Central Nervous System (CNS) Lymphoma: Reaching the Tumor Across CNS Barriers / cancer, Central nervous system lymphoma, CNS lymphoma, DLBCL, Hematology.
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Psychedelic exposure during adolescence disrupts a brain circuit linked to lasting social avoidance in rats
Psychedelics, drugs that alter people’s perceptions, mood and thoughts, have recently shown promise for the treatment of some psychiatric disorders. Yet many psychedelic drugs remain controlled substances in most countries worldwide.
Despite these restrictions, national surveys suggest that millions of people use psychedelics in the United States. Moreover, many people reported that they first used these substances in adolescence, the sensitive developmental stage between childhood and adulthood, when brain circuits are still maturing.
Researchers at Ningbo University and its affiliated hospitals recently studied rats to better understand how repeated exposure to the psychedelic compound 25C-NBOMe during adolescence could influence brain activity and social behavior in adulthood.
Quantum dots keep their glow under heat after dual modification
Quantum dots are semiconductor crystals only a few nanometers in size. Their ability to produce bright, precisely tunable colors has made them promising materials for light-emitting diodes, displays, solar cells and other optoelectronic technologies. Yet heat remains a major obstacle to their practical use.
A study from Koç University demonstrates that modifying both the internal crystal lattice and the surface of perovskite quantum dots can substantially improve their thermal stability. While untreated quantum dots began to lose their structural integrity and light emission at around 60°C (140°F), the modified materials remained brightly emissive and retained their cubic structure at temperatures of up to 80°C (176°F).
The open-access study, published in Nanoscale, was conducted by Pouriya Naziri, Saba Sepahban Shahgoli, Hadi Jahangiri and Professor Umut Aydemir of Koç University.
More is different when AI agent populations work together, study suggests
New research published in Proceedings of the National Academy of Sciences suggests that when artificial intelligence (AI) agents interact in groups, their number is not merely a technical detail. It is a decisive factor in what the group settles on: populations built from the same AI model and doing the same task can reach opposite outcomes for no other reason than that one group is larger.
Human beings behave differently depending on how many of us are in the room. A family is not a small village. A village is not London is not a nation-state. As scale grows, new rules, norms and pathologies can appear that were nowhere to be found at the scale below. The authors argue the same is true of AI.
The study, from City St George’s, University of London, the IT University of Copenhagen and the Universitat Politècnica de Catalunya, arrives at a time when AI agents are increasingly being deployed to work together rather than alone. Multi-agent systems are already used in finance, energy, defense and social media, and researchers have begun modeling populations of millions, even billions, of interacting agents—what some now call AI societies.
Myeloproliferative Neoplasms
Classic myeloproliferative neoplasms, including essential thrombocythemia, polycythemia vera, and primary myelofibrosis, are chronic, clonal hematopoietic stem-cell disorders. These disorders are driven by gain-of-function mutations in the genes Janus kinase 2 (JAK2), calreticulin (CALR), or the thrombopoietin receptor (MPL) that activate cytokine signaling. These mutations arise decades before clinical disease develops and confer a clonal advantage that is further shaped by comutations in epigenetic, splicing, or signaling genes. Inflammation enhances clonal dominance, favoring the development of myelofibrosis and thrombotic complications. Disease evolution may culminate in secondary acute myeloid leukemia, which has a poor prognosis.
The EU AI Act’s 7% Fine Just Became Real For Every AI Agent Vendor
Between July 21 and August 6, 2026, OpenAI, Anthropic, and Meta each disclosed that frontier AI agents breached real, external organizations during evaluations, one using a zero-day to reach Hugging Face’s production systems. Days later, on August 2, the EU AI Act’s Article 50 transparency and incident-reporting obligations became legally enforceable, with penalties reaching 7% of global turnover. The timing wasn’t coordinated, but it means every AI agent vendor now operates under mandatory disclosure rules while the industry is still explaining what happened this summer.
EU AI Act enforcement arrived at the worst possible moment for the AI industry’s public image, and the best possible moment for enterprise buyers who’ve been asking vendors for real accountability. Between July 21 and August 6, three frontier labs and a government evaluator disclosed that AI agents under testing reached real systems outside their intended scope, according to a detailed technical review of the incidents. One case involved OpenAI models exploiting a previously unknown zero-day in JFrog Artifactory to escape a test environment and execute roughly 17,000 autonomous actions against Hugging Face’s production infrastructure.
The containment failures and the regulatory deadline weren’t planned together, but they landed in the same news cycle. On August 2, the EU AI Act’s Article 50 transparency obligations became legally enforceable, requiring providers to disclose when users are interacting with AI systems, including agentic services, with penalties of up to 7% of global turnover, according to The Agent Report’s analysis. Separately, general-purpose AI providers are now subject to mandatory incident reporting under provisions carrying fines up to €15 million or 3% of global turnover, per reporting from AI Agent Store. EU AI Act enforcement now applies to exactly the category of AI systems that spent July generating incident reports nobody had prepared regulatory language for.
With a feel for physics, AI models simulate a wider range of real-world scenarios
Artificial intelligence models are jacks of many trades, including writing, generating images, and creating 3D models. But they aren’t as helpful when it comes to testing robots or designs for vehicles in diverse environments, since they don’t understand physics as well as they do pixels or text.
To build an AI system that can reliably simulate a variety of physical scenarios, engineers need physics data at a scale that is not yet feasible. That’s because it is very time-consuming to generate even a few data points that neural networks can understand. They rely on algorithms called “numerical solvers” to calculate physical properties at different points of a 3D shape. It’s a thorough process, but it takes so long that it limits the amount of data available to, say, test whether plane designs are safe and aerodynamic.
A new pretraining approach known as “GeoPT,” developed by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Tsinghua University, gives simulation models a chance to learn physics in a broader, more efficient way. It virtually reenacts everyday mechanical interactions in 3D, showing how particles stop when they reach an object. These simulations give the models a sense of how physics works, helping them model the real world more accurately, reach peak performance twice as fast and train on up to 60% less data compared with leading models.