To track disease-carrying mosquitoes, researchers tag them with DNA barcodes

 The researchers at a field site in Fort Collins, Colorado collecting mosquitoes for analysis.
Photo Credit: Rebekah Kading/Colorado State University

West Nile, Zika, dengue and malaria are all diseases spread by bites from infected mosquitoes. To track the threat of such diseases over large populations, scientists need to know where the mosquitoes are, where they’ve been, and where they might go.

But take it from Rebekah Kading, a Colorado State University researcher who studies mosquito-borne arboviruses: tracking mosquitoes is no easy task. The capture, tagging and release of single mosquitoes – as is commonly done with bats and other disease carriers – would be ridiculous, if not impossible. A common mosquito-tracking technique involves dousing the insects in fluorescent powder and letting them fly away, but the practice is error-prone and unreliable.

Thanks to a collaboration with CSU engineers, Kading and colleagues are introducing a better way to perform mosquito-tracking for disease applications. Their new method, which involves getting larval mosquitoes to eat harmless particles made entirely of DNA and proteins, has the potential to revolutionize how people study mosquito-borne diseases.

The edible mosquito marker particles are the work of Chris Snow, associate professor in the Department of Chemical and Biological Engineering. For the last several years, Snow’s team has been developing microscopic, porous protein crystals that self-assemble from a protein originally found in Camplyobacter jejuni bacteria. Since inventing these very small, non-toxic protein crystals that feature highly precise arrays of pores, Snow’s team has been exploring diverse applications for them, like capturing virus particles to facilitate wastewater testing.

Machine learning model builds on imaging methods to better detect ovarian lesions

(From left) The top row shows an ultrasound image of a malignant lesion, the blood oxygen saturation, and hemoglobin concentration. The bottom row is an ultrasound image of a benign lesion, the blood oxygen saturation, and hemoglobin concentration.
Image Credit: Zhu lab

Although ovarian cancer is the deadliest type of cancer for women, only about 20% of cases are found at an early stage, as there are no real screening tests for them and few symptoms to prompt them. Additionally, ovarian lesions are difficult to diagnose accurately — so difficult, in fact that there is no sign of cancer in more than 80% of women who undergo surgery to have lesions removed and tested.

Quing Zhu, the Edwin H. Murty Professor of Biomedical Engineering at Washington University in St. Louis’ McKelvey School of Engineering, and members of her lab have applied a variety of imaging methods to diagnose ovarian cancer more accurately. Now, they have developed a new machine learning fusion model that takes advantage of existing ultrasound features of ovarian lesions to train the model to recognize whether a lesion is benign or cancerous from reconstructed images taken with photoacoustic tomography. Machine learning traditionally has been focused on single modality data. Recent findings have shown that multi-modality machine learning is more robust in its performance over unimodality methods. In a pilot study of 35 patients with more than 600 regions of interest, the model’s accuracy was 90%.

New device can control light at unprecedented speeds

Scientists have developed a programmable, wireless spatial light modulator that can manipulate light at the wavelength scale with orders-of-magnitude faster response than existing devices.
Illustration Credit: Sampson Wilcox

In a scene from “Star Wars: Episode IV — A New Hope,” R2D2 projects a three-dimensional hologram of Princess Leia making a desperate plea for help. That scene, filmed more than 45 years ago, involved a bit of movie magic — even today, we don’t have the technology to create such realistic and dynamic holograms.

Generating a freestanding 3D hologram would require extremely precise and fast control of light beyond the capabilities of existing technologies, which are based on liquid crystals or micromirrors.

An international group of researchers, led by a team at MIT, spent more than four years tackling this problem of high-speed optical beam forming. They have now demonstrated a programmable, wireless device that can control light, such as by focusing a beam in a specific direction or manipulating the light’s intensity, and do it orders of magnitude more quickly than commercial devices.

They also pioneered a fabrication process that ensures the device quality remains near-perfect when it is manufactured at scale. This would make their device more feasible to implement in real-world settings.

Rice lab’s catalyst could be key for hydrogen economy

Rice University researchers have engineered a key light-activated nanomaterial for the hydrogen economy. Using only inexpensive raw materials, a team from Rice’s Laboratory for Nanophotonics, Syzygy Plasmonics Inc. and Princeton University’s Andlinger Center for Energy and the Environment created a scalable catalyst that needs only the power of light to convert ammonia into clean-burning hydrogen fuel.

The research is published in the journal Science.

The research follows government and industry investment to create infrastructure and markets for carbon-free liquid ammonia fuel that will not contribute to greenhouse warming. Liquid ammonia is easy to transport and packs a lot of energy, with one nitrogen and three hydrogen atoms per molecule. The new catalyst breaks those molecules into hydrogen gas, a clean-burning fuel, and nitrogen gas, the largest component of Earth’s atmosphere. And unlike traditional catalysts, it doesn’t require heat. Instead, it harvests energy from light, either sunlight or energy-stingy LEDs.

The pace of chemical reactions typically increases with temperature, and chemical producers have capitalized on this for more than a century by applying heat on an industrial scale. The burning of fossil fuels to raise the temperature of large reaction vessels by hundreds or thousands of degrees results in an enormous carbon footprint. Chemical producers also spend billions of dollars each year on thermocatalysts — materials that don’t react but further speed reactions under intense heating.

Improving AI training for edge sensor time series

Engineers at the Tokyo Institute of Technology (Tokyo Tech) have demonstrated a simple computational approach for improving the way artificial intelligence classifiers, such as neural networks, can be trained based on limited amounts of sensor data. The emerging applications of the internet of things often require edge devices that can reliably classify behaviors and situations based on time series. However, training data is difficult and expensive to acquire. The proposed approach promises to substantially increase the quality of classifier training, at almost no extra cost.

In recent times, the prospect of having huge numbers of Internet of Things (IoT) sensors quietly and diligently monitoring countless aspects of human, natural, and machine activities has gained ground. As our society becomes more and more hungry for data, scientists, engineers, and strategists increasingly hope that the additional insight which we can derive from this pervasive monitoring will improve the quality and efficiency of many production processes, also resulting in improved sustainability.

The world in which we live is incredibly complex, and this complexity is reflected in a huge multitude of variables that IoT sensors may be designed to monitor. Some are natural, such as the amount of sunlight, moisture, or the movement of an animal, while others are artificial, for example, the number of cars crossing an intersection or the strain applied to a suspended structure like a bridge. What these variables all have in common is that they evolve over time, creating what is known as time series, and that meaningful information is expected to be contained in their relentless changes. In many cases, researchers are interested in classifying a set of predetermined conditions or situations based on these temporal changes, as a way of reducing the amount of data and making it easier to understand. For instance, measuring how frequently a particular condition or situation arises is often taken as the basis for detecting and understanding the origin of malfunctions, pollution increases, and so on.

New CRISPR-based tool inserts large DNA sequences at desired sites in cells

Building on the CRISPR gene-editing system, MIT researchers designed a new tool that can snip out faulty genes and replace them with new ones.
Image Credit: Sangharsh Lohakare

Building on the CRISPR gene-editing system, MIT researchers have designed a new tool that can snip out faulty genes and replace them with new ones, in a safer and more efficient way.

Using this system, the researchers showed that they could deliver genes as long as 36,000 DNA base pairs to several types of human cells, as well as to liver cells in mice. The new technique, known as PASTE, could hold promise for treating diseases that are caused by defective genes with a large number of mutations, such as cystic fibrosis.

“It’s a new genetic way of potentially targeting these really hard to treat diseases,” says Omar Abudayyeh, a McGovern Fellow at MIT’s McGovern Institute for Brain Research. “We wanted to work toward what gene therapy was supposed to do at its original inception, which is to replace genes, not just correct individual mutations.”

The new tool combines the precise targeting of CRISPR-Cas9, a set of molecules originally derived from bacterial defense systems, with enzymes called integrases, which viruses use to insert their own genetic material into a bacterial genome.

The whole in a part: Synchronizing chaos through a narrow slice of spectrum

Conceptual overview of the coupling scheme between a master and a slave chaotic oscillator via a band-pass filter, and the resulting complex interdependence between their activities.
Credit: Tokyo Institute of Technology

Engineers at the Tokyo Institute of Technology (Tokyo Tech) have uncovered some intricate effects arising when chaotic systems, which typically generate broad spectra, are coupled by conveying only a narrow range of frequencies from one to another. The synchronization of chaotic oscillators, such as electronic circuits, continues to attract considerable fascination due to the richness of the complex behaviors that can emerge. Recently, hypothetical applications in distributed sensing have been envisaged, however, wireless couplings are only practical over narrow frequency intervals. The proposed research shows that, even under such constraints, chaos synchronization can occur and give rise to phenomena that could one day be leveraged to realize useful operations over ensembles of distant nodes.

The abstract notion that the whole can be found in each part of something has for long fascinated thinkers engaged in all walks of philosophy and experimental science: from Immanuel Kant on the essence of time to David Bohm on the notion of order, and from the self-similarity of fractal structures to the defining properties of holograms. It has, however, remained understandably extraneous to electronic engineering, which strives to develop ever more specialized and efficient circuits exchanging signals that possess highly controlled characteristics. By contrast, across the most diverse complex systems in nature, such as the brain, the generation of activity having features that present themselves similarly over different temporal scales, or frequencies, is nearly a ubiquitous observation.

Engineers improve electrochemical sensing by incorporating machine learning

Aida Ebrahimi, Thomas and Sheila Roell Early Career Assistant Professor of Electrical Engineering and assistant professor of biomedical engineering, and Vinay Kammarchedu, 2022-23 Milton and Albertha Langdon Memorial Graduate Fellowship in Electrical Engineering, developed a new approach to improve the performance of electrochemical biosensors by combining machine learning with multimodal measurement.
Photo Credit: Kate Myers | Pennsylvania State University

Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta.

The researchers developed a novel analytical platform that enabled them to selectively measure multiple biomolecules using a single sensor, saving space and reducing complexity as compared to the usual route of using multi-sensor systems. In particular, they showed that their sensor can simultaneously detect small quantities of uric acid and tyrosine — two important biomarkers associated with kidney and cardiovascular diseases, diabetes, metabolic disorders, and neuropsychiatric and eating disorders — in sweat and saliva, making the developed method suitable for personalized health monitoring and intervention.

Many biomarkers have similar molecular structures or overlapping electrochemical signatures, making it difficult to detect them simultaneously. Leveraging machine learning for measuring multiple biomarkers can improve the accuracy and reliability of diagnostics and as a result improve patient outcomes, according to the researchers. Further, sensing using the same device saves resources and biological sample volumes needed for tests, which is critical with clinical samples with scarce amounts.

SARS-CoV-2 detection in 30 minutes using gene scissors

Multiplex chip of a Freiburg research team: On this chip, the viral load in the nasal swab and, if necessary, the antibiotic concentration in the blood of COVID-19 patients could be measured simultaneously.
Photo Credit: AG Disposable Microsystems/University of Freiburg

Researchers of the University of Freiburg introduce biosensor for the nucleic acid amplification-free detection of SARS-CoV-2 RNA

CRISPR-Cas is versatile: Besides the controversial genetically modified organisms (GMOs), created through gene editing, various new scientific studies use different orthologues of the effector protein ‘Cas’ to detect nucleic acids such as DNA or RNA.

In its most recent study, the research group headed by microsystems engineer Dr. Can Dincer of the Department of Microsystems2 Engineering, University of Freiburg introduces a microfluidic multiplexed chip for the simultaneous measurement of the viral load in nasal swabs and (if applicable) the blood antibiotic levels of COVID-19 patients.

Rapid test or PCR?

The market launch of rapid antigen test kits has significantly changed the way in which society handles the effects of the pandemic: Individuals suspecting an infection with SARS-CoV-2 can now test themselves at home with kits that are readily available at most drug stores, pharmacies and supermarkets, instead of making an, oftentimes difficult to acquire, appointment for PCR testing, that requires 1 to 3 additional days to receive the result. This convenience is, however, paid for with test sensitivity. This issue became flagrantly apparent during the wave of infections last winter, when the ‘lateral flow devices’ frequently failed to detect infections with the Omicron-variant until after the onset of symptoms. “The trade-off between sensitivity and sample-to-result time could potentially be bridged using our method,” says Midori Johnston, first author of the study, that is now being published in the journal Materials Today.

Can a new technique for capturing ‘hot’ electrons make solar cells more efficient?

A scanning tunnelling microscope is used to study the dynamics of hot electrons through single molecule manipulation.
Photo Credit: Adrian Hooper

A new way of extracting quantitative information from state-of-the-art single molecule experiments has been developed by physicists at the University of Bath. Using this quantitative information, the researchers will be able to probe the ultra-fast physics of ‘hot’ electrons on surfaces – the same physics that governs and limits the efficacy of silicon-based solar cells.

Solar cells work by converting light into electrons, whose energy can be collected and harvested. A hot solar cell is a novel type of cell that converts sunlight to electricity more efficiently than conventional solar cells. However, the efficiency of this process is limited by the creation of energetic, or ‘hot’, electrons that are extremely short lived and lose most of their energy to their surrounding within the first few femtoseconds of their creation (1 femtosecond equals 1/1,000,000,000,000,000 of a second).

The ultra-short lifetime of hot electrons and the corresponding short distance they can travel mean probing and influencing the properties of hot electrons is experimentally challenging. To date, there have been a few techniques capable of circumventing these challenges, but none has proven capable of spatial resolution – meaning, they can’t tell us about the crucial connection between a material’s atomic structure and the dynamics of hot electrons within that material.

New process developed to extract high purity rare earth element oxides

Pennsylvania stream impacted by acid mine drainage.
Photo Credit: Pennsylvania State University

Critical minerals, including rare earth elements, are used to power devices like smartphones and computers and are essential to our nation’s economy and national security. Penn State’s Center for Critical Minerals has developed a new purification process that extracts mixed rare earth oxides from acid mine drainage and associated sludges at purities of 88.5%

Critical minerals (CMs), including the 17 rare earth elements (REEs), are used in many common household products like smartphones and computers, and in many commercial products such as electric vehicles, batteries and solar panels. Demand for them has skyrocketed, and they are classified as critical because they have high economic importance, high supply risk, and their absence would have significant consequences on the economic and national security of the United States.

Acid mine drainage (AMD) and associated solids and precipitates resulting from AMD treatment have been found to be viable sources of multiple CMs, including REEs, aluminum, cobalt and manganese.

The U.S. Department of Energy (DOE) has funded efforts to demonstrate both the technical feasibility and economic viability of extracting, separating and recovering REEs and CMs from U.S. coal and coal by-product sources, with the goal of achieving mixed rare earth oxides from coal-based resources with minimum purities of 75%.

Flocks of assembler robots show potential for making larger structures

Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots. The new system involves large, usable structures built from an array of tiny identical subunits called voxels (the volumetric equivalent of a 2-D pixel).
Photo Credit: Massachusetts Institute of Technology | Courtesy of the researchers

Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots.

The new work, from MIT’s Center for Bits and Atoms (CBA), builds on years of research, including recent studies demonstrating that objects such as a deformable airplane wing and a functional racing car could be assembled from tiny identical lightweight pieces — and that robotic devices could be built to carry out some of this assembly work. Now, the team has shown that both the assembler bots and the components of the structure being built can all be made of the same subunits, and the robots can move independently in large numbers to accomplish large-scale assemblies quickly.

The new work is reported in the journal Nature Communications Engineering, in a paper by CBA doctoral student Amira Abdel-Rahman, Professor and CBA Director Neil Gershenfeld, and three others.

A fully autonomous self-replicating robot assembly system capable of both assembling larger structures, including larger robots, and planning the best construction sequence is still years away, Gershenfeld says. But the new work makes important strides toward that goal, including working out the complex tasks of when to build more robots and how big to make them, as well as how to organize swarms of bots of different sizes to build a structure efficiently without crashing into each other.

A Solution for Reclaiming Valuable Resources Flushed Down the Drain

A problem at sewage treatment plants - the buildup of 'brown grease' - could yield a bounty of biofuel, thanks to the work of UConn researchers
Photo Credit: kubinger

For the everyday products we use, a pattern has become numbingly familiar: Something is made, we use it, we throw it away. Yet, for a sustainable future – one where we don’t simply extract and toss resources – we need to make this linear process circular, says UConn Department of Chemical and Biomolecular Engineering Emeritus Professor Richard Parnas.

Parnas and his team research biodiesel and how to make it out of waste resources. Parnas also co-founded REA Resource Recovery Systems, which supported UConn Chemical Engineering graduate student Cong Liu Ph.D. ‘22 to develop technology to improve a critical process of removing sulfur from biodiesel made from waste materials. In this case, the materials originate from sewage, and the technology is being implemented in a project at Danbury’s John Oliver Memorial Sewer Plant scheduled to go into operation in January 2023 that will convert fats, oils, and grease into biodiesel whose lifecycle emissions are more than 74% lower than petroleum-based diesel.

Lab discovery leads UAH researchers to a simple, cost-effective electricity generator

Dr. Moonhyung Jang, left, operates the generator to light an LED display as Dr. Gang Wang looks on in the Adaptive Structures Laboratory. 
Photo Credit: Michael Mercier | University of Alabama in Huntsville

A bit of laboratory serendipity led University of Alabama in Huntsville (UAH) researchers to a simple mechanical way to generate electricity to operate electronic devices, says a paper they have published in the journal ACS Omega.

Triboelectric nanogenerators use multiple layers of different materials to generate electricity when pressed. While testing a triboelectric nanogenerator in the Adaptive Structures Laboratory of Dr. Gang Wang at UAH, a part of the University of Alabama System, postdoctoral research assistant Dr. Moonhyung Jang observed something unusual.

“During a finger-tapping test performed by Dr. Jang, a Scotch tape was introduced on the top to prevent electric shock,” says Dr. Wang, an associate professor of mechanical and aerospace engineering and the project’s principal investigator.

“An unexpectedly high voltage was observed. After a careful investigation, we figured out that the tape layer is the reason to cause this,” Dr. Wang says. “This led to our invention that introduces tacky materials to improve the performance of triboelectric generators.”

Electronic/Photonic Chip Sandwich Pushes Boundaries of Computing and Data Transmission Efficiency

Image: The chip sandwich: an electronics chip (the smaller chip on the top) integrated with a photonics chip, sitting atop a penny for scale.
Photo Credit: Arian Hashemi Talkhooncheh

Engineers at Caltech and the University of Southampton in England have collaboratively designed an electronics chip integrated with a photonics chip (which uses light to transfer data)—creating a cohesive final product capable of transmitting information at ultrahigh speed while generating minimal heat.

Though the two-chip sandwich is unlikely to find its way into your laptop, the new design could influence the future of data centers that manage very high volumes of data communication.

"Every time you are on a video call, stream a movie, or play an online video game, you're routing data back and forth through a data center to be processed," says Caltech graduate student Arian Hashemi Talkhooncheh (MS '16), lead author of a paper describing the two-chip innovation that was published in the IEEE Journal of Solid-State Circuits. "There are more than 2,700 data centers in the U.S. and more than 8,000 worldwide, with towers of servers stacked on top of each other to manage the load of thousands of terabytes of data going in and out every second."

Just as your laptop heats up on your lap while you use it, the towers of servers in data centers that keep us all connected also heat up as they work, just at a much greater scale. Some data centers are even built underwater to cool the whole facility more easily. The more efficient they can be made, the less heat they will generate, and ultimately, the greater the volume of information that they will be able to manage.

Fruit flies use corrective movements to maintain stability after injury

Collaborators at the University of Colorado Boulder created a robotic wing made of plastic and cardboard laminate to study the mechanism by which fruit flies compensate for wing damage in flight. Photo Credit: Kaushik Jayaram

Fruit flies can quickly compensate for catastrophic wing injuries, researchers found, maintaining the same stability after losing up to 40% of a wing. This finding could inform the design of versatile robots, which face the similar challenge of having to quickly adapt to mishaps in the field.

The Penn State-led team published their results in Science Advances.

To run the experiment, researchers altered the wing length of anesthetized fruit flies, imitating an injury flying insects can sustain. They then suspended the flies in a virtual reality ring. Mimicking what flies would see when in flight, researchers played virtual imagery on tiny screens in the ring, causing the flies to move as if flying.

“We found flies compensate for their injuries by flapping the damaged wing harder and reducing the speed of the healthy one,” said corresponding author Jean-Michel Mongeau, Penn State assistant professor of mechanical engineering. “They accomplish this by modulating signals in their nervous system, allowing them to fine-tune their flight even after an injury.”

New carbon nanotube-based foam promises superior protection against concussions

Postdoctoral research associate Komal Chawla studies the architected vertically aligned carbon nanotube foam in the lab.
Photo Credit: Joel Hallberg

Developed by University of Wisconsin–Madison engineers, a lightweight, ultra-shock-absorbing foam could vastly improve helmets designed to protect people from strong blows.

The new material exhibits 18 times higher specific energy absorption than the foam currently used in U.S. military combat helmet liners, as well as having much greater strength and stiffness, which could allow it to provide improved impact protection.

Physical forces from an impact can inflict trauma in the brain, causing a concussion. But helmet materials that are better at absorbing and dissipating this kinetic energy before it reaches the brain could help mitigate, or even prevent, concussions and other traumatic brain injuries.

The researchers’ industry partner, helmet manufacturer Team Wendy, is experimenting with the new material in a helmet liner prototype to investigate its performance in real-world scenarios.

“This new material holds tremendous potential for energy absorption and thus impact mitigation, which in turn should significantly lower the likelihood of brain injury,” says Ramathasan Thevamaran, a UW–Madison professor of engineering physics who led the research.

The team detailed its advance in a paper recently published online in the journal Extreme Mechanics Letters.

Brain-Powered Wheelchair Shows Real-World Promise

Source/Credit: The University of Texas at Austin

In one of the first studies of its kind, several people with motor disabilities were able to operate a wheelchair that translates their thoughts into movement.

The study by researchers at The University of Texas at Austin and published today in the journal iScience is an important step forward for brain-machine interfaces — computer systems that turn mind activity into action. The concept of a thought-powered wheelchair has been studied for years, but most projects have used non-disabled subjects or stimuli that leads the device to more or less control the person rather than the other way around.

In this case, three individuals with tetraplegia, the inability to move their arms and legs due to spinal injuries, operated the wheelchair in a cluttered, natural environment to varying degrees of success. The interface recorded their brain activity, and a machine-learning algorithm translated it into commands that drove the wheelchair.

The researchers said this is a sign of future commercial viability for mind-powered wheelchairs that can assist people with limited motor function.

New nanoscale 3D printing material could offer better structural protection for satellites, drones, and microelectronics

A tiny but strong Stanford logo was made using nanoscale 3D printing.
Image credit: John Kulikowski

Engineers have designed a new material for nanoscale 3D printing that is able to absorb twice as much energy as other similarly dense materials and could be used to create better lightweight protective lattices.

Science fiction envisions rapid 3D printing processes that can quickly create new objects out of any number of materials. But in reality, 3D printing is still limited in the properties and types of materials that are available for use, especially when printing at very small scales.

Researchers at Stanford have developed a new material for printing at the nanoscale – creating structures that are a fraction of the width of a human hair – and used it to print minuscule lattices that are both strong and light. In a paper published in Science, the researchers demonstrated that the new material is able to absorb twice as much energy than other 3D-printed materials of a comparable density. In the future, their invention could be used to create better lightweight protection for fragile pieces of satellites, drones, and microelectronics.

Boeing Demonstrates New Autonomous Anti-Jam Capabilities for U.S. Space Force Satellite Communications Program

Boeing engineers recently demonstrated its space-based smart antenna technology, which detects enemy jammers and autonomously reshapes a satellite’s beams to suppress hostile jammers while maintaining communications with the friendly-force PTS terminals. Boeing’s system brings communication to the warfighter at closer range to the enemy.
Photo Credit: Boeing

Boeing engineers recently demonstrated a new, autonomous technology that can successfully prevent jamming attempts on U.S. Department of Defense satellite communications (SATCOM). The test was conducted on the U.S. Space Force’s Protected Tactical SATCOM Prototype (PTS-P), showing how this technology can provide secure communication in contested environments.

“Maintaining communication with our deployed forces during hostility gives us a tactical edge on the battlefield,” said Justin Bruner, PTS-P Program Manager at the U.S. Space Force. “Our adversaries are always attempting to deny our ability to communicate. On-board, autonomous, real-time nulling of jammers greatly enhances our resiliency, ensuring the United States and our allies can provide our warfighters with secure, reliable communications in a contested environment. Boeing has made significant strides in the development and execution of a nulling algorithm with flight-like firmware, demonstrating agile anti-jam capability. PTS-P and all of our Protected Anti-Jam Tactical SATCOM (PATS) programs are critical to this effort.”