigated “quorum sensing,” or QS—a bacterial form of cell-to-cell communication—to engineer communication circuits between bacterial strains to coordinate their behaviors.

To create an autonomous system, Bentley and his team rewired the bacterial QS systems in two strains of E. coli so that the growth rate of communicating cell subpopulations within the consortia would be dictated by signaling between the cells. It’s a sort of feedback loop in which cells are able to sense and react to intercellular signaling molecules called autoinducers, which enable bacteria to work together of their own accord.

The breakthrough could be key to a host of new functions for “smart bacteria” developed through genetic engineering, ranging from drug delivery to water decontamination to new fermentation processes for the latest craft beverage.

“Increasingly, consortia of microbes will be tasked with converting raw materials into valuable products,” Bentley said. “The raw materials may be wastes or byproducts of industrial processes. The synthetic capabilities of consortia may far surpass those of pure monocultures, so methodologies that help to align consortia will be needed.”

University of Maryland Fischell Department of Bioengineering (BIOE) and Institute for Bioscience and Biotechnology Research (IBBR) researcher Kristina Stephens served as first author on the Nature Communications paper titled, “Bacterial co-culture with cell signaling translator and growth controller modules for autonomously regulated culture composition.” Maria Pozo (BIOE), Chen-Yu Tsao (BIOE, IBBR), and Pricila Hauk (BIOE, IBBR) also contributed to the paper.

This work is supported in part by funding from the National Science Foundation, the Defense Threat Reduction Agency (U.S. Department of Defense), and the National Institutes of Health (NIH).

Delivering on Quantum Innovation

Post Syndicated from University of Maryland original https://spectrum.ieee.org/computing/hardware/delivering-on-quantum-innovation

The University of Maryland (UMD) has announced the launch of the Quantum Technology Center (QTC), which aims to translate quantum physics research into innovative technologies.

The center will capitalize on the university’s strong research programs and partnerships in quantum science and systems engineering, and pursue collaborations with industry and government labs to help take promising quantum advances from the lab to the marketplace. QTC will also train students in the development and application of quantum technologies to produce a workforce educated in quantum-related engineering.

The launch of QTC comes at a pivotal time when quantum science research is expanding beyond physics into materials science, engineering, computer science, chemistry, and biology. Scientists across these disciplines are looking for ways to exploit quantum physics to build powerful computers, develop secure communication networks, and improve sensing and imaging capabilities. In the future, quantum technology could also impact fields such as artificial intelligence, energy, and medicine.

Fearless vision

The rules of quantum physics cover the shockingly strange behaviors of atoms and smaller particles. Technologies based on the first century of quantum physics research are close at hand in your daily life—in your smartphone’s billions of transistors and GPS navigation, for instance.

Today more radical quantum technologies are moving toward commercial reality.

UMD has long been a powerhouse in quantum research and is now accelerating this trend with the launch of QTC. Founded jointly by UMD’s A. James Clark School of Engineering and College of Computer, Mathematical, and Natural Sciences, QTC will translate quantum science to the marketplace.

“QTC will be a community that brings together different types of people and ideas to create new quantum technologies and train a new generation of quantum workforce,” says QTC founding Director Ronald Walsworth. “UMD will focus on developing these technologies in the early stages, and then translating them out to the wider world with diverse partners.”

Like UMD’s existing quantum research programs, QTC is expected to draw strong sponsorship from federal research agencies. National support for quantum research is on the upswing—most notably evidenced by the National Quantum Initiative, signed into law in December 2018, which authorizes $1.275 billion over five years for research. 

Quantum research on the rise

UMD already hosts more than 200 researchers in quantum science, one of the greatest concentrations in the world. Much of the effort has been led by the Joint Quantum Institute (JQI) and Joint Center for Quantum Information and Computer Science (QuICS), both partnerships between UMD and the National Institute of Standards and Technology. JQI and QuICS support many projects that cross boundaries in research disciplines and organizations; this trend will only increase with QTC on campus.

One prime example of constructively blurred lines comes from the research of Distinguished University Professor Chris Monroe. An international leader in isolating individual atoms for quantum computing and simulation, Monroe is a member of all three centers, and well-positioned to tap into the expertise of researchers in related disciplines. 

Professor Edo Waks and Associate Professor Mohammad Hafezi, both members of QTC and JQI, are also among the UMD researchers helping to form the next revolution of quantum research with groundbreaking work on devices for quantum information processing and quantum networks.

In one effort, Waks demonstrated the first single-photon transistor using a semiconductor chip. The device is compact; roughly one million of these new transistors could fit inside a single grain of salt. It is also fast and able to process 10 billion photonic qubits every second.

“Using our transistor, we should be able to perform quantum gates between photons,” says Waks. “Software running on a quantum computer would use a series of such operations to attain exponential speedup for certain computational problems.”

Hafezi studies the fundamental behaviors of light–matter interactions down to the single-photon level. He created the first silicon chip that can reliably constrain light to its four corners. The effect, which arises from interfering optical pathways, could eventually enable the creation of robust sources of quantum light.

“We have been developing integrated silicon photonic systems to realize ideas derived from topology in a physical system,” Hafezi says. “The fact that we use components compatible with current technology means that, if these systems are robust, they could possibly be translated into immediate applications.”

Grounding a quantum community

 “QTC will be a crucible for quantum science and engineering,” says Walsworth, a leader in quantum sensing who was recruited from Harvard University to lead the new center. “We’ll be building bridges between people, between sectors, between theories and technologies. There’s a kind of hunger for a community that pulls people together to pool information and find ways to overcome challenges in this exciting new area.”

According to Clark School Dean and Farvardin Professor Darryll Pines, UMD’s hiring of Walsworth signals an important next step in bringing engineering solutions to the forefront. “He’s the perfect representative to bridge the gap between physics and engineering, because he’s already been doing that himself,” says Pines.

In addition to his broad range of research accomplishments, Walsworth has acted as an advisor for corporations and co-founded two companies based in part on his lab’s work. Quantum Diamond Technologies is developing applications in medical diagnostics for quantum measurement technologies that can be generated at room temperatures in synthetic diamonds. Hyperfine Research is creating low-cost portable MRI machines. 

“If you really want a new community of technology to flourish, you’ve got to have the applications right,” Walsworth adds. “You’ve got to be solving someone’s problems. Some people are busy building their technologies, but they don’t always know what the technologies are good for. Other people are out there complaining about how they can’t solve their problems, but they don’t know what technology exists that might help.” 

Making the match will require QTC researchers to seek out groups across and outside the university to talk about actual challenges where quantum technology might help.

“From a United States perspective, this is a big deal,” he says. “Quantum is one of those areas that requires enormous investment from the federal government, to advance our knowledge in this space. We hope this leads to opportunities that translate to real products with positive impact for people, society, and the U.S. economy.”

Saving Lives…With Robots

Post Syndicated from University of Maryland original https://spectrum.ieee.org/biomedical/devices/saving-liveswith-robots

University of Maryland engineer wants to equip ambulances with medical robots enhanced by machine learning to help trauma patients

At the moment of traumatic injury, no physician is present. Emergency medical technicians respond first—they stabilize the patient during ambulance transport, while specialized trauma teams prepare to receive the patient at a hospital.

That is, if the patient makes it there.

“The ride to the hospital is the riskiest part for the trauma patient,” says Axel Krieger, assistant professor of mechanical engineering at the University of Maryland, who specializes in medical robotics and computer vision. Krieger says that estimates suggest one-third of trauma fatalities likely would have survived if they had access to hospital-level of care sooner. He aims to help make that level of care standard on the ambulance ride—a long way from his undergraduate days in Germany, where he studied automotive engineering.

To improve the health-giving capacity for trauma patients during the ambulance ride, Krieger wants to equip the ambulance with a medical robot enhanced by machine learning (ML). “One of the biggest dangers during the ambulance ride is undiagnosed, internal hemorrhagic bleeding,” he says. “It’s currently undetectable with methods available on the ambulance ride. You can’t see it.”

But a robot can.

“Imagine you have a patient in the emergency vehicle, and a robot scans the patient and obtains ultrasound images,” says Krieger, who is a member of the Maryland Robotics Center. “This can provide a critical level of life-saving diagnosis and care not yet possible during an emergency ambulance ride.”

The robot scans and visualizes the injury, then compares and analyzes the scans with its ML algorithm—which was trained using data from similar real-life patient images. It focuses on anatomic areas known to be especially vulnerable to hidden injury and bleeding—such as the pelvic area and space between the lungs, spleen, and liver—to determine severity of wounds based on location, depth, and interaction with vital anatomy; compute volume of blood loss; and assess hemorrhagic potential. Analyzing these characteristics en route would help produce an injury profile useful in triaging the patient so he or she can receive appropriate care as soon as possible—perhaps in the ambulance, and most certainly upon arrival at the hospital.

To develop this ML-based intelligent scanning robot, Krieger and several A. James Clark School of Engineering graduate students collaborated with trauma experts at the University of Maryland Medical Center’s R Adams Cowley Shock Trauma Center.

The research is still experimental and not yet approved for clinical use with patients—but Krieger believes it will be soon.

“It’s the translational aspect to patient care that really excites me,” he says. “If we can help more people survive, this is the best use of our work.”

Algorithms and Autonomous Discovery

Post Syndicated from University of Maryland original https://spectrum.ieee.org/at-work/innovation/algorithms-and-autonomous-discovery

Materials scientist Ichiro Takeuchi uses machine learning-based discovery to help develop new, alternative materials

More than a decade ago, Ichiro Takeuchi, professor of materials science and engineering, started applying the subfield of artificial intelligence (AI) known as machine learning (ML) to help develop new magnetic materials.

At the time, ML was not widely used in materials science. “Now, it’s all the rage,” says Takeuchi, who also holds an appointment with the Maryland Energy Innovation Institute. Its current popularity is due in part to the deep learning revolution of 2012 and related advances in computer chip speed, data storage options, and rapid refinement of the science that drives its predictive analytics of algorithms.

ML-based discovery in materials science is not just a lab exercise. It can provide production solutions to geopolitical challenges—as in the case of deteriorating trade with China about a decade ago, which prompted a supply-chain crisis for electric vehicle motor development in the U.S. Key materials were no longer available to American producers to make the neodymium rare-earth permanent magnet that helps power the vehicles.

The solution: Takeuchi’s team applied ML to discover and develop new, alternative magnet materials so research for electric vehicle motors could continue.

And they bootstrapped it. In the beginning, Takeuchi and his team didn’t have any curated data to feed their ML algorithm. So they built the database themselves. They taught machines to read troves of scientific papers and parse data in search of patterns and predictions. From those papers, they extracted meaningful chemical details on rare-earth magnet performance, properties, and functions. This became the database they needed to enlist the aid of yet another ML algorithm. This time, the task was to identify alternative candidate materials with the desired traits for fabricating rare-earth permanent magnets.

According to Takeuchi, researchers increasingly search for novel materials with specific attributes. “ML helps us in our searches in a way that is computationally inexpensive and highly efficient, so we can understand composition–structure relationships and functional properties.’’

In Takeuchi’s lab, searches for new materials are done with accelerated synthesis of large numbers of compounds called high-throughput experiments, which produce up to 1,000 materials at a time and generate immense quantities of data. “We were inundated with data,” Takeuchi says. Yet prior to applying ML, they lacked a means for leveraging of all that data potential.

ML not only makes sense of enormous datasets—it extends discovery by allowing the algorithm to make predictions from “leads” it discovers in the data. The machine automatically discovers hidden relationships between materials and their properties, which is the knowledge Takeuchi and his team are ultimately seeking.

Takeuchi’s lab continues to innovate with ML-based discovery. Their newest development sprang from the question: “In the search to discover new materials with particular attributes, why don’t we let the computer analyze all the attributes and decide how the experiment should run?”

This new model of autonomous active learning is fast, inexpensive, and highly efficient, because the power and predictive ability of ML minimizes the number of experiments required to solve a problem.

“With an autonomous active learning approach, you don’t need to do 1,000 experiments as we did with high-throughput approaches,” Takeuchi says. “We need only to do about one-tenth or one-fifth of all experiments, because we let the algorithm decide where to go next. You see what the machine comes up with—without you. It predicts, and then we test. We think this is the future.”

Shedding Light on the Future of LASIK

Post Syndicated from University of Maryland original https://spectrum.ieee.org/biomedical/imaging/shedding-light-on-the-future-of-lasik

A University of Maryland-developed microscopy technique could eliminate the “surgery” aspect of LASIK

Fischell Department of Bioengineering (BIOE) researchers have developed a microscopy technique that could one day be used to improve LASIK and eliminate the “surgery” aspect of the procedure. Their findings were published in March in Physical Review Letters.

In the 20 years since the FDA first approved LASIK surgery, more than 10 million Americans have had the procedure done to correct their vision. When performed on both eyes, the entire procedure takes about 20 minutes and can rid patients of the need to wear glasses or contact lenses.

While LASIK has a very high success rate, virtually every procedure involves an element of guesswork. This is because doctors have no way to precisely measure the refractive properties of the eye. Instead, they rely heavily on approximations that correlate with the patient’s vision acuity—how close to 20/20 he or she can see without the aid of glasses or contacts.

In search of a solution, BIOE Assistant Professor Giuliano Scarcelli and members of his Optics Biotech Laboratory have developed a microscopy technique that could allow doctors to perform LASIK using precise measurements of how the eye focuses light, instead of approximations.

“This could represent a tremendous first for LASIK and other refractive procedures,” Scarcelli said. “Light is focused by the eye’s cornea because of its shape and what is known as its refractive index. But until now, we could only measure its shape. Thus, today’s refractive procedures rely solely on observed changes to the cornea, and they are not always accurate.”

The cornea—the outermost layer of the eye—functions like a window that controls and focuses light that enters the eye. When light strikes the cornea, it is bent—or refracted. The lens then fine-tunes the light’s path to produce a sharp image onto the retina, which converts the light into electrical impulses that are interpreted by the brain as images. Common vision problems, such as nearsightedness or farsightedness, are caused by the eye’s inability to sharply focus an image onto the retina.

To fix this, LASIK surgeons use lasers to alter the shape of the cornea and change its focal point. But, they do this without any ability to precisely measure how much the path of light is bent when it enters the cornea. 

To measure the path light takes, one needs to measure a quantity known as the refractive index; it represents the ratio of the velocity of light in a vacuum to its velocity in a particular material.

By mapping the distribution and variations of the local refractive index within the eye, doctors would know the precise degree of corneal refraction. Equipped with this information, they could better tailor the LASIK procedure such that, rather than improved vision, patients could expect to walk away with perfect vision—or vision that tops 20/20.

Even more, doctors might no longer need to cut into the cornea.

“Non-ablative technologies are already being developed to change the refractive index of the cornea, locally, using a laser,” Scarcelli said. “Providing local refractive index measurements will be critical for their success.”

Knowing this, Scarcelli and his team developed a microscopy technique that can measure the local refractive index using Brillouin spectroscopy—a light-scattering technology that was previously used to sense the mechanical properties of tissue and cells without disrupting or destroying either.

“We experimentally demonstrated that, by using a dual Brillouin scattering technology, we could determine the refractive index directly, while achieving three-dimensional spatial resolution,” Scarcelli said. “This means that we could measure the refractive index of cells and tissue at locations in the body—such as the eyes—that can only be accessed from one side.”

In addition to measuring corneal or lens refraction, the group is working on improving its resolution to analyze mass density behavior in cell biology or even cancer pathogenesis, Scarcelli said.

Along with Scarcelli, BIOE Ph.D. student Antonio Fiore (first author) and Carlo Bevilacqua, a visiting student from the University of Bari Aldo Moro in Bari, Italy, contributed to the paper.