Research News

Blow up a long balloon and two things happen: it gets longer and it gets wider.

Now imagine a living cell that inflates itself under enormous pressure and yet only grows longer, never adding width. That is exactly what rod-shaped bacteria do, every time they divide, with a precision that has baffled scientists for decades.

A new study published in Current Biology has finally found the answer. Researchers suggest their discovery could point toward new treatments for antibiotic-resistant bacteria.

Rod-shaped bacteria like Bacillus subtilis — a harmless soil microbe and one of biology's most studied model organisms — are encased in a rigid shell called the cell wall, made of a polymer called peptidoglycan, and pressurized from within at many times the pressure of a car tire.

To grow, bacteria must continuously remodel this wall: snipping out old material with enzymes and weaving in new polymer. This should cause the cell to bulge outward as well as elongate. Yet rod-shaped bacteria hold their width to within 40 nanometers — roughly 1,750 times thinner than a human hair.

"Most antibiotics that target the bacterial cell wall disrupt its structure/architecture," said Paola Bardetti, the study's lead author and an industry assistant professor of chemical and biomolecular engineering at NYU Tandon School of Engineering. "Our work reveals an entirely different vulnerability: the physical mechanism bacteria rely on to maintain their shape. No drug has ever targeted that. Until now, we didn't understand it well enough to try."

The NYU team, led by Bardetti and the paper’s senior author Enrique Rojas, an associate professor of biology at NYU, subjected living bacteria to rapid osmotic shocks — briefly raising or lowering internal pressure — while tracking wall deformations as small as a few nanometers.

 What they found was a sharp mechanical threshold. Below normal pressure the wall behaves like a finger trap toy: reducing pressure makes it expand sideways. Above it, the wall softens and the cell widens. At the transition, width stays constant, precisely where growing bacteria sit.

“The cell wall is a smart material,” said Bardetti. “It responds to mechanical stress in a way that is tuned to keep the cell the right shape. Every time we probed it, it surprised us.”

This tipping-point strategy also confers automatic self-correction. When cells were manipulated to grow wider than normal, the wall slipped into the finger-trap regime, thinning it back toward the target width. The critical pressure of the transition also shifted in response to changes in wall architecture — a second feedback loop — making this a homeostatic system encoded in the physical properties of the material itself.

In scientific terms: the wall is anisotropic, far stiffer circumferentially than longitudinally, with a Poisson ratio of 0.45–0.5 and anisotropy at the physical maximum. The stress-softening non-linearity — an abrupt drop in circumferential stiffness at the critical pressure — parks the cell at the boundary between widening and thinning.

The same phenomenon appeared in Arabidopsis thaliana plant roots, suggesting a shape-control strategy evolution has arrived at independently.

"Finding the same strategy in bacteria and plant roots was genuinely exciting," said Bardetti. "It suggests a fundamental principle of tubular morphogenesis that nature has independently discovered more than once. The next step is identifying the molecular machinery that sets the critical pressure, because once you know that, you have a potential drug target.”

In addition to Bardetti and Rojas, the paper’s authors are Felix Barber (currently Assistant Professor at Ohio State University) and Dylan Fitzmaurice, postdoctoral researcher and PhD candidate respectively in the Rojas Lab at the time of the study. Research funding came from the National Institutes of Health and the National Science Foundation. Microscopy support was provided by the NYU Langone Health Microscopy Lab, partially funded by the National Cancer Institute.

Non-linear stress-softening of peptidoglycan mediates bacterial cell shape homeostasis
Bardetti, Paola et al. Current Biology, Volume 36, Issue 5, 1156 - 1165.e5

Wildlife trafficking is one of the world’s most widespread illegal trades, contributing to biodiversity loss, organized crime, and public health risks. Once concentrated in physical markets, much of this activity has moved online. Today, animals and animal products are advertised on large e-commerce platforms alongside ordinary consumer goods. This shift makes enforcement harder — but it also creates a valuable source of data.

Every online advertisement leaves behind digital information: text descriptions, prices, images, seller details, and timestamps. If collected and analyzed at scale, these traces can help researchers understand how wildlife trafficking operates online. The problem is volume. Online marketplaces contain millions of listings, and most searches for animal names return irrelevant results such as toys, artwork, or souvenirs. Distinguishing illegal wildlife ads from harmless products is difficult to do manually and challenging to automate.

Institute Professor of Computer Science Juliana Freire is part of a team that is taking on the problem head on, building a scalable system designed to address this challenge. They developed a flexible data collection pipeline that automatically gathers wildlife-related advertisements from the web and filters them using modern machine learning techniques. The goal is not to focus on one species or one website, but to enable broad, systematic monitoring across many platforms, regions, and languages, as well as to develop strategies to disrupt illegal markets.

The team is a multi-disciplinary effort, including Gohar Petrossian, Professor of Criminal Justice at John Jay College of Criminal Justice; Jennifer Jacquet, Professor of Environmental Science and Policy at the University of Miami; and Sunandan Chakraborty, Professor of Data Science at Indiana University.

The pipeline begins with web crawling. The researchers generate tens of thousands of search URLs by combining endangered species names with the search structures of major e-commerce websites. A specialized crawler then follows these links, downloading product pages while limiting requests to avoid overwhelming servers. Over just 34 days, the system retrieved more than 11 million ads.

Next comes information extraction. Product pages are messy and inconsistent, varying widely across websites. The pipeline uses a combination of HTML parsing tools and automated scrapers to extract useful details such as titles, descriptions, prices, images, and seller information. These data are stored in structured formats that allow large-scale analysis.

The most critical step is filtering. While machine learning classifiers can be used for this filtering, training specialized classifiers for multiple collection tasks is both time-consuming and expensive, requiring experts to create training data for each task. Freire’s group developed a new approach that leverages large-language models (LLMs) to label data and use the labeled data to automatically create specialized classifiers, which can perform data triage at a low cost and at scale.

The result is essentially a "model factory:" a pipeline that can automatically produce customized, low-cost classifiers on demand for different triage tasks — different species, different product types, different platforms — without requiring experts to label data from scratch each time.

This research has enabled large-scale data collection to answer different scientific questions and shed insights into different aspects of wildlife trafficking. One analysis of 14,000 reptile leather product listings on eBay showed that crocodile, alligator, and python skins dominated the market. Only about 10 animal-product combinations (such as ‘crocodile bags’, ‘alligator bags’ and ‘alligator watches’ made up about 72 percent of all listings, indicating that the trade heavily focuses on a few luxury items. The analysis of all of the listings from these sites showed that while small leather products were shipped from 65 countries, 93 percent came from 10 countries, with the United States, United Kingdom, Australia collectively accounting for over 3/4th of this market.

Similar data from Ebay on shark and ray trophies reveals that, although the platform has introduced policies to restrict threatened or endangered species, their derivatives are still circulated widely on the platform. Tiger shark trophies accounted for one-fifth of such listings, with asking prices up to $3,000. Over 85 percent of listings were linked to sellers in the United States, suggesting a pipeline from deep sea commercial fishing vessels to the US trophy trade.

This research is also being used to determine what would be the most effective way to disrupt this market. For example, the researchers found that targeting key sellers is effective, but targeting key product types — “alligator watch,” for example — breaks the market of reptile leather products equally effectively, and is much easier to enact at a broad scale.

The authors emphasize that this system is a starting point, not a finished solution. The pipeline is designed to be extensible, allowing future researchers to incorporate better classifiers, image-based analysis, or new data sources. By making the code openly available, they aim to support broader collaboration.

As wildlife trade continues to move online, understanding its digital footprint will be increasingly important. Scalable data collection tools like this one offer a way to transform scattered online listings into actionable knowledge, an essential step toward disrupting illegal wildlife trade in the digital era.

Juliana Silva Barbosa, Ulhas Gondhali, Gohar Petrossian, Kinshuk Sharma, Sunandan Chakraborty, Jennifer Jacquet, and Juliana Freire. 2025. A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplaces. Proc. ACM Manag. Data 3, 3, Article 119 (June 2025), 23 pages. https://doi.org/10.1145/3725256

Neuroscience rarely enjoys clean experiments. Most brain disorders are mosaics of risk genes, aging, lifestyle and chance that leave their origins obscured. Huntington’s disease (HD) is different. It begins with a single genetic expansion — a repeated stretch of DNA letters in the HTT gene — that is both measurable and decisive. If you inherit a sufficiently long repeat, you will develop the disease. That stark clarity makes HD scientifically invaluable.

That is the argument put forward by Roy Maimon in a new essay in Trends in Molecular Medicine. Maimon, Assistant Professor of Biomedical Engineering, is an expert in endogenous neural stem cells and their altered regenerative potential in brain diseases like HD. In the piece, he argues that HD’s clear cause and effects offer the ideal starting point to uncover parts of the brain, and the diseases that affect it, that are far less understood.

But it is not only the unique biological properties of HD that make it such a potentially productive topic — it’s the people too. “The HD world is unusually united,” Maimon writes. “It is among the few fields in biomedicine where patients, scientists, and clinicians share the same space. We meet families regularly. We see their courage and humor. We celebrate together and we grieve together.”

“It is this culture that makes HD research not just productive but deeply personal, too. You become part of something when you join this community that truly matters.”

The HTT mutation acts like a molecular clock. The longer the repeat, the earlier symptoms tend to appear. Decades before the first involuntary movement or subtle cognitive shift, a blood test can reveal who carries the expansion. Few neurological diseases offer such foresight. That predictability allows researchers to ask a question that is nearly impossible elsewhere: What happens if we intervene before neurons begin to die?

The brain changes in HD follow a surprisingly consistent pattern. Early damage centers on a deep brain structure called the striatum, which helps control movement and decision-making. Over time, connected regions of the cortex also become involved. Even within the striatum, certain neurons are especially vulnerable, while their neighbors remain relatively resilient. Why some cells are fragile and others hardy is a major puzzle — and HD offers a controlled setting to investigate it.

Because the genetic cause is so precise, HD has become a testing ground for cutting-edge therapies. Drugs called antisense oligonucleotides are designed to lower production of the harmful huntingtin protein. Gene therapies aim to deliver long-lasting genetic instructions that reduce or modify the faulty gene’s output. Other approaches target the DNA repair machinery thought to drive repeat expansion. Not every clinical trial has succeeded, but each has sharpened our understanding of how to measure brain changes, track biomarkers in blood and spinal fluid, and choose the best moment to intervene.

Researchers are also exploring ways to repair or replace damaged cells. The striatum lies near a region of the brain that can generate new neurons, at least in small numbers. In animal studies, boosting this process — or transplanting healthy cells — has shown promise in rebuilding parts of the damaged circuit. While such strategies remain experimental, HD provides a uniquely measurable proving ground: a known mutation, defined target cells and a predictable timeline.

“Every dollar invested in HD yields methods, models, and biomarkers that accelerate discoveries throughout the entire field,” Maimon writes. “For students, it's the quickest path to learning real translational neuroscience. For investors and funders, it’s the most efficient place to deploy resources: high-quality trials, engaged patients, measurable outcomes, and clear readthrough to other diseases. For scientists, it is a bridge between basic biology and the clinic.”

“New York University (NYU) is uniquely positioned to serve as a national hub for Huntington’s disease research”, Maimon adds. “The university bridges engineering, neuroscience, clinical medicine, and data science within a single ecosystem, allowing discoveries to move rapidly from molecular insight to patient-facing trials. With strong programs in gene therapy, RNA therapeutics, biomarker development, and computational modeling, NYU offers the interdisciplinary infrastructure required to tackle a disease that demands precision at every level. Equally important, its proximity to major clinical centers and patient communities enables sustained engagement with families living with HD, ensuring that translational efforts remain both scientifically rigorous and deeply human”.

Maimon, R. (2026). Huntington’s disease is the best investment in Neuroscience Today. Trends in Molecular Medicine. https://doi.org/10.1016/j.molmed.2026.01.001

When energy researchers talk about the future of the grid, they often focus on individual pieces: solar panels, batteries, nuclear plants, or new transmission lines. But in a recent study, urban systems researcher Anton Rozhkov takes a different approach — treating the energy system itself as a complex, evolving organism shaped as much by policy and human behavior as by technology.

Rozhkov’s research, recently published in PLOS Complex Systems, models the electricity system of Northern Illinois, focusing on the service territory of Commonwealth Edison (ComEd). Rather than trying to predict exactly how much electricity the region will generate or consume decades from now, the model explores how the system’s overall behavior changes under different long-term scenarios.

“This work is not intended as a deterministic prediction,” Rozhkov, Industry Assistant Professor in the Center for Urban Science + Progress, explains. “What’s important for complex systems is the general trend, the system’s trajectory, whether something is increasing or decreasing under various scenarios, and how steep and fast that change is.”

The study uses a system dynamics framework, a method designed to capture feedback loops and interactions over time. Rozhkov modeled both electricity generation — reflecting Illinois’s current distinctive mix, including a significant share of nuclear power — and demand, then explored how the balance shifts across a 50-year horizon. Illinois is an especially interesting test case, he notes, because few U.S. states rely as heavily on nuclear energy, making the transition to low-carbon systems more nuanced than a simple fossil-fuel-to-renewables swap.

From this baseline, Rozhkov examined five broad scenarios. Some focused on technology, such as a future dominated by renewable energy, with or without the development of large-scale battery storage. Others reflected policy goals, including a scenario aligned with Illinois’s Climate and Equitable Jobs Act, which sets a target of economy-wide climate neutrality by 2040. Still others explored changes in how people live and consume energy, including denser urban development and widespread adoption of distributed energy resources by households and neighborhoods.

Across these scenarios, the model tracked economic costs and environmental outcomes, particularly greenhouse gas emissions. One striking pattern emerged when decentralized energy production — such as rooftop solar paired with the ability to sell excess power back to the grid — became widespread. In that case, demand for centralized electricity generation steadily declined, and there became a need to support that shift through policies: a clear example of an integrated approach. “We can clearly see that utilities would be needed to produce energy differently in the future, and the energy market should be ready for it,” Rozhkov says.

This is the central concept in the paper is which Rozhkov calls a “policy-driven transition.” Policy, in this context, is not a physical component of the grid but a force that shapes decisions. Incentives, tax credits, and regulations can push households and businesses toward clean energy even when natural conditions are less than ideal. He points to the northeastern states as an example: despite limited sunlight, strong solar policies have made rooftop installations attractive. “Policy can move someone from being on the fence about installing solar to actually doing it,” he says.

The research also highlights the growing role of decentralization, in which individual buildings or districts generate much of their own power. In Illinois’s deregulated electricity market, customers can sell excess energy back to the grid, blurring the line between consumer and producer. Beyond cost savings, decentralization can improve resilience during outages or extreme events, allowing communities to maintain power independently when the main grid fails.

Importantly, Rozhkov’s findings suggest that no single lever — technology, policy, or individual motivation — can drive a successful energy transition on its own. “Isolated single-solution approaches (technology-only, policy-only, planning-only) are rarely enough. The transition emerges from the interactions across all of them, and that’s something we need to consider as urban scientists,” he says. The scenarios that performed best combined strong policy frameworks, technological change, and shifts in behavior and urban design.

Although the model was developed for Northern Illinois, it is designed to be adaptable. With sufficient data, it could be applied to other regions, from New York City to Texas, allowing researchers to explore how different regulatory environments shape energy futures. Rozhkov’s next steps include comparing states with contrasting policies, exploring if policies or natural conditions are driving the transition to a more renewable-based energy profile, and digging deeper into behavioral factors about why people choose to adopt distributed energy in the first place.

Rozhkov, A. (2025). Decentralized Renewable Energy Integration in the urban energy markets: A system dynamics approach. PLOS Complex Systems, 2(12). https://doi.org/10.1371/journal.pcsy.0000083

After mass shootings, Democrats are nearly four times more likely than Republicans to post about guns on social media, but the disparity goes deeper than volume, according to research from NYU Tandon School of Engineering.

The study analyzed the full two-year term of the 117th Congress using computational methods designed to distinguish true cause-and-effect relationships from mere coincidence. The analysis reveals that mass shootings directly trigger Democratic posts within roughly two days, while Republicans show no such causal response.

Topic analysis of gun-related posts, moreover, reveals strikingly different foci. Democrats are far more likely to zero in on legislation, communities, families, and victims, while Republicans more often center on Second Amendment rights, law enforcement, and crime.

The study, in PLOS Global Public Health, analyzed 785,881 total posts from 513 members of the 117th Congress on X over two years, and identified 12,274 gun-related posts using keyword-based filtering. The team tracked how legislators’ posting related to 1,338 mass shooting incidents that occurred between January 2021 and January 2023.

"In this research, we tested a long-running concern, namely that when it comes to gun violence, Americans too often talk past each other instead of with each other,” said Institute Professor Maurizio Porfiri, the paper’s senior author. Porfiri is director of NYU Tandon’s Center for Urban Science + Progress (CUSP) and of its newly formed Urban Institute. “Our findings expose a fundamental difference in the way we mourn and react in the aftermath of a mass shooting. Building consensus with these stark differences across the political aisle becomes exceptionally difficult under these conditions.”

The research team employed the PCMCI+ causal discovery algorithm, which uses statistical methods to identify whether one event genuinely causes another or whether they simply occur around the same time. Combined with mixed-effects logistic regression and topic modeling, this approach revealed that Democrats respond causally to shooting severity — particularly the number of fatalities — both immediately and the day after incidents. Posting likelihood rose especially when incidents occurred in legislators' home states.

"The findings matter because they expose a structural problem in how Americans address gun violence as a nation," said CUSP Assistant Research Scientist Dmytro Bukhanevych, the paper’s lead author. "When Democrats surge onto social media after mass shootings while Republicans have a comparatively smaller level of response, there's no shared moment of attention. The asymmetry itself becomes a barrier to meaningful exchange."

The research may help advocates and policymakers time interventions more strategically. With congressional attention peaking immediately and declining within roughly 48 hours, sustained public pressure needs to extend well beyond the immediate aftermath. And understanding the distinct frames each party uses could help communicators craft messages that bridge ideological divides rather than reinforcing them.

Along with Porfiri and Bukhanevych, CUSP Ph.D. candidate Rayan Succar served as the PLOS paper’s co-author.

This study contributes to Porfiri’s ongoing research related to U.S. gun prevalence and violence, which he and colleagues are pursuing under a National Science Foundation grant to study the “firearm ecosystem” in the United States. Prior published research under the grant explores:

• the degree that political views, rather than race, shape reactions to mass shooting data;
• the role that cities’ population size plays on the incidences of gun homicides, gun ownership and licensed gun sellers;
• motivations of fame-seeking mass shooters;
• factors that prompt gun purchases;
• state-by-state gun ownership trends; and
• forecasting monthly gun homicide rates.