Blood vessels found in T. rex bones are rewriting dinosaur science

 Despite decades of effort, scientists have never recovered dinosaur DNA. Most paleontology research today still focuses on searching for traces of original organic material in fossils, but DNA has not survived the passage of time.

Much of what we understand about dinosaurs comes from fossilized bones and teeth. These durable remains preserve well, but they offer only limited insight into how these animals actually lived.

Soft tissues, on the other hand, can reveal far more. These rare fossilized materials include muscles and ligaments, pigments or even skin (like scales or feathers). They provide important clues about appearance, movement, and behavior.

Another type of soft tissue sometimes preserved inside bones is blood vessels. My research team and I identified preserved blood vessels in a Tyrannosaurus rex fossil, and our findings were recently published in Scientific Reports.

A Discovery That Began With Physics

As an undergraduate physics student at the University of Regina, I joined a research group that used particle accelerators to study fossils. During that time, I used advanced 3D imaging techniques to examine a T. rex bone and noticed structures that appeared to be blood vessels.

Nearly six years later, I am now pursuing a PhD, continuing to apply physics-based methods to improve how fossils are analyzed.

The Largest T. Rex Ever Found

The preserved vessels came from an extraordinary specimen known as Scotty. Housed at the Royal Saskatchewan Museum in Canada, Scotty is the largest T. rex ever discovered and one of the most complete.

Evidence suggests Scotty lived a difficult life around 66 million years ago. Many of its bones show signs of injury, possibly from combat with another dinosaur or from disease. One rib stands out, showing a large fracture that had only partially healed.

When bones are damaged, the body increases blood vessel activity in the affected area to support healing. The structures we observed in Scotty's rib appear to be part of that process, forming a dense network of mineralized vessels that we reconstructed using 3D models.

Advanced Imaging Reveals Hidden Structures

Studying the inside of fossil bones presents two major challenges. First, researchers need to look inside without damaging the specimen. Second, fossilized bones are extremely dense because minerals have replaced the original organic material over millions of years.

We initially considered using an computed topography (CT) scan, similar to those used in medicine. While this method is non-destructive, standard CT scanners cannot penetrate the dense structure of large fossils.

Instead, we turned to synchrotron light, a powerful form of high-intensity x-rays produced at specialized particle accelerator facilities. This technique allowed us to visualize tiny internal features such as blood vessels with remarkable clarity.

Synchrotron imaging also made it possible to analyze the chemical composition of the structures. The vessels had been preserved as iron-rich mineralized casts, which is a common fossilization process. Interestingly, they appeared in two distinct layers, reflecting a complex environmental history that contributed to their preservation.

What Blood Vessels Reveal About Dinosaur Life

The partially healed fracture in Scotty's rib offers a rare opportunity to study how a T. rex recovered from injury. By examining the preserved blood vessels, researchers can gain insight into healing processes and survival strategies in large predatory dinosaurs.

This work may also provide a basis for comparison with other dinosaur species and with modern animals such as birds, which are closely related to dinosaurs.

The findings could guide future fossil discoveries as well. Bones that show signs of injury or disease may be more likely to preserve blood vessels or other soft tissues, helping scientists target promising specimens.

With the combination of physics, paleontology, and advanced imaging technologies, researchers are beginning to uncover details about dinosaur biology that were once thought impossible to study.

Source: ScienceDaily

Panama’s ocean lifeline vanishes for the first time in 40 years

Each year during Central America's dry season (generally between December and April), strong northern trade winds help drive an important ocean process in the Gulf of Panama. These winds push surface waters in a way that allows colder, nutrient-rich water from deep below to rise toward the surface.

This process, known as upwelling, plays a major role in the region's marine life. It fuels highly productive fisheries, helps shield coral reefs from heat stress, and keeps the water along Panama's Pacific beaches cooler during the busy "summer" vacation season.

A 40-Year Pattern Suddenly Changed

Scientists at the Smithsonian Tropical Research Institute (STRI) have tracked this seasonal upwelling for decades. Their records show that from January to April, the event has been a reliable and predictable part of the Gulf of Panama for at least 40 years.

But in 2025, researchers documented something they had never seen before. For the first time in their records, this essential oceanographic process did not occur. The usual seasonal cooling was weakened, and the expected surge in ocean productivity was also reduced.

Weaker Winds May Be the Cause

In a recently published article in the journal PNAS, the scientists suggest that a major drop in wind patterns likely drove the unprecedented failure. The finding shows how quickly climate disruption can interfere with basic ocean processes that have supported coastal fishing communities for thousands of years.

Researchers caution that more work is still needed to identify the exact cause and understand what the event could mean for fisheries.

Tropical Oceans Need Better Monitoring

The discovery points to the rising vulnerability of tropical upwelling systems. These systems are enormously important for ecosystems and coastal economies, yet they remain poorly monitored in many parts of the world.

The findings also highlight the need to improve ocean climate observation and forecasting across tropical regions.

The result is one of the first major outcomes from the collaboration between the S/Y Eugen Seibold research vessel from the Max Planck Institute and STRI.

Source: ScienceDaily

Mezcal worm in a bottle DNA test reveals a surprise

 At the bottom of some mezcal bottles sits one of the most recognizable curiosities in the world of spirits: a pale, curled "worm" preserved in alcohol. It has helped give mezcal an air of mystery for decades, but scientists have now shown that this famous bottle stowaway is not a worm at all.

Mezcal is a distilled drink made from agave, the same plant group used to produce tequila. Most bottles are sold without anything added, but a small number contain larvae known as gusanos de maguey (Spanish for agave worms). The tradition feels ancient, but it is actually much more recent than mezcal itself. While mezcal production reaches back centuries in Mexico, the practice of placing larvae in bottles appears to have begun in the 1940s.

A Longstanding Mezcal Mystery

For years, the true identity of these larvae remained uncertain. They had been described as moth larvae, butterfly larvae, and even weevil larvae. Some people suspected more than one species might be involved, especially because the bottled "worms" can vary in color and appearance.

"It's relatively easy to broadly determine the kind of larva based on the shape of the head, but their identity has never been confirmed," said Akito Kawahara, curator at the Florida Museum's McGuire Center for Lepidoptera and Biodiversity. "This is probably because most biologists are not looking inside mezcal bottles."

To settle the question, Kawahara and his colleagues studied mezcal gusanos in research published in 2023 in PeerJ Life & Environment. In 2022, the team traveled to Oaxaca, Mexico, a region deeply tied to mezcal production. They visited distilleries and gathered as many different brands as they could find so they could sample larvae from a variety of bottles.

The larvae did not offer many obvious clues. After sitting in alcohol, their bodies were preserved, but many visible traits that might help identify them were limited. That preservation, however, also protected something far more useful: DNA.

DNA Revealed a Surprising Answer

The researchers were able to extract and analyze genetic material from 18 specimens. They expected the results might point to several different insects, since gusanos de maguey are harvested from the wild rather than raised through a standardized commercial system.

One leading suspect was the tequila giant skipper (Aegiale hesperiaris), a butterfly whose caterpillars feed on agave plants. Its large, whitish larvae seemed like a strong match for many of the pale gusanos seen in mezcal bottles. Its name also made it an obvious candidate.

But the DNA told a different story. Every larva that produced usable genetic data matched the agave redworm moth (Comadia redtenbacheri). In the PeerJ study, specimens that did not produce usable DNA were also identified morphologically as the same species.

Source: ScienceDaily

This new brain-like chip could slash AI energy use by 70%

 Scientists have created a new type of nanoelectronic device that could significantly reduce how much energy artificial intelligence systems consume. The innovation works by copying how the human brain processes information, offering a more efficient alternative to today's power-hungry AI hardware.

The research team, led by the University of Cambridge, developed a modified version of hafnium oxide that functions as a highly stable, low-energy 'memristor' -- a component designed to replicate how neurons connect and communicate in the brain. Their findings were published in the journal Science Advances.

Why Current AI Systems Use So Much Energy

Modern AI relies on traditional computer chips that constantly move data between memory and processing units. This back-and-forth transfer requires large amounts of electricity, and demand continues to rise as AI becomes more widely used across industries.

Neuromorphic computing offers a different approach. Instead of separating memory and processing, it combines both in one place, similar to how the brain works. This method could cut energy use by as much as 70% while also allowing systems to learn and adapt more naturally.

"Energy consumption is one of the key challenges in current AI hardware," said lead author Dr. Babak Bakhit, from Cambridge's Department of Materials Science and Metallurgy. "To address that, you need devices with extremely low currents, excellent stability, outstanding uniformity across switching cycles and devices, and the ability to switch between many distinct states."

A New Approach to Memristor Design

Most existing memristors operate by forming tiny conductive filaments inside metal oxide materials. These filaments tend to behave unpredictably and often require high voltages, which limits their practicality for large-scale computing.

The Cambridge researchers took a different route. They engineered a hafnium-based thin film that switches states through a more controlled mechanism. By adding strontium and titanium and using a two-step growth process, they created small electronic gates, known as 'p-n junctions', at the interfaces between layers.

Instead of relying on filaments forming and breaking, the device changes its resistance by adjusting the energy barrier at these interfaces. This allows for smoother and more reliable switching.

Bakhit, who is also affiliated with Cambridge's Department of Engineeirng, explained that this design solves a major issue in memristor development. "Filamentary devices suffer from random behavior," he said. "But because our devices switch at the interface, they show outstanding uniformity from cycle to cycle and from device to device."

Source: ScienceDaily

Simple “gut reset” may stop weight gain after Ozempic or Wegovy

 A minimally invasive outpatient procedure may help people avoid regaining weight after stopping popular medications like Ozempic and semaglutide, according to research being presented at Digestive Disease Week® (DDW) 2026. About 70% of people who stop these drugs eventually regain much of the weight they lost, often within 18 months. Nearly one in five adults with obesity has used a glucagon-like peptide-1 (GLP-1), highlighting the scale of this challenge.

Researchers report the first blinded, randomized, sham-controlled evidence that a procedure called duodenal mucosal resurfacing may offer a safe and lasting way to maintain weight loss without ongoing medication. The findings suggest it could help patients hold onto the benefits they achieved while taking drugs such as Ozempic or other GLP-1 therapies.

"As effective as GLP-1 medications are, many people stop taking them because of cost, side effects or simply not wanting to take a drug long-term," said lead author Shelby Sullivan, MD, director of the Endoscopic Bariatric and Metabolic Program at Dartmouth Health Weight Center and professor of medicine, Dartmouth Geisel School of Medicine. "But, if they stop these medications, weight regain occurs in the vast majority of patients, and the metabolic benefits are lost. Finding a treatment that allows patients to stop these medications without weight regain or loss of metabolic benefit is a huge unmet need. These findings indicate that this minimally invasive procedure may provide lasting weight-loss maintenance."

How the "Gut Reset" Procedure Works

Duodenal mucosal resurfacing is an investigational endoscopic treatment that uses controlled heat to remove damaged tissue from the inner lining of the duodenum, the first section of the small intestine just below the stomach. This process, which ablates (burns) the unhealthy mucosal layer, encourages the growth of new, healthier tissue.

The ongoing REMAIN-1 trial is designed to test whether this renewal of the intestinal lining can trigger a lasting metabolic reset, helping the body maintain weight loss after stopping medications like semaglutide or tirzepatide.

Trial Results Show Less Weight Regain

The current findings come from an early group of participants with six months of follow-up data. Among 45 people in this cohort, 29 received the resurfacing treatment while 16 underwent a sham procedure. All participants had previously lost at least 15% of their body weight using tirzepatide before stopping the drug.

On average, patients lost about 40 pounds while on GLP-1 therapy. Six months after discontinuing the medication, those in the control group regained significantly more weight. Participants who received the sham procedure regained about 40% more weight than those who underwent the actual treatment.

In addition, patients who had more extensive resurfacing regained only about 7 pounds and kept more than 80% of their weight loss. By comparison, the control group regained roughly twice as much. The gap between the two groups continued to widen from one to six months after the procedure, suggesting the benefits may persist and even strengthen over time.

"What's particularly encouraging is that the benefit appears to increase over time rather than fade, and that it behaves like a drug in terms of dose response," Dr. Sullivan said. "That gives us confidence that we're targeting the right biology."

Safety and Recovery

No serious complications were reported from either the device or the procedure. Recovery is relatively quick, with most patients returning to normal activities within about a day.

"Other than recovering from the general anesthesia, there isn't much recovery time involved," Dr. Sullivan said. "You can be back to your daily routine in about a day. Participants could not tell if they had the sham or real procedure because there are not a lot of symptoms after the procedure."

Why the Gut Is Key to Weight Regulation

The treatment targets the small intestine, where many of the hormones affected by GLP-1 drugs are produced. Over time, diets high in fat and sugar can alter the lining of the duodenum, changing how the body processes food and regulates hormones. These changes can contribute to insulin resistance and metabolic disease.

By restoring a healthier mucosal layer, the procedure aims to reset how the body responds to food, helping stabilize metabolism at a lower body weight after stopping medications like Ozempic.

Source: ScienceDaily

For the first time, scientists pinpoint the brain cells behind depression

 Researchers at McGill University and the Douglas Institute have discovered that two distinct types of brain cells function differently in people with depression.

The findings, published in Nature Genetics, offer important clues that could lead to new treatments designed to target these specific cells. They also provide a clearer understanding of depression, a condition that affects more than 264 million people worldwide and remains a leading cause of disability.

"This is the first time we've been able to identify what specific brain cell types are affected in depression by mapping gene activity together with mechanisms that regulate the DNA code," said senior author Dr. Gustavo Turecki, a professor at McGill, clinician-scientist at the Douglas Institute and Canada Research Chair in Major Depressive Disorder and Suicide. "It gives us a much clearer picture of where disruptions are happening, and which cells are involved."

Rare Brain Tissue Enables Breakthrough

To make this discovery, the research team relied on post-mortem brain samples from the Douglas-Bell Canada Brain Bank. This collection is one of the few in the world that includes donated brain tissue from individuals who had psychiatric conditions, making it an invaluable resource for studying mental health at a biological level.

Using advanced single-cell genomic techniques, the scientists examined RNA and DNA from thousands of individual brain cells. This approach allowed them to pinpoint which cells behaved differently in people with depression and to identify genetic patterns that might explain those differences. The study included samples from 59 individuals diagnosed with depression and 41 without the condition.

Key Brain Cells Show Altered Activity

The analysis revealed changes in gene activity in two important types of brain cells. One was a group of excitatory neurons that play a role in regulating mood and responding to stress. The other was a subtype of microglia, immune cells in the brain that help control inflammation.

In both cell types, many genes showed different levels of activity in people with depression, suggesting that these systems may not be functioning normally. These disruptions could help explain how depression develops at a biological level.

Rethinking Depression as a Brain Disorder

By identifying the specific cells involved, the study strengthens the case that depression has a clear biological foundation. It also challenges outdated views that treat the condition as purely emotional or psychological.

"This research reinforces what neuroscience has been telling us for years," Turecki said. "Depression isn't just emotional, it reflects real, measurable changes in the brain."

What Comes Next for Depression Research

The researchers now plan to investigate how these cellular differences affect overall brain function. They also hope to determine whether therapies that target these cells could lead to more effective treatments in the future.

About the Study

The paper, titled "Single-nucleus chromatin accessibility profiling identifies cell types and functional variants contributing to major depression" by Anjali Chawla and Gustavo Turecki et al., was published in Nature Genetics.

Funding for the research was provided by the Canadian Institutes of Health Research, Brain Canada Foundation, Fonds de recherche du Québec -- Santé and the Healthy Brains, Healthy Lives initiative at McGill University.

Source: ScienceDaily

Think AI "knows" what it’s doing? Scientists say think again

 Think, know, understand, remember.

These are everyday words people use to describe what goes on in the human mind. But when those same terms are applied to artificial intelligence, they can unintentionally make machines seem more human than they really are.

"We use mental verbs all the time in our daily lives, so it makes sense that we might also use them when we talk about machines -- it helps us relate to them," said Jo Mackiewicz, professor of English at Iowa State. "But at the same time, when we apply mental verbs to machines, there's also a risk of blurring the line between what humans and AI can do."

Mackiewicz and Jeanine Aune, a teaching professor of English and director of the advanced communication program at Iowa State, are part of a research team that studied how writers describe AI using human-like language. This type of wording, known as anthropomorphism, assigns human traits to non-human systems. Their study, "Anthropomorphizing Artificial Intelligence: A Corpus Study of Mental Verbs Used with AI and ChatGPT," was published in Technical Communication Quarterly.

The research team also included Matthew J. Baker, associate professor of linguistics at Brigham Young University, and Jordan Smith, assistant professor of English at the University of Northern Colorado. Both previously studied at Iowa State University.

Why Human-Like Language About AI Can Be Misleading

According to the researchers, using mental verbs to describe AI can create a false impression. Words such as "think," "know," "understand," and "want" suggest that a system has thoughts, intentions, or awareness. In reality, AI does not possess beliefs or feelings. It produces responses by analyzing patterns in data, not by forming ideas or making conscious decisions.

Mackiewicz and Aune also pointed out that this kind of language can overstate what AI is capable of. Phrases like "AI decided" or "ChatGPT knows" can make systems seem more independent or intelligent than they actually are. This can lead to unrealistic expectations about how reliable or capable AI is.

There is also a broader concern. When AI is described as if it has intentions, it can distract from the humans behind it. Developers, engineers, and organizations are responsible for how these systems are built and used.

"Certain anthropomorphic phrases may even stick in readers' minds and can potentially shape public perception of AI in unhelpful ways," Aune said.

How News Writers Actually Use AI Language

To better understand how often this kind of language appears, the researchers analyzed the News on the Web (NOW) corpus. This massive dataset contains more than 20 billion words from English-language news articles published in 20 countries.

They focused on how frequently mental verbs such as "learns," "means," and "knows" were used alongside terms like AI and ChatGPT.

The findings were unexpected.

Mental Verbs Are Less Common Than Expected

The study found that news writers do not frequently pair AI-related terms with mental verbs.

While anthropomorphism is common in everyday speech, it appears far less often in news writing. "Anthropomorphism has been shown to be common in everyday speech, but we found there's far less usage in news writing," Mackiewicz said.

Among the examples identified, the word "needs" appeared most often with AI, showing up 661 times. For ChatGPT, "knows" was the most frequent pairing, but it appeared only 32 times.

The researchers noted that editorial standards may play a role. Associated Press guidelines, which discourage attributing human emotions or traits to AI, could be influencing how journalists write about these technologies.

Context Matters More Than the Words Themselves

Even when mental verbs were used, they were not always anthropomorphic.

For instance, the word "needs" often described basic requirements rather than human-like qualities. Phrases such as "AI needs large amounts of data" or "AI needs some human assistance" are similar to how people describe non-human systems like cars or recipes. In these cases, the language does not imply that AI has thoughts or desires.

In other cases, "needs" was used to express what should be done, such as "AI needs to be trained" or "AI needs to be implemented." Aune explained that these examples were often written in passive voice, which shifts responsibility back to human actors rather than the technology itself.

Anthropomorphism Exists on a Spectrum

The study also showed that not all uses of mental verbs are equal. Some phrases move closer to suggesting human-like qualities.

For example, statements like "AI needs to understand the real world" can imply expectations tied to human reasoning, ethics, or awareness. These uses go beyond simple descriptions and begin to suggest deeper capabilities.

"These instances showed that anthropomorphizing isn't all-or-nothing and instead exists on a spectrum," Aune said

Why Language Choices About AI Matter

Overall, the researchers found that anthropomorphism in news coverage is both less frequent and more nuanced than many might assume.

"Overall, our analysis shows that anthropomorphization of AI in news writing is far less common -- and far more nuanced -- than we might think," Mackiewicz said. "Even the instances that did anthropomorphize AI varied widely in strength."

The findings highlight the importance of context. Simply counting words is not enough to understand how language shapes meaning.

"For writers, this nuance matters: the language we choose shapes how readers understand AI systems, their capabilities and the humans responsible for them," Mackiewicz said.

The research team also emphasized that these insights can help professionals think more carefully about how they describe AI in their work.

"Our findings can help technical and professional communication practitioners reflect on how they think about AI technologies as tools in their writing process and how they write about AI," the research team wrote in the published study.

As AI continues to develop, the way people talk about it will remain important. Mackiewicz and Aune said writers will need to stay mindful of how word choices influence perception.

Looking ahead, the team suggested that future studies could explore how different words shape understanding and whether even rare uses of anthropomorphic language have a strong impact on how people view AI.

Source: ScienceDaily