Deep learning models that predict functional genomic measurements from DNA sequences are powerful tools for deciphering the genetic regulatory code. Existing methods involve a trade-off between input sequence length and prediction resolution, thereby limiting their modality scope and performance1-5. We present AlphaGenome, a unified DNA sequence model, which takes as input 1 Mb of DNA sequence and predicts thousands of functional genomic tracks up to single-base-pair resolution across diverse modalities. The modalities include gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chromatin contact maps, splice site usage and splice junction coordinates and strength. Trained on human and mouse genomes, AlphaGenome matches or exceeds the strongest available external models in 25 of 26 evaluations of variant effect prediction. The ability of AlphaGenome to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically relevant variants near the TAL1 oncogene6. To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.

Controlling quantum matter with light offers a promising route to dynamically tune its many-body properties, ranging from band topology1,2 to superconductivity3. However, achieving such optical control for strongly correlated electron systems in the steady state has remained elusive. Here we demonstrate optical switching of the spin-valley degree of freedom of itinerant ferromagnets in twisted MoTe2 (t-MoTe2) homobilayers. This system uniquely features flat valley-contrasting Chern bands and exhibits a range of strongly correlated phases at various moiré lattice fillings, including Chern insulators and ferromagnetic metals4-7. We show that the spin-valley orientation of all of these phases can be dynamically reversed by resonantly exciting the exciton-polaron8 transitions with circularly polarized light. These findings not only provide direct evidence for non-thermal optical switching of a ferromagnetic spin state at zero magnetic field but also demonstrate the possibility of dynamical control over a topological order parameter, paving the way for optical generation of chiral edge modes and topological quantum circuits.

Galaxy clusters are the most massive gravitationally bound structures in the universe and serve as tracers of the assembly of large-scale structure1. Studying their progenitors, protoclusters, sheds light on the earliest stages of cluster formation. However, detecting protoclusters is demanding: their member galaxies are loosely bound and the emerging hot intracluster medium (ICM) may only be in the initial stages of virialization2-4. Recent James Webb Space Telescope (JWST) observations located several protocluster candidates by identifying overdensities of z ≳ 5 galaxies5-9. However, none of these candidates was detected by X-ray observations, which offer a powerful way to unveil the hot ICM. Here we report the combined Chandra and JWST detection of a protocluster, JADES-ID1, at z ≈ 5.68, merely one billion years after the Big Bang. We measure a bolometric X-ray luminosity of Lbol=(1.5-0.6+0.5)×1044ergs-1 and infer a total gravitating mass of M500=(1.8-0.7+0.6)×1013M⊙ , making this system a progenitor of today's most massive galaxy clusters. The detection of extended, shock-heated gas indicates that substantial ICM heating can occur in massive halos as early as z ≈ 5.7. Also, given the limited survey volume, the discovery of such a massive cluster is statistically unlikely10, implying that the formation of the large-scale structure must have occurred more rapidly in some regions of the early universe than standard cosmological models predict.

Three-dimensional atomic structure is routinely determined for periodic crystals. However, extending such analysis to amorphous materials remains a substantial challenge, despite the scientific and technological importance1,2. In this context, a recent report describing the three-dimensional structure determination of an amorphous solid using atomic-resolution electron tomography (AET) is truly remarkable3. If validated, such an analysis would be groundbreaking. Here we address this issue and investigate whether and when AET can identify all or most atoms in an amorphous nanoparticle. By simulating AET, we reveal limitations on the structural and chemical information AET can determine from noisy electron images. For monoatomic nanoparticles, the structure can be determined with an atomic-position accuracy of tens of picometres under stringent fluence, sampling and projection requirements. For multi-element amorphous nanoparticles, chemical identification resolution is determined by noise and experimental sampling. Heavier atoms are more easily resolved than lighter ones, and large chemical analysis uncertainties emerge when atomic peak and background intensities overlap. Using these insights, we delineate nanoparticle size, composition, electron fluence and image sampling requirements for AET. The results serve as a benchmark for future experiment design and demonstrate a viable approach for amorphous structure determination validation using AET.

Amorphous materials-solids lacking long-range order-underpin technologies from thin-film electronics1, solar cells2 and phase-change memory3 to magnetic components4, medical devices5 and quantum technologies6-8. Yet the absence of periodicity fundamentally limits determination of their three-dimensional (3D) structure at atomic resolution. Despite major theoretical, experimental, and computational advances in characterizing short- and medium-range order9-24, quantitative determination of complete 3D atomic arrangements in amorphous materials remains experimentally demanding. Atomic electron tomography (AET) now provides a pathway to direct 3D atomic mapping in these materials25-27. Here we present a quantitative analysis of AET, showing how robust image preprocessing, denoising, projection alignment and normalization, advanced tomographic reconstruction, atom tracing, elemental classification and atomic position refinement collectively enable reliable determination of 3D atomic coordinates and elemental identities in amorphous materials. Using multislice-simulated datasets of amorphous Si, SiGeSn and CoPdPt nanoparticles under varying noise levels, our workflow outperforms an alternative approach28 in both positional precision and classification accuracy. For CoPdPt, we identify 95.1% of Co, 99.0% of Pd and 100% of Pt atoms, with corresponding 3D positional precisions of 29 pm, 12 pm and 6 pm, respectively, under realistic dose conditions. These results establish practical guidelines and quantitative benchmarks for achieving accurate AET of non-crystalline materials, and the underlying framework can be broadly applied to other tomographic imaging modalities for high-fidelity 3D reconstruction.

Optical control of topology, particularly in the presence of electron correlations, is an interesting topic with broad scientific and technological impact1-4. Twisted MoTe2 bilayer (tMoTe2) is a zero-field fractional Chern insulator (FCI)5-10, exhibiting the fractionally quantized anomalous Hall effect11-14. As the chirality of the edge states and sign of the Chern number are determined by the underlying ferromagnetic polarization15,16, manipulation of ferromagnetism would realize control of the Chern insulator (CI)/FCI states. Here we demonstrate control of ferromagnetic polarization, and thus the CI and FCI states, by circularly polarized optical pumping in tMoTe2. At low excitation power, we achieve on-demand preparation of ferromagnetic polarization by optical training, that is, electrically tuning the system from non-ferromagnetic to desirable ferromagnetic states under helicity-selective optical pumping. With increased excitation power, we further realize direct optical switching of ferromagnetic polarization at a temperature far below the Curie temperature17,18. Both optical training and direct switching are most effective near CI and FCI states, which we attribute to a gap-enhanced valley polarization of optically pumped holes. The magnetization can be dynamically switched by modulating the helicity of optical excitation. Spatially resolved measurements further demonstrate optical writing of ferromagnetic, and thus CI (or FCI) domains. Our work realizes precise optical control of a topological quantum many-body system with potential applications in topological spintronics, quantum memories and creation of exotic edge states by programmable patterning of integer and fractionally quantized anomalous Hall domains4,19.

Flexible electronics, coupled with artificial intelligence, hold the potential to revolutionize robotics, wearable and healthcare devices1, human-machine interfaces2, and other emerging applications3,4. However, the development of flexible computing hardware that can efficiently execute neural-network-inference tasks using parallel computing remains a substantial challenge5. Here we present FLEXI, a thin, lightweight and robust flexible digital artificial intelligence integrated circuit to address this challenge. Our approach uses process-circuit-algorithm co-optimization and a digital dynamically reconfigurable compute-in-memory architecture. Key features include clock frequency operation of up to 12.5 MHz and power consumption as low as 2.52 mW, all while achieving subdollar-per-unit cost and an operational circuit yield of between approximately 70% and 92%. Our circuits can perform 1010 fixed and random multiplications without error, withstand over 40,000 bending cycles and maintain stable performance for a period exceeding 6 months. A one-shot on-chip neural network deployment eliminates the power consumption and latency associated with sequential weight writing, achieving up to 99.2% accuracy in temporal arrhythmia detection tasks on a single 1-kb chip. In addition, FLEXI demonstrates over 97.4% accuracy in human daily activity monitoring using multimodal physiological signals.