Friday, January 09, 2026

 

Never mind how grasshoppers hop. These engineers watch them fly



Princeton University, Engineering School
Grasshopper wing flight test 

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Graduate student Paul Lee launches a 3D-printed model glider across the Princeton Robotics lab to evaluate aerodynamic performance.

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Credit: Princeton University/Sameer A. Khan/Fotobuddy





Grasshoppers may not spring to mind as paragons of graceful flight. But for a team of Princeton engineers, these gangly insects have inspired a new approach to robotic wings.

Typical designs for insect-scale flying robots draw inspiration from bees or flies, relying on constant flapping motion. That flapping draws a lot of power, and delivering that power is difficult because batteries are heavy, particularly for tiny robots. Grasshoppers add another technique to the mix. They don’t just flap, they also jump and glide.

“Gliding is a mode of cheap flight,” said Aimy Wissa, associate professor of mechanical and aerospace engineering and the study’s principal investigator. Her team is studying grasshoppers to build a glider that can fold its wings in and out and change strategies depending on the situation. “When we want to produce thrust, we flap. When we want to conserve energy, we fully deploy the wings and glide.”

By investigating how grasshoppers glide, the team has developed a model that could enable multimodal locomotion for tiny robots. This could give engineers new options in the quest to extend flight time for insect-sized robots.

The engineers teamed up with biologists to uncover the grasshopper’s secret to efficient gliding locomotion. They used those insights to 3D-print model wings between two and four inches wide. The paper was published in the Journal of the Royal Society Interface on Jan. 7.

Reducing the power needed to fly unlocks several possibilities for small, flying robots. Many existing models are tethered to wires to supply them with enough power. Using gliding flight as a part of their locomotion can reduce the power required to fly, allowing for a smaller battery payload and enabling untethered flight. Additionally, more power can be allocated to other forms of movement, such as crawling or jumping.

The project started with one question, according to the researchers: How can we design small robots that, like insects, move seamlessly across ground and air?

Only three insect groups can glide efficiently: grasshoppers, dragonflies and butterflies. While grasshoppers are not necessarily the best at gliding overall, they have the distinct ability to neatly retract their wings by folding them like an accordion.

Collapsing the wing allows for better mobility on the ground, according to Paul Lee, graduate student in mechanical and aerospace engineering and the paper’s lead author. “Dragonfly wings always stick straight out, and butterfly wings can only fold upward, which is limiting,” he said.

This feature also reduces air resistance when the grasshoppers jump from the ground to transition into flight, Wissa added.

When the researchers started deciding which features of the grasshopper’s wing to copy, they found that biology offered no clear answers about how each aspect of the wing impacts flight. “Then the research question flipped,” Wissa said. Instead of copying grasshoppers’ known biological design principles, they had to discover those principles themselves.

The team analyzed the wings of real grasshoppers to find out exactly how they fly so efficiently, collaborating with entomologists at the University of Illinois Urbana-Champaign. They focused on the American grasshopper, also called the bird grasshopper, because of its superior flying skills.

Grasshoppers have two sets of wings, the forewings and hindwings. The grasshopper’s hindwing is corrugated, meaning it has a 3D up-and-down pattern, like sharp hills and valleys, which allows it to fold in neatly. Biologists have long known that the front wing is mainly used for protection and camouflage, so the researchers studied the hindwing to find out how it’s used to combine flapping and gliding.

The researchers took CT scans, an imaging technique that uses X-rays and computing to capture the detailed geometry of an object, of real grasshopper wings in the Princeton Imaging Analysis Center.

They developed a new procedure for turning CT scans into 3D-printable designs, which they detail in the paper and could be useful for future studies of insect locomotion. They used their scans to 3D-print model wings with varying designs, incorporating different principles to test how each detail affects flight performance.

One of the strengths of using engineering to understand biology, according to Wissa, is that you can isolate different features — like a wing’s shape, camber, or corrugation — and test each one separately. “You can’t do that with an actual insect,” she said.

They tested the 3D-printed wings in a water channel, where they evaluated aerodynamic performance based on how water flows around the wing. Then they used the data gathered to further improve the design. They printed new wings and attached them to small frames to create realistic grasshopper-inspired gliders

Finally, they conducted flight experiments where they launched the gliders across the Princeton Robotics lab and used an advanced motion-capture camera system to evaluate flight performance. Using biological benchmarks for comparison, they found that the glider’s performance was on par with that of actual grasshoppers.

They also compared their grasshopper-inspired wing with another standard wing design. The standard design resembled a smooth version of the grasshopper hindwing and revealed insights into how the corrugations impacted performance. The smooth wing allowed the glider to fly more efficiently and more repeatably than one with the natural corrugations.

 “This showed us that these corrugations might have evolved for other reasons,” Wissa said. For example, the tests showed corrugations may help with flying at steep angles. Wissa said this study is a prime example of how engineering can contribute to biology, and vice versa.

The best-performing gliders they tested were smooth rather than corrugated, but future research could shed light on how to incorporate the corrugations to enable wing folding while still maximizing gliding efficiency.

The researchers are continuing to study grasshoppers to find ways to enable the new wings to deploy from a stowed position while minimizing the need for additional motors or power sources. “Very little is known about how grasshoppers deploy their wings," Wissa said. Unlocking this can greatly conserve power and reduce the size and cost of flying insect robots.

Wissa said another next step is to couple the design with jumping abilities. “If you're able to switch from crawling to jumping, then you can scale obstacles that are much bigger than your size,” Wissa said.

“This grasshopper research opens up new possibilities not only for flight, but also for multimodal locomotion,” Lee said. “By combining biology with engineering, we’re able to build and ideate on something completely new.”

The paper “From Grasshoppers to Gliders: Evaluating the Role of Hindwing Morphology in Gliding Flight” was published in the Journal of the Royal Society Interface Jan. 7. Besides Lee and Wissa, authors include Diaa Zekry, Ahmed K. Othman and Marianne Alleyne. The research was partially supported by the NSF CAREER Award and Princeton’s Addy Fund for Excellence in Engineering. The grasshopper specimens were provided by Scott Kirkton in the department of biology at Union College, and the imaging data were acquired with support from Princeton’s Imaging and Analysis Center, and the motion tracking and flight testing were conducted with support from Princeton’s Robotics Lab. 

 

McMaster discovery could lead to new treatments for drug-resistant fungal infections



Canadian lab identifies molecule that could revive entire class of antifungal drugs




McMaster University

Gerry and Xuefei 

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Researchers at McMaster University discovered “a legitimate drug candidate,” but also “an entirely new target to attack with other new drugs.”

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Credit: McMaster University





Fungal infections kill millions of people each year, and modern medicine is struggling to keep up. But researchers at McMaster University have identified a molecule that may help turn the tide — butyrolactol A, a chemical compound that targets a deadly, disease-causing fungi called Cryptococcus neoformans. 

Infections caused by Cryptococcus are extremely dangerous. The pathogen, which can cause pneuomia-like symptoms, is notoriously drug-resistant, and it often preys on people with weakened immune systems, like cancer patients or those living with HIV. And the same can be said about other fungal pathogens, like Candida auris or Aspergillus fumigatus — both of which, like Cryptococcus, have been declared priority pathogens by the World Health Organization.  

Despite the threat, though, doctors have only three treatment options for fungal infections.  

The gold standard is a drug class called amphotericin — though Gerry Wright, a professor in McMaster’s Department of Biochemistry and Biomedical Sciences, jokes that it’s often called “amphoterrible,” because of the major toxic side-effects that it has on humans.  

“Fungal cells are a lot like human cells, so the drugs that hurt them tend to hurt us too,” he says. “That’s why there are so few options available to patients.”  

The other two antifungal drug classes that are available — azoles and echinocandins — are much less effective treatment options, especially against Cryptococcus. Wright says azoles merely stop fungi from growing rather than outright killing them, while Cryptococcus and other fungi have become totally resistant to echinocandins, rendering them completely ineffective.  

So, with a stagnant antifungal drug pipeline, a limited arsenal of approved medicines, and rising rates of drug resistance, scientists are now betting on something called “adjuvants” as a solution to the growing health threat.  

“Adjuvants are helper molecules that don’t actually kill pathogens like drugs do, but instead make them extremely susceptible to existing medicine,” explains Wright, a member of the Michael G. DeGroote Institute for Infectious Disease Research (IIDR). 

Looking for adjuvants that might better sensitize Cryptococcus to existing antifungal drugs, Wright’s lab screened McMaster’s vast chemical collection for candidate molecules.  

Quickly, his team found a hit: butyrolactol A, a known-but-previously-understudied molecule produced by certain Streptomyces bacteria. The researchers found that the molecule could synergize with echinocandin drugs to kill fungi that the drugs alone could not.

But they had no idea how it worked — and almost didn’t bother to find out.

“This molecule was first discovered in the early 1990s, and nobody has ever really looked at it since,” Wright says. “So, when it showed up in our screens, my first instinct was to walk away from it. I thought, ‘it’s a known compound, it kind of looks like amphotericin, it’s just another toxic molecule — not worth our time.’”

But he credits the determination of postdoctoral fellow Xuefei Chen for changing his mind.

“Early on, this molecule’s activity appeared to be quite good,” says Chen, who works in Wright’s lab. “I felt that if there was even a small chance that it could revive an entire class of antifungal medicine, we had to explore it.”

After years of what Wright calls “painstaking sleuthing and detective work” led by Chen, the research team revealed exactly how the adjuvant worked.

Chen discovered that butyrolactol A acts as a plug that clogs up an important protein complex that’s “mission critical” for Cryptococcus — “when it’s jammed, all hell breaks loose,” Wright says. This disturbance renders the fungus completely vulnerable to the drugs that it once resisted.

Working with researchers in the laboratory of McMaster Professor Brian Coombes, also a member of the IIDR, the research team has since shown that butyrolactol A also functions similarly in Candida auris, which gives it broad clinical potential.  

Wright says the findings, published recently in the prestigious journal Cell, are more than a decade in the making.  

“That first screen that put butyrolactol A on our radar took place in 2014,” he notes. “More than eleven years later, thanks almost entirely to Chen, we have identified a legitimate drug candidate and an entirely new target to attack with other new drugs.”  

The discovery marks the second antifungal compound and the third new antimicrobial found by Wright’s lab in the past year.  

 

CUNY Graduate Center and its academic partners awarded more than $1M by Google.org to advance statewide AI education through the Empire AI consortium



The new funding marks the third major gift to CUNY in less than two years to support generative AI best practices and implementation across New York State higher learning.



The Graduate Center, CUNY

Google Empire AI_Josh 

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CUNY Graduate Center President Joshua Brumberg discusses  new funding provided by Google.org to advance the AI usage and ethics aims of academic partners involved in New York State's Empire AI initiative.

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Credit: Carolyn Adams




NEW YORK, January 7, 2026 — The City University of New York Graduate Center (CUNY Graduate Center) has received a grant totaling more than $1 million from Google.org to support the work of Empire AI, a statewide consortium of 11 public and private academic institutions focused on advancing the effective integration of artificial intelligence into higher education. The award—which is the second such source of funding that the CUNY Graduate Center has received from Google.org to support AI literacy in higher education—will further the reach of a comprehensive, multi-institution assessment of how best to prepare students across all degree levels and disciplines for an AI-driven workforce.

“Google recognized the value of the Empire AI consortium to look at issues at scale—in particular looking at how we can help students use AI to learn across degree type, different systems, and different areas of study,” said Joshua C. Brumberg, President of the CUNY Graduate Center. “Our goal is to discern if similar interventions can work across all these areas of post-secondary educational achievement.”

The Empire AI consortium includes the CUNY Graduate Center, Columbia University, Cornell University, Icahn School of Medicine, New York University, Rochester Institute of Technology, Rensselaer Polytechnic Institute, the State University of New York, University at Buffalo, and University of Rochester. Together, these institutions will ensure that New York’s students—from associate to doctoral levels—receive the training necessary to thrive in a rapidly evolving AI-enabled workplace.

Unlike traditional educational research, which often occurs within a single institution, Empire AI enables member campuses to test, evaluate, and share findings rapidly across the consortium. Results—including digital tools, curricula, lesson plans, and other teaching materials—will be widely shared to ensure that best practices in AI pedagogy are adopted and scaled across New York State. By leveraging its collective scope, Empire AI aims to maximize student learning outcomes and strengthen New York’s leadership in AI education.

Each consortium member will explore different dimensions of AI integration in higher education. At the CUNY Graduate Center, researcher fellows will build on current efforts to understand how generative AI is impacting classrooms across the entirety of CUNY. This includes surveys, focus groups, and interviews with thousands of students and faculty, as well as the collection and assessment of syllabi, assignments, and other teaching artifacts.

“This new grant will allow us to expand our research to engage thousands of students inside and outside courses we're supporting through the Critical AI Literacy Institute at CUNY and conduct additional focus groups,” said Luke Waltzer, principal investigator of the grant and director of the CUNY Graduate Center’s Teaching and Learning Center. “The more that we can capture the voices and experiences of students across disciplines, the better. We want to foster critical literacy about generative AI by engaging not just those embracing it, but also those questioning or resisting it.”

Listen to this recent episode of The Thought Project podcast with Luke Waltzer for more information about how the CUNY Graduate Center is advancing critical AI literacy with the support of Google.org and Empire AI.

 

About the Graduate Center of The City University of New York
The CUNY Graduate Center is a leader in public graduate education devoted to enhancing the public good through pioneering research, serious learning, and reasoned debate. The Graduate Center offers ambitious students over 50 doctoral, master’s and certificate programs of the highest caliber, taught by top faculty from throughout CUNY — the nation’s largest urban public university. Through its nearly 40 centers, institutes, initiatives, and the Advanced Science Research Center, the Graduate Center influences public policy and discourse and shapes innovation. The Graduate Center’s extensive public programs make it a home for culture and conversation.

 

New AI tool can take a cattle’s temperature with only a photo





University of Arkansas

Thermal image of cow 

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A thermal image of a calf that was used to determine the animal’s temperature.

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Credit: AICV Lab





What if you could look into a cow’s face and know if it had a fever? A new tool from the Artificial Intelligence and Computer Vision Lab at the University of Arkansas uses artificial intelligence and thermal cameras to estimate the body temperature of cattle.  

The system, called CattleFever, is the first step toward automated tools that ranchers could use to monitor the health of their herd. 

Trong Thang Pham, a doctoral student at the U of A, was the primary researcher on the project. The AICV lab is led by Ngan Le, an associate professor of electrical engineering and computer science, who researches medical imaging, computer vision and robotics. 

Today, cattle’s temperature is measured rectally, a process that can stress the animal. The CattleFever system could both improve animal welfare and reduce the labor needed to track a herd’s health. The technology could help ranchers detect diseases before symptoms appear, leading to earlier treatment and preventing outbreaks. 

BUILDING THE DATASET 

To build CattleFever, the researchers first needed data. A number of datasets exist for dogs, cats, horses and sheep. The existing dataset of cattle, called CattleEyeView, only includesoverhead photos of the animals and was built for tracking herds. The existing animal datasets were also mainly RGB photos of the animals, and for CattleFever the researchers also needed thermal images. 

The researchers had to build their own dataset with short videos and thermal images of thousands of calves. Each calf was held in a pen, then 20 seconds of both video footage and thermal images were recorded. Each animal’s temperature was also recorded with a rectal thermometer to create a benchmark, or ground truth. 

The calf data was collected at the Arkansas Agricultural Experiment Station’s Savoy Research Complex. 

The RGB photos of the calves, which clearly show the animal’s features, then needed to be linked to the thermal image on the computer. The researchers marked the photos with 13 landmarks, such as eyes, ears, muzzle and mouth. The team manually annotated 600 frames, which were then used to train an AI tool that automatically labeled the remaining 4,000 frames in a dataset, named CattleFace-RGBT. The resulting landmark-detection tool can automatically localize a calf’s face and identify its key facial features across RGB and thermal modalities. 

TESTING THE TOOL 

Once they had the dataset, could a computer determine the temperature of the calf from the images alone? 

Through extensive ablation studies examining different facial-landmark combinations, the researchers realized the temperature of the animal’s eyes and nostrils were closest to the reading of the rectal thermometer. Using facial landmarks, the computer focused on the temperature of the thermal images from those spots. 

The researchers then examined the data with various machine learning approaches to determine the animal’s body temperature from those surface readings. The most accurate results came from a random forest regression, a machine learning approach to predict results from complex data. In a random forest regression, many decision trees are created, each one trained on a different portion of the data. Then all the results of these decision trees are averaged together, which helps reduce noise. 

The CattleFever system was able to automatically determine an animal temperature within 1 degree of the reading from a thermometer. 

INTO THE FIELDS? 

The researchers demonstrated that cattle’s temperature could be accurately read from a thermal image, but all the images were taken with the animal directly facing the camera. 

“We probably need to take more photos of them in the real-world settings, such as running around, to capture their motion in the field,” Pham said. 

The next challenge is to handle cattle faces captured from diverse angles and natural poses, essentially teaching the computer to recognize and interpret a cow’s face in real-world field environments, rather than only when the cow is positioned directly in front of the camera inside a pen. 

The U of A researchers have publicly shared their CattleFace-RGBT dataset, so other researchers can build on their work and help develop a system that ranchers could use. 

“If we find something new, we share that with the world. That’s the spirit,” Pham said. 

The results of the CattleFever research were published in the journal Smart Agricultural Technology. Pham was the first author and Le was the senior author. The other authors were Ethan Coffman from Le’s AICV Lab and Beth Kegley, Jeremy G. Powell and Jiangchao Zhao of the Department of Animal Science in the Dale Bumpers College of Agricultural, Food and Life Sciences.