Using an atomic array originally designed for quantum memory, researchers have demonstrated a magnetometer with unprecedented spatial resolution.
A new model, vetted by experiments on lung cancer cells, may help to explain how cancer and other diseases accumulate drug-resistance mutations that can compromise the effectiveness of treatments.
During the past 50 years, researchers have accumulated a massive arsenal in our war on cancer. Well over 500 drugs have been approved to treat tumors, but cancer remains the second leading cause of death in the United States. The problem is partly due to drug resistance—the emergence of treatment-resistant mutants of the original disease. Now a study led by Jeff Maltas of Cleveland Clinic and Case Western Reserve University, both in Ohio, puts forward a model explaining why drug resistance is so common, vetting the model with experiments on lung cancer cells [1]. This model indicates that treatment-resistant mutants can be present in larger-than-expected numbers before treatment begins. The conclusion implies that we cannot understand cancer evolution by looking at individual mutations in isolation; instead, we should consider each tumor as an interacting ecosystem.
Mentally stimulating activities and life experiences can improve cognition in memory clinic patients, but stress undermines this beneficial relationship. This is according to a new study from Karolinska Institutet published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association.
In the late 1980s, researchers discovered that some individuals who showed no apparent symptoms of dementia during their lifetime had brain changes consistent with an advanced stage of Alzheimer’s disease. Since then it has been postulated that so-called cognitive reserve might account for this differential protective effect in individuals.
Cognitively stimulating and enriching life experiences and behaviors such as higher educational attainment, complex jobs, continued physical and leisure activities, and healthy social interactions help build cognitive reserve. However, high or persistent stress levels are associated with reduced social interactions, impaired ability to engage in leisure and physical activities, and an increased risk of dementia.
Quantum simulators are now addressing complex physics problems, such as the dynamics of 1D quantum magnets and their potential similarities to classical phenomena like snow accumulation. Recent research confirms some aspects of this theory, but also highlights challenges in fully validating the KPZ universality class in quantum systems. Credit: Google LLC
Quantum simulators are advancing quickly and can now tackle issues previously confined to theoretical physics and numerical simulation. Researchers at Google Quantum AI and their collaborators demonstrated this new potential by exploring dynamics in one-dimensional quantum magnets, specifically focusing on chains of spin-1/2 particles.
They investigated a statistical mechanics problem that has been the focus of attention in recent years: Could such a 1D quantum magnet be described by the same equations as snow falling and clumping together? It seems strange that the two systems would be connected, but in 2019, researchers at the University of Ljubljana found striking numerical evidence that led them to conjecture that the spin dynamics in the spin-1⁄2 Heisenberg model are in the Kardar-Parisi-Zhang (KPZ) universality class, based on the scaling of the infinite-temperature spin-spin correlation function.
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AI technology is spreading quickly throughout many different industries, and its integration depends on users’ trust and safety concerns. This matter becomes complicated when the algorithms powering AI-based tools are vulnerable to cyberattacks that could have detrimental results.
Dr. David P. Woodruff from Carnegie Mellon University and Dr. Samson Zhou from Texas A&M University are working to strengthen the algorithms used by big data AI models against attacks.
Much like the invigorating passage of a strong cold front, major changes are afoot in the weather forecasting community. And the end game is nothing short of revolutionary: an entirely new way to forecast weather based on artificial intelligence that can run on a desktop computer.
Today’s artificial intelligence systems require one resource more than any other to operate—data. For example, large language models such as ChatGPT voraciously consume data to improve answers to queries. The more and higher quality data, the better their training, and the sharper the results.
Scientists at La Jolla Institute for Immunology (LJI) have developed a new computational method for linking molecular marks on our DNA to gene activity. Their work may help researchers connect genes to the molecular “switches” that turn them on or off.
Marco Di Lucca shared a closer look at his latest digital human, created for NVIDIA’s Computex 2024 keynote.
The Dark Energy Spectroscopic Instrument (DESI) is a robotic instrument and spectrograph mounted on the Mayall Telescope in Kitt Peak, Arizona. The DESI collaboration aims primarily to understand the elusive Dark Energy. This is an energy of unknown source causing the Universe to accelerate in its expansion; this accelerating expansion is not predicted to occur for a universe that is filled with just ordinary matter and radiation (some more detail can be seen in this Astrobite). Since we still know so little about Dark Energy, a large galaxy survey can allow us to explore the history of the expansion of the Universe in more detail. The DESI instrument has 5,000 individual optical fibres controlled by robots that allow it to measure individual spectra of up to 5,000 galaxies in just a mere 20 minutes! Due to this design, and an observing program that optimises targets in the sky based on observing conditions, the survey will measure spectra of up to 35 million galaxies over 5 years. This will allow DESI to perform precise cosmological measurements, as a great volume of space and number of galaxies can be probed, and noise in the data products is reduced. This bite looks at the cosmology results from the collaboration’s analysis of the recently released Year 1 Data (YR1), in particular, via a signal that can be seen in the data known as Baryon Acoustic Oscillations.
DESI tracers
For the cosmological results in this work DESI uses information from various different ‘tracers’ – galaxies that trace the Large Scale Structure of the Universe. These consist of low redshift bright galaxies (BGS) that are measured when the moon lights the sky (and thus dimmer galaxies are less visible), and higher redshift galaxies measured during the dark time. The dimmer objects include luminous red galaxies (LRGs) which are elliptical galaxies that are extremely bright, emission line galaxies (ELGs) which are younger galaxies with emission line features in their spectra, and quasars (QSOs) which are very distant and bright galaxies that contain active galactic nuclei. The sample used also includes QSOs detected using Lyman-alpha forest measurements, or a method of tracing matter that utilises a series of absorption lines detected due to light from distant QSOs passing through neutral hydrogen in the space between us and the distant galaxies.