Two nurses from Lucile Packard Children’s Hospital Stanford gave stem cells to help patients with life-threatening conditions.
Researchers have achieved a breakthrough in wearable health technology by developing a novel self-healing electronic skin (E-Skin) that repairs itself in seconds after damage. This could potentially transform the landscape of personal health monitoring.
In a study published in Science Advances, scientists demonstrate an unprecedented advancement in E-Skin technology that recovers over 80% of its functionality within 10 seconds of being damaged—a dramatic improvement over existing technologies that can take minutes or hours to heal.
The technology seamlessly combines ultra-rapid self-healing capabilities, reliable performance in extreme conditions, advanced artificial intelligence integration, and highly accurate health monitoring systems. This integration enables real-time fatigue detection and muscle strength assessment with remarkable precision.
More than 15 million people worldwide are living with spinal cord injury (SCI), which can affect their sensory and motor functions below the injury level. For individuals with SCI between C5 and C7 cervical levels, this can mean paralysis affecting their limbs and limited voluntary finger and wrist flexion, making it difficult to grasp large, heavy objects.
Now, a team of UC Berkeley engineers from the Embodied Dexterity Group has developed a wearable device to enhance grasping functionality in this population. Dubbed the Dorsal Grasper, this assistive device leverages voluntary wrist extension and uses supernumerary robotic fingers on the back of the hand to facilitate human-robot collaborative grasping.
In a study recently featured in IEEE Transactions on Neural Systems and Rehabilitation Engineering, the researchers demonstrated for the first time how the Dorsal Grasper can expand users’ graspable workspace. Test subjects found that they could easily grasp objects anywhere they could reach their arm, without having to rotate their bodies, which can cause wheelchair users to lose their balance.
We explore numerically the complex dynamics of multilayer networks (consisting of three and one hundred layers) of cubic maps in the presence of noise-modulated interlayer coupling (multiplexing noise). The coupling strength is defined by independent discrete-time sources of color Gaussian noise. Uncoupled layers can demonstrate different complex structures, such as double-well chimeras, coherent and spatially incoherent regimes. Regions of partial synchronization of these structures are identified in the presence of multiplexing noise. We elucidate how synchronization of a three-layer network depends on the initially observed structures in the layers and construct synchronization regions in the plane of multiplexing noise parameters “noise spectrum width – noise intensity”
Our hybrid EOC design extends these concepts by providing continuous tunability and the potential for adding active samples for investigations of intra-cavity light-matter interactions. In the ‘empty’ hybrid cavity investigated here, we observe a rich mode structure, spurring development of both a field-based model to quantify these cavity modes and their properties, as well as a complementary coupled-oscillator description to gain further understanding of the delicate interplay between the various sub-cavities, which thereafter constitute the hybrid EOC modes. Our detailed analysis of these theoretical vantage points will be highly valuable when considering the addition of an active material, after which the hybrid cavity optical response will become even more intricate. Integration of active materials into hybrid EOCs will yield novel access to light-matter interactions—namely access to energy exchange on sub-Rabi-cycle timescales, and furthermore local probing and even control over tunable light-matter superposition—the latter two unavailable when viewed by conventional cavity transmission techniques. Potential ‘active materials’ for these in-situ investigations of tunable light-matter interactions include conventional polar semiconductors40—oftentimes displaying very large oscillator strengths—atomically-thin monolayers or heterostructures ofion-metal dichalcogenides41, hybrid organic-inorganic 3D21,42 and 2D lead-halide perovskites43,44, and novel, magnetically-ordered systems45.
Implementation of EO sampling inside of THz cavities will also significantly advance further areas of contemporary research. As a prominent example, field-resolved probing inside a defined electromagnetic cavity will provide novel opportunities for measurements of electromagnetic vacuum field fluctuations46,47. Most notably, a high-quality factor EOC constitutes an advantageous testing ground for measurement of quantum vacuum fluctuations, by efficiently excluding sources of external radiation. Moreover, EOCs are not limited to either macroscopic environments or the THz spectral region. Although EO sampling is routinely employed up to the mid-IR spectral region9, it has recently been extended even into the visible range48, allowing for future broadband measurements of intra-cavity electric fields. Similar sampling techniques have been used to sample electric fields inside of metallic antenna-based cavities49,50, demonstrating that although on-chip photonic implementations lack the dynamic tunability, the general technique is readily implemented in other near-field contexts, including even tip-based nano-photonic applications51. Furthermore, EOCs utilizing quartz are uniquely suited candidates for chiral THz cavity phenomena52, due to quartz’s capability for straightforward and rapid measurement of vectorial electric field trajectories34.
In conclusion, we have established versatile and compact designs for a new class of active THz cavities, which allow for in-situ retrieval of intra-cavity electric fields. By developing a cavity-correction function formalism for these EOCs, we have demonstrated a rigorous and reliable method to extract absolute fields in a quantitative, and phase-resolved manner. Utilizing straightforward fabrication techniques, we tune the cavities’ quality factors and resonance frequencies. Furthermore, we have introduced a hybrid EOC, offering continuously-tunable cavity modes across the entire THz-frequency range, within a single device. This fundamental advancement lays the groundwork for accommodating additional active materials for in-situ measurement of and control over light-matter coupling. We understand the rich hybrid mode structure, including apparent signatures of strong coupling, via cavity-field and coupled-oscillator formalisms, which will be key to deciphering signatures of light-matter coupling in more complicated devices. Therefore, this work opens new dimensions of THz cavity physics, particularly in the realms of cavity-controlled ground-and excited state material properties. This includes possibilities such as cavity-enhanced THz emission, selectively-driven Floquet states53, and cavity-controlled nonlinear THz driving15,54, thus paving the way for comprehensive investigations of THz cavity quantum electrodynamics.
A game of chess requires its players to think several moves ahead, a skill that computer programs have mastered over the years. Back in 1996, an IBM supercomputer famously beat the then world chess champion Garry Kasparov. Later, in 2017, an artificial intelligence (AI) program developed by Google DeepMind, called AlphaZero, triumphed over the best computerized chess engines of the time after training itself to play the game in a matter of hours.
More recently, some mathematicians have begun to actively pursue the question of whether AI programs can also help in cracking some of the world’s toughest math problems. But, whereas an average game of chess lasts about 30 to 40 moves, these research-level math problems require solutions that take a million or more steps, or moves.
In a paper appearing on the arXiv preprint server, a team led by Caltech’s Sergei Gukov, the John D. MacArthur Professor of Theoretical Physics and Mathematics, describes developing a new type of machine-learning algorithm that can solve math problems requiring extremely long sequences of steps. The team used their new algorithm to solve families of problems related to an overarching decades-old math problem called the Andrews–Curtis conjecture. In essence, the algorithm can think farther ahead than even advanced programs like AlphaZero.
Quantum computers have the potential to revolutionize technology by solving complex calculations and computations that are difficult, if not impossible, for traditional computers. One major roadblock, however, is instability—quantum states can be easily disrupted by “noise” from their surrounding environments, causing errors in the systems. Overcoming instability is important in creating effective and reliable quantum computers and other quantum technologies.
Researchers at the University of Rochester—including John Nichol, an associate professor in the Department of Physics and Astronomy—have taken a key step toward reducing instability in quantum systems, by focusing on an elusive state called a nuclear-spin dark state. Although scientists have long suspected that the nuclear-spin dark state could exist, they haven’t been able to provide direct evidence of it—until now.
“By directly confirming the existence of the dark state and its properties, the findings not only validate decades of theoretical predictions but also open the door to developing more advanced quantum systems,” Nichol says.
Researchers at Washington University School of Medicine in St. Louis have conducted a longitudinal study on an individual carrying the presenilin 2 (PSEN2) p. Asn141Ile mutation, a genetic variant known to cause dominantly inherited Alzheimer’s disease (DIAD). The high risk individual, despite being 18 years past the expected age of clinical onset, has remained cognitively intact. Researchers investigated genetic, neuroimaging, and biomarker data to understand potential protective mechanisms.
Unlike typical DIAD progression, tau pathology in this case was confined to the occipital lobe without spreading, suggesting a possible explanation for the lack of cognitive decline.
DIAD results from highly penetrant mutations in amyloid precursor protein (APP), presenilin 1 (PSEN1), or PSEN2, which lead to abnormal amyloid-β processing and early-onset Alzheimer’s disease. The Dominantly Inherited Alzheimer Network (DIAN) was established to track DIAD mutation carriers and assess clinical, cognitive, and biomarker changes over time.
Combining concepts from statistical physics with machine learning, researchers at the University of Bayreuth have shown that highly accurate and efficient predictions can now be made as to whether a substance will be liquid or gaseous under given conditions. They have published their findings in Physical Review X.
Observation of a glass of water reveals that the water exists in two distinct phases: liquid and gas. Even at room temperature, water molecules are constantly evaporating from the surface of the liquid water and passing into the gas phase. At the same time, some of the water molecules from the gas condense back into the liquid.
The transition from one phase to the other depends on temperature and pressure. Above a critical temperature, the simultaneous coexistence of gas and liquid disappears. The resulting supercritical fluid no longer forms an interface. This is important for industrial processes such as separation, cleaning and production.
The default mode network (DMN) is a set of interconnected brain regions known to be most active when humans are awake but not engaged in physical activities, such as relaxing, resting or daydreaming. This brain network has been found to support a variety of mental functions, including introspection, memories of past experiences and the ability to understand others (i.e., social cognitions).
The DMN includes four main brain regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), the angular gyrus and the hippocampus. While several studies have explored the function of this network, its anatomical structure and contribution to information processing are not fully understood.
Researchers at McGill University, Forschungszentrum Jülich and other institutes recently carried out a study aimed at better understanding the anatomy of the DMN, specifically examining the organization of neurons in the tissue of its connected brain regions, which is known as cytoarchitecture. Their findings, published in Nature Neuroscience, offer new indications that the DMN has a widespread influence on the human brain and its associated cognitive (i.e., mental) functions.