It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Wednesday, May 21, 2025
Mice use chemical cues such as odours to sense social hierarchy
Researchers at the Francis Crick Institute have shown that mice use chemical cues, including odours, to detect the social rank of an unfamiliar mouse and compare it to their own, using this information to determine their behaviour.
Like many mammals, mice live in a social hierarchy where some are more dominant than others. This helps to avoid conflict and establish breeding partners.
It has been suggested previously that some mice might display fixed behaviour regardless of who they are interacting with, or that physical properties can give cues about social ranking. However, new research published today in Current Biology shows that mice instead infer an unfamiliar mouse’s rank through chemical cues transmitted in the air (odours) or through direct contact (non-volatile scent cues).
The Crick team worked this out using a test where male mice enter a transparent tube at opposite ends, meeting in the middle. In this type of confrontation, a more submissive animal will typically retreat1.
The researchers first looked at interactions in mice who shared the same cage, using this to rank each mouse on a hierarchy, before observing how the mice responded to a set of unfamiliar opponents.
They found that the strangers could recognise each other’s rank, compare it to their own, and either retreat or force the other mouse to retreat.
The team then tested the mice in the dark, finding that this did not affect rank recognition, suggesting that cues like physical size or behaviour don’t determine recognition of a more aggressive opponent. Similarly, castrating the mice to remove their sex hormones had no impact.
Finally, the team experimentally blocked the two chemosensory systems that mice use – one for odours in the air (olfactory system) and one for chemical signals transmitted by physical contact (vomeronasal system).
They found no effect when just one of these systems was removed; both needed to be ablated before the mice couldn’t recognise opponent rank. This showed that mice use both olfactory and vomeronasal systems to recognise rank and can compensate if one is missing.
Like mice, people can also infer the social status of others around them relative to their own, also using sensory cues, including language, facial expression or clothing.
The next step for the researchers is to investigate which areas of the brain process the information on opponent rank and own rank and initiate a decision to retreat or advance.
Neven Borak, former PhD student in the State-Dependent Neural Processing Laboratory at the Crick and first author, said: “We’ve shown that mice weigh up strangers using chemical cues and can detect social status without needing an extensive history of confrontations with those specific opponents. This is a fascinating phenomenon that humans do too mostly using visual cues. Our work offers an interesting perspective on social mobility: humans, like mice, can enter a new group of people but still maintain understanding of own social rank and gauge the social status of unfamiliar people.”
Jonny Kohl, Group Leader of the State-Dependent Neural Processing Laboratory at the Crick and senior author, said: “We’ve shown for the first time how mice integrate internal and external information about dominance. This shows that a decision based on relative ranks is made in the brain before mice show either aggression or submissive behaviour, rather than there being fixed differences in behaviours leading to an aggressive or docile mouse.”
The State-Dependent Neural Processing Laboratory studies how processes within the brain are impacted by the state of the body. By studying how physiological states, such as pregnancy, stress or sleep, impact neural circuits in mice, the researchers hope to advance a more integrative view of brain physiology in health and disease.
-ENDS-
For further information, contact: press@crick.ac.uk or +44 (0)20 3796 5252
Notes to Editors
Reference: Borak, N. et al. (2025). Dominance rank inference in mice via chemosensation. Current Biology.
All experimental protocols involving mice were performed in accordance with guidelines of the Francis Crick Institute, and in accordance with the Animals (Scientific Procedures) Act 1986. This study was approved by the UK Home Office.
The Francis Crick Institute is a biomedical discovery institute with the mission of understanding the fundamental biology underlying health and disease. Its work helps improve our understanding of why disease develops which promotes discoveries into new ways to prevent, diagnose and treat disease.
An independent organisation, its founding partners are the Medical Research Council (MRC), Cancer Research UK, Wellcome, UCL (University College London), Imperial College London and King’s College London.
The Crick was formed in 2015, and in 2016 it moved into a brand new state-of-the-art building in central London which brings together 1500 scientists and support staff working collaboratively across disciplines, making it the biggest biomedical research facility under a single roof in Europe.
Research led by Ru-rong Ji, PhD, an anesthesiology and neurobiology professor at Duke University School of Medicine, shows the non-opioid compound SBI-810 relieves pain without side effects.
An experimental drug developed at Duke University School of Medicine could offer powerful pain relief without the dangerous side effects of opioids.
The drug, called SBI-810, is part of a new generation of compounds designed to target a receptor on the nerves and spinal cord. While opioids flood multiple cellular pathways indiscriminately, SBI-810, a non-opioid treatment, takes a more focused approach, activating only a specific pain-relief pathway that avoids the euphoric “high” linked to addiction.
In tests in mice, SBI-810 worked well on its own and, when used in combination, made opioids more effective at lower doses, according to the study published May 19 in Cell.
Even more encouraging: it prevented common side effects like constipation and buildup of tolerance, which often forces patients to need stronger and more frequent doses of opioids over time.
SBI-810 is in early development, but Duke researchers are aiming for human trials soon and they’ve locked in multiple patents for the discovery.
There’s an urgent need for pain relief alternatives. Drug overdose deaths are declining, but more than 80,000 Americans still die each year most often from opioids. Meanwhile, chronic pain affects one-third of the U.S. population.
Researchers said the drug could be a safer option for treating both short-term and chronic pain for those recovering from surgery or living with diabetic nerve pain.
SBI-810 is designed to target the brain receptor neurotensin receptor 1. Using a method known as biased agonism, it switches on a specific signal—β-arrestin-2—linked to pain relief, while avoiding other signals that can cause side effects or addiction.
“The receptor is expressed on sensory neurons and the brain and spinal cord,” Ji said. “It’s a promising target for treating acute and chronic pain.”
SBI-810 effectively relieved pain from surgical incisions, bone fractures, and nerve injuries better than some existing painkillers. When injected in mice, it reduced signs of spontaneous discomfort, such as guarding and facial grimacing.
Duke scientists compared SBI-810 to oliceridine, a newer type of opioid used in hospitals, and found SBI-810 worked better in some situations, with fewer signs of distress.
Unlike opioids like morphine, SBI-810 didn’t cause tolerance after repeated use. It also outperformed gabapentin, a common drug for nerve pain, and didn’t cause sedation or memory problems, which are often seen with gabapentin.
Researchers said the compound’s dual action—on both the peripheral and central nervous systems— could offer a new kind of balance in pain medicine: powerful enough to work, yet specific enough to avoid harm.
The study was supported by the NIH and the Department of Defense.
Additional Duke authors include first authors Ran Guo and Ouyang Chen; Sangsu Bang, Sharat Chandra, Yize Li, Gang Chen, Rou-Gang Xie, Wei He, Jing Xu, Richard Zhou, Shaoyong Song, Ivan Spasojevic, Marc G. Caron, William C. Wetsel and Lawrence S. Barak.
A new AI chip developed at the Technical University of Munich (TUM) works without the cloud server or internet connections needed by existing chips. The AI Pro, designed by Prof Hussam Amrouch, is modelled on the human brain. Its innovative neuromorphic architecture enables it to perform calculations on the spot, ensuring full cyber security. It is also up to ten times more energy efficient.
The professor of AI processor design at TUM has already had the first prototypes produced by semiconductor manufacturer Global Foundries in Dresden. Unlike conventional chips, the computing and memory units of the AI Pro are located together. This is possible because the chip applies the principle of ‘hyperdimensional computing’: This means that it recognizes similarities and patterns, but does not require millions of data records to learn.
Instead of being shown countless images of cars, as with the deep learning method used in conventional AI chips, this chip combines various pieces of information, such as the fact that a car has four wheels, usually drives on the road, and can have different shapes. Like the new chip, explains Prof. Amrouch, ‘humans also draw inferences and learn through similarities.’
An important advantage of brain-like thinking: it saves energy. For the training of a sample task, the new chip consumed 24 microjoules, while comparable chips required ten to a hundred times more energy - ‘a record value,’ notes Prof. Amrouch. ‘This mix of modern processor architecture, algorithm specialization and innovative data processing makes the AI chip something special.’
This also sets it apart from all-rounders like the chips from industry giant NVIDIA. ‘While NVIDIA has built a platform that relies on cloud data and promises to solve every problem, we have developed an AI chip that enables customized solutions. There is a huge market there.’
Neuromorphic chips: Modelled on the human brain
The one square millimeter chip currently costs 30,000 euros. With around 10 million transistors it is not quite as densely packed or as powerful as NVIDIA chips with 200 billion transistors. But that is not Prof. Amrouch's primary concern. His team specializes in AI chips that perform the processing directly on site instead of having to send the data to the cloud to be processed along with millions of other data sets before being sent back again. This saves time and server computing capacity and reduces the carbon footprint of AI.
The chips are also customized for specific applications. ‘That makes them very efficient,’ says chip expert Amrouch. For example, they focus on processing heartrate and other vital data collected via smartwatch or navigation data of a drone. Because this personal and sometimes sensitive data remains on board the device, issues with stable internet connections or cybersecurity do not even arise. The chip expert is convinced: ‘The future belongs to the people who own the hardware.’
Further information:
Prof. Hussam Amrouch started his engagement at TUM two years ago. The Chair of AI Processor Design was created as part of Hightech Agenda Bayern. Further information: https://www.hightechagenda.de/
Prof. Hussam Amrouch is also active in the Munich Institute of Robotics and Machine Intelligence (MIRMI). His chip developments are relevant for health, the environment, and space. Further information on MIRMI: https://www.mirmi.tum.de/mirmi/startseite//
Publications:
Sandy Wasif, Paul Genssler, and Hussam Amrouch. "Domain-Specific Hyperdimensional RISC-V Processor for Edge-AI Training." IEEE Transactions on Circuits and Systems I: Regular Papers (2025). https://ieeexplore.ieee.org/document/10931124
Soliman, Taha, Swetaki Chatterjee, Nellie Laleni, Franz Müller, Tobias Kirchner, Norbert Wehn, Thomas Kämpfe, Yogesh Singh Chauhan, and Hussam Amrouch. "First demonstration of in-memory computing crossbar using multi-level Cell FeFET." Nature Communications 14, no. 1 (2023): 6348. https://www.nature.com/articles/s41467-023-42110-y
Wei-Ji Chao, Paul R. Genssler, Sandy A Wasif, Albi Mema, Hussam Amrouch, “End-to-end Hyperdimensional Computing with 24.65 µJ per Training Sample in 22 nm Technology”, under review at the European Solid-State Electronics Research Conference (ESSERC). Preprint available: https://go.tum.de/440497
Additional material for media outlets
Images for download: https://mediatum.ub.tum.de/1781785
Credit: Sam Beebe: https://www.flickr.com/photos/28585409@N04/3275040520
Using global land use and carbon storage data from the past 175 years, researchers at The University of Texas at Austin and Cognizant AI Labs have trained an artificial intelligence system to develop optimal environmental policy solutions that can advance global sustainability initiatives of the United Nations. The AI tool effectively balances various complex trade-offs to recommend ways of maximizing carbon storage, minimizing economic disruptions and helping improve the environment and people’s everyday lives, according to a paper published today in the journal Environmental Data Science.
The project is among the first applications of the UN-backed Project Resilience, a team of scientists and experts working to tackle global decision-augmentation problems—including ambitious sustainable development goals this decade—through part of a broader effort called AI for Good. University of Texas at Austin computer scientist Risto Miikkulainen, who helped launch Project Resilience, believes the new AI approach, initially focused on land use, can address an even larger set of challenges, from infectious diseases to food insecurity, with artificial intelligence potentially discovering better solutions than humans.
“There’s always an outcome you want to optimize for, but there’s always a cost,” he said. Amid all of the trade-offs, AI can home in on unexpected pathways to desirable outcomes at various costs, helping leaders selectively pick battles and yield better results.
The secret sauce of the researchers’ system is evolutionary AI. Inspired by the process of natural selection in biological systems, this computational approach starts with a few dozen policy scenarios and predicts how each scenario will impact various economic and environmental costs. Then, like a digital version of survival of the fittest, policy combinations that don’t balance the trade-offs well are killed off, while the best ones are allowed to reproduce, giving rise to hybrid offspring. Random mutations also are sprinkled in to help the system explore novel combinations faster. The process then repeats, winnowing poor performers and keeping the best, across hundreds or thousands of scenarios. Like biological evolution, the “generations” of scenarios become ever-more optimized for a set of priorities.
The AI system’s recommendations sometimes surprised the team. Although forests are known to be good at storing carbon, the AI prescription model offered a more nuanced approach than converting as much land as possible into forests, regardless of location. For example, it found that replacing crop land with forest is much more effective than replacing range land (which includes deserts and grasslands). Also, generally, the same land use change at one latitude didn’t yield the same benefits as at another latitude. Ultimately, the system recommended that larger changes should be allocated to locations where it mattered more; in essence, it’s more effective to pick your battles.
“You can obviously destroy everything and plant forests, and that would help mitigate climate change,” said Daniel Young, a researcher at Cognizant AI Labs and a Ph.D. student at UT Austin. “But we would have destroyed rare habitats and our food supply and cities. So we need to find a balance and be smart about where we make changes.”
The researchers have turned their model into an interactive tool that decision makers like legislators can use to explore how incentives, such as tax credits for landowners, would be likely to alter land use and reduce carbon.
Land use activities, including agriculture and forestry are estimated to be responsible for nearly a quarter of all human-caused greenhouse gas emissions. Experts believe smart land use changes will be needed to reduce the amount of carbon in the air and thereby slow climate change. According to Miikkulainen and Young, AI offers options that people, businesses and governments otherwise resistant to change may find easier to accept.
An earlier version of the paper was presented at a major machine learning and computational neuroscience conference, NeurIPS, where it won the “Best Pathway to Impact” award at the Climate Change workshop.
The other authors on the paper are Olivier Francon, Elliot Meyerson, Clemens Schwingshackl, Jakob Bieker, Hugo Cunha and Babak Hodjat.
The annual Wayne E. Sabbe Arkansas Soil Fertility Studies publication guides nutrient management recommendations to improve soil health and crop yield.
Credit: U of A System Division of Agriculture photo
FAYETTEVILLE, Ark. — When you test more than 200,000 soil samples in a year, you not only learn something about how Arkansans grow crops, gardens and lawns, but also the value of recommendations that result from soil test results.
Each year, the Arkansas Agricultural Experiment Station publishes the Wayne E. Sabbe Arkansas Soil Fertility Studies. The latest edition, released in spring, features 12 research reports prepared by scientists with the University of Arkansas System Division of Agriculture and the Dale Bumpers College of Agricultural, Food and Life Sciences at the University of Arkansas.
This edition includes investigations into the effects of fertilization on row crops, blackberries, forage and soil, plant tissue nutrient testing and perceptions of stakeholders when it comes to the state’s public soil testing program.
Each year, the feature article summarizes the chemical properties of soil samples to the Arkansas Soil Testing Program. In 2023, Arkansas clients submitted a record of 201,896 soil samples — representing approximately 1.5 million acres of land — to the experiment station’s Marianna Soil Test Lab. The article found that row crop use accounted for 74 percent of sampled acreage, hay and pasture uses accounted for 15 percent, and home lawns and gardens accounted for 2.3 percent. Mississippi County submitted the most samples, with 26,953; Clay was next at 23,141 and Poinsett County was third with 22,669 samples.
A study led by Aurelie Poncet, assistant professor with the crop, soil, and environmental sciences department, found that 81 percent of those who submitted samples to the soil test lab used lime and fertilizer recommendations from the Division of Agriculture to improve soil fertility.
“We have a very comprehensive record each year about the status of soil fertility across the state of Arkansas,” said Nathan Slaton, who edited the publication and serves as associate vice president for agriculture and assistant director of the experiment station.
Slaton noted how the publication’s reports are of interest to a variety of stakeholders, from horticulturists to rice producers, reflecting the widely applicable nature of the work.
The online publication sees hundreds of downloads from across the United States — and the world — Slaton said. Ultimately, the publication helps university researchers validate or develop new fertilizer and soil nutrient management recommendations.
“It’s important that as production systems change and new genetics are released into the hands of farmers … that soil fertility data that evaluates the reliability of soil test information is checked over time,” Slaton said.
The 2024 Arkansas Soil Fertility Studies include:
Arkansas soil-test summary for samples collected in 2023
Sulfate runoff dynamics from edge-of-field losses at selected Arkansas Discovery Farms
Potassium fertilization effects on cotton yield and tissue-K concentration in Arkansas
Assessment of potassium loss by runoff in different cotton production systems
Bermudagrass forage yield and soil test response to phosphorus and potassium fertilization
Verifying nitrogen rate recommendations for blackberry grown in Arkansas
Effectiveness of in-season potassium fertilization on irrigated corn production
NUMBERS: Nutrient management database for effective rate selection
Assessing producers’ engagement with the services provided by the Marianna Soil Test Laboratory
Updated profit-maximizing potash fertilizer recommendations for corn
Cotton response to nitrogen on silt loam soils: Year two results
Cover crop and phosphorus and potassium application rate effects on soil-test values and cotton yield
Soil testing is conducted at the Marianna Soil Test Lab.
Credit
U of A System Division of Agriculture photo
Leading free soil testing
All Arkansans can submit soil for free testing thanks to the Arkansas Fertilizer Tonnage Fee Program. Fertilizer tonnage fees are used to support routine soil testing services, soil fertility research, and the regulation and enforcement of fertilizer-related laws that benefit both farmers and the broader public.
Residents can submit soil samples to an Arkansas Cooperative Extension Service county office, which will then forward them to the Marianna lab. These extension offices are located in each of Arkansas’ 75 counties. The extension service is the outreach arm of the Division of Agriculture.
The lab’s routine analysis sheds light on soil pH and nutrient availability for selected nutrients, providing recommendations to achieve optimal soil fertility based on crop. The testing is used by individuals from golf course superintendents and farmers to home gardeners and landscapers.
Poncet’s study assessed producers’ use and satisfaction when it comes to the Marianna lab. Researchers collected 98 responses that were considered representative of Arkansas producers’ practices.
Responses revealed that most of the state’s producers collect soil samples to inform their management practices and use the free soil testing services provided by Marianna lab. Overall, most Arkansas producers are satisfied with the lab and its services.
The Marianna lab, which is the second-largest public soil testing program in the United States, accounts for 80 to 85 percent of the analysis for all of the samples collected in Arkansas, according to Slaton.
The University of Arkansas System Division of Agriculture’s mission is to strengthen agriculture, communities, and families by connecting trusted research to the adoption of best practices. Through the Agricultural Experiment Station and the Cooperative Extension Service, the Division of Agriculture conducts research and extension work within the nation’s historic land grant education system.
The Division of Agriculture is one of 20 entities within the University of Arkansas System. It has offices in all 75 counties in Arkansas and faculty on three system campuses.
Pursuant to 7 CFR § 15.3, the University of Arkansas System Division of Agriculture offers all its Extension and Research programs and services (including employment) without regard to race, color, sex, national origin, religion, age, disability, marital or veteran status, genetic information, sexual preference, pregnancy or any other legally protected status, and is an equal opportunity institution.
Article Title
Wayne E. Sabbe Arkansas Soil Fertility Studies 2024
