Search

Self-supervised reconstruction structured illumination microscopy (SSR-SIM) is a reconstruction approach for SIM that improves image reconstruction by including light modulation priors and information on reconstruction artifacts, while simultaneously eliminating the need for ground-truth images. The improvements allow long-term imaging of sensitive cellular processes.

Omnivores like bears can switch between plant and animal diets, potentially helping them respond to changing conditions. By combining modern and fossil data, this study shows that bears shift toward carnivory in harsher climates and toward herbivory in more productive environments.

A histology-informed, diffusion Magnetic Resonance Imaging simulation framework improves the non-invasive assessment of cancer biology in solid tumours in vivo

Floquet engineering is often limited by weak light–matter coupling and heating. Now it is shown that exciton-driven fields in monolayer semiconductors produce stronger, longer-lived Floquet effects and reveal hybridization linked to excitonic phases.

Bruijns et al. present a modeling tool that enables the tracking of learning dynamics across subjects to reveal how behaviors emerge and adapt. Applying the tool to a decision-making task in mice uncovers similarities and differences across individuals.

Tests of the predictions of the renormalization group in biological experiments have not yet been decisive. Now, a study on the collective dynamics of insect swarms provides a long-sought match between experiment and theory.

Hiʻiaka is the largest moon of the distant dwarf planet Haumea. Here, the authors report the first multi-chord stellar occultations of Hiʻiaka, revealing its size, shape, and density, suggesting an origin from Haumea’s icy mantle.

Tokamak walls suffer erosion from steady and bursty heat loads. Here, the authors demonstrate that optimizing 3D magnetic field and cooling gas injection can tame destructive plasma bursts while enabling cooler, safer exhaust conditions.

Stable and robust topological edge modes are observed at finite temperatures in an array of 100 programmable superconducting qubits because of emergent symmetries present in the prethermal regime of this system.

The study provides observational evidence of energy transfer in space plasmas, showing hydrogen and helium ions interact differently with ion-scale waves. Despite helium’s low abundance, they show their interaction can excite electrostatic waves, facilitating energy transfer across scales and challenging traditional models.

Edge localised modes (ELMs) in highly confined plasmas are notoriously difficult to regulate. Here, the authors analyse multiscale modes and interactions by combining experimental measurements from DIII-D and modeling, showing promising results in ELM control.

The CMS Collaboration reports the measurement of the spin, parity, and charge conjugation properties of all-charm tetraquarks, exotic fleeting particles formed in proton–proton collisions at the Large Hadron Collider.

Analysis of human Robertsonian chromosomes originating from 13, 14 and 21 reveal that they result from breaks at the SST1 macrosatellite DNA array and recombination between homologous sequences surrounding SST1.

The MICrONS mouse visual cortex dataset shows that neurons with similar response properties preferentially connect, a pattern that emerges within and across brain areas and layers, and independently emerges in artificial neural networks where these ‘like-to-like’ connections prove important for task performance.

An ensemble of spins from nitrogen–vacancy centres in diamonds is prepared in a spin-squeezed state.

Sandia researchers used machine learning and autonomous experiments to discover new principles for controlling incoherent light emission from metasurfaces. Combining lens and grating elements steers emission more effectively than gratings alone—beyond conventional Fourier optics.

The influence of an animal’s running speed on grid cell spatial representation is unclear. Here, the authors present a Gaussian Process with Kernel Regression method to infer neural representational geometry from data and show that faster movement dilates grid cell representational manifolds, enhancing spatial representation.

Alström syndrome (AöS) is a rare genetic disorder characterized by metabolic problems. Here, the authors show that in AöS models, defects in cilia and autophagy lead to ACBP accumulation, which drives obesity. An anti-ACBP antibody reduces weight gain and metabolic dysfunction, highlighting ACBP as a therapeutic target for this ciliopathy.

Generating highly squeezed states for quantum sensing requires precise entanglement properties, which makes it a hard task. Now a conjecture identifies a realistic regime of magnetic order at finite temperatures that enables scalable spin squeezing.

Hund’s exchange interaction energy in two-dimensional materials is challenging to extract from experiments. Now, this is achieved in rhombohedral graphene, which allows an estimate of the interactions that drive the variety of correlated states in this material.

Artificial intelligence (AI) can greatly improve healthcare delivery and outcomes, but potential embedded biases can affect fairness in clinical deployment. Here, the authors develop a simulation-based approach to explore which formalisations of AI algorithmic fairness translate into long-term outcome fairness, with a focus on breast cancer.

Sparsification techniques can be used to create Ising machines prototyped on field-programmable gate arrays that can quickly and efficiently solve combinatorial optimization problems.

Extrachromosomal circular DNAs (ecDNAs) are prevalent in human cancers and are thought to drive tumor evolution and drug resistance by amplifying oncogenes. Here, authors develop ec3D to reconstruct three-dimensional ecDNA structures, revealing how their spatial organization rewires regulatory circuits.

HIV infection and substance use disorders converge in human midbrain. Here, authors show transcriptional impacts using single-nucleus RNA sequencing, linking dopaminergic vulnerability and microglial dysregulation, and in viremia, GABAergic deficits.

A statistical approach to designing and predicting the fusion yield of cryogenic deuterium–tritium implosions leads to tripled yield in direct-drive laser fusion of deuterium–tritium layered targets.

The effects of detection noise on quantum metrology performances have not been rigorously investigated yet. Here, the authors fill this gap by generalising the quantum Fisher information to the case of noisy readout, and showing the consequences the imperfect measurements bring.

Brain age gaps (BAGs) highlight deviations from healthy brain aging, yet their biophysical underpinnings in aging and dementia are not well understood. Here, the authors use EEG connectivity and generative modeling across diverse populations to reveal that BAGs are influenced by geography, income, sex and education, with implications for understanding accelerated aging and dementia.

The presented Mean-Shift Super Resolution (MSSR) algorithm can extend spatial resolution within a single microscopy image. Its applicability extends across a wide range of experimental and instrumental configurations and it is compatible with other super-resolution microscopy approaches.

As we age, our brains don’t just shrink, their entire geometry changes. This study links specific patterns of brain expansion and compression to cognitive impairment, revealing a new way to understand age-related decline.

The SUMO E3 ligase ZNF451 is a representative member of a new class of SUMO enzymes that execute catalysis via tandem SUMO-interaction motifs, thus allowing efficient SUMO-chain formation.

Cash transfers reduced AIDS incidence and mortality among 12.3 million Brazilian women, especially those with overlapping social vulnerabilities, highlighting the role of social protection in addressing health inequalities.

Developing an understanding of a material’s magnetic behaviour based on neutron scattering measurements often relies on extracting an effective spin model. Samarakoon et al. demonstrate an automated machine learning approach to this problem, leading to more robust inferences from complex data.

Here, using pre-HIV-infection and non-HIV samples in a multi-modal study of microbiomes and metabolomes, the authors develop a taxon-specific measure of DISruption in COrrelations (DISCO) revealing system-wide dysbiosis preceding HIV-1 infection in men who have sex with men.

The advantage of living in cities compared with rural areas with respect to height and BMI in children and adolescents has generally become smaller globally from 1990 to 2020, except in sub-Saharan Africa.

MRI data from more than 100 studies have been aggregated to yield new insights about brain development and ageing, and create an interactive open resource for comparison of brain structures throughout the human lifespan, including those associated with neurological and psychiatric disorders.

ESPRESSO leverages functional information obtained from organelles for deep spatiotemporal phenotyping of single cells.

Experimental measurements of high-order out-of-time-order correlators on a superconducting quantum processor show that these correlators remain highly sensitive to the quantum many-body dynamics in quantum computers at long timescales.

An in-depth microbiological and metagenomic analysis of Colombian farm and fermentation facilities resulted in the design of a defined microbial community that can reproduce the flavour of fine chocolate.

Previous ophthalmic foundation models have struggled to generalize effectively to diverse and rare fundus diseases, restricting their clinical applicability. Here, the authors introduce a vision-language foundation model that demonstrates superior performance in diagnosing both common and rare fundus conditions.

Fine-scale geospatial mapping of overweight and wasting (two components of the double burden of malnutrition) in 105 LMICs shows that overweight has increased from 5.2% in 2000 to 6.0% in children under 5 in 2017. Although overall wasting decreased over the same period, most countries are not on track to meet the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025.

The mechanisms governing energy transport and dissipation in turbulent flows have remained only partially understood. Here, the authors introduce a data-driven framework based on explainable deep learning to assess the relative importance of different flow regions.

Alzheimer’s disease is heterogeneous in its neuroimaging and clinical phenotypes. Here the authors present a semi-supervised deep learning method, Smile-GAN, to show four neurodegenerative patterns and two progression pathways providing prognostic and clinical information.

Canalized polaritons are light-matter excitations characterized by intrinsic collimation of electromagnetic energy along a specific crystal axis. Here, the authors report the observation of intrinsically canalized phonon polaritons in a single thin layer of a van der Waals crystal, LiV₂O₅.

Finding solutions in rugged energy landscapes is hard. Here, authors introduce a generalized Probabilistic Approximate Optimization Algorithm, a classical variational Monte Carlo method that reshapes the landscape and runs on probabilistic computers, recovers simulated annealing, and learns multi-temperature schedules.

Analyses of the relationships between temperature, moisture and seven key plant functional traits across the tundra and over time show that community height increased with warming across all sites, whereas other traits lagged behind predicted rates of change.

Federated learning (FL) algorithms have emerged as a promising solution to train models for healthcare imaging across institutions while preserving privacy. Here, the authors describe the Federated Tumor Segmentation (FeTS) challenge for the decentralised benchmarking of FL algorithms and evaluation of Healthcare AI algorithm generalizability in real-world cancer imaging datasets.