Remembering past events in minute detail, revisiting them methodically, and reliving past emotions—this is the peculiarity of people with an exceptional memory of their own lives, known as autobiographical hypermnesia, or hyperthymesia. This fascinating condition remains poorly understood, and each new case contributes to our understanding.
In an article in Neurocase, researchers from Paris Brain Institute and the Memory, Brain, and Cognition Laboratory describe the extraordinary mental life of a 17-year-old girl.
Autobiographical memory refers to our ability to remember experiences that have shaped our lives since childhood. It consists of emotional and sensory memories of places, moments, and people, as well as a set of factual information—such as names and dates—that allows us to orient ourselves when we try to recall an episode from the past.
Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that causes progressive memory loss and a decline in mental (i.e., cognitive) abilities. Statistics suggest that between 500,000 and 900,000 people are diagnosed with this disease every year, while several hundreds of thousands experience dementia or other aging-related cognitive decline.
While there are some available treatments designed to delay cognitive decline in individuals with mild or moderate AD symptoms, a cure for the disease has not yet been identified. A better understanding of the neural, genetic, cellular and molecular processes that contribute to the disease’s progression, as well as to neurodegeneration in general, could thus be highly valuable, as it could inform the future development of alternative treatments.
Past neuroscience research has identified the key role of microglia in AD. These are specialized immune cells that monitor the environment in the brain, clearing out damaged cells, debris and pathogens. The dysregulation of these cells has been linked to neurodegeneration and to the progression of AD.
Photovoltaics (PVs), technological systems that can convert sunlight into electricity are among the most promising and widely adopted clean energy solutions worldwide. While existing silicon-based solar cells have already achieved remarkable performances, energy engineers have been working to develop other photovoltaic technologies that could be even more durable, efficient and affordable.
An emerging type of solar cells that could be manufactured at a lower cost, while still retaining good efficiencies, are those based on a class of materials with a characteristic arrangement of atoms, known as perovskites. These cells, known as perovskite solar cells (PSCs), have been found to attain high power conversion efficiencies and are based on materials that could be easier to synthesize when compared to silicon wafers.
Despite their potential, PSCs still face considerable limitations that have so far prevented their widespread deployment and commercialization. Most notably, improving the efficiency of these cells has been found to adversely impact their stability over time, and vice versa.
From creating art and writing code to drafting emails and designing new drugs, generative AI tools are becoming increasingly indispensable for both business and personal use. As demand increases, they will require even more computing power, memory and, therefore, energy. That’s got scientists looking for ways to reduce their energy consumption.
In a paper published in the journal Nature, Aydogan Ozcan, from the University of California Los Angeles, and his colleagues describe the development of an AI image generator that consumes almost no power.
AI image generators use a process called diffusion to generate images from text. First, they are trained on a large dataset of images and repeatedly add a statistical noise, a kind of digital static, until the image has disappeared.
Graphene, a single sheet of carbon atoms arranged in a honeycomb lattice, is known for its exceptional strength, flexibility and conductivity. However, despite holding the world record for room-temperature electron mobility, graphene’s performance at cryogenic temperatures has remained below that of the best gallium arsenide (GaAs)-based semiconductor systems, which have benefited from many decades of refinement.
One key obstacle is electronic disorder. In practical devices, graphene is highly sensitive to stray electric fields from charged defects in surrounding materials. These imperfections create spatial fluctuations in charge density, known as electron-hole puddles, that scatter electrons and limit mobility. This disorder has prevented graphene from realizing its full potential as an ultra-clean electronic system.
Now, in two parallel studies, researchers from the National University of Singapore (NUS) and The University of Manchester (UK) report distinct strategies that finally push graphene past this long-standing benchmark. The results set new records for electron mobility, matching and in some cases surpassing GaAs in both transport and quantum mobility, and enabling the observation of quantum effects in unprecedented conditions.
Engineers have long grappled with a fundamental challenge: creating materials that are both strong and tough enough to resist deformation and prevent fractures. These two properties typically exist in opposition, as materials that excel in one area often fail in the other.
Nature, however, has elegantly solved this trade-off in biological materials like bone, teeth, and nacre, which strategically combine soft and hard components in multi-layered architectures. These blueprints have inspired scientists to develop artificial soft–hard composites—from advanced dual-phase steels to specialized gels and reinforced rubbers—that demonstrate performance exceeding that of their individual components.
While artificial soft–hard composites have shown impressive performance in laboratory tests and real-world applications, the fundamental mechanisms behind their enhanced properties remain largely unclear. The inherent complexity of these materials, encompassing nonlinear behaviors, intricate internal structures, and multi-scale interactions, has made it difficult to isolate the essential design principles.
In a first-of-its-kind experiment, engineers at the University of Pennsylvania brought quantum networking out of the lab and onto commercial fiber-optic cables using the same Internet Protocol (IP) that powers today’s web.
Reported in Science, the work shows that fragile quantum signals can run on the same infrastructure that carries everyday online traffic. The team tested their approach on Verizon’s campus fiber-optic network.
The Penn team’s tiny “Q-chip” coordinates quantum and classical data and, crucially, speaks the same language as the modern web. That approach could pave the way for a future “quantum internet,” which scientists believe may one day be as transformative as the dawn of the online era.
Quantum computers, systems that perform computations leveraging quantum mechanical effects, could outperform classical computers in some optimization and information processing tasks. As these systems are highly influenced by noise, however, they need to integrate strategies that will minimize the errors they produce.
One proposed solution for enabling fault-tolerant quantum computing across a wide range of operations is known as magic state distillation. This approach consists of preparing special quantum states (i.e., magic states) that can then be used to perform a universal set of operations. This allows the construction of a universal quantum computer—a device that can reliably perform all operations necessary for implementing any quantum algorithm.
Yet while magic state distillation techniques can achieve good results, they typically consume large numbers of error-protected qubits and need to perform many rounds of error correction. This has so far limited their potential for real-world applications.
Kai Sun of the University of Michigan is a humble physics professor with ambitious goals. “I’m mainly a paper-and-pencil type of theorist, doing analytical calculations mostly,” Sun said. “My interests are pretty broad, but basically searching for new fundamental principles and new phenomena, especially new phenomena and new physics previously believed to be impossible.”