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Artificial intelligence (AI) shows tremendous promise for analyzing vast medical imaging datasets and identifying patterns that may be missed by human observers. AI-assisted interpretation of brain scans may help improve care for children with brain tumors called gliomas, which are typically treatable but vary in risk of recurrence.

Investigators from Mass General Brigham and collaborators at Boston Children’s Hospital and Dana-Farber/Boston Children’s Cancer and Blood Disorders Center trained deep learning algorithms to analyze sequential, post-treatment brain scans and flag patients at risk of cancer recurrence.

Their results are published in NEJM AI.

Irritable bowel syndrome, chronic itching, asthma and migraine are in many cases hard-to-treat conditions. They have in common that they are triggered by an excessive immune response—which in severe cases can be life-threatening.

A team of researchers led by the University of Bonn has now identified a promising bioactive compound that could effectively reduce symptoms and slash fatality risk. The compound blocks a receptor on certain defense cells, thus preventing a derailed immune response. The study findings have been published in the journal Signal Transduction and Targeted Therapy.

If you have ever been bitten by a mosquito, you will know how annoying the resulting itching can be. This is in large part due to mast cells— found in the skin and that are full of inflammatory messengers. When a person is bitten, antibodies bind to substances in the mosquito’s saliva, and this complex can activate the mast cells, which then release their payload all at once. This leads to the symptoms of redness, swelling and itching, which usually subside after a short while, or even quicker, using the right ointment.

The Tesla robotaxi service, as stated, would be a significant leap in capability from what is currently available.

In a new Nature Communications study, researchers have developed an in-memory ferroelectric differentiator capable of performing calculations directly in the memory without requiring a separate processor.

The proposed differentiator promises energy efficiency, especially for edge devices like smartphones, autonomous vehicles, and security cameras.

Traditional approaches to tasks like image processing and motion detection involve multi-step energy-intensive processes. This begins with recording data, which is transmitted to a , which further transmits the data to a microcontroller unit to perform differential operations.

Building GPT-4 took a lot of people. Now, OpenAI says it could rebuild it with as few as five people, all because of what it learned from its latest model, GPT-4.5.

Andreessen Horowitz’s Anjney Midha argues that the US has no choice in terms of how it approaches the artificial intelligence race with China: “We must win.”

Speaking with Semafor’s Reed Albergotti at Semafor’s World Economy Summit on Wednesday, Midha, who is a general partner at the Silicon Valley venture capital firm, said that US AI companies should double down on driving growth rather than stifle innovation over concerns of potentially harmful use cases. People all over the world will choose to use American AI tools, so long as they’re the best available.

“This is why a billion people in India still use WhatsApp. It was invented in Silicon Valley,” Midha said.

Lucid dreaming (LD) is a state of conscious awareness of the ongoing oneiric state, predominantly linked to REM sleep. Progress in understanding its neurobiological basis has been hindered by small sample sizes, diverse EEG setups, and artifacts like saccadic eye movements. To address these challenges in the characterization of the electrophysiological correlates of LD, we introduced an adaptive multi-stage preprocessing pipeline, applied to human data (male and female) pooled across laboratories, allowing us to explore sensor-and source-level markers of LD. We observed that, while sensor-level differences between LD and non-lucid REM sleep were minimal, mixed-frequency analysis revealed broad low-alpha to gamma power reductions during LD compared to wakefulness. Source-level analyses showed significant beta power (12−30 Hz) reductions in right central and parietal areas, including the temporo-parietal junction, during LD. Moreover, functional connectivity in the alpha band (8−12 Hz) increased during LD compared to non-lucid REM sleep. During initial LD eye signaling compared to baseline, source-level gamma1 power (30−36 Hz) increased in right temporo-occipital regions, including the right precuneus. Finally, functional connectivity analysis revealed increased inter-hemispheric and inter-regional gamma1 connectivity during LD, reflecting widespread network engagement. These results suggest that distinct source-level power and connectivity patterns characterize the dynamic neural processes underlying LD, including shifts in network communication and regional activation that may underlie the specific changes in perception, memory processing, self-awareness, and cognitive control. Taken together, these findings illuminate the electrophysiological correlates of LD, laying the groundwork for decoding the mechanisms of this intriguing state of consciousness.

Significance statement Lucid dreaming (LD) is a unique state of oneiric awareness, where individuals recognize they are dreaming while still in the dream. LD neural correlates remain elusive, as it is very rare and difficult to reproduce in the laboratory. Using an advanced preprocessing pipeline, we harmonized diverse EEG datasets to analyze the largest LD sample to date. We observed gamma power increases in the precuneus during initial eye lucidity signaling, beta power reductions in parietal areas, including the temporo-parietal junction, and enhanced alpha and gamma connectivity during LD over non-lucid REM sleep. These findings shed light on how the brain generates self-referential awareness and volitional action even during sleep.